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

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

Effects of ecohydrological variables on current and future ranges,local suitability patterns,and model accuracy in big sagebrush

Forecasting of species and ecosystem responses to novel conditions, including climate change, is one of the major challenges facing ecologists at the start of the 21st century. Climate change studies based on species distribution models (SDMs) have been criticized because they extend correlational relationships beyond the observed data. Here, we compared conventional climate‐based SDMs against ecohydrological SDMs that include information from process‐based simulations of water balance. We examined the current and future distribution of Artemisia tridentata (big sagebrush) representing sagebrush ecosystems, which are widespread in semiarid western North America. For each approach, we calculated ensemble models from nine SDM methods and tested accuracy of each SDM with a null distribution. Climatic conditions included current conditions for 1970–1999 and two IPCC projections B1 and A2 for 2070–2099. Ecohydrological conditions were assessed by simulating soil water balance with SOILWAT, a daily time‐step, multiple layer, mechanistic, soil water model. Under current conditions, both climatic and ecohydrological SDM approaches produced comparable sagebrush distributions. Overall, sagebrush distribution is forecasted to decrease, with larger decreases under the A2 than under the B1 scenario and strong decreases in the southern part of the range. Increases were forecasted in the northern parts and at higher elevations. Both SDM approaches produced accurate predictions. However, the ecohydrological SDM approach was slightly less accurate than climatic SDMs (?1% in AUC, ?4% in Kappa and TSS) and predicted a higher number of habitat patches than observed in the input data. Future predictions of ecohydrological SDMs included an increased number of habitat patches whereas climatic SDMs predicted a decrease. This difference is important for understanding landscape‐scale patterns of sagebrush ecosystems and management of sagebrush obligate species for future conditions. Several mechanisms can explain the diverging forecasts; however, we need better insights into the consequences of different datasets for SDMs and how these affect our understanding of future trajectories.     

Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.     

Improving restoration success through microsite selection: an example with planting sagebrush seedlings after wildfire

Post‐fire restoration of foundation plant species, particularly non‐sprouting shrubs, is critically needed in arid and semi‐arid rangeland, but is hampered by low success. Expensive and labor‐intensive methods, including planting seedlings, can improve restoration success. Prioritizing where these more intensive methods are applied may improve restoration efficiency. Shrubs in arid and semi‐arid environments can create resource islands under their canopies that may remain after fire. Seedlings planted post‐fire in former canopy and between canopies (interspace) may have different survival and growth. We compared planting Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) seedlings post‐fire in former sagebrush canopy and interspace microsites at five locations. Four growing seasons after planting, seedling survival was 46 and 7% in canopy and interspace microsites, respectively. Sagebrush cover was 5.8 times greater in canopy compared to interspace microsites. Sagebrush survival and cover were likely greater because of less competition from herbaceous vegetation as well as benefiting from resource island effects in canopy microsites. Initially, post‐fire abundance of exotic annual grasses was less in canopy microsites, but by the third year post‐fire it was substantially greater in canopy microsites, indicating that resource availability to seedlings was greater, at least initially, in canopy microsites. These results suggest microsites with greater likelihood of success should be identified and then utilized to improve restoration success and efficiency. This is important as the need for restoration greatly exceeds resources available for restoration.     

Effects of invasive plants on fire regimes and postfire vegetation diversity in an arid ecosystem

We assessed the impacts of co‐occurring invasive plant species on fire regimes and postfire native communities in the Mojave Desert, western USA. We analyzed the distribution and co‐occurrence patterns of three invasive annual grasses (Bromus rubens, Bromus tectorum, and Schismus spp.) known to alter fuel conditions and community structure, and an invasive forb (Erodium cicutarium) which dominates postfire sites. We developed species distribution models (SDMs) for each of the four taxa and analyzed field plot data to assess the relationship between invasives and fire frequency, years postfire, and the impacts on postfire native herbaceous diversity. Most of the Mojave Desert is highly suitable for at least one of the four invasive species, and 76% of the ecoregion is predicted to have high or very high suitability for the joint occurrence of B. rubens and B. tectorum and 42% high or very high suitability for the joint occurrence of the two Bromus species and E. cicutarium. Analysis of cover from plot data indicated two or more of the species occurred in 77% of the plots, with their cover doubling with each additional species. We found invasive cover in burned plots increased for the first 20 years postfire and recorded two to five times more cover in burned than unburned plots. Analysis also indicated that native species diversity and evenness as negatively associated with higher levels of relative cover of the four invasive taxa. Our findings revealed overlapping distributions of the four invasives; a strong relationship between the invasives and fire frequency; and significant negative impacts of invasives on native herbaceous diversity in the Mojave. This suggests predicting the distributions of co‐occurring invasive species, especially transformer species, will provide a better understanding of where native‐dominated communities are most vulnerable to transformations following fire or other disturbances.     

An empirical test of the relative and combined effects of land‐cover and climate change on local colonization and extinction

Land‐cover and climate change are two main drivers of changes in species ranges. Yet, the majority of studies investigating the impacts of global change on biodiversity focus on one global change driver and usually use simulations to project biodiversity responses to future conditions. We conduct an empirical test of the relative and combined effects of land‐cover and climate change on species occurrence changes. Specifically, we examine whether observed local colonization and extinctions of North American birds between 1981–1985 and 2001–2005 are correlated with land‐cover and climate change and whether bird life history and ecological traits explain interspecific variation in observed occurrence changes. We fit logistic regression models to test the impact of physical land‐cover change, changes in net primary productivity, winter precipitation, mean summer temperature, and mean winter temperature on the probability of Ontario breeding bird local colonization and extinction. Models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local colonization for 30%, 27%, and 29% of species, respectively. Conversely, models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local extinction for 61%, 7%, and 9% of species, respectively. The quantitative impacts of land‐cover and climate change variables also vary among bird species. We then fit linear regression models to test whether the variation in regional colonization and extinction rate could be explained by mean body mass, migratory strategy, and habitat preference of birds. Overall, species traits were weakly correlated with heterogeneity in species occurrence changes. We provide empirical evidence showing that land‐cover change, climate change, and the combination of multiple global change drivers can differentially explain observed species local colonization and extinction.     

Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil

Land use changes have profound effects on populations of Neotropical primates, and ongoing climate change is expected to aggravate this scenario. The titi monkeys from eastern Brazil (Callicebus personatus group) have been particularly affected by this process, with four of the five species now allocated to threatened conservation status categories. Here, we estimate the changes in the distribution of these titi monkeys caused by changes in both climate and land use. We also use demographic‐based, functional landscape metrics to assess the magnitude of the change in landscape conditions for the distribution predicted for each species. We built species distribution models (SDMs) based on maximum entropy for current and future conditions (2070), allowing for different global circulation models and contrasting scenarios of glasshouse gas concentrations. We refined the SDMs using a high‐resolution map of habitat remnants. We then calculated habitat availability and connectivity based on home‐range size and the dispersal limitations of the individual, in the context of a predicted loss of 10% of forest cover in the future. The landscape configuration is predicted to be degraded for all species, regardless of the climatic settings. This include reductions in the total cover of forest remnants, patch size and functional connectivity. As the landscape configuration should deteriorate severely in the future for all species, the prevention of further loss of populations will only be achieved through habitat restoration and reconnection to counteract the negative effects for these and several other co‐occurring species.     

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long‐term stable habitats. The variability of complex, short‐term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.     

Spatially explicit models of seasonal habitat for greater sage‐grouse at broad spatial scales: Informing areas for management in Nevada and northeastern California

Defining boundaries of species' habitat across broad spatial scales is often necessary for management decisions, and yet challenging for species that demonstrate differential variation in seasonal habitat use. Spatially explicit indices that incorporate temporal shifts in selection can help overcome such challenges, especially for species of high conservation concern. Greater sage‐grouse Centrocercus urophasianus (hereafter, sage‐grouse), a sagebrush obligate species inhabiting the American West, represents an important case study because sage‐grouse exhibit seasonal habitat patterns, populations are declining in most portions of their range and are central to contemporary national land use policies. Here, we modeled spatiotemporal selection patterns for telemetered sage‐grouse across multiple study sites (1,084 sage‐grouse; 30,690 locations) in the Great Basin. We developed broad‐scale spatially explicit habitat indices that elucidated space use patterns (spring, summer/fall, and winter) and accounted for regional climatic variation using previously published hydrographic boundaries. We then evaluated differences in selection/avoidance of each habitat characteristic between seasons and hydrographic regions. Most notably, sage‐grouse consistently selected areas dominated by sagebrush with few or no conifers but varied in type of sagebrush selected by season and region. Spatiotemporal variation was most apparent based on availability of water resources and herbaceous cover, where sage‐grouse strongly selected upland natural springs in xeric regions but selected larger wet meadows in mesic regions. Additionally, during the breeding period in spring, herbaceous cover was selected strongly in the mesic regions. Lastly, we expanded upon an existing joint–index framework by combining seasonal habitat indices with a probabilistic index of sage‐grouse abundance and space use to produce habitat maps useful for sage‐grouse management. These products can serve as conservation planning tools that help predict expected benefits of restoration activities, while highlighting areas most critical to sustaining sage‐grouse populations. Our joint–index framework can be applied to other species that exhibit seasonal shifts in habitat requirements to help better guide conservation actions.     

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

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

Land-cover data improve bioclimatic models for anurans and turtles at a regional scale

Aim We investigated whether accounting for land cover could improve bioclimatic models for eight species of anurans and three species of turtles at a regional scale. We then tested whether accounting for spatial autocorrelation could significantly improve bioclimatic models after statistically controlling for the effects of land cover. Location Nova Scotia, eastern Canada. Methods Species distribution data were taken from a recent (1999–2003) herpetofaunal atlas. Generalized linear models were used to relate the presence or absence of each species to climate and land‐cover variables at a 10‐km resolution. We then accounted for spatial autocorrelation using an autocovariate or third‐order trend surface of the geographical coordinates of each grid square. Finally, variance partitioning was used to explore the independent and joint contributions of climate, land cover and spatial autocorrelation. Results The inclusion of land cover significantly increased the explanatory power of bioclimatic models for 10 of the 11 species. Furthermore, including land cover significantly increased predictive performance for eight of the 11 species. Accounting for spatial autocorrelation improved model fit for rare species but generally did not improve prediction success. Variance partitioning demonstrated that this lack of improvement was a result of the high correlation between climate and trend‐surface variables. Main conclusions The results of this study suggest that accounting for the effects of land cover can significantly improve the explanatory and predictive power of bioclimatic models for anurans and turtles at a regional scale. We argue that the integration of climate and land‐cover data is likely to produce more accurate spatial predictions of contemporary herpetofaunal diversity. However, the use of land‐cover simulations in climate‐induced range‐shift projections introduces additional uncertainty into the predictions of bioclimatic models. Further research is therefore needed to determine whether accounting for the effects of land cover in range‐shift projections is merited.     

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

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

The contributions of topoclimate and land cover to species distributions and abundance: fine‐resolution tests for a mountain butterfly fauna

Aim Models relating species distributions to climate or habitat are widely used to predict the effects of global change on biodiversity. Most such approaches assume that climate governs coarse‐scale species ranges, whereas habitat limits fine‐scale distributions. We tested the influence of topoclimate and land cover on butterfly distributions and abundance in a mountain range, where climate may vary as markedly at a fine scale as land cover. Location Sierra de Guadarrama (Spain, southern Europe) Methods We sampled the butterfly fauna of 180 locations (89 in 2004, 91 in 2005) in a 10,800 km² region, and derived generalized linear models (GLMs) for species occurrence and abundance based on topoclimatic (elevation and insolation) or habitat (land cover, geology and hydrology) variables sampled at 100‐m resolution using GIS. Models for each year were tested against independent data from the alternate year, using the area under the receiver operating characteristic curve (AUC) (distribution) or Spearman's rank correlation coefficient (r_s) (abundance). Results In independent model tests, 74% of occurrence models achieved AUCs of > 0.7, and 85% of abundance models were significantly related to observed abundance. Topoclimatic models outperformed models based purely on land cover in 72% of occurrence models and 66% of abundance models. Including both types of variables often explained most variation in model calibration, but did not significantly improve model cross‐validation relative to topoclimatic models. Hierarchical partitioning analysis confirmed the overriding effect of topoclimatic factors on species distributions, with the exception of several species for which the importance of land cover was confirmed. Main conclusions Topoclimatic factors may dominate fine‐resolution species distributions in mountain ranges where climate conditions vary markedly over short distances and large areas of natural habitat remain. Climate change is likely to be a key driver of species distributions in such systems and could have important effects on biodiversity. However, continued habitat protection may be vital to facilitate range shifts in response to climate change.     

Climate and satellite-derived land cover for predicting breeding bird distribution in the Great Lakes Basin

Aim We examined relationships between breeding bird distribution of 10 forest songbirds in the Great Lakes Basin, large‐scale climate and the distribution of land cover types as estimated by advanced very high resolution radiometer (AVHRR) and multi‐spectral scanner (MSS) land cover classifications. Our objective was to examine the ability of regional climate, AVHRR (1 km resolution) land cover and MSS (200 m resolution) land cover to predict the distribution of breeding forest birds at the scale of the Great Lakes Basin and at the resolution of Breeding Bird Atlas data (5–10 km²). Specifically we addressed the following questions. (1) How well do AVHRR or MSS classifications capture the variation in distribution of bird species? (2) Is one land cover classification more useful than the other for predicting distribution? (3) How do models based on climate compare with models based on land cover? (4) Can the combination of both climate and land cover improve the predictive ability of these models. Location Modelling was conducted over the area of the Great Lakes Basin including parts of Ontario, Canada and parts of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania Wisconsin, and Minnesota, USA. Methods We conducted single variable logistic regression with the forest classes of AVHRR and MSS land cover using evidence of breeding as the response variable. We conducted multiple logistic regression with stepwise selection to select models from five sets of explanatory variables (AVHRR, MSS, climate, AVHRR + climate, MSS + climate). Results Generally, species were related to both AVHRR and MSS land cover types in the direction expected based on the known local habitat use of the species. Neither land cover classification appeared to produce consistently more intuitive results. Good models were generated using each of the explanatory data sets examined here. And at least one but usually all five variable sets produced acceptable or excellent models for each species. Main conclusions Both climate and large scale land cover were effective predictors of the distribution of the 10 forest bird species examined here. Models generated from these data had good classification accuracy of independent validation data. Good models were produced from all explanatory data sets or combinations suggesting that the distribution of climate, AVHRR land cover, and MSS land cover all captured similar variance in the distribution of the birds. It is difficult to separate the effects of climate and vegetation on the species’ distributions at this scale.     

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.     

Predicting Plant Diversity Patterns in Madagascar: Understanding the Effects of Climate and Land Cover Change in a Biodiversity Hotspot

Climate and land cover change are driving a major reorganization of terrestrial biotic communities in tropical ecosystems. In an effort to understand how biodiversity patterns in the tropics will respond to individual and combined effects of these two drivers of environmental change, we use species distribution models (SDMs) calibrated for recent climate and land cover variables and projected to future scenarios to predict changes in diversity patterns in Madagascar. We collected occurrence records for 828 plant genera and 2186 plant species. We developed three scenarios, (i.e., climate only, land cover only and combined climate-land cover) based on recent and future climate and land cover variables. We used this modelling framework to investigate how the impacts of changes to climate and land cover influenced biodiversity across ecoregions and elevation bands. There were large-scale climate- and land cover-driven changes in plant biodiversity across Madagascar, including both losses and gains in diversity. The sharpest declines in biodiversity were projected for the eastern escarpment and high elevation ecosystems. Sharp declines in diversity were driven by the combined climate-land cover scenarios; however, there were subtle, region-specific differences in model outputs for each scenario, where certain regions experienced relatively higher species loss under climate or land cover only models. We strongly caution that predicted future gains in plant diversity will depend on the development and maintenance of dispersal pathways that connect current and future suitable habitats. The forecast for Madagascar’s plant diversity in the face of future environmental change is worrying: regional diversity will continue to decrease in response to the combined effects of climate and land cover change, with habitats such as ericoid thickets and eastern lowland and sub-humid forests particularly vulnerable into the future.     

Restoration of Native Warm Season Grassland Species in a Tall Fescue Pasture Using Prescribed Fire and Herbicides

Pastures dominated by tall fescue (Schedonorus phoenix (Scop.) Holub) cover much of the eastern United States, and there are increasing efforts to restore native grassland plant species to some of these areas. Prescribed fire and herbicide are frequently used to limit the growth of tall fescue and other non‐natives, while encouraging native grasses and forbs. A fungal endophyte, commonly present in tall fescue, can confer competitive advantages to the host plant, and may play a role in determining the ability of tall fescue plants to persist in pastures following restoration practices. We compared vegetation composition among four actively restored subunits of a tall fescue pasture (each receiving different combinations of prescribed fire and/or herbicide) and a control. We also measured the rate of endophyte infection in tall fescue present within each restoration treatment and control to determine if restoration resulted in lower tall fescue cover but higher endophyte infection rates (i.e. selected for endophyte‐infected individuals). Tall fescue cover was low in all restoration treatments and the control (1.1–17.9%). The control (unmanaged) had higher species richness than restoration treatments and plant community composition was indicative of succession to forest. Restoration practices resulted in higher cover of native warm season grasses, but in some cases also promoted a different undesirable species. We found no evidence of higher fungal endophyte presence in tall fescue following restoration, as all subunits had low endophyte infection rates (2.2–9.3%). Restoration of tall fescue systems using prescribed fire and herbicide may be used to promote native grassland species.     

Temporal and spatial patterns in fire occurrence during the establishment of mixed‐oak forests in eastern North America

Question: What was the role of fire during the establishment of the current overstory (ca. 1870–1940) in mixed‐oak forests of eastern North America? Location: Nine sites representing a 240‐km latitudinal gradient on the Allegheny and Cumberland Plateaus of eastern North America. Methods: Basal cross‐sections were collected from 225 trees. Samples were surfaced, and fire scars were dated. Fire history diagrams were constructed and fire return intervals were calculated for each site. Geographic patterns of fire occurrence, and fire‐climate relationships were assessed. Results: Fire was a frequent and widespread occurrence during the formation of mixed‐oak forests, which initiated after large‐scale land clearing in the region ca. 1870. Fire return ranged from 1.7 to 11.1 years during a period of frequent burning from 1875 to 1936. Fires were widespread during this period, sometimes occurring across the study region in the same year. Fires occurred in a variety of climate conditions, including both drought and non‐drought years. Fires were rare from 1936 to the present. Conclusions: A variety of fire regime characteristics were discerned. First, a period of frequent fire lasted approximately 60 years during the establishment of the current oak overstory. Second, fire occurred during a variety of climate conditions, including wet climates and extreme drought. Finally, there was within‐site temporal variability in fire occurrence. These reference conditions could be mimicked in ongoing oak restoration activities, improving the likelihood of restoration success.     

Meta‐replication,sampling bias,and multi‐scale model selection: A case study on snow leopard (Panthera uncia) in western China

Replicated multiple scale species distribution models (SDMs) have become increasingly important to identify the correct variables determining species distribution and their influences on ecological responses. This study explores multi‐scale habitat relationships of the snow leopard (Panthera uncia) in two study areas on the Qinghai–Tibetan Plateau of western China. Our primary objectives were to evaluate the degree to which snow leopard habitat relationships, expressed by predictors, scales of response, and magnitude of effects, were consistent across study areas or locally landcape‐specific. We coupled univariate scale optimization and the maximum entropy algorithm to produce multivariate SDMs, inferring the relative suitability for the species by ensembling top performing models. We optimized the SDMs based on average omission rate across the top models and ensembles’ overlap with a simulated reference model. Comparison of SDMs in the two study areas highlighted landscape‐specific responses to limiting factors. These were dependent on the effects of the hydrological network, anthropogenic features, topographic complexity, and the heterogeneity of the landcover patch mosaic. Overall, even accounting for specific local differences, we found general landscape attributes associated with snow leopard ecological requirements, consisting of a positive association with uplands and ridges, aggregated low‐contrast landscapes, and large extents of grassy and herbaceous vegetation. As a means to evaluate the performance of two bias correction methods, we explored their effects on three datasets showing a range of bias intensities. The performance of corrections depends on the bias intensity; however, density kernels offered a reliable correction strategy under all circumstances. This study reveals the multi‐scale response of snow leopards to environmental attributes and confirms the role of meta‐replicated study designs for the identification of spatially varying limiting factors. Furthermore, this study makes important contributions to the ongoing discussion about the best approaches for sampling bias correction.