Species Distribution Modelling predicts habitat suitability and reduction of suitable habitat under future climatic scenario for Sclerophrys perreti: A critically endangered Nigerian endemic toad

Sclerophrys perreti is a critically endangered Nigerian native frog currently imperilled by human activities. A better understanding of its potential distribution and habitat suitability will aid in conservation; however, such knowledge is limited for S. perreti. Herein, we used a species distribution model (SDM) approach with all known occurrence data (n = 22) from our field surveys and primary literature, and environmental variable predictors (19 bioclimatic variables, elevation and land cover) to elucidate habitat suitability and impact of climate change on this species. The SDM showed that temperature and precipitation were the predictors of habitat suitability for S. perreti with precipitation seasonality as the strongest predictor of habitat suitability. The following variable also had a significant effect on habitat suitability: temperature seasonality, temperature annual range, precipitation of driest month, mean temperature of wettest quarter and isothermality. The model predicted current suitable habitat for S. perreti covering an area of 1,115 km². However, this habitat is predicted to experience 60% reduction by 2050 owing to changes in temperature and precipitation. SDM also showed that suitable habitat exists in south-eastern range of the inselberg with predicted low impact of climate change compared to other ranges. Therefore, this study recommends improved conservation measures through collaborations and stakeholder's meeting with local farmers for the management and protection of S. perreti.     

Modeling plant species distributions under future climates: how fine scale do climate projections need to be?

Recent studies suggest that species distribution models (SDMs) based on fine‐scale climate data may provide markedly different estimates of climate‐change impacts than coarse‐scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse‐scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000‐fold range of spatial scales (0.008–16 km²). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate‐data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine‐ and coarse‐scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.     

Causal or spurious relationship? Climate and the distribution of Phelsuma geckos on Grand Comoro Island

The volcanic island of Grand Comoro, Malagasy biogeographic region, is inhabited by three species of Phelsuma day geckos; two island‐endemic taxa (Phelsuma comorensis and Phelsuma v‐nigra comoraegrandensis) and the introduced Phelsuma dubia. Phelsuma comorensis is restricted to elevations of greater than 150 m above sea level on the northern of the island's two volcanoes and is the only Phelsuma above 300 m. The other species are widespread at low elevations but also reach levels above 900 m at the southern volcano. To investigate these divergent distribution patterns, we used environmental niche models based on climate and habitat data and tested whether predicted climate change may influence species distributions. Analyses of niche overlap did not show significant differences between present‐day and predicted future potential distributions of any Phelsuma species studied, which could be seen as an indicator of resilience towards climate change. Climate models reflected the restricted distribution of P. comorensis with precipitation of the wettest month detected as most important variable, whereas habitat models predicted an island‐wide distribution. While climate appears to determine the distribution of P. comorensis, we propose isolation by migration barriers as an alternative and discuss the detection of causal versus spurious relationships in ecological niche models.     

Future climate change will severely reduce habitat suitability of the Critically Endangered Chinese giant salamander

1. Being the largest extant amphibian in the world, the IUCN Critically Endangered Chinese giant salamander Andrias davidianus is a charismatic species with great international public interest. While threats such as commercial overexploitation and habitat degradation have been extensively documented to affect natural populations of A. davidianus, still no information is available about the species sensitivity to climate change.
2. Here, we develop an ensemble of species distribution models (SDMs) for A. davidianus and projected its habitat suitability under present-day and future climate change scenarios. We based our SDMs on bioclimatic and topographic predictors, and recent (2012–2018) field-collected occurrence data across the whole distribution range of the species.
3. The ensemble SDMs exhibited good predictive capacity and suggested that slope, maximum temperature of warmest month, precipitation of driest month, and isothermality are the most influential predictors in determining distribution patterns in this species. The projections of our models point to a pronounced impact of climate changes over A. davidianus, with more than two-thirds of its suitable range expected to be lost in all scenarios of future climates tested.
4. In concert with the numerous other threats that are affecting this species, climate change poses a serious hindrance to the long-term survival of A. davidianus. We emphasise the urgent need of undertaking strict measures to manage this species and safeguard the few remaining available suitable habitats. We suggest that adaptive management strategies including designation of new reserves should be considered to mitigate the impacts of climate change on A. davidianus.

     

Climatic niche of the Saker Falcon Falco cherrug: predicted new areas to direct population surveys in Central Asia

Accurate species distribution data across remote and extensive geographical areas are difficult to obtain. Here, we use bioclimatic envelope models to determine climatic constraints on the distribution of the migratory Saker Falcon Falco cherrug to identify areas in data-deficient regions that may contain unidentified populations. Sakers live at low densities across large ranges in remote regions, making distribution status difficult to assess. Using presence-background data and eight bioclimatic variables within a species distribution modelling framework, we applied MaxEnt to construct models for both breeding and wintering ranges. Occurrence data were spatially filtered and climatic variables tested for multicollinearity before selecting best fit models using the Akaike information criterion by tuning MaxEnt parameters. Model predictive performance tested using the continuous Boyce index (B) was high for both breeding (B_TEST = 0.921) and wintering models (B_TEST = 0.735), with low omission rates and minimal overfitting. The Saker climatic niche was defined by precipitation in the warmest quarter in the breeding range model, and mean temperature in the wettest quarter in the wintering range model. Our models accurately predicted areas of highest climate suitability and defined the climatic constraints on a wide-ranging rare species, suggesting that climate is a key determinant of Saker distribution across macro-scales. We recommend targeted population surveys for the Saker based on model predictions to areas of highest climatic suitability in key regions with distribution knowledge gaps, in particular the Qinghai-Tibet plateau in western China. Further applications of our models could identify protected areas and reintroduction sites, inform development conflicts, and assess the impact of climate change on distributions.     

Identifying conservation priority areas and predicting the climate change impact on the future habitats of endangered Nepenthes khasiana Hook.f. utilizing ecological niche modelling

Developing strategies for effective species conservation is necessary to counter the ever-fluctuating environmental conditions with increasing anthropogenic activities. Studies have proven Ecological Niche Modelling (ENM) as an effective tool for sustainable conservation. Nepenthes khasiana Hook.f. is an endangered pitcher plant facing a constant decline in population due to anthropogenic activities. This study aimed to locate the most suitable areas for re-establishing the species in natural habitats using Maximum Entropy (MaxEnt) modelling, and to forecast the effects of current and future climate conditions on its distribution throughout Northeast India. The potential suitable areas in future climate under three Representative Concentration Pathway (RCP) scenarios and in the current climate were predicted utilizing the 30 occurrence data, bioclimatic predictors, and variables from BCC-CSM1.1 model and WorldClim respectively. The results of the current study showed significant relationships among annual precipitation, precipitation in the driest month, seasonality of precipitation, annual range iso-thermality of temperature, mean diurnal range [Mean of monthly (max temp - min temp)], and the distribution of the analysed species. The optimum model performance was represented by the AUC value of 0.972 ± 0.007. The model predicted 10.70% of the NE Indian region as climatically suitable, which will expand under RCP4.5 and RCP6.0, reaching 15.35%, and 12.64%, respectively. However, this may degrade significantly under RCP8.5, reducing to 8.14%. Based on the analysis of modelling results it was found that the Nokrek belt and the Khasi hills as highly suitable regions for the reintroduction of the species. The study revalidated ENM as an effective means to identify new populations and predict the influence of climate change on the future habitat which can benefit the concurrent species management strategies.     

Why georeferencing matters: Introducing a practical protocol to prepare species occurrence records for spatial analysis

Species Distribution Models (SDMs) are widely used to understand environmental controls on species’ ranges and to forecast species range shifts in response to climatic changes. The quality of input data is crucial determinant of the model's accuracy. While museum records can be useful sources of presence data for many species, they do not always include accurate geographic coordinates. Therefore, actual locations must be verified through the process of georeferencing. We present a practical, standardized manual georeferencing method (the Spatial Analysis Georeferencing Accuracy (SAGA) protocol) to classify the spatial resolution of museum records specifically for building improved SDMs. We used the high‐elevation plant Saxifraga austromontana Wiegand (Saxifragaceae) as a case study to test the effect of using this protocol when developing an SDM. In MAXENT, we generated and compared SDMs using a comprehensive occurrence dataset that had undergone three different levels of georeferencing: (1) trained using all publicly available herbarium records of the species, minus outliers (2) trained using herbarium records claimed to be previously georeferenced, and (3) trained using herbarium records that we have manually georeferenced to a ≤ 1‐km resolution using the SAGA protocol. Model predictions of suitable habitat for S. austromontana differed greatly depending on georeferencing level. The SDMs fitted with presence locations georeferenced using SAGA outperformed all others. Differences among models were exacerbated for future distribution predictions. Under rapid climate change, accurately forecasting the response of species becomes increasingly important. Failure to georeference location data and cull inaccurate samples leads to erroneous model output, limiting the utility of spatial analyses. We present a simple, standardized georeferencing method to be adopted by curators, ecologists, and modelers to improve the geographic accuracy of museum records and SDM predictions.     

Past distribution of epiphyllous liverworts in China: The usability of historical data

Epiphyllous liverworts form a special group of bryophytes that primarily grow on the leaves of understory vascular plants in tropical and subtropical evergreen broadleaf forests. Being sensitive to moisture and temperature changes, epiphyllous liverworts are often considered to be good indicators of climate change and forest degradation. However, they are a poorly collected and taxonomically complicated group, with an only partly identified distribution pattern. In this study, we built four models based on 24 environmental variables at four different spatial resolutions (i.e., 1 km, 5 km, 10 km, and 15 km) to predict the past distribution of epiphyllous liverworts in China, using Maxent model and 63 historical location records (i.e., presence‐only data). Both area under the curve of the receiver operating characteristic (AUC) and true skill statistic (TSS) methods are used to assess the model performance. Results showed that the model with the predictors at a 15‐km resolution achieved the highest predictive accuracy (AUC=0.946; TSS=0.880), although there was no statistically significant difference between the four models (p > 0.05). The most significant environmental variables included aridity, annual precipitation, precipitation of wettest month, precipitation of wettest quarter, and precipitation of warmest quarter, annual mean NDVI, and minimum NDVI. The predicted suitable areas for epiphyllous liverworts were mainly located in the south of Yangtze River and seldom exceed 35°N, which were consistent with the museum and herbarium records, as well as the historical records in scientific literatures. Our study further demonstrated the value of historical data to ecological and evolutionary studies.     

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.     

A closer look at novel climates: new methods and insights at continental to landscape scales

Novel climates – emerging conditions with no analog in the observational record – are an open problem in ecological modeling. Detecting extrapolation into novel conditions is a critical step in evaluating bioclimatic projections of how species and ecosystems will respond to climate change. However, biologically informed novelty detection methods remain elusive for many modeling algorithms. To assist with bioclimatic model design and evaluation, we present a first‐approximation assessment of general novelty based on a simple and consistent characterization of climate. We build on the seminal global analysis of Williams et al. (2007 PNAS, 104, 5738) by assessing of end‐of‐21st‐century novelty for North America at high spatial resolution and by refining their standardized Euclidean distance into an intuitive Mahalanobian metric called sigma dissimilarity. Like this previous study, we found extensive novelty in end‐of‐21st‐century projections for the warm southern margin of the continent as well as the western Arctic. In addition, we detected localized novelty in lower topographic positions at all latitudes: By the end of the 21st century, novel climates are projected to emerge at low elevations in 80% and 99% of ecoregions in the RCP4.5 and RCP8.5 emissions scenarios, respectively. Novel climates are limited to 7% of the continent's area in RCP4.5, but are much more extensive in RCP8.5 (40% of area). These three risk factors for novel climates – regional susceptibility, topographic position, and the magnitude of projected climate change – represent a priori evaluation criteria for the credibility of bioclimatic projections. Our findings indicate that novel climates can emerge in any landscape. Interpreting climatic novelty in the context of nonlinear biological responses to climate is an important challenge for future research.     

Precipitation and winter temperature predict long‐term range‐scale abundance changes in Western North American birds

Predicting biodiversity responses to climate change remains a difficult challenge, especially in climatically complex regions where precipitation is a limiting factor. Though statistical climatic envelope models are frequently used to project future scenarios for species distributions under climate change, these models are rarely tested using empirical data. We used long‐term data on bird distributions and abundance covering five states in the western US and in the Canadian province of British Columbia to test the capacity of statistical models to predict temporal changes in bird populations over a 32‐year period. Using boosted regression trees, we built presence‐absence and abundance models that related the presence and abundance of 132 bird species to spatial variation in climatic conditions. Presence/absence models built using 1970–1974 data forecast the distributions of the majority of species in the later time period, 1998–2002 (mean AUC = 0.79 ± 0.01). Hindcast models performed equivalently (mean AUC = 0.82 ± 0.01). Correlations between observed and predicted abundances were also statistically significant for most species (forecast mean Spearman′s ρ = 0.34 ± 0.02, hindcast = 0.39 ± 0.02). The most stringent test is to test predicted changes in geographic patterns through time. Observed changes in abundance patterns were significantly positively correlated with those predicted for 59% of species (mean Spearman′s ρ = 0.28 ± 0.02, across all species). Three precipitation variables (for the wettest month, breeding season, and driest month) and minimum temperature of the coldest month were the most important predictors of bird distributions and abundances in this region, and hence of abundance changes through time. Our results suggest that models describing associations between climatic variables and abundance patterns can predict changes through time for some species, and that changes in precipitation and winter temperature appear to have already driven shifts in the geographic patterns of abundance of bird populations in western North America.     

The impact of climate change on the distribution of two threatened Dipterocarp trees

Two ecologically and economically important, and threatened Dipterocarp trees Sal (Shorea robusta) and Garjan (Dipterocarpus turbinatus) form mono‐specific canopies in dry deciduous, moist deciduous, evergreen, and semievergreen forests across South Asia and continental parts of Southeast Asia. They provide valuable timber and play an important role in the economy of many Asian countries. However, both Dipterocarp trees are threatened by continuing forest clearing, habitat alteration, and global climate change. While climatic regimes in the Asian tropics are changing, research on climate change‐driven shifts in the distribution of tropical Asian trees is limited. We applied a bioclimatic modeling approach to these two Dipterocarp trees Sal and Garjan. We used presence‐only records for the tree species, five bioclimatic variables, and selected two climatic scenarios (RCP4.5: an optimistic scenario and RCP8.5: a pessimistic scenario) and three global climate models (GCMs) to encompass the full range of variation in the models. We modeled climate space suitability for both species, projected to 2070, using a climate envelope modeling tool “MaxEnt” (the maximum entropy algorithm). Annual precipitation was the key bioclimatic variable in all GCMs for explaining the current and future distributions of Sal and Garjan (Sal: 49.97 ± 1.33; Garjan: 37.63 ± 1.19). Our models predict that suitable climate space for Sal will decline by 24% and 34% (the mean of the three GCMs) by 2070 under RCP4.5 and RCP8.5, respectively. In contrast, the consequences of imminent climate change appear less severe for Garjan, with a decline of 17% and 27% under RCP4.5 and RCP8.5, respectively. The findings of this study can be used to set conservation guidelines for Sal and Garjan by identifying vulnerable habitats in the region. In addition, the natural habitats of Sal and Garjan can be categorized as low to high risk under changing climates where artificial regeneration should be undertaken for forest restoration.     

Drivers of radial growth and carbon isotope discrimination of bur oak (Quercus macrocarpa Michx.) across continental gradients in precipitation,vapour pressure deficit and irradiance

Tree‐ring characteristics are commonly used to reconstruct climate variables, but divergence from the assumption of a single biophysical control may reduce the accuracy of these reconstructions. Here, we present data from bur oaks (Quercus macrocarpa Michx.) sampled within and beyond the current species bioclimatic envelope to identify the primary environmental controls on ring‐width indices (RWIs) and carbon stable isotope discrimination (Δ¹³C) in tree‐ring cellulose. Variation in Δ¹³C and RWI was more strongly related to leaf‐to‐air vapour pressure deficit (VPD) at the centre and western edge of the range compared with the northern and wettest regions. Among regions, Δ¹³C of tree‐ring cellulose was closely predicted by VPD and light responses of canopy‐level Δ¹³C estimated using a model driven by eddy flux and meteorological measurements (R² = 0.96, P = 0.003). RWI and Δ¹³C were positively correlated in the drier regions, while they were negatively correlated in the wettest region. The strength and direction of the correlations scaled with regional VPD or the ratio of precipitation to evapotranspiration. Therefore, the correlation strength between RWI and Δ¹³C may be used to infer past wetness or aridity from paleo wood by determining the degree to which carbon gain and growth have been more limited by moisture or light.     

Topo‐climatic microrefugia explain the persistence of a rare endemic plant in the Alps during the last 21 millennia

Ongoing rapid climate change is predicted to cause local extinction of plant species in mountain regions. However, some plant species could have persisted during Quaternary climate oscillations without shifting their range, despite the limited evidence from fossils. Here, we tested two candidate mechanisms of persistence by comparing the macrorefugia and microrefugia (MR) hypotheses. We used the rare and endemic Saxifraga florulenta as a model taxon and combined ensembles of species distribution models (SDMs) with a high‐resolution paleoclimatic and topographic dataset to reconstruct its potential current and past distribution since the last glacial maximum. To test the macrorefugia hypothesis, we verified whether the species could have persisted in or shifted to geographic areas defined by its realized niche. We then identified potential MR based on climatic and topographic properties of the landscape and applied refined scenarios of MR dynamics and functions over time. Last, we quantified the number of known occurrences that could be explained by either the macrorefugia or MR model. A consensus of two or three SDM techniques predicted absence between 14–10, 3–4 and 1 ka bp , which did not support the macrorefugia model. In contrast, we showed that S. florulenta could have contracted into MR during periods of absence predicted by the SDMs and later re‐colonized suitable areas according to the macrorefugia model. Assuming a limited and realistic seed dispersal distance for our species, we explained a large number of the current occurrences (61–96%). Additionally, we showed that MR could have facilitated range expansions or shifts of S. florulenta. Finally, we found that the most recent and the most stable MR were the ones closest to current occurrences. Hence, we propose a novel paradigm to explain plant persistence by highlighting the importance of supporting functions of MR when forecasting the fate of plant species under climate change.     

High-resolution bioclimatic dataset derived from future climate projections for plant species distribution modeling

Species distribution modeling is playing an increasingly prominent role in ecology and global change biology, owing to its potential to predict species range shifts, biodiversity losses, and biological invasion risks for future climates. Such models are now well-established as important tools for biological conservation. However, the lack of high-resolution data for future climate scenarios has seriously limited their application, particularly because of the scale gap between general circulation models (GCMs) and species distribution models (SDMs). A recently introduced change-factor downscaling technique provides a convenient way to build high-resolution datasets from GCM projections. Here, we present a high-resolution (10’ × 10’) global bioclimatic dataset (BioPlant) for plant species distribution. The 15 bioclimatic variables we select are considered those most eco-physiologically relevant. They can be easily calculated from climatic variables common to all GCM projections. In addition to the traditional classes of variables regarding temperature and precipitation, the BioPlant dataset emphasizes the interactions between temperature and precipitation, particularly within plant growing seasons. A preliminary visual analysis shows that variations among GCMs are more significant on a species range scale than on a global scale. Thus, the formerly advocated ensemble modeling method should be applied not only to different SDMs, but also to various GCMs. Statistic analysis suggests that divergent behavior among GCM variations for temperature class variables and classes of precipitation variables requires special attention. Our dataset may provide a common platform for ensemble modeling, and can serve as an example to develop higher-resolution regional datasets.     

Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient?

We use observed patterns of species richness and composition of ant communities along a 1000 mm rainfall gradient in northern Australian savanna to assess the accuracy of species richness and turnover predictions derived from stacked species distribution models (S‐SDMs) and constrained by macroecological models (MEMs). We systematically sampled ants at 15 sites at 50 km intervals along the rainfall gradient in 2012 and 2013. Using the observed data, we created MEMs of species richness, composition and turnover. We built distribution models for 135 of the observed species using data from museum collections and online databases. We compared two approaches of stacking SDMs and three modelling algorithms to identify the most accurate way of predicting richness and composition. We then applied the same beta diversity metrics to compare the observed versus predicted patterns. Stacked SDMs consistently over‐predicted local species richness, and there was a mismatch between the observed pattern of richness estimated from the MEM, and the pattern predicted by S‐SDMs. The most accurate richness and turnover predictions occurred when the stacked models were rank‐ordered by their habitat suitability and constrained by the observed MEM richness predictions. In contrast with species richness, the predictions obtained by the MEM of community similarity, composition and turnover matched those predicted by the S‐SDMs. S‐SDMs regulated by MEMs may therefore be a useful tool in predicting compositional patterns despite being unreliable estimators of species richness. Our results highlight that the choice of species distribution model, the stacking method used, and underlying macroecological patterns all influence the accuracy of community assembly predictions derived from S‐SDMS.     

Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current,past, and future climates

How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat‐balance model, to convert macroclimate data to pika‐specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate‐imposed restrictions on activity. This more complete understanding is necessary to inform climate adaptation actions, management strategies, and conservation plans.     

Global thermal niche models of two European grasses show high invasion risks in Antarctica

The two non‐native grasses that have established long‐term populations in Antarctica (Poa pratensis and Poa annua) were studied from a global multidimensional thermal niche perspective to address the biological invasion risk to Antarctica. These two species exhibit contrasting introduction histories and reproductive strategies and represent two referential case studies of biological invasion processes. We used a multistep process with a range of species distribution modelling techniques (ecological niche factor analysis, multidimensional envelopes, distance/entropy algorithms) together with a suite of thermoclimatic variables, to characterize the potential ranges of these species. Their native bioclimatic thermal envelopes in Eurasia, together with the different naturalized populations across continents, were compared next. The potential niche of P. pratensis was wider at the cold extremes; however, P. annua life history attributes enable it to be a more successful colonizer. We observe that particularly cold summers are a key aspect of the unique Antarctic environment. In consequence, ruderals such as P. annua can quickly expand under such harsh conditions, whereas the more stress‐tolerant P. pratensis endures and persist through steady growth. Compiled data on human pressure at the Antarctic Peninsula allowed us to provide site‐specific biosecurity risk indicators. We conclude that several areas across the region are vulnerable to invasions from these and other similar species. This can only be visualized in species distribution models (SDMs) when accounting for founder populations that reveal nonanalogous conditions. Results reinforce the need for strict management practices to minimize introductions. Furthermore, our novel set of temperature‐based bioclimatic GIS layers for ice‐free terrestrial Antarctica provide a mechanism for regional and global species distribution models to be built for other potentially invasive species.     

气候变化情景下外来入侵植物刺苍耳在新疆的潜在分布格局模拟

明确区域尺度上外来入侵种的潜在分布格局及其对气候变化的响应对入侵种的预防和控制具有重要意义。以外来入侵植物刺苍耳(Xanthium spinosum L.)为研究对象,以其扩散蔓延的新疆地区为研究区域,结合中国国家气候中心开发的BCC—CSM1—1模式下的将来气候条件,应用MaxEnt模型和ArcGIS空间分析技术构建了未来不同气候变化情景(RCP4.5,8.5)下2050s和2070s的刺苍耳适宜生境预测模型,定量的展示了气候变化情景下刺苍耳在新疆的扩散趋势及其适宜生境的面积空间变化和分布区中心移动轨迹。结果表明:年降雨量、下层土壤有机碳含量、上层土壤pH值、年温度变化范围、降雨量的季节性变化和年平均温度是影响刺苍耳地理分布的主导环境因子;博州、塔城、阿勒泰西北部、哈密中部、巴州北部、克州中部、阿克苏北部、奎屯市、克拉玛依市、五家渠市、喀什市等地为高危入侵风险区;两种气候模式下刺苍耳的各级适生区面积和总适生面积均呈持续增加的变化趋势,且在RCP8.5情景(最高温室气体排放情景)下响应更为敏感;总体上看,刺苍耳在新疆的分布未达到饱和,呈现以塔城中部为中心,向天山北麓和塔克拉玛干北缘方向辐射状扩散,且两种气候变化情景下至2070s分布区中心均向伊犁州奎屯方向移动。     

Herbarium records identify sensitivity of flowering phenology of eucalypts to climate: Implications for species response to climate change

Flowering phenology is very sensitive to climate and with increasing global warming the flowering time of plants is shifting to earlier or later dates. Changes in flowering times may affect species reproductive success, associated phenological events, species synchrony, and community composition. Long‐term data on phenological events can provide key insights into the impacts of climate on phenology. For Australia, however, limited data availability restricts our ability to assess the impacts of climate change on plant phenology. To address this limitation other data sources must be explored such as the use of herbarium specimens to conduct studies on flowering phenology. This study uses herbarium specimens for investigating the flowering phenology of five dominant and commercially important Eucalyptus species of south‐eastern Australia and the consequences of climate variability and change on flowering phenology. Relative to precipitation and air humidity, mean temperature of the preceding 3 months was the most influential factor on the flowering time for all species. In response to a temperature increment of 1°C, a shift in the timing of flowering of 14.1–14.9 days was predicted for E. microcarpa and E. tricarpa while delays in flowering of 11.3–15.5 days were found for E. obliqua, E. radiata and E. polyanthemos. Eucalyptus polyanthemos exhibited the greatest sensitivity to climatic variables. The study demonstrates that herbarium data can be used to detect climatic signals on flowering phenology for species with a long flowering duration, such as eucalypts. The robust relationship identified between temperature and flowering phenology indicates that shifts in flowering times will occur under predicted climate change which may affect reproductive success, fitness, plant communities and ecosystems.