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Understanding the spatial scales at which environmental factors drive species richness patterns is a major challenge in ecology. Due to the trade-off between spatial grain and extent, studies tend to focus on a single spatial scale, and the effects of multiple environmental variables operating across spatial scales on the pattern of local species richness have rarely been investigated. Here, we related variation in local species richness of ground beetles, landbirds and small mammals to variation in vegetation structure and topography, regional climate, biome diversity and glaciation history for 27 sites across the USA at two different spatial grains. We studied the relative influence of broad-scale (landscape) environmental conditions using variables estimated at the site level (climate, productivity, biome diversity and glacial era ice cover) and fine-scale (local) environmental conditions using variables estimated at the plot level (topography and vegetation structure) to explain local species richness. We also examined whether plot-level factors scale up to drive continental scale richness patterns. We used Bayesian hierarchical models and quantified the amount of variance in observed richness that was explained by environmental factors at different spatial scales. For all three animal groups, our models explained much of the variation in local species richness (85%-89%), but site-level variables explained a greater proportion of richness variance than plot-level variables. Temperature was the most important site-level predictor for explaining variance in landbirds and ground beetles richness. Some aspects of vegetation structure were the main plot-level predictors of landbird richness. Environmental predictors generally had poor explanatory power for small mammal richness, while glacial era ice cover was the most important site-level predictor. Relationships between plot-level factors and richness varied greatly among geographical regions and spatial grains, and most relationships did not hold when predictors were scaled up to the continental scale. Our results suggest that the factors that determine richness may be highly dependent on spatial grain, geography, and animal group. We demonstrate that instead of artificially manipulating the resolution to study multiscale effects, a hierarchical approach that uses fine grain data at broad extents could help solve the issue of scale selection in environment-richness studies.

To be able to protect biodiversity in coming decades, conservation strategies need to consider what sites will be important for species not just today but also in the future. Different methods have been proposed to identify places that will be important for species in the future. Two of the most frequently used methods, ecological niche modeling and climate resilience, have distinct aims. The former focuses on identifying the suitable environmental conditions for species, thus protecting the "actor", namely the species. The later seeks to safeguard the "stage", or the landscape in which species occur. We used the two methods to identify climate refugia for 258 forest vertebrates under short- and long- term climatic changes in a biodiversity hotspot, the Appalachian ecoregion of United States. We also evaluated the spatial congruence of the two approaches for a possible conservation application, that of protecting 30% of the Appalachian region, in line with recent national and international policy recommendations. We detected weak positive correlations between resilience scores and baseline vertebrate richness, estimated with ecological niche models for historical (baseline) climatic conditions. The correlations were stronger for amphibians and mammals than for birds and reptiles. Under climate change scenarios, the correlations between estimated vertebrate richness and resilience were also weakly positive; a positive correlation was detected only for amphibians. Locations with estimated future gain of suitable climatic conditions for vertebrates showed low correlation with resilience. Overall, our results indicate that climate resilience and ecological niche modeling approaches capture different characteristics of projected distributional changes of Appalachian vertebrates. Climate resilience (the stage) approach could be more effective in safeguarding species with low dispersal abilities whereas ecological niche modeling (the actor) approach could be more suitable for species with long-distance dispersal capacity as they may be more broadly impacted by climate and less sensitive to geophysical features captured by climate resilience approach. This article is protected by copyright. All rights reserved.

Boundaries between vegetation types, known as ecotones, can be dynamic in response to climatic changes. The North American Great Plains includes a forest‐grassland ecotone in the southcentral United States that has expanded and contracted in recent decades in response to historical periods of drought and pluvial conditions. This dynamic region also marks a western distributional limit for many passerine birds that typically breed in forests of the eastern United States. To better understand the influence that variability can exert on broad‐scale biodiversity, we explored historical longitudinal shifts in the western extent of breeding ranges of eastern forest songbirds in response to the variable climate of the southern Great Plains. We used climatic niche modeling to estimate current distributional limits of nine species of forest‐breeding passerines from 30‐year average climate conditions from 1980 to 2010. During this time, the southern Great Plains experienced an unprecedented wet period without periodic multi‐year droughts that characterized the region's long‐term climate from the early 1900s. Species’ climatic niche models were then projected onto two historical drought periods: 1952–1958 and 1966–1972. Threshold models for each of the three time periods revealed dramatic breeding range contraction and expansion along the forest‐grassland ecotone. Precipitation was the most important climate variable defining breeding ranges of these nine eastern forest songbirds. Range limits extended farther west into southern Great Plains during the more recent pluvial conditions of 1980–2010 and contracted during historical drought periods. An independent dataset from BBS was used to validate 1966–1972 range limit projections. Periods of lower precipitation in the forest‐grassland ecotone are likely responsible for limiting the western extent of eastern forest songbird breeding distributions. Projected increases in temperature and drought conditions in the southern Great Plains associated with climate change may reverse range expansions observed in the past 30 years., We evaluated distributional shifts of nine eastern forest songbirds at the western edge of their range extent along the transitional ecotone between eastern temperate forests and the central great plains of southcentral United States. Niche modeling of community science data revealed distributional limits of these species are sensitive to variable precipitation and periodic drought events.

Climate adaptation strategies are being developed and implemented to protect biodiversity from the impacts of climate change. A well-established strategy involves the identification and addition of new areas for conservation, and most countries agreed in 2010 to expand the global protected area (PA) network to 17% by 2020 (Aichi Biodiversity Target 11). Although great efforts to expand the global PA network have been made, the potential of newly established PAs to conserve biodiversity under future climate change remains unclear at the global scale. Here, we conducted the first global-extent, country-level assessment of the contribution of PA network expansion toward three key land prioritization approaches for biodiversity persistence under climate change: protecting climate refugia, protecting abiotic diversity, and increasing connectivity. These approaches avoid uncertainties of biodiversity predictions under climate change as well as the issue of undescribed species. We found that 51% of the countries created new PAs in locations with lower mean climate velocity (representing better climate refugia) and 58% added PAs in areas with higher mean abiotic diversity compared to the available, non-human-dominated lands not chosen for protection. However, connectivity among PAs declined in 53% of the countries, indicating that many new PAs were located far from existing PAs. Lastly, we identified potential improvements for climate adaptation, showing that 94% of the countries have the opportunity to improve in executing one or more approaches to conserve biodiversity. Most countries (60%) were associated with multiple opportunities, highlighting the need for integrative strategies that target multiple land protection approaches. Our results demonstrate that a global improvement in the protection of climate refugia, abiotic diversity, and connectivity of reserves is needed to complement land protection informed by existing and projected species distributions. Our study also provides a framework for countries to prioritize land protection for climate adaptation using publicly available data.

Estimating species distributions requires species presence data of sufficient quantity from reputable sources that are geographically representative of the species’ space use. Collecting presence data that meets these standards can be costly and is often complicated by limited land access. Citizen science projects are an appealing alternative source of presence data as these data are freely available and collected globally. Websites such as eBird have become increasingly large repositories of citizen science data. The vulnerable lesser prairie-chicken (LPC; Tympanuchus pallidicinctus) is a species well-represented in the eBird database, with presence observations from 140 unique locations from 2012 to 2014. During that same period, a distribution-wide, standardized aerial survey with state and federal support recorded 106 LPC detections. Our objective was to compare species distribution models (SDMs) made with eBird data to models made with aerial survey data to determine the potential for citizen science data to contribute to conservation planning. We used maximum entropy modeling to create SDMs based on eBird data, aerial survey data, and a combination of both data sets using variables of biological significance to LPCs. We obtained comparable model performance using aerial survey data only [standardized test omission rate (STO): 23.4%, test AUC: 0.76] and with eBird data only (STO: 23.8%, AUC: 0.76). The I statistic confirmed a very high degree of similarity between the outputs of the two model sets (I = 0.929). However, a road bias existed within both data sets (positive and negative biases), potentially confounding some environmental correlates. Despite this bias, our combined model predicted an increase of 1,732,500 ha of unique area suitable for the LPC. Our results indicated that eBird data could be used as a low-cost source for species occurrence data to create species distribution models, though biases in these datasets should be assessed to guide interpretability of the predicted outputs.

Climate change and human‐mediated dispersal are increasingly influencing species’ geographic distributions. Ecological niche models (ENMs) are widely used in forecasting species’ distributions, but are weak in extrapolation to novel environments because they rely on available distributional data and do not incorporate mechanistic information, such as species’ physiological response to abiotic conditions. To improve accuracy of ENMs, we incorporated physiological knowledge through Bayesian analysis. In a case study of the zebra mussel Dreissena polymorpha, we used native and global occurrences to obtain native and global models representing narrower and broader understanding of zebra mussel’ response to temperature. We also obtained thermal limit and survival information for zebra mussel from peer‐reviewed literature and used the two types of information separately and jointly to calibrate native models. We showed that, compared to global models, native models predicted lower relative probability of presence along zebra mussel's upper thermal limit, suggesting the shortcoming of native models in predicting zebra mussel's response to warm temperature. We also found that native models showed improved prediction of relative probability of presence when thermal limit was used alone, and best approximated global models when both thermal limit and survival data were used. Our result suggests that integration of physiological knowledge enhances extrapolation of ENM in novel environments. Our modeling framework can be generalized for other species or other physiological limits and may incorporate evolutionary information (e.g. evolved thermal tolerance), thus has the potential to improve predictions of species’ invasive potential and distributional response to climate change.

Reporting specific modelling methods and metadata is essential to the reproducibility of ecological studies, yet guidelines rarely exist regarding what information should be noted. Here, we address this issue for ecological niche modelling or species distribution modelling, a rapidly developing toolset in ecology used across many aspects of biodiversity science. Our quantitative review of the recent literature reveals a general lack of sufficient information to fully reproduce the work. Over two-thirds of the examined studies neglected to report the version or access date of the underlying data, and only half reported model parameters. To address this problem, we propose adopting a checklist to guide studies in reporting at least the minimum information necessary for ecological niche modelling reproducibility, offering a straightforward way to balance efficiency and accuracy. We encourage the ecological niche modelling community, as well as journal reviewers and editors, to utilize and further develop this framework to facilitate and improve the reproducibility of future work. The proposed checklist framework is generalizable to other areas of ecology, especially those utilizing biodiversity data, environmental data and statistical modelling, and could also be adopted by a broader array of disciplines.

Transferability of species distribution models (SDMs) is key to predicting invasion patterns and can be challenged if niche shift occurs in the invaded range. When using native occurrences to estimate potential invasions with presence-only modeling methods, it is important to constrain the pseudo-absence (PA) sampling to the species’ native range. However, some studies including highly cited ones, do not follow this approach to selecting PA samples. In this research, we addressed two questions using an invasive species in the United States (U.S.), kudzu bug (Megacopta cribraria): 1) is model transferability challenged by a non-adaptive niche shift? and 2) is model performance affected by use of PA samples from outside the native range of the species? Kudzu bug is native to Asia, with recently observed non-adaptive niche shift in the U.S. To answer the first question, we quantified the environmental space anisotropy and non-adaptive niche change, and then evaluated the performances of seven SDMs. To answer the second question, we further compared the interpolation and transferability of seven SDMs trained with PAs from the native range and from both native and invaded ranges. We confirmed that the environmental space anisotropy (P = 0.01) and non-adaptive niche change (P = 0.01) are both statistically significant. Of the seven SDMs used, four models had transferability indices higher than 0.9. Boosted regression tree and random forests both had good interpolation and transferability (AUC>0.80 and kappa>0.60), whereas three other models showed good interpolation and fair transferability (AUC>0.70 and kappa>0.40). Inclusion of pseudo-absences from the invaded range significantly increased the interpolation (P

Wildfires are increasing in frequency and intensity as drier and warmer climates increase plant detrital fuel loads. At the same time, increases in urbanization position 9% of fire-prone land within the United States at the wildland–urban interface. While rarely studied, the compounded effects of urbanization and wildfires may have unknown synergistically negative effects on ecosystems. Previous studies at the wildland–urban interface often focus on aboveground plant communities, but belowground ecosystems may also be affected by this double disturbance. In particular, it is unclear how much fire and urbanization independently or interactively affect nutritional symbioses such as those between arbuscular mycorrhizal (AM) fungi and the majority of terrestrial plants. In November 2016, extreme drought conditions and long-term fire suppression combined to create a wildfire within the Great Smoky Mountains National Park and the neighboring exurban city of Gatlinburg, TN. To understand how the double disturbance of urbanization and fire affected AM fungal communities, we collected fine roots from the 5 dominant understory species in September 2018 at each of 18 sites spanning 3 burn severities in both exurban and natural sites. Despite large variation in burn severity, plant species identity had the largest influence on AM fungi. AM fungal colonization, richness, and composition all varied most among plant species. Fire and urbanization did influence some AM fungal metrics; colonization was lower in burned sites and composition was more variable among exurban locations. There were no interactions among burn severity and urbanization on AM fungi. Our results point to the large influence of plant species identity structuring this obligate nutritional symbiosis regardless of disturbance regime. Therefore, the majority of AM fungal taxa may be buffered from fire-induced ecosystem changes if plant community composition largely remains intact, plant species life history traits allow for AM fungal persistence after fire disturbance, and/or nearby undisturbed habitat can act as an inoculum source for recolonization following fires. Thus, it is critical to maintain natural, undisturbed habitats interspersed within the wildland–urban interface.

About 70% of the world's main crops depend on insect pollination. Climate change is already affecting the abundance and distribution of insects, which could cause geographical mismatches between crops and their pollinators. Crops that rely primarily on wild pollinators (e.g., crops that cannot be effectively pollinated by commercial colonies of honey bees) could be particularly in jeopardy. However, limited information on plant-pollinator associations and pollinator distributions complicate the assessment of climate change impacts on specific crops. To study the potential impacts of climate change on pollination of a specific crop in North America, we use the case of open-field tomato crops, which rely on buzz pollinators (species that use vibration to release pollen, such as bumble bees) to increase their production. We aimed to (1) assess potential changes in buzz pollinator distribution and richness, and (2) evaluate the overlap between areas with high densities of tomato crops and high potential decrease in richness. We used baseline (1961-1990) climate and future (2050s and 2080s) climatic projections in ecological niche models fitted with occurrences of wild bees, documented in the literature as pollinators of tomatoes, to estimate the baseline and future potential distribution of suitable climatic conditions of targeted species and to create maps of richness change across North America. We obtained reliable models for 15 species and found important potential decreases in the distribution of some pollinators (e.g., Lasioglossum pectorale and Augochlorella aurata). We observed geographical discrepancies in the projected change in species richness across North America, detecting important declines in the eastern United States (up to 11 species decrease for 2050s). After overlapping the maps of species richness change with a tomato crop map for the United States, we found spatial correspondence between richness declines and areas with high concentration of tomato crops. Disparities in the effects of climate change on the potential future distribution of different wild pollinators and geographical variation in richness highlight the importance of crop-specific studies. Our study also emphasizes the challenges of compiling and modeling crop-specific pollinator data and the need to improve our understanding of current distribution of pollinators and their community dynamics under climate change.

International audience; Due to their complexity, coral reefs are difficult to study especially when considering the role that the interplay between the terrestrial and marine environments has in shaping distribution of marine, terrestrial, and amphibious species. Many organisms live in remote areas of the ocean and inhabit both terrestrial and marine environments. Such amphibious lifestyle poses analytical difficulties due to broad distribution and scale of coral reefs. Ecological niche modeling is a widely used technique that allows to estimate the environmental set of conditions (niche) in which organisms can survive and reproduce. Estimating the distributions of species with complex life histories (i.e., dependent on various natural resources) at broad geographic scales is crucial, as many of these taxa are threatened (i.e., amphibians, aquatic reptiles, birds, and mammals). However, distribution estimates of such species remain challenging; thus, here we propose an approach to account for marine and terrestrial environmental domains to estimate the distribution of amphibious species. We also test whether inclusion of both environments leads to improved estimates of these species’ distributions. First we calibrated ecological niche models for marine and terrestrial domains separately, and subsequently we outlined a method to combine the marine–terrestrial potential distributions by integrating estimates of the two ecological niches into a single predictive model. Our ecological niche models produced inaccurate distribution predictions of species with amphibious life histories when only one of the environments was used in model calibration. When both aquatic and terrestrial environments were included, our models predicted narrower and more accurate potential distributions. Accounting for the dual environments involved in shaping the niches of amphibious species and their distributions is essential for studying the ecology and proposing conservation management actions for the species studied here. Models that take into account only a subset of the environmental factors are prone to overestimating species’ distributions and should be interpreted with caution.

Revenues from oil and gas development play a key role in the economies of many states in the United States, but there are environmental concerns such as local air and water pollution, land cover changes and fragmentation, and negative effects on wildlife. These concerns were exacerbated as oil and gas production reached historic highs due to the expanded use of hydraulic fracturing technology beginning in the mid-2000s. This paper examines the influence of oil and gas development on breeding bird communities in the High Plains ecoregion of Colorado between 2003 and 2018. Specifically, we investigated whether oil and gas drilling or production affected species richness or evenness of bird communities. Our findings indicate that a decrease in producing well density or the number of producing fracking wells were associated with an increase in evenness of the overall bird community and grassland species richness, but disturbances from well drilling generally did not have a statistically significant influence on bird communities. We also found that a decrease in tree cover was associated with an increase in species richness and evenness of the overall bird community.

Fluvial fishes face increased imperilment from anthropogenic activities, but the specific factors contributing most to range declines are often poorly understood. For example, the range of the fluvial-specialist shoal bass (Micropterus cataractae) continues to decrease, yet how perceived threats have contributed to range loss is largely unknown. We used species distribution models to determine which factors contributed most to shoal bass range loss. We estimated a potential distribution based on natural abiotic factors and a series of currently occupied distributions that incorporated variables characterizing land cover, non-native species, and river fragmentation intensity (no fragmentation, dams only, and dams and large impoundments). We allowed interspecific relationships between non-native congeners and shoal bass to vary across fragmentation intensities. Results from the potential distribution model estimated shoal bass presence throughout much of their native basin, whereas models of currently occupied distribution showed that range loss increased as fragmentation intensified. Response curves from models of currently occupied distribution indicated a potential interaction between fragmentation intensity and the relationship between shoal bass and non-native congeners, wherein non-natives may be favored at the highest fragmentation intensity. Response curves also suggested that >100 km of interconnected, free-flowing stream fragments were necessary to support shoal bass presence. Model evaluation, including an independent validation, suggested that models had favorable predictive and discriminative abilities. Similar approaches that use readily available, diverse, geospatial data sets may deliver insights into the biology and conservation needs of other fluvial species facing similar threats.

Climate change has determined shifts in distributions of species and changing climates are likely to continue to affect species in the future. In this study, we used Maxent, an ecological niche modeling algorithm, to estimate the potential future distributions of apex predator and mesopredator mammals in boreal forest and tundra biomes of North America. We projected the climatic niche models of apex predators and mesopredators on future climate datasets based on three global circulation models (Beijing Climate Center Climate System Model, Hadley Global Environment Model, and Model for Interdisciplinary Research on Climate Earth System Model) and four greenhouse gas emission scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5). Under future climate projections, the potential distributions of most of the predators studied increased by 2050 and 2070. The potential distributions of two apex predators (brown bear, Ursus arctos, and polar bear, U. maritimus) and two mesopredator species (Canadian lynx, Lynx canadensis, and Arctic fox, Vulpes lagopus) were predicted to decline under all emission scenarios, by 2050 and 2070. The only apex predator that was predicted to increase its distribution under all greenhouse gas emission scenarios was U. americanus (American black bear). Similarly, distributions of mesopredators like Mephitis mephitis (striped skunk), and Procyon lotor (raccoon) were predicted to increase greatly under future climate conditions of all four emission scenarios. Predicted expansions of distribution ranges of most mesopredators and contractions of distribution ranges of apex predators included in this study may result in changes of species interactions in North American boreal forests and tundras in the future.

Identifying patterns and drivers of infectious disease dynamics across multiple scales is a fundamental challenge for modern science. There is growing awareness that it is necessary to incorporate multi-host and/or multi-parasite interactions to understand and predict current and future disease threats better, and new tools are needed to help address this task. Eco-phylogenetics (phylogenetic community ecology) provides one avenue for exploring multi-host multi-parasite systems, yet the incorporation of eco-phylogenetic concepts and methods into studies of host pathogen dynamics has lagged behind. Eco-phylogenetics is a transformative approach that uses evolutionary history to infer present-day dynamics. Here, we present an eco-phylogenetic framework to reveal insights into parasite communities and infectious disease dynamics across spatial and temporal scales. We illustrate how eco-phylogenetic methods can help untangle the mechanisms of host-parasite dynamics from individual (e.g. co-infection) to landscape scales (e.g. parasite/host community structure). An improved ecological understanding of multi-host and multi-pathogen dynamics across scales will increase our ability to predict disease threats.

Biological integrity is continuously threatened by human activity. Impacts to biological health may be inferred directly (i.e. bioassessment), or estimated indirectly using rapidly collected field data or computer-based data layer analysis. This study tests whether surrounding land cover alteration (crops, pasture, and development in 200-m buffer) can predict and classify wetland biological health as represented by floristic quality (FQ) and taxonomic distinctness (TD). From surveys of 106 non-forested wetlands in Oklahoma during 2012–2015, we found a limited ability for land use to estimate site-specific FQ and TD values. Although the combined land uses were not a strong predictor overall, pasture land use by itself provided a weak to moderate surrogate when precipitation and ecoregion were taken into account. Floristic quality metrics and average TD decreased with increasing pasture in the 200-m buffer. Land use performed fairly well in classifying sites as biologically intact or not different from a least-altered state (5% FQ and 9% TD misclassification rate), but performed poorly in classifying sites as biologically impaired or deviating from a least-altered state (36% FQ and 24% TD misclassification rate). Importantly, classification analysis suggested that limited land use may separate higher FQ from moderate and lower FQ, whereas more intensive land use may separate lower average TD from moderate and higher average TD. This suggests that both measures (FQ and average TD) are needed for a more complete classification of wetland biological response to anthropogenic disturbance. Despite growing interest in relatively rapid or low-cost methods of wetland assessment, our results do not support using such methods in lieu of direct bioassessment.

Ecological niche modeling has emerged as a notable tool in invasive species risk assessment. However, the advances of the ecological niche theory, the basis of ecological niche modeling, are not matched by availability of detailed biological data. Thus, we proposed a conceptual framework to refine the boundaries of ecological niche, differentiating the species’ existence status (survival, growth, and reproduction) based on physiological limits of major life history characteristics. We discussed differences between the classic and the proposed frameworks and emphasized the importance of the time axis in understanding species’ existence status. We exemplified the implementation potential of this framework by reviewing published studies of physiological limits (temperature and salinity) for survival, growth, and reproduction of invasive freshwater bivalves in USA. We found considerable amount of physiological information through the literature review, though there is a research bias toward more influential invasive species (vs. less influential invasive species), temperature tolerance (vs. salinity tolerance), and survival limits (vs. growth and reproduction limits); filling the knowledge gaps will strengthen the potential of the proposed framework. Our framework addresses the lack of long-term field data, but is limited in that one can only identify unsuitable instead of suitable conditions for species, given incomplete understanding of species’ physiological tolerance. Future studies may consider developing new algorithms that utilize physiological limits as priors in a Bayesian approach.

Survival and reproduction are the two primary life-history traits essential for species' persistence; however, the environmental conditions that support each of these traits may not be the same. Despite this, reproductive requirements are seldom considered when estimating species' potential distributions. We sought to examine potentially limiting environmental factors influencing the distribution of an oviparous reptile of conservation concern with respect to the species' survival and reproduction and to assess the implications of the species' predicted climatic constraints on current conservation practices. We used ecological niche modeling to predict the probability of environmental suitability for the alligator snapping turtle (Macrochelys temminckii). We built an annual climate model to examine survival and a nesting climate model to examine reproduction. We combined incubation temperature requirements, products of modeled soil temperature data, and our estimated distributions to determine whether embryonic development constrained the northern distribution of the species. Low annual precipitation constrained the western distribution of alligator snapping turtles, whereas the northern distribution was constrained by thermal requirements during embryonic development. Only a portion of the geographic range predicted to have a high probability of suitability for alligator snapping turtle survival was estimated to be capable of supporting successful embryonic development. Historic occurrence records suggest adult alligator snapping turtles can survive in regions with colder climes than those associated with consistent and successful production of offspring. Estimated egg-incubation requirements indicated that current reintroductions at the northern edge of the species' range are within reproductively viable environmental conditions. Our results highlight the importance of considering survival and reproduction when estimating species' ecological niches, implicating conservation plans, and benefits of incorporating physiological data when evaluating species' distributions.

The structural heterogeneity of vegetation is a key factor for explaining animal diversity patterns at a local scale. Improvements in airborne light detection and ranging (lidar) technologies have enabled researchers to study forest 3D structure with increasing accuracy. Most structure–animal diversity work has focused on structural metrics derived from lidar returns from canopy and terrain features. Here, we built new lidar structural metrics based on the Leaf Area Density (LAD) at each vegetation height layer, and used these metrics to study how different aspects of forest structural heterogeneity explain variation in bird species richness. Our goals were to test: (1) whether LAD-based metrics better explained bird species richness compared to metrics based on the top of the canopy; and (2) if different aspects of structural heterogeneity had diverse effects on bird richness. We used discrete lidar data together with 61 breeding landbird points provided by the National Ecological Observatory Network at five forest sites of the eastern US. We used the lidar metrics as predictors of bird species richness and analyzed the shape of the response curves against each predictor. Metrics based on LAD measurements had better explanatory power (43% of variance explained) than those based on the variation of canopy heights (32% of variance explained). Dividing the forest plots into smaller grids allowed us to study the within-plot horizontal variation of the vertical heterogeneity, as well as to analyze how the vegetation density is horizontally distributed at each height layer. Bird species richness increased with horizontal heterogeneity, while vertical heterogeneity had negative effects, contrary to previous research. The increasing capabilities of lidar will allow researchers to characterize forest structure with higher detail. Our findings highlight the need for structure–animal diversity studies to incorporate metrics that are able to capture different aspects of forest 3D heterogeneity.

Dispersal is an important, yet overlooked phenomenon when studying species’ distributions using ecological niche models (ENMs). Here, we use species’ dispersal limits to evaluate and refine ENM estimates only to the areas accessible to the species within the study region and, thereby, enhance the model interpretations. First, we ran multiple ENMs to estimate the distribution of sea krait species in both marine and terrestrial environments. Second, we estimated dispersal of the sea kraits using a cost analysis approach. Finally, we outline a new approach that combines dispersal models and ENMs with the purpose of estimating the accessible range when projecting species’ distribution estimates outside the known species range. We found that only a small proportion of the area the ENMs were projected over was accessible to sea kraits. The majority of the suitable areas for sea kraits are within the accessible area. Outside the accessible area, there is only a very limited suitable area for sea kraits. Our results suggest that when dispersal is taken into account, sea kraits seem to occupy most of their suitable available niche and that they may be unable to colonize much of the area outside of their dispersal ranges. Using dispersal estimates to refine species’ distribution predictions is a useful tool for refining the area of focus when ENM results are interpreted. Estimating species’ dispersal also helps evaluate the ability of the models to predict the species’ distributions in areas that are not accessible to the species and, hence, the potential commission error represented by overprediction.

To help conserve biodiversity in coming decades, protected areas need to be located in places that will be important for species as their ranges shift to track suitable climatic conditions. Had past protected areas been optimally targeted to cover today's biodiversity, then we would expect future coverage to decline. However, past protected area siting was often biased towards higher elevations and, with upslope movements of many species predicted under climate change projections, perhaps future coverage may improve as a result. We examine present and future coverage of species potential ranges by protected areas in the Appalachian Mountains, a critical climate refuge and corridor for the biodiversity of the eastern United States. We parameterize climate niche models using the Maxent algorithm with occurrence records for 258 amphibian, bird, mammal, and reptile species of conservation policy concern that depend on forested habitat. We forecast future range shifts by integrating climate projections from six global circulation models and two representative concentration pathways while making contrasting assumptions about species dispersal. We then examine coverage of the estimated present and future ranges of species by existing protected areas. Most of these policy relevant species (93%) have less than 15% of their present estimated range in the region covered by protected areas. We found that species ranked as high conservation priority would be poorly protected under future climate scenarios. We predicted amphibian and mammal coverages by protected areas would worsen whereas coverages of birds and reptiles improve if species are able to disperse freely to future climatically suitable habitat. If species movements are more restricted, then we predict marked losses of species from protected areas, particularly amphibians and mammals. Our results identify obvious candidate locations for adding new protected areas in places that are currently under-protected and that harbor a high diversity of species across all future climate scenarios we consider.

The similar geographic distributions of an extinct (Dasypus bellus) and an extant (D. novemcinctus) armadillo species have long been of interest to scholars because of the unresolved phylogeny. The relationship between the two species has been investigated through morphological and phylogenetic studies, whereas the ecological perspective has been overlooked, the importance of which is more and more acknowledged in speciation events. Here, we used ecological niche models to study the climatic niche similarity of three species of Dasypus (D. bellus, D. novemcinctus, and D. kappleri) and provide new insights on the relationship among them. The climatic niche similarity was compared in two ways: hindcast of ecological niche models based on occurrences and climatic layers, and direct niche boundary comparison along bioclimatic axes. The fossil records of D. bellus were not predicted suitable by the ecological niche models of the two extant armadillos. The direct comparison of niche boundary showed that D. bellus lived in colder and relative dryer climates, with high temperature variation and low precipitation variation. Our results did not support the previously assumed ecological similarity of D. bellus and D. novemcinctus based on their geographic distributions and emphasized the possibility of a cold adapted characteristic of the life history of D. bellus.

We present a first analysis of the ecology and potential distribution of Euscorpius carpathicus (Linnaeus, 1767), a scorpion species endemic to southern Romania, and report on the overwintering habitat selection of this species. Using field data, literature review, species distribution modelling, and habitat selection models, we document the broad scale distribution and ecology of E. carpathicus, as well as habitat selection in the foothills of the Curvature Carpathians, including exclusive microhabitat selection of riverine clay banks. In contrast with other species of the genus that inhabit cracks in cliffs or walls, E. carpathicus has adapted to cracks in clay.

The genus Laticauda (Reptilia: Elapidae), commonly known as sea kraits, comprises eight species of marine amphibious snakes distributed along the shores of the Western Pacific Ocean and the Eastern Indian Ocean. We review the information available on the geographic range of sea kraits and analyze their distribution patterns. Generally, we found that south and south-west of Japan, Philippines Archipelago, parts of Indonesia, and Vanuatu have the highest diversity of sea krait species. Further, we compiled the information available on sea kraits’ occurrences from a variety of sources, including museum records, field surveys, and the scientific literature. The final database comprises 694 occurrence records, with Laticauda colubrina having the highest number of records and Laticauda schistorhyncha the lowest. The occurrence records were georeferenced and compiled as a database for each sea krait species. This database can be freely used for future studies.

In areas with a high density of ephemeral wetlands, traditional mapping protocols may underestimate occurrence of wetlands when single-date base-imagery is utilized. In the Pleistocene Sand Dunes Ecoregion in Oklahoma, National Wetland Inventory (NWI) maps created using base-imagery from a dry year omitted large numbers of ephemeral wetlands. To improve the likelihood of capturing inundated depressions, we classified water pixels from 51 Landsat images (3 images per year: pre/early, peak, and late/post growing season) from 1994 to 2011. Several image classification methods were tested but decision tree analysis with training pixels from multi-season imagery provided the greatest accuracy. Accuracy was determined through manual comparison of two Landsat images with concurrent aerial imagery (Kappa =0.96 and 0.93 for the two images). Wetland polygons were created from water/non-water rasters and given hydroperiod designations based on the number of inundated periods. Landsat-derived wetland maps identified 3156 wetland units, 718 more than the original 1980s NWI, with only 33.9 % agreement between the two maps. Finally, one meter LiDAR data were combined with classified Landsat images to determine the volume of water in wetlands during each image date. These wetland maps can assist with estimating the availability of inundated habitat during wet, dry, and average rainfall periods.

SUMMARY 1. Ecological niche modelling is a technique used to estimate potential distributions of invasive species based on available occurrence data and associated environmental conditions. Maximum entropy (Maxent) is a powerful method for ecological niche modelling and yet has only rarely been applied to aquatic species. 2. Here we applied Maxent to estimate the potential distribution of the invasive Chinese mystery snail (Cipangopaludina chinesis) in Wisconsin and analysed several methodological issues associated with using Maxent for lentic species. 3. To generate Maxent estimates of the potential distribution of C. chinesis, we used presence records from 292 lakes, five spatially explicit climatic variables, and two lake-specific environmental data sets (area, conductivity) from 7995 lakes. Our investigations included three aspects that could affect model prediction accuracy and transferability: (i) combining climate and lake variables into a single data set in two different ways, using each lake as a single observation and as a grid of 1 ha cells; (ii) varying the size of the background data set (locations without presences); and (iii) contrasting environmental conditions between locations with and without C. chinesis. 4. The lake-based model had higher accuracy than the grid-based model, although both models had accuracy values indicative of good performance. Conductivity and lake area were important predictor variables for both models, but had higher contribution to the lake-based model accuracy. Decreasing the background sample size minimally affected model accuracy and thus Maxent can be used even when background sampling does not meet the algorithm’s default settings. Lastly, lakes that were environmentally dissimilar from lakes with known C. chinesis records were more likely to be predicted unsuitable by both grid-based and lake-based models. 5. Overall, the models predicted high potential suitability across Wisconsin lakes for C. chinesis, especially in lakes ≥60 ha. Our study provides evidence that small or environmentally biased presence data sets may underestimate the number of environmentally suitable locations of invasive species.