Effects of ecotourism on forest loss in the Himalayan biodiversity hotspot based on counterfactual analyses

Ecotourism is developing rapidly in biodiversity hotspots worldwide, but there is limited and mixed
empirical evidence that ecotourism achieves positive biodiversity outcomes. We assessed whether ecotourism
influenced forest loss rates and trajectories from 2000 to 2017 in Himalayan temperate forests. We compared forest
loss in 15 ecotourism hubs with nonecotourism areas in 4 Himalayan countries. We used matching statistics to
control for local-level determinants of forest loss, for example, population density, market access, and topography.
None of the ecotourism hubs was free of forest loss, and we found limited evidence that forest-loss trajectories in
ecotourism hubs were different from those in nonecotourism areas. In Nepal and Bhutan, differences in forest loss
rates between ecotourism hubs and matched nonecotourism areas did not differ significantly, and the magnitude
of the estimated effect was small. In India, where overall forest loss rates were the lowest of any country in
our analysis, forest loss rates were higher in ecotourism hubs than in matched nonecotourism areas. In contrast,
in China, where overall forest loss rates were highest, forest loss rates were lower in ecotourism hubs than
where there was no ecotourism. Our results suggest that the success of ecotourism as a forest conservation
strategy, as it is currently practiced in the Himalaya, is context dependent. In a region with high deforestation
pressures, ecotourism may be a relatively environmentally friendly form of economic development relative to
other development strategies. However, ecotourism may stimulate forest loss in regions where deforestation rates
are low.

File: Brandt_etal_-ConsBio_2019.pdf

Ecotourism is developing rapidly in biodiversity hotspots worldwide, but there is limited and mixed
empirical evidence that ecotourism achieves positive biodiversity outcomes. We assessed whether ecotourism
influenced forest loss rates and trajectories from 2000 to 2017 in Himalayan temperate forests. We compared forest
loss in 15 ecotourism hubs with nonecotourism areas in 4 Himalayan countries. We used matching statistics to
control for local-level determinants of forest loss, for example, population density, market access, and topography.
None of the ecotourism hubs was free of forest loss, and we found limited evidence that forest-loss trajectories in
ecotourism hubs were different from those in nonecotourism areas. In Nepal and Bhutan, differences in forest loss
rates between ecotourism hubs and matched nonecotourism areas did not differ significantly, and the magnitude
of the estimated effect was small. In India, where overall forest loss rates were the lowest of any country in
our analysis, forest loss rates were higher in ecotourism hubs than in matched nonecotourism areas. In contrast,
in China, where overall forest loss rates were highest, forest loss rates were lower in ecotourism hubs than
where there was no ecotourism. Our results suggest that the success of ecotourism as a forest conservation
strategy, as it is currently practiced in the Himalaya, is context dependent. In a region with high deforestation
pressures, ecotourism may be a relatively environmentally friendly form of economic development relative to
other development strategies. However, ecotourism may stimulate forest loss in regions where deforestation rates
are low.

What is driving land degradation in the Caucasus Mountains?

Viewed from satellite images, the Caucasus Mountains are a mosaic of forests, rangelands and agriculture stretched across rugged topography between the Caspian and Black Seas. The political and social history in this region is long and turbulent and most recently has resulted in land abandonment. Globally, agricultural land returning to a wilder state is not a trend we are used to seeing. However, land abandonment does not always equate to thriving ecosystems.

Sheep grazing on the eroded mountain side between Steantsminda and Gudauri townlets in Georgia. Photo by Volker Radeloff.

Land degradation is another important force shaping this part of the world, and often it can be traced to livestock overgrazing. In many cases, cattle or sheep are grazed near villages, rather than dispersed across the landscape. This creates a distinctive pattern that turns up in satellite imagery.

The Caucasus Mountains have been farmed intensively for thousands of years. The grasslands and forests that we see today are not in an unaltered state, but rather have changed through time in response to human pressures. Some changes happen on the ground quickly, while others are subtle and have happened over long periods of time, but in general, both can be detected with remote sensing. In the broadest sense, the goal of this research is to find those changes and determine why they are happening.

Katarzyna Ewa Lewinska (Kasia) joined the SILVIS Lab in the summer of 2018 to work with the team studying land use in the Caucasus Mountains. With a strong remote sensing background, an eye for detail, and an intense curiosity about complex problems like this one, she has already begun to make progress.

And why is it important to understand how land cover is changing and what is driving these changes? Land cover influences the ranges of many species, the health of watersheds, nutrient cycling, and the global carbon balance. Our understanding of carbon balance is limited, and carbon sequestration is not uniform across land cover types, but rather influenced by many local-scale factors including land degradation. In models, changing the way degradation is accounted for can yield results that vary by 40-200%, and so it is important to be intentional about the way degradation is defined. Besides being a complex problem, it is also a far reaching one with important implications for understanding the carbon balance and natural resources management of our planet, as 75% of land area is currently degraded and this proportion is projected to increase to 90% in the next 20 years.

Fig 1: High resolution true color images (captured from Google Earth) showing high-mountain summer pasture in North-West Azerbaijan in 2006 and 2017. Yellow arrow in the 2017 scene shows location of a shepherds hut and enclosure. The graph above presents soil endmember time series showing an overall increase in soil reflectance over time. Timing of acquisition of both images is marked in red on the graph.

Because the Caucasus Mountains are so diverse, this region is an ideal natural laboratory for understanding land use and degradation. Kasia explains the incredible variation in her study system: besides the normal degradation, the complex political and social layer, and the projected climate change, the region at its most basic level remains huge and diverse. In the northwest the climate is mild; in the southeast it’s dry and tropical. Only two of the world’s biomes are not represented here. And then there are mountains.

It is no wonder that this region has already been the focus of SILVIS Lab research. Among other projects, current postdoc He Yin and PhD student Johanna Buchner have created land use/land cover maps that track the changes occurring on this landscape every 5 years since 1987.

Kasia hopes to begin disentangling the roles of grazing and climate change in land degradation, and to become more familiar with the region’s ecology through fieldwork in the coming years. Although it’s still in the early stages, this project has a lot of momentum, and it will be interesting to see what is uncovered.

Large land cover and land use change mapping in the Caucasus Mountains since 1987

The Caucuses region (encompassing parts of Russia, Georgia, Armenia and Azerbaijan) has experienced extreme political upheavals. The collapse of the Soviet Union meant that the four countries became sovereign. Their powerful neighbors — Russia, Iran and Turkey – maintained strong geopolitical interests in the newly independent nations of Georgia, Armenia and Azerbaijan. As a result, the Caucasus has experienced four armed conflicts since 1991. In light of such extreme social and political disruption, Johanna wanted to know how cropland and forests had changed.

Figure 1: Land cover/ land use map of the Caucasus.

From previous research, some by former SILVIS lab members Drs. Mihai Nita and Catalina Munteanu, we know that some areas in Eastern Europe saw rapid cropland abandonment after the USSR collapsed. Other areas experienced forest clearing during the soviet era, and forest regrowth afterwards. Why do countries that are ostensibly similar geopolitically show such a wide range of land use outcomes?

“I want to make a clear link between land use and socio-political changes, but to do that you first have to describe where and when the land use changes have taken place.” Johanna says. To do that, she used Landsat imagery from 1987 to 2015 and mapped changes in land use and land cover.

Johanna found that there was some cropland abandonment in the Caucasus, particularly during the transition period in the 1990s and the time of armed conflicts. However, the cropland abandonment rate is far lower than the one apparent in eastern European countries that also experienced the breakdown of the Soviet Union. She has also found that forest as stayed surprisingly steady during the study period in the Caucasus.

Figure 2: Coniferous and mixed forest in Borjomi, Georgia. (Picture: V.Radeloff)

Her findings are rather surprising, since we expect political instability to interfere with cropland, and to make forests vulnerable for illegal harvesting. But in the Caucasus, Johanna explains, the steep, inaccessible terrain may have protected the forests from large clear cuts; even though the extracting of single valuable trees is widespread. Cropland, on the other hand is related to demand for food: cultivation continued wherever possible, unless we find armed conflicts in the region.

Figure 3: Preliminary results of abandoned arable land in Chechnya between 1987 and 2015

“What I can say is that land use patterns and outcomes are extremely dependent on the local context, especially in such a diverse region like the Caucasus” Johanna cautioned.

Indeed.

Payments for ecosystem services in Mexico reduce forest fragmentation

Forest fragmentation can lead to habitat reduction, edge increase, and exposure to disturbances.
A key emerging policy to protect forests is payments for ecosystem services (PES), which
offers compensation to landowners for environmental stewardship. Mexico was one of the first countries
to implement a broad-scale PES program, enrolling over 2.3 Mha by 2010. However, Mexico’s
PES did not completely eliminate deforestation in enrolled parcels and could have increased incentives
to hide deforestation in ways that increased fragmentation. We studied whether Mexican forests
enrolled in the PES program had less forest fragmentation than those not enrolled, and whether the
PES effects varied among forest types, among socioeconomic zones, or compared to the protected
areas system. We analyzed forest cover maps from 2000 to 2012 to calculate forest fragmentation. We
summarized fragmentation for different forest types and in four socioeconomic zones. We then used
matching analysis to investigate the possible causal impacts of the PES on forests across Mexico and
compared the effects of the PES program with that of protected areas. We found that the area covered
by forest in Mexico decreased by 3.4% from 2000 to 2012, but there was 9.3% less forest core area.
Change in forest cover was highest in the southern part of Mexico, and high-stature evergreen tropical
forest lost the most core areas (17%), while oak forest lost the least (2%). Our matching analysis
found that the PES program reduced both forest cover loss and forest fragmentation. Low-PES areas
increased twice as much of the number of forest patches, forest edge, forest islets, and largest area of
forest lost compared to high-PES areas. Compared to the protected areas system in Mexico, high-PES
areas performed similarly in preventing fragmentation, but not as well as biosphere reserve core zones.
We conclude that the PES was successful in slowing forest fragmentation at the regional and country
level. However, the program could be improved by targeting areas where forest changes are more frequent,
especially in southern Mexico. Fragmentation analyses should be implemented in other areas
to monitor the outcomes of protection programs such as REDD+ and PES.

File: Ramirez-Reyes_et_al-2018-Ecological_Applications.pdf

Forest fragmentation can lead to habitat reduction, edge increase, and exposure to disturbances.
A key emerging policy to protect forests is payments for ecosystem services (PES), which
offers compensation to landowners for environmental stewardship. Mexico was one of the first countries
to implement a broad-scale PES program, enrolling over 2.3 Mha by 2010. However, Mexico’s
PES did not completely eliminate deforestation in enrolled parcels and could have increased incentives
to hide deforestation in ways that increased fragmentation. We studied whether Mexican forests
enrolled in the PES program had less forest fragmentation than those not enrolled, and whether the
PES effects varied among forest types, among socioeconomic zones, or compared to the protected
areas system. We analyzed forest cover maps from 2000 to 2012 to calculate forest fragmentation. We
summarized fragmentation for different forest types and in four socioeconomic zones. We then used
matching analysis to investigate the possible causal impacts of the PES on forests across Mexico and
compared the effects of the PES program with that of protected areas. We found that the area covered
by forest in Mexico decreased by 3.4% from 2000 to 2012, but there was 9.3% less forest core area.
Change in forest cover was highest in the southern part of Mexico, and high-stature evergreen tropical
forest lost the most core areas (17%), while oak forest lost the least (2%). Our matching analysis
found that the PES program reduced both forest cover loss and forest fragmentation. Low-PES areas
increased twice as much of the number of forest patches, forest edge, forest islets, and largest area of
forest lost compared to high-PES areas. Compared to the protected areas system in Mexico, high-PES
areas performed similarly in preventing fragmentation, but not as well as biosphere reserve core zones.
We conclude that the PES was successful in slowing forest fragmentation at the regional and country
level. However, the program could be improved by targeting areas where forest changes are more frequent,
especially in southern Mexico. Fragmentation analyses should be implemented in other areas
to monitor the outcomes of protection programs such as REDD+ and PES.

Monitoring the dynamics of abandoned agriculture and fallow with Landsat and Sentinel 2 time series

Figure 2 A field abandoned due to soil salinization (Khorezm, Uzbekistan)
Figure 1: Cotton harvest in Uzbekistan
Figure 1: Cotton harvest in Uzbekistan

Agricultural land abandonment is a prominent land use change across the globe. From Eastern Europe to the core of the Amazonian forest, land dedicated to agriculture has been abandoned. These new unused land areas may provide opportunities for conservation and carbon storage. They can also be seen as potential threats to social security and to the spread of fire. Agriculture abandonment is also an important indicator of economic growth and stability. However, abandoned agriculture, and closely related land cover classes such as fallow fields, and grasslands, are not yet routinely mapped with remote sensing. He Yin wants to contribute to the science of remote sensing by creating agriculture abandonment maps that have high precision in time and location. Better maps (i.e. with greater resolution) would help improve our understanding of the drivers that lead to agricultural land abandonment and would be useful for planning sustainable landscapes.

Figure 2 A field abandoned due to soil salinization (Khorezm, Uzbekistan)
Figure 2 A field abandoned due to soil salinization (Khorezm, Uzbekistan)

Satellite images have precious information on plant phenology, which is the “key” to distinguishing active agriculture from other land use types. For instance, early in the season a plowed field has open soil ready to be planted. Then, as plants grow taller the amount of green leaf area increases. Later in the season, when crops are ready for the market, comes the harvest, and the amount of leaf area decreases. “These abrupt changes in the amount of plants in a field can be detected in the satellite images, and allows us to confirm that the field is under production” – says, He Yin.

Agricultural land abandonment mapping is challenging because of the heterogeneity and complexity of agricultural land use. Some years a field may be left fallow, to give time for the soil to recover. However, fallow land is not abandoned land. A fallow field may return to production after a year or two. An abandoned field contrasts with a production field in that abandoned field presents a natural decay of the vegetation late in the growing season. He Yin used a technique called ‘temporal segmentation’ to map land abandonment from 30-m Landsat time series (Yin et al. 2018). “When a field has no agriculture for more than 5 years, then we classify it as abandoned land”.

Figure 3: Example of a temporal segmentation with related Landsat imagery (RGB: NIR, SWIR 1, Red), for the pixel indicated by either a black or white crosshair (Yin et al., 2018)
Figure 3: Example of a temporal segmentation with related Landsat imagery (RGB: NIR, SWIR 1, Red), for the pixel indicated by either a black or white crosshair (Yin et al., 2018)

However, Landsat images are not immune from problems. From a Landsat time series, there is a set of images that are not useful because they contain clouds or part of the picture is shaded by topographic features. This increases inaccuracy and limits the areas that can be included in analysis. To solve this challenge, He Yin is planning to use a new NASA product, the Harmonized Landsat Sentinel-2. This product merges imagery from Landsat with imagery from Sentinel-2 and the merged product provides increased temporal resolution of the data. “Repeated 5-day observations will likely improve our mapping precision,” says He Yin.

He Yin and his colleagues mapped land abandonment in the Caucasus recently (Yin et al. 2018), and are ready now for a much larger challenge. They are starting to map agriculture abandonment globally. They want to test their time series approach in many regions of the world, using the improved set of satellite imagery provided by Harmonized Landsat Sentinel-2. He Yin hopes that these explorations will help to have better maps of land abandonment from places with varying topographies and biomes, and, most importantly, where agricultural systems are complex.

Land use and climatic causes of environmental novelty in Wisconsin since 1890

Multiple global change drivers are increasing the present and future novelty of
environments and ecological communities. However, most assessments of environmental novelty have focused only on future climate and were conducted at scales too broad to be useful
for land management or conservation. Here, using historical county-level data sets of agricultural land use, forest composition, and climate, we conduct a regional-scale assessment of environmental novelty for Wisconsin landscapes from ca. 1890 to 2012. Agricultural land-use data
include six cropland types, livestock densities for four livestock species, and human populations. Forestry data comprise biomass-weighted relative abundances for 15 tree genera. Climate
data comprise seasonal means for temperature and precipitation. We found that forestry and
land use are the strongest cause of environmental novelty (NoveltyForest = 3.66,
NoveltyAg = 2.83, NoveltyClimate = 1.60, with Wisconsin’s forests transformed by early 20thcentury logging and its legacies and multiple waves of agricultural innovation and obsolescence. Climate change is the smallest contributor to contemporary novelty, with precipitation
signals stronger than temperature. Magnitudes and causes of environmental novelty are
strongly spatially patterned, with novelty in southern Wisconsin roughly twice that in northern
Wisconsin. Forestry is the most important cause of novelty in the north, land use and climate
change are jointly important in the southwestern Wisconsin, and land use and forest composition are most important in central and eastern Wisconsin. Areas of high regional novelty tend
also to be areas of high local change, but local change has not pushed all counties beyond
regional baselines. Seven counties serve as the best historical analogues for over one-half of
contemporary Wisconsin counties (40/72), and so can offer useful historical counterparts for
contemporary systems and help managers coordinate to tackle similar environmental challenges. Multi-dimensional environmental novelty analyses, like those presented here, can help
identify the best historical analogues for contemporary ecosystems, places where new management rules and practices may be needed because novelty is already high, and the main causes
of novelty. Separating regional novelty clearly from local change and measuring both across
many

File: Williams_etal_EcoApps_WI_Novelty_2019.pdf

Multiple global change drivers are increasing the present and future novelty of
environments and ecological communities. However, most assessments of environmental novelty have focused only on future climate and were conducted at scales too broad to be useful
for land management or conservation. Here, using historical county-level data sets of agricultural land use, forest composition, and climate, we conduct a regional-scale assessment of environmental novelty for Wisconsin landscapes from ca. 1890 to 2012. Agricultural land-use data
include six cropland types, livestock densities for four livestock species, and human populations. Forestry data comprise biomass-weighted relative abundances for 15 tree genera. Climate
data comprise seasonal means for temperature and precipitation. We found that forestry and
land use are the strongest cause of environmental novelty (NoveltyForest = 3.66,
NoveltyAg = 2.83, NoveltyClimate = 1.60, with Wisconsin’s forests transformed by early 20thcentury logging and its legacies and multiple waves of agricultural innovation and obsolescence. Climate change is the smallest contributor to contemporary novelty, with precipitation
signals stronger than temperature. Magnitudes and causes of environmental novelty are
strongly spatially patterned, with novelty in southern Wisconsin roughly twice that in northern
Wisconsin. Forestry is the most important cause of novelty in the north, land use and climate
change are jointly important in the southwestern Wisconsin, and land use and forest composition are most important in central and eastern Wisconsin. Areas of high regional novelty tend
also to be areas of high local change, but local change has not pushed all counties beyond
regional baselines. Seven counties serve as the best historical analogues for over one-half of
contemporary Wisconsin counties (40/72), and so can offer useful historical counterparts for
contemporary systems and help managers coordinate to tackle similar environmental challenges. Multi-dimensional environmental novelty analyses, like those presented here, can help
identify the best historical analogues for contemporary ecosystems, places where new management rules and practices may be needed because novelty is already high, and the main causes
of novelty. Separating regional novelty clearly from local change and measuring both across
many

Quantifying the spatial distribution and socio-economic drivers of small holder tree planting in Tanzania

Woodlots located at forest edges. Mbeya, Tanzania

Niwaeli’s interest in smallholder woodlots started when she was conducting her Master’s research in Tanzania, at the southern end of the East African Rift. She saw rural farmers planting pine and eucalyptus in farms that are located near a forest edge on land that had been fallow for 15 years. Her collaborators, working at another forest edge site in the Uganda portion of the Rift, were noticing a similar conversion from cropland to tree plantations. She wanted to know how much smallholder tree planting was occurring near Rift forests, and why.

Niwaeli used Google Earth to get a first impression of the distribution of woodlots. She randomly selected 60 locations in a small portion of the Rift, and digitized all the woodlots in the area. So far the team has digitized more than 4000 individual woodlots. “The really striking thing is how small these woodlots are: 93% are less than 1 hectare, and the average area is 0.45 hectares”, she said. Niwaeli extrapolated the area of woodlots she found in the digitized subset to the entire Tanzanian Southern Highlands area and estimated 80.000 ha of smallholder woodlots. That does not sound like a big number, however, it is only ~ 10.000 ha less than the amount held by the biggest tree plantation owner, the Tanzanian government. A big question remains though: Why would a subsistence farmer plant trees with a slow turnaround rate, instead of growing food crops?

A Google Earth view showing woodlot patches (orange outlines) mixed with other land uses away from the forest edge.
A Google Earth view showing woodlot patches (orange outlines) mixed with other land uses away from the forest edge.

In contrast to the US where farmers tend to own large and contiguous fields, in Niwaeli’s study site, farmers tend to own multiple small parcels of land that are spread throughout the landscape. Niwaeli thinks that the farmers make some allocation choices about which pieces of land will have crops, and which ones will have trees. She assumes that those parcels that are closer to the edge of the forests are not that great for growing food crops and are used for planting trees if the farmer can grow food somewhere else. “This could explain our initial field observations where we saw trees near forest edges” she added.

From the digitization, however, Niwaeli has seen that the woodlots are not limited to just forest edges, so the question of how farmers allocate land to trees is yet to be fully answered. She plans to further investigate the spatial distribution of tree plantations by mapping woodlots using Sentinel-2 satellite imagery. This will also provide a more accurate quantification of woodlot extent; and allow her to explore why some areas end up with woodlots while others do not.

Should I stay or should I go? Patterns of building and re-building after wildfires.

style=margin-left:.25in;”>People enjoy building houses in beautiful places where they are surrounded by the beauty of nature.  Unfortunately, when wildfires rage through forests, these homes are often caught in the fire’s path.  As more and more people attempt to enjoy the amenities of building a home in sparsely populated areas, communities increasingly face tough decisions whether to pay for protecting these homes from wildfires that destroy property and take lives.  Wildfire costs are not trivial.  During the twelve-years from 1999-2011, an average of 1,354 houses were destroyed and approximately $2 billion was spent fighting wildfires, annually.  Ideally, communities would have information to help predict how wildfires spread and how to minimize the number of houses lost during wildfires.  Unfortunately, a lot of basic information about what happens to a community after a wildfire rips though it is unknown.  Patricia Alexandre and her colleagues recently published a study that makes a first step towards describing what happens to communities across the country following wildfire events.  While their results suggest that the conventional wisdom that rebuilding always happens has little support  and how much is rebuilt varies across the country, they were surprised to find that new housing constructed in burned areas was happening at higher rates than rebuilding, and often at higher rates than in surrounding non-fire areas, adding complexity to the discussion. 

Example of a rebuilt building situation when using Google Earth

style=”margin-left:.25in;”>        To be clear, Alexandre’s research is not intended to answer whether people should rebuild following a wildfire, but to provide a snapshot of what the patterns were within affected communities across the country following recent fires.  This is an important step to take to see whether patterns are consistent across a large scale and provides a dataset to begin drawing conclusions from observed rebuilding patterns.  To do this work, Alexandre refined a method utilizing historical images available on Google Earth and then recruited help from students to go through and hand-digitize structures present before a fire as well as all structures that had burned, and consequently been rebuilt within five years following a fire.  Nationally, the team found rebuilding rates averaged 25%, with much higher rates in the western states.  For example, rates in California approached 70% of structures rebuilt following a fire.  The surprising results from Alexandre’s work is first that not all burned communities are re-building within five years following a fire, and second that new buildings were constructed in burned areas at similar or even higher rates.  These results indicate that communities are not just replacing homes lost to wildfires, but many are putting new homes into burned areas. 

Percentage of burned buildings per fire (fires occurred between 2000 and 2005)

Alexandre offers multiple reasons that homeowners may build, and rebuild, in burned areas, which are inherently fire prone.  One reason is that homeowners may find the value they get from living in fire-prone areas worth the fire risk, some insurance policies require rebuilding in the same spot following a fire, and many homeowners do not have the finances to relocate to an area with lower fire risk.  While the reasons to build and rebuild in fire prone areas likely vary widely across the country, Alexandre’s research provides a valuable baseline to evaluate future policies or practices that communities might use to mitigate wildfire damages.  Whether it’s mandating that new or rebuilt structures be constructed with safer materials, or prohibiting rebuilding in burned areas, the best way to evaluate the efficacy of these policies is to compare them to the housing patterns before and after fire events.  Alexandre’s research allows that comparison to take place and hopefully inform local initiatives that could save property, money and lives.”

Finding critical habitat for jaguars when potential distributions of species is not enough: Connectivity to the rescue

Figure 1. Location of the Sierra Gorda Biosphere reserve in central Mexico

In central Mexico, there is a geographically and biologically unique space featuring a mosaic of a varied diversity of flora and fauna, the Sierra Gorda Biosphere Reserve. Yet similar to other reserves in the world, human and climate drivers are changing the unique conditions of this area at an accelerated rate (Figure 2). However, this reserve remains home to important protected and endangered species that are struggling to find optimal habitat conditions elsewhere, thus it is important to identify potential areas that are suitable for keep these animal populations.Jaguar (Panthera onca) is one of these endangered species, and essential in the faunal community because of its position as a top predator (Figure 2).

Figure 2. Jaguar captured in a camera trap by Grupo Ecologico Sierra Gorda

Furthermore, the presence of jaguars is an indicator of the health of the ecosystems and by conserving its habitat we can also protect other species. However, is very difficult to allocate efforts to large areas and therefore we need to identify and prioritize the most important zones capable to host jaguars.In the SILVIS lab, Carlos Ramirez Reyes, created a potential species distribution model for jaguars based on presence data and using factors than can affect their distribution such as topography, landcover and precipitation among others (Figure 3).

Figure 3. Potential distribution for jaguars in the Sierra Gorda Biosphere Reserve

Based on this model, he identified areas inside within and outside the reserve that could host jaguars. Additionally, by adding connectivity parameters to the models (Figure 4), he evaluated if habitat connectivity actually improves the potential distribution model for the reserve. The goal was to find patches of potential habitat that are critical for connectivity of the entire reserve.

Figure 4. Relative importance of potential habitat patches to maintain the connectivity of the system

With his research Carlos hopes to gain better knowledge for the habitat requirements of the jaguar in the region. Ultimately, the project should serve to inform nongovernmental organizations and government agencies with interests in the reserve to make decisions on how to distribute resources for the management of the species and prioritize areas that should be protected. Furthermore, this project includes a new method for obtaining potential areas through the use of landscape connectivity. Similar projects that aim to model potential species distribution can benefit from this technique given that landscape connectivity is also important for animal dispersal and gene flow in fragmented landscapes.  “