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The Effectiveness of natural protected areas in Mexico to reduce forest fragmentation and forest cover loss

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Title:
The Effectiveness of natural protected areas in Mexico to reduce forest fragmentation and forest cover loss
Creator:
Raines, James Edward II
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
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English

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Degree:
Master's ( Master of arts)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Geography and Environmental Sciences, CU Denver
Degree Disciplines:
Applied geography and geospatial sciences
Committee Chair:
Moreno-Sanchez, Rafael
Committee Members:
Anthamatten, Peter
Rojo, Juan Manuel Torres

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Abstract:
This thesis study assessed the effectiveness of 84 natural protected areas in Mexico to reduce forest fragmentation and forest cover loss from the year 2000 to 2010. Mexico is considered megadiverse as it contains around 10% of the worlds biodiversity. Forests are largely responsible for this in addition to carbon sequestration and other ecosystem services. This research is important because natural protected areas are the primary strategy agreed upon in national and international agreements, such as the Aichi Biodiversity Targets and the Paris Agreement, to protect these assets. This research uses GlobeLand30 land use/land cover data sets to delineate the extent of forest cover and INEGI Series III and Series IV data sets to delineate the extent of types of forests within the extent of GlobeLand30 forest cover. Changes in forest canopy cover and forest fragmentation, as determined using the Morphological Spatial Pattern Analysis algorithm, are compared with concentric buffer rings outside of natural protected areas. The results indicate at a national level, the changes in forest canopy cover and forest fragmentation within natural protected areas are indistinguishable from changes occurring within their concentric buffer rings. The national commission of protected areas designates 9 regions across Mexico. When repeating the analysis for each of these 9 regions, we find that natural protected areas in regions 4 and 6 were effective at preventing forest fragmentation. Natural protected areas in region 9 experienced more forest fragmentation than their buffer zones. No significant difference was found between forest canopy cover changes in natural protected areas and their buffer zones for any region.

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Copyright James Edward Raines II. Permission granted to University of Colorado Denver to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

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xi THE EFFECTIVENESS OF NATURAL PROTECTED A REAS IN MEXICO TO RE DUCE FOREST FRAGMENTATION AND FO REST COVER LOSS by JAMES EDWARD RAINES II B.A., University of Texas at Austin, 2015 A thesis submitted to the Faculty of the Graduate School of the Un iversity of Colorado in partial fulfillment of the requirements for the degree of Master of Arts Applied Geography and Geospatial Science 2018

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ii This thesis for the Master of Arts degree by James Edward Raines II has been approved for the Applied Geo graphy and Geospatial Science Program by Rafael Moreno Sanchez, Chair Peter Anthamatten Juan Manuel Torres Rojo Date: July 28 , 2017

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iii Raines II, James Edward ( The Effectiveness of Natural Protected Areas in Mexico to Reduce Forest Fragmentation and Forest Cover Loss Thesis directed by Associate Professor Rafael Moreno Sanchez ABSTRACT This thesis study assessed the effectiveness of 84 natural protected areas in Mexico to reduce forest fra gmentation and forest cover loss from the year 2000 to 2010. Mexico is considered megadiverse as it contains around 10% of the worlds biodiversity. Forests are largely responsible for this in addition to carbon sequestration and other ecosystem services. T his research is important because natural protected areas are the primary strategy agreed upon in national and international agreements, such as the Aichi Biodiversity Targets and the Paris Agreement, to protect these assets. This research uses GlobeLand30 land use/land cover data sets to delineate the extent of forest cover and INEGI Series III and Series IV data sets to delineate the extent of types of forests within the extent of GlobeLand30 forest cover. Changes in forest canopy cover and forest fragmen tation, as determined using the Morphological Spatial Pattern Analysis algorithm, are compared with concentric buffer rings outside of natural protected areas. The results indicate at a national level, the changes in forest canopy cover and forest fragment ation within natural protected areas are indistinguishable from changes occurring within their concentric buffer rings. The national commission of protected areas designates 9 regions across Mexico. When repeating the analysis for each of these 9 regions, we find that natural protected areas in regions 4 and 6 were effective at preventing forest

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iv fragmentation. Natural protected areas in region 9 experienced more forest fragmentation than their buffer zones. No significant difference was found between forest canopy cover changes in natural protected areas and their buffer zones for any region. The form and content of this abstract are approved. I recommend its publication. Approved: Rafael Moreno Sanchez

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v TABLE OF CONTENTS I. INTRODUCTION ................................ ................................ ................................ ....................... 1 Literature Review ................................ ................................ ................................ ........................ 4 Natural Protected Areas ................................ ................................ ................................ .............. 4 Deforestation ................................ ................................ ................................ .............................. 8 Forest Fragmentation ................................ ................................ ................................ ................ 11 Land Cover Data Sets ................................ ................................ ................................ ................. 15 II. METHODS ................................ ................................ ................................ .............................. 18 Hypotheses ................................ ................................ ................................ ................................ 18 Analysis ................................ ................................ ................................ ................................ ...... 18 Identify the extent of the forest cover for years 2000 and 2010. ................................ ......... 18 Calculating the level of fragmentation of forest cover for years 2000 and 2010. ................ 23 Creating concentric buffers around natural protected areas. ................................ .............. 27 Calculating the level of forest fragmentation change and forest cover change in and aro und natural protected areas. ................................ ................................ ................................ ........ 29 Statistical analyses of the relationship between NPAs and concentric buffers with forest fragmentation change and forest cover change. ................................ ................................ .. 30 Determining if NPAs were effective at reducing forest cover loss or increased forest fragmentation. ................................ ................................ ................................ ....................... 33 III. RESULTS ................................ ................................ ................................ .............................. 35 Shapiro Wilk test results ................................ ................................ ................................ ........... 35 Wilcoxon Rank Sum and Kolmogorov Smirnov test results ................................ ...................... 36 All forests ................................ ................................ ................................ ............................... 36

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vi Temperate and tropical Forests ................................ ................................ ............................ 37 Primary and secondary of temperate and tropical Forests ................................ ................... 38 Significant Regional Differences ................................ ................................ ................................ 40 IV. DISCUSSION ................................ ................................ ................................ ........................ 42 National results ................................ ................................ ................................ ......................... 42 Regional results ................................ ................................ ................................ ......................... 42 Comparisons to previous research ................................ ................................ ............................ 54 Forest type subclasses defined in this st udy ................................ ................................ ............. 58 Concentric buffer rings and spillover effects ................................ ................................ ............ 59 Outliers ................................ ................................ ................................ ................................ ...... 61 V. CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH ................................ .... 66 VI. REFERENCES ................................ ................................ ................................ ....................... 71 VII. APPENDIX ................................ ................................ ................................ ........................... 77 Appendix A INEGI keys for temperate and tropical forests ................................ .................... 77 List of Keys for Primary Temperate Forests ................................ ................................ .......... 77 List of Keys for Secondary Temperate Forests ................................ ................................ ...... 77 List of Keys for Primary Tropical Forests ................................ ................................ ............... 78 List of Keys for Secondary Tropica l Forests ................................ ................................ ........... 79 Appendix B Natural Protected Areas in the Analysis ................................ ............................. 80 Appendix C All Regional Results ................................ ................................ ............................. 82 Appendix D Considerable outliers ................................ ................................ ........................ 109

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vii Considerable outliers in the forest cover change analysis ................................ .................. 109 Considerable outliers in the forest fragmentation change analysis ................................ ... 110 Appendix E Results for all NPAs when considering all forests as a single class ................... 112 Forest cover change ranked from greatest increase to greatest decrease ........................ 112 Forest fragmentation change ranked from greatest increase to greatest decrease .......... 116

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viii LIST OF FIGURES FIGURE 1. a) Traditional buffer area shown against b) a buffer area created using matching techniques. Source: Mas (2005) ................................ ................................ ................................ ............................ 7 2. The spatial extent and arrangement of all forests as a single classification in Mexico. .......... 21 3. The spatial extent and arrangement of temperate and tropical forests in Mexico. ............... 22 4. The spatial extent and arrangement of primary temperate, secondary temperate, primary tropical, and secondary tropical forests in Mexico. ................................ ................................ ..... 23 5. An example of the required input for the MSPA algorithm containing two values 1 for background and 2 for foreground. Source: The MSPA Guide (available at: http://forest.jrc.ec.europa.eu/download/software/guidos/mspa/) ................................ ......... 24 6. An example output of the seven fragmentation classes from the MSPA algorithm. Source: (Moreno Sanchez et al., 2018). ................................ ................................ ................................ ...... 25 7. An example of the distribution between all ch anges in NPAs plotted against the distribution of all changes in a buffer zone. ................................ ................................ ................................ ...... 32 8. The nine CONANP regions where regional analyses are performed for each one. ................. 33 9. Box plots illustrating the difference in forest fragmentation change between NPAs and their buffer zones in region 4. ................................ ................................ ................................ ................. 44 10. Box plots illustrating the spillover eff ects in region 6 where the 0.5 and 1 km buffer zones were indistinguishable from NPAs, but the 2 km buffer zones experience more forest fragmentation. ................................ ................................ ................................ ................................ . 45

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ix 11. Density plots of region 9 showing fragm entation increasing in primary temperate forests and stabilizing or decreasing in secondary forests. ................................ ................................ ..... 47 12. Density plots of the 0.5 km buffer zones in region 9, showing forest cover decreasing in primary temperate forests and increasing in secondary forests. ................................ .............. 48 13. Comparing the two figures shows areas where primary temperate forests have been replaced with secondary temperate forests in th e NPA of Lagunas de Montebello. .............. 49 14. Density plots of the 0.5 km buffer zones in region 9, showing forest cover decreasing in primary tropical forests and increasing in secondary forests. ................................ ................... 50 15. Comparing the two figures shows areas where primary tropical forests have been replaced with secondary tropical forests in the NPA of La Cantun. ................................ .......................... 51 16. The distribution of forest fragmentation change observing all forests in region 9 shows higher fragmentation increases occurring in NPAs than all buffer zones. ................................ 52 17. Spillover effect s are noticeable in the 0.5 km and 1 km buffer zones where the distributions overlap the changes in NPAs. The 2 km buffer zones experienced significantly more forest cover loss than NPAs. ................................ ................................ ................................ ...................... 60 18. Sp illover effects are noticeable in the 0.5 km and 1 km buffer zones where the distributions overlap the changes in NPAs. The 2 km buffer zones experienced significantly more forest fragmentation than NPAs. ................................ ................................ ................................ .............. 61 19. Forest fragmentation increasing by 62,598.51% in primary temperate forests from 2000 to 2010. ................................ ................................ ................................ ................................ .................. 63

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x 20. Secondary tropical forests increased by 9,756.14% within the boundaries of the NP A of El Veladero, state of Guerrero. ................................ ................................ ................................ .......... 64 21. Primary temperate forests in the NPA of Porción Sierra de Arteaga (CADNR026) increasing by 5,855.56% in the 0.5 km buffer zone. ................................ ................................ ...................... 65

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xi LIST OF TABLES TABLE 1. Fragmentation classes created by performing the MSPA method. (following Wickham, Riitters, Wade, & Vogt, 2010) ................................ ................................ ................................ . 24 2. Shapiro Wilk test results for forest fragmentation change. ................................ ................... 35 3. Shapiro Wilk test results for forest cover change. ................................ ................................ . 35 4. All forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. ................................ ........... 36 5. Temperate forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. ............................. 37 6. Tropical forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. ................................ .. 37 7. Temperate Primary forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. ............... 38 8. Temperate Secondary forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. ........... 39 9. Tropical Primary forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. .................... 39 10. Tropical Secondary forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. ................ 39 11. NPAs with significant differences from buffer zones in Nor este y Sierra Madre Oriental, region 4. ................................ ................................ ................................ ................................ .. 40 12. NPAs with significant differences from buffer zones in Peninsula de Yucatan y Caribe Mexicano, region 6. ................................ ................................ ................................ ................ 41 13. NPAs with significant differences from buffer zones in Frontera Sur, Istmo y Pacifico Sur, region 9. ................................ ................................ ................................ ................................ .. 41

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1 I. INTRODUCTION Decreasing deforestation and forest fragmentation in Mexico are import to conserve ecosystems, biodiversity, soil composition, and to sequester carbon (Blackman, Pfaff, & Robalino, 2015; Ochoa Gaona & González Espinosa, 1999; Pineda Jaimes et al., 2010) . Mexico is said to species richness (Ceballos, 2007; Groombridge & Jenkins, 2000) . Forests cover one third of the co untry and are responsible for much of the biodiversity (Pfaff & Santiago Avila, 2014) . been made in the Paris Agreement, at the 13 th Convention of Biological Diversity (CBD), and in an effort to reach the Aichi Biodiversity Targets (CBD, 2016; Paris Agreement, 2015) . In accordance with the Paris Agreement, Mexico must reach a deforestation rate of 0% by 2030, increase ecological connectivity for all natural protected areas (NPAs), increase carbon storage, and the resi lience of its most vulnerable ecosystems (Paris Agreement, 2015) . According to the CBD agreements and Aichi Biodiversity Targets, Mexico must declare 17 % of its terrestrial land and inland water a NPA by 2020 (CBD, 2016) . While the importance of Mexican forests is stressed through these agreements, few studies have evaluated the effectiveness of NPAs to protect forests as a national strategy. Most studies focus on local and regional scales , examining the effectiveness of NPAs to mitigate a variety of issues including deforestation, ecosystem degradation, mining, fires, and loss of mammals and biologic al diversity (Armendáriz Villegas et al., 2015; Ceballos, 2007; Ceballos,

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2 Rodrigue z, & Medellin, 1998; Chapa Vargas & Monzalvo Santos, 2012; Cortina Villar et al., 2012; Ochoa Gaona & González Espinosa, 1999; Román Cuesta & Martínez Vilalta, 2006) . At a national scale in Mexico , forest cover change has been studied in NPAs before and a fter becoming federally decreed (Rayn & Sutherland, 2011) . It has also been compared from within NPAs to the immediately adjacent area outside protected area boundaries (Blackman et al., 2015; Rayn & Sutherland, 2011) . These studies have suggested , as a national strategy, NPAs do not prevent forest cover loss . However, t he effectiveness of NPAs has also been shown to vary geographically (Blackman et al., 2015) . The use of different methods to assess effec tiveness has also shown to alter results and conclusions (Mas, 2005) . Furthermore, the methods used between studies vary , making comparion of results difficult or not possible and none of them have considered the aspect of forest cover fragmentation. Deforestation and forest fragmentation are key metrics in assessin g the sustainability and condition of forests . However, ambiguity exist s in defining these terms. For the purpose of this research, deforestation is defined as the loss in forest canopy cover as represented by land cover data sets . This method of studying deforestation is commonly found in the literature (Bocco, Mendoza, & Masera, 2 001; Mas et al., 2004; Ochoa Gaona & González Espinosa, 1999; Pineda Jaimes et al., 2010; Vidal & Rendón Salinas, 2014) . Forest fragmentation is the division of a conterminous forest into smaller, patchier, and/or more exposed units. A way of examining f ragmentation is by using the Morphological Spatial Pattern Analysis (MSPA) algorithm. This allows fragmentation to be quantified and compared. The MSPA is sanctioned and endorsed by international forestry and environmental

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3 agencies and has been used in num erous forest fragmentation studies (Estreguil, De Rigo, & Caudullo, 2014; O stapowicz et al., 2008; Riitters et al., 2009; Saura et al., 2011; Vogt et al., 2007) . Any amount or combination of forest canopy cover loss and increased forest fragmentation will be defined as forest degradation . Forest degradation in Mexico has been st udied in relation to sprawling colonization, increased and intensified agriculture and livestock production, and various types of resource extraction, including timber. The establishment of NPAs has been characterized as the primary strategy to mitigate fo rest degradation in Mexico (Figueroa et al., 2009; Geldmann et al., 2013; López Granados, Mendoza, & González, 2014; Rayn & Sutherland, 2011; Román Cuesta & Martínez Vilalta, 2006) . However, few studies have examined the effectiveness of NPAs at the national sca le in Mexico and none of them have done it for dates beyond the year 2000 . Despite the lack of research on the effectiveness of NPAs , this strategy for protecting forests continues to be prescribed. The purpose of this research is to analyze the relationsh ip between the presence of NPAs and the change in forest canopy cover and forest fragmentation during the 2000 2010 period . Forest change inside NPAs is compared to forest change in three concentric buffer rings around NPAs to determine if any significan t difference in the two metrics exists. More specifically, this research address es the question : Were natural protected areas (NPA) in Mexico effective in preventing forest cover loss and increases in forest fragmentation during the 2000 2010 period when compared with the areas around the NPAs ?

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4 Literature Review Natural Protected Areas Natural protected areas (NPAs) in Mexico are federally decreed lands that are preserved or restored under the administration of the National Commission of Natural Protecte d Areas (CONANP). The ownership of these lands can be: federal, state, municipal, community, ejido (state supported, community owned lands) , or private . They have many purposes, including biosphere reserves, national parks, national monuments, protection a reas of natural resources, p rotection areas of flora and fa una, and sanctuaries. As of February 2018, CONANP lists 174 NPAs , ( http:// www.conanp.gob.mx/ ). ecosystems, biodiversity, forests, and carbon stocks (Figueroa et al., 2009; Figueroa & Sánchez Cordero, 2008; Geldmann et al., 2013; Rayn & Sutherland, 2011; Román Cuesta & Martí nez Vilalta, 2006) . However , non state ownership of NPAs ha s led to concern about the consistency of land management with respect to tenure. Blackman et al. (2015) found that NPAs farther from cities and managed by communities largely populated by indigenous people experienced more deforestation than non protected surrounding areas. This is thought to be the result of a lack of monetary and management resources , invoking the tragedy of the commons. Conversely, a meta analysis of community managed forests uncovered lower rates of deforestation than in protected areas in the tropics (Porter Bolland et al., 2012) .

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5 NPAs in Mexico operate with far lower budgets than protected areas in the US, Canada, and Europe . In 2010, NPAs in Mexico receive d an average of $ 2.12 USD per hectare from the government, about 61% of their total funding (Bovarnick et al., 2010) . This can be compared to $ 28 USD per hectare in other western countries (Blackman et al., 2015) . Management costs are important considering that 47% of tropical forests and 23% of temperate forests are in effecti ve proximity to anthropogenic pressures , such as population centers and roads (Moreno Sanchez et al., 2012) . Additionally, 87% of land use/land cover change ( LULCC ) in biosphere reserves has been linked to socioeconomic conditions , the key three being population density, percent indigenou s population, and population size (Figueroa et al., 2009) . A common method for assessing the effectiveness of NPAs is to compare changes within NPA boundaries to buffer areas outside the protected area (Mas, 2005) . This has been performed for protected area in Mexico (Blackman et al., 2015; Figueroa & Sánchez Cordero, 2008; Mas, 2005; Rayn & Sutherland, 2011) , as well as other parts of the world (Nagendra, 2008; Spracklen et al., 2015) . There is no standard or consensus for the appropri ate buffer distance to compare LULCC within and outside of NPAs. Distances from 0.5 km to the equivalent of the NPA 10 km is most frequently found in the literature (Mas, 2005; Rayn & Sutherland, 2011; Sánchez Azofeifa et al., 2003; Southworth et al., 2004; Spracklen et al., 2015) . This literature review reveals that studies report very little or no validation for choosing buffer zone distances to compare LULCC or forest cover change against changes occurring in NPAs. Mas (2005) does not explain why a 10 km buffer zone is appropriate . Rayn and Sutherland (2011) reports the 10 km buffer is recommended by Sánchez Azofeifa et al. (2003)

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6 and Southworth et al. (2004) . Sánchez Azofeifa et al. (2003) provides no sources or recommendations for using a 10 km buffer, but does use it in addition to a 0.5 km and 1 km actually use a 10 km buffer, but rather a 5 km buffer an d explictly refers to it as arbirary. Spracklen et al. (2015) uses a number of buffer zones at 1 km increments between 0 and 5 km and 5 km increments between 5 and 15 km, but provides no sources of validation. Blackman et al. (2015) used a buffer distance of 20 km with no sources of validation. Figueroa et al. (2008) used a dynamic buffer distance that created adjacent buffers containing the equivalent square area of the NPA (± 100 ha). No sources were provided for this method, but the logic is to compare the same amount of area. Critiques of using buffer areas have noted significant geophysical differences between the area inside NPAs and their buffers (Mas, 2005) . Specifically, distance from roads, distance from settlements, soil type, elevation, and slope. Differences inside and outside protected areas can potentially alter the propensity for forest cover change to occur. Sixty percent of NPAs in Mexico, analyzed by Mas (2005), showed significant geophysical differences (regarding the five variables listed) from their surrounding areas. The author describes soil type and distance from population centers and roads as the best predictors of forest cover change, but only for one NPA, the Calakmul Biosphere Reserve. To compare areas with the same geophysical characteristics, matching techniques are employed. Mat ching techniques select areas within a buffer zone under the same geophysical circumstances occurring within the NPA and exclude the rest of the buffer zone from being compared (see Figure 1).

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7 Figure 1. a) Traditional buffer area shown against b) a buffe r area created using matching techniques. Source: Mas (2005) Matching techniques have been used to evaluate the effectiveness of NPAs in Mexico to prevent forest cover loss in one other study (Blackman et al., 2015) . The authors matched buffer zone area where several variables were similar within NPAs , including land tenure , percentage indigenous population, travel time to nearest city (population > 15,000), elevat ion, slope, and median annual precipitation. The variables were chosen using Mahalanobis distance metric, explained in Andam et al. (2008) . Studies outside of Mexico that implement simi lar matching techniques for NPAs include: Ament & Cumming (2016) and Andam et al. (2008) . Another fa ctor to consider in analyses that compare NPAs to their buffers is the concept of spillover effects , sometimes referred to as leakage . Spillover effects occur when LULC C in the NPA or its adjacent surrounding area spur changes across the protected area boundaries. It is thought that the protection by NPAs makes the surrounding area more resilient . Spillover effects have been argued as a main reason for overestimating the effectiveness of NPAs (Ament & Cumming, 2016) . However, spillovers can produce negative as well as positive effects (Ament & Cumming, 2016; Andam et al., 2008) . Stra tegies to prevent confusing the effects of NPA with

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8 spillover effects include matching methods and using concentric buffer rings (Blackman et al., 2015; Spracklen et al., 2015) . Other methods to assess t he effectiveness of NPAs on forest cover change compare the rate of change within the protected area boundaries before and after being decreed. Of these studies, there is no agreement on the appropriate ranges of time to compare (Nagendra, 2008; Rayn & Sutherland, 2011) . Usin g this method can be problematic because looking only at forest cover does not consider changes outside of protected area boundaries. For example: A protected area may experience less forest cover loss afte r being decreed, but it is still not an indication of how well it is protecting forests relative to outside protected area boundaries . Additionally, changes in anthropogenic pressure s or climatic events such as El Niño, fires, or storms can confound result s . Deforestation Deforestation in Mexico has remained a concern for ecosystems, biodiversity, carbon sequestration, the chemical composition of soils, erosion, and flooding (Blackman et al., 2015; Ochoa Gaona & González Espinosa , 1999; Pineda Jaimes et al., 2010) . Between 1976 and 2000 Mexico experienced an average annual rate of deforestation of 0.25% for temperate forests and 0.76% for tropical forests (Mas et al., 2004) . During this time period, forest cover loss has been also been observed at a state level, regionally, and in individual protected a reas (Bocco et al., 2001; Ochoa Gaona & González Espinosa, 1999; Pineda Jaimes et al., 2010; Vidal, López García, & Rendón Salinas, 2014) . Deforestation has been attributed to incentivized colonization,

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9 increased agricultural and livestock activities, as well as commercial and illegal logging (Pineda Jaim es et al., 2010) . Although deforestation has been found at multiple scales, d efining and measuring deforestation is not consistent throughout the literature . Hansen et al. (2013) defines forest land uses excludes case s in which natural forests are cleared and natural regeneration occurs or a managed forest is established. Ha forest does not include plantations, orchards, or other agricultural areas where tree cover is present. Most studies do not include a definition of deforestation, but rather imply their definition through how deforestation is c alculated in their methods. Deforestation has often been measured through calculating the annual rate of forest cover loss (Blackman et al., 2015; Mas, 2005; Mas et al., 2004; Ochoa Gaona & González Espinosa, 1999; Pineda Jaimes et al., 2010) . Others differentiate between deforestation and forest degradation . Vidal et al. (2014) considered areas with <10% forest canopy cover deforested and degra dation as forest experiencing considerable loss while still having more than 10% canopy cover. All of the methods identify the extent of forest cover either through land cover (LC) data sets or through classification methods of satellite or aerial imagery. Forest cover change has been used to evaluate the effectiveness of NPAs in Mexico in several studies. Rayn and Sutherland (2011) examined 28 NPAs from 19 73 to 2000 using two methods: the authors compared the extent of forest cover in NPAs before and after being federally decreed and compared forest cover change within and outside of NPA boundaries. No

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10 significant difference was found in the rate of forest cover change before and after becoming decreed or within and outside of NPAs. The LC data used in this paper is not presented in detail and therefore, it is difficult to assume a level of confidence in the results. The analysis begins before officially san ctioned national LC data sets were released and unsupervised classifications were performed on LANDSAT imagery to define the extent of forest cover. No validation methods of the classification or accuracy assessments are mentioned. Blackman et al. (2015) evaluated the effectiveness of NPAs in Mexico to reduce deforestation from 1993 to 2000 . Th e analysis included all 56 NPAs decreed prior to 1993. The evaluation compared plots within and outside the borders of NPAs with similar socioeconomic, geophysical, and climatic variables to eliminate any confounding variables that may make an area more prone to forest cover change. The authors found no difference , at a national level , between forest cover change inside NPAs and outside their borders . H owever, geographic variation did exist. The analysis was repeated at a regional level for each of the n ine regions designated by the Comisión Nacional de Áreas Naturales Protegidas ( CONANP ) . They found that NPAs in region 3 (Norte y Sierra Madre Occidental) spurred forest cover loss; NPAs in region 6 (Peninsula de Yucatan y Caribe Mexicano) experienced less forest cover loss; and in region 9 (Frontera Sur, Istmo y Pacifico Sur), forest cover loss increased outside of NPA borders. Across all regions, forest cover loss was significantly lower in 36% of NPAs compared to outside their borders, while 27% of NPAs were found to experience more forest cover loss. At a local level , Mas (2005) compared forest cover change in the Calakmul Biosphere Reserve against two buffer areas; the first, a 10 km buffer, and the second, a buffer area

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11 delineated using matc h ing methods which determine d an area with similar environment al characteristics . The analysis uses Instituto Nacional de Estadística y Geografía (INEGI) Series II and Series III LC data sets to determine the extent of the forest cover . Mas (2005) found the rate of deforestation to be 1% highe r in the 10 km buffer zone than in the NPA , and 0.3% higher in the matched buffer area and than in the the NPA. The study concludes that the Calakmul Bioshpere Reserve is effective at prevent ing forest cover loss and that standard buffer zones (without mat ching methods) can inflate the effectivesness of NPAs . However, this study only looks at one NPA and does not use any statistical tests to determine if these results are statistically significant . Forest Fragmentation Forest fragmentation is the division of a conterminous forest into smaller, patchier, and/or more exposed forest units (Clay et al., 2016; Moreno Sanchez et al., 2012) . It is important to consider because it contributes to the decreases in the connectivity of habitats and increases the ir vulnerability through increased exposure, known as edge effects (Harper et al., 2005; Mas et al., 2004; Moreno Sanchez et al., 2012; Vidal et al., 2014) . Forest fragmentation has been known to result in the loss of species, due to isolation, changes in available resources, and altering the i nteractions between ecosystems (Fischer & Lindenmayer, 2007; Murcia, 1995; Tabarelli, Lopes , & Peres, 2008; Tabarelli, Silva, & Gascon, 2012; Vellend et al., 2006) . These effects have been found in both temperate and tropical forests and can take up to a century to occur (Clay et al., 2016; Tabarelli et al., 2012; Vellend et al., 2006) . Fragmentation has also been shown to alter forest microclimates, decreas e carbon

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12 sequestration, perpetuate early succession, and increase tree mortality (Harper et al., 2005; Laurance, 2000; Laurance et al., 2011; Tabarelli et al., 2008) . Fragmentation is often measured using pattern oriented landscape model s (Fischer & Lindenmayer, 2007) . These models use human perceived patterns in land cover to analyze the exposure, isolation, and connectivity of vegetation or land uses. Since pattern oriented landscape models use abstractions of ecological processes, limitations exist in their consideration of the complex interactions between environmental processes and individual species. Additionally, the scale of the data set s b eing used for the analys e s can drastically alter the results . Interior patches of fragmentation, or perforation, as well as exterior intricacies of land cover may not be visible using small scale data sets and are reported as core or edge areas (Riitters et al., 2000) . Therefore, small scale data sets can lead to underestimation of fragmentation. The difference in measurements between sca les also makes comparisons across scales difficult . One of the most common pattern oriented landscape models is the Morphological Spatial Pattern Analysis (MSPA) algorithm. Based on the framework of Soille & Vogt (2009) , the algorithm segments a binary spatial pattern ( e.g., a land use/land cover class such as forest and a background land cover class such as non forest) into seven mutually exclusive fragmentation classes : core, i slet, perforation, edge, bridge, loop, and branch (see Methods for definitions ). This allows fragmentation to be quantified and compared spatially and temporally . The MSPA is sanctioned and endorsed internationally by forestry and environmental agencies an d has been used in numerous studies (Clay et al., 2016; Estreguil et al., 2014; Ostapowicz et al., 2008; Riitters et al., 2009; Saura et al., 2011; Vogt et al., 2007) .

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13 However, output s from pattern oriented landscape models can be interpreted in different ways. McIntyre and Hobbs (1999) present a framework to classify the state of alter ation experienced by a particular habitat. This includes four states of increased alteration : intact , where more than 90% of habitat remains; variegated, 60 90% of habitat remains; fragmented, 10 60% of habitat remains; and relictual, 10% or less of ha bitat remains. This method can be seen in later studies (Fischer & Lindenmayer, 2007) . However, this framework has li mitations. It requires performing a change detection analysis to quantify the remnant habitat . It cannot quantify fragmentation in a snapshot of time, but rather alteration between two time periods . The framework introduced by McIntyre and Hobbs (1999) do es not completely rely on percent change in habitat to classify the level of alteration. It is also concerned with connectivity, the degree of modification, and the pattern of modification. When considering all of these factors, it can be difficult, even s ubjective, to classify the level of alteration. For example, the authors indicate intact habitat should have a high level of connectivity, while fragmented habitat should have a low level of connectivity. However, if a habitat is reduced by 40 to 90%, but the habitat is still highly contiguous and connected, disagreement in the classification may occur in this framework. Other studies group particular classifications together to quantify fragmented and non fragmentated areas at particular points in time (Clay et al., 2016; Moreno Sanchez et al., 2012; Moreno Sánchez et al., 2014) . Moreno Sanchez et al. (2012, 2014) utilizes a moving wind ow approach, devised by Riitters et al. (2000) , which analyzes each pixel relative to a local neighborhood grid of pixels, segmenting a binary spatial pattern into one of six classes: interior,

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14 perforated, edge, transitional, patch, or undetermined. Fragmented categories were considered edge, transitional, and patch. This has been performed s imilarly with the MSPA algorithm. Clay et al. (2016) fragmentation classes loop, islet, bridge, and branch were defin ed as fragmented classes. Results from these methods can vary , depending on what is defined as fragmented classes. Additionally, they do not reveal any temporal trends in fragmentation, such as the McIntyre & Hobbs ( 1999) framework. However, these methods are easily replicable and by quantifying fragmented classes, they make defining the extent of fragmentation explicit. Landscape metrics that capture fragmentation have also been generated using FragStats . Moreno Sanchez et al. (2011) use d 9 of the 60 metrics produced by FragStats to provide a national a ssessment of forest fragmentation in Mexico. Th e selected metrics include: the area covered by forest, percent of land covered by forest, the number of patches, the largest patch index, average patch size, the shape of patches, average patch fractal dimens ion, average patch proximity, and average nearest neighbor difference . Definitions can be found in Moreno Sanchez et al. (2011) ). Although FragStats offers uniqu e statistics from other fragmentation analysis methods, it comes with its own set of limitations. The results are largely affected by the resolution of the input image. The metrics are computed from rooks case contiguity, as opposed to queens case or bisho ps case. Additional limitations include the double count method and fractal dimension moving windows (further explained here: http://www.umass.edu/landeco/research/fragstats/documents/Metrics/Contagion%20 %20Interspersion%20Metrics/Limitations.htm ). Due to the large number of metrics and the ir

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15 complexity, it is difficult to consistently and confidently measure fragmentati on in the most appropriate way using FragStats . Land Cover Data Sets Land cover (LC) is described as the biophysical attributes that comprise the surface and immediate sub surface of the Earth, including LC such as: forest, shrubland, cropland, grassland, barren land, waterbodies, and built up areas (Grekousis, Mountrakis, & Kavouras, 2015; Giri, 2012; Turner, Meyer, & Skole, 1994) . Land cover change (LCC) occurs when one LC classification is converted to another, or modified within a LC (Grekousis et al., 2015; Turner et al., 1994) . National Institute of Statistics and Geography (INEGI) produ ces the national authoritative LC data set s for the country . There are six data sets available known as Series I through VI. They correspond to the years 1985, 1993, 2002, 2007, 2011, and 2014 respectively (Victoria Hernandez et al. 2008; INEGI 2012; Niño Alcocer et al. 2013; INEGI 2014 ; INEGI 2017 ) . Each series is produced at a scale of 1:250,000 with a minimum mapping unit (MMU) of 25 50 ha and contain 73 thematic classes (Gebhardt et al., 2015) . The datasets are provided by I NEGI in an ESRI (Redlands, CA) S hapefile format , in Lambert Conical Conformal projection. The data sets , excluding Series I, have been homogenized with respect to their land cover/use classes and therefore are appropriate for carrying out temporal change d etection . They also have undergone accuracy validations through ground truthing (Figueroa & Sánchez Cordero, 2008; Gebhardt et al., 2015) . Change detection using Series I is not advised due to numerous inconsi stencies found between data sets (Velázquez et al., 2010) .

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16 The INEGI LC data sets have been considered highly accurate due to incorporation of over 10,000 validation points (Díaz Gallegos, Mas, & Velázquez, 2010; Figueroa & Sánchez Cordero, 2008; Gebhardt et al., 2015; Mas et al., 2004) . However, INEGI does not formally release any accuracy assessment information of the extent of each LULC class and few independent assessments have been performed. Overall , the evaluations from Mas et al. (2004) indicate s a thematic accuracy of more than 70%. MAD Mex, a land cover product produced using INEGI LC as reference data, has a producer accuracy in primary temperate for est between 59.54% and 73.54% while primary tropical forests were found between 52.28 and 82.97%. Secondary forests were found between 45 and 52.34% with the majority of confusion occurring between primary and secondary forests (Gebhardt et al., 2015) . The MAD Mex LC data set s are produced through an automated supervised classification, rather than a visual interpretation of land cover that is used to make the INEGI LC data sets . Although these methods of producing LC data sets are different, the MAD Mex data sets offer the most comparable LC classes to classes in the INEGI LC data sets due to the use of the same reference data sets . GlobeLand30 (GL30), produced by the National Geomatics Center of China (NGCC), is a global LC dat a set that has potential to enable forest cover and f orest f ragmentation analysis in Mexico (available at: www.globallandcover.com ) . It is distributed at a spatial resolution of 30 meters and contains 10 thema tic classes (Ran & L i, 2015) . A global accuracy assessment reported an overall accuracy of 80%, which can be compared to less than 65% for other global LC data sets (Chen et al., 2015) . In G L 30, f orests showed accuracies between 83 % and 84%. The overall accuracy of thematic classification has been verified by other studies in Ita ly, Germany, and Iran (Arsanjani, See, & Tayyebi, 2016; Arsanjani, Tayyebi, & Vaz, 2016; Brovelli et al., 2015) .

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17 In Mexico , forests can be highly diverse and be found in complex landscape mosaics. To assess the accuracy of the GL30 forests class in Mexico , Carver ( 2017) used two methods : 1) intersection with reference data set points ; and 2) percent forest coverage by area . To determine the accuracy of the GL30 forest classification algorithm , two GL30 data sets were assessed, GL30 2000 and 2010, and compared to respective forest inventory data sets from the Inventario Nacional Forestal y de Suelos data set for 2004 2009 ( INFyS0 ) and for 2009 2013 (INFyS1) . The intersection method returned correct classification matches with 77.2% of the INF yS0 reference data points and 79.6% of INFyS1. For temperate forest s , the results averaged 74.3% accuracy and tropical forests 89.2%. In the second method, the reference data sets characterized the forest into three categories: all forest, partial forest, or no forest. T he area surrounding the point was compared to the characterization . The overall accuracy of the classification was 84.4% for INFyS0 and 80.4 % for INFyS1. Temperate forests averaged 77.5% correct classification across both forest inventory da ta sets (INFyS0 and INFyS1) and which topical forests averaged 91.5% for the same data sets . Carver (2017) is the only study to analyze the accuracy of the forest class in GL30 for Mexico. The results were found to differ from other studies around the world. Chen et al. (2015) and Brovelli et al. (2015) were the only other studies to in dividually examine the forest class. These studies found an overall forest accuracy of 92.4% and 81% respectively. All t hese studies validate the high level of accuracy for the forest class in GL30 across different environmental conditions and forest type s .

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18 II. METHODS Hypotheses H 0 : There is no difference in forest cover and forest fragmentation change between natural protected areas and their buffer areas. H 1 : There is less forest cover loss and decreased fragmentation in natural protected areas than in their buffer areas. H 2 : There is more forest cover loss and increased fragmentation in natural protected areas than in their buffer zones Analysis Identify the extent of the forest cover for years 2000 and 2010. The spatial extent and arrangement of the forest canopy cover is delineated through the use of land cover (LC) data sets. These spatial data provide geographic reference of forest boundaries for the quantification of forest area in change detection . LC data sets are also necessary inputs for assessing t he level of forest fragmentation in pattern oriented landscape models , such as the one used here ( s ee Methods section 3.2.2) . Performing change detection through a post classification method, such as comparing LC data sets, has shown to be the most accurat e method in Mexico (Mas, 1999) . GlobeLand30 (GL30) LC data set s for year s 2000 and 201 0 were used to determine the spatial extent and arrangement of forest cover at the national level . These data sets contain 10

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19 thematic class es : water bodies, wetland, artificial surfaces, tundra, permanent snow and ice, grassland, bareland, cultivated land , shrubland, and forest. For the purposes of this analysis, only the forest class was considered. The G L 30 data sets were used to delineate forests due to the high classification accuracy and also because the ir 30 meter spatial resolution enables greater r epresentation of forest fragmentation than the INEGI LC S eries, the authoritative national data sets. T he GL30 forest class does not include differences in more specific types of forests . T he different t ypes of forest s existing in Mexico are classified wit h varying degrees of accurac y (Carver, 2017; Gebhardt et al., 2015) and have been shown to experience different rates of defores tation and forest fragmentation (Mas et al., 2004; Moreno Sanc hez et al., 2012, 2011; Moreno Sánchez et al., 2014) . To examine the relationships of different forest types with natural protected areas, the INEGI Series LC data sets are used to further delineate the extent of forest cover in GL30 into more specific th ematic classes . INEGI Series III (2002) is used for the delineation of GL30 2000 and INEGI Series IV (2008) to further delineate GL30 2010. Seven classes were defined for the analysis : 1) all forests; 2) tropical forests; 3) temperate forests; 4 ) tropical primary forest; 5 ) temperate primary forest; 6 ) tropical secondary forest; and 7 ) temperate secondary forest . Tropical and temperate forests were distinguished due to a precedence in the literature demonstrating differences in deforestation and fragmentat ion rates in Mexico (Mas et al., 2004; Moreno Sanchez et al., 2012, 2011; Moreno Sánchez et al., 2014) . Primary and secondary forests were distinguished based on the assumption that disturbance and loss of primary forests leads to increases in secondary forest (Mas et al., 2004) .

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20 GL30 2000 and 2010 data sets were acquired as GeoTiffs from the National Geomatics Center of China for the ext ent of Mexico (available at: http://www.globallandcover.com/GLC30Download/index.aspx). INEGI Series III and Series IV were acquired as Shapefiles from INEGI (available at: http://www.inegi.org.mx/ ). In preparing the data sets , GL30 and INEGI data sets were each tiled (mosaicked) to create contiguous rasters across Mexico . An administrative was acquired as a Shapefile (available at: http://www.naturalearthdata.com/). All data sets were then re projected into North American Albers Equal Area Conic (EPSG: 102008). GL30 and INEGI LC Series data sets were masked to the extent of the Mexico boundary Shapefile. Due to the high thematic resolution of INEGI LC data sets, many classes were merged to create the subclasses of forest s defined for our study (see Appendix I) . However, the forest extent in the INEGI LC classes did not completely match the forest extent in GL30. Two problems existed: 1) the extent of INEGI LC exist s beyond the exte nt of GL30; and 2) the extent on GL30 is not completely covered by INEGI LC. For the first problem, the forest extent found in GL30 was determined as the authoritative extent due to the high spatial resolution and reported high accuracy. The second problem pose d challenges in determining which subclass of forest should be present when multiple classes exist in a contiguous patch of forest. To resolve these spatial uncertainties , the nearest forest class was assigned to the unclaimed extent of GL30. To deter mine the nearest forest class, for each subclass of forest , Euclidean distance was calculated from the forest sub class boundar y at a cell size of 30 meters . This produce d six Euclidian distance raster s for each year, 2000 and 2010. The six rasters were the n compared ,

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21 assigning each pixel the classification of the raster with the least distance . The new raster was then masked to the extent of the GL30 forest class. This create d a forest cover data set with spatial extent of the forest cover found in GL30, bu t included of all the forest subclasses constructed from the INEGI Series LC data sets . Figure 2 shows all forests as a single class defined for this study ; F igure 3 shows temperate and tropical forests ; and F igure 4 shows primary temperate, secondary temp erate, primary tropical, and secondary tropical forests. Figure 2. The spatial extent and arrangement of all forests as a single classification in Mexico.

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22 Figure 3. The spatial extent and arrangement of temperate and tropical forests in Mexico.

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23 Figu re 4. The spatial extent and arrangement of primary temperate, secondary temperate, primary tropical, and secondary tropical forests in Mexico. Calculat ing the level of fragmentation of forest cover for years 2000 and 2010. The Morphological Spatial Patter n Analysis ( MSPA ) algorithm is used to classify forest fragmentation levels . This is performed using Guido s Toolbox 2.5 (available at: http://forest.jrc.ec.europa.eu/download/software/guidos/) . In accordance with the MSPA guide, input data is required to be stored in an 8 bit data type and is re s trained to contain three different values: 0 = missing data (optional); 1 = background (required); and 2 = foreground (required) (Vogt, 2010) . F igure 5 illustr ates generic input data for the MSPA.

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24 Figure 5. An example of the required input for the MSPA algorithm containing two values 1 for background and 2 for foreground. Source: The MSPA Guide (available at: http://forest.jrc.ec.europa.eu/download/software/gu idos/mspa/) This algorithm produces a fragmentation raster by reclassifying the foreground area into one of seven classes: Core, Islet, Perforation, Edge, Bridge, Loop, and Branch. A description of each class is provided in Table 1 and an example illustrat ion of the output in Figure 6 . Table 1. Fragmentation classes created by performing the MSPA method. (following Wickham, Riitters, Wade, & Vogt, 2010) Fragmentation Class Definition Core Forest cells without any side exposed to non forest cells and separa ted from non forest cells by the declared MSPA edge width. Islet Forest cells exposed to forest cells and non forest cells on at least one side with no Core area. Islet is isolated from all other classes. Perforation The interior edge separating Core and non forest area in contiguous forest.

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25 Table 1 . Fragmentation classes created by performing the MSPA method. (following Wickham, Riitters, Wade, & Vogt, 2010) Edge The exterior edge separating Core and non forest area. Bridge Forest cells conne cting multiple separate Core areas. Loop Forest cells extending from and returning to a single area of Core. Branch Forest cells extending from Core without intersecting any Core area. Figure 6. An example output of the seven fragmentation classes fro m the MSPA algorithm . Source: (Moreno Sanchez et al., 2018). The MSPA analysis parameters require designating either queens case contiguity (8 neighbors) or rooks case (four neighbors; adjacent cells). The default queens case was selected. The Edge Width w as then set to a value of 3 pixels (90 meters). This parameter designates the cell width of non core pixels. This edge width was determined by a number of studies that indicate core area can be established at approximately 100 meters from the forest edge ( Cayuela, Murcia, Hawk, Fernandez Vega, & Oviedo Brenes, 2009; Galetti, Alves Costa, & Cazetta, 2003; Oosterhoorn & Kappelle, 2000; Schedlbauer, Finegan, & Kavanagh, 2007;

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26 Schlaepfer & Gavin, 2001). The option to show transition areas was selected. Transiti on areas show where multiple core areas are connected when the width of the connection is less than or distinguishes internal and external classes of fragmentation. When Intext is on, the output will contain 14 classes. The base 7 fragmentation classes for the exterior and for the interior. Interior fragmentation is defined as occurring within perforation. All seven forest types defined for our study were then prepared fo r the MSPA analysis . For each forest type , a new raster was created by assigning the extent of the forest cover to a value of 2 , foreground. All data values that do not include the forest class were assigned a value of 1 , background. The area s that contain ed no data or null values were assigned a value of 0, missing data. The GDAL command line utilities (gdal_calc ; http://www.gdal.org/gdal_calc.html ) were used to reclassify the data and convert it into an 8 bit GeoTiff file . After pr eparing the data, a batch MSPA was performed ( batch, due to the large size of the data) on each GeoTiff file. Once the batch MSPA analysis had completed , GDAL command line utilities (gdal_calc) were used to reclassify each GeoTiff into three classes: 1) fr agmented forest (composed of the MSPA fragmentation classes branch, loop, bridge, and islet) ; 2) non fragmented forest (composed of the MSPA fragmentation classes core, edge, and perforation) ; and 3) non forest. No data values were left with a value of nul l. All other values were classified as non forest. The designation of these classes follo wed the methods of Clay et al. (2016) building off the assumptions in Moreno Sánchez et al. (2014) . These state the order of f ragmentation, from least to most, as: interior, perforated, edge, transition, then patch. Islet, loop, branch, and

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27 bridge were not defined in Moreno Sánchez et al. (2014) , but these classes exhibit edge exposure more closely to transition and patch than other classes. This compari son ranked them as the more fragmented classes. Creat ing concentric buffers around natural protected areas. Spatial data on n atural protected areas were acquired from the Comisón Nacional de Áreas Naturales Protegidas (CONANP) ( https://www.gob.mx/conanp ). This data set contains the authoritative boundaries for all federally decreed NPAs in Mexico. It includes 176 NPAs, terrestrial and marine , and is distributed in an ESRI (Redlands, CA) S hapefile format , in Lambert Conical Conformal projection . This data s et was prepared by re projecting it into North American Albers Equal Area Conic (EPSG: 102008) for consistency with the LC data sets . NPAs analyzed in the study when they met the following requirements: a) forest cover was greater than or equal to 1,000 ha . This size was chosen, because it has been suggested as the minimum for ecosystem conservation in a NPA (Figueroa & Sánchez Cordero, 2008) ; b) The NPA must have been federally decreed prior to the year 20 00. The analysis covers forest transition s from year 2000 to 2010 . T herefore , NPAs decreed after the year 2000 were not protected for the full time period of the analysis and were excluded . 84 NPAs met these requirements and were included in this study (se e Appendix II) . Buffer areas have proven useful in assessing the effectiveness of protected areas (Mas, 2005; Spracklen et al., 201 5) . To determine the appropriate buffer areas outside of NPAs , previous studies consider ed three factors: spillover effects, buffer sizes , and matching the

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28 vegetative composition . Spillover effects are k nown to artificially produce over estimates and under estimates of NPA effectiveness (Ament & Cumming, 2016; Andam et al., 2008) . As a method to avoid spillover effects , three concentric buffer rings were created around NPA bo undaries (Spracklen et al., 2015) . T hese concentric buffer rings were set at distances of 0 0.5 km, 0.5 1 km, and 1 2 km. They are hereinafter referred to as the 0.5 km, 1 km, and 2 km buffer zones respectively. The first two sizes follow Sánchez Azofeifa et al. ( 2002) . However, the third distance used in Sánchez Azofeifa et al. (2002) , 10 km, has been cited as arbitrary (So uthworth et al., 2004) . To remain consistent with the current buffer size increments, 2 km was chosen. These distances are assumed to contain spillover effects. This assumption was based on the work by Rayn and Sutherland (2011) who found that interior ar eas of NPAs experienced the least amount of forest change. This suggests that spillover effects, on average, begin to decrease even before the y reach NPA boundaries. The relatively small distances used in the concentric buffer rings helps to minimize topo logical, vegetative, and climatic differences between the NPAs and their buffer areas . No matching techniques were used to compare areas inside NPAs to similar areas outside. Matching techniques are only as reliable as their ability to correctly identify c omparable areas outside NPAs, or control points (Blackman et al., 2015) . This process involves selecting the proper covariates and weig hting them appropriately. Due to the lack of literature describing successful implementation of matching techniques for the evaluation of NPAs, matching techniques were not incorporated to reduce basis. However, the fact that areas inside and outside NPAs are not always comparable , produces limitations to this study .

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29 To create the concentric buffer rings , the B uffer ( A nalysis) tool in ArcMap 10.4 was used for each buffer distance. Using the Multiple Ring Buffer (Analysis) tool creates inconsistencies where the buffer areas of different NPAs meet , compared to using the Buffer (Analysis) tool three times independently. Following the creation of the buffers, the Erase (Analysis) tool was used to remove any interio r NPA or buffer area . For the 0.5 km buffer, th e interior NPA was erased. For the 1 km buffer area, the interior 0.5 km buffer and NPA was erased. For the 2 km buffer area, the interior 1 km and 0.5 km buffer areas and the NPA was erased. The result was three Shapefiles containing only the buffer zones outside the NPAs: 0 0.5 km , 0.5 1 km , and 1 2 km. Some NPAs are located along coastlines, therefore some buffer area s extended beyond the terrestrial boundaries of Mexico and into marine areas. None of these marine areas are able to support the pre sence of forests. To remove these areas from the study, all buffer areas were masked to the Mexico boundary Shapefile. Calculat ing the level of forest fragmentation change and forest cover change in and around natural protected areas. The ArcGIS 10.4 Tabul ate Area Tool (http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial analyst toolbox/tabulate area.htm) was used to quantify the area covered by forests within the NPA and each of the 3 concentric buffer rings. The same tool was used to calculate the area cover by each MSPA fragmentation class also within the NPA and each of the 3 concentric buffer rings. These calculations were performed for all the forest types defined in this study for year s 2000 and 2010. The results

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30 were stored as comma separated valu e (CSV) files. To determine the change in forest cover and forest fragmentation, the CSV files were read into R Studio using R (https://www.rstudio.com/) . In R Studio, the CSV files were converted to data frames. All data frames were then consolidated int o a single data frame with columns for: A unique NPA identification number, forest cover area for year 2000, forest fragmentation area for year 2000, forest cover area for year 2010, forest fragmentation area for year 2010, the forest type (all forests, tr opical forests, temperate forests, primary tropical forests, primary temperate forests, secondary tropical forests, and secondary temperate forests), and the buffer zone (NPA, 0.5 km, 1 km, 2 km) . The change s in forest cover and forest fragmentation were c alculated as a percent using the formula : where is the forests for year 2000 and is the forests for year 2010. Changes for each NPA and concentric buffer were stored in two new columns , forest cover c hange and forest fragmentation change. Statistical analys e s of the relationship between NPAs and concentric buffers with forest fragmentation change and forest cover change. The statistical analysis was performed in R Studio using the statistical programmi ng language R. First, a Shapiro Wilk test was performed for each output to determine if parametric or non parametric statistics were applicable. The Shapiro Wilk is a test of normality, where the null hypothesis is that the data are normally distributed. T his test was performed for the

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31 distribution of percent change of forest cover and forest fragmentation for each forest type. The consolidated data frame is indexed by each forest type. For each index, the shapiro.test() method in R is used (https://stat.et hz.ch/R manual/R devel/library/stats/html/shapiro.test.html) . Following the Shapiro Wilk test, two statistical tests were used to compare NPAs to buffer zones. The Wilcoxon Rank Sum test , using the wilcox.test() method in R (https://stat.ethz.ch/R manual/ R devel/library/stats/html/wilcox.test.html) , and the Kolmogorov Smirnov (KS) test , using the ks.test() method in R (https://stat.ethz.ch/R manual/R devel/library/stats/html/ks.test.html) . A Wilcoxon Rank Sum test compares two groups by ranking the values in ascending order and then comparing the medians. The null hypothesis being there is no significant difference between medians. This is often thought of as the non parametric T test. The Kolmogorov Smirnov (KS) test compares two groups by quantifying the difference between the distributions and determining the probability of the difference existing by random chance. The null hypothesis is that the distributions are drawn from the same population . The significance threshold for both tests was an alpha value of 0.05. These two tests were performed on the same data to augment the statistical robustness of the results. To implement these tests , the distribution of changes in NPAs was compared to the distribution of changes for each buffer zone. Figure 7 illust rates the distribution of values.

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32 Figure 7. An example of the distribution between all changes in NPAs plotted against the distribution of all changes in a buffer zone . These comparisons were made for forest cover change and forest fragmentation change f or each of the 7 forest types defined in this study . In the national analysis, this result ed in 42 statistical comparisons (2 analys es * 3 buffer comparisons against the NPA * 7 forest types ). Following the national analysis, regional analyses were perform ed in each of the 9 Comisión Nacional de Áreas Naturales Protegidas ( CONANP ) regions to determine if geographic variation exist ed in the effectiveness of NPAs. This method follow ed the regional divisions in Blackman et al. (2015) to compare results . The NP As Shapefile came with the regions listed in the attribute table . CONANP regions can be accessed in an ESRI Shapefile format from CONANP (available at: http://sig.conanp.gob.mx/website/p agsig/info_shape.htm ). Figure 8 depicts the CONANP regions.

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33 Figure 8 . The nine CONANP regions where regional analyses are performed for each one . For the regional analysis 3 7 8 distributions are compared (2 analys es * 3 buffer comparisons against NPA * 7 forest types * 9 regions). Determining if NPAs were effective at reducing forest cover loss or increased forest fragmentation. For the purpose of this thesis, a NPA was defined effective at protecting forests when it met two conditions : 1) the distributi on of either forest cover change or forest fragmentation change occurring within the interior areas of NPAs is statistically significantly different from changes occurring within buffer zone s (for both the Wilcoxon Rank Sum and KS test); and 2) the median forest cover change that occurred in the interior areas of NPAs is higher than occurred

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34 within buffer zones, or the median forest fragmentation change that occurred in the interior areas of NPAs is lower than occurred within buffer zones. These conditions ensure d that changes within NPAs were significantly different than buffer zones and that the NPAs experienced either less forest cover loss or less forest fragmentation. Independent of statistical analysis, all forest cover changes and forest fragmentation changes were visually reviewed using density plots and box plots (explained in the Discussion) . The visual inspection sought notable trends that were not identified using the Wilcoxon Rank Sum test and the KS test. This was performed for each forest type class defined in this study , at a national level and for each CONANP region. The visual inspection compared the median forest cover changes and forest fragmentation changes between interior areas of NPAs and their buffer areas. Additionally, the overlap of their distributions was compared. Notable differences in medians or where distributions had little overlap were highlighted in the D iscussion . These steps looked to determine if trends existed beyond statistical significance. Visual inspection of the fore st change distributions was deemed necessary because of the small number of NPAs in CONANP regions. Significant p values in the Wilcoxon Rank Sum test and KS test are less likely with small sample sizes . Therefore, it is possible the statistical tests coul d miss significant differences.

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35 III. RESULTS Shapiro Wilk test results All outputs from the analyses returned p values of < 0.0 5 in the Shapiro Wilk test . This warranted the use of non parametric statistics. The distributions of the data largely resembled a ga ussian curve but were extremely leptokurtic and contained some extreme outliers. Table 2 contains the Shapiro Wilk test results for forest fragmentation change and Table 3, the Shapiro Wilk test results for forest cover change. Table 2 . Shapiro Wilk test r esults for forest fragmentation change. Forest Fragmentation Change Forest type Shapiro Wilk Statistic Shapiro Wilk p value All forests 0.66492 < 2.2e 16 Tropical forests 0.38061 < 2.2e 16 Temperate forests 0.26058 < 2.2e 16 Primary tropical forests 0 .084773107097657 4.96597952477498e 32 Primary temperate forests 0.120790185867175 2.1767651313543e 29 Secondary tropical forests 0.219740756523236 9.35297456758887e 24 Secondary temperate forests 0.288713869515284 6.21326067223933e 26 Table 3 . Shapiro Wilk test results for forest cover change. Forest Cover Change Forest type Shapiro Wilk Statistic Shapiro Wilk p value All forests 0.050051 < 2.2e 16 Tropical forests 0.1657 < 2.2e 16 Temperate forests 0.31517 < 2.2e 16 Primary tropical forests 0.046 722357602579 8.4927354008875e 33 Primary temperate forests 0.088875961671936 1.14214282592568e 30

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36 Secondary tropical forests 0.779696661199628 2.78441527039946e 13 Secondary temperate forests 0.257997289465273 6.85848267483149e 27 Wilcoxon Rank Sum an d Kolmogorov Smirnov test results These results describe how each statistical test relates to the hypothesis. The tables provide the test statistic and p value for each forest type and buffer zone. All forests When looking at forests as one thematic clas s across Mexico, neither the Wilcoxon Rank Sum or KS test indicate any significant difference between forest cover or forest fragmentation changes occurring in NPA and any concentric buffer ring ( Table 4 ) . The p values for both statistical tests are consis tently well above the alpha. Therefore, these tests show no evidence that NPAs experienced any difference in forest fragmentation or forest cover change than their concentric buffers. Any difference between them has a high probability of being due to rando m chance. We therefore are unable to reject H 0 for all forests . Table 4 . All forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. Forest Fragmentation Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 3280 0.43238 0.14286 0.35809 1 km buffer 3521 0.98355 0.08333 0.93245 2 km buffer 3358.5 0.59189 0.11905 0.59110 Forest Cover Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 3321 0.51242 0.09524 0.84070 1 km buffer 3652 0.69 523 0.14286 0.35809

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37 2 km buffer 3681 0.62855 0.11905 0.59110 Temperate and t ropical Forests When dividing forests into two thematic class es , temperate (Table 5) and tropical (Table 6) , neither the Wilcoxon Rank Sum or KS test indicate any significant d ifference between forest cover or forest fragmentation changes occurring in NPA and any concentric buffer ring . The p values for both statistical tests are consistently well above the alpha. Therefore, these tests show no evidence that NPAs experienced any difference in forest fragmentation or forest cover change than their concentric buffers. Any difference between them has a high probability of being due to random chance. We therefore are unable to reject H 0 for temperate and tropical forests . Table 5 . Te mperate forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. Forest Fragmentation Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 1914 0.78610 0.11111 0.83157 1 km buffer 1970.5 0.57501 0.13261 0.64179 2 km buf fer 1996 0.59708 0.10590 0.86860 Forest Cover Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 1839 0.91437 0.08567 0.97771 1 km buffer 1924 0.74701 0.111819 0.78363 2 km buffer 2031 0.48040 0.11290 0.82428 Table 6 . Tropical forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. Forest Fragmentation Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 1091 0.35015 0.13846 0.72428 1 km buffer 1284 0.91736 0.14027 0.69157

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38 2 km buffer 1188 0.36774 0.14095 0.68010 Tropical forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. Forest Cover Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 1118 0.45606 0.15412 0.58631 1 km buffer 1160 0.35040 0.16101 0.51675 2 km buffer 1161 0.22006 0.18736 0.31590 Primary and s econdary of t emperate and t ropical Forests When dividing forests into four thematic classifications, primary temperate (Table 7) , secondary temperate (Table 8) , p rimary tropical (Table 9) , and secondary tropical (Table 10) , neither the Wilcoxon Rank Sum or KS test indicate any significant difference between forest cover or forest fragmentation changes occurring in NPA and any concentric buffer ring . The p values fo r both statistical tests are consistently well above the alpha. Therefore, these tests show no evidence that NPAs experienced any difference in forest fragmentation or forest cover change than their concentric buffers. Any difference between them has a hig h probability of being due to random chance. We therefore are unable to reject H 0 for primary and secondary of temperate and tropical forests . Table 7 . Temperate Primary forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. Forest Fragmentation Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 1835.5 0.72973 0.08136 0.98927 1 km buffer 1743.5 0.86151 0.08475 0.98389 2 km buffer 1656.5 0.89237 0.10170 0.92044 Forest Cover Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value

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39 0.5 km buffer 1712.5 0.64793 0.13333 0.66038 1 km buffer 1717 0.78022 0.09237 0.96145 2 km buffer 1932 0.49006 0.11666 0.80882 Table 8 . Temperate Secondary forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. Forest Fragmentation Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 1351.5 0.28322 0.17647 0.40509 1 km buffer 1303.5 0.46417 0.14932 0.61424 2 km buffer 1316.5 0.40972 0.19608 0.28074 Forest Cover Change Zone Wilcox on Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 1451 0.63747 0.12809 0.79675 1 km buffer 1476 0.52770 0.11835 0.84399 2 km buffer 1473 0.43141 0.11835 0.84399 Table 9 . Tropical Primary forests Wilcoxon Rank Sum and Kolmogorov Smi rnov test. Forest Fragmentation Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 544.5 0.44092 0.16718 0.69754 1 km buffer 622.5 0.71811 0.08978 0.99870 2 km buffer 569.5 0.49707 0.20526 0.42650 Forest Cover Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 655 0.60469 0.19659 0.49183 1 km buffer 720 0.41725 0.17895 0.60396 2 km buffer 732 0.46986 0.14011 0.85535 Table 10 . Tropical Secondary forests Wilcoxon Rank Sum and K olmogorov Smirnov test. Forest Fragmentation Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 863.5 0.49144 0.13902 0.78909 1 km buffer 1034.5 0.56999 0.15042 0.68983

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40 2 km buffer 976 0.93123 0.12914 0.84804 Table . Tropical Secondary forests Wilcoxon Rank Sum and Kolmogorov Smirnov test. Forest Cover Change Zone Wilcoxon Statistic Wilcoxon p value KS statistic KS p value 0.5 km buffer 893 0.33691 0.17391 0.48985 1 km buffer 857 0.11825 0.20583 0.2782 6 2 km buffer 919 0.09414 0.20162 0.27921 Significant Regional Differences This section provides tables showing all instances where changes in NPAs were significantly different than changes in buffer zones. Significance was considered where the p value for both the Wilcoxon Rank Sum test and the KS test were below 0.05. Significant differences were only found in CONANP regions 4 (Table 10) , 6 (Table 11) , and 9 (Table 12) . In all other regions the changes in buffer zones were statistically indistinguisha ble from changes in side the NPAs. Table 1 1 . NPAs with significant differences from buffer zones in Noreste y Sierra Madre Oriental, region 4. Analysis Type Forest Type Zone Wilcox statistic Wilcox p value KS statistic KS p value Median Difference Frag ment ation All forests 0.5 km 3.477 0.001 0.800 0.001 54.613 Fragment ation All forests 1 km 2.721 0.007 0.600 0.031 26.411 Fragment ation Temperat e 0.5 km 2.495 0.013 0.700 0.007 62.844 Fragment ation Temperat e 1 km 2.495 0.013 0.700 0.007 16.956

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41 Table 1 2 . NPAs with significant differences from buffer zones in Peninsula de Yucatan y Caribe Mexicano, region 6. Table 1 3 . NPAs with significant differences from buffer zones in Frontera Sur, Istmo y Pacifico Sur, region 9. Fragment atio n Temperat e 2 2km 2.419 0.016 0.700 0.007 20.132 Fragment ation Primary temperate 0.5 km 2.343 0.019 0.600 0.031 69.199 Analysis Type Fore st Type Zone Wilcox statistic Wilcox p value KS statistic KS p value Median Difference Fragment ation All forests 2 km 1.995 0.046 0.625 0.050 42.789 Fragment ation Tropical 2 km 2.205 0.027 0.625 0.050 38.229 Analysis Type Forest Type Zone Wilcox statistic Wilcox p value KS statistic KS p value Median Difference Fragment ation Temperat e 0.5 km 2.847 0.004 0.667 0.000 44.481 Fragment ation Pri mary Temperat e 0.5 km 2.927 0.003 0.667 0.000 183.131 Forest Tropical 0.5 km 3.006 0.003 0.556 0.004 0.487 Fragment ation Tropical 0.5 km 2.341 0.019 0.556 0.004 11.464 Forest Tropical Secondary 0.5 km 3.417 0.001 0.611 0.001 9.645 Forest Tropical S econdary 1 km 2.594 0.009 0.444 0.039 11.744 Forest Tropical Secondary 2 km 2.183 0.029 0.444 0.039 6.362 Fragment ation Tropical Secondary 0.5 km 2.246 0.025 0.500 0.014 13.048

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42 IV. DISCUSSION National results Using forest cover change and forest fragmenta tion as indicators , at the national level, NPAs show ed no evidence of better protecting the forest s within their boundaries when compared to any of their buffer zones . Nor do they appear to spur more forest change than occurred within any buffer zone . The Wilcoxon Ranked Sum test s and the KS test s both returned p values well above the alpha of 0.05 for all tests. Statistically, the percent changes in forest cover and forest fragmentation zones and within their bord ers cannot be distinguished from random chance . This was the case when analyzing all forests as one class and for all forest types defined in this study . These results at the national level are concerning since NPAs are used as the primary strategy for the country to reach a 0% deforestation rate, sequester carbon, and protect ecosystems (Paris Agreement, 2015) . Especially as Mexico plans to increase the amount of NPA to cover 17% of its terrestrial territory by 2020, compared to the current 12.92%. If the current government allocation of 2.12 USD per hectare is not sufficient to provide protection for its forests presently , it is difficult to imagine adeq uate funding will be provided as the total NPA increases . Regional results At a regional level (using the CONANP regions) , the effectiveness of NPAs showed some variability. In regions 4 and 6 the re is evidence suggesting that NPAs helped to reduce forest

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43 fragmentation , w hile the results show no significant regional differences in the effectiveness of NPAs regarding changes in forest cover . Changes in forest cover between NPAs and buffer zones was statically indistinguishable in all regions, but apart from region 9. Here, NPAs are associated with greater forest fragmentati on. Forest cover in NPAs remained relatively unchanged or decreased while many classes of forest saw increases in forest cover in their buffer zones. This section serves to elaborate on the regional difference found in this study . Forest fragmentation in NPAs in region 4 was significantly lower than in the 0.5 km and 1 km NPA buffer zones . When considering all forests as a single class, NPAs experienced less forest fragmentation than all o f their buffer zones . Only the 0.5 km and 1 km buffer zones displayed significant differences, but the 2 km buffer zones experienced a median percentage of 28.85% more fragmentation than the NPAs. Figure 9 illustrates changes in forest fragmentation for al l forests. B uffer zones experiencing notably higher forest fragmentation than within the NPA s , is clearly visible, even though changes in the 2 km buffer zone were not significantly different from changes inside the NPAs.

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44 Figure 9. Box plots illustratin g the difference in forest fragmentation change between NPAs and their buffer zones in region 4. When considering temperate forests in region 4 , NPAs experienced significantly less forest fragmentation than in their buffer zones. However, when dividing tem perate forests into primary and secondary temperate forests, forest fragmentation was mostly indistinguishable between NPAs and buffer zones. The only significant difference was found between NPAs and 0.5 km buffer zones for primary temperate forests. Howe ver, fragmentation was observably higher in all buffer zones than NPAs. NPAs in region 4 experienced a median decrease in forest fragmentation for all forest types , while all buffer zones experienced a median increase. Comments only pertain to temperate fo rests given that tropical forests do not exist in region 4 . Forest fragmentation in NPAs in region 6 was significantly lower than in 2 km buffer zones when considering he forest types all forests and tropical forests. Since neither the 0.5 km or 1 km buffer zones were significantly different , these results suggest spillover effects are occurring outside the NPAs in this region . The protected areas appear to be decreasing the

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45 amount of forest fragmentation within 1 km outside of their borders. Beyond t his distance , fragmentation is noticeably higher. F igure 10 illustrates the forest fragmentation changes occurring within NPAs and buffer zones in region 6 when considering all forests . The distributions of changes in the NPAs, the 0.5 km, and 1 km buffer zone largely overlap and their median change shows fragmentation levels around zero or decreasing . In the 2 km buffer zone fragmentation has noticeably increased relative to the NPA and other buffer zones . When considering primary and secondary tropical fo rests, none of the buffer zones were statistically distinguishable from NPAs. However, similar to the other forest classes, the median fragmentation changes show decreasing levels within the observed spillover distance of 1 km and increasing fragmentation in the 2 km buffer zone. Comments only pertain to tropical forests due the absence of temperate forests in region 6. Figure 10 . Box plots illustrating the spillover effects in region 6 where the 0.5 and 1 km buffer zones were indistinguishable from NPAs, but the 2 km buffer zones experience more forest fragmentation.

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46 Region 9 is distinctive in that it showed significant changes in both forest fragmentation and forest cover change . Based purely o ff the statistical analyses results , forest fragmentation is significantly higher in this region in NPAs than their 0.5 km buffer zones for all forest types , excluding secondary temperate and primary tropical . The process of secondary forests replacing primary forests could result in secondary temperate and primary tropical forest types showing no significant differences in the 0.5 km buffers from inside the NPAs . When comparing the distributions of primary and secondary temperate forests we see increasing fragmentation of primary forests relative to secondary ones . These distributions are illustrated in density plots below . The x axis shows the percent forest fragmentation change and similar to a histogram, the y axis shows the percentage of data at each value o n the x axis. Unlike histograms, density plots do not re ly on a discrete number of bins (ranges) to place the data in and show a continous distribution. Figure 1 1 shows a density plot of forest fragmentation change in the primary and secondary forest types in region 9 . Primary temperate forest fragmentation can be seen increasing at a rate of between 400 and 800% , while secondary temperate forest fragmentation has a larger percentage of its distribution at or below zero percent change .

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47 Figure 1 1 . Density plots of region 9 showing fragmentation increasing in pr imary temperate forests and stabilizing or decreasing in secondary forests . Comparing the distributions in forest cover change of primary and secondary temperate forests in region 9 we see decreases in primary forest and increases in secondary forest. Prim ary temperate forest cover has decreas ed at a rate of between 50% and 140%, much more than decreases in secondary forests . Secondary temperate forests have a higher percentage of their distribution around zero, showing no change, or increasing between 6 0 % and 1 4 0%. Although the bulk of the distributions overlap, their relative differences suggest that degraded primary forests being are replaced by secondary forests (See Figure 12) .

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48 Figure 1 2 . Density plots of the 0.5 km buffer zones in region 9 , showing forest cover decreasing in primary temperate forests and increasing in secondary forests. When visually inspecting the NPAs , it can be seen that secondary forests are replacing primary forests . For example, Figure 1 3 shows temperate forests in Lagunas de Montebello where primary forests are being converted into secondary forests in the NPA and all three buffer zones. It is important to notice that t his is occurring more in areas where forests are more fragmented . The denser core areas of primary forests wi thin the NPA appear to be more resilient to switching to secondary forests .

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49 Figure 1 3 . Comparing the two figures shows areas where primary temperate forests have been replaced with secondary temperate forests in the NPA of Lagunas de Montebello. There w ere no observed significant differences between forest fragmentation change in primary and secondary tropical forests, as in temperate forests. However, when considering forest cover change, we can see secondary tropical forests increasing and primary trop ical forests decreasing. This is true across all buffer zones and easily discernable when comparing distribution s in the 0.5 km buffer zones. Figure 1 4 shows primary tropical forest cover largely

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50 around zero or decreasing between 40 and 60%, while secondar y tropical forest cover is increasing almost inversely. Figure 1 4 . Density plots of the 0.5 km buffer zones in region 9 , showing forest cover decreasing in primary tropical forests and increasing in secondary forests. The conversion of primary tropical forests to secondary tropical forests is also visible in a number of NPAS. Figure 1 5 displays an example in the NPA of La Cantun, where secondary tropical forests expand ed across buffer zone s and replaced primary forests . Here, the secondary forests overla p the more fragmented areas and expand further into fragmented primary forests from 2000 to 2010.

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51 Figure 1 5 . Comparing the two figures shows areas where primary tropical forests have been replaced with secondary tropical forests in the NPA of La Cantun . Although the Wil coxon test and KS test suggest that only the 0.5 km buffer zones experienced significantly different changes than NPAs, forest fragmentation in NPAs was consistently higher than in all buffer zones when considering any forest type define d in this study . When observing median change s , the NPAs show increases in forest fragmentation while the median changes in all other buffer zones show no change or a decrease in fragmentation. The distributions of forest fragmentation changes in the buffe r zones also have more overlap than the distribution of forest fragmentation changes in the NPAs. This suggests that the presence of NPAs is related to increased forest fragmentation in region 9 . Figure 1 6 shows a

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52 boxplot of forest fragmentation changes wh en considering all forests . Forest fragmentation is visibly increasing in the NPAs and remaining centered around zero in all buffer zones. Figure 1 6 . The distribution of forest fragmentation change observing all forests in region 9 shows higher fragmenta tion increases occurring in NPAs than all buffer zones. The regional analyses revealed 2 regions region 4 and region 6 where NPAs demonstrated some effectiveness in preventing forest fragmentation compared to their buffer zones. Region 6 even shows sp illover effects where forest fragmentation decreased within 1 km of NPA boundaries. The presence of NPAs in region 9 appeared to have some relationship to increased forest fragmentation relative to buffer zones . Additionally, region 9 experienced primary f orest cover replaced by secondary forest cover for both temperate and tropical forests . M isclassifications between primary and secondary forests could be contributing to the conclusions about primary forest degradation. However, if this was the case, we wo uld expect to see this happing more explicitly across more regions .

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53 Regional analyses are not necessary representative of individual NPAs , just as the national analysis did not reveal the variation in the regions. Analyzing NPAs regionally through compari ng distributions can also be problematic due to the different number of NPAs in each CONANP region . Region 1 contains 4 NPAs; region 2 : 4; region 3 : 6; region 4 : 10; region 5 : 7; region 6: 8; region 7: 10; region 8: 17; and region 9: 18. To determine if si gnificant changes were not being detected due to the small number of NPAs , the results were inspected independent of statistical analysis . Boxplots and density plots were visually inspected for every CONANP region. These revealed spillover effects into the 0.5 km and 1 km buffer zones in region 6 and secondary forest cover replacing primary forest cover in region 9. However, the visual analysis did not reveal any other notable changes. When considering all forests as a single class, individual NPAs experie nced forest cover increases as high as 91.7% and forest cover loss as high as 94.3%. Forest fragmentation ranged between increases as high as 31,232% and decreases as high as 93%. Appendix V displays forest cover change and forest fragmentation change for every NPA included in this study. Within each CONANP region, individual NPAs experienced a wide variety forest cover and forest fragmentation changes. Increases and decreases were found among NPAs in each CONANP region. A nalyzing changes at a national or r egional level, as done in this thesis, does not allow for the explanation of these individual variations . While this thesis suggests no evidence of NPAs being effective at a national level and only for 2 regions at a regional level, forest cover increased in 42 of 84 NPA included in this study. Forest fragmentation decreased in 47 of 84 NPAs included in this study. Analyzing changes across individual NPAs in future research may

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54 help determine why some are better able to prevent forest cover loss and forest fragmentation than others . Comparisons to previous research When observing forest cover change and NPAs , national results corroborate previous studies. Blackman et al. (2015) evaluated the effectiveness of NPAs to prevent deforestation nationally. The auth ors considered the years 1993 to 2000 and conclude that NPAs had no noticeable difference between inside and outside their borders in preventing deforestation. However, the study did see geographic variation i n the ability of NPAs to prevent deforestation. Thi s study did see geographic variation in the ability of NPAs to prevent deforestation. The authors repeated the analysis for each CONANP region , as done in our study. They concluded that NPAs in region 3 spurred forest cover loss; NPAs in region 6 expe rienced less forest cover loss; and in region 9, forest cover loss increased outside of NPA borders. These results do not match what was found in our study . From 2000 to 2010 we observed NPAs in region 3 los ing forest cover , but their distributions were no t statistically distinguishable from buffer zones. When visually examining our data, it appear s the 1 km buffer zones performed better than NPAs with a marginal increase in forest cover. The median change in region 6 shows NPAs losing forest cover as well. When compared to the distribution to the 2 km buffer zone, NPAs experienced a larger forest cover increase . However, these differences were not large enough to be deemed beyond random chance. In region 9, the median forest cover change for NPAs and all bu ffer zones was around zero. The distributions did not show increased forest cover loss outside of NPA boundaries. However, when considering forest degradation ( primary forests

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55 converted into secondary ) , NPAs experienced more degradation of temperate forest s than buffer zones , but no noticeable differences were observed when considering tropical forests. The differences in results between this thesis study and Blackman et al. (2015) could be attributed to varying time periods and different methodologies use d . Blackman et al. (2015) included 56 NPAs in their study, compared to the 84 included i n this study. This is partially due to the temporal extent of the studies. The authors observed NPAs from 1993 to 2000, whereas this study looks at years 2000 to 2010. Blackman et al. (2015) did not have prerequisites for the NPAs that were included, while this study excludes the NPAs with less than 1,000 ha of forest cover . This size restriction is aimed at includ ing only established ecosystems and it attempts to reduce confounding results by anomalies occurring in NPAs with smaller forest covers (Figueroa & Sánchez Cordero, 2008) . Blackman et al. (2015) also used a matching technique to compare similar areas within and outside of NPAs to control for spillover effects and nonrandom sitting (the influence o f geophysical, climatological, and socioeconomic factors). These techniques are based o n the methods in Andam et al. (2008) and Nelson & Chomitz (2011) and are the only matching techniques that have been used for national studies of NPAs in Mexico. Plots with similar environmental characteristics to the NPAs were identified within a 20 km buffer of their boundaries . Changes in forest cover in these plots were compared to changes in forest cover within NPAs. This thesis study differs in that matching techniques are not used. Concentric buffer rings are used to control for spillover effects. These effects are assumed to diminish before reaching the NPA boundaries, as found in Rayn and Sutherland (2011) , but spillover effects were obser ved the in 0.5 km and 1 km buffer zones when analyzing forest

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56 fragmentation in region 6. It should be assumed in the future that spillover effects may exist outside of NPA boundaries in Mexico. Spillover effects were not observed in any other regio n. Match ing techniques are not used in this thesis study due to uncertainty in selecting the proper covariates. While matching techniques can provide a method to compare areas with similar susceptibility to change, they can also artificially lower the variability between NPAs and buffer zones. Two studies have implemented them for assessing NPAs in Mexico, Blackman et al. (2015) and Mas (2005) . These studies use geophysical char acteristics such as slope, elevation, soil type, and also include proximity to anthropogenic activity. S ome biosphere reserves considered in those studies contain populations within protected area boundaries. This is not addressed in either paper. Neverthe less, the proper use of matching techniques could improve this thesis study by excluding areas where it was not possible for forest to grow. Some examples would be water bodies, agricultural land, or built up areas. Natural disasters, pests, and disease wo uld be worth examining as well. In another study , Rayn and Sutherland (2011) examined the effectiveness of NPAs to reduce forest cover loss. While it was found that the core area of the NPAs experienced the least forest cover loss, no significant differenc e was found between the rate of forest loss before and after the NPA decrees, or inside and outside the borders of NPAs when considering changes at the national level (Rayn & Sutherland, 2011) . Rayn and Sutherland (2011) used two methods: 1) observing forest cover change before and after each NPAs decree; and 2) comparing forest cover change within and outsid e of NPAs over time. The first method cannot be compared to this thesis study. The second method is

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57 difficult to compare to this thesis stud y , because t he temporal extent and number of NPAs differs. Rayn and Sutherland (2011) studied forest cover change in 28 NPAs from 1973 to 2000 , whereas this thesis study analyses 84 NPAs from 2000 to 2010 . Due to the unavailability of LC datasets for years as early as 1973, Rayn and Sutherland (2011) performed unsupervised classifications on LANDSAT satellite imagery to delineate forest cover. This is problematic, because the heterogeneity of LANDSAT imagery produced by different satellites over time is not discussed. Additionally, no accuracy assessments of the classification of forest is mentioned. A single buffer area of 10 km is used for comparison . Additionally, as many concentric 5 km internal buffers as the area of each NPA would allow were created for comparison . The effects of spillover and nonrandom sitting are not mentioned. Due to the number of inconsistencies between Rayn and Sutherland (2011) and this thesis study , methodological comparisons are left out . Other main differences between previous studies and this thesis , are the spatial resolution of the data and the analysis of forest fragmentation . Blackman e t al. (2015) used INEGI Series data sets, which are produced at a scale of 1:250,000. This study utilizes GlobeLand 3 0 (GL30), which is produced at a 30 meter cell size spatial resolution. Rayn and Sutherland (2011) did not list the cell size at which the L C dataset they used was produced . LC data sets with a spatial resolution of 30 meters provide s more precise edges and extent s of LC classes. The smaller pixel size also allows more landscape variation to be captured . This is particularly important when an alyzing forest fragmentation. The use of the GL30 data set s allow better detection of shape intricacies and perforation s in fragmentated forest areas. Due to the increased precision, the 30 meter spatial resolution used in this study may quantify the

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58 exten t of forest cover differently than coarser spatial resolutions used in previous studies. The effect of scale in measuring forests in NPAs has not been studied. Additionally, this thesis study is the first to analyze the effectiveness of NPAs in regards to reduc ing forest fragmentation. Forest type subclasses defined in this study This thesis study is the first to examine the effectiveness of NPAs relative to different and more specific forest types . All comparable previous research examines forest as a sin gle land cover class . This was considered important to determine if particular forest types were more vulnerable at different scale s or regions and to look for transitions between primary and secondary forest classes as an indicator of degradation . Differe nces have been observed in the total amount of forest cover loss between tropical and temperate forests (Mas et al., 2004) and differences in exposure to anthropogenic pressures (Moreno Sanchez et al., 2012) . However, no noticeable differences were found in the ability of NPAs to mitigate forest cover loss or forest fragmentation between forest types defined in this thesis . Previous research has also suggested that decreases in primary forest can result in increases in secondary forests (Mas et al., 2004) . When observing forests as a single LC class , where primary and secondary forests are not distinguished, it is likely that change detection will falsely indicate no forest cover loss has occurred. The results in region 9 directly imply this. No significant difference between changes in NPAs and buffer zones was found when considering all forests as one class . However, when looking at the forest type subclasses, secondary forests are in creasing and primary forests are decreasing in both temperate and tropical forests . At this point, it is premature to conclude that primary forests are being degraded into secondary .

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59 While the results of this research suggest this is happening, another pos sibility is th at LC data sets are being misclassifi ed and artificially portraying these changes. Misclassification can potentially be extended through error propagation . Combining the INEGI LC data sets with the GL30 LC data sets has the potential to p ro paga te of error due to in accuracies between data sets . The accuracy of INEGI Series III and Series IV has never been released for the forest classes used in these data sets but is reviewed as highly accurate due to extensive ground truthing done for these data sets (Figueroa & Sánchez Cordero, 2008; Gebhardt et al., 2015) . GL30 is reported to have an average producer accuracy of 74.3% for temperate forests and 89.2% for tropical forests in Mexico (Carver, 2017) . While accuracy of the combined LC data sets cannot be directly measured, the most accurate and authoritative data sets we re used to derive the best possible results. However, p rimary and secondary forest classes are difficult to differentiate in satellite imagery and present the greater number of misclassifications (Gebhardt et al., 2015) . Concen tric buffer rings and spillover effects Three concentric buffer rings were evaluated independently against the internal areas of the NPAs for percent changes in forest cover and forest fragmentations. The buffer ring distances were declared at 0.5 km, 1 k m, and 2 km. These distances were based on buffer distances used in previous research and it is assumed that spillover effects do not permeate beyond all buffer rings. This assumption is based on the work on Rayn and Sutherland (2011) , which indicated that core areas of NPAs experienced the least amount of forest cover loss, therefore spillover effects should begin to diminish within NPA boundaries. At a nat ional level ,

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60 since no differences between NPAs and their buffer zones were found , spillover effects also appeared absent. When observing changes at a regional level , spillover effects appeared in region 6 . Changes in the first two buffer zones from the N PA boundaries were not significantly different than changes occurring in NPAs , while the 2 km buffer zone experienced decreases in forest cover and increases in forest fragmentation. This appears true in all forest type subclasses but is most noticeable wh en observing forests as a single LC class. F igure 1 7 shows forest cover change in 0.5 km and 1 km buffer zones largely around zero, while forest cover noticeably decreases in the 2 km buffer zone. Figure 18 shows forest fragmentation at zero or decreasing in the 0.5 km and 1 km buffer zones, while noticeably increasing the in 2 km buffer zone. Figure 1 7 . Spillover effects are noticeable in the 0.5 km and 1 km buffer zones where the distributions overlap the changes in NPAs. The 2 km buffer zones experienc ed significantly more forest cover loss than NPAs.

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61 Figure 1 8 . Spillover effects are noticeable in the 0.5 km and 1 km buffer zones where the distributions overlap the changes in NPAs. The 2 km buffer zones experienced significantly more forest fragmentat ion than NPAs. For all other regions, changes in NPAs were not found significantly different than changes in buffer zones. This indicates one of two scenarios: NPAs are not causing spillover effects, because they have no influence on forest change or spill over effects are permeating beyond all buffer zones making differences in forest change impossible to detect. Repeating the analysis with additional buffer zones would aid in determining which scenario is true. Increasing buffer zone distances beyond 2 km increases the chance that the climatic and geophysical characteristics would be less comparable to NPAs. Some form of matching technique could be used to mitigate these differences. Outliers When considering percent forest cover change, 6 of the results w ere values above 1000% forest cover increase . When considering percent forest fragmentation change, 22 of the

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62 results were values above 1000% forest fragmentation increase . These values were well beyond 3 times the interquartile range of the results the number used to identify outliers beyond the outer fence of boxplots , or extreme outliers (Dawson, 2011) . The outer fence was 25.64% for forest cover change and 123.26% for forest fragmentation change . The cause s of these outliers are beyond the scope of this research. However, considerable outliers, defined as beyond 1000% forest change, w ere visually inspected using the GL30 and INEGI LC data sets to verify their existence in the data sets. This section serves to highlight some of the greatest considerable outliers. All considerable outliers can be found in Appendix IV . The largest outlier , being an increase in forest fragmentation of 62,598.51 % , occurred when considering primary temperate forest in the NPA of Zona de Protección Forestal en los terrenos que se encuentran en los municipios de La Concordia, Ángel Albino Corzo, Villa Flores y Jiquipilas, Chiapas . This is highlighted in Figure 1 9 below . In the year 2000, the NPA is almost completely forested with very little forest fragmentation. By the year 2010, forest fragmentation is pervasive within the NPA and increases outside its borders . Situations such as this occurred in other NPAs.

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63 Figure 1 9 . Forest fragmentation increasing by 62,598.51% in primary temperate forests from 2000 to 2010 . Other considerable outliers included situations where areas classified as primary forests in 2000 w ere classified as secondary forests in 2010, suggesting the forests were degraded. Figure 20 displays primary and secondary tropical forests in the NPA of El Veladero , state of Guerrero , where secondary tropical forests increased by 9,756.14% within the NP A.

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64 Figure 20 . Secondary tropical forests increased by 9,756.14% within the boundaries of the NPA of El Veladero, state of Guerrero. Additional considerable outliers occurred when forest cover increased and filled in previously fragmented areas . Figure 2 1 displays primary temperate forests in the NPA of P orci ó n S ierra de A rteaga ( CADNR026 ) increasing by 5,855.56% in the 0.5 km buffer zone.

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65 Figure 2 1 . Primary temperate forests in the NPA of Porción Sierra de Arteaga (CADNR026) increasing by 5,855.56% in t he 0.5 km buffer zone.

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66 V. CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH This thesis research asks if NPAs in Mexico experience any difference in forest cover loss or forest fragmentation than their surrounding buffer areas . The analysis compares forest cover, as represented by GL30 and INEGI LC data sets , for years 2000 and 2010. The 84 NPAs analyzed in this study were decreed prior to the year 2000 and had more than 1,000 ha of forest. The methods used compare d the percent forest cover and forest fragmentation changes in each NPA to three concentric buffer rings at distances of 0.5 km, 1 km, and 2km from the edges of the NPAs . This analysis was performed for all forests as a single LC class as well as for the following forest types : tropical fores ts, temperate forests, primary tropical forests, primary temperate forests, secondary tropical forests, and secondary temperate forests. INEGI Series LC data sets were used to delineate the forest type subclasses within the extent of forest cover reported in the 2000 and 2010 GL30 data sets . Forests that are not assigned a forest type subclass through overlap with the INEGI Series LC data sets were assigned as the closest neighboring forest type subclass using Euclidean distance. When analyzing the percent changes between NPAs and their concentric buffer zones at a national level , neither the Wilcoxon Rank Sum test or the KS test indicate d any significant differences between the NPAs internal areas and their external buffer areas of any size . These results were consistent for changes in forest cover as well as for forest fragmentation. This is true for all three concentric buffer rings when considering all types of forests defined in this study . At a national level, this thesis results are unable to reject H 0 : Natural protected areas

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67 have no difference from their buffer areas in protecting forests from forest cover loss and increased fragmentation at a national scale. C onclusions become more complex when examining regional variations. For regions 1, 2, 3, 5, 7, and 8 this research is unable to reject H 0 and follows the same results as the national analysis. However, f or region 4 , NPAs were effective at reducing forest fragmentation when compared to their 0.5 and 1 km buffer zones . NPAs in region 4 were not con sidered effective at reducing forest fragmentation when compared with their 2 km buffer zones , due to the lack of statistical significant differences. Although, the median forest fragmentation change shows NPAs experiencing less forest fragmentation than t heir 2 km buffer zones and their distributions share little overlap (See Figure 9) . This suggests that NPAs could be considered effective at reducing forest fragmentation when compared to all buffer zones defined in this study. NPAs in region 4 were not co nsidered effective at reducing forest cover loss when compared to any of their buffer zones . NPAs in region 6 were considered effective at reducing forest fragmentation when comparing their internal areas to their 2 km buffer zones. The 0.5 km and 1 km bu ffer zones appeared to experience spillover effects from the NPAs, making the changes forest fragmentation within these buffer zones indistinguishable from changes occurring in NPA internal areas . Therefore, NPAs in region 6 were not considered effective a t reducing forest fragmentation when compared to their 0.5 km or 1 km buffer zones. NPAs in region 6 were not considered effective at reducing forest cover loss when compared to any of their buffer zones. Some evidence of effectiveness at reducing forest f ragmentation existed for both region 4 and region 6, therefore, it is necessary to reject H 0. When considering forest cover change, no

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68 evidence existed demonstrating the effectiveness of NPAs in region 4 or region 6 to reduce forest cover loss , t herefore, this research is unable to reject H 0. In region 9, NPAs experienced significantly higher level s of forest fragmentation in their internal areas than their 0.5 km buffer zones. When considering median changes, NPAs show median increased in forest fragmentat ion, whereas all buffer zones show median decreases in forest fragmentation. This suggests a relationship between the presence of NPAs and increases in forest fragmentation in region 9. This was true when considering all forest type subclasses, but not for forests as a single class. This can likely be attributed to primary forests becoming secondary forests. When considering forest cover change, NPAs appeared to be losing both temperate and tropical primary forests and having increases in secondary forests. Due to decreases in primary forests and roughly equivalent increases in secondary forests , examining all forests as a single class does not show any significant differences from buffer areas. The evidence showing significantly more forest fragmentation in interior areas of NPA than buffer zones makes it necessary to reject H 0. The comparison of previous national studies , e.g., Blackman et al. (2015) and Rayn et al. (2011) , which indicate NPAs show no difference from their surrounding buffer areas, raises concern about the prescription of NPAs to protect forests as part of compliance with international agreements. Mexico has agreed to declare 17% of its terrestrial land and inland water a s NPA by 2020 to meet the Aichi Biodiversity Targets and achieve a deforestation rate of 0% by 2030 to be in accordance with the P aris Agreement (CBD, 2016; Paris Agreement, 2015) . NPAs have been explicitly designated as the strategy to reach the Aichi Biodive rsity Targets and have traditionally been the strategy to reduce deforestation in Mexico . In the results of this

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69 thesis , NPAs have demonstrated regional effectiveness to reduce forest fragmentation in the CONANP regions 4 and 5. In Blackman et al. (2015) , region 6 demonstrated effectiveness to reduce forest cover loss , but overall , and when analyzed nationally , NPAs show little evidence to be an effective tool to reduce forest cover loss or increased forest fragmentation . It is necessary to better understan d the mechanisms and under what context NPAs are effective if they are continued to be used to protect forests. Until then, agreements advocating their use to reach environmental and sustainability goals at a national level are insufficiently warranted. F uture research on the effectiveness of NPAs to protect forests should consider primary and secondary forest types. As seen in the results of this thesis, in region 9, primary forests were being replaced by secondary forests. When considering all forests as a single class, this conversion was not observed , underestimating forest cover change . Excluding primary and secondary forest types in future studies may similarly result in underestimating forest cover change. Buffer areas outside the boundaries of NPAs should also be extended to account for spillover effects. No significant differences between forest cover changes within NPAs and their buffer areas were found in 6 CONANP regions. Extending buffer areas beyond 2 km from NPA boundaries may help answer if s pillover effects were responsible for the lack of significant differences. Furthermore, this thesis is the first national study to observe the relationship between NPAs and forest fragmentation. Region 4 and region 6 demonstrated some effectiveness at redu cing forest fragmentation, while region 9 experienced more forest fragmentation than its buffer areas. Due to the implications for fragmentation to impact ecosystems and increase the vulnerability of forests , further research is encouraged to compare

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70 these findings and determine the relationship with NPAs and fragmentation through time and under what context fragmentation can be mitigated. To further explore the context in which NPAs are effective or ineffective , articles in this thesis literature review su ggest the following factors are important : the spending on NPAs; management strategies; ownership; socio economic factors, such as poverty, industry, population, and proximity to anthropogenic activities (Blackman et al., 2015; Bovarnick et al., 2010; Cortina Villar et al., 2012; Porter Bolland et al., 2012) . De termining the causality of forest cover loss and forest fragmentation in NPAs is fundamental to their utilization in achieving environmental and sustainability goals, such as the Aichi Biodiversity Targets and the Paris Agreement. Little research h as been done on the characteristics and context of effective NPAs. T he most successful NPAs in mitigat ing deforestation have been the ones having a larger area, more recently decreed, designated as mixed use, and received greater funding (Blackman et al., 2015) . How NPAs are purposed , for example, as biosphere reserves, national parks, or protected areas , may also play a role in their effectivene ss . To expand on the results of this thesis, these contextual factors could be explored in relation to the most and least effective NPAs and CONANP regions in this thesis results. This could help develop hypotheses about how to improve the effectiveness of NPAs to reduce forest cover loss and forest fragmentation.

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71 VI. REFERENCES Ament, J. M., & Cumming, G. S. (2016). Scale dependency in effectiveness, isolation, and social ecological spillover of protected ar eas. Conservation Biology , 30 (4), 846 855. https://doi.org/10.1111/cobi.12673 Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez Azofeifa, G. A., & Robalino, J. A. (2008). Measuring the effectiveness of protected area networks in reducing deforestation. Proc eedings of the National Academy of Sciences , 105 (42), 16089 16094. https://doi.org/10.1073/pnas.0800437105 Armendáriz Villegas, E. J., Covarrubias García, M. de los Á., Troyo Diéguez, E., Lagunes, E., Arreola Lizárraga, A., Nieto Rubi o, A. (2015). Metal mining and natural protected areas in Mexico: Geographic overlaps and environmental implications. Environmental Science and Policy , 48 , 9 19. https://doi.org/10.1016/j.envsci.2014.12.016 Arsanjani, J., See, L., & Tayyebi, A. (2016). Ass essing the suitability of GlobeLand30 for mapping land cover in Germany. International Journal of Digital Earth , 9 (9), 873 891. https://doi.org/10.1080/17538947.2016.1151956 Arsanjani, J., Tayyebi, A., & Vaz, E. (2016). GlobeLand30 as an alternative fine s cale global land cover map: Challenges, possibilities, and implications for developing countries. Habitat International , 55 , 25 31. https://doi.org/10.1016/j.habitatint.2016.02.003 Blackman, A., Pfaff, A., & Robalino, J. (2015). Paper park performance: Mex protected areas in the 1990s. Global Environmental Change , 31 , 50 61. https://doi.org/10.1016/j.gloenvcha.2014.12.004 Bocco, G., Mendoza, M., & Masera, O. (2001). Michoacán . Una propuesta metodológica para el, (May 2015). Bovarnick, A., Baca , J. F., Galindo, J., & Negret, H. (2010). Financial sustainability of protected areas in Latin America and the Caribbean: Investment policy guidance. Brovelli, M., Molinari, M., Hussein, E., Chen, J., & Li, R. (2015). The First Comprehensive Accuracy Asse ssment of GlobeLand30 at a National Level: Methodology and Results. Remote Sensing , 7 (4), 4191 4212. https://doi.org/10.3390/rs70404191 Carver, D. P. (2017). ASSESSMENT OF THE REPRESENTATIONAL ACCURACY OF GLOBELAND30 CLASSIFICATION OF THE TEMPERATE AND TRO PICAL FOREST OF MEXICO. University of Colorado Denver . CBD. (2016). No Title. CONFERENCE OF THE PARTIES TO THE CONVENTION ON BIOLOGICAL DIVERSITY , XIII/7 .

PAGE 83

72 Efficiency of OF RESERVE NETWORKS. Ecological Applications , 17 (2), 569 578. Ceballos, G., Rodriguez, P., & Medellin, R. (1998). Asses sing Conservation Priorities in of the Ecological Society of America Sta. Ecol ogical Applications , 8 (1), 8 17. Chapa Vargas, L., & Monzalvo Santos, K. (2012). Natural protected areas of San Luis Potosí, Mexico: ecological representativeness, risks, and conservation implications across scales. International Journal of Geographical In formation Science , 26 (9), 1625 1641. https://doi.org/10.1080/13658816.2011.643801 at 30 m resolution: A POK based operational approach. ISPRS Journal of Ph otogrammetry and Remote Sensing , 103 , 7 27. https://doi.org/10.1016/j.isprsjprs.2014.09.002 Clay, E., Moreno Sanchez, R., Torres Rojo, J. M., & Moreno Sanchez, F. (2016). National assessment of the fragmentation levels and fragmentation class transitions o f the forests in Mexico for 2002, 2008 and 2013. Forests , 7 (3), 6 8. https://doi.org/10.3390/f7030048 Cortina Villar, S., Plascencia Vargas, H., Vaca, R., Schroth, G., Zepeda, Y., Soto Pinto, L., & Nahed Toral, J. (2012). Resolving the conflict between eco system protection and land use in protected areas of the sierra madre de chiapas, Mexico. Environmental Management , 49 (3), 649 662. https://doi.org/10.1007/s00267 011 9799 9 Dawson, R. (2011). How Significant Is A Boxplot Outlier? Journal of Statistics Edu cation , 19 (2), 1 13. Retrieved from www.amstat.org/publications/jse/v19n2/dawson.pdf Díaz Gallegos, J. R., Mas, J. F., & Velázquez, A. (2010). Trends of tropical deforestation in Southeast Mexico. Singapore Journal of Tropical Geography , 31 (2), 180 196. ht tps://doi.org/10.1111/j.1467 9493.2010.00396.x Estreguil, C., De Rigo, D., & Caudullo, G. (2014). A proposal for an integrated modelling framework to characterise habitat pattern. Environmental Modelling and Software , 52 , 176 191. https://doi.org/10.1016/j .envsoft.2013.10.011 Figueroa, F., & Sánchez Cordero, V. (2008). Effectiveness of natural protected areas to prevent land use and land cover change in Mexico. Biodiversity and Conservation , 17 (13), 3223 3240. https://doi.org/10.1007/s10531 008 9423 3 Figue roa, SÁNCHEZ CORDERO, V., MEAVE, J. A., & TREJO, I. (2009). Socioeconomic context of land use and land cover change in Mexican biosphere reserves. Environmental Conservation , 36 (03), 180. https://doi.org/10.1017/S0376892909990221

PAGE 84

73 Fischer, J., & Lindenmaye r, D. (2007). Landscape modification and habitat \ rfragmentation: a synthesis. Global Ecology and Biogeography , 17 (August), 265 280. https://doi.org/10.1111/j.1466 8238.2007.00287 Gebhardt, S., Maeda, P., Wehrmann, T., Argumedo Espinoza, J., & Schmidt, M. ( 2015). A proper Land Cover and Forest Type Classification Scheme for Mexico. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , XL 7/W3 (May), 383 390. https://doi.org/10.5194/isprsarchives XL 7 W3 383 201 5 Geldmann, J., Barnes, M., Coad, L., Craigie, I. D., Hockings, M., & Burgess, N. D. (2013). Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biological Conservation , 161 , 230 238. https://doi.org/10.1016/j.bio con.2013.02.018 Grekousis, G., Mountrakis, G., & Kavouras, M. (2015). An overview of 21 global and 43 regional land cover mapping products. International Journal of Remote Sensing , 36 (21), 5309 5335. https://doi.org/10.1080/01431161.2015.1093195 Groombridg the 21st Century. Global Biodiversity . https://doi.org/10.1016/S0006 3207(02)00364 6 Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, Townshend, J. R. G. (2013). High Resolution Global Maps of 21st Century Forest Cover Change. Science , 342 (6160), 850 853. https://doi.org/10.1126/science.1244693 Harper, K. a., Macdonald, S. E. S., Burton, P. J., Chen, J., Brosofske, K. D., Saunders, Esseen, P. A. (2005). Edge influence on forest structure and composition in fragmented landscapes. Conservation Biology , 19 (3), 768 782. https://doi.org/10.1111/j.1523 1739.2005.00045.x Laurance, W. F. (2000). Rainforest fragmentation kills big t rees. Nature , 404 (April), 2000 2000. Lovejoy, T. E. (2011). The fate of Amazonian forest fragments: A 32 year investigation. Biological Conservation , 144 (1) , 56 67. https://doi.org/10.1016/j.biocon.2010.09.021 López Granados, E., Mendoza, M. E., & González, D. I. (2014). A tecnologia de remoção de fósforo: Gerenciamento do elemento em resíduos industriais. Revista Ambiente e Agua , 9 (3), 445 458. https://doi.o rg/10.4136/1980 993X Mas. (1999). Monitoring land cover changes: a com parison of change detection techniques. International Journal of Remote Sensing , 20 (1), 139 152. https://doi.org/10.1080/014311699213659 Mas, J. F. (2005). Assessing protected area effe ctiveness using surrounding (buffer) areas environmentally similar to the target area. Environmental Monitoring and Assessment , 105 (1 3), 69 80. https://doi.org/10.1007/s10661 005 3156 5

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74 Mas, J. F., Velázquez, A., Díaz Gallegos, J. R., Mayorga Saucedo, R. Pérez Vega, A. (2004). Assessing land use/cover changes: A nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation , 5 (4), 249 261. https://doi.org/10.1016/j.jag.2 004.06.002 McIntyre, S., & Hobbs, R. (1999). A framework for conceptualizing human effects on lanfscapes and its relevance fo management and research models. Conservation Biology , 13 (6), 1282 1292. https://doi.org/10.1046/j.1523 1739.1999.97509.x Moreno Sá nchez, R., Buxton Torres, T., Sinbernagel, K., & Moreno Sánchez, R. (2014). Fragmentation of the forests in Mexico: national level assessments for 1993, 2002 and 2008. Revista Internacional de Estadística y Geografía , 5 (2), 4 17. Moreno Sanchez, R., Moreno Sanchez, F., & Torres Rojo, J. M. (2011). National assessment of the evolution of forest fragmentation in Mexico. Journal of Forestry Research , 22 (2), 167 174. https://doi.org/10.1007/s11676 011 0145 0 Moreno Sanchez, R., Torres Rojo, J. M., Moreno Sanche z, F., Hawkins, S., Little, J., & McPartland, S. (2012). National assessment of the fragmentation, accessibility and anthropogenic pressure on the forests in Mexico. Journal of Forestry Research , 23 (4), 529 541. https://doi.org/10.1007/s11676 012 0293 x Mu rcia, C. (1995). Edge effects in fragmented forests: implications for conservation. Trends in Ecology & Evolution , 10 (2), 58 62. https://doi.org/10.1016/S0169 5347(00)88977 6 Nagendra, H. (2008). Do Parks Work? Impact of Protected Areas on Land Cover Clear ing. AMBIO: A Journal of the Human Environment , 37 (5), 330 337. https://doi.org/10.1579/06 R 184.1 Nelson, A., & Chomitz, K. M. (2011). Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: A global analysis using matc hing methods. PLoS ONE , 6 (8). https://doi.org/10.1371/journal.pone.0022722 Ochoa Gaona, S., & González Espinosa, M. (1999). Land use and deforestation in the highlands of Chiapas, Mexico. Applied Geography , 20 (1), 17 42. https://doi.org/10.1016/S0143 6228( 99)00017 X Ostapowicz, K., Vogt, P., Riitters, K. H., Kozak, J., & Estreguil, C. (2008). Impact of scale on morphological spatial pattern of forest. Landscape Ecology , 23 (9), 1107 1117. https://doi.org/10.1007/s10980 008 9271 2 Paris Agreement. (2015). Int ended Nationally Determined Contribution (INDC) Mexico. SECRETARÍA DE MEDIO AMBIENTE Y RECURSOS NATURALES , No. 54113 , 1 8. https://doi.org/10.1007/s13398 014 0173 7.2 Pfaff, A., & Santiago exico: the need to add politics and dynamics to static landuse economics. Sanford School of Public Policy, Duke University, Durhan, NC , Working pa . Retrieved from http://ageconsearch.umn.edu/bitstream/177195/2/Pfaff, Alexander.pdf

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75 Pineda Jaimes, N. B., Bos que Sendra, J., Gómez Delgado, M., & Franco Plata, R. (2010). Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression. Applied Geography , 30 (4), 576 591. https://doi.org/10.1016/j.apgeog.20 10.05.004 Porter Bolland, L., Ellis, E. A., Guariguata, M. R., Ruiz Mallén, I., Negrete Yankelevich, S., & Reyes García, V. (2012). Community managed forests and forest protected areas: An assessment of their conservation effectiveness across the tropics. Forest Ecology and Management , 268 , 6 17. https://doi.org/10.1016/j.foreco.2011.05.034 Ran, Y. H., & Li, X. (2015). First comprehensive fine resolution global land cover map in the world from China Comments on global land cover map at 30 m resolution. Scie nce China Earth Sciences , 58 (9), 1677 1678. https://doi.org/10.1007/s11430 015 5132 4 Rayn, D., & Sutherland, W. J. (2011). Impact of nature reserve establishment on deforestation: A test. Biodiversity and Conservation , 20 (8), 1625 1633. https://doi.org/10 .1007/s10531 011 0051 y Riitters, K., Vogt, P., Soille, P., & Estreguil, C. (2009). Landscape patterns from mathematical morphology on maps with contagion. Landscape Ecology , 24 (5), 699 709. https://doi.org/10.1007/s10980 009 9344 x Riitters, Wickham, J., Scale Patterns of Forest Fragmentation. Ecology and Society , 4 (2). Retrieved from habitat+fragmentation%5CRiitters2000.pdf Román Cuesta, R. M., & Martínez Vilalta, J. (2006). Effectiveness of protected are as in mitigating fire within their boundaries: Case study of Chiapas, Mexico. Conservation Biology , 20 (4), 1074 1086. https://doi.org/10.1111/j.1523 1739.2006.00478.x Sánchez Azofeifa, G., Daily, G. C., Pfaff, A. S. P., & Busch, C. (2003). Integrity and is olation of cover change. Biological Conservation , 109 (1), 123 135. https://doi.org/10.1016/S0006 3207(02)00145 3 Saura, S., Vogt, P., Velázquez, J., Hernando, A., & Tejera, R. (2011). Key structural forest connectors can be identified by combining landscape spatial pattern and network analyses. Forest Ecology and Management , 262 (2), 150 160. https://doi.org/10.1016/j.foreco.2011.03.017 Soille, P., & Vogt, P. (2009). Morpholo gical segmentation of binary patterns. Pattern Recognition Letters , 30 (4), 456 459. https://doi.org/10.1016/j.patrec.2008.10.015 Southworth, J., Nagendra, H., Carlson, L. A., & Tucker, C. (2004). Assessing the impact of Celaque National Park on forest frag mentation in western Honduras. Applied Geography , 24 (4), 303 322. https://doi.org/10.1016/j.apgeog.2004.07.003

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76 Spracklen, B. D., Kalamandeen, M., Galbraith, D., Gloor, E., & Spracklen, D. V. (2015). A global analysis of deforestation in moist tropical for est protected areas. PLoS ONE , 10 (12), 1 17. https://doi.org/10.1371/journal.pone.0143886 Tabarelli, M., Lopes, A. V., & Peres, C. A. (2008). Edge effects drive tropical forest fragments towards an early successional system. Biotropica , 40 (6), 657 661. htt ps://doi.org/10.1111/j.1744 7429.2008.00454.x Tabarelli, M., Silva, J. M. C. da, & Gascon, C. (2012). Forest fragmentation, synergisms and the impoverishment of neotropical forests. European Science Editing , 38 (2), 35 37. https://doi.org/10.1023/B Turner, B. L., Meyer, W. B., & Skole, D. L. (1994). Global land use land cover change towards an integrated study. Ambio , 23 (1), 91 95. https://doi.org/10.2307/4314168 Velázquez, A., Mas, J. F., Bocco, G., & Palacio Prieto, J. L. (2010). Mapping land cover chang es in Mexico, 1976 2000 and applications for guiding environmental management policy. Singapore Journal of Tropical Geography , 31 (2), 152 162. https://doi.org/10.1111/j.1467 9493.2010.00398.x Vellend, M., Verheyen, K., Jacquemyn, H., Kolb, A., Van Calster, H., Peterken, G., & Hermy, M. (2006). Extinction debt of forest plants persists for more than a century following habitat fragmentation. Ecology , 87 (3), 542 548. https://doi.org/10.1890/05 1182 Vidal, O., López García, J., & Rendón Salinas, E. (2014). Tre nds in Deforestation and Forest Degradation after a Decade of Monitoring in the Monarch Butterfly Biosphere Reserve in Mexico. Conservation Biology , 28 (1), 177 186. https://doi.org/10.1111/cobi.12138 Vidal, O., & Rendón Salinas, E. (2014). Dynamics and tre nds of overwintering colonies of the monarch butterfly in Mexico. Biological Conservation , 180 , 165 175. https://doi.org/10.1016/j.biocon.2014.09.041 Vogt, P. (2010). MSPA Guide. European Commission, Joint Research Centre , (I 21027), 1 7. Vogt, P., Riitter s, K. H., Estreguil, C., Kozak, J., Wade, T. G., & Wickham, J. D. (2007). Mapping spatial patterns with morphological image processing. Landscape Ecology , 22 (2), 171 177. https://doi.org/10.1007/s10980 006 9013 2

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77 VII. APPENDIX Appendix A INEGI keys for temperate and tropical forests The below keys were used to create the subclasses of forests used in this research. The temperate forest subclass used all keys in the tables: List of Keys for Primary Temperate Forest and List of Keys for Secondary Temperate Forest. The tropical forest subclass used all keys in the tables: List of Keys for Primary Tropical Forest and List of Keys for Secondary Tropical Forest. For primary and secondary subclasses of temperate and tropical forests, only the keys in their respective tab les were used. List of Keys for Primary Temperate Forest s List of Keys for Primary Temperate Forest s BA Bosque de Oyamel BB Bosque de Cedro BC Bosque Cultivado BG Bosque de Galeria BI Bosque inducido BJ Bosque de Tascate BM Bosque Mesofilo de Monta na BP Bosque de Pino BPQ Bosque de Pino Encino BQ Bosque de Encino BQP Bosque de Encino Pino BS Bosque de Ayarin List of Keys for Secondary Temperate Forest s List of Keys for Secondary Temperate Forest s VSa/BA Vegetación Secundaria Arbustiva de Bosque De Oyamel VSA/BA Vegetación Secundaria Arbórea de Bosque De Oyamel VSa/BB Vegetación Secundaria Arbustiva de Bosque De Cedro VSA/BB Vegetación Secundaria Arbórea de Bosque De Cedro VSa/BJ Vegetación Secundaria Arbustiva de Bosque De Tascate VSA /BJ Vegetación Secundaria Arbórea de Bosque De Tascate

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78 VSa/BM Vegetación Secundaria Arbustiva de Bosque Mesófilo De Montana VSA/BM Vegetación Secundaria Arbórea de Bosque Mesófilo De Montana VSa/BP Vegetación Secundaria Arbustiva de Bosque De Pino VSA/ BP Vegetación Secundaria Arbórea de Bosque De Pino VSa/BPQ Vegetación Secundaria Arbustiva de Bosque De Pino Encino VSA/BPQ Vegetación Secundaria Arbórea de Bosque De Pino Encino VSa/BQ Vegetación Secundaria Arbustiva de Bosque De Encino VSA/BQ Vegetac ión Secundaria Arbórea de Bosque De Encino VSa/BQP Vegetación Secundaria Arbustiva de Bosque De Encino Pino VSA/BQP Vegetación Secundaria Arbórea de Bosque De Encino Pino VSa/BS Vegetación Secundaria Arbustiva de Bosque De Ayarin VSA/BS Vegetación Secu ndaria Arbórea de Bosque De Ayarin VSa/MJ Vegetación Secundaria Arbustiva de Matorral De Coníferas VSh/BJ Vegetación Secundaria Herbacea de Bosque De Tascate VSh/BM Vegetación Secundaria Herbacea de Bosque Mesófilo De Montana VSh/BP Vegetación Secundar ia Herbacea de Bosque De Pino VSh/BPQ Vegetación Secundaria Herbacea de Bosque De Encino Pino VSh/BQ Vegetación Secundaria Herbacea de Bosque De Encino VSh/BQP Vegetación Secundaria Herbacea de Bosque De Encino Pino List of Keys for Primary Tropical F orest s List of Keys for Primary Tropical Forest s MKE Vegetación Primaria de Mezquital Tropical MST Vegetación Primaria de Matorral Subtropical SAP Selva Alta Perennifolia SAQ Selva Alta Subperennifolia SBC Selva Baja Caducifolia SBK Selva Baja Espino sa SBP Selva Baja Perennifolia SBQ Selva Baja Subperennifolia SBS Selva Baja Subcaducifolia SG Selva de Galeria SMC Selva Mediana Caducifolia SMP Selva Mediana Perennifolia SMQ Selva Mediana Subperennifolia SMS Selva Mediana Subcaducifolia

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79 List o f Keys for Secondary Tropical Forests List of Keys for Secondary Tropical Forest s VSa/MKE Vegetación Secundaria Arbustiva de Mezquital Tropical VSA/MKE Vegetación Secundaria Arbórea de Mezquital Tropical VSa/MST Vegetación Secundaria Arbustiva de Mator ral Subtropical VSA/MST Vegetación Secundaria Arbórea de Matorral Subtropical VSa/SG Vegetación Secundaria Arbustiva de selva de galería VSA/SG Vegetación Secundaria Arbórea de selva de galería VSa/SAP Vegetación Secundaria Arbustiva de Selva Alta P erennifolia VSA/SAP Vegetación Secundaria Arbórea de Selva Alta Perennifolia VSa/SAQ Vegetación Secundaria Arbustiva de Selva Alta Subperennifolia VSA/SAQ Vegetación Secundaria Arbórea de Selva Alta Subperennifolia VSa/SBC Vegetación Secundaria Arbus tiva de Selva Baja Caducifolia VSA/SBC Vegetación Secundaria Arbórea de Selva Baja Caducifolia VSa/SBK Vegetación Secundaria Arbustiva de Selva Baja Espinosa Caducifolia VSA/SBK Vegetación Secundaria Arbórea de Selva Baja Espinosa Caducifolia VSa/SBP Vegetación Secundaria Arbustiva de Selva Baja Perennifolia VSA/SBP Vegetación Secundaria Arbórea de Selva Baja Perennifolia VSa/SBQ Vegetación Secundaria Arbustiva de Selva Baja Subperennifolia VSA/SBQ Vegetación Secundaria Arbórea de Selva Baja Subp erennifolia VSa/SBS Vegetación Secundaria Arbustiva de Selva Baja Subcaducifolia VSA/SBS Vegetación Secundaria Arbórea de Selva Baja Subcaducifolia VSa/SMC Vegetación Secundaria Arbustiva de Selva Mediana Caducifolia VSa/SMC Vegetación Secundaria Arbó rea de Selva Mediana Caducifolia VSa/SMP Vegetación Secundaria Arbustiva de Selva Mediana Perennifolia VSA/SMP Vegetación Secundaria Arbórea de Selva Mediana Perennifolia VSa/SMQ Vegetación Secundaria Arbustiva de Selva Mediana Subperennifolia VSA/SM Q Vegetación Secundaria Arbórea de Selva Mediana Subperennifolia VSa/SMS Vegetación Secundaria Arbustiva de Selva Mediana Subcaducifolia VSA/SMS Vegetación Secundaria Arbórea de Selva Mediana Subcaducifolia VSh/SAP Vegetación Secundaria Herbaceo de Se lva Alta Perennifolia VSh/SAQ Vegetación Secundaria Herbacea de Selva Alta Subperennifolia VSh/SBC Vegetación Secundaria Herbacea de Selva Baja Caducifolia VSh/SBK Vegetación Secundaria Herbacea de Selva Baja Espinosa Caducifolia VSh/SBQ Vegetación Sec undaria Herbacea de Selva Baja Subperennifolia VSh/SMC Vegetación Secundaria Herbacea de Selva Mediana Caducifolia VSh/SMQ Vegetación Secundaria Herbacea de Selva Mediana Subperennifolia VSh/SMS Vegetación Secundaria Herbacea de Selva Mediana Subcaducif olia

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80 Appendix B Natural Protected Areas in the Analysis Natural Protected Area Name State ALTO GOLFO DE CALIFORNIA Y DELTA DEL RIO COLORADO B. C. Y SONORA ARRECIFES DE XCALAK QUINTANA ROO BARRANCA DE METZTITLAN HIDALGO BENITO JUAREZ OAXACA BONAMPAK CHIAPAS C AoON DE RIO BLANCO VERACRUZ Y PUEBLA CArON DEL SUMIDERO CHIAPAS CADNR001 AGUASCALIENTES Y ZACATECAS CADNR004 COAHUILA DE ZARAGOZA CADNR026 COAHUILA DE ZARAGOZA Y NUEVO LESN CADNR043 AGUASCALIENTES, JALISCO, DURANGO, NAYARIT Y ZACATECAS CALAKMUL CAMPE CHE CAMPO VERDE CHIHUAHUA Y SONORA CASCADA DE AGUA AZUL CHIAPAS CASCADA DE BASSASEACHIC CHIHUAHUA CERRO DE LA SILLA NUEVO LEON CHAMELA CUIXMALA JALISCO CHAN KIN CHIAPAS COFRE DE PEROTE VERACRUZ CORREDOR BIOLOGICO CHICHINAUTZIN DF, MORELOS Y EDO. ME XICO CUMBRES DE MONTERREY NUEVO LEON Y COAHUILA DE ZARAGOZA DESIERTO DE LOS LEONES DISTRITO FEDERAL EL CHICO HIDALGO EL JABALI COLIMA EL PINACATE Y GRAN DESIERTO DE ALTAR SONORA EL POTOSI SAN LUIS POTOSI EL TEPOZTECO MORELOS Y D. F. EL TRIUNFO CHIA PAS EL VELADERO GUERRERO EL VIZCAINO BAJA CALIFORNIA SUR Y BAJA CALIFORNIA

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81 GOGORRON SAN LUIS POTOSI GRUTAS DE CACAHUAMILPA GUERRERO HUATULCO OAXACA INSUR. MIGUEL HIDALGO Y COSTILLA EDO. MEXICO Y D.F. INSURG. JOSE MARIA MORELOS MICHOACAN DE OCAMPO I ZTACCIHUATL POPOCATEPETL EDO. MEXICO, PUEBLA Y MORELOS LA ENCRUCIJADA CHIAPAS LA MICHILIA DURANGO LA PRIMAVERA JALISCO LA SEPULTURA CHIAPAS LACANTUN CHIAPAS LAGUNA DE TERMINOS CAMPECHE Y TABASCO LAGUNAS DE CHACAHUA OAXACA LAGUNAS DE MONTEBELLO CHIA PAS LAGUNAS DE ZEMPOALA EDO. MEXICO Y MORELOS LOS MARMOLES HIDALGO LOS PETENES CAMPECHE LOS TUXTLAS VERACRUZ MADERAS DEL CARMEN COAHUILA DE ZARAGOZA MALINCHE o MATLALCUEYATL TLAXCALA Y PUEBLA MAPIMI DGO, CHIH. Y COAH MESETA DE CACAXTLA SINALOA MET ZABOK CHIAPAS MONTES AZULES CHIAPAS NAHA CHIAPAS PANTANOS DE CENTLA TABASCO Y CAMPECHE PAPIGOCHIC CHIHUAHUA PICO DE ORIZABA VERACRUZ Y PUEBLA PICO DE TANCITARO MICHOACAN DE OCAMPO RIA CELESTUN YUCATAN Y CAMPECHE RIA LAGARTOS QUINTANA ROO Y YUCATAN SELVA EL OCOTE CHIAPAS SIAN KAAN QUINTANA ROO SIERRA DE ALAMOS RIO CUCHUJAQUI CHIHUAHUA, SINALOA Y SONORA SIERRA DE ALVAREZ SAN LUIS POTOSI SIERRA DE HUAUTLA MORELOS, PUEBLA Y GUERRERO SIERRA DE MANANTLAN JALISCO Y COLIMA SIERRA DE QUILA JALISCO SI ERRA DE SAN PEDRO MARTIR BAJA CALIFORNIA SIERRA DEL ABRA TANCHIPA SAN LUIS POTOSI Y TAMAULIPAS SIERRA LA LAGUNA BAJA CALIFORNIA SUR

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82 TEHUACAN CUICATLAN PUEBLA Y OAXACA TUTUACA CHIHUAHUA Y SONORA UAYMIL QUINTANA ROO VALLE DE LOS CIRIOS BAJA CALIFORNIA VOLCAN NEVADO DE COLIMA JALISCO Y COLIMA YAXCHILAN CHIAPAS YUM BALAM QUINTANA ROO ZONA DE PROTEC. FORESTAL TERR.MPIOS.LA CONCORDIA,ANGELALBINOCORZO, ETC CHIAPAS ZONA PROTECTORA FORESTAL VEDADA LA CUENCA HIDROGRAFICA DEL RIO NECAXA HIDALGO Y PUEBLA ZP FTC CUENCAS DE LOS RIOS VALLE DE BRAVO, MALACATEPEC, TILOSTOC Y TEMASCALTEPEC EDO. MEXICO Y MICHOACAN NEVADO DE TOLUCA ESTADO DE MEXICO SIERRA GORDA QUERETARO DE ARTEGA, GUANAJUATO, SLP E HIDALGO MARIPOSA MONARCA EDO. MEXICO Y MICHOACAN Appendix C All Regional Results Analy sis Type Forest Type Zone Region Region Numb er Wilc ox Stat Wilco x p value KS Stat KS p valu e Differen ce from NPA forest all 0.5 km Peninsula de Baja California y Pacifico Norte 1 0.57 735 0.563 703 0.5 0.53 441 6 1.00604 forest all 1 km Penins ula de Baja California y Pacifico Norte 1 0 1 0.2 5 0.99 687 6 1.83646 forest all 2 km Peninsula de Baja California y Pacifico Norte 1 0.57 735 0.563 703 0.2 5 0.99 687 6 0.98841 2 forest bosqu e 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.44 3376 0.1 48 915 0.7 5 0.10 749 24.5983 forest bosqu e 1 km Peninsula de Baja California y Pacifico Norte 1 0.86 6025 0.386 476 0.5 0.53 441 6 5.60415

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83 forest bosqu e 2 km Peninsula de Baja California y Pacifico Norte 1 0.57 735 0.563 703 0.5 0.53 441 6 0.30603 forest bosqu ePrim aria 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.44 3376 0.148 915 0.7 5 0.10 749 24.5983 forest bosqu ePrim aria 1 km Peninsula de Baja California y Pacifico Norte 1 0.28 8675 0.772 83 0.5 0.53 441 6 56.3005 forest bosqu ePrim aria 2 km Peninsul a de Baja California y Pacifico Norte 1 0.28 8675 0.772 83 0.2 5 0.99 687 6 0.12372 forest bosqu eSecu ndaria 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.15 47 0.248 213 0.2 5 0.99 687 6 forest bosqu eSecu ndaria 1 km Peninsula de Baja California y Pac ifico Norte 1 1.44 338 0.148 915 0.2 5 0.99 687 6 1.47363 forest bosqu eSecu ndaria 2 km Peninsula de Baja California y Pacifico Norte 1 1.73 205 0.083 265 0.5 0.53 441 6 7.41033 forest selva 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.15 47 0.248 2 13 0.5 0.53 441 6 5.80627 7 forest selva 1 km Peninsula de Baja California y Pacifico Norte 1 1.15 47 0.248 213 0.5 0.53 441 6 4.70089 6 forest selva 2 km Peninsula de Baja California y Pacifico Norte 1 1.15 47 0.248 213 0.2 5 0.99 687 6 5.02619 forest selvaP rimar i a 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.15 47 0.248 213 0.2 5 0.99 687 6 7.96005 2 forest selvaP rimari a 1 km Peninsula de Baja California y Pacifico Norte 1 1.15 47 0.248 213 0.2 5 0.99 687 6 9.14285 7 forest selvaP rimari a 2 km Peninsula de Baj a California y Pacifico Norte 1 1.44 338 0.148 915 0.2 5 0.99 687 6 17.8265 forest selvaS ecund aria 0.5 km Peninsula de Baja California y Pacifico Norte 1 2.30 94 0.020 921 0.7 5 0.10 749

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84 forest selvaS ecund aria 1 km Peninsula de Baja California y Pacifico Nort e 1 2.30 94 0.020 921 0.7 5 0.10 749 forest selvaS ecund aria 2 km Peninsula de Baja California y Pacifico Norte 1 2.30 94 0.020 921 0.5 0.53 441 6 frag all 0.5 km Peninsula de Baja California y Pacifico Norte 1 0.28 8675 0.772 83 0.5 0.53 441 6 5.91532 frag all 1 km Peninsula de Baja California y Pacifico Norte 1 1.44 338 0.148 915 0.7 5 0.10 749 13.4289 frag all 2 km Peninsula de Baja California y Pacifico Norte 1 1.44 3376 0.148 915 0.7 5 0.10 749 3.69700 4 frag bosqu e 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.44 3376 0.148 915 0.7 5 0.10 749 19.5661 frag bosqu e 1 km Peninsula de Baja California y Pacifico Norte 1 1.15 4701 0.248 213 0.5 0.53 441 6 4.05078 frag bosqu e 2 km Peninsula de Baja California y Pacifico Norte 1 0.86 6025 0.386 476 0.5 0.53 441 6 3.90 894 1 frag bosqu ePrim aria 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.44 3376 0.148 915 0.7 5 0.10 749 20.0432 frag bosqu ePrim aria 1 km Peninsula de Baja California y Pacifico Norte 1 0.28 8675 0.772 83 0.5 0.53 441 6 57.9035 frag bosqu ePrim aria 2 km Peninsula de Baja California y Pacifico Norte 1 0.86 6025 0.386 476 0.5 0.53 441 6 25.2056 frag bosqu eSecu ndaria 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.15 47 0.248 213 0.2 5 0.99 687 6 frag bosqu eSecu ndaria 1 km Peninsula de Baja Californ ia y Pacifico Norte 1 1.44 338 0.148 915 0.2 5 0.99 687 6 18.7523 frag bosqu eSecu ndaria 2 km Peninsula de Baja California y Pacifico Norte 1 2.02 073 0.043 308 0.7 5 0.10 749 26.1035

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85 frag selva 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.44 338 0 .148 915 0.5 0.53 441 6 9.13317 3 frag selva 1 km Peninsula de Baja California y Pacifico Norte 1 2.02 073 0.043 308 0.7 5 0.10 749 8.17808 frag selva 2 km Peninsula de Baja California y Pacifico Norte 1 1.44 338 0.148 915 0.2 5 0.99 687 6 0.5929 frag selvaP rima ri a 0.5 km Peninsula de Baja California y Pacifico Norte 1 1.44 338 0.148 915 0.2 5 0.99 687 6 43.4374 frag selvaP rimari a 1 km Peninsula de Baja California y Pacifico Norte 1 1.44 338 0.148 915 0.2 5 0.99 687 6 72.9857 frag selvaP rimari a 2 km Peninsula de Baja California y Pacifico Norte 1 1.44 338 0.148 915 0.2 5 0.99 687 6 53.8958 frag selvaS ecund aria 0.5 km Peninsula de Baja California y Pacifico Norte 1 2.30 94 0.020 921 0.7 5 0.10 749 frag selvaS ecund aria 1 km Peninsula de Baja California y Pacifico Norte 1 2 .30 94 0.020 921 0.7 5 0.10 749 frag selvaS ecund aria 2 km Peninsula de Baja California y Pacifico Norte 1 2.30 94 0.020 921 0.5 0.53 441 6 forest all 0.5 km Noroeste y Alto Golfo de California 2 0.57 735 0.563 703 0.5 0.53 441 6 5.15329 6 forest all 1 km Noroeste y Alto Golfo de California 2 1.44 3376 0.148 915 0.5 0.53 441 6 8.74122 8 forest all 2 km Noroeste y Alto Golfo de California 2 0.57 735 0.563 703 0.5 0.53 441 6 5.72373 8 forest bosqu e 0.5 km Noroeste y Alto Golfo de California 2 1.44 3376 0.148 915 0.7 5 0.10 749 23.1227 forest bosqu e 1 km Noroeste y Alto Golfo de California 2 2.02 0726 0.043 308 0.7 5 0.10 749 52.2349 7

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86 forest bosqu e 2 km Noroeste y Alto Golfo de California 2 0 1 0.2 5 0.99 687 6 10.8188 forest bosqu ePrim aria 0.5 km Noroeste y Alto Golfo de California 2 0.28 8675 0.772 83 0.5 0.53 441 6 91.2354 forest bosqu ePrim aria 1 km Noroeste y Alto Golfo de California 2 1.15 4701 0.248 213 0.7 5 0.10 749 25.85 forest bosqu ePrim aria 2 km Noroeste y Alto Golfo de California 2 0.28 868 0.772 83 0.2 5 0.99 687 6 36.5023 fore st bosqu eSecu ndaria 0.5 km Noroeste y Alto Golfo de California 2 1.15 4701 0.248 213 0.7 5 0.10 749 20.9903 forest bosqu eSecu ndaria 1 km Noroeste y Alto Golfo de California 2 2.02 0726 0.043 308 0.7 5 0.10 749 53.1227 3 forest bosqu eSecu ndaria 2 km Noroeste y Al to Golfo de California 2 0.28 868 0.772 83 0.5 0.53 441 6 10.2653 forest selva 0.5 km Noroeste y Alto Golfo de California 2 1.15 47 0.248 213 0.7 5 0.10 749 5.08568 9 forest selva 1 km Noroeste y Alto Golfo de California 2 0.57 735 0.563 703 0.2 5 0.99 687 6 5.983 81 5 forest selva 2 km Noroeste y Alto Golfo de California 2 0 1 0.5 0.53 441 6 12.2168 8 forest selvaP rimari a 0.5 km Noroeste y Alto Golfo de California 2 0.57 735 0.563 703 0.2 5 0.99 687 6 0.32272 9 forest selvaP rimari a 1 km Noroeste y Alto Golfo de Californi a 2 0 1 0.5 0.53 441 6 12.0033 7 forest selvaP rimari a 2 km Noroeste y Alto Golfo de California 2 0.57 735 0.563 703 0.2 5 0.99 687 6 0.88554 2 forest selvaS ecund aria 0.5 km Noroeste y Alto Golfo de California 2 0.57 735 0.563 703 0.5 0.53 441 6 23.4945 5

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87 forest sel vaS ecund aria 1 km Noroeste y Alto Golfo de California 2 0.86 603 0.386 476 0.2 5 0.99 687 6 23.2119 forest selvaS ecund aria 2 km Noroeste y Alto Golfo de California 2 0.28 868 0.772 83 0.2 5 0.99 687 6 6.18047 2 frag all 0.5 km Noroeste y Alto Golfo de California 2 0 1 0.2 5 0.99 687 6 0.46739 frag all 1 km Noroeste y Alto Golfo de California 2 0.57 735 0.563 703 0.5 0.53 441 6 17.1099 9 frag all 2 km Noroeste y Alto Golfo de California 2 0.28 868 0.772 83 0.2 5 0.99 687 6 25.4748 frag bosqu e 0.5 km Noroeste y Alto Golfo de California 2 1.44 3376 0.148 915 0.7 5 0.10 749 25.1597 frag bosqu e 1 km Noroeste y Alto Golfo de California 2 1.73 2051 0.083 265 0.7 5 0.10 749 60.5425 1 frag bosqu e 2 km Noroeste y Alto Golfo de California 2 0 1 0.2 5 0.99 687 6 7.39566 1 frag bosqu ePrim aria 0.5 km Noroeste y Alto Golfo de California 2 0.28 8675 0.772 83 0.5 0.53 441 6 91.4053 frag bosqu ePrim aria 1 km Noroeste y Alto Golfo de California 2 0.86 6025 0.386 476 0.5 0.53 441 6 62.6269 2 frag bosqu ePrim aria 2 km Noroeste y Alto Golfo de California 2 0 1 0.2 5 0.99 687 6 22.112 frag bosqu eSecu ndaria 0.5 km Noroeste y Alto Golfo de California 2 0.86 6025 0.386 476 0.5 0.53 441 6 0.33249 frag bosqu eSecu ndaria 1 km Noroeste y Alto Golfo de California 2 0.86 6025 0.386 476 0.5 0.53 441 6 60.3843 frag bosqu eSecu ndari a 2 km Noroeste y Alto Golfo de California 2 0.86 603 0.386 476 0.5 0.53 441 6 16.0836

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88 frag selva 0.5 km Noroeste y Alto Golfo de California 2 1.73 205 0.083 265 0.7 5 0.10 749 111.367 frag selva 1 km Noroeste y Alto Golfo de California 2 1.15 47 0.248 213 0. 5 0.53 441 6 91.758 frag selva 2 km Noroeste y Alto Golfo de California 2 0.86 603 0.386 476 0.2 5 0.99 687 6 136.481 frag selvaP rimari a 0.5 km Noroeste y Alto Golfo de California 2 0.86 603 0.386 476 0.2 5 0.99 687 6 192.82 frag selvaP rimari a 1 km Noroeste y Alto Golfo de California 2 0.86 603 0.386 476 0.2 5 0.99 687 6 181.357 frag selvaP rimari a 2 km Noroeste y Alto Golfo de California 2 0.86 603 0.386 476 0.2 5 0.99 687 6 195.951 frag selvaS ecund aria 0.5 km Noroeste y Alto Golfo de California 2 1.73 205 0.083 265 0.7 5 0.10 749 57.7239 frag selvaS ecund aria 1 km Noroeste y Alto Golfo de California 2 0.86 603 0.386 476 0.2 5 0.99 687 6 88.1124 frag selvaS ecund aria 2 km Noroeste y Alto Golfo de California 2 0.57 735 0.563 703 0.2 5 0.99 687 6 78.7831 forest all 0.5 km No rte y Sierra Madre Occidental 3 0.96 0769 0.336 668 0.5 0.31 802 8 3.49783 1 forest all 1 km Norte y Sierra Madre Occidental 3 2.40 1922 0.016 309 0.6 666 67 0.07 664 5 5.12389 3 forest all 2 km Norte y Sierra Madre Occidental 3 0.64 0513 0.521 839 0.5 0.31 802 8 2.7127 3 forest bosqu e 0.5 km Norte y Sierra Madre Occidental 3 0.32 0256 0.748 774 0.3 333 33 0.80 955 7 0.27534 3 forest bosqu e 1 km Norte y Sierra Madre Occidental 3 0.80 0641 0.423 34 0.5 0.31 802 8 4.46717

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89 forest bosqu e 2 km Norte y Sierra Madre Occidental 3 0.96 07 69 0.336 668 0.5 0.31 802 8 2.18381 forest bosqu ePrim aria 0.5 km Norte y Sierra Madre Occidental 3 0.96 0769 0.336 668 0.5 0.31 802 8 3.16643 forest bosqu ePrim aria 1 km Norte y Sierra Madre Occidental 3 1.60 1282 0.109 315 0.6 666 67 0.07 664 5 0.00759 forest bosq u ePrim aria 2 km Norte y Sierra Madre Occidental 3 1.76 141 0.078 169 0.6 666 67 0.07 664 5 2.96319 1 forest bosqu eSecu ndaria 0.5 km Norte y Sierra Madre Occidental 3 0.80 064 0.423 34 0.3 333 33 0.80 955 7 2.97462 forest bosqu eSecu ndaria 1 km Norte y Sierra Madre O ccidental 3 0.48 0384 0.630 954 0.3 333 33 0.80 955 7 1.12235 forest bosqu eSecu ndaria 2 km Norte y Sierra Madre Occidental 3 0.32 026 0.748 774 0.3 333 33 0.80 955 7 9.8473 forest selva 0.5 km Norte y Sierra Madre Occidental 3 0.32 0256 0.748 774 0.3 333 33 0.80 955 7 6.15823 1 forest selva 1 km Norte y Sierra Madre Occidental 3 0.64 051 0.521 839 0.5 0.31 802 8 8.75959 6 forest selva 2 km Norte y Sierra Madre Occidental 3 0 1 0.3 333 33 0.80 955 7 6.34312 4 forest selvaP rimari a 0.5 km Norte y Sierra Madre Occidental 3 0.80 06 4 0.423 34 0.1 666 67 0.99 995 7 24.0342 forest selvaP rimari a 1 km Norte y Sierra Madre Occidental 3 1.76 141 0.078 169 0.5 0.31 802 8 17.5364 forest selvaP rimari a 2 km Norte y Sierra Madre Occidental 3 0.64 051 0.521 839 0.1 666 67 0.99 995 7 1.89941 forest selv aS ecund aria 0.5 km Norte y Sierra Madre Occidental 3 0.48 038 0.630 954 0.1 666 67 0.99 995 7 12.9888 3

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90 forest selvaS ecund aria 1 km Norte y Sierra Madre Occidental 3 0.80 064 0.423 34 0.5 0.31 802 8 16.9988 1 forest selvaS ecund aria 2 km Norte y Sierra Madre Occide ntal 3 0.80 064 0.423 34 0.1 666 67 0.99 995 7 4.79821 1 frag all 0.5 km Norte y Sierra Madre Occidental 3 1.60 128 0.109 315 0.6 666 67 0.07 664 5 19.2998 frag all 1 km Norte y Sierra Madre Occidental 3 1.12 09 0.262 332 0.5 0.31 802 8 17.414 frag all 2 km Norte y Sierra Madre Occidental 3 0.96 077 0.336 668 0.5 0.31 802 8 18.3774 frag bosqu e 0.5 km Norte y Sierra Madre Occidental 3 1.44 115 0.149 541 0.5 0.31 802 8 25.8087 frag bosqu e 1 km Norte y Sierra Madre Occidental 3 0.48 0384 0.630 954 0.5 0.31 802 8 25.0756 fr ag bosqu e 2 km Norte y Sierra Madre Occidental 3 0.64 0513 0.521 839 0.5 0.31 802 8 17.602 frag bosqu ePrim aria 0.5 km Norte y Sierra Madre Occidental 3 1.76 141 0.078 169 0.5 0.31 802 8 42.2084 frag bosqu ePrim aria 1 km Norte y Sierra Madre Occidental 3 0.80 06 41 0.423 34 0.5 0.31 802 8 15.1577 frag bosqu ePrim aria 2 km Norte y Sierra Madre Occidental 3 0.80 0641 0.423 34 0.5 0.31 802 8 16.5936 frag bosqu eSecu ndaria 0.5 km Norte y Sierra Madre Occidental 3 0.96 077 0.336 668 0.5 0.31 802 8 53.1109 frag bosqu eSecu ndar ia 1 km Norte y Sierra Madre Occidental 3 0.16 0128 0.872 78 0.3 333 33 0.80 955 7 44.0113 frag bosqu eSecu ndaria 2 km Norte y Sierra Madre Occidental 3 0.16 0128 0.872 78 0.3 333 33 0.80 955 7 79.1624

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91 frag selva 0.5 km Norte y Sierra Madre Occidental 3 0.32 0256 0. 748 774 0.3 333 33 0.80 955 7 4.28382 frag selva 1 km Norte y Sierra Madre Occidental 3 0.96 077 0.336 668 0.3 333 33 0.80 955 7 1.89231 frag selva 2 km Norte y Sierra Madre Occidental 3 0 1 0.1 666 67 0.99 995 7 1.40031 6 frag selvaP rimari a 0.5 km Norte y Sierra Mad re Occidental 3 0.48 038 0.630 954 0.1 666 67 0.99 995 7 11.4093 frag selvaP rimari a 1 km Norte y Sierra Madre Occidental 3 1.12 09 0.262 332 0.3 333 33 0.80 955 7 32.9195 frag selvaP rimari a 2 km Norte y Sierra Madre Occidental 3 0.32 026 0.748 774 0.3 333 33 0.80 95 5 7 7.65651 frag selvaS ecund aria 0.5 km Norte y Sierra Madre Occidental 3 1.28 103 0.200 185 0.1 666 67 0.99 995 7 16.3713 7 frag selvaS ecund aria 1 km Norte y Sierra Madre Occidental 3 2.08 167 0.037 373 0.6 666 67 0.07 664 5 14.219 frag selvaS ecund aria 2 km Norte y Sierra Madre Occidental 3 1.60 128 0.109 315 0.3 333 33 0.80 955 7 61.3660 2 forest all 0.5 km Noreste y Sierra Madre Oriental 4 0.30 237 0.762 369 0.3 0.67 507 8 0.54409 forest all 1 km Noreste y Sierra Madre Oriental 4 1.20 949 0.226 476 0.5 0.11 084 1.3565 forest all 2 km Noreste y Sierra Madre Oriental 4 0.90 7115 0.364 346 0.3 0.67 507 8 2.03606 9 forest bosqu e 0.5 km Noreste y Sierra Madre Oriental 4 0 1 0.2 0.97 478 9 0.5898 forest bosqu e 1 km Noreste y Sierra Madre Oriental 4 0.60 474 0.545 35 0.3 0.67 507 8 1.35798

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92 forest bosqu e 2 km Noreste y Sierra Madre Oriental 4 0.90 7115 0.364 346 0.3 0.67 507 8 0.42070 9 forest bosqu ePrim aria 0.5 km Noreste y Sierra Madre Oriental 4 0 1 0.2 0.97 478 9 1.71828 forest bosqu ePrim aria 1 km Noreste y Sierra Madre Oriental 4 0.83 152 0.405 679 0.4 0.31 285 3 2.55739 forest bosqu ePrim aria 2 km Noreste y Sierra Madre Oriental 4 0.30 2372 0.762 369 0.3 0.67 507 8 1.5963 forest bosqu eSecu ndaria 0.5 km Noreste y Sierra Madre Oriental 4 0.52 915 0.596 701 0.3 0.67 507 8 6.94397 forest bo squ eSecu ndaria 1 km Noreste y Sierra Madre Oriental 4 0.98 271 0.325 751 0.4 0.31 285 3 9.47209 forest bosqu eSecu ndaria 2 km Noreste y Sierra Madre Oriental 4 0.45 3557 0.650 147 0.3 0.67 507 8 2.51477 forest selva 0.5 km Noreste y Sierra Madre Oriental 4 2. 64 575 0.008 151 0.6 0.03 104 7 0.68328 forest selva 1 km Noreste y Sierra Madre Oriental 4 1.88 982 0.058 782 0.5 0.11 084 3.87800 9 forest selva 2 km Noreste y Sierra Madre Oriental 4 1.66 304 0.096 304 0.4 0.31 285 3 3.12426 9 forest selvaP rimari a 0.5 km Nores te y Sierra Madre Oriental 4 0.90 7115 0.364 346 0.2 0.97 478 9 3.7935 forest selvaP rimari a 1 km Noreste y Sierra Madre Oriental 4 2.34 338 0.019 11 0.1 1 14.1899 forest selvaP rimari a 2 km Noreste y Sierra Madre Oriental 4 1.05 8301 0.289 918 0.2 0.97 478 9 0.3 3482 forest selvaS ecund aria 0.5 km Noreste y Sierra Madre Oriental 4 2.64 575 0.008 151 0.6 0.03 104 7 0.50311

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93 forest selvaS ecund aria 1 km Noreste y Sierra Madre Oriental 4 1.81 423 0.069 642 0.5 0.11 084 5.54604 4 forest selvaS ecund aria 2 km Noreste y Sierr a Madre Oriental 4 1.66 304 0.096 304 0.4 0.31 285 3 16.4870 4 frag all 0.5 km Noreste y Sierra Madre Oriental 4 3.47 7273 0.000 507 0.8 0.00 121 6 54.6129 2 frag all 1 km Noreste y Sierra Madre Oriental 4 2.72 1344 0.006 502 0.6 0.03 104 7 26.4113 6 frag all 2 km No reste y Sierra Madre Oriental 4 2.11 6601 0.034 294 0.5 0.11 084 25.8539 3 frag bosqu e 0.5 km Noreste y Sierra Madre Oriental 4 2.49 4566 0.012 611 0.7 0.00 689 9 62.8436 6 frag bosqu e 1 km Noreste y Sierra Madre Oriental 4 2.49 4566 0.012 611 0.7 0.00 689 9 16.9562 4 frag bosqu e 2 km Noreste y Sierra Madre Oriental 4 2.41 8973 0.015 564 0.7 0.00 689 9 20.1321 4 frag bosqu ePrim aria 0.5 km Noreste y Sierra Madre Oriental 4 2.34 338 0.019 11 0.6 0.03 104 7 69.1992 1 frag bosqu ePrim aria 1 km Noreste y Sierra Madre Oriental 4 1.8 8 9822 0.058 782 0.6 0.03 104 7 32.9114 6 frag bosqu ePrim aria 2 km Noreste y Sierra Madre Oriental 4 1.58 7451 0.112 411 0.5 0.11 084 35.0156 6 frag bosqu eSecu ndaria 0.5 km Noreste y Sierra Madre Oriental 4 1.05 8301 0.289 918 0.5 0.11 084 97.4549 7 frag bosqu eSecu n daria 1 km Noreste y Sierra Madre Oriental 4 0.52 915 0.596 701 0.3 0.67 507 8 58.1580 3 frag bosqu eSecu ndaria 2 km Noreste y Sierra Madre Oriental 4 1.28 5079 0.198 765 0.4 0.31 285 3 78.2492 2

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94 frag selva 0.5 km Noreste y Sierra Madre Oriental 4 2.34 338 0.019 11 0.6 0.03 104 7 92.1597 8 frag selva 1 km Noreste y Sierra Madre Oriental 4 0.68 034 0.496 292 0.5 0.11 084 111.227 1 frag selva 2 km Noreste y Sierra Madre Oriental 4 0.60 474 0.545 35 0.4 0.31 285 3 80.5507 9 frag selvaP rimari a 0.5 km Noreste y Sierra Madre Orie ntal 4 1.66 304 0.096 304 0.2 0.97 478 9 87.1140 6 frag selvaP rimari a 1 km Noreste y Sierra Madre Oriental 4 2.41 8973 0.015 564 0.1 1 40.0339 1 frag selvaP rimari a 2 km Noreste y Sierra Madre Oriental 4 1.20 9486 0.226 476 0.2 0.97 478 9 102.532 frag selvaS ecund ar ia 0.5 km Noreste y Sierra Madre Oriental 4 2.19 219 0.028 366 0.7 0.00 689 9 frag selvaS ecund aria 1 km Noreste y Sierra Madre Oriental 4 1.51 186 0.130 57 0.6 0.03 104 7 frag selvaS ecund aria 2 km Noreste y Sierra Madre Oriental 4 0.90 711 0.364 346 0.5 0.11 0 84 forest all 0.5 km Golfo de Mexico y Planicie Costera 5 1.08 609 0.277 439 0.4 285 71 0.42 321 8 1.66819 forest all 1 km Golfo de Mexico y Planicie Costera 5 1.34 164 0.179 712 0.5 714 29 0.12 865 7 17.1619 forest all 2 km Golfo de Mexico y Planicie Costera 5 0.19 166 0.848 006 0.4 285 71 0.42 321 8 0.74690 5 forest bosqu e 0.5 km Golfo de Mexico y Planicie Costera 5 0.83 054 0.406 234 0.2 857 14 0.88 274 7 1.35385 forest bosqu e 1 km Golfo de Mexico y Planicie Costera 5 1.46 942 0.141 72 0.5 714 29 0.12 865 7 19.1651

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95 for est bosqu e 2 km Golfo de Mexico y Planicie Costera 5 1.72 497 0.084 533 0.5 714 29 0.12 865 7 5.82849 forest bosqu ePrim aria 0.5 km Golfo de Mexico y Planicie Costera 5 0.57 499 0.565 299 0.2 857 14 0.88 274 7 0.88674 forest bosqu ePrim aria 1 km Golfo de Mexico y Planicie Costera 5 0.83 054 0.406 234 0.4 285 71 0.42 321 8 15.0603 forest bosqu ePrim aria 2 km Golfo de Mexico y Planicie Costera 5 1.72 497 0.084 533 0.5 714 29 0.12 865 7 32.2976 forest bosqu eSecu ndaria 0.5 km Golfo de Mexico y Planicie Costera 5 0.83 054 0.40 6 234 0.4 285 71 0.42 321 8 9.82228 forest bosqu eSecu ndaria 1 km Golfo de Mexico y Planicie Costera 5 0.70 276 0.482 203 0.4 285 71 0.42 321 8 48.288 forest bosqu eSecu ndaria 2 km Golfo de Mexico y Planicie Costera 5 0.31 9438 0.749 394 0.4 285 71 0.42 321 8 3.04434 8 forest selva 0.5 km Golfo de Mexico y Planicie Costera 5 0.19 1663 0.848 006 0.2 857 14 0.88 274 7 0.46091 7 forest selva 1 km Golfo de Mexico y Planicie Costera 5 0.70 2764 0.482 203 0.4 285 71 0.42 321 8 0.29845 forest selva 2 km Golfo de Mexico y Planicie Costera 5 0.44 7214 0.654 721 0.4 285 71 0.42 321 8 19.9148 9 forest selvaP rimari a 0.5 km Golfo de Mexico y Planicie Costera 5 0.95 8315 0.337 904 0.5 714 29 0.12 865 7 1.39293 forest selvaP rimari a 1 km Golfo de Mexico y Planicie Costera 5 0.44 7214 0.654 721 0.4 285 71 0.42 32 1 8 2.53661 forest selvaP rimari a 2 km Golfo de Mexico y Planicie Costera 5 0.57 4989 0.565 299 0.4 285 71 0.42 321 8 29.686 forest selvaS ecund aria 0.5 km Golfo de Mexico y Planicie Costera 5 0.83 054 0.406 234 0.2 857 14 0.88 274 7 17.2001

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96 forest selvaS ecund aria 1 km Golfo de Mexico y Planicie Costera 5 0.19 166 0.848 006 0.2 857 14 0.88 274 7 1.67037 forest selvaS ecund aria 2 km Golfo de Mexico y Planicie Costera 5 0.44 721 0.654 721 0.2 857 14 0.88 274 7 29.0891 1 frag all 0.5 km Golfo de Mexico y Planicie Costera 5 1. 34 164 0.179 712 0.5 714 29 0.12 865 7 18.3866 frag all 1 km Golfo de Mexico y Planicie Costera 5 0.19 166 0.848 006 0.4 285 71 0.42 321 8 9.77715 7 frag all 2 km Golfo de Mexico y Planicie Costera 5 0.70 276 0.482 203 0.4 285 71 0.42 321 8 6.53441 frag bosqu e 0.5 km Golfo de Mexico y Planicie Costera 5 1.34 164 0.179 712 0.5 714 29 0.12 865 7 45.439 frag bosqu e 1 km Golfo de Mexico y Planicie Costera 5 0.95 831 0.337 904 0.4 285 71 0.42 321 8 47.8325 frag bosqu e 2 km Golfo de Mexico y Planicie Costera 5 1.59 719 0.110 223 0. 5 714 29 0.12 865 7 39.9987 frag bosqu ePrim aria 0.5 km Golfo de Mexico y Planicie Costera 5 1.34 164 0.179 712 0.5 714 29 0.12 865 7 56.2682 frag bosqu ePrim aria 1 km Golfo de Mexico y Planicie Costera 5 0.44 721 0.654 721 0.2 857 14 0.88 274 7 24.3016 frag bosqu eP rim aria 2 km Golfo de Mexico y Planicie Costera 5 1.72 497 0.084 533 0.5 714 29 0.12 865 7 72.4894 frag bosqu eSecu ndaria 0.5 km Golfo de Mexico y Planicie Costera 5 0.95 831 0.337 904 0.4 285 71 0.42 321 8 28.641 frag bosqu eSecu ndaria 1 km Golfo de Mexico y Plan icie Costera 5 0.06 3888 0.949 06 0.4 285 71 0.42 321 8 102.67 frag bosqu eSecu ndaria 2 km Golfo de Mexico y Planicie Costera 5 0.19 166 0.848 006 0.2 857 14 0.88 274 7 19.0579

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97 frag selva 0.5 km Golfo de Mexico y Planicie Costera 5 0.57 4989 0.565 299 0.4 285 71 0.42 3 21 8 1.83642 frag selva 1 km Golfo de Mexico y Planicie Costera 5 1.21 3865 0.224 799 0.5 714 29 0.12 865 7 39.2125 5 frag selva 2 km Golfo de Mexico y Planicie Costera 5 0.06 3888 0.949 06 0.4 285 71 0.42 321 8 7.56298 frag selvaP rimari a 0.5 km Golfo de Mexico y Pl anicie Costera 5 1.34 1641 0.179 712 0.7 142 86 0.02 75 42.4667 5 frag selvaP rimari a 1 km Golfo de Mexico y Planicie Costera 5 1.21 3865 0.224 799 0.5 714 29 0.12 865 7 21.4107 4 frag selvaP rimari a 2 km Golfo de Mexico y Planicie Costera 5 0.06 3888 0.949 06 0.4 285 71 0 .42 321 8 71.3076 frag selvaS ecund aria 0.5 km Golfo de Mexico y Planicie Costera 5 0.57 499 0.565 299 0.2 857 14 0.88 274 7 41.8091 frag selvaS ecund aria 1 km Golfo de Mexico y Planicie Costera 5 0.19 1663 0.848 006 0.4 285 71 0.42 321 8 8.16908 1 frag selvaS ecund ar ia 2 km Golfo de Mexico y Planicie Costera 5 0.70 276 0.482 203 0.2 857 14 0.88 274 7 3.40148 forest all 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 1.36 5273 0.172 167 0.6 25 0.04 965 4 6.71902 5 forest all 1 km Peninsula de Yucatan y Caribe Mexicano 6 0.63 01 26 0.528 612 0.5 0.18 768 4 5.90638 3 forest all 2 km Peninsula de Yucatan y Caribe Mexicano 6 0.42 008 0.674 424 0.3 75 0.51 894 2 2.78927 7 forest bosqu e 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 forest bosqu e 1 km Penins ula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6

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98 forest bosqu e 2 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 forest bosqu ePrim aria 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 forest bosqu ePrim aria 1 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 forest bosqu ePrim aria 2 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 forest bosqu eSecu ndaria 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 forest bosqu eSecu ndaria 1 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.6 25 0.04 965 4 forest bosqu eSecu ndaria 2 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 77 8 0.7 5 0.00 976 6 forest selva 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 1.68 0336 0.092 892 0.5 0.18 768 4 4.52160 1 forest selva 1 km Peninsula de Yucatan y Caribe Mexicano 6 1.89 0378 0.058 707 0.5 0.18 768 4 4.67780 7 forest selva 2 km Peninsula de Yucat an y Caribe Mexicano 6 0.63 0126 0.528 612 0.3 75 0.51 894 2 2.93916 9 forest selvaP rimari a 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 0.21 0042 0.833 635 0.2 5 0.92 895 5 2.65663 9 forest selvaP rimari a 1 km Peninsula de Yucatan y Caribe Mexicano 6 0.84 0168 0.4 00 814 0.3 75 0.51 894 2 5.64520 2 forest selvaP rimari a 2 km Peninsula de Yucatan y Caribe Mexicano 6 0.10 5021 0.916 359 0.3 75 0.51 894 2 12.8516 forest selvaS ecund aria 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 1.15 5231 0.247 996 0.5 0.18 768 4 7.94113 6

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99 for est selvaS ecund aria 1 km Peninsula de Yucatan y Caribe Mexicano 6 1.05 021 0.293 622 0.3 75 0.51 894 2 7.21073 8 forest selvaS ecund aria 2 km Peninsula de Yucatan y Caribe Mexicano 6 0.21 0042 0.833 635 0.2 5 0.92 895 5 3.50136 3 frag all 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 1.15 5231 0.247 996 0.5 0.18 768 4 30.7446 frag all 1 km Peninsula de Yucatan y Caribe Mexicano 6 0.21 0042 0.833 635 0.3 75 0.51 894 2 20.1754 2 frag all 2 km Peninsula de Yucatan y Caribe Mexicano 6 1.99 5399 0.045 999 0.6 25 0.04 965 4 42.7886 9 frag bosqu e 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.8 75 0.00 142 9 frag bosqu e 1 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 frag bosqu e 2 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0. 000 778 0.7 5 0.00 976 6 frag bosqu ePrim aria 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 frag bosqu ePrim aria 1 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 frag bosqu ePrim aria 2 km Penins ula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 frag bosqu eSecu ndaria 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.7 5 0.00 976 6 frag bosqu eSecu ndaria 1 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 7 78 0.6 25 0.04 965 4 frag bosqu eSecu ndaria 2 km Peninsula de Yucatan y Caribe Mexicano 6 3.36 067 0.000 778 0.6 25 0.04 965 4

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100 frag selva 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 0.73 5147 0.462 25 0.3 75 0.51 894 2 17.3477 2 frag selva 1 km Peninsula de Yuc atan y Caribe Mexicano 6 1.68 0336 0.092 892 0.5 0.18 768 4 21.5258 3 frag selva 2 km Peninsula de Yucatan y Caribe Mexicano 6 2.20 5441 0.027 423 0.6 25 0.04 965 4 38.2291 3 frag selvaP rimari a 0.5 km Peninsula de Yucatan y Caribe Mexicano 6 0.21 004 0.833 635 0.2 5 0.92 895 5 12.5026 8 frag selvaP rimari a 1 km Peninsula de Yucatan y Caribe Mexicano 6 0.63 0126 0.528 612 0.3 75 0.51 894 2 15.4945 8 frag selvaP rimari a 2 km Peninsula de Yucatan y Caribe Mexicano 6 1.26 0252 0.207 578 0.5 0.18 768 4 80.3499 5 frag selvaS ecund aria 0. 5 km Peninsula de Yucatan y Caribe Mexicano 6 0.10 502 0.916 359 0.1 25 0.99 999 9 6.32262 frag selvaS ecund aria 1 km Peninsula de Yucatan y Caribe Mexicano 6 0.52 5105 0.599 51 0.2 5 0.92 895 5 15.2025 frag selvaS ecund aria 2 km Peninsula de Yucatan y Caribe Mex icano 6 0.84 0168 0.400 814 0.3 75 0.51 894 2 5.87098 forest all 0.5 km Occidente y Pacifico Centro 7 0.37 7964 0.705 457 0.2 0.97 478 9 1.03173 8 forest all 1 km Occidente y Pacifico Centro 7 0.75 593 0.449 692 0.4 0.31 285 3 0.57453 forest all 2 km Occidente y P acifico Centro 7 0.75 593 0.449 692 0.3 0.67 507 8 0.14873 forest bosqu e 0.5 km Occidente y Pacifico Centro 7 1.20 9486 0.226 476 0.4 0.31 285 3 1.06467 3 forest bosqu e 1 km Occidente y Pacifico Centro 7 0.98 2708 0.325 751 0.4 0.31 285 3 0.89532 1

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101 forest bosqu e 2 km Occidente y Pacifico Centro 7 0 1 0.2 0.97 478 9 0.61274 8 forest bosqu ePrim aria 0.5 km Occidente y Pacifico Centro 7 0.52 915 0.596 701 0.3 0.67 507 8 1.31033 forest bosqu ePrim aria 1 km Occidente y Pacifico Centro 7 0.30 2372 0.762 369 0.4 0.31 285 3 6.71963 forest bosqu ePrim aria 2 km Occidente y Pacifico Centro 7 0.37 796 0.705 457 0.3 0.67 507 8 4.75022 forest bosqu eSecu ndaria 0.5 km Occidente y Pacifico Centro 7 0.98 2708 0.325 751 0.3 0.67 507 8 0.46337 6 forest bosqu eSecu ndaria 1 km Occidente y Pacifico Centr o 7 1.51 1858 0.130 57 0.4 0.31 285 3 35.6834 8 forest bosqu eSecu ndaria 2 km Occidente y Pacifico Centro 7 0.45 3557 0.650 147 0.3 0.67 507 8 0.01092 forest selva 0.5 km Occidente y Pacifico Centro 7 0.22 6779 0.820 596 0.2 0.97 478 9 0.84632 forest selva 1 km Occi dente y Pacifico Centro 7 0.22 678 0.820 596 0.2 0.97 478 9 1.08187 2 forest selva 2 km Occidente y Pacifico Centro 7 0.83 152 0.405 679 0.3 0.67 507 8 0.97496 5 forest selvaP rimari a 0.5 km Occidente y Pacifico Centro 7 0.07 5593 0.939 743 0.2 0.97 478 9 1.19746 7 f orest selvaP rimari a 1 km Occidente y Pacifico Centro 7 0.07 559 0.939 743 0.3 0.67 507 8 1.36312 forest selvaP rimari a 2 km Occidente y Pacifico Centro 7 1.05 83 0.289 918 0.3 0.67 507 8 1.47365 forest selvaS ecund aria 0.5 km Occidente y Pacifico Centro 7 0 1 0.1 1 0.22487 4

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102 forest selvaS ecund aria 1 km Occidente y Pacifico Centro 7 0 1 0.2 0.97 478 9 33.1230 6 forest selvaS ecund aria 2 km Occidente y Pacifico Centro 7 0.75 593 0.449 692 0.2 0.97 478 9 8.46144 9 frag all 0.5 km Occidente y Pacifico Centro 7 1.20 9486 0 .226 476 0.4 0.31 285 3 12.6464 7 frag all 1 km Occidente y Pacifico Centro 7 1.13 389 0.256 839 0.3 0.67 507 8 15.7324 frag all 2 km Occidente y Pacifico Centro 7 1.05 8301 0.289 918 0.4 0.31 285 3 3.04326 5 frag bosqu e 0.5 km Occidente y Pacifico Centro 7 1.88 98 22 0.058 782 0.5 0.11 084 11.8826 2 frag bosqu e 1 km Occidente y Pacifico Centro 7 0.22 6779 0.820 596 0.4 0.31 285 3 20.9961 frag bosqu e 2 km Occidente y Pacifico Centro 7 0.98 2708 0.325 751 0.3 0.67 507 8 1.43757 8 frag bosqu ePrim aria 0.5 km Occidente y Pacific o Centro 7 1.28 5079 0.198 765 0.4 0.31 285 3 1.75641 8 frag bosqu ePrim aria 1 km Occidente y Pacifico Centro 7 0.45 3557 0.650 147 0.3 0.67 507 8 27.3601 frag bosqu ePrim aria 2 km Occidente y Pacifico Centro 7 1.13 3893 0.256 839 0.4 0.31 285 3 5.08891 3 frag bosqu eS ecu ndaria 0.5 km Occidente y Pacifico Centro 7 1.20 9486 0.226 476 0.5 0.11 084 15.6345 7 frag bosqu eSecu ndaria 1 km Occidente y Pacifico Centro 7 1.05 8301 0.289 918 0.4 0.31 285 3 10.3356 frag bosqu eSecu ndaria 2 km Occidente y Pacifico Centro 7 0.22 6779 0.820 596 0.3 0.67 507 8 4.2167

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103 frag selva 0.5 km Occidente y Pacifico Centro 7 0.60 4743 0.545 35 0.3 0.67 507 8 69.3371 frag selva 1 km Occidente y Pacifico Centro 7 0.15 119 0.879 829 0.2 0.97 478 9 32.4066 2 frag selva 2 km Occidente y Pacifico Centro 7 0.37 796 0 .705 457 0.2 0.97 478 9 48.4907 1 frag selvaP rimari a 0.5 km Occidente y Pacifico Centro 7 0.22 6779 0.820 596 0.2 0.97 478 9 99.7051 5 frag selvaP rimari a 1 km Occidente y Pacifico Centro 7 0.07 5593 0.939 743 0.2 0.97 478 9 22.4894 2 frag selvaP rimari a 2 km Occidente y Pacifico Centro 7 1.05 83 0.289 918 0.4 0.31 285 3 23.2558 5 frag selvaS ecund aria 0.5 km Occidente y Pacifico Centro 7 0.68 0336 0.496 292 0.4 0.31 285 3 90.6739 frag selvaS ecund aria 1 km Occidente y Pacifico Centro 7 0.52 915 0.596 701 0.2 0.97 478 9 10.9631 5 frag selvaS ecund aria 2 km Occidente y Pacifico Centro 7 0.30 2372 0.762 369 0.3 0.67 507 8 57.7996 3 forest all 0.5 km Centro y Eje Neovolcanico 8 0.63 7205 0.523 991 0.2 941 18 0.38 739 0.25226 forest all 1 km Centro y Eje Neovolcanico 8 0.63 721 0.523 991 0.2 352 9 4 0.67 250 4 0.54666 forest all 2 km Centro y Eje Neovolcanico 8 0.67 165 0.501 807 0.2 352 94 0.67 250 4 0.70603 forest bosqu e 0.5 km Centro y Eje Neovolcanico 8 1.60 1624 0.109 239 0.3 529 41 0.19 000 8 0.23202 6 forest bosqu e 1 km Centro y Eje Neovolcanico 8 0.0 5 1665 0.958 795 0.1 176 47 0.99 944 8 0.18985

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104 forest bosqu e 2 km Centro y Eje Neovolcanico 8 1.32 6076 0.184 815 0.4 705 88 0.03 050 3 0.36262 7 forest bosqu ePrim aria 0.5 km Centro y Eje Neovolcanico 8 1.56 7181 0.117 072 0.4 705 88 0.03 050 3 0.24292 forest bosqu ePrim aria 1 km Centro y Eje Neovolcanico 8 2.18 7164 0.028 731 0.4 117 65 0.08 130 2 2.52858 3 forest bosqu ePrim aria 2 km Centro y Eje Neovolcanico 8 1.56 7181 0.117 072 0.4 705 88 0.03 050 3 2.32058 4 forest bosqu eSecu ndaria 0.5 km Centro y Eje Neovolcanico 8 0.77 4979 0.4 38 352 0.2 941 18 0.38 739 0.36970 2 forest bosqu eSecu ndaria 1 km Centro y Eje Neovolcanico 8 1.56 718 0.117 072 0.4 117 65 0.08 130 2 4.6182 forest bosqu eSecu ndaria 2 km Centro y Eje Neovolcanico 8 0.60 2762 0.546 667 0.2 352 94 0.67 250 4 0.72714 forest selva 0.5 k m Centro y Eje Neovolcanico 8 1.05 053 0.293 476 0.2 352 94 0.67 250 4 0.40830 2 forest selva 1 km Centro y Eje Neovolcanico 8 0.25 833 0.796 155 0.1 764 71 0.93 031 2.13534 forest selva 2 km Centro y Eje Neovolcanico 8 0.80 942 0.418 272 0.1 764 71 0.93 031 1.1417 forest selvaP rimari a 0.5 km Centro y Eje Neovolcanico 8 1.23 997 0.214 988 0.1 764 71 0.93 031 1.35623 1 forest selvaP rimari a 1 km Centro y Eje Neovolcanico 8 1.70 4955 0.088 203 0.1 176 47 0.99 944 8 1.92607 5 forest selvaP rimari a 2 km Centro y Eje Neovolcanico 8 1.67 0511 0.094 818 0.1 176 47 0.99 944 8 2.81005 forest selvaS ecund aria 0.5 km Centro y Eje Neovolcanico 8 0.32 721 0.743 506 0.2 352 94 0.67 250 4 3.37346

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105 forest selvaS ecund aria 1 km Centro y Eje Neovolcanico 8 0.43 0544 0.666 8 0.1 764 71 0.93 031 2.22363 forest selvaS ecund aria 2 km Centro y Eje Neovolcanico 8 0.36 1657 0.717 608 0.1 176 47 0.99 944 8 1.59644 7 frag all 0.5 km Centro y Eje Neovolcanico 8 0.22 3883 0.822 848 0.2 941 18 0.38 739 4.17915 8 frag all 1 km Centro y Eje Neovolcanico 8 0.91 2754 0.361 372 0.2 941 18 0.3 8 739 4.88734 9 frag all 2 km Centro y Eje Neovolcanico 8 0.30 999 0.756 567 0.3 529 41 0.19 000 8 0.32585 frag bosqu e 0.5 km Centro y Eje Neovolcanico 8 1.05 0528 0.293 476 0.3 529 41 0.19 000 8 2.10196 frag bosqu e 1 km Centro y Eje Neovolcanico 8 0.49 9431 0.617 4 76 0.1 764 71 0.93 031 5.84099 5 frag bosqu e 2 km Centro y Eje Neovolcanico 8 1.15 3858 0.248 558 0.3 529 41 0.19 000 8 1.27787 5 frag bosqu ePrim aria 0.5 km Centro y Eje Neovolcanico 8 2.11 8277 0.034 152 0.4 117 65 0.08 130 2 9.99119 frag bosqu ePrim aria 1 km Centro y E je Neovolcanico 8 1.42 9407 0.152 887 0.3 529 41 0.19 000 8 14.5592 3 frag bosqu ePrim aria 2 km Centro y Eje Neovolcanico 8 1.39 4963 0.163 027 0.4 117 65 0.08 130 2 16.9210 3 frag bosqu eSecu ndaria 0.5 km Centro y Eje Neovolcanico 8 1.18 8302 0.234 715 0.2 941 18 0.38 739 6 .63549 9 frag bosqu eSecu ndaria 1 km Centro y Eje Neovolcanico 8 0.43 054 0.666 8 0.1 764 71 0.93 031 10.3089 2 frag bosqu eSecu ndaria 2 km Centro y Eje Neovolcanico 8 0.18 9439 0.849 748 0.2 941 18 0.38 739 18.3403

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106 frag selva 0.5 km Centro y Eje Neovolcanico 8 0. 46 499 0.641 94 0.1 764 71 0.93 031 2.53007 8 frag selva 1 km Centro y Eje Neovolcanico 8 0.39 6101 0.692 031 0.1 764 71 0.93 031 1.9789 frag selva 2 km Centro y Eje Neovolcanico 8 0.98 1641 0.326 277 0.2 352 94 0.67 250 4 2.85282 8 frag selvaP rimari a 0.5 km Centro y Ej e Neovolcanico 8 1.17 108 0.241 567 0.1 764 71 0.93 031 14.5964 1 frag selvaP rimari a 1 km Centro y Eje Neovolcanico 8 1.73 9398 0.081 965 0.1 176 47 0.99 944 8 7.62187 frag selvaP rimari a 2 km Centro y Eje Neovolcanico 8 1.67 0511 0.094 818 0.1 176 47 0.99 944 8 13.1463 frag selvaS ecund aria 0.5 km Centro y Eje Neovolcanico 8 0.08 6109 0.931 38 0.1 176 47 0.99 944 8 4.08686 frag selvaS ecund aria 1 km Centro y Eje Neovolcanico 8 1.05 0528 0.293 476 0.1 764 71 0.93 031 9.13105 frag selvaS ecund aria 2 km Centro y Eje Neovolcanico 8 0.91 2754 0.361 372 0.1 764 71 0.93 031 0.36798 forest all 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 0.34 8025 0.727 822 0.2 222 22 0.70 876 8 0.09473 forest all 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.44 294 0.657 809 0.3 333 33 0.21 816 6 0.34831 forest all 2 km Frontera Sur, Istmo y Pacifico Sur 9 1.07 571 0.282 056 0.4 444 44 0.03 902 8 0.96165 forest bosqu e 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 1.10 735 0.268 142 0.3 888 89 0.09 818 1 1.30629 5 forest bosqu e 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.31 639 0.751 71 0.2 222 22 0.70 876 8 1.22089 4

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107 forest bosqu e 2 km Frontera Sur, Istmo y Pacifico Sur 9 0.82 26 0.410 733 0.3 333 33 0.21 816 6 3.10153 forest bosqu ePrim aria 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 1.21 809 0.223 191 0.4 444 44 0.03 902 8 27.7487 2 forest bosqu ePrim aria 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.07 91 0.936 956 0.2 777 78 0.42 547 2 28.6851 forest bosqu ePrim aria 2 km Frontera Sur, Istmo y Pacifico Sur 9 0.94 916 0.342 54 0.2 777 78 0.42 547 2 24.7835 2 forest bosqu eSecu ndaria 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 0.12 6554 0.899 293 0.2 222 22 0.70 876 8 4.64497 9 forest bosqu eSecu ndaria 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.06 328 0.949 546 0.2 222 22 0.70 876 8 3.11817 9 forest bosqu eSecu ndaria 2 km Frontera Sur, Istmo y Pacifico Sur 9 0.11 074 0.911 826 0.1 666 67 0.94 475 3 1.38148 1 forest selva 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 3.00 5667 0.002 65 0.5 555 56 0.00 426 3 0.48689 forest selva 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.94 9158 0.342 54 0.2 777 78 0.42 547 2 0.32513 forest selva 2 km Frontera Sur, Istmo y Pacifico Sur 9 1.04 4074 0.296 451 0.2 777 78 0.42 547 2 0.93584 7 forest selvaP rimari a 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 1.70 8484 0.087 546 0.3 333 33 0.21 816 6 3.98003 forest selvaP rimari a 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.06 3277 0.949 546 0.2 222 22 0.70 876 8 3.43715 forest selvaP rimari a 2 km Frontera Sur, Istmo y Pacifico Sur 9 0.34 8025 0.727 822 0.1 666 67 0.94 475 3 0.30543 forest selvaS ecund aria 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 3.41 6969 0.000 633 0.6 111 11 0.00 1 17 1 9.64473 1

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108 forest selvaS ecund aria 1 km Frontera Sur, Istmo y Pacifico Sur 9 2.59 4365 0.009 477 0.4 444 44 0.03 902 8 11.7438 9 forest selvaS ecund aria 2 km Frontera Sur, Istmo y Pacifico Sur 9 2.18 3063 0.029 031 0.4 444 44 0.03 902 8 6.36237 1 frag all 0.5 km Fron tera Sur, Istmo y Pacifico Sur 9 1.45 538 0.145 565 0.3 888 89 0.09 818 1 7.4667 frag all 1 km Frontera Sur, Istmo y Pacifico Sur 9 1.36 046 0.173 684 0.3 333 33 0.21 816 6 12.177 frag all 2 km Frontera Sur, Istmo y Pacifico Sur 9 0.94 916 0.342 54 0.3 333 33 0.21 8 16 6 8.20888 frag bosqu e 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 2.84 747 0.004 407 0.6 666 67 0.00 028 5 44.4805 frag bosqu e 1 km Frontera Sur, Istmo y Pacifico Sur 9 1.29 718 0.194 568 0.3 888 89 0.09 818 1 51.2187 frag bosqu e 2 km Frontera Sur, Istmo y P acifico Sur 9 1.74 012 0.081 837 0.4 444 44 0.03 902 8 49.803 frag bosqu ePrim aria 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 2.92 657 0.003 427 0.6 666 67 0.00 028 5 183.131 frag bosqu ePrim aria 1 km Frontera Sur, Istmo y Pacifico Sur 9 1.43 956 0.149 993 0.5 0.0 1 371 7 180.384 frag bosqu ePrim aria 2 km Frontera Sur, Istmo y Pacifico Sur 9 1.59 775 0.110 099 0.3 888 89 0.09 818 1 177.76 frag bosqu eSecu ndaria 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 1.07 571 0.282 056 0.3 888 89 0.09 818 1 75.7722 frag bosqu eSecu ndaria 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.79 096 0.428 964 0.2 777 78 0.42 547 2 65.637 frag bosqu eSecu ndaria 2 km Frontera Sur, Istmo y Pacifico Sur 9 0.79 096 0.428 964 0.2 777 78 0.42 547 2 60.4106

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109 frag selva 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 2.3 4 1256 0.019 219 0.5 555 56 0.00 426 3 11.4638 frag selva 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.15 819 0.874 305 0.2 222 22 0.70 876 8 18.9271 frag selva 2 km Frontera Sur, Istmo y Pacifico Sur 9 0.22 147 0.824 726 0.1 666 67 0.94 475 3 9.00513 frag selvaP rimar i a 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 2.02 487 0.042 881 0.3 333 33 0.21 816 6 30.5331 frag selvaP rimari a 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.45 876 0.646 407 0.1 666 67 0.94 475 3 24.7412 1 frag selvaP rimari a 2 km Frontera Sur, Istmo y Pacifico Sur 9 1.07 5712 0.282 056 0.2 222 22 0.70 876 8 35.5367 8 frag selvaS ecund aria 0.5 km Frontera Sur, Istmo y Pacifico Sur 9 2.24 6341 0.024 682 0.5 0.01 371 7 13.0481 frag selvaS ecund aria 1 km Frontera Sur, Istmo y Pacifico Sur 9 0.82 2604 0.410 733 0.2 777 78 0.42 547 2 20 .8002 frag selvaS ecund aria 2 km Frontera Sur, Istmo y Pacifico Sur 9 0.18 9832 0.849 441 0.1 666 67 0.94 475 3 10.6657 Appendix D Considerable outliers Considerable outliers in the forest cover change analysis NPA Name Percent Change Forest Type Zon e Region R egion num ber EL VELADERO 9756.14 bosqueSec undaria NPA Centro y Eje Neovolcanico 8 CADNR026 5855.55 6 bosquePri maria 500 Noreste y Sierra Madre Oriental 4

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110 CUMBRES DE MONTERREY 2003.92 2 bosquePri maria 200 0 Noreste y Sierra Madre Oriental 4 LA MICHILIA 1692.77 1 bos queSec undaria NPA Norte y Sierra Madre Occidental 3 CALAKMUL 1411.53 8 selva 100 0 Peninsula de Yucatan y Caribe Mexicano 6 CALAKMUL 1411.53 8 selvaSecun daria 100 0 Peninsula de Yucatan y Caribe Mexicano 6 Considerable outliers in the forest fragmentation change analysis NPA Name Percent Change Forest Type Zon e Region Region number ZONA DE PROTEC. FORESTAL TERR.MPIOS.LA CONCORDIA,ANGELALBINOCORZO, ETC 62598.5 1 bosqueP rimaria NPA Frontera Sur, Istmo y Pacifico Sur 9 CERRO DE LA SILLA 34252.9 4 bosqueS ecunda ri a 500 Noreste y Sierra Madre Oriental 4 ZONA DE PROTEC. FORESTAL TERR.MPIOS.LA CONCORDIA,ANGELALBINOCORZO, ETC 31231.9 8 all NPA Frontera Sur, Istmo y Pacifico Sur 9 ZONA DE PROTEC. FORESTAL TERR.MPIOS.LA CONCORDIA,ANGELALBINOCORZO, ETC 25439.3 9 bosqueS ecundari a NPA Frontera Sur, Istmo y Pacifico Sur 9 CUMBRES DE MONTERREY 24151.1 6 bosqueS ecundari a 100 0 Noreste y Sierra Madre Oriental 4 TEHUACAN CUICATLAN 4500 bosqueP rimaria 100 0 Centro y Eje Neovolcanic o 8 CERRO DE LA SILLA 3884.61 5 bosqueP rimaria 50 0 Noreste y Sierra Madre Oriental 4 CERRO DE LA SILLA 3636.06 2 all 500 Noreste y Sierra Madre Oriental 4

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111 SELVA EL OCOTE 3550 bosqueP rimaria NPA Frontera Sur, Istmo y Pacifico Sur 9 CERRO DE LA SILLA 3155.30 2 bosque 500 Noreste y Sierra Madre Oriental 4 GOGORRON 2977.77 8 selvaPri maria 200 0 Noreste y Sierra Madre Oriental 4 ZONA DE PROTEC. FORESTAL TERR.MPIOS.LA CONCORDIA,ANGELALBINOCORZO, ETC 2959.64 9 selvaSec undaria NPA Frontera Sur, Istmo y Pacifico Sur 9 ZONA DE PROTEC. FORESTAL TERR.MPIOS.LA CONCOR DIA,ANGELALBINOCORZO, ETC 2513.12 7 bosque NPA Frontera Sur, Istmo y Pacifico Sur 9 CUMBRES DE MONTERREY 2475 bosqueP rimaria 100 0 Noreste y Sierra Madre Oriental 4 CUMBRES DE MONTERREY 2015.56 bosque 100 0 Noreste y Sierra Madre Oriental 4 BONAMPAK 1762.5 selva 200 0 Frontera Sur, Istmo y Pacifico Sur 9 CUMBRES DE MONTERREY 1602.81 7 all 100 0 Noreste y Sierra Madre Oriental 4 GRUTAS DE CACAHUAMILPA 1567.61 5 bosqueS ecundari a 200 0 Centro y Eje Neovolcanic o 8 LOS TUXTLAS 1466.27 9 bosqueP rimaria NPA Golfo de Mexico y Planicie Costera 5 CADNR026 1405.55 6 bosqueP rimaria 500 Noreste y Sierra Madre Oriental 4 ZONA DE PROTEC. FORESTAL TERR.MPIOS.LA CONCORDIA,ANGELALBINOCORZO, ETC 1260 selva NPA Frontera Sur, Istmo y Pacifico Sur 9

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112 MADERAS DEL CARMEN 1100 bosqueS ecundari a 200 0 Noreste y Sierra Madre Oriental 4 Appendix E Results for all NPAs when considering all forests as a single class Forest cover change ranked from greatest increase to greatest decrease NPA Name Percent Change Region Region Number Hectares Category L OS PETENES 91.70114 Peninsula de Yucatan y Caribe Mexicano 6 100866.5 RB RIA CELESTUN 72.2884 Peninsula de Yucatan y Caribe Mexicano 6 61987.27 RB GOGORRON 65.0366 Noreste y Sierra Madre Oriental 4 38231.52 PN LA ENCRUCIJADA 54.17731 Frontera Sur, Istmo y Pacifico Sur 9 115652.7 RB VOLCAN NEVADO DE COLIMA 24.53876 Occidente y Pacifico Centro 7 6525.472 PN LOS TUXTLAS 18.09849 Golfo de Mexico y Planicie Costera 5 155122.5 RB CUMBRES DE MONTERREY 11.96452 Noreste y Sierra Madre Oriental 4 177396 PN LAG UNA DE TERMINOS 11.53889 Golfo de Mexico y Planicie Costera 5 547278.7 APFyF CADNR001 11.30497 Occidente y Pacifico Centro 7 97699.69 APRN SIERRA DE QUILA 9.806526 Occidente y Pacifico Centro 7 15192.5 APFyF CADNR026 9.544867 Noreste y Sierra Madre Orie ntal 4 197156.8 APRN METZABOK 5.429392 Frontera Sur, Istmo y Pacifico Sur 9 3368.359 APFyF HUATULCO 3.174965 Frontera Sur, Istmo y Pacifico Sur 9 6578.786 PN LAGUNAS DE MONTEBELLO 2.55738 Frontera Sur, Istmo y Pacifico Sur 9 6425.493 PN

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113 CHAMELA CUIXMAL A 2.134992 Occidente y Pacifico Centro 7 13142.78 RB CERRO DE LA SILLA 1.964963 Noreste y Sierra Madre Oriental 4 6039.399 MN BONAMPAK 1.559843 Frontera Sur, Istmo y Pacifico Sur 9 4357.4 MN MESETA DE CACAXTLA 1.295953 Noroeste y Alto Golfo de Californi a 2 50862.31 APFyF EL VIZCAINO 1.231129 Peninsula de Baja California y Pacifico Norte 1 2258931 RB EL VELADERO 0.899542 Centro y Eje Neovolcanico 8 3617.413 PN CASCADA DE AGUA AZUL 0.883904 Frontera Sur, Istmo y Pacifico Sur 9 2580 APFyF EL TEPOZTECO 0 .748878 Centro y Eje Neovolcanico 8 23258.73 PN YAXCHILAN 0.687578 Frontera Sur, Istmo y Pacifico Sur 9 2621.252 MN SIERRA GORDA 0.586059 Centro y Eje Neovolcanico 8 383567.4 RB INSUR. MIGUEL HIDALGO Y COSTILLA 0.576612 Centro y Eje Neovolcanico 8 1889. 966 PN LOS MARMOLES 0.452384 Centro y Eje Neovolcanico 8 23150 PN SIERRA DE HUAUTLA 0.376757 Centro y Eje Neovolcanico 8 59030.94 RB LACANTUN 0.346968 Frontera Sur, Istmo y Pacifico Sur 9 61873.96 RB MONTES AZULES 0.261567 Frontera Sur, Istmo y Pacific o Sur 9 331200 RB EL POTOSI 0.229764 Noreste y Sierra Madre Oriental 4 2000 PN NAHA 0.221713 Frontera Sur, Istmo y Pacifico Sur 9 3847.416 APFyF SIERRA DEL ABRA TANCHIPA 0.183122 Noreste y Sierra Madre Oriental 4 21464.44 RB PICO DE ORIZABA 0.182417 Go lfo de Mexico y Planicie Costera 5 19750.01 PN SIERRA DE ALVAREZ 0.164426 Noreste y Sierra Madre Oriental 4 16900 APFyF

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114 CORREDOR BIOLOGICO CHICHINAUTZIN 0.127399 Centro y Eje Neovolcanico 8 37302.41 APFyF SIERRA DE MANANTLAN 0.111529 Occidente y Pacific o Centro 7 139577.1 RB CAMPO VERDE 0.104676 Norte y Sierra Madre Occidental 3 108067.5 APFyF LA PRIMAVERA 0.085155 Occidente y Pacifico Centro 7 30500 APFyF LAGUNAS DE CHACAHUA 0.084098 Frontera Sur, Istmo y Pacifico Sur 9 14896.07 PN CHAN KIN 0.049303 Frontera Sur, Istmo y Pacifico Sur 9 12184.99 APFyF SIERRA LA LAGUNA 0.012624 Peninsula de Baja California y Pacifico Norte 1 112437.1 RB DESIERTO DE LOS LEONES 0.005913 Centro y Eje Neovolcanico 8 1529 PN BARRANCA DE METZTITLAN 0.02718 Centro y Eje N eovolcanico 8 96042.95 RB EL CHICO 0.04684 Centro y Eje Neovolcanico 8 2739.026 PN BENITO JUAREZ 0.06681 Frontera Sur, Istmo y Pacifico Sur 9 2591.516 PN LAGUNAS DE ZEMPOALA 0.10079 Centro y Eje Neovolcanico 8 4790 PN PICO DE TANCITARO 0.21659 Occi dente y Pacifico Centro 7 23405.92 APFyF MARIPOSA MONARCA 0.22131 Occidente y Pacifico Centro 7 56259.05 RB EL PINACATE Y GRAN DESIERTO DE ALTAR 0.2221 Noroeste y Alto Golfo de California 2 714556.5 RB CALAKMUL 0.26531 Peninsula de Yucatan y Caribe M exicano 6 723185.1 RB GRUTAS DE CACAHUAMILPA 1.0815 Centro y Eje Neovolcanico 8 1600 PN ZPFTC CUENCAS DE LOS RIOS VALLE DE BRAVO, MALACATEPEC, TILOSTOC Y TEMASCALTEPEC 1.32291 Centro y Eje Neovolcanico 8 172879.4 APRN

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115 CAoON DE RIO BLANCO 1.3433 Golfo de Mexico y Planicie Costera 5 48799.78 PN ZONA PROTECTORA FORESTAL VEDADA LA CUENCA HIDROGRAFICA DEL RIO NECAXA 1.35024 Golfo de Mexico y Planicie Costera 5 42129.35 APRN TUTUACA 1.52608 Norte y Sierra Madre Occidental 3 436985.7 APFyF INSURG. JOSE MARIA MORELOS 1.78453 Occidente y Pacifico Centro 7 7191.769 PN VALLE DE LOS CIRIOS 2.17173 Peninsula de Baja California y Pacifico Norte 1 2521988 APFyF SELVA EL OCOTE 2.43555 Frontera Sur, Istmo y Pacifico Sur 9 101288.2 RB CASCADA DE BASSASEACHIC 2.94758 Norte y Sierra Madre Occidental 3 5802.851 PN TEHUACAN CUICATLAN 3.00026 Centro y Eje Neovolcanico 8 490186.9 RB MALINCHE o MATLALCUEYATL 3.0432 Centro y Eje Neovolcanico 8 46112.24 PN CADNR043 3.58093 Norte y Sierra Madre Occidental 3 23290 27 APRN COFRE DE PEROTE 3.8075 Golfo de Mexico y Planicie Costera 5 11530.73 PN PANTANOS DE CENTLA 4.48772 Golfo de Mexico y Planicie Costera 5 302706.6 RB CADNR004 4.85607 Noreste y Sierra Madre Oriental 4 1519385 APRN PAPIGOCHIC 5.04122 Norte y S ierra Madre Occidental 3 222763.9 APFyF IZTACCIHUATL POPOCATEPETL 5.4843 Centro y Eje Neovolcanico 8 39819.09 PN YUM BALAM 5.77048 Peninsula de Yucatan y Caribe Mexicano 6 52307.62 APFyF UAYMIL 7.20167 Peninsula de Yucatan y Caribe Mexicano 6 89118.1 5 APFyF CArON DEL SUMIDERO 7.39567 Frontera Sur, Istmo y Pacifico Sur 9 21789.42 PN

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116 RIA LAGARTOS 8.02824 Peninsula de Yucatan y Caribe Mexicano 6 60347.83 RB NEVADO DE TOLUCA 9.32392 Centro y Eje Neovolcanico 8 53590.68 APFyF EL JABALI 10.3196 Occi dente y Pacifico Centro 7 5178.56 APFyF SIERRA DE ALAMOS RIO CUCHUJAQUI 11.2794 Noroeste y Alto Golfo de California 2 92889.69 APFyF LA MICHILIA 11.7375 Norte y Sierra Madre Occidental 3 35000 RB ARRECIFES DE XCALAK 13.0888 Peninsula de Yucatan y Car ibe Mexicano 6 4521.839 PN EL TRIUNFO 13.418 Frontera Sur, Istmo y Pacifico Sur 9 119177.3 RB SIAN KAAN 13.8033 Peninsula de Yucatan y Caribe Mexicano 6 375011.9 RB MADERAS DEL CARMEN 13.8577 Noreste y Sierra Madre Oriental 4 208381.2 APFyF ZONA DE PROTEC. FORESTAL TERR.MPIOS.LA CONCORDIA,ANG ELALBINOCORZO, ETC 32.9264 Frontera Sur, Istmo y Pacifico Sur 9 177546.2 APRN LA SEPULTURA 34.6425 Frontera Sur, Istmo y Pacifico Sur 9 167309.9 RB SIERRA DE SAN PEDRO MARTIR 39.3329 Peninsula de Baja Califo rnia y Pacifico Norte 1 72908.58 PN MAPIMI 85.8111 Noreste y Sierra Madre Oriental 4 342388 RB ALTO GOLFO DE CALIFORNIA Y DELTA DEL RIO COLORADO 94.2987 Noroeste y Alto Golfo de California 2 407147.5 RB Forest fragmentation change ranked from greates t increase to greatest decrease NPA Name Percent Change Region Region Number Hectares Categor y

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117 ZONA DE PROTEC. FORESTAL TERR.MPIOS.LA CONCORDIA,ANGELALBINOCORZ O, ETC 31231.9 8 Frontera Sur, Istmo y Pacifico Sur 9 177546. 2 APRN SIERRA DE ALAMOS RIO CUCHUJA QUI 277.531 5 Noroeste y Alto Golfo de California 2 92889.6 9 APFyF EL TRIUNFO 264.174 5 Frontera Sur, Istmo y Pacifico Sur 9 119177. 3 RB LA SEPULTURA 135.674 9 Frontera Sur, Istmo y Pacifico Sur 9 167309. 9 RB CASCADA DE BASSASEACHIC 106.892 1 Norte y Sierra Madre Occidental 3 5802.85 1 PN IZTACCIHUATL POPOCATEPETL 67.8652 5 Centro y Eje Neovolcanic o 8 39819.0 9 PN EL JABALI 63.9344 3 Occidente y Pacifico Centro 7 5178.56 APFyF CALAKMUL 55.8812 3 Peninsula de Yucatan y Caribe Mexicano 6 723185. 1 RB MALINCHE o MATLALCUEYATL 46.8763 5 Centro y Eje Neovolcanic o 8 46112.2 4 PN YUM BALAM 45.0470 9 Peninsula de Yucatan y Caribe Mexicano 6 52307.6 2 APFyF ZONA PROTECTORA FORESTAL VEDADA LA CUENCA HIDROGRAFICA DEL RIO NECAXA 42.5155 5 Golfo de Mexico y Planicie Costera 5 42129.3 5 APRN METZABOK 40.5694 Frontera Sur, Istmo y Pacifico Sur 9 3368.35 9 APFyF COFRE DE PEROTE 36.9586 8 Golfo de Mexico y 5 11530.7 3 PN

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118 Planicie Costera LA ENCRUCIJADA 34.4414 6 Frontera Sur, Istmo y Pacifico Sur 9 115652. 7 RB LAGUNAS DE CHACAHUA 33 .7775 1 Frontera Sur, Istmo y Pacifico Sur 9 14896.0 7 PN TUTUACA 31.1034 9 Norte y Sierra Madre Occidental 3 436985. 7 APFyF NEVADO DE TOLUCA 26.7521 4 Centro y Eje Neovolcanic o 8 53590.6 8 APFyF CAoON DE RIO BLANCO 19.1251 1 Golfo de Mexico y Planicie Coster a 5 48799.7 8 PN PAPIGOCHIC 18.8226 3 Norte y Sierra Madre Occidental 3 222763. 9 APFyF CAMPO VERDE 16.9458 4 Norte y Sierra Madre Occidental 3 108067. 5 APFyF BENITO JUAREZ 16.5441 2 Frontera Sur, Istmo y Pacifico Sur 9 2591.51 6 PN CHAN KIN 14.5833 3 Fronter a Sur, Istmo y Pacifico Sur 9 12184.9 9 APFyF INSURG. JOSE MARIA MORELOS 10.7526 9 Occidente y Pacifico Centro 7 7191.76 9 PN LAGUNA DE TERMINOS 9.00218 6 Golfo de Mexico y Planicie Costera 5 547278. 7 APFyF LAGUNAS DE MONTEBELLO 7.12076 8 Frontera Sur, Istmo y Pacifico Sur 9 6425.49 3 PN SELVA EL OCOTE 6.83190 3 Frontera Sur, Istmo y Pacifico Sur 9 101288. 2 RB

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119 EL CHICO 5.19031 1 Centro y Eje Neovolcanic o 8 2739.02 6 PN ZPFTC CUENCAS DE LOS RIOS VALLE DE BRAVO, MALACATEPEC, TILOSTOC Y TEMASCALTEPEC 5.10386 8 Cen tro y Eje Neovolcanic o 8 172879. 4 APRN MARIPOSA MONARCA 3.65489 7 Occidente y Pacifico Centro 7 56259.0 5 RB VOLCAN NEVADO DE COLIMA 3.20088 3 Occidente y Pacifico Centro 7 6525.47 2 PN EL POTOSI 0.92539 Noreste y Sierra Madre Oriental 4 2000 PN TEHUACAN C UICATLAN 0.82758 9 Centro y Eje Neovolcanic o 8 490186. 9 RB EL VIZCAINO 0.67275 4 Peninsula de Baja California y Pacifico Norte 1 2258931 RB LA PRIMAVERA 0.36399 8 Occidente y Pacifico Centro 7 30500 APFyF PICO DE ORIZABA 0.25067 3 Golfo de Mexico y Planicie Costera 5 19750.0 1 PN BARRANCA DE METZTITLAN 0.11019 9 Centro y Eje Neovolcanic o 8 96042.9 5 RB SIERRA LA LAGUNA 0.06294 9 Peninsula de Baja California y Pacifico Norte 1 112437. 1 RB DESIERTO DE LOS LEONES 0 Centro y Eje Neovolcanic o 8 1529 PN

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120 SIERRA DE HUAUTLA 0.1588 Centro y Eje Neovolcanic o 8 59030.9 4 RB SIERRA DE ALVAREZ 0.4885 Noreste y Sierra Madre Oriental 4 16900 APFyF EL PINACATE Y GRAN DESIERTO DE ALTAR 0.54263 Noroeste y Alto Golfo de California 2 714556. 5 RB LOS MARMOLES 1.02913 Centro y Eje Neovolcanic o 8 23150 PN NAHA 1.25428 Frontera Sur, Istmo y Pacifico Sur 9 3847.41 6 APFyF MONTES AZULES 1.5732 Frontera Sur, Istmo y Pacifico Sur 9 331200 RB PICO DE TANCITARO 1.88535 Occidente y Pacifico Centro 7 23405.9 2 APFyF GOGORRON 1.950 24 Noreste y Sierra Madre Oriental 4 38231.5 2 PN GRUTAS DE CACAHUAMILPA 2.27372 Centro y Eje Neovolcanic o 8 1600 PN CASCADA DE AGUA AZUL 2.73899 Frontera Sur, Istmo y Pacifico Sur 9 2580 APFyF CORREDOR BIOLOGICO CHICHINAUTZIN 3.21537 Centro y Eje Neo volcanic o 8 37302.4 1 APFyF VALLE DE LOS CIRIOS 3.5185 Peninsula de Baja California y Pacifico Norte 1 2521988 APFyF CArON DEL SUMIDERO 3.61848 Frontera Sur, Istmo y Pacifico Sur 9 21789.4 2 PN PANTANOS DE CENTLA 3.72545 Golfo de Mexico y 5 302706. 6 RB

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121 Planicie Cost era LOS TUXTLAS 3.8635 Golfo de Mexico y Planicie Costera 5 155122. 5 RB EL TEPOZTECO 4.43767 Centro y Eje Neovolcanic o 8 23258.7 3 PN SIERRA GORDA 4.65976 Centro y Eje Neovolcanic o 8 383567. 4 RB CADNR043 5.06316 Norte y Sierra Madre Oc cidental 3 2329027 APRN CADNR004 5.97964 Noreste y Sierra Madre Oriental 4 1519385 APRN INSUR. MIGUEL HIDALGO Y COSTILLA 7.68688 Centro y Eje Neovolcanic o 8 1889.96 6 PN RIA LAGARTOS 8.7785 Peninsula de Yucatan y Caribe Mexicano 6 60347.8 3 RB MADERAS DEL CARMEN 10.4802 Noreste y Sierra Madre Oriental 4 208381. 2 APFyF EL VELADERO 11.2211 Centro y Eje Neovolcanic o 8 3617.41 3 PN MESETA DE CACAXTLA 11.7912 Noroeste y Alto Golfo de California 2 50862.3 1 APFyF CADNR001 13.1073 Occidente y Pacifico Ce ntro 7 97699.6 9 APRN SIERRA DE MANANTLAN 16.5513 Occidente y Pacifico Centro 7 139577. 1 RB LA MICHILIA 18.3074 Norte y Sierra Madre Occidental 3 35000 RB

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122 SIAN KAAN 28.6463 Peninsula de Yucatan y Caribe Mexicano 6 375011. 9 RB ARRECIFES DE XCALAK 30. 3614 Peninsula de Yucatan y Caribe Mexicano 6 4521.83 9 PN LAGUNAS DE ZEMPOALA 32.2981 Centro y Eje Neovolcanic o 8 4790 PN RIA CELESTUN 32.7387 Peninsula de Yucatan y Caribe Mexicano 6 61987.2 7 RB LACANTUN 36.4225 Frontera Sur, Istmo y Pacifico Sur 9 61873.9 6 RB SIERRA DE SAN PEDRO MARTIR 37.3814 Peninsula de Baja California y Pacifico Norte 1 72908.5 8 PN LOS PETENES 39.8681 Peninsula de Yucatan y Caribe Mexicano 6 100866. 5 RB CADNR026 41.0808 Noreste y Sierra Madre Oriental 4 197156. 8 APRN CHAMELA CUIXMALA 50.602 Occidente y Pacifico Centro 7 13142.7 8 RB UAYMIL 57.9217 Peninsula de Yucatan y Caribe Mexicano 6 89118.1 5 APFyF YAXCHILAN 61.5385 Frontera Sur, Istmo y Pacifico Sur 9 2621.25 2 MN HUATULCO 68.9273 Frontera Sur, Istmo y Pacifi co Sur 9 6578.78 6 PN

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123 CUMBRES DE MONTERREY 71.7204 Noreste y Sierra Madre Oriental 4 177396 PN SIERRA DE QUILA 73.3271 Occidente y Pacifico Centro 7 15192.5 APFyF CERRO DE LA SILLA 76.1515 Noreste y Sierra Madre Oriental 4 6039.39 9 MN MAPIMI 82.0748 Noreste y Sierra Madre Oriental 4 342388 RB SIERRA DEL ABRA TANCHIPA 91.4439 Noreste y Sierra Madre Oriental 4 21464.4 4 RB ALTO GOLFO DE CALIFORNIA Y DELTA DEL RIO COLORADO 92.9949 Noroeste y Alto Golfo de California 2 407147. 5 RB BONAMPAK 96.8421 F rontera Sur, Istmo y Pacifico Sur 9 4357.4 MN