Uncovering Trends Between Environmental Features and Feline Camera Trap Occurrences in the Alto Chagres Region of Panama

Home , Giant panda

Uncovering trends between environmental features and feline camera trap occurrences in the Alto Chagres region of Panama

Juliana Balluffi-Fry and Laura Milena Molina González

Sociedad Mastozoológica de Panamá (SOMASPA) Address: Aptdo. Postal 0835-00680 - Parque Lefevre , Zona 10, Panamá Telephone: (507) 395-3021 / 395-4004 E-mail: [email protected]

Project 1: Bats

Number of equivalent full days spent on the project in Panama: 11 days

Number of equivalent full days spent in the "field": 2 days

Project 2: Felines

Number of equivalent full days spent on the project in Panama: 13 days

Number of equivalent full days spent in the "field": 1 day (Panama students exchange session)

Uncovering trends between environmental features and feline camera trap occurrences in

the Alto Chagres region of Panama

Juliana Balluffi-Fry and Laura Molina González

Introduction:

Evidence shows that humans and our ancestors have been changing the earth’s landscape and animal populations long before modern time (Steffen 2007). The worldwide expansion of the human race along with our urbanization, industrialization, fossil fuel exhaustion, and pollution is the reason why scientists are calling this geographic time period the “Anthropocene” (Crutzen

2006). A side effect of our prominent presence is an increased rate of extinction; roughly 322 terrestrial vertebrates have disappeared since 1500 (Dirzo 2014).

Another condition of the Anthropocene is habitat fragmentation, which Fahrig (2009) defines as “a landscape-scale process involving the breaking apart of [a specific] habitat”- a type of habitat loss. Fragmentation, whether arising from habitat loss or man-made borders such as highways, can reduce animal movement, leading to less gene flow, genetic diversity, and ultimately lower reproductive fitness (Frankham 2006; Urquiza-Haas 2009). These thoroughly studied realities are the case for many fauna types, including mammals, birds, and invertebrates

(Largiader 2003; Riley 2006; Wilson 2015; Herkert 2010). Eventually, lower reproduction rates can lead to local extinctions, and eventually lower species richness (Fahrig 2010; Herkert 2010).

To promote animal movement in our anthropogenic environments, suitable land in between populations must be present. This can be done through corridors, defined by Kindlmann and Burel (2008) as “narrow, continuous strips of habitat that structurally connect two otherwise non-contiguous habitat patches”. Some suggest that continuity can also be achieved through

3 many smaller, close-knit patches of ideal habitat (Tulloch 2015). In certain cases, such as the giant panda habitats in the Min Mountains, a single strip of land can be fully responsible for connecting two populations (Yin 2006). Choosing which areas to protect for corridor use can be determined through least cost analysis using GIS, expert advices, or GPS tracking of animals

(Epps 2007).

Besides the act of preservation, confidently knowing whether or not a species will actually move through an area makes creating corridors challenging. The combination of a landscape’s physical structure, distance between habitat patches, and an individual unique behavior are the primary influences on animal’s movement (Henein and Merriam 1990; Vasudev et al. 2015). In traditional conservation ecology, it has been assumed that species will tend to disperse through habitats similar to their own, yet late research has found that these preferences vary across circumstances and species (Vasudev et al. 2015). Therefore, a distinction among types of connectivity has been established. Structural connectivity refers to the spatial configuration and number of habitat patches in a given area (Baudry J and Merriam HG.1988).

Another meaning is strictly the animal’s capacity to move through a landscape (Castilho et al.

2015; Henein and Merriam 1990). Even though the exact factors that constrain successful dispersal remain unknown, is thought that animals face spatial, environmental and intrinsic limitations (Castilho et al. 2015). Thus, the determination of successful corridors requires general knowledge of a species’ habitat and environmental preferences, like the research in this paper discusses.

Despite the challenges of building viable corridors, initiatives to create landscape connectivity are common; an exceptional case is the Mesoamerican Biological Corridor (MBC), the largest bioregional conservation program (Dettman 2005). In 1997, seven countries of central

America cooperated to create a transnational environmental and security trade (Finley-Brook

2007; Crespin and García-Villalta 2014; Godoy 2003 ). The area covers 768,000 km² with 600 protected areas and 40 billion residents (Díaz-Gallegos et al. 2008; Finley-Brook 2007). The

MBC was envisioned to link critical habitats in Central America through the protection of zoning areas that include national parks, corridors, buffer zones and moderately human transformed land

(Godoy 2003; Barquet 2015). Furthermore, this initiative followed the “path of the jaguar” project, a precursor idea seeking to protect threatened predatory felines, and other large mammals alike, by creating areas that allow these species to move through the American continent (Finley-Brook 2007). A least cost path analysis in GIS of this area, found it to be of very high importance to jaguar conservation (Rabinowitz and Zeller 2010). However, despite the implementation of the BMC efforts, the feline populations in Central America continue to decline (Rabinowitz and Zeller 2010).

Felids are particularly susceptible to decline in the Anthropocene (Azevedo and

Murray 2007). Their vulnerability is due to their natural low densities, large territories, low birth rates, and high trophic level position (Crooks 2002; Lewis et al. 2015; Zanin 2015). Therefore, deforestation, habitat fragmentation and human hunting are all factors that threaten the survival of this taxonomic group (Foster 2016). In particular fragmentation due to road development represents the largest threat to cats (Lewis et al. 2015; Colchero et al. 2011). In the case of jaguars (panthera onca) the maintenance of a well conserved uninterrupted habitat is crucial for their survival since the species is less prone to cross modified sites (Roques 2016). Even more, the transformation of forest habitats to cattle ranching pushes cats to begin eating livestock

(Foster et al. 2016; Gutiérrez et al. 2015). The resulting monetary loss to farmers creates constant conflict and persecution of felids, further complicating conservation efforts (Foster et al. 2016).

MBC’s establishment and the prohibition of big cat hunting in different Central American countries is crucial for the survival of these species, yet monitoring and regulating the application of this measures remains challenging.

The lack of research regarding Neotropic cats (compared to predators elsewhere) intensifies their conservation challenges (Wultsch 2016). Felines tend to be secretive and nocturnal, making them specifically difficult to study (Lewis et al. 2015). As mentioned earlier, understanding a species’ natural history is crucial to approach effective connectivity and habitat protection initiatives. Felid species found in the MBC and more specifically on the Panamanian portion include the jaguar (Panthera onca), puma (Puma concolor), ocelot (Leopardus pardalis) and jaguarondi (Puma yagouaroundi). All four of these species vary in sensitivity, but are nonetheless known to co-occur (Goulart et al. 2009; Castilho et al. 2015; Harmsen et al. 2010).

The IUCN red list states that all four are in decline (IUCN 2015). It names the jaguar, the most anthropogenic sensitive of the four, as near threatened while the remaining three species are of least concern (Castilho et al. 2015; IUCN 2015).

Contributing to the understanding of a species’ ecology, particularly its habitat preferences, is a critical step in its protection. This study will focus on assessing the relationships between environmental features and feline occurrences in a portion of the Mesoamerican biological corridor. To answer this question, we used previously collected camera trap data taken in the Alto Chagres region, of Colon, Panama by a pilot project undertaken by the

Sociedad Mastozoologica de Panama (SOMASPA). The camera trap sites varied in regards to both natural and anthropogenic variables, making this analysis possible. We used Quantum GIS to gather the values of elevation, hydrography, forest cover, distance to roads and human

6 settlements for each camera location. Finally, we associated the habitat features with feline occurrence.

Methods:

Study Site

Figure 1. Location of study area in the Alto Chagres of the Province of Colon, Panama.

The study area is located in the Alto Chagres region in the Colon province of

Panama (Figure 1). Data collection was taken in two different locations, each in a separate year.

One study site is found inside of the Chagres National park and the other is located in the buffer zone of the park which corresponds to the Colon Corridor.

In the year 2008, the camera traps sites were located in the northeastern side of the

Chagres National Park and the limits of the Portobelo National Park. The Chagres National Park covers a total area of 129 000 hectares, with an average temperature of 30 ˚C and an annual precipitations exceeding 4 000mm. Time of year is missing for this data set.

In the year 2014 from February to April, the camera traps were placed in the Colon corridor. This land connects Chagres National Park to the banks of the Gatun river close to the

7 northern limits of the Soberania National Park (USAID 2009). The average temperature is of 20

˚C and the annual precipitation varies between 2000mm to 3000mm (McKay 2000). For the purpose of this study, the cameras were placed in three locations within the corridor: Natural

Reserve Alfonso Lee, Siera Llorona Panama Lodge and Dr. David Roubik’s property.

Figure 2. Location of the camera traps in the Alto Chagres region including the anthropogenic factors used for the research. The purple dots represent small human settlements and the roads are also included and classified according to their respective characteristics.

Figure 3. Location of the camera traps in the Alto Chagres including both elevation and hydrography.

Figure 4. Location of the camera traps in the Alto Chagres including forest cover type.

Camera Traps

Camera traps were stationed in the years of 2008 and 2014. A total of 19 camera stations were set in 2008 for a total of 58 nights, all within the Chagres boundaries. 12 stations were placed in corridor during 2014 for a total of 71 nights (see Table 1).

We used the program RECONYX MapView to view and categorize the pictures taken from every camera trap site (31 in total). For each image, the species seen and the number of individuals (if more than one) were recorded. Because certain species of interest for this research (puma and jaguarondi) could not be individually identified with our resources, due to their lack of distinguishable markings, we did not attempt to distinguish individuals of the any species in this study. Hence, the data for this research is not able to compare species abundances across the camera trap sites, only feline occurrences.

Site North East Year Effort (nights) 1 1031746 633679 2014 71 2 1031937 633699 2014 71 3 1031761 633822 2014 71 4 1033438 634128 2014 71 5 1031985 634129 2014 71 6 1033466 634360 2014 71 7 1032827 634438 2014 71 8 1033423 634681 2014 71 9 1034270 640825 2014 71 10 1034407 641718 2014 71 11 1035008 642198 2014 71 12 1034617 642251 2014 71 13 1046005 651443 2008 58 14 1043369 651573 2008 58 15 1045106 652299 2008 58 16 1045702 653664 2008 58 17 1046301 653664 2008 58 18 1043827 653962 2008 58 19 1044846 654165 2008 58 20 1041561 654919 2008 58 21 1045168 655437 2008 58 22 1045193 655612 2008 58 23 1046145 655683 2008 58 24 1045781 655737 2008 58 25 1046222 655902 2008 58 26 1042426 656069 2008 58 27 1041150 656082 2008 58 28 1042528 656108 2008 58 29 1045974 656681 2008 58 30 1045081 656682 2008 58 31 1042311 654279 2008 58 Table 1. A list of the camera trap sites with their location, year of installment and effort (days) listed.

Geographic Informational System

The regional characteristics and geographic data were mapped and extracted using

QGIS 2.14.1 (refer to Appendix 1). WGS84/ Universal Transverse Mercator (UTM) zone 17N was adopted as the coordinate system of the maps. Shapefiles with hydrography (2012) (Figure

3), towns and cities (2012) (Figure 1), forest cover type (2000) (Figure 2), roads network (2011)

(Figure 1), Panama administrative division, Panama protected areas (2006) and a raster file of a digital elevation model 30m (DEM) were obtained from STRI GIS portal

(http://strimaps.si.edu/portal/home/index.html). Maps were scaled at a 1: 50 000 in the preview display and the final maps generated had a scale of 1:130000.

The distance of every camera trap to a water source was evaluated through the creation of buffers of 100m, 200m, 300m and 400m around the hydrography shapefile. Four distance categories were made 1) 0-100m, 2) 100-200m, 3) 200m-300m, 4) 300-400m and 5)

>400m and each camera trap’s distance to water was determined by the presence or absence of each coordinate within the 5 categories.

To evaluate the distance of the camera traps to the nearest road, the measure line tool was used with a scale of 1: 50 000 in the overview map. The village density around each camera trap was determined by counting the amount of towns in a 3km radius from the camera traps (buffer tool). The such distance was assumed to be the influence zone of each settlement and it minimized the overlap between different stations.

The elevation of each station was extracted from the DEM 30m raster, by joining both the coordinates and the DEM shapefile. The forest cover variable was evaluated by determining the percentage of mature forest cover on an area of 1km radius around each camera trap. A buffer of 1 km was created, the two layers (forest cover and the 1 km buffer shapefiles) were joined and the area of each mature forest type polygon was calculated in order to obtain the percentage mature forest cover within the 1 km buffer.

Analysis

Each data point was one camera trap station. The response variable in this analysis was the feline count rate (number of felines observed per camera trap night). We had to measure in rate form due to the difference in sample effort (camera trap nights) between the 2 years of this study (Table 1). The independent variables were distance to water (categorical by meter), elevation (numeric variable in meters), forest type (percent mature forest in 1 km radius), human settlement density (number of settlements within 3 km), and distance to nearest road (meters).

Forest type was taken as percentage; therefore we conducted an arcsine transformation on the vector. Year was not used as a factor because it is heavily tied to the location of the camera trap site (Table 1), therefore we assume that the 6 year difference between the two sets of data did not significantly change the feline populations and hence did not affect the chance of detection.

Because the human settlement density (x) and distance to road (y) were found to be the only independent variables highly correlated (t = -4.549), we combined them using a Principal

Component Analysis (PCA) and created a principal component to represent total human influence. This reduced our dimensions down to 4 independent variables.

To correctly analyze our data, we ran a multiple poisson regression instead of attempting to transform the negatively skewed data (O’Hara 2010). The R system function “glm” was used to obtained effect sizes (z-values). However, to properly run a poisson regression, which works with integral data, we had to use the original count data as our y-variable with the number of days accounted for using the function “offset(log(days))”. Insignificant effects (|z|<2) were eliminated and the model re-run with remaining variables until all effects were significant.

Results:

Observed Feline Summary

Four species of cats were observed via camera traps in the total data collection from both 2008 and 2014. These species include jaguar (Panthera onca), puma (Puma concolor), ocelot (Leopardus pardalis) and jaguarundi (Puma yagouaroundi). The most observations were of ocelot and puma while the least observed species were jaguar and jaguarundi (see Table 2).

The total number of observations across the different species and 2 years/areas was 56.

The Chagres camera traps observed about 4 times as many individuals compared to the corridor stations, while having more camera traps but fewer trap nights (see Table 1). A simple comparison between the two areas shows that the average rate of cat occurrence in the

Chagres area dataset was higher than that in the corridor, with numbers being 0.0390 and 0.0153 individuals/camera trap/ night respectively. No jaguarundis were seen and only one jaguar was spotted by the corridor camera traps (Table 1).

Area Jaguar Puma Ocelot Jaguarundi Total Chagres 6 12 19 6 43 Corridor 1 8 4 0 13 Total 7 20 23 6 56 Table 2. The total counts for each species observed in the Colon corridor and the Chagres National Park. In this chart, sample effort is not accounted for.

Camera Trap Site Comparisons Elevation showed a negative relationship and % Mature Forest Cover showed a positive relationship in the model’s first run, but neither was significant. Distance to Water and the Human Principal Component did show significance, at a 99.9% confidence level, with

Distance to Water having a positive relationship (slope of 0.3457) and Human influences having

14 a negative correlation (slope of -0.0002078) (Table. 3, Figure 5, Figure 6). The human variable had a much higher standard error than the water factor (Table 3).

Independent Variable Estimate Standard Error z-value Water Factor 0.3457 0.1063 3.252 Human PC -0.0002078 0.00005442 -3.818 Table 3. Statistics generated by the poisson regression for the two significant factors, distance to water and the human principal component.

Figure 5. The residual scatter plot of the water factor, with the 5 categorical levels shown on the x-axis. It shows the relationship between feline occurrence rate and distance from a water source while accounting for the influence of the human variable. The R² value and line equation is also displayed.

Figure 6. The residual scatter plot of the Human Pricipal Component variable. It shows the relationship between the feline occurrence rate and the amount of human influence while accounting for the influence of the distance-to- water variable. The R² value and the line equation is also displayed.

Discussion:

Basic Observations

Overall, the camera traps for this study saw 4 types of feline species, two showing to be rare- jaguar and jaguarundi, and two showing to be common- puma and ocelot. The camera traps inside the national park observed many more predator cats than the cameras in the park’s buffer zone. Most notably, there were zero observations of jaguarundi and only one jaguar spotting outside the park’s boundaries (Table 2). These differences did not come as a surprise since the park is larger and more heavily forested compared to the buffer zone (Figures 3) and jaguars and jaguarundi are the most sensitive to human activity out of the four studied species

(Castilho et al. 2015; IUCN 2015).

Anthropogenic Trends

The analysis using individual camera trap sites as data points, with biotic and abiotic variables as the independent effects, showed a highly significant relationship between human influence (roads and towns) and cat occurrence (Table 3, Figure 6). This finding supports the literature on these species, which are known to avoid human inhabited areas (Crooks 2002).

However, human settlement data was taken from the year 2000, 8 years prior to the first camera trap year. It is possible that if a more updated map is used, the trend would appear differently.

Our discovered trend also describes the effects of roads and towns together as one PC. We were not able to understand the dynamics between these two effects separately on feline behavior because other variables needed to be accounted for.

There is some discrepancy in the literature about whether or not cat species actually avoid roads. Colchero (2011) has found avoidance to be the case; contrarily Rabinowitz (1986) has found some species actually actively use them. It is possible that a road’s size is the determiner of this behavioral choice. Though our research could not answer this question, we believe this location would be a perfect for such a study because as we saw, there are 4 different resident feline species, with different types of roads inside and outside the park (Figure 2). We suggest that future researchers place camera traps on different road types (primary, secondary, and tertiary) and if possible use GPS collars to associate movement with road type. Depending on the findings, this type of research could potentially lead to road size limit regulations within the corridor to better allow for the space to be shared by humans and wildlife.

Water Source Trend

Our results show that with a greater distance from a water source, there was a decreased rate of cat observations, but with less significance and a much higher standard error compared to the human factor (Table 3, Figure 5). The reasons behind this correlation remains unclear because there could be other influencing factors associated with water locations not visible using our GIS method of collection. There was also not a lot of diversity in regards to this variable; only 3 cameras were placed more than 400m away from a stream or river (Figure 5).

The small sample size also allowed for outliers to skew any trends, and increase the likelihood of this significance being by chance.

Overall, the incredibly significant, and literature supporting, correlation between human activity and feline occurrence, despite the small data, set shows the strength of the influence that human activity has on wild cat species in this region. Our project is a good example of the research possibilities available by using data sets collected for other intentions, like monitoring purposes, given the proper statistical analysis.

Implications for Conservation

This study proves that these predator cats are in fact avoiding humans, meaning their ecological impacts, mainly predation, are to be felt more in locations further from human settlements. Therefore, if monitoring of their abundances is needed in the future, we suggest that camera traps are placed away from anthropogenic activity to achieve the highest number of observations.

In regards to the corridor, our results shows that felines do use the area but, not surprisingly, to a lesser extent than in the protected zone. However, we cannot answer the

18 question of whether or not it acts as a movement or gene flow promoter. We suggest that to answer this question, future research must be done using other methods. A least cost efficient path analysis could be run through this landscape using GIS to define the areas in which cats theoretically will most likely travel through (Castilho et al. 2015). To conclusively test the theory though, tracking via radio collars should be used on individual cats to follow their movements

(Cavalcanti and Gese, 2009). Finally, a Fecal DNA analysis can allow for a long term genetic diversity comparison between the areas of interest (Foster et al. 2016). At the very least, if camera traps are set along the supposed corridor in areas of lowest human activity, jaguars and ocelots could be identified and tracked to see if they are in fact using the area as a corridor.

Acknowledgements:

We would like to thank SOMASPA, an organization dedicated to the conservation, education, and research of Panama’s vertebrate wildlife, for previously collecting this camera trap data and granting us permission to analyze it. We are also very grateful for McGill for offering us this research opportunity through its Panama Field Study Semester.

References

Azevedo FCC, Murray DL (2007). Evaluation of potential factors predisposing livestock to predation by jaguars. The journal of wildlife management, 71(7), 2379-2386. Baudry J, Merriam HG.1988. Connectivity and connectedness: functional versus structural patterns in landscapes. Connectivity in landscape ecology. In: Proceedings of the 2nd international seminar of the ‘‘international association for landscape ecology’’ Mu¨nster 1987.Paderborn. 23–28. Barquet, K. 2015. Building a bioregion through transboundary conservation in Central America. Norsk Geografisk Tidsskrift-Norwegian Journal of Geography. 69(5): 265-276. Cavalcanti SM. Gese EM. 2009. Spatial ecology and social interactions of jaguars (Panthera onca) in the southern Pantanal, Brazil. Journal of Mammalogy. 90(4): 935-945. Castilho CS, Hackbart VCS, Pivello VR, and dos SRF. 2015. Evaluating Landscape Connectivity for Puma concolor and Panthera onca Among Atlantic Forest Protected Areas. Environmental Management. 55(6):1377-1389. Crespin SJ, and García-Villalta JE. 2014. Integration of Land-Sharing and Land-Sparing Conservation Strategies Through Regional Networking: The Mesoamerican Biological Corridor as a Lifeline for Carnivores in El Salvador. Ambio : a Journal of the Human Environment.43 (6): 820-824. Crooks K R. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology. 16(2): 488–502. Crutzen P J. 2006. The anthropocene. In: Earth System Science in the Anthropocene. Berlin Heidelberg: Springer. p. 13-18. Colchero F, Conde D A, Manterola C, Chávez C, Rivera A and Ceballos G. 2011. Jaguars on the move: modeling movement to mitigate fragmentation from road expansion in the Mayan Forest. Animal Conservation, 14(2), 158-166. Dettman, S. 2005. The Mesomaerican Biological Corridor in Panama and Costa Rica Integrating Bioregional Planning and Local Initiatives. Journal of Sustainable Forestry, 22, 15-34. Díaz-Gallegos, JR, Mas, JF, and MontesA V. 2008. Monitoreo de los patrones de deforestación en el Corredor Biológico Mesoamericano, México.Interciencia: Revista de ciencia y tecnología de América, 33(12), 882-890.

Dirzo R, Young H, Galetti M. 2014. Defaunation in the anthropocene. Science. 345: 401-406. Goulart FVB, Cáceres NC, Graipel ME, Tortato AM, Ghizoni IR, and Oliveira-Santos LGR. 2009. Habitat selection by large mammals in a southern Brazilian Atlantic Forest. Mammalian Biology, 74, (3): 182-190.

Gutiérrez-González CE, Gómez-Ramírez MA, López-González C A and Doherty PFJ 2015. Are Private Reserves Effective for Jaguar Conservation?.Plos One, 10 (9). Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS. 2007. Optimizing dispersal and corridor models using landscape genetics. Journal of Applied Ecology. 44(4): 714-724.

Fahrig L. 2001. How much habitat is enough? Biological Conservation. 100: 65-74. Fahrig L. 2010. Effects of habitat fragmentation on biodiversity. Review Literature And Arts Of The Americas. 34: 487-515. Finley-Brook. 2007. Green Neoliberal Space: The Mesoamerican Biological Corridor. Journal of Latin American Geography, 6, (1), 101-124. Frankham R. Genetics and landscape connectivity. 2006. In: Connectivity Conservation. Cambridge, U.K.: Cambridge University Press. Foster RJ, Harmsen BJ, Macdonald DW, Collins J, Urbina Y, Garcia R, and Doncaster CP. 2016. Wild meat: a shared resource amongst people and predators. Oryx. 50(01): 63-75. Godoy Herrera, J. C. 2003. Mesoamerican Biological Corridor: regional initiative for the promotion of forest conservation. Quebec City, Canada: XII World Forestry Congress. Harmsen BJ, Foster RJ, Gutierrez SM, Marin SY and Doncaster CP. 2010. Scrape-marking behavior of jaguars (Panthera onca) and pumas (Puma concolor). Journal of Mammalogy, 91(5): 1225-1234. Henein, K., & Merriam, G, 1990. The elements of connectivity where corridor quality is variable. Landscape Ecology, 4, 157-170. Herkert Jr. 2010. The effects of habitat fragmentation on midwestern grassland bird communities. Ecological Applications. 4(3): 461-471. IUCN Red list of threatened species [Internet].2015. [cited 2016 April 13]. Available from: http://www.iucnredlist.org/details/11511/0. Largiader, CR. 2003. Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proceedings of the Royal Society B: Biological Sciences. 270(1513):417-423. Lewis JS, Logan KA, Alldredg MW, Bailey LL, VandeWoude S, and Crooks KR (2015). The effects of urbanization on population density, occupancy, and detection probability of wild felids. Ecological Applications,25(7), 1880-1895. McKay AA. 2000. Clima y biodiversidad: una nueva clasificación de los climas de Panamá. Rev. Cultural Lotería 431:47-61. O’Hara RB and Kotze DJ. 2010. Do not log-transform count data. Methods in Ecology and Evolution. 1: 118-122.

Rabinowitz A. Zeller K A. 2010. A range-wide model of landscape connectivity and conservation for the jaguar, Panthera once. Biological Conservation. 134: 939-945. Rabinowitz AR. Jr BG. 1986. Ecology and behaviour of the jaguar (Panthers onca) in Belize, Central America. Journal of Zoology, 210(1):149-159. Riley SPD, Pollinger JP, Sauvajot RM, York EC, Bromley C, Fuller TK and Wayne RK. 2006. A southern California freeway is a physical and social barrier to gene flow in carnivores. Molecular Ecology. 15(7): 1733–1741. Roques S, Sollman R, Jácomo A, Tôrres N, Silveira L, Chávez C and Luz XBG (2016). Effects of habitat deterioration on the population genetics and conservation of the jaguar. Conservation Genetics. 17(1): 125-139. Steffen W, Crutzen J, McNeill J, John R. 2007. The anthropocene: are humans now overwhelming the great forces of nature? Ambio. 36(8): 614-621. Tulloch AIT, Barnes MD, Ringma J, Fuller RA, Watson JEM.2015. Understanding the importance of small patches of habitat for conservation. Journal of Applied Ecology. 53: 418– 429. Urquiza-Haas T, Peres C A, Dolman P M. 2009. Regional scale effects of human density and forest disturbance on large-bodied vertebrates throughout the Yucatan Peninsula, Mexico. Biological Conservation. 142:134–48. USAID. 2009. Marco conceptual y planes de acción de los corredores biológicos del Filo de Santa Rita y Campo Chagres para la conectividad de los Parques Nacionales Soberanía y Chagres. Proyecto Conservación de la Biodiversidad en la Cuenca del Canal. Informe final, Panamá. 102. Vasudev, Divya, Robert J. Fletcher, Varun R. Goswami, and Meghna Krishnadas. 2015.From dispersal constraints to landscape connectivity: lessons from species distribution modeling. Ecography. 38 (10): 967-978.

Wilson RE, Farley SD, McDonough TJ, Talbot SL, Barboza PS. 2015. A genetic discontinuity in moose (Alces alces) in Alaska corresponds with fenced transportation infrastructure. Conservation Genetics. 16: 791–800. Wultsch C, Waits L, Kelly M. 2016. A comparative analysis of genetic diversity and structure in jaguars ( Panthera onca ), pumas ( Puma concolor ), and ocelots ( Leopardus pardalis ) in fragmented landscapes of a critical Mesoamerican linkage zone. PLoS ONE. 11(3): 1-30. Yin K, Xie Y, Wu N. 2006. Corridor connecting giant panda habitats from north to south in the Min Mountains, Sichuan, China. Zanin M, Palomares F, Brito D (2015). What we (don't) know about the effects of habitat loss and fragmentation on felids. Oryx. 49(01): 96-106.

Appendices

Appendix 1. Figure 5. Cartographic model illustrating the tools and processes used to derive the data from QGIS.