Projected Loss of a Salamander Diversity Hotspot as a Consequence of Projected Global Climate Change Joseph R. Milanovich1*, William E. Peterman2, Nathan P. Nibbelink1, John C. Maerz1 1 D.B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, United States of America, 2 Department of Biological Sciences, University of Missouri, Columbia, Missouri, United States of America Abstract Background: Significant shifts in climate are considered a threat to plants and animals with significant physiological limitations and limited dispersal abilities. The southern Appalachian Mountains are a global hotspot for plethodontid salamander diversity. Plethodontids are lungless ectotherms, so their ecology is strongly governed by temperature and precipitation. Many plethodontid species in southern Appalachia exist in high elevation habitats that may be at or near their thermal maxima, and may also have limited dispersal abilities across warmer valley bottoms. Methodology/Principal Findings: We used a maximum-entropy approach (program Maxent) to model the suitable climatic habitat of 41 plethodontid salamander species inhabiting the Appalachian Highlands region (33 individual species and eight species included within two species complexes). We evaluated the relative change in suitable climatic habitat for these species in the Appalachian Highlands from the current climate to the years 2020, 2050, and 2080, using both the HADCM3 and the CGCM3 models, each under low and high CO2 scenarios, and using two-model thresholds levels (relative suitability thresholds for determining suitable/unsuitable range), for a total of 8 scenarios per species. Conclusion/Significance: While models differed slightly, every scenario projected significant declines in suitable habitat within the Appalachian Highlands as early as 2020. Species with more southern ranges and with smaller ranges had larger projected habitat loss. Despite significant differences in projected precipitation changes to the region, projections did not differ significantly between global circulation models. CO2 emissions scenario and model threshold had small effects on projected habitat loss by 2020, but did not affect longer-term projections. Results of this study indicate that choice of model threshold and CO2 emissions scenario affect short-term projected shifts in climatic distributions of species however, these factors and choice of global circulation model have relatively small affects on what is significant projected loss of habitat for many salamander species that currently occupy the Appalachian Highlands. Citation: Milanovich JR, Peterman WE, Nibbelink NP, Maerz JC (2010) Projected Loss of a Salamander Diversity Hotspot as a Consequence of Projected Global Climate Change. PLoS ONE 5(8): e12189. doi:10.1371/journal.pone.0012189 Editor: Justin Wright, Duke University, United States of America Received March 23, 2010 Accepted July 14, 2010 Published August 16, 2010 Copyright: �� 2010 Milanovich et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The contributions of J. C. Maerz and J. R. Milanovich were partially supported by the Coweeta LTER (http://coweeta.uga.edu/), which is funded by the National Science Foundation and has among its current objectives to understand the role of climate change in patterns of species losses and gains in southern Appalachia. This study was also partially funded by the American Museum of Natural History (http://www.amnh.org/) and the North Carolina Herpetological Society (http://www.ncherps.org/). This material is based upon work supported by the National Science Foundation under grant DEB-0823293. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the authors and do not reflect the views of the National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: milanovichj@warnell.uga.edu Introduction Understanding how species distributions and patterns of diversity shift with changing climates has been a long-standing theme of ecology that has grown less academic with the specter of rapid climate change. Not surprisingly, there is an increasing effort to project the effects of climate change on species��� distributions and regions of high biodiversity [1,2,3,4,5]. Knowing whether particular species or hotspots of biodiversity are vulnerable to decline is important to planning management actions and understanding how ecosystem functions may change [6]. Species distribution modeling is one tool for evaluating the potential impact of climate change on the distributions of biota [7,8]. Distribution models characterize dimensions, generally mean climatic variables, of the current realized niche of a species based on presence-absence data and then use future climate forecasts to project changes in the distribution of suitable habitat for a species. Climate-driven species distribution models have several limitations including exclusion of other biotic, physiolog- ical, and geographic controls on a species��� distribution. Addition- ally, these models cannot mechanistically account for the role of climate in determining species distributions or quantify the limits of species abilities to migrate. Furthermore, this technique ignores the capability of evolutionary change to compensate for species responses to changing climate and they assume reliance upon credible climatic projections by assuming that the ������suitable������ habitat is saturated and the data input into models is accurate [9,10,11,12,13,14]. Projections from climate distribution modeling are also dependent upon the global circulation model selected, how well that model can be downscaled to predict local climate [15], and assumptions about future atmospheric CO2 levels. To deal with the potential limitations of model projections, increas- PLoS ONE | www.plosone.org 1 August 2010 | Volume 5 | Issue 8 | e12189

ingly studies often take an ensemble forecasting approach by modeling a number of future scenarios that bracket ranges of model assumptions or predicted climate change scenarios [16]. The most common approach is to integrate different global circulation models and CO2 emissions scenarios and forecast out to multiple future time points. A potential criticism of forecasts from species distribution modeling is the self-fulfilling nature of the endeavor. Based on relationships between climate variables at sites occupied by a species, climate distribution models such as Maxent [17] subsequently provide a continuous probability surface which can be classified (based on a threshold) into suitable or non-suitable climatic space. The user determines the threshold, which is often set to a single value, and then generates a current climate-driven distribution to best fit the known species distribution [18]. In other words, the user makes the species��� distribution a strict function of the variables that are put into the model (e.g., climate, land cover, soil type). Because the threshold may be a somewhat arbitrary cutoff depicting presence/absence of a species, applying a more liberal threshold in climate distribution models may dampen projected effects of climate change on species��� distributions, such as the inability to cross geographic barriers. We used a combination of Global Circulation Models (GCM), atmospheric CO2 scenarios, and both strict and liberal model thresholds to generate a range of projected shifts in potential suitable climatic habitat for plethodontid salamanders in the southern Appalachian region of the eastern United States. Areas with high biodiversity or endemism are of high conservation value, and the Appalachian Highlands are regarded as a biodiversity hotspot with some of the most biologically diverse forests and freshwater systems in the United States [19]. At broad spatial scales, amphibian diversity is related strongly to the direct and indirect (via net primary production) effects of climate and regional phylogeography [20,21]. The Appalachian Highlands are a global hotspot for salamander diversity, nearly all of which is determined by the family Plethodontidae [22]. Plethodontid distributions are determined by a number of factors including land forms (e.g., major river boundaries), history and biotic interactions such as interspecific competition [23,24] however, because plethodontids are lungless ectotherms, their activity, life- history traits, and consequently geographic distributions and patterns of diversity appear predominantly controlled by climate. [25,26,27,28,29]. Consistent with global patterns of amphibian diversity [20,21], plethodontid species richness throughout the southern Appalachian Highlands is positively linked to the cool, moist montane climate [28] with most species occupying mid or high elevation climatic zones that were colonized millions of years earlier when those climatic zones occurred in valley bottoms [28,29]. Recent evidence suggests temperature is a direct limiting factor of dispersal and range size of some species within the family [26], further supporting the use of climate-based models to examine species distributions within this family. Because pletho- dontid salamanders are the most abundant vertebrate predators in eastern North American forests and headwater streams and are influential in a number of ecosystem processes [30,31,32,33], understanding shifts in their distributions or abundance will be important to predicting changes to ecosystem processes. Methods Species Distribution Modeling using Maximum Entropy We developed distribution models using Maxent version 3.30a [17,34] for 41 plethodontid species (33 individual species and eight species included within two species complexes) with distributions in the eastern United States that included a portion of the species range within the Appalachian Mountain region (defined by a geographic boundary that includes all ecoregions found within the Appalachian Highland region). The two species complexes were the Plethodon glutinosus complex, which was composed of six species (P. glutinosus, P. cylindraceus, P. kentucki, P. teyahalee, P. chlorobryonis, and P. chattahoochee) and the Desmognathus fuscus complex, composed of two species (D. fuscus and D. conanti). We treated these groups as complexes because their members were historically identified as one species but were later broken up into parapatric, morpholog- ically cryptic species based on patterns of genetic divergence suggesting that geographic features and isolation promoted speciation [23,35,36], and they are nearly indistinguishable in hand (although evidence suggest there are differences in body size [37]). There are no data indicating that they function differently with regard to ecological factors such as climate. The 33 species (and complexes) represent ,90% of plethodontid species in the southern Appalachian Highlands and ,50% of plethodontid species occurring in the southeastern United States. Maxent is a machine learning method that utilizes the principle of maximum entropy to model species distributions using presence- only data coupled with environmental data [34]. This approach finds a probability distribution of maximum entropy using a set of environmental variables to estimate a species��� ecological niche using the defined Maxent probability distribution. For each species or species complex, current species distribution models were created using point data from two natural history databases intersected with georeferenced climatic variables. Salamander presence data were obtained from HerpNET (www.herpnet.org) and Global Biodiver- sity Information Facility (GBIF www.gbif.org). To maximize model quality, only species with greater than 30 point locations were used [38]. We downloaded 1-km resolution temperature and precipita- tion bioclimatic layers, which are based on the 30-year period from 1960���1990, from the WorldClim database [39]. We used the 11 bioclimatic layers utilized by Rissler and Apodaca [40] in their bioclimatic distribution modeling of Aneides flavipunctatus, a pletho- dontid species distributed in the western United States. Those 11 bioclimatic layers were winnowed from a larger set of 19 variables using correlations to estimate redundancy between variables and retaining the more biologically meaningful and interpretable variables (e.g., annual mean temperature, mean temperature of the wettest quarter, and precipitation of the wettest quarter). Maxent was run from the command line using the default settings with the exception of background points. A total of 4215 target- group background data points representing localities of plethodon- tid salamanders in the eastern United States were used to develop an initial climatic envelope that represents the range of environmental conditions within the modeled region. In turn, this method is expected to reduce the bias inherent in our sample of museum locality data [41]. This approach uses background data (also known as pseudo-absences), chosen with the same bias as the occurrence data used, to develop the models. By using this approach we can produce an unbiased estimate of the geographic distribution of species, since the background data provides an equable sample of the environmental conditions within the region modeled. We used a threshold approach to designate a location as climatically suitable for a species. When modeling a single species, each location modeled is represented by a probability that the location is climatically suitable for that species however, it is logistically unfeasible to present each location as a probability of occupancy for every species modeled. Therefore, it was necessary to delineate a threshold at which a location was deemed climatically suitable or un-suitable. As was discussed in the introduction, the use of a single threshold will create a strict Salamanders and Climate Change PLoS ONE | www.plosone.org 2 August 2010 | Volume 5 | Issue 8 | e12189