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.     

Alien Invasive Slider Turtle in Unpredicted Habitat: A Matter of Niche Shift or of Predictors Studied?

Background

Species Distribution Models (SDMs) aim on the characterization of a species'' ecological niche and project it into geographic space. The result is a map of the species'' potential distribution, which is, for instance, helpful to predict the capability of alien invasive species. With regard to alien invasive species, recently several authors observed a mismatch between potential distributions of native and invasive ranges derived from SDMs and, as an explanation, ecological niche shift during biological invasion has been suggested. We studied the physiologically well known Slider turtle from North America which today is widely distributed over the globe and address the issue of ecological niche shift versus choice of ecological predictors used for model building, i.e., by deriving SDMs using multiple sets of climatic predictor.

Principal Findings

In one SDM, predictors were used aiming to mirror the physiological limits of the Slider turtle. It was compared to numerous other models based on various sets of ecological predictors or predictors aiming at comprehensiveness. The SDM focusing on the study species'' physiological limits depicts the target species'' worldwide potential distribution better than any of the other approaches.

Conclusion

These results suggest that a natural history-driven understanding is crucial in developing statistical models of ecological niches (as SDMs) while “comprehensive” or “standard” sets of ecological predictors may be of limited use.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

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.

     

Improved spatial estimates of climate predict patchier species distributions

Aim

Correlative species distribution models (SDMs) combined with spatial layers of climate and species' localities represent a frequently utilized and rapid method for generating spatial estimates of species distributions. However, an SDM is only as accurate as the inputs upon which it is based. Current best‐practice climate layers commonly utilized in SDM (e.g. ANUCLIM) are frequently inaccurate and biased spatially. Here, we statistically downscale 30 years of existing spatial weather estimates against empirical weather data and spatial layers of topography and vegetation to produce highly accurate spatial layers of weather. We proceed to demonstrate the effect of inaccurately quantified spatial data on SDM outcomes.

Location

The Australian Wet Tropics.

Methods

We use Boosted Regression Trees (BRTs) to generate 30 years of spatial estimates of daily maximum and minimum temperature for the study region and aggregate the resultant weather layers into ‘accuCLIM’ climate summaries, comparable with those generated by current best‐practice climate layers. We proceed to generate for seven species of rainforest skink comparable SDMs within species; one model based on ANUCLIM climate estimates and another based on accuCLIM climate estimates.

Results

Boosted Regression Trees weather layers are more accurate with respect to empirically measured temperature, particularly for maximum temperature, when compared to current best‐practice weather layers. ANUCLIM climate layers are least accurate in heavily forested upland regions, frequently over‐predicting empirical mean maximum temperature by as much as 7°. Distributions of the focal species as predicted by accuCLIM were more fragmented and contained less core distributional area.

Conclusion

Combined these results reveal a source of bias in climate‐based SDMs and indicate a solution in the form of statistical downscaling. This technique will allow researchers to produce fine‐grained, ground‐truthed spatial estimates of weather based on existing estimates, which can be aggregated in novel ways, and applied to correlative or process‐based modelling techniques.

     

Weak climatic associations among British plant distributions

Aim Species distribution models (SDMs) are used to infer niche responses and predict climate change‐induced range shifts. However, their power to distinguish real and chance associations between spatially autocorrelated distribution and environmental data at continental scales has been questioned. Here this is investigated at a regional (10 km) scale by modelling the distributions of 100 plant species native to the UK. Location UK. Methods SDMs fitted using real climate data were compared with those utilizing simulated climate gradients. The simulated gradients preserve the exact values and spatial structure of the real ones, but have no causal relationships with any species and so represent an appropriate null model. SDMs were fitted as generalized linear models (GLMs) or by the Random Forest machine‐learning algorithm and were either non‐spatial or included spatially explicit trend surfaces or autocovariates as predictors. Results Species distributions were significantly but erroneously related to the simulated gradients in 86% of cases (P < 0.05 in likelihood‐ratio tests of GLMs), with the highest error for strongly autocorrelated species and gradients and when species occupied 50% of sites. Even more false effects were found when curvilinear responses were modelled, and this was not adequately mitigated in the spatially explicit models. Non‐spatial SDMs based on simulated climate data suggested that 70–80% of the apparent explanatory power of the real data could be attributable to its spatial structure. Furthermore, the niche component of spatially explicit SDMs did not significantly contribute to model fit in most species. Main conclusions Spatial structure in the climate, rather than functional relationships with species distributions, may account for much of the apparent fit and predictive power of SDMs. Failure to account for this means that the evidence for climatic limitation of species distributions may have been overstated. As such, predicted regional‐ and national‐scale impacts of climate change based on the analysis of static distribution snapshots will require re‐evaluation.     

The ability of climate envelope models to predict the effect of climate change on species distributions

Climate envelope models (CEMs) have been used to predict the distribution of species under current, past, and future climatic conditions by inferring a species' environmental requirements from localities where it is currently known to occur. CEMs can be evaluated for their ability to predict current species distributions but it is unclear whether models that are successful in predicting current distributions are equally successful in predicting distributions under different climates (i.e. different regions or time periods). We evaluated the ability of CEMs to predict species distributions under different climates by comparing their predictions with those obtained with a mechanistic model (MM). In an MM the distribution of a species is modeled based on knowledge of a species' physiology. The potential distributions of 100 plant species were modeled with an MM for current conditions, a past climate reconstruction (21 000 years before present) and a future climate projection (double preindustrial CO₂ conditions). Point localities extracted from the currently suitable area according to the MM were used to predict current, future, and past distributions with four CEMs covering a broad range of statistical approaches: Bioclim (percentile distributions), Domain (distance metric), GAM (general additive modeling), and Maxent (maximum entropy). Domain performed very poorly, strongly underestimating range sizes for past or future conditions. Maxent and GAM performed as well under current climates as under past and future climates. Bioclim slightly underestimated range sizes but the predicted ranges overlapped more with the ranges predicted with the MM than those predicted with GAM did. Ranges predicted with Maxent overlapped most with those produced with the MMs, but compared with the ranges predicted with GAM they were more variable and sometimes much too large. Our results suggest that some CEMs can indeed be used to predict species distributions under climate change, but individual modeling approaches should be validated for this purpose, and model choice could be made dependent on the purpose of a particular study.     

Climatic associations of British species distributions show good transferability in time but low predictive accuracy for range change

Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs) are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time--due to their accuracy to predict large areas retained by species--but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records--as assessed using widespread metrics--need not indicate a model's ability to predict the future.     

Predicting distributions,habitat preferences and associated conservation implications for a genus of rare fishes,seahorses (Hippocampus spp.)

Aim

To identify useful sources of species data and appropriate habitat variables for species distribution modelling on rare species, with seahorses as an example, deriving ecological knowledge and spatially explicit maps to advance global seahorse conservation.

Location

The shallow seas.

Methods

We applied a typical species distribution model (SDM), maximum entropy, to examine the utility of (1) two versions of habitat variables (habitat occurrences vs. proximity to habitats) and (2) three sources of species data: quality research‐grade (RG) data, quality‐unknown citizen science (CS) and museum‐collection (MC) data. We used the best combinations of species data and habitat variables to predict distributions and estimate species–habitat relations and threatened status for seahorse species.

Results

We demonstrated that using “proximity to habitats” and integrating all species datasets (RG, CS and MC) derived models with the highest accuracies among all dataset variations. Based on this finding, we derived reliable models for 33 species. Our models suggested that only 0.4% of potential seahorse range was suitable to more than three species together; seahorse biogeographic epicentres were mainly in the Philippines; and proximity to sponges was an important habitat variable. We found that 12 “Data Deficient” species might be threatened based on our predictions according to IUCN criteria.

Main conclusions

We highlight that using proper habitat variables (e.g., proximity to habitats) is critical to determine distributions and key habitats for low‐mobility animals; collating and integrating quality‐unknown occurrences (e.g., CS and MC) with quality research data are meaningful for building SDMs for rare species. We encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment.

     

Creating individual accessible area hypotheses improves stacked species distribution model performance

Aim

Stacked species distribution models (SDMs) are an important step towards estimating species richness, but frequently overpredict this metric and therefore erroneously predict which species comprise a given community. We test the idea that developing hypotheses about accessible area a priori can greatly improve model performance. By integrating dispersal ability via accessible area into SDM creation, we address an often‐overlooked facet of ecological niche modelling.

Innovation

By limiting the training and transference areas to theoretically accessible areas, we are creating more accurate SDMs on the basis of a taxon's explorable environments. This limitation of space and environment is a more accurate reflection of a taxon's true dispersal properties and more accurately reflects the geographical and environmental space to which a taxon is exposed. Here, we compare the predictive performance of stacked SDMs derived from spatially constrained and unconstrained training areas.

Main conclusions

Restricting a species’ training and transference areas to a theoretically accessible area greatly improves model performance. Stacked SDMs drawn from spatially restricted training areas predicted species richness and community composition more accurately than non‐restricted stacked SDMs. These accessible area‐based restrictions mimic true dispersal barriers to species and limit training areas to the suite of environments to those which a species is exposed to in nature. Furthermore, these restrictions serve to ‘clip’ predictions in geographical space, thus removing overpredictions in adjacent geographical regions where the species is known to be absent.     

No‐analog climates and shifting realized niches during the late quaternary: implications for 21st‐century predictions by species distribution models

Empirically derived species distributions models (SDMs) are increasingly relied upon to forecast species vulnerabilities to future climate change. However, many of the assumptions of SDMs may be violated when they are used to project species distributions across significant climate change events. In particular, SDM's in theory assume stable fundamental niches, but in practice, they assume stable realized niches. The assumption of a fixed realized niche relative to climate variables remains unlikely for various reasons, particularly if novel future climates open up currently unavailable portions of species’ fundamental niches. To demonstrate this effect, we compare the climate distributions for fossil‐pollen data from 21 to 15 ka bp (relying on paleoclimate simulations) when communities and climates with no modern analog were common across North America to observed modern pollen assemblages. We test how well SDMs are able to project 20th century pollen‐based taxon distributions with models calibrated using data from 21 to 15 ka. We find that taxa which were abundant in areas with no‐analog late glacial climates, such as Fraxinus, Ostrya/Carpinus and Ulmus, substantially shifted their realized niches from the late glacial period to present. SDMs for these taxa had low predictive accuracy when projected to modern climates despite demonstrating high predictive accuracy for late glacial pollen distributions. For other taxa, e.g. Quercus, Picea, Pinus strobus, had relatively stable realized niches and models for these taxa tended to have higher predictive accuracy when projected to present. Our findings reinforce the point that a realized niche at any one time often represents only a subset of the climate conditions in which a taxon can persist. Projections from SDMs into future climate conditions that are based solely on contemporary realized distributions are potentially misleading for assessing the vulnerability of species to future climate change.     

Potential effects of future climate change on global reptile distributions and diversity

Aim

Until recently, complete information on global reptile distributions has not been widely available. Here, we provide the first comprehensive climate impact assessment for reptiles on a global scale.

Location

Global, excluding Antarctica.

Time period

1995, 2050 and 2080.

Major taxa studied

Reptiles.

Methods

We modelled the distribution of 6296 reptile species and assessed potential global and realm-specific changes in species richness, the change in global species richness across climate space, and species-specific changes in range extent, overlap and position under future climate change. To assess the future climatic impact on 3768 range-restricted species, which could not be modelled, we compared the future change in climatic conditions between both modelled and non-modelled species.

Results

Reptile richness was projected to decline significantly over time, globally but also for most zoogeographical realms, with the greatest decreases in Brazil, Australia and South Africa. Species richness was highest in warm and moist regions, with these regions being projected to shift further towards climate extremes in the future. Range extents were projected to decline considerably in the future, with a low overlap between current and future ranges. Shifts in range centroids differed among realms and taxa, with a dominant global poleward shift. Non-modelled species were significantly stronger affected by projected climatic changes than modelled species.

Main conclusions

With ongoing future climate change, reptile richness is likely to decrease significantly across most parts of the world. This effect, in addition to considerable impacts on species range extent, overlap and position, was visible across lizards, snakes and turtles alike. Together with other anthropogenic impacts, such as habitat loss and harvesting of species, this is a cause for concern. Given the historical lack of global reptile distributions, this calls for a re-assessment of global reptile conservation efforts, with a specific focus on anticipated future climate change.     

Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers

Aim

Climate change is affecting the distribution of species and subsequent biotic interactions, including hybridization potential. The imperiled Golden-winged Warbler (GWWA) competes and hybridizes with the Blue-winged Warbler (BWWA), which may threaten the persistence of GWWA due to introgression. We examined how climate change is likely to alter the breeding distributions and potential for hybridization between GWWA and BWWA.

Location

North America.

Methods

We used GWWA and BWWA occurrence data to model climatically suitable conditions under historical and future climate scenarios. Models were parameterized with 13 bioclimatic variables and 3 topographic variables. Using ensemble modeling, we estimated historical and modern distributions, as well as a projected distribution under six future climate scenarios. We quantified breeding distribution area, the position of and amount of overlap between GWWA and BWWA distributions under each climate scenario. We summarized the top explanatory variables in our model to predict environmental parameters of the distributions under future climate scenarios relative to historical climate.

Results

GWWA and BWWA distributions are projected to substantially change under future climate scenarios. GWWA are projected to undergo the greatest change; the area of climatically suitable breeding season conditions is expected to shift north to northwest; and range contraction is predicted in five out of six future climate scenarios. Climatically suitable conditions for BWWA decreased in four of the six future climate scenarios, while the distribution is projected to shift east. A reduction in overlapping distributions for GWWA and BWWA is projected under all six future climate scenarios.

Main Conclusions

Climate change is expected to substantially alter the area of climatically suitable conditions for GWWA and BWWA, with the southern portion of the current breeding ranges likely to become climatically unsuitable. However, interactions between BWWA and GWWA are expected to decline with the decrease in overlapping habitat, which may reduce the risk of genetic introgression.     

How biotic interactions may alter future predictions of species distributions: future threats to the persistence of the arctic fox in Fennoscandia

Aim With climate change, reliable predictions of future species geographic distributions are becoming increasingly important for the design of appropriate conservation measures. Species distribution models (SDMs) are widely used to predict geographic range shifts in response to climate change. However, because species communities are likely to change with the climate, accounting for biotic interactions is imperative. A shortcoming of introducing biotic interactions in SDMs is the assumption that biotic interactions remain the same under changing climatic factors, which is disputable. We explore the performance of SDMs while including biotic interactions. Location Fennoscandia, Europe. Methods We investigate the appropriateness of the inclusion of biotic factors (predator pressure and prey availability) in assessing the future distribution of the arctic fox (Alopex lagopus) in Fennoscandia by means of SDM, using the algorithm MaxEnt. Results Our results show that the inclusion of biotic interactions enhanced the accuracy of SDMs to predict the current arctic fox distribution, and we argue that the accuracy of future predictions might also be enhanced. While the range of the arctic fox is predicted to have decreased by 43% in 2080 because of temperature‐related variables, projected increases in predator pressure and reduced prey availability are predicted to constrain the potential future geographic range of the arctic fox in Fennoscandia 13% more. Main conclusions The results indicate that, provided one has a good knowledge of past changes and a clear understanding of interactions in the community involved, the inclusion of biotic interactions in modelling future geographic ranges of species increases the predictive power of such models. This likely has far‐reaching impacts upon the design and implementation of possible conservation and management plans. Control of competing predators and supplementary feeding are suggested as necessary management actions to preserve the Fennoscandian arctic fox population in the face of climate change.     

Causal inference and large-scale expert validation shed light on the drivers of SDM accuracy and variance

Aim

To develop a causal understanding of the drivers of Species distribution model (SDM) performance.

Location

United Kingdom (UK).

Methods

We measured the accuracy and variance of SDMs fitted for 518 species of invertebrate and plant in the UK. Our measure of variance reflects variation among replicate model fits, and taxon experts assessed model accuracy. Using directed acyclic graphs, we developed a causal model depicting plausible effects of explanatory variables (e.g. species' prevalence, sample size) on SDM accuracy and variance and quantified those effects using a multilevel piecewise path model.

Results

According to our model, sample size and niche completeness (proportion of a species' niche covered by sampling) directly affect SDM accuracy and variance. Prevalence and range completeness have indirect effects mediated by sample size. Challenging conventional wisdom, we found that the effect of prevalence on SDM accuracy is positive. This reflects the facts that sample size has a positive effect on accuracy and larger sample sizes are possible for widespread species. It is possible, however, that the omission of an unobserved confounder biased this effect. Previous studies, which reported negative correlations between prevalence and SDM accuracy, conditioned on sample size.

Main conclusions

Our model explicates the causal basis of previously reported correlations between SDM performance and species/data characteristics. It also suggests that niche completeness has similarly large effects on SDM accuracy and variance as sample size. Analysts should consider niche completeness, or proxies thereof, in addition to sample size when deciding whether modelling is worthwhile.     

Modelling current and future potential distributions of two desert jerboas under climate change in Iran

Species distribution models (SDMs) are excellent tools to understand the factors that affect the potential distribution of several organisms at different scale. In this study, we analyzed the current potential distribution of the Blanford's Jerboa Jaculus blanfordi and the Arabian Jerboa Jaculus loftusi (Mammalia: Rodentia) in Iran and predicted the impact of climate change on their future potential distributions using two different modelling software packages: Maxent and sdm. Our results showed that precipitation was the most important variable affecting the potential distributions of J. blanfordi and J. loftusi in Iran. We also showed that the potential distributions of the two jerboas species are unlikely to be affected by climate change. All our models showed high levels of predictive performances. Thus, SDMs are a promising tool to complement data from laboratory and field studies to illuminate the biology and ecology of jerboa and inform management decisions.     

Biotic interactions influence the projected distribution of a specialist mammal under climate change

Aim

To measure the effects of including biotic interactions on climate‐based species distribution models (SDMs) used to predict distribution shifts under climate change. We evaluated the performance of distribution models for an endangered marsupial, the northern bettong (Bettongia tropica), comparing models that used only climate variables with models that also took into account biotic interactions.

Location

North‐east Queensland, Australia.

Methods

We developed separate climate‐based distribution models for the northern bettong, its two main resources and a competitor species. We then constructed models for the northern bettong by including climate suitability estimates for the resources and competitor as additional predictor variables to make climate + resource and climate + resource + competition models. We projected these models onto seven future climate scenarios and compared predictions of northern bettong distribution made by these differently structured models, using a ‘global’ metric, the I similarity statistic, to measure overlap in distribution and a ‘local’ metric to identify where predictions differed significantly.

Results

Inclusion of food resource biotic interactions improved model performance. Over moderate climate changes, up to 3.0 °C of warming, the climate‐only model for the northern bettong gave similar predictions of distribution to the more complex models including interactions, with differences only at the margins of predicted distributions. For climate changes beyond 3.0 °C, model predictions diverged significantly. The interactive model predicted less contraction of distribution than the simpler climate‐only model.

Main conclusions

Distribution models that account for interactions with other species, in particular direct resources, improve model predictions in the present‐day climate. For larger climate changes, shifts in distribution of interacting species cause predictions of interactive models to diverge from climate‐only models. Incorporating interactions with other species in SDMs may be needed for long‐term prediction of changes in distribution of species under climate change, particularly for specialized species strongly dependent on a small number of biotic interactions.     

Higher spring temperatures increase food scarcity and limit the current and future distributions of crossbills

Aim

Understanding how climate affects species distributions remains a major challenge, with the relative importance of direct physiological effects versus biotic interactions still poorly understood. We focus on three species of resource specialists (crossbill Loxia finches) to assess the role of climate in determining the seasonal availability of their food, the importance of climate and the occurrence of their food plants for explaining their current distributions, and to predict changes in their distributions under future climate change scenarios.

Location

Europe.

Methods

We used datasets on the timing of seed fall in European Scots pine Pinus sylvestris forests (where different crossbill species occur) to estimate seed fall phenology and climate data to determine its influence on spatial and temporal variation in the timing of seed fall to provide a link between climate and seed scarcity for crossbills. We used large‐scale datasets on crossbill distribution, cover of the conifers relied on by the three crossbill species and climate variables associated with timing of seed fall, to assess their relative importance for predicting crossbill distributions. We used species distribution modelling to predict changes in their distributions under climate change projections for 2070.

Results

We found that seed fall occurred 1.5–2 months earlier in southern Europe than in Sweden and Scotland and was associated with variation in spring maximum temperatures and precipitation. These climate variables and area covered with conifers relied on by the crossbills explained much of their observed distributions. Projections under global change scenarios revealed reductions in potential crossbill distributions, especially for parrot crossbills.

Main conclusions

Ranges of resource specialists are directly influenced by the presence of their food plants, with climate conditions further affecting resource availability and the window of food scarcity indirectly. Future distributions will be determined by tree responses to changing climatic conditions and the impact of climate on seed fall phenology.

     

Weather, not climate, defines distributions of vagile bird species

Background

Accurate predictions of species distributions are essential for climate change impact assessments. However the standard practice of using long-term climate averages to train species distribution models might mute important temporal patterns of species distribution. The benefit of using temporally explicit weather and distribution data has not been assessed. We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species.

Methodology

We tested our hypothesis by generating distribution models for 157 bird species found in Australian tropical savannas (ATS) using modelling algorithm Maxent. The variable weather of the ATS supports a bird assemblage with variable movement patterns and a high incidence of nomadism. We developed “weather” models by relating climatic variables (mean temperature, rainfall, rainfall seasonality and temperature seasonality) from the three month, six month and one year period preceding each bird record over a 58 year period (1950–2008). These weather models were compared against models built using long-term (30 year) averages of the same climatic variables.

Conclusions

Weather models consistently achieved higher model scores than climate models, particularly for wide-ranging, nomadic and desert species. Climate models predicted larger range areas for species, whereas weather models quantified fluctuations in habitat suitability across months, seasons and years. Models based on long-term climate averages over-estimate availability of suitable habitat and species'' climatic tolerances, masking species potential vulnerability to climate change. Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions.     

Traits help explain species' performance away from their climate niche centre

Aim

Climate change impacts on biota are variable across sites, among species and throughout individual species' ranges. Niche theory predicts that population performance should decline as site climate becomes increasingly different from the species' climate niche centre, though studies find significant variation from these predictions. Here, we propose that predictions about climate responses can be improved by incorporating species' trait information.

Location

Europe.

Methods

We used observations of plant species abundance change over time to assess variation in climate difference sensitivity (CDS), defined as how species performance (colonization, extinction and abundance change) relates to the difference of site climate from the mean temperature and precipitation of each species' range. We then investigated if leaf economics, plant size and seed mass traits were associated with the species' CDS.

Results

Species that performed better (e.g. increased in abundance) towards sites progressively cooler than their niche centre were shorter and had more resource-acquisitive leaves (i.e. lower leaf dry matter content or LDMC) relative to species with zero or the opposite pattern of temperature difference sensitivity. This result supports the hypothesis that if sites cooler than niche centres are more stressful for a species, then shorter stature is advantageous compared with taller species. The LDMC result suggests the environment selects for more resource-acquisitive leaf strategies towards relatively cooler climates with shorter growing seasons, counter to expectations that conservative strategies would be favoured in such environments. We found few consistent relationships between precipitation difference sensitivities and traits.

Main Conclusions

The results supported key a priori foundations on how trait-based plant strategies dictate species responses to climate variation away from their niche centre. Furthermore, plant height emerged as the most consistent trait that varied with species CDS, suggesting height will be key for theory development around species response to climate change.