The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Incorporating plant phenological responses into species distribution models reduces estimates of future species loss and turnover

Anthropogenetic climate change has caused range shifts among many species. Species distribution models (SDMs) are used to predict how species ranges may change in the future. However, most SDMs rarely consider how climate-sensitive traits, such as phenology, which affect individuals' demography and fitness, may influence species' ranges. Using > 120 000 herbarium specimens representing 360 plant species distributed across the eastern United States, we developed a novel 'phenology-informed' SDM that integrates phenological responses to changing climates. We compared the ranges of each species forecast by the phenology-informed SDM with those from conventional SDMs. We further validated the modeling approach using hindcasting. When examining the range changes of all species, our phenology-informed SDMs forecast less species loss and turnover under climate change than conventional SDMs. These results suggest that dynamic phenological responses of species may help them adjust their ecological niches and persist in their habitats as the climate changes. Plant phenology can modulate species' responses to climate change, mitigating its negative effects on species persistence. Further application of our framework will contribute to a generalized understanding of how traits affect species distributions along environmental gradients and facilitate the use of trait-based SDMs across spatial and taxonomic scales.

Prediction of Potential Suitable Areas and Priority Protection for Cupressus gigantea on the Tibetan Plateau

Cupressus gigantea (C. gigantea) is an endemic endangered species on the Tibetan Plateau; its potential suitable areas and priority protection in the context of global climate change remain poorly predicted. This study utilized Biomod2 and Marxan to assess the potential suitable areas and priority protection for C. gigantea. Our study revealed that the suitable areas of C. gigantea were concentrated in the southeastern Tibetan Plateau, with the center in Lang County. Temperature was identified as a crucial environmental factor influencing the distribution of C. gigantea. Over the coming decades, the suitable range of C. gigantea expanded modestly, while its overall distribution remained relatively stable. Moreover, the center of the highly suitable areas tended to migrate towards Milin County in the northeast. Presently, significant areas for improvement are needed to establish protected areas for C. gigantea. The most feasible priority protected areas were located between the Lang and Milin counties in Tibet, which have more concentrated and undisturbed habitats. These results provide scientific guidance for the conservation and planning of C. gigantea, contributing to the stability and sustainability of ecosystems.

Effects of Climate Change on the Distribution of Prosthechea mariae (Orchidaceae) and within Protected Areas in Mexico

The impact of climate change on the distribution of native species in the Neotropics remains uncertain for most species. Prosthechea mariae is an endemic epiphytic orchid in Mexico, categorized as threatened. The objective of this study was to assess the effect of climate change on the natural distribution of P. mariae and the capacity of protected areas (PAs) to safeguard optimal environmental conditions for the species in the future. Historical records were obtained from herbaria collections and through field surveys. We utilized climate variables from WorldClim for the baseline scenario and for the 2050 period, using the general circulation models CCSM4 and CNRM-CM5 (RCP 4.5). Three sets of climate data were created for the distribution models, and multiple models were evaluated using the kuenm package. We found that the species is restricted to the eastern region of the country. The projections of future scenarios predict not only a substantial reduction in habitat but also an increase in habitat fragmentation. Ten PAs were found within the current distribution area of the species; in the future, the species could lose between 36% and 48% of its available habitat within these PAs. The results allowed for the identification of locations where climate change will have the most severe effects, and proposals for long-term conservation are addressed.

Unravelling the role of tropical cyclones in shaping present species distributions

Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored. Using TC data from the National Hurricane Center, we developed spatially and temporally explicit species distribution models (SDMs) to examine the role of TCs in shaping present-day distributions of Puerto Rico's 10 Anolis lizard species. We created six predictor variables to represent the intensity and frequency of TCs. For each occurrence of a species, we calculated these variables for TCs that came within 500 km of the center of Puerto Rico and occurred within the 1-year window prior to when that occurrence was recorded. We also included predictor variables related to landcover, climate, topography, canopy cover and geology. We used random forests to assess model performance and variable importance in models with and without TC variables. We found that the inclusion of TC variables improved model performance for the majority of Puerto Rico's 10 anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. Range-restricted species experienced small, almost negligible, improvements but also had more predictive models both with and without the inclusion of TC variables compared to generalist species. Our findings suggest that incorporating data on TCs into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events.

The future of endemic and threatened birds of the Amazon in the face of global climate change

The anthropogenic impacts on the environment, including deforestation and the escalating emissions of greenhouse gases, have significantly contributed to global climate change that can lead to alterations in ecosystems. In this context, protected areas (PAs) are pillars for biodiversity conservation by being able, for example, to maintain the viability of populations of endangered species. On the other hand, the species range shifts do not follow the limits of PAs, jeopardizing the conservation of these species. Furthermore, the effectiveness of PAs is consistently undermined by impacts stemming from land use, hunting activities, and illegal exploitation, both within the designated areas and in their adjacent zones. The objectives of this study are to quantify the impacts of climate change on the distribution of threatened and endemic birds of the Amazon biome, evaluate the effectiveness of PAs in protecting the richness of threatened birds, and analyze the representativeness of species within PAs. We found with our results that climate suitability loss is above 80 for 65% of taxa in the optimistic scenario and above 93% in the pessimistic scenario. The results show that PAs are not effective in protecting the richness of Amazonian birds, just as they are ineffective in protecting most of the taxa studied when analyzed individually Although some taxa are presented as "Protected," in future scenarios these taxa may suffer major shrinkages in their distributions and consequently present population unviability. The loss of climatically suitable areas and the effectiveness of PAs can directly influence the loss of ecosystem services, fundamental to maintaining the balance of biodiversity. Therefore, our study paves the way for conservation actions aimed at these taxa so that they can mitigate current and future extinctions due to climate change.

Characterization and Future Distribution Prospects of "Carciofo di Malegno" Landrace for Its In Situ Conservation

"Carciofo di Malegno" is a little-known landrace of Cynara cardunculus subsp. scolymus cultivated in Camonica Valley (northern Italy). The morphological and phytochemical characteristics of this landrace were investigated; furthermore, a species distribution model (MaxEnt algorithm) was used to explore its ecological niche and the geographical area where it could be grown in the future. Due to its spiky shape, "Carciofo di Malegno" was distinct from any other artichoke sample considered, and it appears to be similar to those belonging to the "Spinosi" group. The concentration of chlorogenic acid (497.2 ± 116.0 mg/100 g DW) and cynarine (7.4 ± 1.2 mg/100 g DW) in "Carciofo di Malegno" was comparable to that of the commercial cultivars. In "Carciofo di Malegno," luteolin was detected in a significant amount (9.4 ± 1.5 mg/100 g DW) only in the stems and in the edible parts of the capitula. A MaxEnt distribution model showed that in the coming decades (2040-2060s), the cultivation of this landrace could expand to the pre-Alps and Alps of Lombardy. Climate change may promote the diffusion of "Carciofo di Malegno", contributing to preservation and the enhancement of this landrace and generating sustainable income opportunities in mountain areas through exploring new food or medicinal applications.

Global Trends in Climate Suitability of Bees: Ups and Downs in a Warming World

Bees represent vital natural assets contributing significantly to global food production and the maintenance of ecosystems. While studies on climate change effects impacting major pollinators like honeybees and bumblebees raise concerns about global diversity and crop productivity, comprehensive global-scale analyses remain limited. This study explores the repercussions of global warming on 1365 bees across seven families of bees worldwide. To compile a robust global bee occurrence dataset, we utilized the innovative 'BeeBDC' R package that amalgamated over 18.3 million bee occurrence records sourced from various repositories. Through species distribution models under the SSP585 scenario in the year 2070, we assessed how climate change influences the climate suitability of bees on a global scale, examining the impacts across continents. Our findings suggested that approximately 65% of bees are likely to witness a decrease in their distribution, with reductions averaging between 28% in Australia and 56% in Europe. Moreover, our analysis indicated that climate change's impact on bees is projected to be more severe in Africa and Europe, while North America is expected to witness a higher number (336) of bees expanding their distribution. Climate change's anticipated effects on bee distributions could potentially disrupt existing pollinator-plant networks, posing ecological challenges that emphasize the importance of pollinator diversity, synchrony between plants and bees, and the necessity for focused conservation efforts.

Predicting the current habitat refugia of Himalayan Musk deer (Moschus chrysogaster) across Nepal

Himalayan Musk deer, Moschus chrysogaster is widely distributed but one of the least studied species in Nepal. In this study, we compiled a total of 429 current presence points of direct observation of the species, pellets droppings, and hoofmarks based on field-based surveys during 2018-2021 and periodic data held by the Department of National Park and Wildlife Conservation. We developed the species distribution model using an ensemble modeling approach. We used a combination of bioclimatic, anthropogenic, topographic, and vegetation-related variables to predict the current suitable habitat for Himalayan Musk deer in Nepal. A total of 16 predictor variables were used for habitat suitability modeling after the multicollinearity test. The study shows that the 6973.76 km2 (5%) area of Nepal is highly suitable and 8387.11 km2 (6%) is moderately suitable for HMD. The distribution of HMD shows mainly by precipitation seasonality, precipitation of the warmest quarter, temperature ranges, distance to water bodies, anthropogenic variables, and land use and land cover change (LULC). The probability of occurrence is less in habitats with low forest cover. The response curves indicate that the probability of occurrence of HMD decreases with an increase in precipitation seasonality and remains constant with an increase in precipitation of the warmest quarter. Thus, the fortune of the species distribution will be limited by anthropogenic factors like poaching, hunting, habitat fragmentation and habitat degradation, and long-term forces of climate change.

Environmental drivers behind the genetic differentiation in mountain chickadees (Poecile gambeli)

Anthropogenic climate change has a large impact on wildlife populations and the scale of the impacts has been increasing. In this study, we utilised 3dRAD sequence data to investigate genetic divergence and identify the environmental drivers of genetic differentiation between 12 populations of mountain chickadees, family Paridae, sampled across North America. To examine patterns of genetic variation across the range, we conducted a discriminant analysis of principal components (DAPC), admixture analysis, and calculated pairwise Fst values. The DAPC revealed four clusters: southern California, eastern Rocky Mountains, northwestern Rocky Mountains, and Oregon/northern California. We then used BayeScEnv to highlight significant outlier SNPs associated with the five environmental variables. We identified over 150 genes linked to outlier SNPs associated with more than 15 pathways, including stress response and circadian rhythm. We also found a strong signal of isolation by distance and local temperature was highly correlated with genetic distance. Maxent simulations showed a northward range shift over the next 50 years and a decrease in suitable habitat, highlighting the need for immediate conservation action.

Genetic Structure and Population History of the Zaisan Toad-Headed Agama (Phrynocephalus melanurus) Inferred from Mitochondrial DNA

The agamid lizard Phrynocephalus melanurus is restricted to Northwest China (Dzungar Basin) and the adjacent Eastern Kazakhstan (Zaisan and Alakol basins). To elucidate the phylogeography of P. melanurus, we obtained the mitochondrial DNA COI segments of 175 sampled lizards from 44 localities across the whole distribution. Phylogenetic analyses revealed two main Clades comprising five geographically structured lineages (I, IIa, IIb1, IIb2, and IIb3) that fit an isolation-by-distance (IBD) model. The divergence from the most recent common ancestor was dated to ~1.87 million years ago (Ma). Demographic analyses demonstrated lineage-specific response to past climate change: stable population for Clade I, Subclade IIb1; past population expansion for IIb3 since 0.18 Ma, respectively. Bayesian phylogeographic diffusion analyses detected initial spreading at the Saur Mount vicinity, approximately 1.8 Ma. Historical species distribution model (SDM) projected expansion of the suitable habitat in the last interglacial and shift and contraction in the last glacial maximum and Holocene epochs. The SDM predicted a drastic reduction in suitable area throughout the range as a response to future climate change. Our findings suggest that the evolution of P. melanurus followed a parapatric divergence with subsequent dispersal and adaptation to cold and dry environments during the Quaternary. Overall, this work improves our understanding of the lineage diversification and population dynamics of P. melanurus, providing further insights into the evolutionary processes that occurred in Northwest China and adjacent Eastern Kazakhstan.

Projected climate and canopy change lead to thermophilization and homogenization of forest floor vegetation in a hotspot of plant species richness

Mountain forests are plant diversity hotspots, but changing climate and increasing forest disturbances will likely lead to far-reaching plant community change. Projecting future change, however, is challenging for forest understory plants, which respond to forest structure and composition as well as climate. Here, we jointly assessed the effects of both climate and forest change, including wind and bark beetle disturbances, using the process-based simulation model iLand in a protected landscape in the northern Alps (Berchtesgaden National Park, Germany), asking: (1) How do understory plant communities respond to 21st-century change in a topographically complex mountain landscape, representing a hotspot of plant species richness? (2) How important are climatic changes (i.e., direct climate effects) versus forest structure and composition changes (i.e., indirect climate effects and recovery from past land use) in driving understory responses at landscape scales? Stacked individual species distribution models fit with climate, forest, and soil predictors (248 species currently present in the landscape, derived from 150 field plots stratified by elevation and forest development, overall area under the receiving operator characteristic curve = 0.86) were driven with projected climate (RCP4.5 and RCP8.5) and modeled forest variables to predict plant community change. Nearly all species persisted in the landscape in 2050, but on average 8% of the species pool was lost by the end of the century. By 2100, landscape mean species richness and understory cover declined (-13% and -8%, respectively), warm-adapted species increasingly dominated plant communities (i.e., thermophilization, +12%), and plot-level turnover was high (62%). Subalpine forests experienced the greatest richness declines (-16%), most thermophilization (+17%), and highest turnover (67%), resulting in plant community homogenization across elevation zones. Climate rather than forest change was the dominant driver of understory responses. The magnitude of unabated 21st-century change is likely to erode plant diversity in a species richness hotspot, calling for stronger conservation and climate mitigation efforts.

Soil properties constrain predicted poleward migration of plants under climate change

Many plant species are predicted to migrate poleward in response to climate change. Species distribution models (SDMs) have been widely used to quantify future suitable habitats, but they often neglect soil properties, despite the importance of soil for plant fitness. As soil properties often change along latitudinal gradients, higher-latitude soils might be more or less suitable than average conditions within the current ranges of species, thereby accelerating or slowing potential poleward migration. In this study, we built three SDMs - one with only climate predictors, one with only soil predictors, and one with both - for each of 1870 plant species in Eastern North America, in order to investigate the relative importance of soil properties in determining plant distributions and poleward shifts under climate change. While climate variables were the most important predictors, soil properties also had a substantial influence on continental-scale plant distributions. Under future climate scenarios, models including soil predicted much smaller northward shifts in distributions than climate-only models (c. 40% reduction). Our findings strongly suggest that high-latitude soils are likely to impede ongoing plant migration, and they highlight the necessity of incorporating soil properties into models and predictions for plant distributions and migration under environmental change.

Potential sources of time lags in calibrating species distribution models

The Anthropocene is characterized by a rapid pace of environmental change and is causing a multitude of biotic responses, including those that affect the spatial distribution of species. Lagged responses are frequent and species distributions and assemblages are consequently pushed into a disequilibrium state. How the characteristics of environmental change-for example, gradual 'press' disturbances such as rising temperatures due to climate change versus infrequent 'pulse' disturbances such as extreme events-affect the magnitude of responses and the relaxation times of biota has been insufficiently explored. It is also not well understood how widely used approaches to assess or project the responses of species to changing environmental conditions can deal with time lags. It, therefore, remains unclear to what extent time lags in species distributions are accounted for in biodiversity assessments, scenarios and models; this has ramifications for policymaking and conservation science alike. This perspective piece reflects on lagged species responses to environmental change and discusses the potential consequences for species distribution models (SDMs), the tools of choice in biodiversity modelling. We suggest ways to better account for time lags in calibrating these models and to reduce their leverage effects in projections for improved biodiversity science and policy.

A standard approach for including climate change responses in IUCN Red List assessments

The International Union for Conservation of Nature (IUCN) Red List is a central tool for extinction risk monitoring and influences global biodiversity policy and action. But, to be effective, it is crucial that it consistently accounts for each driver of extinction. Climate change is rapidly becoming a key extinction driver, but consideration of climate change information remains challenging for the IUCN. Several methods can be used to predict species' future decline, but they often fail to provide estimates of the symptoms of endangerment used by IUCN. We devised a standardized method to measure climate change impact in terms of change in habitat quality to inform criterion A3 on future population reduction. Using terrestrial nonvolant tetrapods as a case study, we measured this impact as the difference between the current and the future species climatic niche, defined based on current and future bioclimatic variables under alternative model algorithms, dispersal scenarios, emission scenarios, and climate models. Our models identified 171 species (13% out of those analyzed) for which their current red-list category could worsen under criterion A3 if they cannot disperse beyond their current range in the future. Categories for 14 species (1.5%) could worsen if maximum dispersal is possible. Although ours is a simulation exercise and not a formal red-list assessment, our results suggest that considering climate change impacts may reduce misclassification and strengthen consistency and comprehensiveness of IUCN Red List assessments.

Climate Change Potentially Leads to Habitat Expansion and Increases the Invasion Risk of Hydrocharis (Hydrocharitaceae)

Climate change is a crucial factor impacting the geographical distribution of plants and potentially increases the risk of invasion for certain species, especially for aquatic plants dispersed by water flow. Here, we combined six algorithms provided by the biomod2 platform to predict the changes in global climate-suitable areas for five species of Hydrocharis (Hydrocharitaceae) (H. chevalieri, H. dubia, H. laevigata, H. morsus-ranae, and H. spongia) under two current and future carbon emission scenarios. Our results show that H. dubia, H. morsus-ranae, and H. laevigata had a wide range of suitable areas and a high risk of invasion, while H. chevalieri and H. spongia had relatively narrow suitable areas. In the future climate scenario, the species of Hydrocharis may gain a wider habitat area, with Northern Hemisphere species showing a trend of migration to higher latitudes and the change in tropical species being more complex. The high-carbon-emission scenario led to greater changes in the habitat area of Hydrocharis. Therefore, we recommend strengthening the monitoring and reporting of high-risk species and taking effective measures to control the invasion of Hydrocharis species.

Bayesian areal disaggregation regression to predict wildlife distribution and relative density with low-resolution data

For species of conservation concern and human-wildlife conflict, it is imperative that spatial population data be available to design adaptive-management strategies and be prepared to meet challenges such as land use and climate change, disease outbreaks, and invasive species spread. This can be difficult, perhaps impossible, if spatially explicit wildlife data are not available. Low-resolution areal counts, however, are common in wildlife monitoring, that is, the number of animals reported for a region, usually corresponding to administrative subdivisions, for example, region, province, county, departments, or cantons. Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high-resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyze data for wildlife populations. Based on high-resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5 × 5 km or finer resolution), thereby converting low-resolution data into a high-resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika, and fallow) in Ireland from 2000 to 2018. We show an application of the Bayesian areal disaggregation regression method and document marked increases in relative population density and extensive range expansion for each of the three deer species across Ireland. We challenged our disaggregated model predictions by correlating them with independent deer surveys carried out in field sites and alternative deer distribution models built using presence-only and presence-absence data. Finding a high correlation with both independent data sets, we highlighted the ability of Bayesian areal disaggregation regression to accurately capture fine-scale spatial patterns of animal distribution. This study uncovers new scenarios for wildlife managers and conservation biologists to reliably use regional count data disregarded so far in species distribution modeling. Thus, it represents a step forward in our ability to monitor wildlife population and meet challenges in our changing world.

Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir

Predicting the spatial occurrence of wildlife is a major challenge for ecology and management. In Latin America, limited knowledge of the number and locations of vampire bat roosts precludes informed allocation of measures intended to prevent rabies spillover to humans and livestock. We inferred the spatial distribution of vampire bat roosts while accounting for observation effort and environmental effects by fitting a log Gaussian Cox process model to the locations of 563 roosts in three regions of Peru. Our model explained 45% of the variance in the observed roost distribution and identified environmental drivers of roost establishment. When correcting for uneven observation effort, our model estimated a total of 2340 roosts, indicating that undetected roosts (76%) exceed known roosts (24%) by threefold. Predicted hotspots of undetected roosts in rabies-free areas revealed high-risk areas for future viral incursions. Using the predicted roost distribution to inform a spatial model of rabies spillover to livestock identified areas with disproportionate underreporting and indicated a higher rabies burden than previously recognized. We provide a transferrable approach to infer the distribution of a mostly unobserved bat reservoir that can inform strategies to prevent the re-emergence of an important zoonosis.

Modeling Spatio-Temporal Variations in the Habitat Utilization of Swordtip Squid (Uroteuthis edulis) in the East China Sea and Southern Yellow Sea

Accurately modeling the distribution of keystone species is of utmost importance to gain a comprehensive understanding of their complex ecological dynamics and to develop effective strategies for sustainable scientific management. In the coastal China ecosystem, the swordtip squid (Uroteuthis edulis) stands out as a keystone species with significant commercial and ecological value. Despite its importance, research on the ecological dynamics of this species remains limited and requires further investigation. To investigate the spatial and temporal variability in the distribution of U. edulis and identify the key environmental drivers in the East China Sea (ECS) and southern Yellow Sea across different seasons, we generated ensemble models using oceanographic variables and fishery-independent scientific survey data collected from 2016 to 2018. Our results revealed that U. edulis predominantly inhabited the central and southern regions of the ECS throughout the year. The primary environmental variables driving its distribution varied by season, with the sea surface temperature being the most important in spring, sea surface height in summer and autumn, and depth in winter. During summer and autumn, the suitable habitats of U. edulis were found to be largest and extended northwards towards the coastline. However, they migrated southwards to the waters near the edge of the ECS continental shelf with smaller suitable areas in the spring and winter. These results suggested that U. edulis exhibited season-specific habitat preferences and responded to changing environmental conditions throughout the year. The observed seasonal distribution patterns were likely influenced by the fluctuating mixture of waters (ocean currents) from different sources, with varying physical and chemical characteristics throughout the year. Our study provides baseline data for comprehending the population dynamics of U. edulis and highlights the significance of considering species' habitat preferences in a dynamic environment.

Spatial and seasonal variation in thermal sensitivity within North American bird species

Responses of wildlife to climate change are typically quantified at the species level, but physiological evidence suggests significant intraspecific variation in thermal sensitivity given adaptation to local environments and plasticity required to adjust to seasonal environments. Spatial and temporal variation in thermal responses may carry important implications for climate change vulnerability; for instance, sensitivity to extreme weather may increase in specific regions or seasons. Here, we leverage high-resolution observational data from eBird to understand regional and seasonal variation in thermal sensitivity for 21 bird species. Across their ranges, most birds demonstrated regional and seasonal variation in both thermal peak and range, or the temperature and range of temperatures when observations peaked. Some birds demonstrated constant thermal peaks or ranges across their geographical distributions, while others varied according to local and current environmental conditions. Across species, birds typically demonstrated either geographical or seasonal adaptation to climate. Local adaptation and phenotypic plasticity are likely important but neglected aspects of organismal responses to climate change.

The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models

How to effectively obtain species-related low-dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high-dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.

Niche dynamics along two centuries of multiple crayfish invasions

The realised ecological niches of species may change in response to dynamic abiotic and biotic environments, particularly under fast global change. To fully understand the dynamics of niche features and their drivers, it is essential to have a long-term view of species distributions and the factors that may have influenced them. Here, we analysed the distribution and niche dynamics of the Italian crayfish (Austropotamobius fulcisianus) in the Iberian Peninsula over the past 200 years. The Italian crayfish was introduced to Spain in the 16th century, and spread due to multiple stocking events until the 1970s, when two North American crayfish (red swamp crayfish Procambarus clarkii, and signal crayfish Pacifastacus leniusculus) were introduced. Both North American species are carriers of a pathogen (Aphanomyces astaci, the causal agent of crayfish plague) lethal to the Italian crayfish. We hypothesised that the realised niche of the Italian crayfish, both in breadth and in position, has changed over time following changes in its range. The distribution of the Italian crayfish expanded from the mid-19th century until the mid-20th century, in association with an enlargement of its realised niched, mostly towards less abrupt and more coastal-influenced areas. After the introduction of the North American crayfishes, the collapse of the Italian crayfish involved a niche shift towards rough terrains in mountain areas. North American crayfish have eventually occupied most of the Italian crayfish's niche space, with the few no-coexistence areas being relegated to the most abrupt and high-elevation territories. Our historical approach allowed us to document and understand the highly dynamic distribution and niche of the Italian crayfish in the presence of invader counterparts, and to explore the environmental conditions under which their coexistence is minimised.

Estimating the Spatial Distribution and Future Conservation Requirements of the Spotted Seal in the North Pacific

Local adaptation has been increasingly involved in the designation of species conservation strategies to response to climate change. Marine mammals, as apex predators, are climatechange sensitive, and their spatial distribution and conservation requirements are critically significant for designing protection strategies. In this study, we focused on an ice-breeding marine mammal, the spotted seal (Phoca largha), which exhibits distinct morphological and genetic variations across its range. Our objectives were to quantify the ecological niches of three spotted seal populations, construct the species-level model and population-level models that represent different regions in the Bering population (BDPS), Okhotsk population (ODPS) and southern population (SDPS), and conduct a conservation gap analysis. Our findings unequivocally demonstrated a clear niche divergence among the three populations. We predicted habitat contraction for the BDPS and ODPS driven by climate change; in particular, the spotted seals inhabiting Liaodong Bay may face breeding habitat loss. However, most spotted seal habitats are not represented in existing marine protected areas. Drawing upon these outcomes, we propose appropriate conservation policies to effectively protect the habitat of the different geographical populations of spotted seals. Our research addresses the importance of incorporating local adaptation into species distribution modeling to inform conservation and management strategies.

Selection of planning unit size in dynamic management strategies to reduce human-wildlife conflict

Conservation planning traditionally relies upon static reserves; however, there is increasing emphasis on dynamic management (DM) strategies that are flexible in space and time. Due to its novelty, DM lacks best practices to guide design and implementation. We assessed the effect of planning unit size in a DM tool designed to reduce entanglement of protected whales in vertical ropes of surface buoys attached to crab traps in the lucrative U.S. Dungeness crab (Metacarcinus magister) fishery. We conducted a retrospective analysis from 2009 to 2019 with modeled distributions of blue (Balaenoptera musculus) and humpback (Megaptera novaeangliae) whales and observed fisheries effort and revenue to evaluate the effect of 7 planning unit sizes on DM tool performance. We measured performance as avoided whale entanglement risk and protected fisheries revenue. Small planning units avoided up to $47 million of revenue loss and reduced entanglement risk by up to 25% compared to the large planning units currently in use by avoiding the incidental closure of areas with low biodiversity value and high fisheries revenue. However, large planning units were less affected by an unprecedented marine heat wave in 2014-2016 and by delays in information on the distributions of whales and the fishery. Our findings suggest that the choice of planning unit size will require decision-makers to navigate multiple socioecological considerations-rather than a one-size-fits-all approach-to separate wildlife from threats under a changing climate.

Assessing the Potential Distribution of Oxalis latifolia, a Rapidly Spreading Weed, in East Asia under Global Climate Change

Oxalis latifolia, a perennial herbaceous weed, is a highly invasive species that poses a threat to agricultural lands worldwide. East Asia is under a high risk of invasion of O. latifolia under global climate change. To evaluate this risk, we employed maximum entropy modeling considering two shared socio-economic pathways (SSP2-4.5 and SSP5-8.5). Currently, a small portion (8.02%) of East Asia is within the O. latifolia distribution, with the highest coverages in Chinese Taipei, China, and Japan (95.09%, 9.8%, and 0.24%, respectively). However, our projections indicated that this invasive weed will likely be introduced to South Korea and North Korea between 2041 and 2060 and 2081 and 2100, respectively. The species is expected to cover approximately 9.79% and 23.68% (SSP2-4.5) and 11.60% and 27.41% (SSP5-8.5) of the total land surface in East Asia by these time points, respectively. South Korea and Japan will be particularly susceptible, with O. latifolia potentially invading up to 80.73% of their territory by 2081-2100. Mongolia is projected to remain unaffected. This study underscores the urgent need for effective management strategies and careful planning to prevent the introduction and limit the expansion of O. latifolia in East Asian countries.

Building use-inspired species distribution models: Using multiple data types to examine and improve model performance

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.

Fast Bayesian inference for large occupancy datasets

In recent years, the study of species' occurrence has benefited from the increased availability of large-scale citizen-science data. While abundance data from standardized monitoring schemes are biased toward well-studied taxa and locations, opportunistic data are available for many taxonomic groups, from a large number of locations and across long timescales. Hence, these data provide opportunities to measure species' changes in occurrence, particularly through the use of occupancy models, which account for imperfect detection. These opportunistic datasets can be substantially large, numbering hundreds of thousands of sites, and hence present a challenge from a computational perspective, especially within a Bayesian framework. In this paper, we develop a unifying framework for Bayesian inference in occupancy models that account for both spatial and temporal autocorrelation. We make use of the Pólya-Gamma scheme, which allows for fast inference, and incorporate spatio-temporal random effects using Gaussian processes (GPs), for which we consider two efficient approximations: subset of regressors and nearest neighbor GPs. We apply our model to data on two UK butterfly species, one common and widespread and one rare, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years. Our framework can be applied to a wide range of taxa, providing measures of variation in species' occurrence, which are used to assess biodiversity change.

Need for shared internal mound conditions by fungus-growing Macrotermes does not predict their species distributions, in current or future climates

The large, iconic nests constructed by social species are engineered to create internal conditions buffered from external climatic extremes, to allow reproduction and/or food production. Nest-inhabiting eusocial Macrotermitinae (Blattodea: Isoptera) are outstanding palaeo-tropical ecosystem engineers that evolved fungus-growing to break down plant matter ca 62 Mya; the termites feed on the fungus and plant matter. Fungus-growing ensures a constant food supply, but the fungi need temperature-buffered, high humidity conditions, created in architecturally complex, often tall, nest-structures (mounds). Given the need for constant and similar internal nest conditions by fungi farmed by different Macrotermes species, we assessed whether current distributions of six African Macrotermes correlate with similar variables, and whether this would reflect in expected species' distribution shifts with climate change. The primary variables explaining species' distributions were not the same for the different species. Distributionally, three of the six species are predicted to see declines in highly suitable climate. For two species, range increases should be small (less than 9%), and for a single species, M. vitrialatus, 'very suitable' climate could increase by 64%. Mismatches in vegetation requirements and anthropogenic habitat transformation may preclude range expansion, however, presaging disruption to ecosystem patterns and processes that will cascade through systems at both landscape and continental scales. This article is part of the theme issue 'The evolutionary ecology of nests: a cross-taxon approach'.

Identifying and Managing Areas under Threat in the Iberian Peninsula: An Invasion Risk Atlas for Non-Native Aquatic Plant Species as a Potential Tool

Predicting the likelihood that non-native species will be introduced into new areas remains one of conservation's greatest challenges and, consequently, it is necessary to adopt adequate management measures to mitigate the effects of future biological invasions. At present, not much information is available on the areas in which non-native aquatic plant species could establish themselves in the Iberian Peninsula. Species distribution models were used to predict the potential invasion risk of (1) non-native aquatic plant species already established in the peninsula (32 species) and (2) those with the potential to invade the peninsula (40 species). The results revealed that the Iberian Peninsula contains a number of areas capable of hosting non-native aquatic plant species. Areas under anthropogenic pressure are at the greatest risk of invasion, and the variable most related to invasion risk is temperature. The results of this work were used to create the Invasion Risk Atlas for Alien Aquatic Plants in the Iberian Peninsula, a novel online resource that provides information about the potential distribution of non-native aquatic plant species. The atlas and this article are intended to serve as reference tools for the development of public policies, management regimes, and control strategies aimed at the prevention, mitigation, and eradication of non-native aquatic plant species.

Range Dynamics of Striped Field Mouse (Apodemus agrarius) in Northern Eurasia under Global Climate Change Based on Ensemble Species Distribution Models

The striped field mouse (Apodemus agrarius Pallas, 1771) is a widespread species in Northern Eurasia. It damages crops and carries zoonotic pathogens. Its current and future range expansion under climate change may negatively affect public health and the economy, warranting further research to understand the ecological and invasive characteristics of the species. In our study, we used seven algorithms (GLM, GAM, GBS, FDA, RF, ANN, and MaxEnt) to develop robust ensemble species distribution models (eSDMs) under current (1970-2000) and future climate conditions derived from global circulation models (GCMs) for 2021-2040, 2041-2060, 2061-2080, and 2081-2100. Simulation of climate change included high-, medium-, and low-sensitivity GCMs under four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We analyzed the habitat suitability across GCMs and scenarios by constructing geographical ranges and calculating their centroids. The results showed that the range changes depended on both the sensitivity of GCMs and scenario. The main trends were range expansion to the northeast and partial loss of habitat in the steppe area. The striped field mouse may form a continuous range from Central Europe to East Asia, closing the range gap that has existed for 12 thousand years. We present 49 eSDMs for the current and future distribution of A. agrarius (for 2000-2100) with quantitative metrics (gain, loss, change) of the range dynamics under global climate change. The most important predictor variables determining eSDMs are mean annual temperature, mean diurnal range of temperatures, the highest temperature of the warmest month, annual precipitation, and precipitation in the coldest month. These findings could help limit the population of the striped field mouse and predict distribution of the species under global climate change.

Time to Step Up Conservation: Climate Change Will Further Reduce the Suitable Habitats for the Vulnerable Species Marbled Polecat (Vormela peregusna)

Habitat loss and human threats are putting the marbled polecat (Vormela peregusna) on the brink of extinction. Numerous recent studies have found that climate change will further deteriorate the living environment of endangered species, leading to their eventual extinction. In this study, we used the results of infrared camera surveys in China and worldwide distribution data to construct an ensemble model consisting of 10 commonly used ecological niche models to specify potential suitable habitat areas for V. peregusna under current conditions with similar environments to the sighting record sites. Changes in the suitable habitat for V. peregusna under future climate change scenarios were simulated using mid-century (2050s) and the end of the century (2090s) climate scenarios provided by the Coupled Model Intercomparison Project Phase 6 (CMIP6). We evaluated the accuracy of the model to obtain the environmental probability values (cutoff) of the V. peregusna distribution, the current distribution of suitable habitats, and future changes in moderately and highly suitable habitat areas. The results showed that the general linear model (GLM) was the best single model for predicting suitable habitats for V. peregusna, and the kappa coefficient, area under the curve (AUC), and true skill statistic (TSS) of the ensemble model all exceeded 0.9, reflecting greater accuracy and stability than single models. Under the current conditions, the area of suitable habitat for V. peregusna reached 3935.92 × 10⁴ km², suggesting a wide distribution range. In the future, climate change is predicted to severely affect the distribution of V. peregusna and substantially reduce the area of suitable habitats for the species, with 11.91 to 33.55% of moderately and highly suitable habitat areas no longer suitable for the survival of V. peregusna. This shift poses an extremely serious challenge to the conservation of this species. We suggest that attention be given to this problem in Europe, especially the countries surrounding the Black Sea, Asia, China, and Mongolia, and that measures be taken, such as regular monitoring and designating protected areas for the conservation of vulnerable animals.

Unveiling the Potential Distribution of the Highly Threatened Madeira Pipistrelle (Pipistrellus maderensis): Do Different Evolutionary Significant Units Exist?

The isolation of islands has played a significant role in shaping the unique evolutionary histories of many species of flora and fauna, including bats. One notable example is the Madeira pipistrelle (Pipistrellus maderensis), which inhabits the Macaronesian archipelagos of the Azores, Madeira, and the Canary Islands. Despite the high biogeographic and conservation importance of this species, there is limited information on its ecology and evolutionary history across different archipelagos. In our study, we employed species distribution models (SDMs) to identify suitable habitats for the Madeira pipistrelle and determine the environmental factors influencing its distribution. Additionally, we conducted molecular comparisons using mitochondrial DNA data from various Macaronesian islands. Molecular analyses provided compelling evidence for the presence of distinct Evolutionary Significant Units on the different archipelagos. We identified distinct haplotypes in the populations of Madeira and the Canary Islands, with a genetic distance ranging from a minimum of 2.4% to a maximum of 3.3% between samples from different archipelagos. In support of this, SDMs highlighted relevant dissimilarities between the environmental requirements of the populations of the three archipelagos, particularly the climatic niche. Our research demonstrates that deeper investigations that combine ecological, morphological, and genetic areas are necessary to implement tailored conservation strategies.

Are threatened species special? An assessment of Dutch bees in relation to land use and climate

Red Lists are widely used as an indicator of the status and trends of biodiversity and are often used in directing conservation efforts. However, it is unclear whether species with a Least Concern status share a common relationship to environmental correlates compared to species that are on the Red List. To assess this, we focus here on the contribution and correlates of land use, climate, and soil to the occurrence of wild bees in the Netherlands. We used observation data and species distribution models to explain the relation between wild bees and the environment. Non-threatened bees had a relatively higher variable importance of the land use variables to their models, as opposed to the climate variables for the threatened bees. The threatened bees had a smaller extent of occurrence and occupied areas with more extreme climatic conditions. Bees with a Least Concern status showed more positive responses to urban green spaces and Red List species showed a different response to climatic variables, such as temperature and precipitation. Even though Red List bees were found in areas with a higher cover of natural areas, they showed a more selective response to natural land use types. Pastures and crops were the main contributing land use variables and showed almost exclusively a negative correlation with the distribution of all wild bees. This knowledge supports the implementation of appropriate, species-specific conservation measures, including the preservation of natural areas, and the improvement of land use practices in agricultural and urban areas, which may help mitigate the negative impacts of future global change on species' distributions.

Forecasting Outbreaks of Hantaviral Disease: Future Directions in Geospatial Modeling

Hantaviral diseases have been recognized as 'place diseases' from their earliest identification and, epidemiologically, are tied to single host species with transmission occurring from infectious hosts to humans. As such, human populations are most at risk when they are in physical proximity to suitable habitats for reservoir populations, when numbers of infectious hosts are greatest. Because of the lags between improving habitat conditions and increasing infectious host abundance and spillover to humans, it should be possible to anticipate (forecast) where and when outbreaks will most likely occur. Most mammalian hosts are associated with specific habitat requirements, so identifying these habitats and the ecological drivers that impact population growth and the dispersal of viral hosts should be markers of the increased risk for disease outbreaks. These regions could be targeted for public health and medical education. This paper outlines the rationale for forecasting zoonotic outbreaks, and the information that needs to be clarified at various levels of biological organization to make the forecasting of orthohantaviruses successful. Major challenges reflect the transdisciplinary nature of forecasting zoonoses, with needs to better understand the implications of the data collected, how collections are designed, and how chosen methods impact the interpretation of results.

How can physiology best contribute to wildlife conservation in a warming world?

Global warming is now predicted to exceed 1.5°C by 2033 and 2°C by the end of the 21st century. This level of warming and the associated environmental variability are already increasing pressure on natural and human systems. Here we emphasize the role of physiology in the light of the latest assessment of climate warming by the Intergovernmental Panel on Climate Change. We describe how physiology can contribute to contemporary conservation programmes. We focus on thermal responses of animals, but we acknowledge that the impacts of climate change are much broader phylogenetically and environmentally. A physiological contribution would encompass environmental monitoring, coupled with measuring individual sensitivities to temperature change and upscaling these to ecosystem level. The latest version of the widely accepted Conservation Standards designed by the Conservation Measures Partnership includes several explicit climate change considerations. We argue that physiology has a unique role to play in addressing these considerations. Moreover, physiology can be incorporated by institutions and organizations that range from international bodies to national governments and to local communities, and in doing so, it brings a mechanistic approach to conservation and the management of biological resources.

Potential decline in the distribution and food provisioning services of the mopane worm (Gonimbrasia belina) in southern Africa

The mopane worm (Gonimbrasia belina) is an edible insect distributed across southern Africa. As a culturally important source of food, the mopane worm provides nutrition, livelihoods and improves wellbeing for rural communities across its range. However, this is strong evidence that insect populations are declining worldwide, and climate change is likely to cause many insect species to shift in their distributions. For these reasons, we aimed to model how the ecosystem service benefits of the mopane worm are likely to change in the coming decades. We modelled the distribution of the mopane worm under two contrasting climate change scenarios (RCPs 4.5 and 8.5). Moreover, given that the mopane worm shows strong interactions with other species, particularly trees, we incorporated biotic interactions in our models using a Bayesian network. Our models project significant contraction across the species' range, with up to 70% decline in habitat by the 2080s. Botswana and Zimbabwe are predicted to be the most severely impacted countries, with almost all habitat in Botswana and Zimbabwe modelled to be lost by the 2080s. Decline of mopane worm habitat would likely have negative implications for the health of people in rural communities due to loss of an important source of protein as well as household income provided by their harvest. Biogeographic shifts therefore have potential to exacerbate food insecurity, socio-economic inequalities, and gender imbalance (women are the main harvesters), with cascading effects that most negatively impact poor rural communities dependent on natural resources.

Boosting freshwater fish conservation with high-resolution distribution mapping across a large territory

The lack of high-resolution distribution maps for freshwater species across large extents fundamentally challenges biodiversity conservation worldwide. We devised a simple framework to delineate the distributions of freshwater fishes in a high-resolution drainage map based on stacked species distribution models and expert information. We applied this framework to the entire Chinese freshwater fish fauna (>1600 species) to examine high-resolution biodiversity patterns and reveal potential conflicts between freshwater biodiversity and anthropogenic disturbances. The correlations between spatial patterns of biodiversity facets (species richness, endemicity, and phylogenetic diversity) were all significant (r = 0.43-0.98, p < 0.001). Areas with high values of different biodiversity facets overlapped with anthropogenic disturbances. Existing protected areas (PAs), covering 22% of China's territory, protected 25-29% of fish habitats, 16-23% of species, and 30-31% of priority conservation areas. Moreover, 6-21% of the species were completely unprotected. These results suggest the need for extending the network of PAs to ensure the conservation of China's freshwater fishes and the goods and services they provide. Specifically, middle to low reaches of large rivers and their associated lakes from northeast to southwest China hosted the most diverse species assemblages and thus should be the target of future expansions of the network of PAs. More generally, our framework, which can be used to draw high-resolution freshwater biodiversity maps combining species occurrence data and expert knowledge on species distribution, provides an efficient way to design PAs regardless of the ecosystem, taxonomic group, or region considered.

Population-Based Evidence of Climate Change Adaptation in an Endangered Plant Endemic to a Biodiversity Hotspot

Climate change is expected to impact both the population structure and geographic distribution of plants. Species distribution models are widely used to assess range shifts and the vulnerability of plants to climate change. Despite the abundance of modeling studies, little is known about how existing populations respond to climate change. We investigated the demographic structure and vulnerability to climate change in Anemone moorei, a sub-shrub with a highly restricted distribution in a biodiversity hotspot. We improved the distribution knowledge through intensive field work. We conducted a census of stem length as a proxy for age for all known populations. We used ensemble forecasting to project distributions considering 10 future climate scenarios and developed a novel climate change vulnerability index for the species' distribution. We found that the mean stem length decreases and the proportion of young plants increases, while the size of fruiting plants decreases as A. moorei faces greater climate change vulnerability. We interpret these results as evidence for the onset of recent adaptation to climate change, consisting of reduced adult longevity and an earlier onset of reproduction. As a result of these changes, the proportion of juveniles in the population increases.

Invasive Trends of Spartina alterniflora in the Southeastern Coast of China and Potential Distributional Impacts on Mangrove Forests

Mangrove forests are one of the most productive and seriously threatened ecosystems in the world. The widespread invasion of Spartina alterniflora has seriously imperiled the security of mangroves as well as coastal mudflat ecosystems. Based on a model evaluation index, we selected RF, GBM, and GLM as a predictive model for building a high-precision ensemble model. We used the species occurrence records combined with bioclimate, sea-land topography, and marine environmental factors to predict the potentially suitable habitats of mangrove forests and the potentially suitable invasive habitats of S. alterniflora in the southeastern coast of China. We then applied the invasion risk index (IRI) to assess the risk that S. alterniflora would invade mangrove forests. The results show that the suitable habitats for mangrove forests are mainly distributed along the coastal provinces of Guangdong, Hainan, and the eastern coast of Guangxi. The suitable invasive habitats for S. alterniflora are mainly distributed along the coast of Zhejiang, Fujian, and relatively less in the southern provinces. The high-risk areas for S. alterniflora invasion of mangrove forests are concentrated in Zhejiang and Fujian. Bioclimate variables are the most important variables affecting the survival and distribution of mangrove forests and S. alterniflora. Among them, temperature is the most important environmental variable determining the large-scale distribution of mangrove forests. Meanwhile, S. alterniflora is more sensitive to precipitation than temperature. Our results can provide scientific insights and references for mangrove forest conservation and control of S. alterniflora.

Combining environmental niche models, multi-grain analyses, and species traits identifies pervasive effects of land use on butterfly biodiversity across Italy

Understanding how species respond to human activities is paramount to ecology and conservation science, one outstanding question being how large-scale patterns in land use affect biodiversity. To facilitate answering this question, we propose a novel analytical framework that combines environmental niche models, multi-grain analyses, and species traits. We illustrate the framework capitalizing on the most extensive dataset compiled to date for the butterflies of Italy (106,514 observations for 288 species), assessing how agriculture and urbanization have affected biodiversity of these taxa from landscape to regional scales (3-48 km grains) across the country while accounting for its steep climatic gradients. Multiple lines of evidence suggest pervasive and scale-dependent effects of land use on butterflies in Italy. While land use explained patterns in species richness primarily at grains ≤12 km, idiosyncratic responses in species highlighted "winners" and "losers" across human-dominated regions. Detrimental effects of agriculture and urbanization emerged from landscape (3-km grain) to regional (48-km grain) scales, disproportionally affecting small butterflies and butterflies with a short flight curve. Human activities have therefore reorganized the biogeography of Italian butterflies, filtering out species with poor dispersal capacity and narrow niche breadth not only from local assemblages, but also from regional species pools. These results suggest that global conservation efforts neglecting large-scale patterns in land use risk falling short of their goals, even for taxa typically assumed to persist in small natural areas (e.g., invertebrates). Our study also confirms that consideration of spatial scales will be crucial to implementing effective conservation actions in the Post-2020 Global Biodiversity Framework. In this context, applications of the proposed analytical framework have broad potential to identify which mechanisms underlie biodiversity change at different spatial scales.

Projected northward shifts in eastern red-backed salamanders due to changing climate

Many species' distributions are being impacted by the acceleration of climate change. Amphibians in particular serve numerous ecosystem functions and are useful indicators of environmental change. Understanding how their distributions have been impacted by climate change and will continue to be impacted is thus important to overall ecosystem health. Plethodon cinereus (Eastern Red-Backed Salamander) is a widespread species of lungless salamander (Plethodontidae) that ranges across northeastern North America. To better understand future potential lungless salamander range shifts, we quantify environmental favorability, the likelihood of membership in a set of sites where environmental conditions are favorable for a species, for P. cinereus in multiple time periods, and examine shifts in the species' distribution. First, utilizing a large data set of georeferenced records, we assessed which bioclimatic variables were associated with environmental favorability in P. cinereus. We then used species distribution modeling for two time periods (1961-1980 and 2001-2020) to determine whether there was a regional shift in environmental favorability in the past 60 years. Models were then used to project future distributions under eight climate change scenarios to quantify potential range shifts. Shifts were assessed using fuzzy logic, avoiding thresholds that oversimplify model predictions into artificial binary outputs. We found that P. cinereus presence is strongly associated with environmental stability. There has been a substantial northward shift in environmental favorability for P. cinereus between 1961-1980 and 2001-2020. This shift is predicted to continue by 2070, with larger shifts under higher greenhouse gas emission scenarios. As climate change accelerates, it is differentially impacting species but has especially strong impacts on dispersal-limited species. Our results show substantial northward shifts in climatic favorability in the last 60 years for P. cinereus, which are likely to be exacerbated by ongoing climate change. Since P. cinereus is dispersal-limited, these models may imply local extirpations along the southern modern range with limited northward dispersal. Continued monitoring of amphibians in the field will reveal microclimatic effects associated with climate change and the accuracy of the model predictions presented here.

Occurrence Prediction of Riffle Beetles (Coleoptera: Elmidae) in a Tropical Andean Basin of Ecuador Using Species Distribution Models

Genera and species of Elmidae (riffle beetles) are sensitive to water pollution; however, in tropical freshwater ecosystems, their requirements regarding environmental factors need to be investigated. Species distribution models (SDMs) were established for five elmid genera in the Paute river basin (southern Ecuador) using the Random Forest (RF) algorithm considering environmental variables, i.e., meteorology, land use, hydrology, and topography. Each RF-based model was trained and optimised using cross-validation. Environmental variables that explained most of the Elmidae spatial variability were land use (i.e., riparian vegetation alteration and presence/absence of canopy), precipitation, and topography, mainly elevation and slope. The highest probability of occurrence for elmids genera was predicted in streams located within well-preserved zones. Moreover, specific ecological niches were spatially predicted for each genus. Macrelmis was predicted in the lower and forested areas, with high precipitation levels, towards the Amazon basin. Austrelmis was predicted to be in the upper parts of the basin, i.e., páramo ecosystems, with an excellent level of conservation of their riparian ecosystems. Austrolimnius and Heterelmis were also predicted in the upper parts of the basin but in more widespread elevation ranges, in the Heterelmis case, and even in some areas with a medium level of anthropisation. Neoelmis was predicted to be in the mid-region of the study basin in high altitudinal streams with a high degree of meandering. The main findings of this research are likely to contribute significantly to local conservation and restoration efforts being implemented in the study basin and could be extrapolated to similar eco-hydrological systems.

Prediction of the Potential Distribution of the Endangered Species Meconopsis punicea Maxim under Future Climate Change Based on Four Species Distribution Models

Climate change increases the extinction risk of species, and studying the impact of climate change on endangered species is of great significance to biodiversity conservation. In this study, the endangered plant Meconopsis punicea Maxim (M. punicea) was selected as the research object. Four species distribution models (SDMs): the generalized linear model, the generalized boosted regression tree model, random forest and flexible discriminant analysis were applied to predict the potential distribution of M. punicea under current and future climates scenarios. Among them, two emission scenarios of sharing socio-economic pathways (SSPs; i.e., SSP2-4.5 and SSP5-8.5) and two global circulation models (GCMs) were considered for future climate conditions. Our results showed that temperature seasonality, mean temperature of coldest quarter, precipitation seasonality and precipitation of warmest quarter were the most important factors shaping the potential distribution of M. punicea. The prediction of the four SDMs consistently indicated that the current potential distribution area of M. punicea is concentrated between 29.02° N-39.06° N and 91.40° E-105.89° E. Under future climate change, the potential distribution of M. punicea will expand from the southeast to the northwest, and the expansion area under SSP5-8.5 would be wider than that under SSP2-4.5. In addition, there were significant differences in the potential distribution of M. punicea predicted by different SDMs, with slight differences caused by GCMs and emission scenarios. Our study suggests using agreement results from different SDMs as the basis for developing conservation strategies to improve reliability.

Global Distribution of Culex tritaeniorhynchus and Impact Factors

Culex tritaeniorhynchus is the primary vector of Japanese encephalitis (JE) and has a wide global distribution. However, the current and future geographic distribution maps of Cx. tritaeniorhynchus in global are still incomplete. Our study aims to predict the potential distribution of Cx. tritaeniorhynchus in current and future conditions to provide a guideline for the formation and implementation of vector control strategies all over the world. We collected and screened the information on the occurrence of Cx. tritaeniorhynchus by searching the literature and online databases and used ten algorithms to investigate its global distribution and impact factors. Cx. tritaeniorhynchus had been detected in 41 countries from 5 continents. The final ensemble model (TSS = 0.864 and AUC = 0.982) indicated that human footprint was the most important factor for the occurrence of Cx. tritaeniorhynchus. The tropics and subtropics, including southeastern Asia, Central Africa, southeastern North America and eastern South America, showed high habitat suitability for Cx. tritaeniorhynchus. Cx. tritaeniorhynchus is predicted to have a wider distribution in all the continents, especially in Western Europe and South America in the future under two extreme emission scenarios (SSP5-8.5 and SSP1-2.6). Targeted strategies for the control and prevention of Cx. tritaeniorhynchus should be further strengthened.

Current and Future Habitat Suitability Models for Four Ticks of Medical Concern in Illinois, USA

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.

Scale-sensitivity in the measurement and interpretation of environmental niches

Species environmental niches are central to ecology, evolution, and global change research, but their characterization and interpretation depend on the spatial scale (specifically, the spatial grain) of their measurement. We find that the spatial grain of niche measurement is usually uninformed by ecological processes and varies by orders of magnitude. We illustrate the consequences of this variation for the volume, position, and shape of niche estimates, and discuss how it interacts with geographic range size, habitat specialization, and environmental heterogeneity. Spatial grain significantly affects the study of niche breadth, environmental suitability, niche evolution, niche tracking, and climate change effects. These and other fields will benefit from a more mechanism-informed choice of spatial grain and cross-grain evaluations that integrate different data sources.

Sampling from commercial vessel routes can capture marine biodiversity distributions effectively

Collecting fine-scale occurrence data for marine species across large spatial scales is logistically challenging but is important to determine species distributions and for conservation planning. Inaccurate descriptions of species ranges could result in designating protected areas with inappropriate locations or boundaries. Optimizing sampling strategies therefore is a priority for scaling up survey approaches using tools such as environmental DNA (eDNA) to capture species distributions. In a marine context, commercial vessels, such as ferries, could provide sampling platforms allowing access to undersampled areas and repeatable sampling over time to track community changes. However, sample collection from commercial vessels could be biased and may not represent biological and environmental variability. Here, we evaluate whether sampling along Mediterranean ferry routes can yield unbiased biodiversity survey outcomes, based on perfect knowledge from a stacked species distribution model (SSDM) of marine megafauna derived from online data repositories. Simulations to allocate sampling point locations were carried out representing different sampling strategies (random vs regular), frames (ferry routes vs unconstrained), and number of sampling points. SSDMs were remade from different sampling simulations and compared with the "perfect knowledge" SSDM to quantify the bias associated with different sampling strategies. Ferry routes detected more species and were able to recover known patterns in species richness at smaller sample sizes better than unconstrained sampling points. However, to minimize potential bias, ferry routes should be chosen to cover the variability in species composition and its environmental predictors in the SSDMs. The workflow presented here can be used to design effective sampling strategies using commercial vessel routes globally for eDNA and other biodiversity survey techniques. This approach has potential to provide a cost-effective method to access remote oceanic areas on a regular basis and can recover meaningful data on spatiotemporal biodiversity patterns.

Can suitability indices predict plant growth in the invaded range? The case of Acacias species

INTRODUCTION

Forestry in many parts of the world depends on exotic species, making this industry a source of invasions in some countries. Among others, plantations of the genus Pinus, Eucalyptus, Acacia, Populus, and Pseudotsuga underpin the forestry industry and are a vital component of many countries economies. Among woody plants, the cosmopolitan genus Acacia includes some of the most commonly planted trees worldwide. In order to prevent, manage and control invasive plant species, one of the most used tools is species distribution models. The output of these models can also be used to obtain information about population characteristics, such as spatial abundance patterns or species performance. Although ecological theory suggests a direct link between fitness and suitability, this link is often absent. The reasons behind the lack of this relationship are multiple. Chile is one of the countries where Acacia species, in particular, A. dealbata and A. melanoxylon, have become invaders.

METHODS

Here, we used climatic and edaphic variables to predict thepotentially suitable habitats for A. dealbata and A. melanoxylon in continental Chile and evaluate if the suitability indices obtained from these models are associated with the observed performance of the trees along the country.

RESULTS

Our models show that variable importance showed significant similarities between the variables that characterize each species' niche. However, despite the high accuracy of our models, we did not observe an association between suitability and tree growth.

DISCUSSION

This disconnection between suitability and performance can result from multiple causes, from structural limitations, like the lack of biotic interactions in the models, to methodological issues, like the usefulness of the performance metric used. Whatever the scenario, our results suggest that plans to control invasive species should be cautious in assuming this relationship in their design and consider other indicators such as species establishment success.

Whole genome demographic models indicate divergent effective population size histories shape contemporary genetic diversity gradients in a montane bumble bee

Understanding historical range shifts and population size variation provides an important context for interpreting contemporary genetic diversity. Methods to predict changes in species distributions and model changes in effective population size (N _e) using whole genomes make it feasible to examine how temporal dynamics influence diversity across populations. We investigate N _e variation and climate-associated range shifts to examine the origins of a previously observed latitudinal heterozygosity gradient in the bumble bee Bombus vancouverensis Cresson (Hymenoptera: Apidae: Bombus Latreille) in western North America. We analyze whole genomes from a latitude-elevation cline using sequentially Markovian coalescent models of N _e through time to test whether relatively low diversity in southern high-elevation populations is a result of long-term differences in N _e. We use Maxent models of the species range over the last 130,000 years to evaluate range shifts and stability. N _e fluctuates with climate across populations, but more genetically diverse northern populations have maintained greater N _e over the late Pleistocene and experienced larger expansions with climatically favorable time periods. Northern populations also experienced larger bottlenecks during the last glacial period, which matched the loss of range area near these sites; however, bottlenecks were not sufficient to erode diversity maintained during periods of large N _e. A genome sampled from an island population indicated a severe postglacial bottleneck, indicating that large recent postglacial declines are detectable if they have occurred. Genetic diversity was not related to niche stability or glacial-period bottleneck size. Instead, spatial expansions and increased connectivity during favorable climates likely maintain diversity in the north while restriction to high elevations maintains relatively low diversity despite greater stability in southern regions. Results suggest genetic diversity gradients reflect long-term differences in N _e dynamics and also emphasize the unique effects of isolation on insular habitats for bumble bees. Patterns are discussed in the context of conservation under climate change.