Use Of Species Distribution Models Research Articles

Assessment of potential invasion for six phytophagous quarantine pests in Taiwan

Pest risk assessment is typically performed by expert taxonomists using a pest’s biological data. However, the biological data or expert taxonomists may be difficult to find. Here, we used species distribution modelling to predict potential invasion in which phytophagous quarantine pests survive in Taiwan; the pests (unrecorded yet in Taiwan) included were three notorious quarantine whiteflies (Crenidorsum aroidephagus, Aleurothrixus trachoides, and Paraleyrodes minei) and three aphids (Nasonovia ribisnigri, Macrosiphum euphorbiae, and Viteus vitifoliae). In brief, maximum entropy modelling (MaxEnt) was used to predict the suitability of the pests’ habitats under certain climatic conditions, and then receiver operating characteristic curve analysis was performed (to verify the prediction result). We then analysed environmental variables affecting the habitat suitability and matched them with Taiwan’s crop cultivation areas for the assessment of potential invasion. We observed that the habitat suitability of the cultivation areas of host plants was low for C. aroidephagus, A. trachoides, and N. ribisnigri but was high for the remaining three species. Moreover, precipitation of coldest quarter negatively affected habitat suitability for C. aroidephagus, P. minei, N. ribisnigri, and M. euphorbiae. Seasonal temperature changes also negatively affected the habitat suitability for A. trachoides. This is the first study to demonstrate the use of species distribution modelling as the preliminary step for the pest risk assessment of these emerging pests with limited biological data before their invasion.

Embracing Ensemble Species Distribution Models to Inform At-Risk Species Status Assessments

AbstractConservation planning depends on reliable information regarding the geographic distribution of species. However, our knowledge of species' distributions is often incomplete, especially when species are cryptic, difficult to survey, or rare. The use of species distribution models has increased in recent years and proven a valuable tool to evaluate habitat suitability for species. However, practitioners have yet to fully adopt the potential of species distribution models to inform conservation efforts for information-limited species. Here, we describe a species distribution modeling approach for at-risk species that could better inform U.S. Fish and Wildlife Service's species status assessments and help facilitate conservation decisions. We applied four modeling techniques (generalized additive, maximum entropy, generalized boosted, and weighted ensemble) to occurrence data for four at-risk species proposed for listing under the U.S. Endangered Species Act (Papaipema eryngii, Macbridea caroliniana, Scutellaria ocmulgee, and Balduina atropurpurea) in the Southeastern United States. The use of ensemble models reduced uncertainty caused by differences among modeling techniques, with a consequent improvement of predictive accuracy of fitted models. Incorporating an ensemble modeling approach into species status assessments and similar frameworks is likely to benefit survey efforts, inform recovery activities, and provide more robust status assessments for at-risk species. We emphasize that co-producing species distribution models in close collaboration with species experts has the potential to provide better calibration data and model refinements, which could ultimately improve reliance and use of model outputs.

Modeling the Distribution of Habitat-Forming, Deep-Sea Sponges in the Barents Sea: The Value of Data

The use of species occurrence as a proxy for habitat type is widespread, probably because it allows the use of species distribution modeling (SDM) to cost-effectively map the distribution of e.g., vulnerable marine ecosystems. We have modeled the distribution of epibenthic megafaunal taxa typical of soft-bottom, Deep-Sea Sponge Aggregations (DSSAs), i.e., “indicators,” to discover where in the Barents Sea region this habitat is likely to occur. The following taxa were collectively modeled: Hexadella cf. dedritifera, Geodia spp., Steletta sp., Stryphnus sp. The data were extracted from MarVid, the video database for the Marine AREAl database for NOrwegian waters (MAREANO). We ask whether modeling density data may be more beneficial than presence/absence data, and whether using this list of indicator species is enough to locate the target habitat. We use conditional inference forests to make predictions of probability of presence of any of the target sponges, and total density of all target sponges, for an area covering a large portion of the Norwegian Barents Sea and well beyond the data’s spatial range. The density models explain <31% of the variance, and the probability models have high classificatory power (AUC > 0.88), depending on the variables/samples used to train the model. The predicted surfaces were then classified on the basis of a probability threshold (0.75) and a density threshold (13 n/100 m2) to obtain polygons of “core area” and “hotspots” respectively (zones). The DSSA core area comprises two main regions: the Egga shelf break/Tromsøflaket area, and the shelf break southwest of Røst bank in the Træna trench. Four hotspots are detected within this core area. Zones are evaluated in the light of whole-community data which have been summarized as taxon richness and density of all megafauna. Total megafaunal density was significantly higher inside the hotspots relative to the background. Richness was not different between zones. Hotspots appeared different to one another in their richness and species composition although no tests were possible. We make the case that the effectiveness of the indicator species approach for conservation planning rests on the availability of density data on the target species, and data on co-occurring species.

Evolutionary Studies Combined with Species Distribution Models are Useful to Understand COVID-19 Spread and Aid Vaccine Design

Different opinions on the use of Species Distribution Models (SDMs) to understand COVID-19 spread resulted in an ongoing debate in the scientific community. We agree SDMs alone cannot suffice the purpose of understanding COVID-19 spread. However, evolutionary studies combined with SDMs can overcome these limitations and, mutational insights gained can aid design of an ideal vaccine against SARS-CoV-2.

The future representativeness of Madagascar's protected area network in the face of climate change

AbstractWith many species predicted to respond to a changing climate by shifting their distribution to climatically suitable areas, the effectiveness of static protected areas (PAs) is in question. The Madagascan PA network area has quadrupled over the past 15 years, and, although conservation planning techniques were employed to prioritise suitable areas for protection during this process, climate change impacts were not considered. We make use of species distribution models for 750 Madagascan vertebrate species to assess the potential impacts of climate change on (1) species richness across Madagascar, (2) species gain, loss and turnover in Madagascar's PAs and (3) PA network representativeness. Results indicate that Madagascar is predicted to experience substantial shifts in species richness, with most PAs predicted to experience high rates of species turnover. Provided there are no barriers to species movements, the representativeness of the current PA network will remain high for the species that are predicted to survive changes in climate by 2070, suggesting that little benefit will be gained from establishing new PAs. However, this rests on the assumption of mobility through areas currently characterised by fragmentation and anthropogenic activity, something that will require considerable expansion in conservation efforts in order to achieve.

Earth and field observations underpin metapopulation dynamics in complex landscapes: Near-term study on carabids

Understanding risks to biodiversity requires predictions of the spatial distribution of species adapting to changing ecosystems and, to that end, Earth observations integrating field surveys prove essential as they provide key numbers for assessing landscape-wide biodiversity scenarios. Here, we develop, and apply to a relevant case study, a method suited to merge Earth/field observations with spatially explicit stochastic metapopulation models to study the near-term ecological dynamics of target species in complex terrains. Our framework incorporates the use of species distribution models for a reasoned estimation of the initial presence of the target species and accounts for imperfect and incomplete detection of the species presence in the study area. It also uses a metapopulation fitness function derived from Earth observation data subsuming the ecological niche of the target species. This framework is applied to contrast occupancy of two species of carabids (Pterostichus flavofemoratus, Carabus depressus) observed in the context of a large ecological monitoring program carried out within the Gran Paradiso National Park (GPNP, Italy). Results suggest that the proposed framework may indeed exploit the hallmarks of spatially explicit ecological approaches and of remote Earth observations. The model reproduces well the observed in situ data. Moreover, it projects in the near term the two species' presence both in space and in time, highlighting the features of the metapopulation dynamics of colonization and extinction, and their expected trends within verifiable timeframes.

Modeling the potential distribution of Zelkova schneideriana under different human activity intensities and climate change patterns in China

The influence of climate change and human activities on the distribution and biodiversity of endangered plants has been a hot topic over recent years. In the future, the use of species distribution modeling will be of great theoretical and practical significance for the prediction of trends in the spatial change of suitable habitats of threatened species as a result of climate change. Since 2018, Zelkova schneideriana has been listed in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species as “Vulnerable”. Based on current species location data, the MaxEnt model was used to simulate potential habitat changes for Z. schneideriana under predicted climate change scenarios. The simulation results showed the precipitation of the driest quarter was determined to be the key influential variable that affected the natural habitat distribution of Z. schneideriana the most (accounting for 70.8%). The precipitation of driest month was the most influential factor for the human-influenced Z. schneideriana (accounting for 29.2%). The simulated distribution area of Z. schneideriana showed possibly future loss (more lost suitable areas and less expansion suitable area). Meanwhile, the trees may not be able to reach the simulated expansion of suitable areas under natural conditions because of the dispersal limitation and geographic barriers. Therefore, the distribution of Z. schneideriana may be greatly diminished in the future, even under human-managed environment in the future if in situ conservation measures are not instigated in time.

Assessment of the extinction risks of medicinal plants in Egypt under climate change by integrating species distribution models and IUCN Red List criteria

The IUCN Red List of Threatened Species is one of the most important of all conservation indicators, but most developing countries do not have enough information with which to make assessments. The use of species distribution models (SDMs) to predict habitat suitability, both currently and in the future under the effects of climate change, offers a potential solution for estimating extinction risk. With a set of validated observations, we used SDMs to make preliminary evaluations of the risk of extinction for 114 Egyptian medicinal plants based on IUCN Red-List Criteria and Categories. Using MaxEnt and eleven environmental variables, distributions were projected for 2020, 2050, and 2080 under two emission scenarios (A2a and B2a) assuming unlimited and no dispersal. The IUCN assessments used the predicted distributions as well as the actual records to measure both extent of occurrence (EOO) and area of occupancy (AOO). There was a positive correlation between EOO estimates based on actual records and those based on SDMs, demonstrating the ability of SDMs to compensate for a lack of data. Most species could be classified as Least Concern (LC) at the current time, whilst in the future under climate change, many species face some risk of extinction, depending on assumptions. We conclude that it is possible to make regional risk assessments even in data-sparse countries, in order to plan conservation management in the future. Using species distribution modelling together with IUCN Red-List assessment is a good method for countries where data are sparse in order to conserve and protect threatened species.

Modelling common dolphin (Delphinus delphis) coastal distribution and habitat use: Insights for conservation

The world's ecosystems are altered to different extents by anthropogenic activities. Marine habitats, especially coastal areas, are subjected to an increasing pressure derived from human activities on both land and ocean. Information about species distribution is fundamental to develop effective conservation and management measures and counteract negative anthropogenic impacts. The present work explores the use of species distribution models by using the Environmental Niche Factor Analysis (ENFA) to assess the habitat suitability of common dolphins (Delphinus delphis) in Northwest Spain, and its application to the development of effective conservation and management measures. The relationship between presence-only data and ecogeographical variables (EGV) was used to assess the potential distribution of the species. Data was collected during 273 days at sea, covering a total distance of 9 417 km between March 2014 and October 2017 with a total of 91 common dolphin encounters. This study shows that tide level and sea surface salinity are the main EGVs driving the distribution of the species in coastal areas especially in waters above the continental shelf. Additionally, this study reveals the most suitable habitats for common dolphin and outlines the need to develop conservation measures and management plans to promote the protection of this species. Findings of the study contribute to a more accurate and comprehensive understanding of the common dolphin distribution and emphasize the importance of species distribution models in the development of effective conservation and management strategies.

Species distribution models with field validation, a key approach for successful selection of receptor sites in conservation translocations

Conservation translocations of threatened species are being widely used to mitigate human impacts. However, their effects are surrounded by some controversy since these actions have often failed to meet planned objectives. Despite the limited number of published studies, existing evidence indicates that a main constraint for the long-term success of translocation actions is the selection of suitable receptor sites. In this study, we present a methodological approach to identify suitable receptor sites that combines the use of species distribution models (SDMs) and in situ field validation trials. This method was successfully applied to translocate a population of Critically Endangered Narcissus cavanillesii, which was going to be destroyed by the construction of the Alqueva dam (Portugal), the largest dam in Europe. The results of the SDM developed for the target species were biologically validated through in situ germination trials in sites with contrasting species suitability values. The population translocated to the site selected with this approach has experienced a stable demographic trend for more than ten years and established new mature plants outside the translocated patches. This methodology, which has proven to be a fast and reliable approach for the selection of appropriate receptor sites for conservation translocations, could be useful in other studies.

Species distribution models (SDM): applications, benefits and challenges in invasive species management.

Abstract The use of species distribution models (SDM) is of increasing popularity when studying biological invasions, e.g. to assess the impact of climate change on invasive species, to prioritize conservation measures, or to study invasive evolutionary biology. SDM correlate known occurrences of species with environmental variables and predict a species' potential distribution on other geographies over space and time. Today, SDM are widely used to produce invasion risk maps by delineating probable risk areas based on climatic suitability for a species. These maps can guide early detection and rapid response measures. While recent developments in modelling approaches and wider availability of environment datasets have helped to create better and more accurate SDM, in many cases the used models ignore the associated underlying ecological processes for e.g. dispersal and biotic interactions and thus provide only an incomplete picture of invasion risks. In this paper, we present a review of the most common applications of SDM in invasive species management and provide an overview of possible solutions to various challenges like uncertainty and transferability associated with them. Based on our review we conclude that ways towards more accurate model outputs include fitting models with existing ecological knowledge (hybrid models), address uncertainty and biotic interactions and link species dispersal traits with projections of species distributions. In the future, work is required on developing more hybrid approaches and models that addresses both local diffusion and long distance movement of alien species.

Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation.

The increasing use of species distribution modeling (SDM) has raised new concerns regarding the inaccuracies, misunderstanding, and misuses of this important tool. One of those possible pitfalls − collinearity among environmental predictors − is assumed as an important source of model uncertainty, although it has not been subjected to a detailed evaluation in recent SDM studies. It is expected that collinearity will increase uncertainty in model parameters and decrease statistical power. Here we use a virtual species approach to compare models built using subsets of PCA-derived variables with models based on the original highly correlated climate variables. Moreover, we evaluated whether modelling algorithms and species data characteristics generate models with varying sensitivity to collinearity. As expected, collinearity among predictors decreases the efficiency and increases the uncertainty of species distribution models. Nevertheless, the intensity of the effect varied according to the algorithm properties: more complex procedures behaved better than simple envelope models. This may support the claim that complex models such as Maxent take advantage of existing collinearity in finding the best set of parameters. The interaction of the different factors with species characteristics (centroid and tolerance in environmental space) highlighted the importance of the so-called “idiosyncrasy in species responses” to model efficiency, but differences in prevalence may represent a better explanation. However, even models with low accuracy to predict suitability of individual cells may provide meaningful information on the estimation of range-size, a key species-trait for macroecological studies. We concluded that the use of PCA-derived variables is advised both to control the negative effects of collinearity and as a more objective solution for the problem of variable selection in studies dealing with large number of species with heterogeneous responses to environmental variables.

Combining social media photographs and species distribution models to map cultural ecosystem services: The case of a Natural Park in Portugal

Developing spatially explicit models of Ecosystem Services (ES) distribution and diversity across the territory has been increasingly attracting the interest of researchers and policy-makers due to its potential to operacionalize and mainstream the ES concept into existing planning and policy tools.In this paper we explore the use of social media photographs to model the spatial distribution of people preferences for cultural ecosystem services (CES), map their hotspots, identify the determinant variables as well as the spatial correlation between CES. This research was applied in the Sudoeste Alentejano and Costa Vicentina Natural Park (PNSACV) located in Southwestern Alentejo, Portugal.A collection of 1378 geo-tagged digital images taken inside the Park and posted in the Flickr web platform between 2004 and 2015 were analyzed and classified according to a tailored list of CES. To model CES spatial distribution it was used a species distribution model – Maxent – adapted to combine the observation of CES occurrence with biophysical and infrastructural variables.This method allowed us to identify and map the social preferences for CES in this area. The distance to the ocean and distance to touristic and cultural infrastructure were the most determinant variables to explain CES distribution in PNSACV. Another relevant result of this study was the identification of pairs of CES (such as Recreation & Aesthetics services) with a significant spatial overlap.Using social media data can be an expedite and cost-effective way to identify and map CES, although this approach embodies some challenges and biases that need to be considered. The use of species distribution models, such as Maxent, can be particularly valuable to support the design of future scenarios and assist decision-making on land use planning.

Environmental and anthropogenic determinants of the spread of alien plant species: insights from South Africa’s quaternary catchments

Alien plants invasion has negative impacts on the structure and functionality of ecosystems. Understanding the determinants of this process is fundamental for addressing environmental issues, such as the water availability in South Africa’s catchments. Both environmental and anthropogenic factors determine the invasion of alien species; however, their relative importance has to be quantified. The aim of this paper was to estimate the importance of 32 explanatory variables in predicting the distribution of the major invasive alien plant species (IAPS) of South Africa, through the use of Species Distribution Models. We used data from the National Invasive Alien Plants Survey, delineated at a quaternary catchment level, coupled with climatic, land cover, edaphic, and anthropogenic variables. Using two-part generalized linear models, we compared the accuracy of two different sets of variables in predicting the spatial distribution of IAPS; the first included environmental correlates alone, and the second included both environmental and anthropogenic variables. Using Random Forest, we explored the relative importance of the variables in producing a map of potential distribution of IAPS. Results showed that the inclusion of anthropogenic variables did not significantly improve model predictions. The most important variables influencing the distribution of IAPS appeared to be the climatic ones. The modeled potential distribution was analyzed in relation to provinces, biomes, and species’ minimum residence time.

Monitoring protected areas from space: A multi-temporalassessment using raptors as biodiversity surrogates.

Monitoring protected areas (PAs) is essential for systematic evaluation of their effectiveness in terms of habitat protection, preservation and representativeness. This study illustrates how the use of species distribution models that combine remote sensing data and information about biodiversity surrogates can contribute to develop a systematic protocol for monitoring PAs. In particular, we assessed the effectiveness of the Natura 2000 (N2000) network, for conserving and preserving the representativeness of seven raptor species in a highly-dynamic landscape in northwest Spain between 2001 and 2014. We also evaluated the cost-effectiveness of the N2000 network by using the total area under protection as a proxy for conservation costs. Overall, the N2000 network was found to poorly represent the habitats of the raptor species. Despite the low representativeness, this network showed a high degree of effectiveness due to increased overall habitat availability for generalist and forest specialist species between 2001 and 2014. Nevertheless, additional protected areas should be established in the near future to increase their representativeness, and thus ensure the protection of open-habitat specialist species and their priority habitats. In addition, proactive conservation measures in natural and semi-natural ecosystems (in particular, montane heathlands) will be essential for long-term protection of Montagu’s harrier (species listed in the Annex I of the Bird Directive), and thus complying with the current European Environmental Legislation. This study sheds light on how the development and application of new protected area indices based on the combined use of freely-available satellite data and species distribution models may contribute substantially to the cost-efficiency of the PA monitoring systems, and to the ‘Fitness Check’ process of EU Nature Directives.

Integrating multiple data sources in species distribution modeling: a framework for data fusion.

The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species' occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches ("Shared," "Correlation," "Covariates") for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source ("Single"). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.

Eriocaulaceae in the Brazilian Amazon and the use of Species Distribution Modelling in its conservation

Abstract Following a survey in herbaria in Brazil and abroad, complemented by a survey of the literature and searches of biodiversity data, 66 taxa of Eriocaulaceae have been identified in the Brazilian Amazon, as defined by the states of Acre, Amapá, Amazonas, Pará, Rondônia and Roraima. Six genera of Eriocaulaceae were found in the Amazon with the following taxon numbers: Comanthera (3 taxa), Eriocaulon (10), Paepalanthus (15), Rondonanthus (2), Syngonanthus (35) and Tonina (1). Of this total, 25 taxa present distributions in the states of the Amazon and other Brazilian states (considered as widely distributed) and the remaining 41 taxa are restricted to the Amazon Rainforest biome. The distribution data for 31 species were used for Species Distribution Modelling, due to the low number of occurrence points reported for the other taxa, 17 of those are known from a single location. The overlap of these models indicates areas from Amapá, Amazonas, Pará and Roraima where modelled species are most likely to occur. These data can further contribute to the location of new populations of species of Eriocaulaceae. New detection of Eriocaulaceae species can assist in filling the gaps on their geographic distribution and ecology, contributing to the protection or restoration of priority areas set aside for their conservation.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Impacts of climate change and renewable energy development on habitat of an endemic squirrel, Xerospermophilus mohavensis, in the Mojave Desert, USA

Predicting changes in species distributions under a changing climate is becoming widespread with the use of species distribution models (SDMs). The resulting predictions of future potential habitat can be cast in light of planned land use changes, such as urban expansion and energy development to identify areas with potential conflict. However, SDMs rarely incorporate an understanding of dispersal capacity, and therefore assume unlimited dispersal in potential range shifts under uncertain climate futures. We use SDMs to predict future distributions of the Mojave ground squirrel, Xerospermophilus mohavensis Merriam, and incorporate partial dispersal models informed by field data on juvenile dispersal to assess projected impact of climate change and energy development on future distributions of X. mohavensis. Our models predict loss of extant habitat, but also concurrent gains of new habitat under two scenarios of future climate change. Under the B1 emissions scenario- a storyline describing a convergent world with emphasis on curbing greenhouse gas emissions- our models predicted losses of up to 64% of extant habitat by 2080, while under the increased greenhouse gas emissions of the A2 scenario, we suggest losses of 56%. New potential habitat may become available to X. mohavensis, thereby offsetting as much as 6330km2 (50%) of the current habitat lost. Habitat lost due to planned energy development was marginal compared to habitat lost from changing climates, but disproportionately affected current habitat. Future areas of overlap in potential habitat between the two climate change scenarios are identified and discussed in context of proposed energy development.