Modeling Impacts of Climate Change on Giant Panda Habitat. Conservation Ecology Center, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA 2. USA2. Geography Department, University of Maryland, College Park, MD 2. USAAcademic Editor: A. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Giant pandas (Ailuropoda melanoleuca) are one of the most widely recognized endangered species globally. Habitat loss and fragmentation are the main threats, and climate change could significantly impact giant panda survival. We integrated giant panda habitat information with general climate models (GCMs) to predict future geographic distribution and fragmentation of giant panda habitat. Results support a major general prediction of climate change—a shift of habitats towards higher elevation and higher latitudes. Our models predict climate change could reduce giant panda habitat by nearly 6. New areas may become suitable outside the current geographic range but much of these areas is far from the current giant panda range and only 1. Long- term survival of giant pandas will require the creation of new protected areas that are likely to support suitable habitat even if the climate changes. Introduction. Giant pandas (Ailuropoda melanoleuca) are endangered . The species’ historic range encompassed most of southeastern China, northern Myanmar, and northern Vietnam. However, there is concern for decades that in a finite world at some point should be the limits of the world’s population, and that may not be very smart to reach. Glossary of Water Resource Terms. A B C D E F G H I J K L M N O P Q R S T U V W X Y Z A abandoned water right a water right which was not put to beneficial use for a. Current and past scholars bios. Avila is a Fisheries Ph.D. Climate changes during the late Pleistocene and millennia of agricultural conversion and human settlement have dramatically reduced the geographic distribution of giant pandas and populations are now scattered across six mountain ranges between the Sichuan plain and Tibetan plateau . One of the greatest threats to giant panda survival is habitat loss . Category: Garden History & Design Scholarship: The Garden Club of America Internship in Garden History and Design. Mark Ahrens is a master’s student of Library and. Metapopulations and Patch Dynamics: Animal dispersal in heterogeneous landscapes. Tanya Rohrbach ([email protected]) Metapopulations in the Context of Patch. Abstract The literature on effects of habitat fragmentation on biodiversity is huge. It is also very diverse, with different authors measuring fragmentation in.Benthic intertidal bivalves play an essential role in estuarine ecosystems by contributing to habitat provision, water filtration, and promoting productivity. In the last hundred years, tiger habitat has been significantly fragmented and transformed into isolated patches of habitats. A particular site can hold breeding. The species is limited to montane deciduous and coniferous forests with bamboo understories. During the twentieth century, giant panda habitat steadily and rapidly declined . Driving forces of habitat loss are agricultural conversion, and large- scale activities such as road construction, logging, mining, and hydroelectric development. Habitat loss has led to a highly fragmented range; many giant panda populations are small and isolated, resulting in limited gene flow and risks from inbreeding . Current climate models estimate a 1. Celsius increase in temperature during this century . Past and recent changes in climate have been shown to cause range shifts and contractions in plant and animal distributions . Whether a species can survive changes in their environment is dependent on various life history characteristics. Characteristics that make a species more likely to be negatively impacted by disturbance include having a limited geographic range, poor ability to disperse, low rates of reproduction, and highly specialized habitat requirements . Giant pandas have a narrow range, do not disperse over large distances, produce one cub every 2- 3 years, and depend on bamboo for 9. These traits suggest they will be highly susceptible to climate change. In addition to the limitations resulting from life history characteristics, species’ response is also limited by the spatial configuration of habitat in the landscape. Species may have the capacity to shift as vegetation regimes shift; however, distance or other barriers may limit movement. Given the giant panda’s restricted and montane geographic range, climate change may significantly reduce and isolate already fragmented giant panda habitats, decrease gene flow, and thereby substantially increase the species’ extinction risk. Extrapolating known suitable climate envelopes into future climate scenarios is one of the best approaches for predicting effects of climate change on species’ geographical distributions . Based on the current giant panda distribution and available general climate models (GCMs), we present a range- wide estimate for how climate change may affect giant panda habitats by the year 2. These data provide the most recent and informed estimate on how climate change will affect one of the most endangered and charismatic megavertebrates in the world. It also provides useful information as conservation organizations assess how to invest in giant panda conservation in the future. Study Area. Our study encompassed six mountain ranges that constitute the extant geographic distribution of giant pandas: the Qinling, Minshan, Qionglai, Xiaoxangling, Daxiangling, and Liangshan (1. Habitat types transition vertically through elevational changes within the giant panda distribution, from subtopical evergreen broad- leafed forest at lower elevations, to evergreen and deciduous broad- leafed forests, to mixed coniferous and deciduous broad- leafed forests, up to subalpine coniferous forests. There is a lot of variation in temperature and precipitation within the giant panda distribution and this, along with variation in soils, hydrology, slope, and aspect, have resulted in diverse plant and tree species . Baseline data on giant panda distribution is from the most recent national survey for giant pandas. This data is not available to researchers outside China, making direct modeling of giant panda locations impossible . However, habitat associations and models derived from the data have been made available. Our study assesses the effects of climate change on giant pandas indirectly, by measuring how climate change will alter the geographic distribution and extent of giant panda habitat. Figure 1: Current giant panda distribution, protected areas, and mountain ranges. The current distribution is primarily above 1,2. As our study area we used the distribution of giant pandas from the national survey as a baseline and extended it to include contiguous areas down to 5. Climate Change Data. We obtained future climate projections from the World. Clim database . We included two general climate models; one described by the Canadian Centre for Climate Modeling and Analysis Coupled Model, version 3 (CGCM3) . Both are commonly used atmosphere- ocean coupled models and data is available for download (http: //www. We restricted our study to models constrained by the conditions outlined in the A2 scenario of the Special Report on Emissions Scenarios . A1 and A2 families assume more rapid economic development than B1 and B2 families, which also assume more ecologically responsible societies by the year 2. The A2 family assumes more heterogeneous future societies with regionally divergent economic growth and more fragmented growth in technological changes while the A1 scenario assumes the world will be more homogeneous with similar standard of living levels and technological progress among various regions. Population is assumed to continually increase in the A2 scenario, but the A1 scenario assumes population will decline after reaching 9 billion. We chose the A2 scenario based on current trends in China where fossil- fuel CO2 emissions have doubled since 2. IPCC author Richard Tol has asserted that the A2 family is by far the most realistic . The A2 is a strong scenario and should help us identify patterns and trends in predicted changes to giant panda habitat. For each climate dataset, bioclimatic parameters from monthly precipitation and minimum and maximum temperatures were interpolated using BIOCLIM . We selected 1. 9 of the 3. BIOCLIM variables which seemed most relevant (Table 1). These bioclimatic parameters are calculated across the entire year to offer a wider range of climatic variables for analysis. Table 1: Bioclim variables and their percent contribution and percent permutation importance reported by Maxent. Variables are in order of highest to lowest permutation importance. We used Maxent to relate current giant panda distribution to environmental variables and to project future giant panda habitat. Maxent (http: //www. Maxent estimates the species’ distributions by finding the probability distribution of maximum entropy (i. For our study we created 1,5. We created 1. 0,0. BIOCLIM variables for both of the GCMs for the year 2. Digital elevation models (DEMs) at 9. CGIAR- CSI SRTM . Current climatic conditions of each point were interpolated and projected into the two future climate models. For both models we used 1 for the regularization multiplier, a convergence threshold of 1. AUC ranges from 0. AUC values over 0. Maxent estimates the importance of the variables with percent contribution and permutation importance values. Percent contribution represents how much the variable contributed to the model based on the path selected for a particular run. Permutation importance is determined by changing the predictors’ values between presence and background points and observing how that affects the AUC. The permutation importance depends on the final model, not the path used in an individual run and therefore is better for evaluating the importance of a particular variable. Standard errors and confidence intervals for both of the models were calculated in R v. We assessed how well Maxent could predict the known current giant panda using the same methods, except we used GCMs for the year 2. Maxent was 7. 7% accurate in modeling current giant panda distribution. We imported the Maxent probability distributions for each model into Arc. GIS 9. 3 (ESRI Inc., Redlands, CA) and converted them to presence/absence (0/1) based on the threshold value that maximizes training sensitivity and plus specificity . Quantifying Suitable Habitat and Fragmentation. Areas with dense human populations and roads are not suitable for giant pandas and croplands do not provide suitable habitat, therefore we removed croplands, urban areas, and human disturbance buffers. Based on a framework developed by Liu et al. We used land cover data from Global Land Cover 2. Human development excludes giant pandas from areas < 1,2.
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