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Spain accounts for 13.1% of the world's vineyard area, and viticulture is crucial for the socioeconomic and cultural sectors. Vineyards are among the perennial crops that can suffer most negative impacts under climate change which can pose challenges to the sustainability of viticulture. Local and regional studies are needed to assess these impacts to help implement effective strategies in response to climate change. To this end, our approach involves integrating both conventional agroclimatic indices and those new bioclimatic indices that have proven to be essential for the characterization and demarcation of vineyards into species distribution models to assess areas suitable for viticulture under climate change projections. The proposed methodology was tested in a viticultural region located in northwestern Spain (DO León). An ensemble platform was used to build consensus models encompassing three general circulation models, two emission scenario pathways and two time horizons. Only the predictors that effectively characterize each grape variety were included in the models. The results revealed increases in the continentality index, compensated thermicity index, hydrothermic index of Branas, and temperature range during ripening in all the future scenarios analyzed in comparison to current conditions. Conversely, the values for the annual ombrothermic index and growing season precipitation may decrease in the future. The pattern of changes for 2070 will be more pronounced than for 2050. A significant loss of future habitat suitability was detected within the limits of the study area for the grape varieties analyzed. This negative impact could be counteracted to some degree with new and favorable areas for the cultivation of vineyards in territories located at the north of the DO limits. We suggest that our results could help policymakers to develop practices and strategies to conserve existing grape varieties and to implement efficient adaptation measures for mitigating or anticipating the effects of climate change on viticulture. [ABSTRACT FROM AUTHOR]

This study aimed to construct species distribution models (SDMs) to predict present and future (2050 and 2070) potential distribution ranges of the endemic leaf beetle Psylliodes anatolicus Gök and Çilbiroğlu, 2004 under different climate change scenarios (Representative Concentration Pathway (RCP) 4.5 and 8.5). The distribution records were gathered from the related literature and unpublished data of the authors. SDMs were constructed by the maximum entropy (MaxEnt) method using the bioclimatic variables of Community Climate System Model 4 (CCSM4). As a result of this study, the most effective bioclimatic factors determining the distribution of species were isothermality, temperature seasonality, and mean temperature of the wettest quarter. The SDM conducted for the present distribution showed that the species may occur in large parts of the Aegean and Mediterranean Regions of Türkiye, beyond the known records. The SDMs for 2050 and 2070 suggest that the range of the species will shrink considerably or go extinct totally in the next 50 years, probably due to the changing climate. In conclusion, this study revealed that changing climate threatens the endemic members of Anatolian biodiversity, especially the endemic species living in mountain ecosystems. [ABSTRACT FROM AUTHOR]

The Hyrcanian climate in the northern parts of Iran has warmed over the past 50 years, but the impacts on plant species are unknown. As the longest-lived tree in the Hyrcanian forest, English yew, Taxus baccata L., is a rare and endangered species in the forests along the Iranian coasts of the Caspian Sea, which is likely affected by climate change. This paper explores the current and future distribution of this species, using four species distribution models. In order to project the effect of climate change on the distribution of English yew by 2050 and 2070, output from the HadGEM2-ES climate model was used for two RCPs scenarios (2.6 and 8.5). The results showed a good accuracy of all the models for the distribution of this species with a mean area under the receiver operating curve (AUC) of 0.92. Using ensemble forecasting as an algorithm for reducing the uncertainty in species distribution modeling shows that the suitable habitats for this species is about 6000 km2 for the current climate conditions in the study area. Range size analysis indicates that in 2050, in the most optimistic scenario (RCP 2.6), only 17% of the habitats will retain their suitability, while in the most pessimistic scenario (RCP 8.5), this amount will decrease to 2%. In 2070, in the most optimistic scenario, only 10% of the currently suitable habitats will retain their suitability, while in the RCP 8.5, no stable suitable habitats will be left. It is strongly recommended that the impacts of climate change on English yew should be considered in the management decisions and conservation plans in the Hyrcanian forests. [ABSTRACT FROM AUTHOR]

Using a case study of an isolated management unit of Sichuan snub‐nosed monkey (Rhinopithecus roxellana), we assess the extent that climate change will impact the species' habitat distribution in the current period and projected into the 2050s. We identify refugia that could maintain the population under climate change and determine dispersal paths for movement of the population to future suitable habitats. Hubei Province, China. We identified climate refugia and potential movements by integrating bioclimatic models with circuit theory and least‐cost model for the current period (1960–1990) and the 2050s (2041–2060). We coupled a maximum entropy algorithm to predict suitable habitat for the current and projected future periods. Suitable habitat areas that were identified during both time periods and that also satisfied home range and dispersal distance conditions were delineated as refugia. We mapped potential movements measured as current flow and linked current and future habitats using least‐cost corridors. Our results indicate up to 1,119 km2 of currently suitable habitat within the study range. Based on our projections, a habitat loss of 67.2% due to climate change may occur by the 2050s, resulting in a reduced suitable habitat area of 406 km2 and very little new habitat. The refugia areas amounted to 286 km2 and were located in Shennongjia National Park and Badong Natural Reserve. Several connecting corridors between the current and future habitats, which are important for potential movements, were identified. Our assessment of the species predicted a trajectory of habitat loss following anticipated future climate change. We believe conservation efforts should focus on refugia and corridors when planning for future species management. This study will assist conservationists in determining high‐priority regions for effective maintenance of the endangered population under climate change and will encourage increased habitat connectivity. This article evaluates the trajectory of habitat loss of the Sichuan snub‐nosed monkey (Rhinopithecus roxellana) in Hubei Province, China, and identified climate refugia and potential movement following anticipated future climate change. Several connecting corridors between the current and future habitats, which are important for potential movements, were identified. [ABSTRACT FROM AUTHOR]

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Many tree species are predicted to shift their geographic ranges with changing climate, but the extents, timing, and magnitude of these shifts remain uncertain. Comparing various modeling strategies is crucial for reducing uncertainty related to these responses and for guiding the interpretation of model results. Here, we compared outputs of a dynamic vegetation model (DVM) and an ensemble of statistical bioclimatic envelope models (BEMs) in predicting range shifts of 14 representative tree species in continental Europe. Expanding the number of species and geographic extent compared to previous model comparisons, we found that the DVM produced more conservative range shift estimates, even in long-term equilibrium simulations. The differences in range shift projections were greatest for Mediterranean species, whose expansion northwards was inhibited in the DVM by more competitive prevailing temperate species. In contrast to our expectation, competitive traits of the species studied did not consistently affect the differences. The agreement between BEM and DVM results was highest in boreal species, suggesting that BEMs are an efficient method for modeling species under strong control of abiotic factors. BEMs produced substantially larger range contractions at the southern edge of distribution, in contrary to the DVM, where contractions were more modest. Despite these differences, both approaches also yielded consistent northwards shifts of forest types, which may have substantial negative impacts on forest economy, and alter species composition in natural forest stands. [ABSTRACT FROM AUTHOR]

Fagus sylvatica forests are considered to be of Community interest according to Directive 92/43/EEC. Climate change predictions for Spain point to a warming scenario, coupled with decreasing rainfall, which may have an impact on their future distribution particularly at the extremes of its distribution area. Species distribution models incorporating bioclimatic, topographic and phytogeographic variables were used as predictors to assess their habitat suitability under current conditions and a climate change projection. Ten single models were generated and an ensemble-forecasting model was subsequently built by computing a consensus of single-model projections. The results revealed that ombrothermic indices are the main factors controlling the distribution of Spanish beech forests. They are highly vulnerable to climate change and could suffer a decline in their habitat suitability if climate trends observed are maintained in the future. The least favoured areas for them will be located close to the limit between the Temperate and Mediterranean climates, where they could suffer a loss of habitat suitability. Conversely, suitable new areas could be found mainly in western areas of the Cantabrian Range and in the Central Pyrenees. [ABSTRACT FROM AUTHOR]

The term 'thermal niche' is frequently used in ecology. Using the Web of Science database, I reviewed how the term has been used in current literature (2000–2015). While the use of 'thermal niche' has increased notably over the last 15 years, its meaning differs between studies. Only 10% of studies measured thermal niche directly as the range of body temperatures enabling non‐zero population growth. Indirect thermal niche estimates were based on distributional data (47%), thermal reaction norms (36%) and preferred body temperatures (7%). Here, I present a new conceptual unification model for the term in which I propose that the choice of commonly used thermal physiology traits for indirect thermal niche estimates depends on behavioural ability to buffer variation in thermal environments during both activity and inactivity (diel or seasonal). The resulting body temperature distribution determines the relative importance of key thermal physiology traits, thermal tolerance, thermal preferenda and temperature dependence of metabolic rate. This framework provides a guide for using the term 'thermal niche' in ecological literature and identifies key areas for further research. [ABSTRACT FROM AUTHOR]

Harmful algae can cause death in fish, shellfish, marine mammals, and humans, via their toxins or from effects associated with their sheer quantity. There are many species, which cause a variety of problems around north-west Europe, and the frequency and distribution of algal blooms have altered in the recent past. Species distribution modelling was used to understand how harmful algal species may respond in the future to climate change, by considering environmental preferences and how these may shift. Most distribution studies to date use low resolution global model outputs. In this study, high resolution, downscaled shelf seas climate projections for the north-west European shelf were nested within lower resolution global projections, to understand how the distribution of harmful algae may change by the mid to end of century. Projections suggest that the habitat of most species (defined by temperature, salinity, depth, and stratification) will shift north this century, with suitability increasing in the central and northern North Sea. An increase in occurrence here might lead to more frequent detrimental blooms if wind, irradiance and nutrient levels are also suitable. Prioritizing monitoring of species in these susceptible areas could help in establishing early-warning systems for aquaculture and health protection schemes. [ABSTRACT FROM AUTHOR]

Juniperus procera is the most preferred tree in Ethiopia. It is an endangered tree species enumerated in IUCN red list. Accordingly, this study investigates the future suitable habitat of the J. procera under climate change in northern Ethiopia. Three occurrence districts were visited and 124 presence observations were taken. The records, altitude, and 19 bio-climatic variables were used to run a species distribution model to account for the climate change effect on the species. Maxent, Diva-GIS, and ArcGIS were used to evaluate the outputs. Future suitable habitats were projected into mid and end-century time frames with two Representative Concentration Pathways (RCP2.6 and 8.5) under one General Circulation Model, namely the Climate Community System Model Version-4. Our results showed that minimum temperature of the coldest month and altitude are main predictors of the distribution of the species. Suitable habitats of the species will be decreased by 79.84%, 91.17%, 75.31%, and 96.25% in Mid-century RCP2.6, Mid-century RCP8.5, End-century RCP2.6, and End-century RCP8.5 when compared with current distributions, respectively. This indicates that climate change will affect the future distribution of the species. The results of the study indicate that appropriate management strategies must be taken to ensure the long-term survival of J. procera. [ABSTRACT FROM AUTHOR]

Natural history collections are alternative data sources to plot‐based species inventories for analysing macroecological species turnover. Herbarium records sample diversity well at regional level and are taxonomically validated. However, they are ad hoc from a sampling perspective, generating spatial and taxonomic biases. The implications of biased sampling on beta diversity (β) estimation, and use of herbarium data to identify macroecological transitions, remain unexplored. We tested sampling influences by comparing herbarium data with systematically collected inventory data from the Mount Lofty–Flinders Ranges region of Australia. We calculated β within moving windows across bioclimatic gradients using metrics varying in sensitivity to richness differences (pairwise/multi‐site Sørensen β; Simpson β; Harrison et al. β‐2), and correlated β to species sampling and between herbarium and plot data. We tested whether generalised dissimilarity modelling (GDM) revealed the same compositional transitions in herbarium and plot data along environmental gradients. Sørensen, Simpson and multi‐site Sørensen β had strong negative correlations with richness (indicating sampling bias) for herbarium data (Pearson's r = –0.85, –0.80, –0.81, respectively) but not plots (r = –0.27, –0.28, –0.11). Harrison et al. β‐2 correlated poorly with richness (herbarium: r = –0.16; plots: r = –0.14) but herbarium and plot data were only weakly correlated (r = 0.18). All other metrics correlated poorly (–0.03 < r < 0.16) between datasets, suggesting biases. GDMs differed in variable importance but revealed similar transition zones for key gradients. We conclude that untransformed herbarium data are unsuitable for detecting macroecological transitions because turnover is linearly related to sampling intensity and correlates poorly with systematic surveys. Herbarium data should be used cautiously for β, even with methods insensitive to richness differences. However, herbarium data can robustly reproduce transition zones when modelled along environment gradients. We recommend this approach for detecting macroecological transitions using natural history data in the absence of plot data. [ABSTRACT FROM AUTHOR]

Ocean acidification (OA) driven by anthropogenic CO 2 emissions affects marine ecosystems, fisheries and aquaculture. Assessing the impacts of OA using projection models facilitates the development of future scenarios and potential solutions. Here, we explored various ways to incorporate OA impacts into a multi-stressor dynamic bioclimatic envelope model to project biogeographic changes of ten commercially exploited invertebrate species. We examine three dimensions of uncertainties in modelling biophysical OA effects: model structure, parameterization, and scenario uncertainty. Our results show that projected OA impacts are most sensitive to the choice of structural relationship between OA and biological responses, followed by the choice of climate change emission scenarios and parameterizations of the size of OA effects. Species generally showed negative effects to OA but sensitivity to the various sources of uncertainty were not consistent across or within species. For example, some species showed higher sensitivity to structural uncertainty and very low sensitivity to parameter uncertainty, while others showed greatest sensitivity to parameter uncertainty. This variability is largely due to geographic variability and difference in life history traits used to parameterize model simulations. Our model highlights the variability across the sources of uncertainty and contributes to the development of integrating OA impacts in species distribution models. We further stress the importance of defining the limitations and assumptions, as well as exploring the range of uncertainties associated with modelling OA impacts. [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]

Abstract: The morphology of an organism is limited by genetic and environmental factors, and the precise mechanism is inconsistent between species. Tetrix japonica Bolivar, 1987, is a widely distributed pygmy grasshopper in East Asia. However, the population clustering and relationships between the morphology and bioclimatic factors have not been previously investigated. Here, 32 geographic populations were sampled from China, and morphometrics and multiple statistical analyses were applied to detect the population clustering and relationship between the morphology and bioclimatic factors. The results suggested that T. japonica with females are significantly bigger than males in the eight morphological traits. The 32 populations do not obviously cluster according to the natural geographic area. The body sizes of females are mainly related to the lowest temperature and precipitation; by contrast, males only have a significant relationship with the lowest temperature. The forewing size is significantly related to the maximum precipitation. Furthermore, the Mantel test showed that the morphological size variation of females has a weak positive correlation to geographic distance, but is insignificant in males. It was concluded that Chinese geographic populations of T. japonica mixed and that the size of the morphological structure is limited by bioclimatic factors. [ABSTRACT FROM AUTHOR]

A sensitivity analysis can categorize levels of parameter influence on a model's output. Identifying parameters having the most influence facilitates establishing the best values for parameters of models, providing useful implications in species modelling of crops and associated insect pests. The aim of this study was to quantify the response of species models through a CLIMEX sensitivity analysis. Using openfield Solanum lycopersicum and Neoleucinodes elegantalis distribution records, and 17 fitting parameters, including growth and stress parameters, comparisons were made in model performance by altering one parameter value at a time, in comparison to the best-fit parameter values. Parameters that were found to have a greater effect on the model results are termed Bsensitive^. Through the use of two species, we show that even when the Ecoclimatic Index has a major change through upward or downward parameter value alterations, the effect on the species is dependent on the selection of suitability categories and regions of modelling. Two parameters were shown to have the greatest sensitivity, dependent on the suitability categories of each species in the study. Results enhance user understanding of which climatic factors had a greater impact on both species distributions in our model, in terms of suitability categories and areas, when parameter values were perturbed by higher or lower values, compared to the best-fit parameter values. Thus, the sensitivity analyses have the potential to provide additional information for end users, in terms of improving management, by identifying the climatic variables that are most sensitive. [ABSTRACT FROM AUTHOR]

Crucifer flea beetle, Phyllotreta cruciferae (Goeze) and striped flea beetle, Phyllotreta striolata (Fabricius) (Coleoptera: Chrysomelidae) are the most chronic and economically important flea beetle pests of cruciferous (Brassicaceae) crops in western Canada. There have been reports that populations of P. striolata are increasing in numbers and expanding their geographic range. Climate is the fundamental factor regulating the distribution and abundance of most insect species. Bioclimate simulation models of the two flea beetle species were developed to assess climatic factors influencing shifts in their geographic distribution and density. The results fostered a better understanding of how the two species responded to selected climate variables. Growing seasons with above average precipitation were predicted to favour the geographic distribution of P. striolata more than P. cruciferae. Both P. cruciferae and P. striolata were sensitive to temperature changes in the range of −2 °C to +2 °C. The ecoclimatic index (suitability index) for P. cruciferae increased with increasing temperatures, whereas the index for P. striolata declined with increasing temperatures. This study highlights the regions of the Prairies and Boreal Plains Ecozones that are most sensitive to shifts of the two populations and which may require changes in insecticidal seed treatments for effective control. [ABSTRACT FROM PUBLISHER]

It is generally assumed that ectothermic vertebrates show a reversed Bergmann's cline, but several studies suggest the opposite for turtles. Here, we assess this issue using the widely distributed European pond turtle (Emys orbicularis), which displays strong geographic size variation. We tested carapace lengths of more than 2000 individuals from the entire distribution range against latitude and climatic factors and estimated the effects of those on female-specific traits such as clutch size and frequency. Also, we compared our data against Pleistocene (Eemian) E. orbicularis from Germany. Regional fluctuations of temperature and precipitation are better predictors of body size than latitudinal temperature clines, especially in females. In addition, female body size, activity period and nesting season might be correlated, as small southern females lay several smaller clutches per season, whereas larger-bodied northern females produce only a single large clutch. In conclusion, although climatic conditions appear to have an effect on the body size of E. orbicularis, the species does not follow Bergmann's rule. Moreover, comparison of extant and fossil E. orbicularis from Germany revealed that fossil pond turtles were generally smaller than extant conspecifics from the same region. [ABSTRACT FROM AUTHOR]

Transects that traverse substantial climate gradients are important tools for climate change research and allow questions on the extent to which phenotypic variation associates with climate, the link between climate and species distributions, and variation in sensitivity to climate change among biomes to be addressed. However, the potential limitations of individual transect studies have recently been highlighted. Here, we argue that replicating and networking transects, along with the introduction of experimental treatments, addresses these concerns. Transect networks provide cost-effective and robust insights into ecological and evolutionary adaptation and improve forecasting of ecosystem change. We draw on the experience and research facilitated by the Australian Transect Network to demonstrate our case, with examples, to clarify how population- and community-level studies can be integrated with observations from multiple transects, manipulative experiments, genomics, and ecological modeling to gain novel insights into how species and systems respond to climate change. This integration can provide a spatiotemporal understanding of past and future climate-induced changes, which will inform effective management actions for promoting biodiversity resilience. [ABSTRACT FROM AUTHOR]

Species distribution models are increasingly used in regional biodiversity assessments, pest management strategies, conservation biology, ecology and evolution. The Maximum Entropy model was applied to predict the potential distribution of four egg parasitoids, e.g., Psix saccharicola, Trissolcus agriope, Trissolcus basalis and Trissolcus volgensis (all Hymenoptera: Scelionidae) in Kerman province, south of Iran. Presence records of the species sampled during 2012-2014 were used alongside with seven environmental predictors. Besides describing the climatic profile of the species, the contribution percentage of the bioclimatic variables was explored. The accuracy and performance of distribution models were also evaluated by the area under receiver operating characteristic curve (AUC) index. According to Jackknife, the minimum temperature of the coldest month was the most important predictor for the P. saccharicola distribution model. The temperature annual range and the minimum temperature of the coldest quarter were the most effective variables of species distribution for T. agriope and T. basalis, respectively. The mean diurnal range was the most important environmental factor for T. volgensis. The AUC values, based on training data, were 0.87 for P. saccharicola, 0.92 for T. agriope, 0.95 for T. basalis, and 0.89 for T. volgensis, confirming the high accuracy of MaxEnt in predicting the distribution model of these scelionid wasps. [ABSTRACT FROM AUTHOR]

Biotic interactions can have large effects on species distributions yet their role in shaping species ranges is seldom explored due to historical difficulties in incorporating biotic factors into models without a priori knowledge on interspecific interactions. Improved SDMs, which account for biotic factors and do not require a priori knowledge on species interactions, are needed to fully understand species distributions. Here, we model the influence of abiotic and biotic factors on species distribution patterns and explore the robustness of distributions under future climate change. We fit hierarchical spatial models using Integrated Nested Laplace Approximation (INLA) for lagomorph species throughout Europe and test the predictive ability of models containing only abiotic factors against models containing abiotic and biotic factors. We account for residual spatial autocorrelation using a conditional autoregressive (CAR) model. Model outputs are used to estimate areas in which abiotic and biotic factors determine species’ ranges. INLA models containing both abiotic and biotic factors had substantially better predictive ability than models containing abiotic factors only, for all but one of the four species. In models containing abiotic and biotic factors, both appeared equally important as determinants of lagomorph ranges, but the influences were spatially heterogeneous. Parts of widespread lagomorph ranges highly influenced by biotic factors will be less robust to future changes in climate, whereas parts of more localised species ranges highly influenced by the environment may be less robust to future climate. SDMs that do not explicitly include biotic factors are potentially misleading and omit a very important source of variation. For the field of species distribution modelling to advance, biotic factors must be taken into account in order to improve the reliability of predicting species distribution patterns both presently and under future climate change. [ABSTRACT FROM AUTHOR]

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models ( SDMs) including Generalized Linear Model ( GLM), MaxEnt, Random Forest ( RF), Boosted Regression Tree ( BRT), Bioclim, together with CLIMEX ( CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic ( ROC) curves ( AUC), true skill statistic ( TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a 'large' number of species into novel environments or in an independent area, the selection of the 'best' model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models ( CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models. [ABSTRACT FROM AUTHOR]

Long-term monitoring datasets provide a solid framework for ecological research. Such a dataset from the German long-term ecological research (LTER) site Rhine-Main-Observatory was used to set up a species distribution model (SDM) for the Kinzig catchment. The extensive knowledge on the monitoring data provided by the LTER-site framework allowed to calibrate a robust model for 175 taxa of stream macroinvertebrates and to project their distributions on the Kinzig River stream network using bioclimatic, topographical, hydrological, land use and geological predictors. On average, model performance was good, with a TSS of 0.83 (±0.09 SD) and a ROC of 0.95 (±0.03 SD). The model delivered valuable insights on three sources of bias that plague SDMs in general: (a) level of taxonomic identification of the modeled organisms, (b) the spatial arrangement of sampling sites, and (c) the sampling intensity at each sampling site. Taxonomic resolution did not affect SDM performance. The distribution of high predicted probabilities of occurrence in the stream network coincided with those segments in the stream network most densely and frequently sampled, indicating both a spatial and temporal sampling bias. Species richness curves confirmed the temporal sampling bias. Next to spatial bias, sampling frequency also plays an important role in data collection, affecting further analysis and modeling procedures. Results indicate an underrepresentation of low order streams, an important aspect that should be addressed by both monitoring schemes and modeling approaches. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are an effective way of predicting the potential distribution of species and their response to environmental change. Most SDMs apply presence data to a relatively generic set of predictive variables such as climate. However, this weakens the modelling process by overlooking the responses to more cryptic predictive variables. In this paper we demonstrate a means by which data gathered from an intensive animal trapping study can be used to enhance SDMs by combining field data with bioclimatic modelling techniques to determine the future potential distribution for the koomal (Trichosurus vulpecula hypoleucus). The koomal is a geographically isolated subspecies of the common brushtail possum, endemic to south-western Australia. Since European settlement this taxon has undergone a significant reduction in distribution due to its vulnerability to habitat fragmentation, introduced predators and tree/shrub dieback caused by a virulent group of plant pathogens of the genus Phytophthora. An intensive field study found: 1) the home range for the koomal rarely exceeded 1 km in in length at its widest point; 2) areas heavily infested with dieback were not occupied; 3) gap crossing between patches (>400 m) was common behaviour; 4) koomal presence was linked to the extent of suitable vegetation; and 5) where the needs of koomal were met, populations in fragments were demographically similar to those found in contiguous landscapes. We used this information to resolve a more accurate SDM for the koomal than that created from bioclimatic data alone. Specifically, we refined spatial coverages of remnant vegetation and dieback, to develop a set of variables that we combined with selected bioclimatic variables to construct models. We conclude that the utility value of an SDM can be enhanced and given greater resolution by identifying variables that reflect observed, species-specific responses to landscape parameters and incorporating these responses into the model. [ABSTRACT FROM AUTHOR]

Climate change may drastically alter patterns of species distributions and richness, but predicting future species patterns in occurrence is challenging. Significant shifts in distributions have already been observed, and understanding these recent changes can improve our understanding of potential future changes. We assessed how past climate change affected potential breeding distributions for landbird species in the conterminous United States. We quantified the bioclimatic velocity of potential breeding distributions, that is, the pace and direction of change for each species' suitable climate space over the past 60 years. We found that potential breeding distributions for landbirds have shifted substantially with an average velocity of 1.27 km yr−1, about double the pace of prior distribution shift estimates across terrestrial systems globally (0.61 km yr−1). The direction of shifts was not uniform. The majority of species' distributions shifted west, northwest, and north. Multidirectional shifts suggest that changes in climate conditions beyond mean temperature were influencing distributional changes. Indeed, precipitation variables that were proxies for extreme conditions were important variables across all models. There were winners and losers in terms of the area of distributions; many species experienced contractions along west and east distribution edges, and expansions along northern distribution edges. Changes were also reflected in the potential species richness, with some regions potentially gaining species (Midwest, East) and other areas potentially losing species (Southwest). However, the degree to which changes in potential breeding distributions are manifested in actual species richness depends on landcover. Areas that have become increasingly suitable for breeding birds due to changing climate are often those attractive to humans for agriculture and development. This suggests that many areas might have supported more breeding bird species had the landscape not been altered. Our study illustrates that climate change is not only a future threat, but something birds are already experiencing. [ABSTRACT FROM AUTHOR]

Aim It has been suggested that predicting species distributions requires a process-based and preferably dynamic approach. If dynamic models are to contribute towards understanding species distributions, uncertainties related to their spatial extrapolation and bioclimatic parameters need to be addressed. Here, we analyse the potential of a forest gap model for predicting species distributions. Location Pacific Northwest of North America ( PNW). Methods We used the dynamic forest gap model ForClim, which includes climate, competition and demographic processes, to simulate the distribution of 18 tree species outside the domain of the data used for fitting. We explored model accuracy for species distributions at the regional scale by: (1) estimating species climatic tolerances so as to maximize agreement with regional distribution maps versus (2) employing a bioclimatic parameter set that produces high accuracy at the local scale. We then performed the opposite tests and simulated local forest composition in a small area in the PNW, using (3) the local bioclimatic parameters and (4) the bioclimatic parameters that produced the highest accuracy at the regional scale. We also compared the ForClim results with predictions from a standard correlative species distribution model ( SDM). Results ForClim produced regional species distributions with fair to very good agreement for 12 tree species. The optimized bioclimatic parameters consistently improved the accuracy of regional predictions compared with simulations run with the local parameters, and were consistent with SDM results. At the local scale, predictions using the local parameters conformed to descriptions of forest composition, but accuracy decreased strongly when using the regionally calibrated parameters. Main conclusions Forest gap models can predict regional species distributions, but at the cost of reduced accuracy at the local scale. Future applications of gap models to understand regional species distributions should include robust parameterization schemes and additional ecological processes that are important at large spatial scales (e.g. dispersal, disturbances). [ABSTRACT FROM AUTHOR]

Species distribution models may provide realistic scenarios to explain the influence of bioclimatic variables in the context of emerging plant pathogens. Xylella fastidiosa is a xylem-limited Gram-negative bacterium causing severe diseases in man}' plant species. We developed a maximum entropy model for X. fastidiosa in Italy. Our objectives were to carry out a preliminary analysis of the species' potential geographical distribution and determine which eco-geographical variables ma}' favour its presence in other Italian regions besides Apulia. The analysis of single variable contribution showed that precipitation of the driest (40.3%) and wettest (30.4%) months were the main factors influencing model performance. Altitude, precipitation of warmest quarter, mean temperature of coldest quarter, and land cover provided a total contribution of 19.5%. Based on the model predictions, X. fastidiosa has a high probability (> 0.8) of colonizing areas characterized by: i) low altitude (0-150 m a.s.L); ii) precipitations iii the driest month < 10 mm, in the wettest month ranging between 80-110 mm and during the warmest quarter < 60 mm; iii) mean temperature of coldest quarter ⩾ 8°C; iv) agricultural areas comprising intensive agriculture, complex cultivation patterns, olive groves, annual crops associated with permanent crops, orchards and vineyards; forest (essentially oak woodland); and Mediterranean shrubland. Species distribution models showed a high probability of X fastidiosa occurrence in the regions of Apulia, Calabria, Rasiiicata, Sicily, Sardinia and coastal areas of Campania, Lazio and south Tuscany. Maxent models achieved excellent levels of predictive performance according to area under curve (AUC), true skill statistic (TSS) and minimum difference between training and testing AUC data (AUCdiff). Our study indicated that X. fastidiosa has the potential to overcome the current boundaries of distribution and affect areas of Italy outside Apuiia. [ABSTRACT FROM AUTHOR]

The Izvoarele Nerei Forest (in the southeastern part of Romania) on the southern slopes of the Semenic Mountain has been bryofloristically surveyed. This is one of the largest old beech forests of Europe, covering more than 5000 ha of pure beech stands. Quantitative relevés were set systematically and the Index of Ecological Significance for each taxon was calculated. Seventy-five taxa were identified, including 62 mosses and 13 liverworts. Two Red List species were found: Dicranum viride (EN) and Lophozia ascendens (VU). The bryoflora of the site is strikingly distinct from that of other old-growth forests. The overall bryophyte cover is scarce, CWD liverworts are mostly absent or very infrequent, and pleurocarpous mosses dominate almost everywhere to the detriment of pioneer specialized liverworts. The beech old-growth forest of Nerei is mentioned as escaping the simplistic interpretations in regard to the indicator value of bryophytic assemblages. The unfavourable bioclimatic context is pointed out as the most likely potential explaining factor. [ABSTRACT FROM AUTHOR]

Many uncertainties emerge dealing with future climate conditions and their possible impacts on forests. In this paper we suggest a probabilistic approach extending existing ensemble species distribution modeling concepts by addressing important sources of uncertainty. We exemplify our approach using European beech as the target tree species and bioclimatic predictors derived from WorldClim data. Model parameter uncertainty is represented by 1000 parameter samples from a Bayesian generalized linear model. Climate change impact ( CCI ) is based on 63 different climate model outputs using four RCP-scenarios ( R epresentative C oncentration P athways) in addition to the parameter uncertainty. The proposed difference of the probability of occurrence p occ to a predefined threshold, allows for evaluation of parameter uncertainty as well as for the uncertainty of future climate and describes the changing niche position. Further, we suggest the probability that the probability of occurrence exceeds a predefined threshold ( p exc ) as a metric for the distance of a site to the niche edge. These metrics are unambiguously determinable, intuitive and evident. Stands at a central, a marginal and an intermediate niche position are taken to exemplify deduction and application of p occ and p exc . A regional exercise shows how a map of p exc may support forest management planning and decision making under severe uncertainty. A key advantage of our novel metrics is the reformulation of common species distribution model (SDM) outputs in terms of risk thereby accounting for two important sources of uncertainty. [ABSTRACT FROM AUTHOR]

This study investigated the importance of the use of appropriate species distribution records in projecting potential distributions under climate change using comparative bioclimatic models and alternative sets of data (native and exotic) to project a species in a new environment. We built bioclimatic models for date palm ( Phoenix dactylifera L.), using the MaxEnt correlative model and the CLIMEX mechanistic niche model, and fitted the models using three training data sets: native data only, exotic data only and entire data. We compared the ability of the different data sets using the different modelling approaches to project suitable climate envelope for independent records of the species at a global scale. We found that the output of projected species distributions was closely related to the modelling approach as well as the specific categorized distribution of species data used (native data only, exotic data only and entire data). [ABSTRACT FROM AUTHOR]

Concerns are rising about the capacity of species to adapt quickly enough to climate change. In long-lived organisms such as trees, genetic adaptation is slow, and how much phenotypic plasticity can help them cope with climate change remains largely unknown. Here, we assess whether, where and when phenological plasticity is and will be adaptive in three major European tree species. We use a process-based species distribution model, parameterized with extensive ecological data, and manipulate plasticity to suppress phenological variations due to interannual, geographical and trend climate variability, under current and projected climatic conditions. We show that phenological plasticity is not always adaptive and mostly affects fitness at the margins of the species' distribution and climatic niche. Under current climatic conditions, phenological plasticity constrains the northern range limit of oak and beech and the southern range limit of pine. Under future climatic conditions, phenological plasticity becomes strongly adaptive towards the trailing edges of beech and oak, but severely constrains the range and niche of pine. Our results call for caution when interpreting geographical variation in trait means as adaptive, and strongly point towards species distribution models explicitly taking phenotypic plasticity into account when forecasting species distribution under climate change scenarios. [ABSTRACT FROM AUTHOR]

We assessed if the relative importance of biotic and abiotic factors for plant community composition differs along environmental gradients and between functional groups, and asked which implications this may have in a warmer and wetter future. The study location is a unique grid of sites spanning regional-scale temperature and precipitation gradients in boreal and alpine grasslands in southern Norway. Within each site we sampled vegetation and associated biotic and abiotic factors, and combined broad- and fine-scale ordination analyses to assess the relative explanatory power of these factors for species composition. Although the community responses to biotic and abiotic factors did not consistently change as predicted along the bioclimatic gradients, abiotic variables tended to explain a larger proportion of the variation in species composition towards colder sites, whereas biotic variables explained more towards warmer sites, supporting the stress gradient hypothesis. Significant interactions with precipitation suggest that biotic variables explained more towards wetter climates in the sub alpine and boreal sites, but more towards drier climates in the colder alpine. Thus, we predict that biotic interactions may become more important in alpine and boreal grasslands in a warmer future, although more winter precipitation may counteract this trend in oceanic alpine climates. Our results show that both local and regional scales analyses are needed to disentangle the local vegetation-environment relationships and their regional-scale drivers, and biotic interactions and precipitation must be included when predicting future species assemblages. [ABSTRACT FROM AUTHOR]

The aim of this paper was firstly to describe the ecological and geographical differentiation of the four maple species ( Acer spp.) in Serbia and Kosovo based on floristic and phytocoenological data, and secondly, to model their distributions in order to predict which areas the species can be expected in. The intention was also to compare the resulting prediction maps with the available field records and see whether there are any differences between the actual and the predicted ranges. The data set included 1979 species records and each record was accompanied by geographic coordinates. The geographi-cal analysis was performed on the chorological data (latitude, longitude, altitude), while the ecological was based on vegetation data relating to the association, alliance, order and class as well as on 19 bioclimatic parameters. The data set was georeferenced using GIS tools. The results demonstrated that the distribution patterns of all the analyzed species are mostly affected by the limiting effects of the variables related to precipitation and temperature of the dry and also the warm period. Their synergic limiting effects are the most important force shaping distribution patterns within a territory. These findings highlight the importance of defining bioclimatic profiles of species using different techniques of distribution modelling. [ABSTRACT FROM AUTHOR]

Remote sensing techniques offer an opportunity to improve biodiversity modelling and prediction world-wide. Yet, to date, the weather station-based WorldClim data set has been the primary source of temperature and precipitation information used in correlative species distribution models. WorldClim consists of grids interpolated from in situ station data recorded primarily from 1960 to 1990. Those data sets suffer from uneven geographic coverage, with many areas of Earth poorly represented. , Here, we compare two remote sensing data sources for the purposes of biodiversity prediction: MERRA climate reanalysis data and AMSR-E, a pure remote sensing data source. We use these data to generate novel temperature-based bioclimatic information and to model the distributions of 20 species of vertebrates endemic to four regions of South America: Amazonia, the Atlantic Forest, the Cerrado and Patagonia. We compare the bioclimatic data sets derived from MERRA and AMSR-E information with in situ station data and contrast species distribution models based on these two products to models built with WorldClim. , Surface temperature estimates provided by MERRA and AMSR-E showed warm temperature biases relative to the in situ data fields, but the reliability of these data sets varied in geographic space. Species distribution models derived from the MERRA data performed equally well (in Cerrado, Amazonia and Patagonia) or better (Atlantic Forest) than models built with the WorldClim data. In contrast, the performance of models constructed with the AMSR-E data was similar to (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of models built with WorldClim data. , Whereas this initial comparison assessed only temperature fields, efforts to estimate precipitation from remote sensing information hold great promise; furthermore, other environmental data sets with higher spatial and temporal fidelity may improve upon these results. [ABSTRACT FROM AUTHOR]

Species distributions are known to be limited by biotic and abiotic factors at multiple temporal and spatial scales. Species distribution models, however, frequently assume a population at equilibrium in both time and space. Studies of habitat selection have repeatedly shown the difficulty of estimating resource selection if the scale or extent of analysis is incorrect. Here, we present a multi-step approach to estimate the realized and potential distribution of the endangered giant kangaroo rat. First, we estimate the potential distribution by modeling suitability at a range-wide scale using static bioclimatic variables. We then examine annual changes in extent at a population-level. We define “available” habitat based on the total suitable potential distribution at the range-wide scale. Then, within the available habitat, model changes in population extent driven by multiple measures of resource availability. By modeling distributions for a population with robust estimates of population extent through time, and ecologically relevant predictor variables, we improved the predictive ability of SDMs, as well as revealed an unanticipated relationship between population extent and precipitation at multiple scales. At a range-wide scale, the best model indicated the giant kangaroo rat was limited to areas that received little to no precipitation in the summer months. In contrast, the best model for shorter time scales showed a positive relation with resource abundance, driven by precipitation, in the current and previous year. These results suggest that the distribution of the giant kangaroo rat was limited to the wettest parts of the drier areas within the study region. This multi-step approach reinforces the differing relationship species may have with environmental variables at different scales, provides a novel method for defining “available” habitat in habitat selection studies, and suggests a way to create distribution models at spatial and temporal scales relevant to theoretical and applied ecologists. [ABSTRACT FROM AUTHOR]

The so-called BIOCLIM variables have played a central role in the advancement of ecologicalmodellingmethods for correlative species distribution modelling., We propose to establish a register of the BIOCLIMvariables, allowing for the extension of the suite of described, readily available covariate data tosupport bioclimatic modelling. The registry will provide a simple means for researchers to add usefuldata sets in a transparent, documented manner., As a case study, we introduce and describe the set offivemost significant principal components of the first 35 BIOCLIM variables (Bio01 to Bio35) and proposethey be assigned as Bio36 to Bio40.Together, these five PCA variables capture more than 90%of thevariability in the full suite of 35 BIOCLIM variables., The PCA variables may provide a means to explore the climaticlimits of poorly known species, with a reduced risk of overfitting the models. The BIOCLIM variable registry should facilitate the expansion of the suite of variables commonly used in species distribution modelling. [ABSTRACT FROM AUTHOR]

We evaluate genetic test plantations of North American Douglas-fir provenances in Europe to quantify how tree populations respond when subjected to climate regime shifts, and we examined whether bioclimate envelope models developed for North America to guide assisted migration under climate change can retrospectively predict the success of these provenance transfers to Europe. The meta-analysis is based on long-term growth data of 2800 provenances transferred to 120 European test sites. The model was generally well suited to predict the best performing provenances along north-south gradients in Western Europe, but failed to predict superior performance of coastal North American populations under continental climate conditions in Eastern Europe. However, model projections appear appropriate when considering additional information regarding adaptation of Douglas-fir provenances to withstand frost and drought, even though the model partially fails in a validation against growth traits alone. We conclude by applying the partially validated model to climate change scenarios for Europe, demonstrating that climate trends observed over the last three decades warrant changes to current use of Douglas-fir provenances in plantation forestry throughout Western and Central Europe. [ABSTRACT FROM AUTHOR]

Maximum entropy ecological niche modeling was utilized to describe the global geographic distribution of Trichinella species and genotypes and to assess their invasive risk in new areas other than the ones currently known. Also, space–time scan statistic was utilized to identify global spatiotemporal clusters of infection. A database containing 3209 records for 12 species and genotypes identified at the International Trichinella Reference Center (ITRC) as well as climate, elevation, and land cover data extracted from various databases were used. Ecological niche modeling implemented in the Maxent program indicated new potential ranges for T. spiralis (T1), T. nativa (T2), T. britovi (T3), T. pseudospiralis (T4), T. murrelli (T5), T6, T. papuae (T10), and T. zimbabwensis (T11). The area under the curve values for the test data of the models ranged from 0.901 to 0.998, indicating that the models were very good to excellent. The most important bioclimatic factor in modeling the ranges for T. spiralis (T1), T. nativa (T2), T. britovi (T3), T6, and T. zimbabwensis (T11) was temperature, for T. pseudospiralis (T4) and T. papuae (T10) was precipitation, and for T. murrelli (T5) was land cover. T. spiralis (T1), T. britovi (T3), and T. pseudospiralis (T4) had the same primary land cover which was “Grass Crops”. The primary land covers were “Conifer Boreal Forest” for T. nativa (T2), “Cool Fields and Woods” for T. murrelli (T5), “Upland Tundra” for T6, “Tropical Rainforest” for T. papuae (T10), and “Crops and Town” for T. zimbabwensis (T11). The scan statistic analyses revealed the presence of significant spatiotemporal clusters ( p < 0.05) for T. spiralis (T1), T. nativa (T2), T. britovi (T3), T. pseudospiralis (T4), T. murrelli (T5), T6, and T. nelsoni (T7). No significant clusters were found for T. papuae (T10) and T. zimbabwensis ( T11 ). [ABSTRACT FROM AUTHOR]

Tanzania's Ruaha landscape is an international priority area for large carnivores, supporting over 10% of the world's lions and important populations of leopards and spotted hyaenas. However, lack of ecological data on large carnivore distribution and habitat use hinders the development of effective carnivore conservation strategies in this critical landscape. Therefore, the study aimed to (i) identify the most significant ecogeographical variables influencing the potential distribution of lions, leopards and spotted hyaenas across the Ruaha landscape; (ii) identify zones with highest suitability for harbouring those species; and (iii) use species distribution modelling algorithms (SDMs) to define important areas for conservation of large carnivores. Habitat suitability was calculated based on environmental features from georeferenced presence-only carnivore location data. Potential distribution of large carnivores appeared to be strongly influenced by water availability; highly suitable areas were situated close to rivers and experienced above average annual precipitation. Net primary productivity and tree cover also exerted some influence on habitat suitability. All three species showed relatively narrow niche breadth and low tolerance to changes in habitat characteristics. From 21,050 km2 assessed, 8.1% (1,702 km2) emerged as highly suitable for all three large carnivores collectively. Of that area, 95.4% (1,624 km2) was located within 30 km of the Park-village border, raising concerns about human-carnivore conflict. This was of particular concern for spotted hyaenas, as they were located significantly closer to the Park boundary than lions and leopards. This study provides the first map of potential carnivore distribution across the globally important Ruaha landscape, and demonstrates that SDMs can be effective for understanding large carnivore habitat requirements in poorly sampled areas. This approach could have relevance for many other important wildlife areas that only have limited, haphazard presence-only data, but which urgently require strategic conservation planning. [ABSTRACT FROM AUTHOR]

Questions (1) Does shrub encroachment affect the regeneration of two contrasting species of oak along an elevation gradient in the west-central Iberian Peninsula? (2) Do different nurse shrubs have any relevant species-specific effects along the gradient? (3) Does shrub encroachment affect the location of the bioclimatic limit between these two oak species along the elevation gradient? Location Twelve sites distributed from the Arribes del Duero plains to the Sistema Central mountains ( Salamanca province, Spain). Methods We measured the regeneration of two contrasting oak tree species ( Quercus ilex subsp. ballota Samp - sclerophyllous, Q. pyrenaica Willd - marcescent) as the relative percentage cover of seedlings and saplings (<1.3-m high) in plots 10 m in diameter distributed in stands of different age. We fixed the bioclimatic limit at 50% relative proportion of species in each plot, i.e. whether one species was more abundant than the other in a plot. Shrub stand age was estimated by counting growth rings in the principal stems of the oldest shrubs in a plot. We fitted generalized linear mixed models to analyse the effects of elevation, specific nurse shrub and shrub stand age on regeneration and the probability of one species being more abundant than the other. Results The regeneration and relative proportion of Q. ilex (sclerophyllous) decreased with elevation. Regeneration did not vary with shrub encroachment, although the relative proportion increased notably in the youngest stands. In turn, regeneration and relative proportion of Q. pyrenaica (deciduous) significantly increased towards the upper sites and mature shrub stands. We found no evidence of specific nurse shrub effects on regeneration or the probability of one species being more abundant than the other in either of the two species of oak. Conclusions The bioclimatic limit between the two contrasting species of oak shifted with shrub stand age along the elevation gradient. Land practices preventing shrub encroachment can thus indirectly shift this limit towards higher elevations. The effects of shrubs may be critical in resolving the climatic and land-use effects on elevation shifts of species under changing conditions in bioclimatic transitions. [ABSTRACT FROM AUTHOR]

Aim To investigate the velocity of species-specific exposure to climate change for mid- and late 21st century and develop metrics that quantify exposure to climate change over space and time. Location California Floristic Province, south-western USA. Methods Occurrences from presence/absence inventories of eight Californian endemic tree species ( Pinus balfouriana [Grev.&Balf.], Pinus coulteri [D.Don] , Pinus muricata [D.Don.], Pinus sabiniana [D.Don], Quercus douglasii [Hook.&Arn.], Quercus engelmannii [Greene], Quercus lobata [Nee] and Quercus wislizeni [A.DC.]) were used to develop eight species distribution models ( SDMs) for each species with the BIOMOD platform, and this ensemble was used to construct current suitability maps and future projections based on two global circulation models in two time periods [mid-century: 2041-2070 and late century ( LC): 2071-2100]. From the resulting current and future suitability maps, we calculated a bioclimatic velocity as the ratio of temporal gradient to spatial gradient. We developed and compared eight metrics of temporal exposure to climate change for mid- and LC for each species. Results The velocity of species exposure to climate change varies across species and time periods, even for similarly distributed species. We find weak support among the species analysed for higher velocities in exposure to climate change towards the end of the 21st century, coinciding with harsher conditions. The variation in the pace of exposure was greater among species than for climate projections considered. Main conclusions The pace of climate change exposure varies depending on period of analysis, species and the spatial extent of conservation decisions (potential ranges versus current distributions). Translating physical climatic space into a biotic climatic space helps informing conservation decisions in a given time frame. However, the influence of spatial and temporal resolution on modelled species distributions needs further consideration in order to better characterize the dynamics of exposure and species-specific velocities. [ABSTRACT FROM AUTHOR]

Accurate models for species' distributions are needed to forecast the progress and impacts of alien invasive species and assess potential range-shifting driven by global change. Although this has traditionally been achieved through data-driven correlative modelling, robustly extrapolating these models into novel climatic conditions is challenging. Recently, a small number of process-based or mechanistic distribution models have been developed to complement the correlative approaches. However, tests of these models are lacking, and there are very few process-based models for invasive species. We develop a method for estimating the range of a globally invasive species, common ragweed ( Ambrosia artemisiifolia L.), from a temperature- and photoperiod-driven phenology model. The model predicts the region in which ragweed can reach reproductive maturity before frost kills the adult plants in autumn. This aligns well with the poleward and high-elevation range limits in its native North America and in invaded Europe, clearly showing that phenological constraints determine the cold range margins of the species. Importantly, this is a 'forward' prediction made entirely independently of the distribution data. Therefore, it allows a confident and biologically informed forecasting of further invasion and range shifting driven by climate change. For ragweed, such forecasts are extremely important as the species is a serious crop weed and its airborne pollen is a major cause of allergy and asthma in humans. Our results show that phenology can be a key determinant of species' range margins, so integrating phenology into species distribution models offers great potential for the mechanistic modelling of range dynamics. [ABSTRACT FROM AUTHOR]

Aim Interest in species distribution models ( SDMs) and related niche studies has increased dramatically in recent years, with several books and reviews being prepared since 2000. The earliest SDM studies are dealt with only briefly even in the books. Consequently, many researchers are unaware of when the first SDM software package ( bioclim) was developed and how a broad range of applications using the package was explored within the first 8 years following its release. The purpose of this study is to clarify these early developments and initial applications, as well as to highlight bioclim's continuing relevance to current studies. Location Mainly Australia and New Zealand, but also some global applications. Methods We outline the development of the bioclim package, early applications (1984-1991) and its current relevance. Results bioclim was the first SDM package to be widely used. Early applications explored many of the possible uses of SDMs in conservation biogeography, such as quantifying the environmental niche of species, identifying areas where a species might be invasive, assisting conservation planning and assessing the likely impacts of climate change on species distributions. Main conclusions Understanding this pioneering work is worthwhile as bioclim was for many years one of the leading SDM packages and remains widely used. Climate interpolation methods developed for bioclim were used to create the WorldClim database, the most common source of climate data for SDM studies, and bioclim variables are used in about 76% of recent published MaxEnt analyses of terrestrial ecosystems. Also, some of the bioclim studies from the late 1980s, such as measuring niche (both realized and fundamental) and assessing possible impacts of climate change, are still highly relevant to key conservation biogeography issues. [ABSTRACT FROM AUTHOR]

Climate change is altering phenology and distributions of many species and further changes are projected. Can species physiologically adapt to climate warming? We analyse thermal tolerances of a large number of terrestrial ectotherm ( n = 697), endotherm ( n = 227) and plant ( n = 1816) species worldwide, and show that tolerance to heat is largely conserved across lineages, while tolerance to cold varies between and within species. This pattern, previously documented for ectotherms, is apparent for this group and for endotherms and plants, challenging the longstanding view that physiological tolerances of species change continuously across climatic gradients. An alternative view is proposed in which the thermal component of climatic niches would overlap across species more than expected. We argue that hard physiological boundaries exist that constrain evolution of tolerances of terrestrial organisms to high temperatures. In contrast, evolution of tolerances to cold should be more frequent. One consequence of conservatism of upper thermal tolerances is that estimated niches for cold-adapted species will tend to underestimate their upper thermal limits, thereby potentially inflating assessments of risk from climate change. In contrast, species whose climatic preferences are close to their upper thermal limits will unlikely evolve physiological tolerances to increased heat, thereby being predictably more affected by warming. [ABSTRACT FROM AUTHOR]

Aim To investigate the use of bioclimatic envelope models for predicting distributions of species that have experienced severe human-induced geographical range contractions. Bioclimatic envelope model predictions of current and future distributions were contrasted with those from models that used biotic indicators of suitable habitat as predictors rather than climate. Location Temperate grassy woodlands of South Australia. Methods We modelled the distribution of two native grassland plant species, key habitat indicators of the endangered and geographically restricted pygmy bluetongue lizard ( Tiliqua adelaidensis), using climate and landscape variables with aggregated boosted regression trees. We forecast annual changes in the plant species distributions from 2000 to 2100 under a no-climate-policy 'Reference' scenario (high global greenhouse gas emissions) and a climate stabilization 'Policy' scenario. We compared current and future predicted distributions of the lizard estimated directly using bioclimatic envelope models with those derived indirectly from climate-driven changes in habitat suitability of the grassland plant species with which the lizard has a strong association (termed plant-habitat models). Results Both coupled plant-habitat models and bioclimatic envelope models described the current distribution of the pygmy bluetongue lizard almost equally well; however, future projections of changes in the species range were markedly more pessimistic (i.e. greater range contraction) for bioclimatic envelope models. Further, bioclimatic envelope models that included interactions among variables projected rapid increases in area of occupancy that are unlikely to be attainable given dispersal constraints, but no such increases were projected from plant-habitat models. Main conclusions Capturing species-environment relationships for threatened and range-restricted species using surrogate biotic variables that represent resource requirements of the focal species - which themselves respond to environmental variation and are in stable equilibrium - allows more confident and ecologically realistic forecasts of potential range changes for species most susceptible to climate change. [ABSTRACT FROM AUTHOR]

Climate change is projected to be particularly strong in northern latitudes, and subarctic species are thus likely to be especially susceptible to the effects of climate warming. We forecast potential effects of climate change on the extent of the suitable habitat of the common frog,Rana temporaria,at the margin of its northern range. We investigated 179 potential breeding sites in subarctic Finland and subjected the data to detailed bioclimate envelope modelling using three state-of-the-art techniques: generalized additive models, maximum entropy and generalized boosting methods. Moreover, we included local environmental factors in the models to investigate whether they improve model performance. Under all tested climate change projections and irrespective of the modelling method, the suitable habitat forR. temporariaincreased in warming climate. The inclusion of local abiotic variables significantly improved the performance of the models. However, June temperature appeared to be the most informative variable in all modelling approaches: a major increase in the extent of suitable habitat occurred when it increased by 1°C. Overall, the modelling results indicate that the distribution of northernR. temporariais likely to be very sensitive to climate warming. The results also highlight the fact that overlooking local abiotic variation can significantly bias bioclimatic modelling results. [ABSTRACT FROM PUBLISHER]

Abstract: Global bioclimatic datasets are being widely used in ecological research to estimate the potential distribution of species using Climate Envelope Models (CEMs). These datasets are easily available and offer high resolution information for all land areas globally. However, they have not been tested rigorously in smaller regions, and their use in regional CEM studies may pose problems derived from their poor representation of local climate features. Moreover, these problems may be enhanced when using CEMs for future climate projections—a topic of current active research—due to the uncertainty derived from the future altered climate scenarios. In this paper we use distributional data of European beech (Fagus sylvatica) in Northern Iberian Peninsula to analyze the discrepancies of the CEMs (predictive skill, variable importance and consistency using different predictor subsets) resulting from three alternative public, high-resolution climate datasets: a benchmarking regional climate dataset developed for the area of study (UC), the University of Barcelona Atlas for the Iberian Peninsula (UAB) and the worldwide WorldClim bioclimatic dataset (WC). The same CEM techniques (multiple logistic regression and multivariate adaptive regression splines) were applied to the different datasets, showing that the quality of the baseline climate has a great impact on the resulting models, as manifested by the different contributions of the bioclimatic predictors to the resulting models. Artifactual bioclimatic variables were found in some datasets, representing topographical features and spatial gradients, rather than true climatic patterns, thus significantly contributing to the models, although not for the right reasons. This causes a misleading model interpretation and problems for extrapolation in future climate conditions, as evidenced analyzing the future projections obtained using state-of-the-art regional climate projections from the ENSEMBLES project. [Copyright &y& Elsevier]