Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long‐term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat‐induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long‐term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range – with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot‐spells, in driving species–climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species.     

The fate of European breeding birds under climate,land‐use and dispersal scenarios

Many species have already shifted their distributions in response to recent climate change. Here, we aimed at predicting the future breeding distributions of European birds under climate, land‐use, and dispersal scenarios. We predicted current and future distributions of 409 species within an ensemble forecast framework using seven species distribution models (SDMs), five climate scenarios and three emission and land‐use scenarios. We then compared results from SDMs using climate‐only variables, habitat‐only variables or both climate and habitat variables. In order to account for a species’ dispersal abilities, we used natal dispersal estimates and developed a probabilistic method that produced a dispersal scenario intermediate between the null and full dispersal scenarios generally considered in such studies. We then compared results from all scenarios in terms of future predicted range changes, range shifts, and variations in species richness. Modeling accuracy was better with climate‐only variables than with habitat‐only variables, and better with both climate and habitat variables. Habitat models predicted smaller range shifts and smaller variations in range size and species richness than climate models. Using both climate and habitat variables, it was predicted that the range of 71% of the species would decrease by 2050, with a 335 km median shift. Predicted variations in species richness showed large decreases in the southern regions of Europe, as well as increases, mainly in Scandinavia and northern Russia. The partial dispersal scenario was significantly different from the full dispersal scenario for 25% of the species, resulting in the local reduction of the future predicted species richness of up to 10%. We concluded that the breeding range of most European birds will decrease in spite of dispersal abilities close to a full dispersal hypothesis, and that given the contrasted predictions obtained when modeling climate change only and land‐use change only, both scenarios must be taken into consideration.     

Habitat availability and gene flow influence diverging local population trajectories under scenarios of climate change: a place‐based approach

Ecological niche theory holds that species distributions are shaped by a large and complex suite of interacting factors. Species distribution models (SDMs) are increasingly used to describe species’ niches and predict the effects of future environmental change, including climate change. Currently, SDMs often fail to capture the complexity of species’ niches, resulting in predictions that are generally limited to climate‐occupancy interactions. Here, we explore the potential impact of climate change on the American pika using a replicated place‐based approach that incorporates climate, gene flow, habitat configuration, and microhabitat complexity into SDMs. Using contemporary presence–absence data from occupancy surveys, genetic data to infer connectivity between habitat patches, and 21 environmental niche variables, we built separate SDMs for pika populations inhabiting eight US National Park Service units representing the habitat and climatic breadth of the species across the western United States. We then predicted occurrence probability under current (1981–2010) and three future time periods (out to 2100). Occurrence probabilities and the relative importance of predictor variables varied widely among study areas, revealing important local‐scale differences in the realized niche of the American pika. This variation resulted in diverse and – in some cases – highly divergent future potential occupancy patterns for pikas, ranging from complete extirpation in some study areas to stable occupancy patterns in others. Habitat composition and connectivity, which are rarely incorporated in SDM projections, were influential in predicting pika occupancy in all study areas and frequently outranked climate variables. Our findings illustrate the importance of a place‐based approach to species distribution modeling that includes fine‐scale factors when assessing current and future climate impacts on species’ distributions, especially when predictions are intended to manage and conserve species of concern within individual protected areas.     

Choice of predictor variables as a source of uncertainty in continental‐scale species distribution modelling under climate change

Aim Species distribution modelling is commonly used to guide future conservation policies in the light of potential climate change. However, arbitrary decisions during the model‐building process can affect predictions and contribute to uncertainty about where suitable climate space will exist. For many species, the key climatic factors limiting distributions are unknown. This paper assesses the uncertainty generated by using different climate predictor variable sets for modelling the impacts of climate change. Location Europe, 10° W to 50° E and 30° N to 60° N. Methods Using 1453 presence pixels at 30 arcsec resolution for the great bustard (Otis tarda), predictions of future distribution were made based on two emissions scenarios, three general climate models and 26 sets of predictor variables. Twenty‐six current models were created, and 156 for both 2050 and 2080. Map comparison techniques were used to compare predictions in terms of the quantity and the location of presences (map comparison kappa, MCK) and using a range change index (RCI). Generalized linear models (GLMs) were used to partition explained deviance in MCK and RCI among sources of uncertainty. Results The 26 different variable sets achieved high values of AUC (area under the receiver operating characteristic curve) and yet introduced substantial variation into maps of current distribution. Differences between maps were even greater when distributions were projected into the future. Some 64–78% of the variation between future maps was attributable to choice of predictor variable set alone. Choice of general climate model and emissions scenario contributed a maximum of 15% variation and their order of importance differed for MCK and RCI. Main conclusions Generalized variable sets produce an unmanageable level of uncertainty in species distribution models which cannot be ignored. The use of sound ecological theory and statistical methods to check predictor variables can reduce this uncertainty, but our knowledge of species may be too limited to make more than arbitrary choices. When all sources of modelling uncertainty are considered together, it is doubtful whether ensemble methods offer an adequate solution. Future studies should explicitly acknowledge uncertainty due to arbitrary choices in the model‐building process and develop ways to convey the results to decision‐makers.     

Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change

Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models.     

MigClim: Predicting plant distribution and dispersal in a changing climate

Aim Many studies have forecasted the possible impact of climate change on plant distributions using models based on ecological niche theory, but most of them have ignored dispersal‐limitations, assuming dispersal to be either unlimited or null. Depending on the rate of climatic change, the landscape fragmentation and the dispersal capabilities of individual species, these assumptions are likely to prove inaccurate, leading to under‐ or overestimation of future species distributions and yielding large uncertainty between these two extremes. As a result, the concepts of ‘potentially suitable’ and ‘potentially colonizable’ habitat are expected to differ significantly. To quantify to what extent these two concepts can differ, we developed Mig Clim, a model simulating plant dispersal under climate change and landscape fragmentation scenarios. Mig Clim implements various parameters, such as dispersal distance, increase in reproductive potential over time, landscape fragmentation or long‐distance dispersal. Location Western Swiss Alps. Methods Using our Mig Clim model, several simulations were run for two virtual species by varying dispersal distance and other parameters. Each simulation covered the 100‐year period 2001–2100 and three different IPCC‐based temperature warming scenarios were considered. Results of dispersal‐limited projections were compared with unlimited and no‐dispersal projections. Results Our simulations indicate that: (1) using realistic parameter values, the future potential distributions generated using Mig Clim can differ significantly (up to more than 95% difference in colonized surface) from those that ignore dispersal; (2) this divergence increases under more extreme climate warming scenarios and over longer time periods; and (3) the uncertainty associated with the warming scenario can be as large as the one related to dispersal parameters. Main conclusions Accounting for dispersal, even roughly, can importantly reduce uncertainty in projections of species distribution under climate change scenarios.     

Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France

Species distribution modelling is an easy, persuasive and useful tool for anticipating species distribution shifts under global change. Numerous studies have used only climate variables to predict future potential species range shifts and have omitted environmental factors important for determining species distribution. Here, we assessed the importance of the edaphic dimension in the niche‐space definition of Quercus pubescens and in future spatial projections under global change over the metropolitan French forest territory. We fitted two species distribution models (SDM) based on presence/absence data (111 013 plots), one calibrated from climate variables only (mean temperature of January and climatic water balance of July) and the other one from both climate and edaphic (soil pH inferred from plants) variables. Future predictions were conducted under two climate scenarios (PCM B2 and HadCM3 A2) and based on 100 simulations using a cellular automaton that accounted for seed dispersal distance, landscape barriers preventing migration and unsuitable land cover. Adding the edaphic dimension to the climate‐only SDM substantially improved the niche‐space definition of Q. pubescens, highlighting an increase in species tolerance in confronting climate constraints as the soil pH increased. Future predictions over the 21st century showed that disregarding the edaphic dimension in SDM led to an overestimation of the potential distribution area, an underestimation of the spatial fragmentation of this area, and prevented the identification of local refugia, leading to an underestimation of the northward shift capacity of Q. pubescens and its persistence in its current distribution area. Spatial discrepancies between climate‐only and climate‐plus‐edaphic models are strengthened when seed dispersal and forest fragmentation are accounted for in predicting a future species distribution area. These discrepancies highlight some imprecision in spatial predictions of potential distribution area of species under climate change scenarios and possibly wrong conclusions for conservation and management perspectives when climate‐only models are used.     

An Objective Approach to Select Climate Scenarios when Projecting Species Distribution under Climate Change

An impressive number of new climate change scenarios have recently become available to assess the ecological impacts of climate change. Among these impacts, shifts in species range analyzed with species distribution models are the most widely studied. Whereas it is widely recognized that the uncertainty in future climatic conditions must be taken into account in impact studies, many assessments of species range shifts still rely on just a few climate change scenarios, often selected arbitrarily. We describe a method to select objectively a subset of climate change scenarios among a large ensemble of available ones. Our k-means clustering approach reduces the number of climate change scenarios needed to project species distributions, while retaining the coverage of uncertainty in future climate conditions. We first show, for three biologically-relevant climatic variables, that a reduced number of six climate change scenarios generates average climatic conditions very close to those obtained from a set of 27 scenarios available before reduction. A case study on potential gains and losses of habitat by three northeastern American tree species shows that potential future species distributions projected from the selected six climate change scenarios are very similar to those obtained from the full set of 27, although with some spatial discrepancies at the edges of species distributions. In contrast, projections based on just a few climate models vary strongly according to the initial choice of climate models. We give clear guidance on how to reduce the number of climate change scenarios while retaining the central tendencies and coverage of uncertainty in future climatic conditions. This should be particularly useful during future climate change impact studies as more than twice as many climate models were reported in the fifth assessment report of IPCC compared to the previous one.     

Probabilistic measures of climate change vulnerability,adaptation action benefits,and related uncertainty from maximum temperature metric selection

Predictions of the projected changes in species distributions and potential adaptation action benefits can help guide conservation actions. There is substantial uncertainty in projecting species distributions into an unknown future, however, which can undermine confidence in predictions or misdirect conservation actions if not properly considered. Recent studies have shown that the selection of alternative climate metrics describing very different climatic aspects (e.g., mean air temperature vs. mean precipitation) can be a substantial source of projection uncertainty. It is unclear, however, how much projection uncertainty might stem from selecting among highly correlated, ecologically similar climate metrics (e.g., maximum temperature in July, maximum 30‐day temperature) describing the same climatic aspect (e.g., maximum temperatures) known to limit a species’ distribution. It is also unclear how projection uncertainty might propagate into predictions of the potential benefits of adaptation actions that might lessen climate change effects. We provide probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty stemming from the selection of four maximum temperature metrics for brook trout (Salvelinus fontinalis), a cold‐water salmonid of conservation concern in the eastern United States. Projected losses in suitable stream length varied by as much as 20% among alternative maximum temperature metrics for mid‐century climate projections, which was similar to variation among three climate models. Similarly, the regional average predicted increase in brook trout occurrence probability under an adaptation action scenario of full riparian forest restoration varied by as much as .2 among metrics. Our use of Bayesian inference provides probabilistic measures of vulnerability and adaptation action benefits for individual stream reaches that properly address statistical uncertainty and can help guide conservation actions. Our study demonstrates that even relatively small differences in the definitions of climate metrics can result in very different projections and reveal high uncertainty in predicted climate change effects.     

The influence of coarse‐scale environmental features on current and predicted future distributions of narrow‐range endemic crayfish populations

1. A major limitation to effective management of narrow‐range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2. Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate‐change scenarios. 3. The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 65–87% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4. Current models created using two spatial resolutions (1 and 4.5 km²) showed that fine‐resolution data more accurately represented current distributions. For three of the four species, the 1‐km² resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1‐km² resolution models were more accurate than 4.5‐km² resolution models. 5. Future projected (4.5‐km² resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low‐emission scenario, whereas two of four species would be severely restricted in range under moderate–high emissions. Discrepancies in the two emission scenarios probably relate to the exclusion of behavioural adaptations from species‐distribution models. 6. These model predictions illustrate possible impacts of climate change on narrow‐range endemic crayfish populations. The predictions do not account for biotic interactions, migration, local habitat conditions or species adaptation. However, we identified the constraining landscape features acting on these populations that provide a framework for addressing habitat needs at a fine scale and developing targeted and systematic monitoring programmes.     

Cetacean range and climate in the eastern North Atlantic: future predictions and implications for conservation

There is increasing evidence that the distributions of a large number of species are shifting with global climate change as they track changing surface temperatures that define their thermal niche. Modelling efforts to predict species distributions under future climates have increased with concern about the overall impact of these distribution shifts on species ecology, and especially where barriers to dispersal exist. Here we apply a bio‐climatic envelope modelling technique to investigate the impacts of climate change on the geographic range of ten cetacean species in the eastern North Atlantic and to assess how such modelling can be used to inform conservation and management. The modelling process integrates elements of a species' habitat and thermal niche, and employs “hindcasting” of historical distribution changes in order to verify the accuracy of the modelled relationship between temperature and species range. If this ability is not verified, there is a risk that inappropriate or inaccurate models will be used to make future predictions of species distributions. Of the ten species investigated, we found that while the models for nine could successfully explain current spatial distribution, only four had a good ability to predict distribution changes over time in response to changes in water temperature. Applied to future climate scenarios, the four species‐specific models with good predictive abilities indicated range expansion in one species and range contraction in three others, including the potential loss of up to 80% of suitable white‐beaked dolphin habitat. Model predictions allow identification of affected areas and the likely time‐scales over which impacts will occur. Thus, this work provides important information on both our ability to predict how individual species will respond to future climate change and the applicability of predictive distribution models as a tool to help construct viable conservation and management strategies.     

Individualistic vs community modelling of species distributions under climate change

Studies investigating the consequences of future climate changes on species distributions usually start with the assumption that species respond to climate changes in an individualistic fashion. This assumption has led researchers to use bioclimate envelope models that use present climate-range relationships to characterize species' limits of tolerance to climate, and then apply climate-change scenarios to enable projections of altered species distributions. However, there are techniques that combine climate variables together with information on the composition of assemblages to enable projections that are expected to mimic community dynamics. Here, we compare, for the first time, the performance of GLM (generalized linear model) and CQO (canonical quadratic ordination; a type of community-based GLM) for projecting distributions of species under climate change scenarios. We found that projections from these two methods varied both in terms of accuracy (GLM providing generally more accurate projections than CQO) and in the broad diversity patterns yielded (higher species richness values projected with CQO). Model outputs were also affected by species-specific traits, such as species range size and species geographical positions, supporting the view that methods are sensitive to different degrees of equilibrium of species distributions with climate. This study reveals differences in projections between individual- and community-based approaches that require further scrutiny, but it does not find support for unsupervised use community-based models for investigating climate change impacts on species distributions. Reasons for this lack of support are discussed.     

Climate versus weather extremes: Temporal predictor resolution matters for future rather than current regional species distribution models

Aim

Climate is considered a major driver of species distributions. Long‐term climatic means are commonly used as predictors in correlative species distribution models (SDMs). However, this coarse temporal resolution does not reflect local conditions that populations experience, such as short‐term weather extremes, which may have a strong impact on population dynamics and local distributions. We here compare the performance of climate‐ and weather‐based predictors in regional SDMs and their influence on future predictions, which are increasingly used in conservation planning.

Location

South‐western Germany.

Methods

We built different SDMs for 20 Orthoptera species based on three predictor sets at a regional scale for current and future climate scenarios. We calculated standard bioclimatic variables and yearly and seasonal sets of climate change indicating variables of weather extremes. As the impact of extreme events may be stronger for habitat specialists than for generalists, we distinguished species’ degrees of specialization. We computed linear mixed‐effects models to identify significant effects of algorithm, predictor set and specialization on model performance and calculated correlations and geographical niche overlap between spatial predictions.

Results

Current predictions were rather similar among all predictor sets, but highly variable for future climate scenarios. Bioclimatic and seasonal weather predictors performed slightly better than yearly weather predictors, though performance differences were minor. We found no evidence that specialists are more sensitive to weather extremes than generalists.

Main conclusions

For future projections of species distributions, SDM predictor selection should not solely be based on current performances and predictions. As long‐term climate and short‐term weather predictors represent different environmental drivers of a species’ distribution, we argue to interpret diverging future projections as complements. Even if similar current performances and predictions might imply their equivalency, favouring one predictor set neglects important aspects of future distributions and might mislead conservation decisions based on them.

     

Species distribution models in climate change scenarios are still not useful for informing policy planning: an uncertainty assessment using fuzzy logic

We compared the effect of general circulation models and greenhouse gas emission scenarios on the uncertainty associated with models predicting changes in areas favourable to animal species. Given that mountain species are particularly at risk due to climate warming, we selected one amphibian (Baetic midwife toad), one reptile (Lataste's viper), one bird (Bonelli's eagle), and one mammal (Iberian wild goat) present in Spanish mountains to model their distributional response to climate change during this century. Climate forecasts for the whole century were provided by the Agencia Estatal de Meteorología (AEMET; National Meteorological Agency) of Spain, which adapted the general circulation models CGCM2 and ECHAM4 and produced expected temperature and precipitation values for Spain according to the A2 and B2 emission scenarios. We constructed separate models of the species response to spatial, topographic, human, and climate variables using current values of the corresponding variables. We predicted future areas favourable to the species by replacing the current climate values with those expected according to each climate change scenario, while keeping spatial, topographic and human variables constant. Fuzzy logic was used to compute the coincidence between predictions for different emission scenarios in the same global circulation model, and the consistency between predictions for the same emission scenario applying different general circulation models. In general, coincidences were higher than consistencies and, thus, discrepancies between predictions were more attributable to uncertainty in global circulation models, i.e. our insufficient knowledge concerning the effect of the oceans and atmosphere on climate, than to the putative effect of different emission scenarios on future climates. Our conclusion is that species distribution models in climate warming scenarios are still not useful for informing emission policy planning, although they have great potential as tools once consistencies become higher than coincidences.     

Choice of baseline climate data impacts projected species' responses to climate change

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species‐climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040–2069, 2070–2099), using downscaled climate projections, and calculated species turnover and changes in species‐specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species‐specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site‐level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses.     

Optimizing resiliency of reserve networks to climate change: multispecies conservation planning in the Pacific Northwest,USA

The effectiveness of a system of reserves may be compromised under climate change as species' habitat shifts to nonreserved areas, a problem that may be compounded when well‐studied vertebrate species are used as conservation umbrellas for other taxa. The Northwest Forest Plan was among the first efforts to integrate conservation of wide‐ranging focal species and localized endemics into regional conservation planning. We evaluated how effectively the plan's focal species, the Northern Spotted Owl, acts as an umbrella for localized species under current and projected future climates and how the regional system of reserves can be made more resilient to climate change. We used the program maxent to develop distribution models integrating climate data with vegetation variables for the owl and 130 localized species. We used the program zonation to identify a system of areas that efficiently captures habitat for both the owl and localized species and prioritizes refugial areas of climatic and topographic heterogeneity where current and future habitat for dispersal‐limited species is in proximity. We projected future species' distributions based on an ensemble of contrasting climate models, and incorporating uncertainty between alternate climate projections into the prioritization process. Reserve solutions based on the owl overlap areas of high localized‐species richness but poorly capture core areas of localized species' distribution. Congruence between priority areas across taxa increases when refugial areas are prioritized. Although core‐area selection strategies can potentially increase the conservation value and resilience of regional reserve systems, they accentuate contrasts in priority areas between species and over time and should be combined with a broadened taxonomic scope and increased attention to potential effects of climate change. Our results suggest that systems of fixed reserves designed for resilience can increase the likelihood of retaining the biological diversity of forest ecosystems under climate change.     

Where will species go? Incorporating new advances in climate modelling into projections of species distributions

Bioclimatic models are the primary tools for simulating the impact of climate change on species distributions. Part of the uncertainty in the output of these models results from uncertainty in projections of future climates. To account for this, studies often simulate species responses to climates predicted by more than one climate model and/or emission scenario. One area of uncertainty, however, has remained unexplored: internal climate model variability. By running a single climate model multiple times, but each time perturbing the initial state of the model slightly, different but equally valid realizations of climate will be produced. In this paper, we identify how ongoing improvements in climate models can be used to provide guidance for impacts studies. In doing so we provide the first assessment of the extent to which this internal climate model variability generates uncertainty in projections of future species distributions, compared with variability between climate models. We obtained data on 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985–1995) and future (2025–2035). Initially, we compared the simulated values for each climate variable (P, T_max, T_min, and T_mean) for the current period to observed climate data. This showed that climates simulated by realizations from the same climate model were more similar to each other than to realizations from other models. However, when projected into the future, these realizations followed different trajectories and the values of climate variables differed considerably within and among climate models. These had pronounced effects on the projected distributions of nine Australian butterfly species when modelled using the BIOCLIM component of DIVA-GIS. Our results show that internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability. Further, different conclusions regarding the vulnerability of species to climate change can be reached due to internal model variability. Clearly, several climate models, each represented by multiple realizations, are required if we are to adequately capture the range of uncertainty associated with projecting species distributions in the future.     

The predictive skill of species distribution models for plankton in a changing climate

Statistical species distribution models (SDMs) are increasingly used to project spatial relocations of marine taxa under future climate change scenarios. However, tests of their predictive skill in the real‐world are rare. Here, we use data from the Continuous Plankton Recorder program, one of the longest running and most extensive marine biological monitoring programs, to investigate the reliability of predicted plankton distributions. We apply three commonly used SDMs to 20 representative plankton species, including copepods, diatoms, and dinoflagellates, all found in the North Atlantic and adjacent seas. We fit the models to decadal subsets of the full (1958–2012) dataset, and then use them to predict both forward and backward in time, comparing the model predictions against the corresponding observations. The probability of correctly predicting presence was low, peaking at 0.5 for copepods, and model skill typically did not outperform a null model assuming distributions to be constant in time. The predicted prevalence increasingly differed from the observed prevalence for predictions with more distance in time from their training dataset. More detailed investigations based on four focal species revealed that strong spatial variations in skill exist, with the least skill at the edges of the distributions, where prevalence is lowest. Furthermore, the scores of traditional single‐value model performance metrics were contrasting and some implied overoptimistic conclusions about model skill. Plankton may be particularly challenging to model, due to its short life span and the dispersive effects of constant water movements on all spatial scales, however there are few other studies against which to compare these results. We conclude that rigorous model validation, including comparison against null models, is essential to assess the robustness of projections of marine planktonic species under climate change.     

Effects of climate change on species turnover and body mass frequency distributions of South African bird communities

Climate change will lead to substantial shifts in species distributions. Most of the predictions of shifting distributions rely on modelling future distributions with ecological niche models. We used these models to investigate (i) the expected species turnover, loss and gain within bird communities of four South African biomes and (ii) the expected changes in the body mass frequency distributions of these communities. We used distributional data of the Southern African Bird Atlas Project, current climate data and two scenarios of future climate change for 2050 to build ensemble models of bird distributions. Our results indicate that future species loss, gain and turnover within the four biomes will be considerable. Climate change will also have statistically significant effects on body mass frequency distributions, and these effects differ substantially depending on the severity of future climate change. We discuss the possible ecological effects of these predicted changes on ecosystem interactions and functions.