Climate‐change winners and losers: stream macroinvertebrates of a submontane region in Central Europe

1. Freshwater ecosystems will be profoundly affected by global climate change, especially those in mountainous areas, which are known to be particularly vulnerable to warming temperatures. We modelled impacts of climate change on the distribution ranges of 38 species of benthic stream macroinvertebrates from nine macroinvertebrate orders covering all river zones from the headwaters to large river reaches. 2. Species altitudinal shifts as well as range changes up to the year 2080 were simulated using the A2a and B2a Intergovernmental Panel on Climate Change climate‐warming scenarios. Presence‐only species distribution models were constructed for a stream network in Germany’s lower mountain ranges by means of consensus projections of four algorithms, as implemented in the BIOMOD package in R (GLM, GAM, GBM and ANN). 3. Species were predicted to shift an average of 122 and 83 m up in altitude along the river continuum by the year 2080 under the A2a and B2a climate‐warming scenarios, respectively. No correlation between altitudinal shifts and mean annual air temperature of species’ occurrence could be detected. 4. Depending on the climate‐warming scenario, most or all (97% for A2a and 100% for B2a) of the macroinvertebrate species investigated were predicted to survive under climate change in the study area. Ranges were predicted to contract for species that currently occur in streams with low annual mean air temperatures but expand for species that inhabit rivers where air temperatures are higher. 5. Our models predict that novel climate conditions will reorganise species composition and community structure along the river continuum. Possible effects are discussed, including significant reductions in population size of headwater species, eventually leading to a loss of genetic diversity. A shift in river species composition is likely to enhance the establishment of non‐native macroinvertebrates in the lower reaches of the river continuum.     

Evaluating the Significance of Paleophylogeographic Species Distribution Models in Reconstructing Quaternary Range-Shifts of Nearctic Chelonians

The climatic cycles of the Quaternary, during which global mean annual temperatures have regularly changed by 5–10°C, provide a special opportunity for studying the rate, magnitude, and effects of geographic responses to changing climates. During the Quaternary, high- and mid-latitude species were extirpated from regions that were covered by ice or otherwise became unsuitable, persisting in refugial retreats where the environment was compatible with their tolerances. In this study we combine modern geographic range data, phylogeny, Pleistocene paleoclimatic models, and isotopic records of changes in global mean annual temperature, to produce a temporally continuous model of geographic changes in potential habitat for 59 species of North American turtles over the past 320 Ka (three full glacial-interglacial cycles). These paleophylogeographic models indicate the areas where past climates were compatible with the modern ranges of the species and serve as hypotheses for how their geographic ranges would have changed in response to Quaternary climate cycles. We test these hypotheses against physiological, genetic, taxonomic and fossil evidence, and we then use them to measure the effects of Quaternary climate cycles on species distributions. Patterns of range expansion, contraction, and fragmentation in the models are strongly congruent with (i) phylogeographic differentiation; (ii) morphological variation; (iii) physiological tolerances; and (iv) intraspecific genetic variability. Modern species with significant interspecific differentiation have geographic ranges that strongly fluctuated and repeatedly fragmented throughout the Quaternary. Modern species with low genetic diversity have geographic distributions that were highly variable and at times exceedingly small in the past. Our results reveal the potential for paleophylogeographic models to (i) reconstruct past geographic range modifications, (ii) identify geographic processes that result in genetic bottlenecks; and (iii) predict threats due to anthropogenic climate change in the future.     

Untangling climate and water chemistry to predict changes in freshwater macrophyte distributions

Forecasting changes in the distributions of macrophytes is essential to understanding how aquatic ecosystems will respond to climate and environmental changes. Previous work in aquatic ecosystems has used climate data at large scales and chemistry data at small scales; the consequence of using these different data types has not been evaluated. This study combines a survey of macrophyte diversity and water chemistry measurements at a large regional scale to demonstrate the feasibility and necessity of including ecological measurements, in addition to climate data, in species distribution models of aquatic macrophytes. A survey of 740 water bodies stratified across 327,000 square kilometers was conducted to document Characeae (green macroalgae) species occurrence and water chemistry data. Chemistry variables and climate data were used separately and in concert to develop species distribution models for ten species across the study area. The impacts of future environmental changes on species distributions were modeled using a range of global climate models (GCMs), representative concentration pathways (RCPs), and pollution scenarios. Models developed with chemistry variables generally gave the most accurate predictions of species distributions when compared with those using climate variables. Calcium and conductivity had the highest total relative contribution to models across all species. Habitat changes were most pronounced in scenarios with increased road salt and deicer influences, with two species predicted to increase in range by >50% and four species predicted to decrease in range by >50%. Species of Characeae have distinct habitat ranges that closely follow spatial patterns of water chemistry. Species distribution models built with climate data alone were insufficient to predict changes in distributions in the study area. The development and implementation of standardized, large‐scale water chemistry databases will aid predictions of habitat changes for aquatic ecosystems.     

Microclimate species distribution models estimate lower levels of climate-related habitat loss for salamanders

Species distribution models (SDMs) largely rely on free-air temperatures at coarse spatial resolutions to predict habitat suitability, potentially overlooking important microhabitat. Integrating microclimate data into SDMs may improve predictions of organismal responses to climate change and support targeting of conservation assets at biologically relevant scales, especially for small, dispersal-limited species vulnerable to climate-change-induced range loss. We integrated microclimate data that account for the buffering effects of forest vegetation into SDMs at a very high spatial resolution (3 m²) for three plethodontid salamander species in Great Smoky Mountains National Park (North Carolina and Tennessee). Microclimate SDMs were used to characterize potential changes to future plethodontid habitat, including habitat suitability and habitat spatial patterns. Additionally, we evaluated spatial discrepancies between predictions of habitat suitability developed with microclimate and coarse-resolution, free-air climate data. Microclimate SDMs indicated substantial losses to plethodontid ranges and highly suitable habitat by mid-century, but at much more conservative levels than coarse-resolution models. Coarse-resolution SDMs generally estimated higher mid-century losses to plethodontid habitat compared to microclimate models and consistently undervalued areas containing highly suitable microhabitat. Furthermore, microclimate SDMs revealed potential areas of future gain in highly suitable habitat within current species’ ranges, which may serve as climatic microrefugia. Taken together, this study highlights the need to develop microclimate SDMs that account for vegetation and its biophysical effects on near-surface temperatures. As microclimate datasets become increasingly available across the world, their integration into correlative and mechanistic SDMs will be imperative for accurately estimating organismal responses to climate change and helping environmental managers tasked with spatially prioritizing conservation assets.     

Climate change increases risk of plant invasion in the Eastern United States

Invasive plant species threaten native ecosystems, natural resources, and managed lands worldwide. Climate change may increase risk from invasive plant species as favorable climate conditions allow invaders to expand into new ranges. Here, we use bioclimatic envelope modeling to assess current climatic habitat, or lands climatically suitable for invasion, for three of the most dominant and aggressive invasive plants in the southeast United States: kudzu (Pueraria lobata), privet (Ligustrum sinense; L. vulgare), and cogongrass (Imperata cylindrica). We define climatic habitat using both the Maxent and Mahalanobis distance methodologies, and we define the best climatic predictors based on variables that best ‘constrain’ species distributions and variables that ‘release’ the most land area if excluded. We then use an ensemble of 12 atmosphere-ocean general circulation models to project changes in climatic habitat for the three invasive species by 2100. The combined methodologies, predictors, and models produce a robust assessment of invasion risk inclusive of many of the approaches typically used individually to assess climate change impacts. Current invasion risk is widespread in southeastern states for all three species, although cogongrass invasion risk is more restricted to the Gulf Coast. Climate change is likely to enable all three species to greatly expand their ranges. Risk from privet and kudzu expands north into Ohio, Pennsylvania, New York, and New England states by 2100. Risk from cogongrass expands as far north as Kentucky and Virginia. Heightened surveillance and prompt eradication of small pockets of invasion in northern states should be a management priority.     

The responses of grassland plants to experimentally simulated climate change depend on land use and region

Macroclimatic niche properties derived from species distribution ranges are fundamental for projections of climate change impacts on biodiversity. However, it has been recognized that changes in regional or local distribution patterns also depend on interactions with land use. The reliability and transferability of large scale geographic predictions to small scale plant performance need to be tested experimentally. Thus, we asked how grassland plant species pairs with different macroclimatic niche properties respond to increased spring temperature and decrease summer precipitation in three different land‐use types. An experiment was carried out in the framework of the German Biodiversity Exploratories simulating climate change in 45 experimental plots in three geographical regions (Schorfheide‐Chorin, Hainich‐Dün, Schwäbische Alb) and three grassland management types (meadow, pasture, mown pasture). We planted six plant species as phytometers, each two of them representing congeneric species with contrasting macroclimatic niches and recorded plant survival and growth over 1 year. To quantify the species macroclimatic niches with respect to drought tolerance, the species’ distribution ranges were mapped and combined with global climate data. The simulated climate change had a general negative effect on plant survival and plant growth, irrespective of the macroclimatic niche characteristics of the species. Against expectation, species with ranges extending into drier regions did not generally perform better under drier conditions. Growth performance and survival was best in mown pastures, representing a quite intensive type of land use in all study regions. Species with higher macroclimatic drought tolerance were generally characterized by lower growth rates and higher survival rates in land‐use types with regular mowing regimes, probably because of reduced competition in the growing season. In conclusion, plant species with similar climatic niche characteristics cannot be expected to respond consistently over different regions owing to complex interactions of climate change with land use practices.     

Interrogating recent range changes in South African birds: confounding signals from land use and climate change present a challenge for attribution

Aim Apparent anthropogenic warming has been underway in South Africa for several decades, a period over which significant range shifts have been observed in some indigenous bird species. We asked whether these range shifts by birds are clearly consistent with either climate change or land use change being the primary driver. Location South Africa. Methods We categorized recent range changes among 408 South African terrestrial bird species and, using generalized linear mixed models, analysed ecological attributes of those species that have and have not changed their ranges. Results Fifty‐six of the 408 taxa studied have undergone significant range shifts. Most extended their ranges towards the south (towards cooler latitudes, consistent with climate‐change drivers) or west (towards drier and warmer habitats, inconsistent with climate drivers but consistent with land use drivers); very few moved east or north. Both southward and westward movers were habitat generalists. Furthermore, southward movers were mobile taxa (migrants and nomads), whereas westward movers were associated with human‐modified elements in the landscape, such as croplands, plantations or buildings. Main conclusions The results suggest that both land use changes and climate change may simultaneously be influencing dynamic range shifts by South African birds, but separating the relative strengths of these two drivers is challenging, not least because both are operating concurrently and may influence some species simultaneously. Those species that respond to land use change by contracting their ranges are likely to be among the species that will be most impacted by climate change if land use practices with negative impacts are occurring in areas anticipated to become climatic refugia for these species. This highlights a pressing need to develop dynamic models of species’ potential range shifts and changing abundances that incorporate population and dispersal processes, as well as ecological processes that influence habitat suitability.     

The Relative Impacts of Climate and Land-Use Change on Conterminous United States Bird Species from 2001 to 2075

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be “suitable” for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges.     

Forecasting the future of biodiversity: a test of single‐ and multi‐species models for ants in North America

The geographic distributions of many taxonomic groups remain mostly unknown, hindering attempts to investigate the response of the majority of species on Earth to climate change using species distributions models (SDMs). Multi‐species models can incorporate data for rare or poorly‐sampled species, but their application to forecasting climate change impacts on biodiversity has been limited. Here we compare forecasts of changes in patterns of ant biodiversity in North America derived from ensembles of single‐species models to those from a multi‐species modeling approach, Generalized Dissimilarity Modeling (GDM). We found that both single‐ and multi‐species models forecasted large changes in ant community composition in relatively warm environments. GDM predicted higher turnover than SDMs and across a larger contiguous area, including the southern third of North America and notably Central America, where the proportion of ants with relatively small ranges is high and where data limitations are most likely to impede the application of SDMs. Differences between approaches were also influenced by assumptions regarding dispersal, with forecasts being more similar if no‐dispersal was assumed. When full‐dispersal was assumed, SDMs predicted higher turnover in southern Canada than did GDM. Taken together, our results suggest that 1) warm rather than cold regions potentially could experience the greatest changes in ant fauna under climate change and that 2) multi‐species models may represent an important complement to SDMs, particularly in analyses involving large numbers of rare or poorly‐sampled species. Comparisons of the ability of single‐ and multi‐species models to predict observed changes in community composition are needed in order to draw definitive conclusions regarding their application to investigating climate change impacts on biodiversity.     

To Be Or Not to Be? Variable selection can change the projected fate of a threatened species under future climate

Species distribution models (SDMs) are commonly used to project future changes in the geographic ranges of species, to estimate extinction rates and to plan biodiversity conservation. However, these models can produce a range of results depending on how they are parameterized, and over‐reliance on a single model may lead to overconfidence in maps of future distributions. The choice of predictor variable can have a greater influence on projected future habitat than the range of climate models used. We demonstrate this in the case of the Ptunarra Brown Butterfly, a species listed as vulnerable in Tasmania, Australia. We use the Maxent model to develop future projections for this species based on three variable sets; all 35 commonly used so‐called ‘bioclimatic’ variables, a subset of these based on expert knowledge, and a set of monthly climate variables relevant to the species’ primary activity period. We used a dynamically downscaled regional climate model based on three global climate models. Depending on the choice of variable set, the species is projected either to experience very little contraction of habitat or to come close to extinction by the end of the century due to lack of suitable climate. The different conclusions could have important consequences for conservation planning and management, including the perceived viability of habitat restoration. The output of SDMs should therefore be used to define the range of possible trajectories a species may be on, and ongoing monitoring used to inform management as changes occur.     

Where are all the fish: potential of biogeographical maps to project current and future distribution patterns of freshwater species

The dendritic structure of river networks is commonly argued against use of species atlas data for modeling freshwater species distributions, but little has been done to test the potential of grid-based data in predictive species mapping. Using four different niche-based models and three different climate change projections for the middle of the 21st century merged pairwise as well as within a consensus modeling framework, we studied the variability in current and future distribution patterns of 38 freshwater fish species across Germany. We used grid-based (11×11 km) fish distribution maps and numerous climatic, topographic, hydromorphologic, and anthropogenic factors derived from environmental maps at a finer scale resolution (250 m-1 km). Apart from the explicit predictor selection, our modeling framework included uncertainty estimation for all phases of the modeling process. We found that the predictive performance of some niche-based models is excellent independent of the predictor data set used, emphasizing the importance of a well-grounded predictor selection process. Though important, climate was not a primary key factor for any of the studied fish species groups, in contrast to substrate preferences, hierarchical river structure, and topography. Generally, distribution ranges of cold-water and warm-water species are expected to change significantly in the future; however, the extent of changes is highly uncertain. Finally, we show that the mismatch between the current and future ranges of climatic variables of more than 90% is the most limiting factor regarding reliability of our future estimates. Our study highlighted the underestimated potential of grid cell information in biogeographical modeling of freshwater species and provides a comprehensive modeling framework for predictive mapping of species distributions and evaluation of the associated uncertainties.     

Habitat stability affects dispersal and the ability to track climate change

Habitat persistence should influence dispersal ability, selecting for stronger dispersal in habitats of lower temporal stability. As standing (lentic) freshwater habitats are on average less persistent over time than running (lotic) habitats, lentic species should show higher dispersal abilities than lotic species. Assuming that climate is an important determinant of species distributions, we hypothesize that lentic species should have distributions that are closer to equilibrium with current climate, and should more rapidly track climatic changes. We tested these hypotheses using datasets from 1988 and 2006 containing all European dragon- and damselfly species. Bioclimatic envelope models showed that lentic species were closer to climatic equilibrium than lotic species. Furthermore, the models over-predicted lotic species ranges more strongly than lentic species ranges, indicating that lentic species track climatic changes more rapidly than lotic species. These results are consistent with the proposed hypothesis that habitat persistence affects the evolution of dispersal.     

Shifting ranges and conservation challenges for lemurs in the face of climate change

Geospatial modeling is one of the most powerful tools available to conservation biologists for estimating current species ranges of Earth's biodiversity. Now, with the advantage of predictive climate models, these methods can be deployed for understanding future impacts on threatened biota. Here, we employ predictive modeling under a conservative estimate of future climate change to examine impacts on the future abundance and geographic distributions of Malagasy lemurs. Using distribution data from the primary literature, we employed ensemble species distribution models and geospatial analyses to predict future changes in species distributions. Current species distribution models (SDMs) were created within the BIOMOD2 framework that capitalizes on ten widely used modeling techniques. Future and current SDMs were then subtracted from each other, and areas of contraction, expansion, and stability were calculated. Model overprediction is a common issue associated Malagasy taxa. Accordingly, we introduce novel methods for incorporating biological data on dispersal potential to better inform the selection of pseudo‐absence points. This study predicts that 60% of the 57 species examined will experience a considerable range of reductions in the next seventy years entirely due to future climate change. Of these species, range sizes are predicted to decrease by an average of 59.6%. Nine lemur species (16%) are predicted to expand their ranges, and 13 species (22.8%) distribution sizes were predicted to be stable through time. Species ranges will experience severe shifts, typically contractions, and for the majority of lemur species, geographic distributions will be considerably altered. We identify three areas in dire need of protection, concluding that strategically managed forest corridors must be a key component of lemur and other biodiversity conservation strategies. This recommendation is all the more urgent given that the results presented here do not take into account patterns of ongoing habitat destruction relating to human activities.     

Why Would Plant Species Become Extinct Locally If Growing Conditions Improve?

Two assumptions underlie current models of the geographical ranges of perennial plant species: 1. current ranges are in equilibrium with the prevailing climate, and 2. changes are attributable to changes in macroclimatic factors, including tolerance of winter cold, the duration of the growing season, and water stress during the growing season, rather than to biotic interactions. These assumptions allow model parameters to be estimated from current species ranges. Deterioration of growing conditions due to climate change, e.g. more severe drought, will cause local extinction. However, for many plant species, the predicted climate change of higher minimum temperatures and longer growing seasons means, improved growing conditions. Biogeographical models may under some circumstances predict that a species will become locally extinct, despite improved growing conditions, because they are based on an assumption of equilibrium and this forces the species range to match the species-specific macroclimatic thresholds. We argue that such model predictions should be rejected unless there is evidence either that competition influences the position of the range margins or that a certain physiological mechanism associated with the apparent improvement in growing conditions negatively affects the species performance. We illustrate how a process-based vegetation model can be used to ascertain whether such a physiological cause exists. To avoid potential modelling errors of this type, we propose a method that constrains the scenario predictions of the envelope models by changing the geographical distribution of the dominant plant functional type. Consistent modelling results are very important for evaluating how changes in species areas affect local functional trait diversity and hence ecosystem functioning and resilience, and for inferring the implications for conservation management in the face of climate change.     

The role of climate,habitat, and species co‐occurrence as drivers of change in small mammal distributions over the past century

Species distribution models are commonly used to predict species responses to climate change. However, their usefulness in conservation planning and policy is controversial because they are difficult to validate across time and space. Here we capitalize on small mammal surveys repeated over a century in Yosemite National Park, USA, to assess accuracy of model predictions. Historical (1900–1940) climate, vegetation, and species occurrence data were used to develop single‐ and multi‐species multivariate adaptive regression spline distribution models for three species of chipmunk. Models were projected onto the current (1980–2007) environmental surface and then tested against modern field resurveys of each species. We evaluated models both within and between time periods and found that even with the inclusion of biotic predictors, climate alone is the dominant predictor explaining the distribution of the study species within a time period. However, climate was not consistently an adequate predictor of the distributional change observed in all three species across time. For two of the three species, climate alone or climate and vegetation models showed good predictive performance across time. The stability of the distribution from the past to present observed in the third species, however, was not predicted by our modeling approach. Our results demonstrate that correlative distribution models are useful in understanding species' potential responses to environmental change, but also show how changes in species‐environment correlations through time can limit the predictive performance of models.     

An empirical test of the relative and combined effects of land‐cover and climate change on local colonization and extinction

Land‐cover and climate change are two main drivers of changes in species ranges. Yet, the majority of studies investigating the impacts of global change on biodiversity focus on one global change driver and usually use simulations to project biodiversity responses to future conditions. We conduct an empirical test of the relative and combined effects of land‐cover and climate change on species occurrence changes. Specifically, we examine whether observed local colonization and extinctions of North American birds between 1981–1985 and 2001–2005 are correlated with land‐cover and climate change and whether bird life history and ecological traits explain interspecific variation in observed occurrence changes. We fit logistic regression models to test the impact of physical land‐cover change, changes in net primary productivity, winter precipitation, mean summer temperature, and mean winter temperature on the probability of Ontario breeding bird local colonization and extinction. Models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local colonization for 30%, 27%, and 29% of species, respectively. Conversely, models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local extinction for 61%, 7%, and 9% of species, respectively. The quantitative impacts of land‐cover and climate change variables also vary among bird species. We then fit linear regression models to test whether the variation in regional colonization and extinction rate could be explained by mean body mass, migratory strategy, and habitat preference of birds. Overall, species traits were weakly correlated with heterogeneity in species occurrence changes. We provide empirical evidence showing that land‐cover change, climate change, and the combination of multiple global change drivers can differentially explain observed species local colonization and extinction.     

Consensus Forecasting of Species Distributions: The Effects of Niche Model Performance and Niche Properties

Ensemble forecasting is advocated as a way of reducing uncertainty in species distribution modeling (SDM). This is because it is expected to balance accuracy and robustness of SDM models. However, there are little available data regarding the spatial similarity of the combined distribution maps generated by different consensus approaches. Here, using eight niche-based models, nine split-sample calibration bouts (or nine random model-training subsets), and nine climate change scenarios, the distributions of 32 forest tree species in China were simulated under current and future climate conditions. The forecasting ensembles were combined to determine final consensual prediction maps for target species using three simple consensus approaches (average, frequency, and median [PCA]). Species’ geographic ranges changed (area change and shifting distance) in response to climate change, but the three consensual projections did not differ significantly with respect to how much or in which direction, but they did differ with respect to the spatial similarity of the three consensual predictions. Incongruent areas were observed primarily at the edges of species’ ranges. Multiple stepwise regression models showed the three factors (niche marginality and specialization, and niche model accuracy) to be related to the observed variations in consensual prediction maps among consensus approaches. Spatial correspondence among prediction maps was the highest when niche model accuracy was high and marginality and specialization were low. The difference in spatial predictions suggested that more attention should be paid to the range of spatial uncertainty before any decisions regarding specialist species can be made based on map outputs. The niche properties and single-model predictive performance provide promising insights that may further understanding of uncertainties in SDM.     

Impact of twenty-first century climate change on diadromous fish spread over Europe, North Africa and the Middle East

Climate change is expected to drive species ranges towards the poles and to have a strong influence on species distributions. In this study, we focused on diadromous species that are of economical and ecological importance in the whole of Europe. We investigated the potential distribution of all diadromous fish regularly encountered in Europe, North Africa and the Middle East (28 species) under conditions predicted for twenty‐first century climate change. To do so, we investigated the 1900 distribution of each species in 196 basins spread across all of Europe, North Africa and the Middle East. Four levels were used to semiquantitatively describe the abundance of species, that is missing, rare, common and abundant. We then selected five variables describing the prevailing climate in the basins, the physical nature of the basins and reflecting historical events known to have affected freshwater fish distribution. Logistic regressions with a four‐level ordinal response variable were used to develop species‐specific models. These predictive models related the observed distribution of these species in 1900 to the most explanatory combination of variables. Finally, we selected the A2 SRES scenario and the HadCM3 (Hadley Centre Coupled Model version 3) global climate model (GCM) to obtain climate variables (temperature and precipitation) at the end of this century. We used these 2100 variables in our models and obtained maps of climatically suitable and unsuitable basins, percentages of contraction or expansion for each species. Twenty‐two models were successfully built, that is there were five species for which no model could be established because their distribution range was too narrow and the Acipenser sturio model failed during calibration. All the models selected temperature or/and precipitation as explanatory variables. Responses to climate change were species‐specific but could be classified into three categories: little or no change in the distribution (five species), expansion of the distribution range (three species gaining suitable basins mainly northward) and contraction of the distribution (14 species losing suitable basins). Shifting ranges were in accordance with those found in other studies and underlined the high sensitivity of diadromous fish to modifications in their environment.     

Habitat shifts of endangered species under altered climate conditions: importance of biotic interactions

Predicting changes in potential habitat for endangered species as a result of global warming requires considering more than future climate conditions; it is also necessary to evaluate biotic associations. Most distribution models predicting species responses to climate change include climate variables and occasionally topographic and edaphic parameters, rarely are biotic interactions included. Here, we incorporate biotic interactions into niche models to predict suitable habitat for species under altered climates. We constructed and evaluated niche models for an endangered butterfly and a threatened bird species, both are habitat specialists restricted to semiarid shrublands of southern California. To incorporate their dependency on shrubs, we first developed climate‐based niche models for shrubland vegetation and individual shrub species. We also developed models for the butterfly's larval host plants. Outputs from these models were included in the environmental variable dataset used to create butterfly and bird niche models. For both animal species, abiotic–biotic models outperformed the climate‐only model, with climate‐only models over‐predicting suitable habitat under current climate conditions. We used the climate‐only and abiotic–biotic models to calculate amounts of suitable habitat under altered climates and to evaluate species' sensitivities to climate change. We varied temperature (+0.6, +1.7, and +2.8 °C) and precipitation (50%, 90%, 100%, 110%, and 150%) relative to current climate averages and within ranges predicted by global climate change models. Suitable habitat for each species was reduced at all levels of temperature increase. Both species were sensitive to precipitation changes, particularly increases. Under altered climates, including biotic variables reduced habitat by 68–100% relative to the climate‐only model. To design reserve systems conserving sensitive species under global warming, it is important to consider biotic interactions, particularly for habitat specialists and species with strong dependencies on other species.