Prediction of plant species distributions across six millennia

The usefulness of species distribution models (SDMs) in predicting impacts of climate change on biodiversity is difficult to assess because changes in species ranges may take decades or centuries to occur. One alternative way to evaluate the predictive ability of SDMs across time is to compare their predictions with data on past species distributions. We use data on plant distributions, fossil pollen and current and mid-Holocene climate to test the ability of SDMs to predict past climate-change impacts. We find that species showing little change in the estimated position of their realized niche, with resulting good model performance, tend to be dominant competitors for light. Different mechanisms appear to be responsible for among-species differences in model performance. Confidence in predictions of the impacts of climate change could be improved by selecting species with characteristics that suggest little change is expected in the relationships between species occurrence and climate patterns.     

From global change to a butterfly flapping: biophysics and behaviour affect tropical climate change impacts

Difficulty in characterizing the relationship between climatic variability and climate change vulnerability arises when we consider the multiple scales at which this variation occurs, be it temporal (from minute to annual) or spatial (from centimetres to kilometres). We studied populations of a single widely distributed butterfly species, Chlosyne lacinia, to examine the physiological, morphological, thermoregulatory and biophysical underpinnings of adaptation to tropical and temperate climates. Microclimatic and morphological data along with a biophysical model documented the importance of solar radiation in predicting butterfly body temperature. We also integrated the biophysics with a physiologically based insect fitness model to quantify the influence of solar radiation, morphology and behaviour on warming impact projections. While warming is projected to have some detrimental impacts on tropical ectotherms, fitness impacts in this study are not as negative as models that assume body and air temperature equivalence would suggest. We additionally show that behavioural thermoregulation can diminish direct warming impacts, though indirect thermoregulatory consequences could further complicate predictions. With these results, at multiple spatial and temporal scales, we show the importance of biophysics and behaviour for studying biodiversity consequences of global climate change, and stress that tropical climate change impacts are likely to be context-dependent.     

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross‐validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland‐dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross‐validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross‐validation results were correlated with extrapolation results, the use of cross‐validation performance metrics to guide modeling choices where knowledge is limited was supported.     

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.     

Assessing the vulnerability of species richness to anthropogenic climate change in a biodiversity hotspot

Aim To compare theoretical approaches towards estimating risks of plant species loss to anthropogenic climate change impacts in a biodiversity hotspot, and to develop a practical method to detect signs of climate change impacts on natural populations. Location The Fynbos biome of South Africa, within the Cape Floristic Kingdom. Methods Bioclimatic modelling was used to identify environmental limits for vegetation at both biome and species scale. For the biome as a whole, and for 330 species of the endemic family Proteaceae, tolerance limits were determined for five temperature and water availability‐related parameters assumed critical for plant survival. Climate scenarios for 2050 generated by the general circulation models HadCM2 and CSM were interpolated for the region. Geographic Information Systems‐based methods were used to map current and future modelled ranges of the biome and 330 selected species. In the biome‐based approach, predictions of biome areal loss were overlayed with species richness data for the family Proteaceae to estimate extinction risk. In the species‐based approach, predictions of range dislocation (no overlap between current range and future projected range) were used as an indicator of extinction risk. A method of identifying local populations imminently threatened by climate change‐induced mortality is also described. Results A loss of Fynbos biome area of between 51% and 65% is projected by 2050 (depending on the climate scenario used), and roughly 10% of the endemic Proteaceae have ranges restricted to the area lost. Species range projections suggest that a third could suffer complete range dislocation by 2050, and only 5% could retain more than two thirds of their range. Projected changes to individual species ranges could be sufficient to detect climate change impacts within ten years. Main conclusions The biome‐level approach appears to underestimate the risk of species diversity loss from climate change impacts in the Fynbos Biome because many narrow range endemics suffer range dislocation throughout the biome, and not only in areas identified as biome contractions. We suggest that targeted vulnerable species could be monitored both for early warning signs of climate change and as empirical tests of predictions.     

Predicting species distribution: offering more than simple habitat models

In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.     

Land Use Compounds Habitat Losses under Projected Climate Change in a Threatened California Ecosystem

Given the rapidly growing human population in mediterranean-climate systems, land use may pose a more immediate threat to biodiversity than climate change this century, yet few studies address the relative future impacts of both drivers. We assess spatial and temporal patterns of projected 21^st century land use and climate change on California sage scrub (CSS), a plant association of considerable diversity and threatened status in the mediterranean-climate California Floristic Province. Using a species distribution modeling approach combined with spatially-explicit land use projections, we model habitat loss for 20 dominant shrub species under unlimited and no dispersal scenarios at two time intervals (early and late century) in two ecoregions in California (Central Coast and South Coast). Overall, projected climate change impacts were highly variable across CSS species and heavily dependent on dispersal assumptions. Projected anthropogenic land use drove greater relative habitat losses compared to projected climate change in many species. This pattern was only significant under assumptions of unlimited dispersal, however, where considerable climate-driven habitat gains offset some concurrent climate-driven habitat losses. Additionally, some of the habitat gained with projected climate change overlapped with projected land use. Most species showed potential northern habitat expansion and southern habitat contraction due to projected climate change, resulting in sharply contrasting patterns of impact between Central and South Coast Ecoregions. In the Central Coast, dispersal could play an important role moderating losses from both climate change and land use. In contrast, high geographic overlap in habitat losses driven by projected climate change and projected land use in the South Coast underscores the potential for compounding negative impacts of both drivers. Limiting habitat conversion may be a broadly beneficial strategy under climate change. We emphasize the importance of addressing both drivers in conservation and resource management planning.     

Niche dynamics in space and time

Niche conservatism, the tendency of a species niche to remain unchanged over time, is often assumed when discussing, explaining or predicting biogeographical patterns. Unfortunately, there has been no basis for predicting niche dynamics over relevant timescales, from tens to a few hundreds of years. The recent application of species distribution models (SDMs) and phylogenetic methods to analysis of niche characteristics has provided insight to niche dynamics. Niche shifts and conservatism have both occurred within the last 100 years, with recent speciation events, and deep within clades of species. There is increasing evidence that coordinated application of these methods can help to identify species which likely fulfill one key assumption in the predictive application of SDMs: an unchanging niche. This will improve confidence in SDM-based predictions of the impacts of climate change and species invasions on species distributions and biodiversity.     

Predicting range shifts of Davidia involucrata Ball. under future climate change

Understanding and predicting how species will respond to climate change is crucial for biodiversity conservation. Here, we assessed future climate change impacts on the distribution of a rare and endangered plant species, Davidia involucrate in China, using the most recent global circulation models developed in the sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC6). We assessed the potential range shifts in this species by using an ensemble of species distribution models (SDMs). The ensemble SDMs exhibited high predictive ability and suggested that the temperature annual range, annual mean temperature, and precipitation of the driest month are the most influential predictors in shaping distribution patterns of this species. The projections of the ensemble SDMs also suggested that D. involucrate is very vulnerable to future climate change, with at least one‐third of its suitable range expected to be lost in all future climate change scenarios and will shift to the northward of high‐latitude regions. Similarly, at least one‐fifth of the overlap area of the current nature reserve networks and projected suitable habitat is also expected to be lost. These findings suggest that it is of great importance to ensure that adaptive conservation management strategies are in place to mitigate the impacts of climate change on D. involucrate.     

Assessing the impacts of climate change and land transformation on Banksia in the South West Australian Floristic Region

Aim To determine the potential combined effects of climate change and land transformation on the modelled geographic ranges of Banksia. Location Mediterranean climate South West Australian Floristic Region (SWAFR). Methods We used the species distribution modelling software Maxent to relate current environmental conditions to occurrence data for 18 Banksia species, and subsequently made spatial predictions using two simple dispersal scenarios (zero and universal), for three climate‐severity scenarios at 2070, taking the impacts of land transformation on species’ ranges into account. The species were chosen to reflect the biogeography of Banksia in the SWAFR. Results Climate‐severity scenario, dispersal scenario, biogeographic distribution and land transformation all influenced the direction and magnitude of the modelled range change responses for the 18 species. The predominant response of species to all climate change scenarios was range contraction, with exceptions for some northern and widespread species. Including land transformation in estimates of modelled geographic range size for the three climate‐severity scenarios generally resulted in smaller gains and larger declines in species ranges across both dispersal scenarios. Including land transformation and assuming zero dispersal resulted, as expected, in the greatest declines in projected range size across all species. Increasing climate change severity greatly increased the risk of decline in the 18 Banksia species, indicating the critical role of mitigating future emissions. Main conclusions The combined effects of climate change and land transformation may have significant adverse impacts on endemic Proteaceae in the SWAFR, especially under high emissions scenarios and if, as expected, natural migration is limiting. Although these results need cautious interpretation in light of the many assumptions underlying the techniques used, the impacts identified warrant a clear focus on monitoring across species ranges to detect early signs of change, and experiments that determine physiological thresholds for species in order to validate and refine the models.     

A framework for assessing the vulnerability of species to climate change: a case study of the Australian elapid snakes

Frameworks that provide a system for assessing species according to their vulnerability to climate change can offer considerable guidance to conservation managers who need to allocate limited resources among a large number of taxa. To date, climate change vulnerability assessments have largely been based on projected changes in range size derived from the output of species distribution models (SDMs). A criticism of risk assessments based solely on these models is that information on species ecological and life history traits is lacking. Accordingly, we developed a points-based framework for assessing species vulnerability to climate change that considered species traits together with the projections of SDMs. Applying this method to the Australian elapid snakes (family Elapidae), we determined which species may be particularly susceptible in the future and assessed broad-scale biogeographic patterns in species vulnerability. By offering a more comprehensive and rigorous method for assessing vulnerability than those based solely on SDMs, this framework provides greater justification for resource allocation, and can help guide decisions regarding the most appropriate adaptation strategies.     

Potential impacts of future land use and climate change on the Red List status of the Proteaceae in the Cape Floristic Region, South Africa

Using spatial predictions of future threats to biodiversity, we assessed for the first time the relative potential impacts of future land use and climate change on the threat status of plant species. We thus estimated how many taxa could be affected by future threats that are usually not included in current IUCN Red List assessments. Here, we computed the Red List status including future threats of 227 Proteaceae taxa endemic to the Cape Floristic Region, South Africa, and compared this with their Red List status excluding future threats. We developed eight different land use and climate change scenarios for the year 2020, providing a range of best‐ to worst‐case scenarios. Four scenarios include only the effects of future land use change, while the other four also include the impacts of projected anthropogenic climate change (HadCM2 IS92a GGa), using niche‐based models. Up to a third of the 227 Proteaceae taxa are uplisted (become more threatened) by up to three threat categories if future threats as predicted for 2020 are included, and the proportion of threatened Proteaceae taxa rises on average by 9% (range 2–16%), depending on the scenario. With increasing severity of the scenarios, the proportion of Critically Endangered taxa increases from about 1% to 7% and almost 2% of the 227 Proteaceae taxa become Extinct because of climate change. Overall, climate change has the most severe effects on the Proteaceae, but land use change also severely affects some taxa. Most of the threatened taxa occur in low‐lying coastal areas, but the proportion of threatened taxa changes considerably in inland mountain areas if future threats are included. Our approach gives important insights into how, where and when future threats could affect species persistence and can in a sense be seen as a test of the value of planned interventions for conservation.     

Benchmarking novel approaches for modelling species range dynamics

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process‐based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process‐based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species.     

A framework integrating physiology,dispersal and land‐use to project species ranges under climate change

To study the potential effects of climate change on species, one of the most popular approaches are species distribution models (SDMs). However, they usually fail to consider important species‐specific biological traits, such as species’ physiological capacities or dispersal ability. Furthermore, there is consensus that climate change does not influence species distributions in isolation, but together with other anthropogenic impacts such as land‐use change, even though studies investigating the relative impacts of different threats on species and their geographic ranges are still rare. Here we propose a novel integrative approach which produces refined future range projections by combining SDMs based on distribution, climate, and physiological tolerance data with empirical data on dispersal ability as well as current and future land‐use. Range projections based on different combinations of these factors show strong variation in projected range size for our study species Emberiza hortulana. Using climate and physiological data alone, strong range gains are projected. However, when we account for land‐use change and dispersal ability, future range‐gain may even turn into a future range loss. Our study highlights the importance of accounting for biological traits and processes in species distribution models and of considering the additive effects of climate and land‐use change to achieve more reliable range projections. Furthermore, with our approach we present a new tool to assess species’ vulnerability to climate change which can be easily applied to multiple species.     

Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species

Species distribution models (SDMs) correlate species occurrences with environmental predictors, and can be used to forecast distributions under future climates. SDMs have been criticized for not explicitly including the physiological processes underlying the species response to the environment. Recently, new methods have been suggested to combine SDMs with physiological estimates of performance (physiology-SDMs). In this study, we compare SDM and physiology-SDM predictions for select marine species in the Mediterranean Sea, a region subjected to exceptionally rapid climate change. We focused on six species and created physiology-SDMs that incorporate physiological thermal performance curves from experimental data with species occurrence records. We then contrasted projections of SDMs and physiology-SDMs under future climate (year 2100) for the entire Mediterranean Sea, and particularly the ‘warm’ trailing edge in the Levant region. Across the Mediterranean, we found cross-validation model performance to be similar for regular SDMs and physiology-SDMs. However, we also show that for around half the species the physiology-SDMs substantially outperform regular SDM in the warm Levant. Moreover, for all species the uncertainty associated with the coefficients estimated from the physiology-SDMs were much lower than in the regular SDMs. Under future climate, we find that both SDMs and physiology-SDMs showed similar patterns, with species predicted to shift their distribution north-west in accordance with warming sea temperatures. However, for the physiology-SDMs predicted distributional changes are more moderate than those predicted by regular SDMs. We conclude, that while physiology-SDM predictions generally agree with the regular SDMs, incorporation of the physiological data led to less extreme range shift forecasts. The results suggest that climate-induced range shifts may be less drastic than previously predicted, and thus most species are unlikely to completely disappear with warming climate. Taken together, the findings emphasize that physiological experimental data can provide valuable supplemental information to predict range shifts of marine species.     

Why is the choice of future climate scenarios for species distribution modelling important?

Species distribution models (SDMs) are common tools for assessing the potential impact of climate change on species ranges. Uncertainty in SDM output occurs due to differences among alternate models, species characteristics and scenarios of future climate. While considerable effort is being devoted to identifying and quantifying the first two sources of variation, a greater understanding of climate scenarios and how they affect SDM output is also needed. Climate models are complex tools: variability occurs among alternate simulations, and no single 'best' model exists. The selection of climate scenarios for impacts assessments should not be undertaken arbitrarily - strengths and weakness of different climate models should be considered. In this paper, we provide bioclimatic modellers with an overview of emissions scenarios and climate models, discuss uncertainty surrounding projections of future climate and suggest steps that can be taken to reduce and communicate climate scenario-related uncertainty in assessments of future species responses to climate change.     

Consequences of warming up a hotspot: species range shifts within a centre of bee diversity

Aim Bees are the most important pollinators of flowering plants and essential ecological keystone species contributing to the integrity of most terrestrial ecosystems. Here, we examine the potential impact of climate change on bees’ geographic range in a global biodiversity hotspot. Location South Africa with a focus on the Cape Floristic Region (CFR) diversity hotspot. Methods  Geographic ranges of 12 South African bee species representing dominant distribution types were studied, and the climate change impacts upon bees were examined with A2 and B2 climate scenarios of HadCM3 model, using MaxEnt for species distribution modelling. Results The predicted levels of climate change‐induced impacts on species ranges varied from little shifts and range expansion of 5–50% for two species to substantial range contractions between 32% and 99% in another six species. Four species show considerable range shifts. Bees of the winter rainfall area in the west of South Africa generally have smaller range sizes than in the summer rainfall area and generally show eastward range contractions toward the dry interior. Bee species prevalent in summer rainfall regions show a tendency for a south‐easterly shift in geographic range. Main conclusions The bee fauna of the CFR is identified as the most vulnerable to climate change due to the high level of endemism, the small range sizes and the island‐like isolation of the Mediterranean‐type climate region at the SW tip of Africa. For monitoring climate change impact on bees, we suggest to establish observatories in the coastal plains of the west coast that are predicted to be worst affected and areas where persistence of populations is most likely. Likely impacts of climate change on life history traits of bees (phenology, sociality, bee‐host plant synchronization) are discussed but require further investigation.     

Contribution of disturbance to distribution and abundance in a fire‐adapted system

Species distribution modeling (SDM) is an essential tool in understanding species ranges, but models haven't incorporated disturbance‐related variables. This is true even for regions where long histories of disturbance have resulted in disturbance‐adapted species. Therefore, the degree to which including disturbance‐related variables in SDMs might improve their performance is unclear. We used hierarchical partitioning to determine how fire patterns contribute to variation in species abundance and presence, examining both the total variation disturbance‐related variables explained, and how much of this variation is independent of soil and climate variables. For 27 Proteaceae species in the fire‐adapted Cape Floristic Region of South Africa , we found that fire variability, frequency, and area burned tended to have explanatory power similar in size to that of soil and climate variables. Importantly, for SDMs of abundance, fire‐related variables explained additional variation not captured by climatic variables, resulting in markedly increased model performance. In systems with high disturbance rates, species are less likely to be in equilibrium with their environment, and SDMs including variables describing disturbance regimes may be better able to capture the probability of a species being present at a site. Finally, the differential effect of fire on species abundance and presence suggests functional differences between these responses, which could hamper attempts to make predictions about species abundances using models of presence.     

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.     

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.