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Species distribution models (SDMs) are algorithms designed to infer the distribution of species using environmental and biotic variables and have become an important tool for ecologists and conservation biologists seeking to understand the implications of environmental change.Global datasets of environmental variables at resolutions of a few metres are increasingly available. SDMs fitted using such high‐resolution data allow researchers to investigate how local factors affect species occurrences at unprecedented fine spatial scales.As the spatial resolution of SDMs increases, we see a critical need to consider the characteristics of habitat types within or around raster pixels. In particular, we argue that the effects of habitat patches (EHPs, including habitat area, habitat configuration, and habitat diversity), measured focusing on patches or landscapes, have yet to be fully realized in SDMs.We provide guidelines to incorporate EHPs in SDMs. We explain why this development is important, describe approaches to properly conduct such analyses, and discuss pitfalls we foresee in testing EHPs.Synthesis. Ensuring that SDMs incorporating EHPs are properly designed will be key to increasing model predictive performance and to understanding which environmental factors influence the distribution of species at fine spatial scales. At a crucial time for nature conservation, we foresee that this will be a key step forward to understanding and protecting biodiversity. [ABSTRACT FROM AUTHOR]

Societal Impact Statement Native medicinal plants contribute essential health benefits to populations globally, constituting a major natural resource that human societies rely on. Being an integral part of terrestrial biodiversity, medicinal plants are detrimentally affected by ongoing climate and land‐use change, yet comprehensive studies on the risk that extinction will pose to medicinal biodiversity are lacking. Responding to ongoing scientific calls for conserving medicinal biodiversity, this study provides an integrated assessment of the impacts of environmental change on ironwort (Sideritis), a group of closely related endemic plants of great cultural significance as local medicinal resources in the Balkan Mountains. Summary Mountain habitats harbour unique biodiversity and provide vital resources for human well‐being, including natural medicinal resources, yet they are amongst the environments most impacted by global change. While there is ample evidence of recent rapid climate and land‐use change on mountain ecosystems, the impacts of these processes on the habitats of culturally important medicinal plants are still poorly understood. Here, we assess the potential loss of mountain habitats for medicinal plant resources over the past four decades using the culturally important ironwort, a group of endemic medicinal plants of the Balkan Mountains extensively used by local human populations and the pharmaceutical industry for treating cough and cold and gastrointestinal disorders. We used information collected from major European natural history museums to guide extensive field campaigns across 15 separate mountain ranges. We integrate field data with thousands of satellite images, station‐validated climate reanalysis data and habitat suitability modelling. We finally used machine learning to assess the relative roles of climate and vegetation rates of change in driving rates of habitat suitability change. We show that rising temperatures and ‘mountain greening’ erode the habitats of ironwort at alarming rates. About 50% of the total habitat area across all considered mountain ranges shows a significant decline in habitat suitability. These past trends will most likely continue in the future and could lead to widespread local extinction of the species and other medicinal plants that share similar ecological preferences, threatening their future contributions to societal well‐being.

Aim: Various species distributed in the Alps have their disjunct occurrences in the Carpathians. Fossil evidence for some woodland snails of Alpine distribution suggests that they colonized the Carpathians during the Holocene forest optimum or later. Here, we focus on disjunct Carpathian populations of the rock‐dwelling alpine snail Pyramidula saxatilis. As it occupies very stable habitats, that is, high‐elevation limestone rocks, one can assume its potential survival in Carpathian refugia over the glacial period(s). For comparison, the more widespread and warm‐climate P. pusilla is analysed. Location: Europe. Taxon: Pyramidula saxatilis and P. pusilla. Methods: We analysed the genetic diversity of P. saxatilis and P. pusilla populations across their entire ranges using mitochondrial DNA and nuclear DNA markers. Seven fossil mollusc successions from the area where P. saxatilis occurs in the Carpathians were analysed and dated using the radiocarbon method. Habitat suitability models of both species were calculated for selected time periods since the Last Glacial Maximum. Results: All Carpathian P. saxatilis populations were genetically homogeneous and similar to those from the Eastern Alps. In P. pusilla, we found a genetically distinct lineage in the southern Western Carpathians. Both species were found in three fossil profiles since the Middle Holocene, and in one already in the Late Glacial. Habitat suitability models suggested the fluctuations of suitable conditions in the Carpathians driven by summer precipitation, especially for P. saxatilis. Main Conclusions: Our results suggest that the Carpathian populations of P. saxatilis represent a post‐LGM colonization from the Eastern Alps triggered by climatic changes, mainly by the increase in summer precipitation during the Late Glacial interstadials and Middle Holocene. The evidence for P. pusilla is similar, but also suggests rare long‐term survival in the Carpathian microrefugia. The palaeoecological evidence indicates a possible threat to high‐elevation species from future fluctuations in precipitation regimes. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Native medicinal plants contribute essential health benefits to populations globally, constituting a major natural resource that human societies rely on. Being an integral part of terrestrial biodiversity, medicinal plants are detrimentally affected by ongoing climate and land‐use change, yet comprehensive studies on the risk that extinction will pose to medicinal biodiversity are lacking. Responding to ongoing scientific calls for conserving medicinal biodiversity, this study provides an integrated assessment of the impacts of environmental change on ironwort (Sideritis), a group of closely related endemic plants of great cultural significance as local medicinal resources in the Balkan Mountains. Summary: Mountain habitats harbour unique biodiversity and provide vital resources for human well‐being, including natural medicinal resources, yet they are amongst the environments most impacted by global change. While there is ample evidence of recent rapid climate and land‐use change on mountain ecosystems, the impacts of these processes on the habitats of culturally important medicinal plants are still poorly understood. Here, we assess the potential loss of mountain habitats for medicinal plant resources over the past four decades using the culturally important ironwort, a group of endemic medicinal plants of the Balkan Mountains extensively used by local human populations and the pharmaceutical industry for treating cough and cold and gastrointestinal disorders.We used information collected from major European natural history museums to guide extensive field campaigns across 15 separate mountain ranges. We integrate field data with thousands of satellite images, station‐validated climate reanalysis data and habitat suitability modelling. We finally used machine learning to assess the relative roles of climate and vegetation rates of change in driving rates of habitat suitability change.We show that rising temperatures and 'mountain greening' erode the habitats of ironwort at alarming rates. About 50% of the total habitat area across all considered mountain ranges shows a significant decline in habitat suitability.These past trends will most likely continue in the future and could lead to widespread local extinction of the species and other medicinal plants that share similar ecological preferences, threatening their future contributions to societal well‐being. [ABSTRACT FROM AUTHOR]

Aim: Eucalypts are important and popular urban tree species across cities worldwide. However, little is known about how their climatic niche breadth (CNB) and functional traits predict their success, and vulnerability, to current climate change in cities. We assessed the relationship between the CNB of eucalypts and key traits to understand their tolerance to climate change. Location: Global urban areas, 66 cities in 21 countries. Time period: 1981 to 2022. Major species studied: Fifty 'eucalypt' species belonging to the genera Eucalyptus, Angophora and Corymbia. Methods: We used the species' safety margin concept to determine cities where eucalypts were planted outside the limits of their CNB, as defined from the native range, considering two extreme variables, maximum temperature of the warmest month (MTWM) and precipitation of the driest month (PDM). We assessed correlations between functional traits (leaf δ13C, leaf dry mass, leaf length, leaf N per dry mass, wood density) and negative safety margins, indicative of tolerance to non‐native conditions. Results: In total, 42 species planted in 40 cities exceeded their safety margins for MTWM, while 43 species in 38 cities exceeded their safety margins for PDM. Within 24 cities, all species exceeded their native CNB for both MTWM and PDM. The cities of Atakpame (Togo), Chennai (India), Chongqing (China) and the US cities of Phoenix and Riverside had the highest richness of eucalypt species growing outside their native CNB. Broadly, species with wide CNB, small leaves, high δ13C, high leaf N per dry mass and high wood density were more likely to persist in cities where climatic conditions exceeded their native CNB. Main conclusions: Eucalypts occupy many cities experiencing climatic conditions outside their native CNB. Species with traits characteristic of heat and drought tolerance are more often planted in cities where climatic conditions may exceed their CNB native limits. [ABSTRACT FROM AUTHOR]

Spatial truncation in species distribution models (SDMs) might cause niche truncation and model transferability issues, particularly when extrapolating models to non‐analog environmental conditions. While broad calibration extents reduce truncation issues, they usually overlook local ecological factors driving species distributions at finer resolution. Spatially‐nested hierarchical SDMs (HSDMs) address truncation by merging (a) a global model calibrated with broadly extended, yet typically low‐resolution, basic, and imprecise data; and (b) a regional model calibrated with spatially restricted but more precise and reliable data. This study aimed to examine HSDMs' efficacy to overcome spatial truncation in national‐scale studies. We compared two hierarchical strategies (‘covariate', which uses the global model output as a covariate for the regional model, and ‘multiply', which calculates the geometric mean of the global and regional models) and a non‐hierarchical strategy. The three strategies were compared in terms of niche truncation, environmental extrapolation, model performance, species' predicted distributions and shifts, and trends in species richness. We examined the consistency of the results over two study areas (Spain and Switzerland), 108 tree species, and four future climate scenarios. Only the non‐hierarchical strategy was susceptible to niche truncation, and environmental extrapolation issues. Hierarchical strategies, particularly the ‘covariate' one, presented greater model accuracy than non‐hierarchical strategies. The non‐hierarchical strategy predicted the highest overall values and the lowest decreases over time in species distribution ranges and richness. Differences between strategies were more evident in Switzerland, which was more affected by niche truncation issues. Spain was more negatively affected by climate change and environmental extrapolation. The ‘covariate' strategy exhibited higher model performance than the ‘multiply' one. However, uncertainties regarding model temporal transferability advocate for adopting and further examining multiple hierarchical approaches. This research underscores the importance of adopting spatially‐nested hierarchical SDMs given the compromised reliability of non‐hierarchical approaches due to niche truncation and extrapolation issues. [ABSTRACT FROM AUTHOR]

Aim: Understanding the processes underlying the distribution of species through space and time is fundamental in several research fields spanning from ecology to spatial epidemiology. Correlative species distribution models rely on the niche concept to infer or explain the distribution of species, though often focusing only on the abiotic component of the niche (e.g. temperature, precipitation), without clear causal links to the biology of the species under investigation. This might result in an oversimplification of the complex niche hypervolume, resulting in a single model formula whose estimates and predictions lack ecological realism. Location: Not applicable. Time Period: Not applicable. Major Taxa Studied: Virtual species. Materials and Methods: We believe that a causal perspective associated with a finer definition of the modelling target is necessary to develop more ecologically realistic outputs. Here, we propose to infer the geographical distribution of a species by applying the modelling relation approach, a causal conceptual framework developed by the theoretical biologist Robert Rosen, which can be formalized through structural equation modelling. Results: Our findings suggest that building a model relying on a strong conceptual basis improves the stability of the estimated model's coefficients, without necessarily increasing the predictive accuracy metrics of the model. Main Conclusions: Including causal processes underlying the spatial distribution of species into an inferential formal system highlights the methodological steps where uncertainty can arise and results in model outputs which are tightly linked to the ecology of the target species. [ABSTRACT FROM AUTHOR]

The conservation of threatened and rare species in remote areas often presents two challenges: there may be unknown populations that have not yet been documented and there is a need to identify suitable habitat to translocate individuals and help populations recover. This is the case of the reticulated giraffe (Giraffa reticulata), a species of high conservation priority for which: (a) there may be unknown populations in remote areas, and (b) detailed maps of suitable habitat available within its range are lacking. We implemented a species distribution modeling (SDM) workflow in Google Earth Engine, combining GPS telemetry data of 31 reticulated giraffe with Landsat 8 OLI, Advanced Land Observing Satellite Phased Arrayed L‐band Synthetic Aperture Radar, and surface ruggedness layers to predict suitable habitat at 30‐m spatial resolution across the potential range of the species. Models had high predictive power, with a mean AUC‐PR of 0.88 (SD: 0.02; range: 0.86–0.91), mean sensitivity of 0.85 (SD: 0.04; range: 0.80–0.91), and mean precision was 0.81 (SD: 0.02; range: 0.79–0.83). Model predictions were also consistent with two independent validation datasets, with higher predicted suitable habitat values at known occurrence locations than at a random set of locations (P < 0.01). Our model predicted a total of 5519 km2 of potentially suitable habitat in Kenya, 963 km2 in Ethiopia, and 147 km2 in Somalia. Our results indicate that is possible to combine moderate spatial resolution imagery with telemetry data to guide conservation programs of threatened terrestrial species. We provide a free web app where managers can visualize and interact with the 30 m resolution map to help guide future surveys to search for existing populations and to inform future reintroduction assessments. We present all analysis code as a framework that could be adapted for other species across the globe. [ABSTRACT FROM AUTHOR]

Similar to other single-island endemic Hawaiian honeycreepers, the critically endangered 'ākohekohe (Palmeria dolei) is threatened by climate-driven disease spread. To avert the imminent risk of extinction, managers are considering novel measures, including the conservation introduction (CI) of 'ākohekohe from Maui to higher elevation habitats on the Island of Hawai'i. This study integrated lidar-based habitat suitability models (LHSMs) and population viability analyses (PVAs) to assess five candidate sites currently considered by managers for CI. We first developed an LHSM for the species' native range on Maui. We then projected habitat suitability across candidate CI sites, using forest structure and topography metrics standardized across sensor types. Given the structural variability observed within the five candidate sites, we identified clusters of contiguous, highly suitable habitat as potential release sites. We then determined how many adult individuals could be supported by each cluster based on adult home range estimates. To determine which clusters could house the minimum number of 'ākohekohe birds necessary for a stable or increasing future population, we conducted PVAs under multiple scenarios of bird releases. We found that canopy height and relative height 90 had the greatest effects on model performance, possibly reflecting 'ākohekohe's preference for taller canopies. We found that a small release of at least nine pairs of equal sex ratios were sufficient for an 80% chance of success and a <1% chance of extirpation in 20 years, resulting in a minimum release area of 4.5 ha in size. We integrated the results of the LHSM and PVA into an interactive web application that allowed managers to consider the caveats and uncertainties associated with both LHSMs and PVAs in their decision-making process. As climate change continues to threaten species worldwide, this research demonstrates the value of lidar remote sensing combined with species-specific models to enable rapid, quantitative assessments that can inform the increasing consideration of time-sensitive conservation introductions. [ABSTRACT FROM AUTHOR]

The threat of invasive species to biodiversity and ecosystem structure is exacerbated by the increasingly concerning outlook of predicted climate change and other human influences. Developing preventative management strategies for invasive plant species before they establish is crucial for effective management. To examine how climate change may impact habitat suitability, we modeled the current and future habitat suitability of two terrestrial species, Geranium lucidum and Pilosella officinarum, and two aquatic species, Butomus umbellatus and Pontederia crassipes, that are relatively new invasive plant species regionally, and are currently spreading in the Pacific Northwest (PNW, North America), an area of unique natural areas, vibrant economic activity, and increasing human population. Using North American presence records, downscaled climate variables, and human influence data, we developed an ensemble model of six algorithms to predict the potential habitat suitability under current conditions and projected climate scenarios RCP 4.5, 7.0, and 8.5 for 2050 and 2080. One terrestrial species (P. officinarum) showed declining habitat suitability in future climate scenarios (contracted distribution), while the other terrestrial species (G. lucidum) showed increased suitability over much of the region (expanded distribution overall). The two aquatic species were predicted to have only moderately increased suitability, suggesting aquatic plant species may be less impacted by climate change. Our research provides a template for regional-scale modelling of invasive species of concern, thus assisting local land managers and practitioners to inform current and future management strategies and to prioritize limited available resources for species with expanding ranges. [ABSTRACT FROM AUTHOR]

Context: The forestry industry provides important goods, services and economic benefits, but timber harvest can adversely impact ecosystem services, including wildlife habitat. Timber harvest planning can integrate wildlife habitat quality through multi-objective optimization for timber harvest and wildlife habitat suitability. Objectives: Our objective was to develop a method to find optimal solutions for timber harvest and wildlife habitat suitability individually and concurrently, then apply the method to Roosevelt elk (Cervus elaphus roosevelti) on Afognak Island, Alaska. Methods: We developed three seasonal habitat suitability models using elk locations and landscape variables including historical timber harvest on Afognak Island, Alaska. We used threshold-accepting optimization over a 50-year planning horizon to maximize timber harvest yield and habitat suitability in each season, then used multi-objective goal-deviation optimization to simultaneously maximize timber harvest volume and seasonal habitat suitability. Results: The optimal solution for timber yield decreased seasonal average habitat suitability by 5.7%. Elk habitat suitability and corresponding optimal solutions varied seasonally; elk generally selected open landcovers and early- to mid-successional timber stands over late-successional and mature stands. Therefore, in the optimal solutions, stands were harvested before they reached maximum volume and few stands were harvested in early planning periods, resulting in a seasonal average loss of 17.5% yield. Multi-objective optimization decreased seasonal average suitability by 3.9% and yield by 1.4% compared to single-objective optimization. Conclusions: Our multi-objective optimization approach that incorporates data-driven habitat suitability models using open-source software can enable managers to achieve desired quantity and quality of wildlife habitat while providing for resource extraction. [ABSTRACT FROM AUTHOR]

Climate change is one of the most impactful global phenomena, affecting multiple ecosystems, particularly wetlands and water bodies, as well as important species that depend on these areas. Flamingos are unique and distinctive species that live exclusively in these environments and are highly impacted by any changes in their breeding or non-breeding wetlands. To address and measure the direct impact that future climatic changes could have on the distribution of the six extant species of flamingos, I used citizen science data and climatic variables to construct habitat suitability models. These models were used to predict the future gain or loss of climatic suitability areas in the short, medium, and long term, under four different Shared Socioeconomic Pathways and eight Global Circulation Models. The results predicted that five out of the six species of flamingos will experience continued loss of habitat over the next few decades in all scenarios. Dramatic changes in distribution are expected for all species. The data also indicate a higher impact of climate change on more habitat restrictive species and on wetlands along the borders of their distributions. Finally, the research highlights the importance of combined efforts from public communities, scientists, and policymakers to create mitigation and conservation plans that could avoid the intensification of climate change effects on wetlands and prevent the future reduction of flamingo populations. [ABSTRACT FROM AUTHOR]

Freshwater mussels are among the most imperiled freshwater taxa, and translocation of these endangered species is one option for conservation. However, the long-term success of translocations is difficult to determine, in part because the amount of suitable habitat for a translocated species is often not known. Two federally endangered freshwater mussel species, the Northern Riffleshell Epioblasma rangiana (Lea, 1837) and the Clubshell Pleurobema clava (Lamarck, 1819), were recently translocated to the Middle Fork and Salt Fork of the Vermilion River in East Central Illinois, USA; however, the total amount of suitable habitat in both rivers remains unknown. To identify potential suitable habitat for these mussels in the Middle Fork and Salt Fork of the Vermilion River, we sampled 79 sites for surrogate mussel species of E. rangiana and P. clava along with their host fishes. We investigated habitat suitability by modeling presence/absence and abundance of surrogate mussel species to determine the landscape variables most strongly associated with surrogate mussel species presence/absence and abundance. We used these model results to create a habitat suitability index. We then validated our model predictions using timed searches in y 2 of our study. Our results indicate there may be more suitable habitat than previously believed throughout both rivers and that ample habitat is available to support large populations of mussels. Our approach also demonstrates how landscape variables can be used to successfully identify potential sites for future translocations. [ABSTRACT FROM AUTHOR]

Data availability remains a principal factor limiting the use of species distribution models (SDMs) as tools for wildlife conservation and management of rare species. Although data collected in systematic and rigorous fashion are preferable, available data for most species of conservation interest are usually low in both quality and number. Here we show that combining records published in peer-reviewed journals and gray literature sources (e.g., theses, government, and NGO reports) with unpublished records obtained by personal communications from relevant stakeholders affect the predicted distribution of spectacled bears (Tremarctos ornatus) in Peru. We built SDMs using generalized linear models, random forest, and Maxent, first using a dataset that only included published records, and second with a dataset using both published and unpublished records. All models were replicated ten times with random subsets with controlled sample size. Models that combined published and unpublished spectacled bear records had a better performance, irrespective of with SDM method used, increasing the connectivity of the species' range, and increasing the overall predicted distribution area than models that only included published records. This was because unpublished records added key new localities, reducing spatial sampling biases. Our study shows that the inclusion of commonly disregarded data such as opportunistic records, reports from natural park rangers, student theses, and data-deficient small studies can make an important contribution to the overall ecological knowledge of rare and difficult-to-study species such as the spectacled bear. [ABSTRACT FROM AUTHOR]

Similar to other single-island endemic Hawaiian honeycreepers, the critically endangered ‘ākohekohe (Palmeria dolei) is threatened by climate-driven disease spread. To avert the imminent risk of extinction, managers are considering novel measures, including the conservation introduction (CI) of ‘ākohekohe from Maui to higher elevation habitats on the Island of Hawai’i. This study integrated lidar-based habitat suitability models (LHSMs) and population viability analyses (PVAs) to assess five candidate sites currently considered by managers for CI. We first developed an LHSM for the species’ native range on Maui. We then projected habitat suitability across candidate CI sites, using forest structure and topography metrics standardized across sensor types. Given the structural variability observed within the five candidate sites, we identified clusters of contiguous, highly suitable habitat as potential release sites. We then determined how many adult individuals could be supported by each cluster based on adult home range estimates. To determine which clusters could house the minimum number of ‘ākohekohe birds necessary for a stable or increasing future population, we conducted PVAs under multiple scenarios of bird releases. We found that canopy height and relative height 90 had the greatest effects on model performance, possibly reflecting ‘ākohekohe’s preference for taller canopies. We found that a small release of at least nine pairs of equal sex ratios were sufficient for an 80% chance of success and a

Simple Summary: The variable landscape of Illinois creates a patchwork of tickborne disease risk to humans and domestic animals that can be predicted in part based on climate and landscape features. We fit individual and mean-weighted ensemble species distribution models for Ixodes scapularis, Amblyomma americanum, Dermacentor variabilis, and a newly invading tick species, Amblyomma maculatum using a variety of landscape and mean climate variables and identified numerous environmental niche factors that are associated with the presence of these vectors in current and future climate scenarios within the state. As the environment changes over the coming decades, the distribution of these tick species will change as they adapt to the increasing temperatures and precipitation alterations. Knowing where ticks may concentrate will be important to anticipating, preventing, and treating tickborne disease. The greater U.S. Midwest is on the leading edge of tick and tick-borne disease (TBD) expansion, with tick and TBD encroachment into Illinois occurring from both the northern and the southern regions. To assess the historical and future habitat suitability of four ticks of medical concern within the state, we fit individual and mean-weighted ensemble species distribution models for Ixodes scapularis, Amblyomma americanum, Dermacentor variabilis, and a newly invading species, Amblyomma maculatum using a variety of landscape and mean climate variables for the periods of 1970–2000, 2041–2060, and 2061–2080. Ensemble model projections for the historical climate were consistent with known distributions of each species but predicted the habitat suitability of A. maculatum to be much greater throughout Illinois than what known distributions demonstrate. The presence of forests and wetlands were the most important landcover classes predicting the occurrence of all tick species. As the climate warmed, the expected distribution of all species became strongly responsive to precipitation and temperature variables, particularly precipitation of the warmest quarter and mean diurnal range, as well as proximity to forest cover and water sources. The suitable habitat for I. scapularis, A. americanum, and A. maculatum was predicted to significantly narrow in the 2050 climate scenario and then increase more broadly statewide in the 2070 scenario but at reduced likelihoods. Predicting where ticks may invade and concentrate as the climate changes will be important to anticipate, prevent, and treat TBD in Illinois. [ABSTRACT FROM AUTHOR]

Bats are important components of global ecosystems, providing essential ecosystem services with substantial economic benefit. Yet North American bat populations have been negatively affected by numerous factors (e.g., disease, habitat loss, wind energy development) with compounding effects. Bats use habitats at a variety of scales, from small, isolated patches to large, contiguous corridors. Landscape‐level research is necessary to identify important habitats, patches, and corridors to strategically target management interventions. We created habitat suitability models (HSMs) for hoary bats (Lasiurus cinereus), eastern red bats (L. borealis), and tri‐colored bats (Perimyotis subflavus) across Illinois, USA, using species‐specific landscape and climate variables. With the 3 models from this study and a previously published HSM for Indiana bats (Myotis sodalis), we stacked binary HSMs, thereby identifying priority conservation areas across Illinois. Species exhibited different distributional patterns and habitat preferences across Illinois. Multi‐species HSMs highlight high quality habitat (i.e., ecologically important habitat that provides preferred resources for roosting, foraging, and raising young) in southern Illinois and along river riparian areas. This approach identified priority conservation areas mainly following hydrologic zones, which allows managers to strategically target restoration and conservation measures, invest funds in habitat likely to have high return‐on‐investment, and assist with decisions that affect bats (e.g., siting wind turbines, purchasing mitigation lands). [ABSTRACT FROM AUTHOR]

Ecological niche models (ENMs) and species distribution models (SDMs) have been widely applied to various studies relevant to biogeography, conservation biology, and ecology. These modelling techniques seek to develop spatial maps for projecting, among others past, current, and future species distributions. Born in the field of terrestrial ecology, only in recent years have these models been applied to marine environmental issues, especially to improve the forecasting of the distribution of occurrences and capturing of fishery resources. This study aimed to present through bibliometric analysis the characteristics of articles related to the use of ENMs and SDMs in marine fishery resources considering three main points: (1) state of the art: number of articles over the years, journals, countries, collaborations, and focus of research; (2) characteristics linked to fishery resources: marine biogeographic realms, taxonomic groups, life phases, oceanographic zones, and behaviours; (3) characteristics linked to methods: type of method, type of biological and, environmental data. We provide a list of 378 articles (derived from 930 screened ones), the results, and a discussion of our findings, which represent a baseline for the current status (strengths, limits, and gaps) of the interface between ENMs/SDMs and fishery resources. [ABSTRACT FROM AUTHOR]

Predicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other environmental factors can also be essential, particularly for species with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wetland, alpine, coastal, serpentine). Here, we demonstrate a novel approach for capturing strong effects of both hydrologic and climatic predictors in SDMs for riparian plants, by merging analyses targeted at environmental drivers within riparian ecosystems and across the western USA (3.8 × 106 km2). We developed presence‐background SDMs from five algorithms for three invasive riparian trees (Tamarix ramossisima/chinensis [saltcedar], Elaeagnus angustifolia [Russian olive], and Ulmus pumila [Siberian elm]) and three native Populus spp. (cottonwoods). We used separate background datasets to develop models with different spatial scales of inference: (1) spatially filtered random points to represent available habitat across the study area and (2) target‐group points from Salix (willow) occurrences to represent available riparian habitat. Random‐background models captured hydrologic drivers of riparian tree distributions relative to the largely upland western USA, whereas Salix‐background models captured climatic drivers within the context of riparian ecosystems. Combining predictions from the two backgrounds identified hydrologically suitable habitats within climatically suitable regions, resulting in fewer false "absences" than either background alone, improving predictions over previous SDMs, and providing more complete information to guide management decisions. Surprisingly, the predicted habitat for U. pumila, a newly recognized riparian invader, was as or more extensive than Populus deltoides/fremontii, T. ramossisima/chinensis, and E. angustifolia, the most common riparian tree complexes in the western USA. Watersheds constituting 20% of U. pumila predicted habitat contained no occurrence records, indicating high risk of future and unrecognized invasions. Combining models from random and ecosystem‐specific target‐group backgrounds may improve SDMs for species from many specialized habitats, providing a method to link predicted distributions to localized geographic features while capturing broad‐scale climatic requirements. [ABSTRACT FROM AUTHOR]

Deep‐water corals are protected in the seas around New Zealand by legislation that prohibits intentional damage and removal, and by marine protected areas where bottom trawling is prohibited. However, these measures do not protect them from the impacts of a changing climate and ocean acidification. To enable adequate future protection from these threats we require knowledge of the present distribution of corals and the environmental conditions that determine their preferred habitat, as well as the likely future changes in these conditions, so that we can identify areas for potential refugia. In this study, we built habitat suitability models for 12 taxa of deep‐water corals using a comprehensive set of sample data and predicted present and future seafloor environmental conditions from an earth system model specifically tailored for the South Pacific. These models predicted that for most taxa there will be substantial shifts in the location of the most suitable habitat and decreases in the area of such habitat by the end of the 21st century, driven primarily by decreases in seafloor oxygen concentrations, shoaling of aragonite and calcite saturation horizons, and increases in nitrogen concentrations. The current network of protected areas in the region appear to provide little protection for most coral taxa, as there is little overlap with areas of highest habitat suitability, either in the present or the future. We recommend an urgent re‐examination of the spatial distribution of protected areas for deep‐water corals in the region, utilising spatial planning software that can balance protection requirements against value from fishing and mineral resources, take into account the current status of the coral habitats after decades of bottom trawling, and consider connectivity pathways for colonisation of corals into potential refugia. [ABSTRACT FROM AUTHOR]

The 19 BIOCLIM variables available from the WorldClim databases have become the most widely used set of climatic variables for creating species distribution models (SDMs) and ecological niche models (ENMs). Nevertheless, in recent years, there has been an increasing trend to exclude four of these variables from SDM/ENM analyses. The four values concerned are interactive as they combine both temperature and precipitation assessments. Their exclusion was justified initially due to discontinuities in their interpolated surfaces observed in a study using data from version 1 of WorldClim. The discontinuities were thought to be artefacts of the measures used, and such sudden changes were unlikely to be biologically meaningful. A freely available biodiversity database and an open access geographic information system were used here to check the four interactive variables for discontinuities in data from both versions 1 and 2 of WorldClim. Over most of the world, the four interactive BIOCLIM variables from WorldClim were found to change smoothly. Major and minor discontinuities, however, were noted for all four variables in specific parts of some of the six continents (excluding Antarctica) covered by WorldClim databases. Discontinuities were related to sudden changes in the quarterly periods used to calculate the variables. These discontinuities were often found in equatorial regions where there are only small changes in temperature across the year. Bimodal precipitation distributions may also cause problems. It is recommended that the quick and simple method described here should be used to facilitate visual inspection and numerical checking for possible discontinuities of these variables before SDM/ENM analyses. If discontinuities are found for an interactive variable in the study area, it is recommended that the variable should not be used. Ways in which the four variables could be recalculated when new databases are created are suggested to reduce discontinuity problems. [ABSTRACT FROM AUTHOR]

Abstract Predicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other environmental factors can also be essential, particularly for species with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wetland, alpine, coastal, serpentine). Here, we demonstrate a novel approach for capturing strong effects of both hydrologic and climatic predictors in SDMs for riparian plants, by merging analyses targeted at environmental drivers within riparian ecosystems and across the western USA (3.8 × 106 km2). We developed presence‐background SDMs from five algorithms for three invasive riparian trees (Tamarix ramossisima/chinensis [saltcedar], Elaeagnus angustifolia [Russian olive], and Ulmus pumila [Siberian elm]) and three native Populus spp. (cottonwoods). We used separate background datasets to develop models with different spatial scales of inference: (1) spatially filtered random points to represent available habitat across the study area and (2) target‐group points from Salix (willow) occurrences to represent available riparian habitat. Random‐background models captured hydrologic drivers of riparian tree distributions relative to the largely upland western USA, whereas Salix‐background models captured climatic drivers within the context of riparian ecosystems. Combining predictions from the two backgrounds identified hydrologically suitable habitats within climatically suitable regions, resulting in fewer false “absences” than either background alone, improving predictions over previous SDMs, and providing more complete information to guide management decisions. Surprisingly, the predicted habitat for U. pumila, a newly recognized riparian invader, was as or more extensive than Populus deltoides/fremontii, T. ramossisima/chinensis, and E. angustifolia, the most common riparian tree complexes in the western USA. Watersheds constituting 20% of U. pumila predicted habitat contained no occurrence records, indicating high risk of future and unrecognized invasions. Combining models from random and ecosystem‐specific target‐group backgrounds may improve SDMs for species from many specialized habitats, providing a method to link predicted distributions to localized geographic features while capturing broad‐scale climatic requirements.

Despite their ecological and socio-economic importance, seagrasses are often overlooked in comparison with terrestrial ecosystems. In the Canarian archipelago (Spain), Cymodocea nodosa is the best-established species, sustaining the most important marine ecosystem and providing ecosystem services (ES) of great relevance. Nevertheless, we lack accurate and standardized information regarding the distribution of this species and its ES supply. As a first step, the use of species distribution models is proposed. Various machine learning algorithms and ensemble model techniques were considered along with freely available remote sensing data to assess Cymodocea nodosa's potential distribution. In a second step, we used InVEST software to estimate the ES provision by this phanerogam on a regional scale, providing spatially explicit monetary assessments and a habitat degradation characterization due to human impacts. The distribution models presented great predictive capabilities and statistical significance, while the ES estimations were in concordance with previous studies. The proposed methodology is presented as a useful tool for environmental management of important communities sensitive to human activities, such as C. nodosa meadows. [ABSTRACT FROM AUTHOR]

Abstract Transferability of habitat suitability models (HSMs), essential to accurately predict outside calibration conditions, has been seldom investigated at intraspecific level. We targeted Vipera ursinii ursinii, a meadow viper from southeastern France and central Italy, to assess determinants of transferability among geographically disjunct populations. We fitted HSMs upon occurrences of the Italian and French populations separately, as well as on the combined occurrence dataset. Internal transferability of HSMs, on spatially independent test data drawn from the calibration region, and their external transferability on the geographically disjunct populations were evaluated according to (a) use of full or spatially rarefied presence datasets; (b) ecology‐driven or statistics‐driven filtering of predictors; (c) modeling algorithm, testing generalized additive models and gradient boosting models; and (d) multivariate environmental novelty within test data. Niche overlap between French and Italian populations was also tested. Niche overlap was low, but niche divergence between the two populations’ clusters was not corroborated. Nonetheless, wider niche breadth and heterogeneity of background environmental conditions characterizing the French populations led to low intercluster transferability. Although models fitted on the combined datasets did not attain consistently higher internal transferability than those separately fitted for the French and Italian populations, ensemble projection from the HSMs fitted on the joint occurrences produced more consistent suitability predictions across the full range of V. u. ursinii. Spatial thinning of occurrences ameliorated internal transferability but did not affect external transferability. The two approaches to predictors filtering did not differ in transferability of the respective HSMs but led to discrepant estimated environment–occurrence relationships and spatial predictions, while the two algorithms attained different relative rankings depending on the considered prediction task. Multivariate novelty of projection sites was negatively correlated to both internal transferability and external transferability. Our findings clarify issues researchers should keep in mind when using HSMs to get predictions across geographically disjunct populations.

Area of habitat (AOH) is defined as the "habitat available to a species, that is, habitat within its range" and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land‐cover classes, which generates a source of uncertainty in AOH maps. We developed a data‐driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land‐cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land‐cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land‐cover class. We then compared the performance of our data‐driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land‐cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land‐cover classes with more confidence than others (e.g., wetlands and artificial). The data‐driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups. Article Impact Statement: Point‐locality data can be used to translate IUCN habitat classes to land cover to produce area of habitat maps. [ABSTRACT FROM AUTHOR]

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Aim: Google Earth Engine (GEE) is a free Web‐based spatial analysis platform that requires only a web browser and an Internet connection to programmatically access and analyse data from its multi‐petabyte catalog of regularly updated satellite imagery (e.g. MODIS, Landsat, Sentinel) and other geospatial datasets. The high computing capacity of GEE can make computationally demanding analyses more accessible to researchers and practitioners, especially those with limited access to advanced computational resources. Here, we present a workflow in GEE to fit species distribution models, offering direct access to a multi‐petabyte catalog of raster products to obtain estimates of habitat suitability. Innovation: We implemented a workflow for species distribution modelling in GEE that includes importing species occurrence data into the GEE platform, selecting and preparing predictor variables, and performing model fitting with spatial or temporal split‐block cross‐validation techniques. We present three case studies that demonstrate: (i) a baseline SDM workflow that produces informative model predictions, (ii) a workflow that accounts for temporal variability in predictor variables to study changes in habitat suitability over time and (iii) a complex and computationally demanding analysis incorporating thousands of satellite images for modelling habitat suitability at high spatial resolution. Main Conclusions: Our SDM workflow allows users to benefit from the high speed and performance of GEE without the need for significant computing infrastructure. This workflow may be especially beneficial to researchers in countries where computing power is limited, as SDMs frequently require the download, storage and processing of large raster datasets. We also discuss key limitations of implementing SDMs in GEE, such as user memory limits and the lack of high‐level functions. We include a step‐by‐step guide for the general model workflow and for each of the case studies presented to facilitate its implementation. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are widely used to hindcast or forecast suitable habitat conditions during climate change. Although distant populations of a given species may show local adaptations to diverging environmental conditions, traditional SDMs disregard intraspecific variation. Yet, incorporating genetic information into SDMs could improve predictions. Here we aimed at investigating whether genetically informed SDMs would outperform traditional SDMs. Using published information on the spatial genetic structure of the European Beech Fagus sylvatica L. (1753), we built lineage-specific SDMs for each phylogenetic group of the species. We then combined all lineage-specific SDMs into a single genetically informed SDM that we compared against a traditional SDM approach. We finally compared SDMs' predictions against independent datasets of present-day distribution as well as fossil distribution data from the Mid-Holocene, using six metrics of model performance. We found that aggregating lineage-specific SDMs into a single genetically informed SDM increased model performances to identify suitable areas currently occupied by F. sylvatica. In comparison to a traditional SDM, the genetically informed SDM we built for F. sylvatica assigned higher probabilities of occurrence during the Mid-Holocene at locations where fossil records were found. Aggregating lineage-specific SDMs into a single genetically informed SDM seems to outperform the traditional SDM approach, especially so when the aim is to identify potentially suitable areas of occupancy. This could be particularly useful for the identification of cryptic refugia that remain undetected by traditional SDMs. Genetically informed SDMs have the potential to improve our understanding of species redistribution under climate change. [ABSTRACT FROM AUTHOR]

Continental‐ and regional‐scale assessments of gaps in protected area networks typically use relatively coarse range maps for well documented species groups, creating uncertainty about the fate of unexamined biodiversity and providing insufficient guidance for land managers. By building habitat suitability models for a taxonomically diverse group of 2216 imperiled plants and animals, we revealed comprehensive and detailed protection opportunities in the conterminous United States. Summing protection‐weighted range‐size rarity (PWRSR, the product of the percent of modeled habitat outside of protected areas and the inverse of modeled habitat extent) uncovered novel patterns of biodiversity importance. Concentrations of unprotected imperiled species in places such as the northern Sierra Nevada, central and northern Arizona, the Rocky Mountains of Utah and Colorado, southeastern Texas, southwestern Arkansas, and Florida's Lake Wales Ridge have rarely if ever been featured in continental‐ and regional‐scale analyses. Inclusion of diverse taxa (vertebrates, freshwater mussels, crayfishes, bumble bees, butterflies, skippers, and vascular plants) partially drove these new patterns. When analyses were restricted to groups typically included in previous studies (birds, mammals, and amphibians), up to 53% of imperiled species in other groups were left out. The finer resolution of modeled inputs (990 m) also resulted in a more geographically dispersed pattern. For example, 90% of the human population of the conterminous United States lives within 50 km of modeled habitat for one or more species with high PWRSR scores. Over one‐half of the habitat for 818 species occurs within federally lands managed for biodiversity protection; an additional 360 species have over one‐half of their modeled habitat on federal multiple use land. Freshwater animals occur in places with poorer landscape condition but with less exposure to climate change than other groups, suggesting that habitat restoration is an important conservation strategy for these species. The results provide fine‐scale, taxonomically diverse inputs for local and regional priority‐setting and show that although protection efforts are still widely needed on private lands, notable gains can be achieved by increasing protection status on selected federal lands. [ABSTRACT FROM AUTHOR]

Monitoring and preserving natural habitats has become an essential activity in many countries today. As a native tree species in Korea, Paulownia coreana has periodically been surveyed in national ecological surveys and was identified as an important target for conservation as well as habitat monitoring and management. This study explores habitat suitability models (HSMs) for Paulownia coreana in conjunction with national ecological survey data and various environmental factors. Together with environmental variables, the national ecological survey data were run through machine learning algorithms such as Artificial Neural Network and Decision Tree & Rules, which were used to identify the impact of individual variables and create HSMs for Paulownia coreana, respectively. Unlike other studies, which used remote sensing data to create HSMs, this study employed periodical on-site survey data for enhanced validity. Moreover, localized environmental resources such as topography, soil, and rainfall were taken into account to project habitat suitability. Among the environment variables used, the study identified critical attributes that affect the habitat conditions of Paulownia coreana. Therefore, the habitat suitability modelling methods employed in this study could play key roles in planning, monitoring, and managing plants species in regional and national levels. Furthermore, it could shed light on existing challenges and future research needs. [ABSTRACT FROM AUTHOR]

BackgroundGlobally, tick-borne disease is a pervasive and worsening problem that impacts human and domestic animal health, livelihoods, and numerous economies. Species distribution models are useful tools to help address these issues, but many different modeling approaches and environmental data sources exist.ObjectiveWe conducted a scoping review that examined all available research employing species distribution models to predict occurrence and map tick species to understand the diversity of model strategies, environmental predictors, tick data sources, frequency of climate projects of tick ranges, and types of model validation methods.DesignFollowing the PRISMA-ScR checklist, we searched scientific databases for eligible articles, their references, and explored related publications through a graphical tool (www.connectedpapers.com). Two independent reviewers performed article selection and characterization using a priori criteria.ResultsWe describe data collected from 107 peer-reviewed articles that met our inclusion criteria. The literature reflects that tick species distributions have been modeled predominantly in North America and Europe and have mostly modeled the habitat suitability for Ixodes ricinus (n = 23; 21.5%). A wide range of bioclimatic databases and other environmental correlates were utilized among models, but the WorldClim database and its bioclimatic variables 1–19 appeared in 60 (56%) papers. The most frequently chosen modeling approach was MaxEnt, which also appeared in 60 (56%) of papers. Despite the importance of ensemble modeling to reduce bias, only 23 papers (21.5%) employed more than one algorithm, and just six (5.6%) used an ensemble approach that incorporated at least five different modeling methods for comparison. Area under the curve/receiver operating characteristic was the most frequently reported model validation method, utilized in nearly all (98.9%) included studies. Only 21% of papers used future climate scenarios to predict tick range expansion or contraction. Regardless of the representative concentration pathway, six of seven genera were expected to both expand and retract depending on location, while Ornithodoros was predicted to only expand beyond its current range.ConclusionSpecies distribution modeling techniques are useful and widely employed tools for predicting tick habitat suitability and range movement. However, the vast array of methods, data sources, and validation strategies within the SDM literature support the need for standardized protocols for species distribution and ecological niche modeling for tick vectors.

At the start of the UN Decade of Ecosystem Restoration (2021–2030), the restoration of degraded ecosystems is more than ever a global priority. Tree planting will make up a large share of the ambitious restoration commitments made by countries around the world, but careful planning is needed to select species and seed sources that are suitably adapted to present and future restoration site conditions and that meet the restoration objectives.Here we present a scalable and freely available online tool, Diversity for Restoration (D4R), to identify suitable tree species and seed sources for climate‐resilient tropical forest landscape restoration.The D4R tool integrates (a) species habitat suitability maps under current and future climatic conditions; (b) analysis of functional trait data, local ecological knowledge and other species characteristics to score how well species match the restoration site conditions and restoration objectives; (c) optimization of species combinations and abundances considering functional trait diversity or phylogenetic diversity, to foster complementarity between species and to ensure ecosystem multifunctionality and stability; and (d) development of seed zone maps to guide sourcing of planting material adapted to present and predicted future environmental conditions. We outline the various elements behind the tool and discuss how it fits within the broader restoration planning process, including a review of other existing tools.Synthesis and applications. The Diversity for Restoration tool enables non‐expert users to combine species traits, environmental data and climate change models to select tree species and seed sources that best match restoration site conditions and restoration objectives. Originally developed for the tropical dry forests of Colombia, the tool has now been expanded to the tropical dry forests of northwestern Peru–southern Ecuador and the countries of Burkina Faso and Cameroon, and further expansion is underway. Acknowledging that restoration has a wide range of meanings and goals, our tool is intended to support decision making of anyone interested in tree planting and seed sourcing in tropical forest landscapes, regardless of the purpose or restoration approach. [ABSTRACT FROM AUTHOR]

The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs‐based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between‐version differences). To this aim, we simulated occurrence probability and presence‐absence across Europe of four virtual species (VS) with differing climate‐occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate‐occurrence relationships for the climate‐tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between‐version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between‐version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between‐version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re‐evaluating previous biodiversity‐related works relying on geographical predictions from Worldclim‐based HSMs. [ABSTRACT FROM AUTHOR]

In Europe, agricultural practices have progressively evolved towards high productivity leading either to the intensification of productive and accessible areas or to the abandonment of less profitable sites. Both processes have led to the degradation of semi‐natural habitats like extensive grasslands, threatening species such as the Eurasian Scops Owl Otus scops that rely on extensively managed agricultural landscapes. In this work, we aimed to assess the habitat preferences of the Scops Owl using habitat suitability models combined with a multi‐scale approach. We generated a set of multi‐scale predictors, considering both biotic and abiotic variables, built on two newly developed vegetation management and orthopteran abundance models. To select the variables to incorporate in a 'best multi‐scale model', we chose the best spatial scale for each variable using univariate models and by calculating their relative importance through multi‐model inference. Next, we built ensembles of small models (ESMs) at 10 different scales from 50 to 1000 m, and an additional model with each variable at its best scale ('best multi‐scale model'). The latter performed better than most of the other ESMs and allowed the creation of a high‐resolution habitat suitability map for the species. Scops Owls showed a preference for dry sites with extensive and well‐structured habitats with 30–40% bush cover, and relied strongly on semi‐extensive grasslands covering at least 30% of the surface within 300 m of the territory centre and with high orthopteran availability near the centre (50‐m radius), revealing a need for good foraging grounds near the nest. At a larger spatial scale within a radius of 1000 m, the habitat suitability of Scops Owls was negatively related to forest cover. The resulting ESM predictions provide valuable tools for conservation planning, highlighting sites in need of particular conservation efforts together with offering estimates of the percentage of habitat types and necessary prey abundance that could be used as targets in future management plans to ensure the persistence of the population. [ABSTRACT FROM AUTHOR]

Abstract Background In the Netherlands, Aedes albopictus has been found each year since 2010 during routine exotic mosquito species surveillance at companies that import used tires. We developed habitat suitability models to investigate the potential risk of establishment and spread of this invasive species at these locations. Methods We used two methodologies: first, a species distribution model based on the maximum entropy modelling approach (MaxEnt) taking into consideration updated occurrence data of the species in Europe, and secondly, a spatial logic conditional model based on the temperature requirements of the species and using land surface temperature data (LST model). Results Suitability assessment obtained with the MaxEnt model at European level accurately reflect the current distribution of the species and these results also depict moderately low values in parts of the Netherlands, Belgium, Denmark, the British islands and southern parts of Scandinavia. Winter temperature was the variable that contributed most to the performance of the model (47.3%). The results of the LST model showed that: (i) coastal areas are suitable for overwintering of eggs; (ii) large areas in the northern part of the country have a low suitability for adult survival; and (iii) the entire country is suitable for successful completion of the life-cycle if the species is introduced after the winter months. Results of the LST model revealed that temperatures in 2012 and 2014 did not limit the overwintering of eggs or survival of adults at the locations where the species was found. By contrast, for the years 2010, 2011 and 2013, overwintering of eggs at these locations is considered unlikely. Conclusions Results using two modelling methodologies show differences in predicted habitat suitability values. Based on the results of both models, the climatic conditions could hamper the successful overwintering of eggs of Ae. albopictus and their survival as adults in many areas of the country. However, during warm years with mild winters, many areas of the Netherlands offer climatic conditions suitable for developing populations. Regular updates of the models, using updated occurrence and climatic data, are recommended to study the areas at risk.

Abstract Habitat suitability models (HSMs) are widely used to plan actions for species of conservation interest. Models that will be turned into conservation actions need predictors that are both ecologically pertinent and fit managers’ conceptual view of ecosystems. Remote sensing technologies such as light detection and ranging (LiDAR) can describe landscapes at high resolution over large spatial areas and have already given promising results for modeling forest species distributions. The point‐cloud (PC) area‐based LiDAR variables are often used as environmental variables in HSMs and have more recently been complemented by object‐oriented (OO) metrics. However, the efficiency of each type of variable to capture structural information on forest bird habitat has not yet been compared. We tested two hypotheses: (1) the use of OO variables in HSMs will give similar performance as PC area‐based models; and (2) OO variables will improve model robustness to LiDAR datasets acquired at different times for the same area. Using the case of a locally endangered forest bird, the capercaillie (Tetrao urogallus), model performance and predictions were compared between the two variable types. Models using OO variables showed slightly lower discriminatory performance than PC area‐based models (average ΔAUC = −0.032 and −0.01 for females and males, respectively). OO‐based models were as robust (absolute difference in Spearman rank correlation of predictions ≤ 0.21) or more robust than PC area‐based models. In sum, LiDAR‐derived PC area‐based metrics and OO metrics showed similar performance for modeling the distribution of the capercaillie. We encourage the further exploration of OO metrics for creating reliable HSMs, and in particular testing whether they might help improve the scientist–stakeholder interface through better interpretability.

Abstract Habitat suitability models (HSM) based on remotely sensed data are useful tools in conservation work. However, they typically use species occurrence data rather than robust demographic variables, and their predictive power is rarely evaluated. These shortcomings can result in misleading guidance for conservation. Here, we develop and evaluate a HSM based on correlates of long‐term breeding success of an open nest building boreal forest bird, the Siberian jay. In our study site in northern Sweden, nest failure of this permanent resident species is driven mainly by visually hunting corvids that are associated with human settlements. Parents rely on understory nesting cover as protection against these predators. Accordingly, our HSM includes a light detection and ranging (LiDAR) based metric of understory density around the nest and the distance of the nest to the closest human settlement to predict breeding success. It reveals that a high understory density 15–80 m around nests is associated with increased breeding success in territories close to settlements (

Species’ vulnerability to climate change is often assessed by focusing on potential changes of species’ ranges. This study aimed to develop community-level vulnerability indices which measure bryophyte community vulnerability to climate change, based on the best set of factors summarizing species' niche or geographic properties expected to respond to climate change.We used a dataset on 39 saxicolous bryophytes from the Iberian Peninsula, highly sensitive to climate shifts. Niche metrics were calculated using a recently described hypervolume-based approach. Spatial metrics were derived from habitat suitability model (HSM) projections. We then compared regression models based on niche or spatial metrics to evaluate which ones improve species range shifts forecast. The final vulnerability score for each species, the Species Vulnerability Index (SVI), was calculated by applying a weighted sum of all the relevant parameters. We then generated a spatial representation of vulnerability values for the whole community through HSMs and obtained three Community Vulnerability Indices (CVIs), according to different statistical aggregation measures (average, maximum and standard deviation).SVI assigns maximum vulnerability to species with smaller niche breadth and higher marginality in the community environmental niche space continuum, allowing to rank bryophyte species according to their vulnerability. Given the overall importance of niche-hypervolume metrics in SVI and CVIs, we rename it, respectively, as Niche Hypervolume Species Vulnerability Index (NHSVI) and Niche Hypervolume Community Vulnerability Indices (NHCVIs).Overall, saxicolous bryophyte communities with the greatest average vulnerability to climate change are those at the high mountains of the northern, central and southern regions of the Iberian Peninsula. Results suggest that vulnerability patterns are structured locally not only due to species richness but also to community composition. The three NHCVIs provided complementary insights into the study area's community vulnerability distribution.This study shows that NHSVI can prioritise vulnerable species to climate change, and NHCVIs can depict community-wise vulnerability hotspots, thereby informing policymakers in the definition of bryophyte species conservation measures.

Abstract The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs‐based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between‐version differences). To this aim, we simulated occurrence probability and presence‐absence across Europe of four virtual species (VS) with differing climate‐occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate‐occurrence relationships for the climate‐tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between‐version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between‐version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between‐version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re‐evaluating previous biodiversity‐related works relying on geographical predictions from Worldclim‐based HSMs.

The greater U.S. Midwest is on the leading edge of tick and tick-borne disease (TBD) expansion, with tick and TBD encroachment into Illinois occurring from both the northern and the southern regions. To assess the historical and future habitat suitability of four ticks of medical concern within the state, we fit individual and mean-weighted ensemble species distribution models for Ixodes scapularis, Amblyomma americanum, Dermacentor variabilis, and a newly invading species, Amblyomma maculatum using a variety of landscape and mean climate variables for the periods of 1970–2000, 2041–2060, and 2061–2080. Ensemble model projections for the historical climate were consistent with known distributions of each species but predicted the habitat suitability of A. maculatum to be much greater throughout Illinois than what known distributions demonstrate. The presence of forests and wetlands were the most important landcover classes predicting the occurrence of all tick species. As the climate warmed, the expected distribution of all species became strongly responsive to precipitation and temperature variables, particularly precipitation of the warmest quarter and mean diurnal range, as well as proximity to forest cover and water sources. The suitable habitat for I. scapularis, A. americanum, and A. maculatum was predicted to significantly narrow in the 2050 climate scenario and then increase more broadly statewide in the 2070 scenario but at reduced likelihoods. Predicting where ticks may invade and concentrate as the climate changes will be important to anticipate, prevent, and treat TBD in Illinois.

Forecasting the effects of climate change on the distribution of Andean trees (Polylepis, Rosaceae) is important to understand how species respond to climate variability and to assess their resilience to the ongoing climate crisis. Here, paleodistribution modelling is used to assess distribution shifts of 17 Polylepis species during the Last Glacial Maximum (LGM), mid-Holocene (MH), and the Anthropocene in the central Andes. Species distribution models (SDMs) were computed by combining presence records and current climatic conditions using Maxent and projected onto three climatic scenarios for the LGM (~22,000 yr BP), the MH (~6,000 yr BP), and the Anthropocene (1,970-2,000). Subsequently, current refugia hotspots were identified by intersecting suitable habitat over the LGM, MH, and anthropogenic conditions for all the studied species. SDMs for the LGM and MH showed a contraction of climate suitable habitat for most of the species in comparison to the Anthropocene. Four current refugia hotspots were identified: central Cordillera of Peru, Lake Titicaca basin, western Cordillera of Bolivia, and northern Chile. In general, SDMs were consistent with patterns proposed with pollen records, and partially with available phylogeographic studies. Current hotspots are highly important areas for the conservation of Polylepis and associated biota. This study assists in understanding distribution shifts over millennia of Andean tree species in response to climate change and identifying key conservation areas for the delineation of future conservation strategies. [ABSTRACT FROM AUTHOR]

Context: Habitat suitability models (HSM) have been used to understand the impacts of landscape-scale habitat connectivity and gene flow mostly by assuming a regular decrease in the cost of movement as habitat improves. Yet, habitat selection and gene flow are governed by different behavioural processes which may limit the reliability of this approach as individuals are likely to disperse through unsuitable habitat for breeding. Objectives: The aim of this study was to identify the optimal relationship between gene flow and HSMs for two bat species (Myotis bechsteinii and Eptesicus serotinus) in Britain by testing a range of nonlinear negative exponential functions for the transformation of HSMs into resistance surfaces. Methods: We modelled habitat suitability using a hierarchical, multi-level approach that integrates models across three nested levels. Then, we measured the relationship between published genetics data of both species and six negative exponential transformations of the predicted outputs. Results: The two most extreme transformations provided the best fit to genetic data for both M. bechsteinii (c = 32; R2 = 0.87) and E. serotinus (c = 16; R2 = 0.42). The negative linear transformations had the poorest fit. Conclusions: These results suggest that bats are able to disperse through areas of poor habitat for breeding, but will avoid the most unsuitable areas. We recommend comparing multiple transformations of HSMs at different resolutions to gain a more accurate representation of gene flow across heterogeneous landscapes and to inform cost-effective, targeted management. [ABSTRACT FROM AUTHOR]

Habitat modelling aims to predict changes in the structure of aquatic communities as a function of habitat availability. It is a primary tool to inform management actions and to search for the best compromise between biodiversity conservation and water supply. The construction of these models requires in-depth knowledge about the main hydrological and geomorphological drivers that affect local communities. However, these drivers act at different spatial scales and determining which investigation scale is the best trade-off between model accuracy and model transferability is crucial. The present work aims to evaluate the mesoscale for habitat modelling of aquatic macroinvertebrates, by testing the effect of microhabitat (flow velocity, water depth, substrate), mesohabitat (riffles, glides, backwaters, isolated ponds) and spatial position (coordinates and derived spatial variables) on macroinvertebrate community variability. Multivariate spatial analyses were used to analyse the macroinvertebrate data collected in a braided reach of the Trebbia River (N Italy). Mesohabitat was a good predictor of macroinvertebrate community composition, although both microhabitat and space also showed a significant effect. The outcome of this work highlights the transferability of the results across mesohabitats, which supports the use of mesoscale modelling for macroinvertebrate distribution in braided rivers. [ABSTRACT FROM AUTHOR]

Habitat loss is a major reason for population declines and thus increases extinction risk, particularly for endemic or specialist species. Protected areas are an important biodiversity conservation strategy in the face of native vegetation conversion, but local and regional factors such as anthropogenic pressure can jeopardize their effectiveness. The Pantanal biome is one of the largest inland wetlands in the world. Currently, only 11% of the Pantanal is protected, 7% with full protection and 4% under sustainable use. However, 14% of the natural vegetation of the biome was lost between 2002 and 2014, negatively affecting its biodiversity. Here, we analyse how the availability of protected areas and habitat loss affects the conservation of the Hyacinth Macaw Anodorhynchus hyacinthinus, as well as the Acuri Palm Attalea phalerata and the Manduvi Tree Sterculia apetala, its two main resources for food and nesting, respectively. We modelled potential distributions to assess the spatio‐temporal patterns of habitat loss due to the conversion of native vegetation and to evaluate to what degree currently protected areas contribute to the conservation of these three species in the Pantanal. We found that, on average, 11.8% of the suitable habitat had been converted and that 10.6% of habitat predicted as suitable is currently protected. In addition, our results indicated important areas for the three species, which are of high priority for conservation and restoration. We also identified priorities for protection and restoration to maintain native vegetation, such as the creation of corridors. We believe that the high conversion rate and the low protection of suitable areas are related to the fact that these species are associated with areas that are rarely or never flooded. In the Pantanal, these areas are more prone to the conversion of native vegetation and are poorly protected given their higher economic value compared with areas with a high frequency of flooding. These facts, and the inadequate management of these areas, such as inappropriate fire regimens, threaten the persistence of these iconic species in the Pantanal. [ABSTRACT FROM AUTHOR]

The MesoHABitat SImulation Model (MesoHABSIM) is the preferred method to calculate spatio-temporal variation in the fish habitat availability in Italian rivers. With the aim of improving the applicability of the MesoHABSIM approach in the Italian territory, we carried out a systematic review of physical habitat preferences for 31 freshwater fish species and three freshwater lampreys, representing 75% of the total indigenous freshwater fish community of Italy. Information related to suitable ranges of depth, flow velocity, biotic/abiotic substrates, covers/shelters was collected and summarized for two critical life stages (adult and juvenile) and two bioperiods (rearing/growth and spawning). Overall, 250 publications were reviewed, classified as 206 peer-reviewed papers, 20 books, 7 PhD thesis, and 17 grey literature sources. Our analysis revealed substantial deficits of information about habitat requirements for more than 30% of Italian freshwater fish species. This information is particularly scarce for the most threatened endemic species, especially for their most critical bioperiod (i.e., spawning). With the aim of preserving freshwater fish biodiversity as required in the EU Biodiversity Strategy for 2030 (European Commission, 2020), accurate information on physical habitat requirements for spawning is crucial. As an example application of MesoHABSIM, the collected habitat preference information was used to define and apply mesohabitat suitability criteria for one fish species (Telestes muticellus) in a regulated river reach of Argentina Creek (Province of Imperia, Italy). This analysis demonstrates the potential for applying information from the current review to other fish species.

Habitability has been generally defined as the capability of an environment to support life. Ecologists have been using Habitat Suitability Models (HSMs) for more than four decades to study the habitability of Earth from local to global scales. Astrobiologists have been proposing different habitability models for some time, with little integration and consistency among them, being different in function to those used by ecologists. Habitability models are not only used to determine whether environments are habitable, but they also are used to characterize what key factors are responsible for the gradual transition from low to high habitability states. Here we review and compare some of the different models used by ecologists and astrobiologists and suggest how they could be integrated into new habitability standards. Such standards will help improve the comparison and characterization of potentially habitable environments, prioritize target selections, and study correlations between habitability and biosignatures. Habitability models are the foundation of planetary habitability science, and the synergy between ecologists and astrobiologists is necessary to expand our understanding of the habitability of Earth, the Solar System, and extrasolar planets. [ABSTRACT FROM AUTHOR]

Ecological restoration plays a vital role in the management of degraded ecosystems; nevertheless, the success of restoration plans depends to a large extent on determining optimal habitats for target species' growth and survival. Ecological niche modeling can be used to predict where climate is presently suitable for a particular species used in restoration, and where suitable climates will be located in the future. Here, we used ensemble ecological modeling to identify areas suitable for restoration of the arid shrub Haloxylon persicum in southeast Iran. We generated environmental suitability predictions for H. persicum restorations from eight different niche modeling algorithms and averaged these predictions to generate an ensemble model prediction. We present projections for the climate suitability of H. persicum for the present climate and the years 2050 and 2070 under the RCP4.5 and RCP8.5 emissions scenarios and for three different global circulation models. Our model projected the optimum mean annual temperature for this species to be between 35.5 and 37°C, the optimum temperature of the coldest quarter to be between 3.5 and 13°C and the optimum precipitation of the coldest quarter to be between 65 to 90 mm. Based on our results, H. persicum is predicted to persist north of 27°N latitude in southeast Iran under current and future conditions; however, the availability of habitat suitable for H. persicum restoration is expected to dramatically diminish under future climate change. This study will help identify and prioritize locations for ecological restoration of this species in the face of climate change. [ABSTRACT FROM AUTHOR]