To explore relationships between spatial distributions of northern shrimp Pandalus eous and environmental factors along the coastline of Honshu Island in the Sea of Japan, we built delta-type two-step generalized additive models (delta-GAM) based on bottom trawl surveys conducted in 2013–2022. The models provide the first quantitative analysis of the species’ habitat, showing that its distribution is associated with bottom sediment type, depth, slope and topographic position index (TPI) alongside the effects of year and region. From the delta-GAM response plots, species habitat preferences in the Sea of Japan were estimated as follows: seafloor deeper than 283 m; muddy rather than sandy bottoms; gently sloped to flat bottoms (<0.8° in slope); and valleys rather than ridges (TPI < 0.9). These results were reviewed in detail along with previously reported distribution records of northern shrimp. Standardized density (s-density) per fishing grid cell (10′ square latitude–longitude mesh) estimated from the delta-GAM results indicated that this species is widely distributed on the continental slope along the coast of Honshu Island. To test plausibility of the s-density analysis, we compared s-density per fishing grid cell with nominal CPUE (kg per haul) per fishing grid cell based on official logbook data from large offshore trawl fisheries. The two were generally positively correlated by year, and the delta-GAM results were assumed to be fairly robust. Finally, potential fishing grounds were explored based on the delta-GAM results.

When one or several classes are much less prevalent than another class (unbalanced data), class error rates and variable importances of the machine learning algorithm random forest can be biased, particularly when sample sizes are smaller, imbalance levels higher, and effect sizes of important variables smaller. Using simulated data varying in size, imbalance level, number of true variables, their effect sizes, and the strength of multicollinearity between covariates, we evaluated how eight versions of random forest ranked and selected true variables out of a large number of covariates despite class imbalance. The version that calculated variable importance based on the area under the curve (AUC) was least adversely affected by class imbalance. For the same number of true variables, effect sizes, and multicollinearity between covariates, the AUC variable importance ranked true variables still highly at the lower sample sizes and higher imbalance levels at which the other seven versions no longer achieved high ranks for true variables. Conversely, using the Hellinger distance to split trees or downsampling the majority class already ranked true variables lower and more variably at the larger sample sizes and lower imbalance levels at which the other algorithms still ranked true variables highly. In variable selection, a higher proportion of true variables were identified when covariates were ranked by AUC importances and the proportion increased further when the AUC was used as the criterion in forward variable selection. In three case studies, known species–habitat relationships and their spatial scales were identified despite unbalanced data.

Tropical montane systems are characterized by a high plant species diversity and complex environmental gradients. Climate warming may force species to track suitable climatic conditions and shift their distribution upward, which may be particularly problematic for species with narrow elevational ranges. To better understand the fate of montane plant species in the face of climate change, we evaluated a) which environmental factors best predict the distribution of 277 plant species along the Himalayan elevational gradient in Nepal, and b) whether species elevational ranges increase with increasing elevation. To this end, we developed ecological niche models using MaxEnt by combining species survey and presence data with 19 environmental predictors. Key environmental factors that best predicted the distribution of Himalayan plant species were mean annual temperature (for 54.5% of the species) followed by soil clay content (10.2%) and slope (9.4%). Although temperature is the best predictor, it is associated with many other covariates that may explain species distribution, such as irradiance and potential evapotranspiration. Species at both ends of the Himalayan elevational gradient had narrower elevational ranges than species in the middle. Our results suggest that with further global warming, most Himalayan plant species have to migrate upward, which is especially critical for upland species with narrow distribution ranges.

In April 2019, the U.S. Fish and Wildlife Service (USFWS) released its recovery plan for the jaguar Panthera onca after several decades of discussion, litigation and controversy about the status of the species in the USA. The USFWS estimated that potential habitat, south of the Interstate-10 highway in Arizona and New Mexico, had a carrying capacity of c. six jaguars, and so focused its recovery programme on areas south of the USA–Mexico border. Here we present a systematic review of the modelling and assessment efforts over the last 25 years, with a focus on areas north of Interstate-10 in Arizona and New Mexico, outside the recovery unit considered by the USFWS. Despite differences in data inputs, methods, and analytical extent, the nine previous studies found support for potential suitable jaguar habitat in the central mountain ranges of Arizona and New Mexico. Applying slightly modified versions of the USFWS model and recalculating an Arizona-focused model over both states provided additional confirmation. Extending the area of consideration also substantially raised the carrying capacity of habitats in Arizona and New Mexico, from six to 90 or 151 adult jaguars, using the modified USFWS models. This review demonstrates the crucial ways in which choosing the extent of analysis influences the conclusions of a conservation plan. More importantly, it opens a new opportunity for jaguar conservation in North America that could help address threats from habitat losses, climate change and border infrastructure.

The White-winged Snowfinch Montifringilla nivalis nivalis is assumed to be highly threatened by climate change, but this high elevation species has been little studied and the current breeding distribution is accurately known only for a minor portion of its range. Here, we provide a detailed and spatially explicit identification of the potentially suitable breeding areas for the Snowfinch. We modelled suitable areas in Europe and compared them with the currently known distribution. We built a distribution model using 14,574 records obtained during the breeding period that integrated climatic, topographic and land-cover variables, working at a 2-km spatial resolution with MaxEnt. The model performed well and was very robust; average annual temperature was the most important occurrence predictor (optimum between c.-3°C and 0°; unsuitable conditions below -10° and above 5°). The current European breeding range estimated by BirdLife International was almost three times greater than that classified as potentially suitable by our model. Discrepancies between our model and the distribution estimated by BirdLife International were particularly evident in eastern Europe, where the species is poorly monitored. Southern populations are likely more isolated and at major risk because of global warming. These differences have important implications for the supposed national responsibility for conservation of the species and highlight the need for new investigations on the species in the eastern part of its European range.

Species distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. ‘Integrated SDMs’ (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications and predictions for rare species, where data are often limited and prediction errors may have significant management consequences. Despite this potential importance, ISDMs remain largely untested under a variety of scenarios. We performed an exploration of key modelling decisions and assumptions on an ISDM using the endangered Baird’s tapir (Tapirus bairdii) as a test species. We found that site area had the strongest effect on the magnitude of population estimates and underlying intensity surface and was driven by estimates of model intercepts. Selecting a site area that accounted for the individual movements of the species within an average home range led to population estimates that coincided with expert estimates. ISDMs that do not account for the individual movements of species will likely lead to less accurate estimates of species intensity (number of individuals per unit area) and thus overall population estimates. This bias could be severe and highly detrimental to conservation actions if uninformed ISDMs are used to estimate global populations of threatened and data-deficient species, particularly those that lack natural history and movement information. However, the ISDM was consistently the most accurate model compared to other approaches, which demonstrates the importance of this new modelling framework and the ability to combine opportunistic data with systematic survey data. Thus, we recommend researchers use ISDMs with conservative movement information when estimating population sizes of rare and data-deficient species. ISDMs could be improved by using a similar parameterization to spatial capture–recapture models that explicitly incorporate animal movement as a model parameter, which would further remove the need for spatial subsampling prior to implementation.

Asiatic black bear Ursus thibetanus and sun bear Helarctos malayanus populations are declining throughout South-east Asia as a result of habitat loss and human disturbance. Knowledge of the distribution and status of each species is limited and largely anecdotal. Range maps are coarse, compiled by expert opinion, and presence or absence is unknown over large portions of South-east Asia. These two species co-occur in Lao People's Democratic Republic and may be faring better there than in neighbouring countries. During 2010–2013 we searched for bear sign along 99 transects within eight study sites throughout Lao. To explore countrywide relative abundance and habitat suitability, we modelled bear sign as a log-linear function of biological and anthropogenic predictors that were associated with habitat assemblages and human disturbance. Bears favored higher elevations and rugged terrain in areas less accessible to humans, and were most abundant in the north and east of Lao. Suitable habitats were rare in the southern lowland plains where bear abundance was relatively low. Our model predicted that Nam Et–Phou Louey National Protected Area had the largest areas of suitable bear habitat, followed by the Nakai-Nam Teun and Nam Ha National Protected Areas. Using transects to survey for bear sign, we created a replicable geographical information system based assessment tool for bears in Lao that can be used to identify conservation opportunities and monitor changes in bear distribution over time.

Translocation of species to areas of former habitat after threats have been mitigated is a common conservation action. However, the long-term success of reintroduction relies on identification of currently available habitat and areas that will remain, or become, habitat in the future. Commonly, a short-term view is taken, focusing on obvious and assumed threats such as predators and habitat degradation. However, in areas subject to significant climate change, challenges include correctly identifying variables that define habitat, and considering probable changes over time. This poses challenges with species such as the western ground parrot Pezoporus flaviventris, which was once relatively common in near-coastal south-western Australia, an area subject to major climate change. This species has declined to one small population, estimated to comprise < 150 individuals. Reasons for the decline include altered fire regimes, introduced predators and habitat clearing. The establishment of new populations is a high priority, but the extent to which a rapidly changing climate has affected, and will continue to affect, this species remains largely conjecture, and understanding probable climate change impacts is essential to the prioritization of potential reintroduction sites. We developed high-resolution species distribution models and used these to investigate climate change impacts on current and historical distributions, and identify locations that will remain, or become, bioclimatically suitable habitat in the future. This information has been given to an expert panel to identify and prioritize areas suitable for site-specific management and/or translocation.

Bivalves are among the main groups of invasive freshwater species, with the Asian clam genus Corbicula in particular being widely distributed. While global studies have focused on Corbicula fluminea (Müller, 1774), the invasive potential of Corbicula largillierti (Philippi, 1844) and Corbicula fluminalis (Müller, 1774) is still unknown. The spread of invasive species may be intensified by climate change. We estimated and compared environmentally suitable areas for these species under hypothetical climate scenarios, generating global maps of invasion risk. We found large climatically suitable areas for C. largillierti and C. fluminalis (under species distribution models) and that their invasive potential is currently underestimated. The analysis revealed many areas in which changing climate may favour the invasion of Corbicula spp.

The Endangered Arabian tahr Arabitragus jayakari is a rare and little known mountain ungulate, endemic to the 650 km mountain chain of northern Oman and the United Arab Emirates. To investigate the species’ status and distribution we conducted a systematic camera-trap survey across its entire range. We used occupancy modelling to quantify habitat associations and create a predictive distribution model for the species. We found that tahr preferred steep, rugged mountain habitats, and occupancy was much higher in protected areas. Arabian tahr were subject to anthropogenic threats, with occupancy decreasing with closer proximity to villages, and with increasing numbers of domestic goats. Tahr occupancy was also negatively associated with elevation and rainfall, with peak occupancy at 800–1,000 m. Although previous assessments have associated the entire Hajar Mountain range with the Arabian tahr, we found that only 23.9%, or 6,986 km2, of the mountain range was occupied. This reduction in area of occupancy reflects recent population declines, but also our improved methods of assessment. Based on our findings, future conservation efforts should focus on creating more protected areas, control measures to partition goats from core habitats of the Arabian tahr, and restoration and captive reinforcement within suitable habitats unoccupied by Arabian tahr. As infrastructure development is a threat to the Arabian tahr, our occurrence probability map provides a useful tool for spatial planning of developments to reduce impacts on the species.

Tamarisk species are shrubs or small trees considered by some to be among the most aggressively invasive and potentially detrimental exotic plants in the United States. Although extensively studied in the southern and interior west, northwestern (Oregon, Washington, and Idaho) distribution and habitat information for tamarisk is either limited or lacking. We obtained distribution data for the northwest, developed a habitat suitability map, and projected changes in habitat due to climate change in a smaller case study area using downscaled climate data. Results show extensive populations of tamarisk east of the Cascade Mountains. Despite the perceived novelty of tamarisk in the region, naturalized populations were present by the 1920s. Major population centers are limited to the warmest and driest environments in the central Snake River Plain, Columbia Plateau, and Northern Basin and Range. Habitat suitability model results indicate that 21% of the region supports suitable tamarisk habitat. Less than 1% of these areas are occupied by tamarisk; the remainder is highly vulnerable to invasion. Although considerable uncertainty exists regarding future climate change, we project a 2- to 10-fold increase in highly suitable tamarisk habitat by the end of the century. Our habitat suitability maps can be used in “what if” exercises as part of planning, detection, restoration, management, and eradication purposes.

Host–parasite associations are complex interactions dependent on aspects of hosts (e.g. traits, phylogeny or coevolutionary history), parasites (e.g. traits and parasite interactions) and geography (e.g. latitude). Predicting the permissive host set or the subset of the host community that a parasite can infect is a central goal of parasite ecology. Here we develop models that accurately predict the permissive host set of 562 helminth parasites in five different parasite taxonomic groups. We developed predictive models using host traits, host taxonomy, geographic covariates, and parasite community composition, finding that models trained on parasite community variables were more accurate than any other covariate group, even though parasite community covariates only captured a quarter of the variance in parasite community composition. This suggests that it is possible to predict the permissive host set for a given parasite, and that parasite community structure is an important predictor, potentially because parasite communities are interacting non-random assemblages.

The contribution of protected areas to biodiversity conservation is well attested but many taxa in many regions remain dependent on the unprotected wider landscape. To develop conservation plans for large mammals such as the Endangered pygmy hippopotamus Choeropsis liberiensis of West Africa's Upper Guinea Forests it is critical to understand the importance of unprotected land. Despite being a conservation priority, little is known about the habitat associations of this species, or its distribution across its range. Through a combination of field surveys, species distribution models and community questionnaires we investigated the use of unprotected areas by the pygmy hippopotamus in the Sierra Leone–Liberia border region. We found signs of the species in 128 of 525 1-km2 cells surveyed. Our analysis suggested that the species is reasonably widespread in this region and is associated with major rivers. It occurred close to, but rarely within, large areas of intact forest, and 80.4% of pygmy hippopotamus signs were recorded outside protected areas. The expansion of the protected area network in this area is unrealistic in Sierra Leone and to some extent in Liberia, mainly because of anthropogenic pressure and the overlap of proposed protected areas with mining and logging concessions. Thus pygmy hippopotamus conservation activities in the region need to include programmes on community lands while maintaining a robust network of protected forests. Community-based conservation of the pygmy hippopotamus may prove valuable for other threatened and endemic species that are not confined to protected areas in this region.

We tested a series of hypotheses on drivers of habitat selection by the Vulnerable wild yak Bos mutus, combining distribution-wide sighting data with species distribution modelling approaches. The results indicate that climatic conditions are of paramount importance in shaping the wild yak's distribution on the Tibetan Plateau. Habitat selection patterns were seasonal, with yaks appearing to select areas closer to villages during the vegetation-growing season. Unexpectedly, our index of forage quantity had a limited effect in determining the distribution of the species. Overall, our results suggest that expected changes in climate for this region could have a significant impact on habitat availability for wild yaks, and we call for more attention to be focused on the unique wildlife in this ecosystem.

Habitat loss is one of the main threats to wildlife, particularly large mammals. Estimating the potential distribution of threatened species to guide surveys and conservation is crucial, primarily because such species tend to exist in small fragmented populations. The Endangered huemul deer Hippocamelus bisulcus is endemic to the southern Andes of Chile and Argentina. Although the species occurs in the Valdivian Ecoregion, a hotspot for biodiversity, we have no information on its occupancy and potential distribution in this region. We built and compared species distribution models for huemul using the maximum entropy approach, using 258 presence records and sets of bioclimatic and geographical variables as predictors, with the objective of assessing the potential distribution of the species in the Valdivian Ecoregion. Annual temperature range and summer precipitation were the predictive variables with the greatest influence in the best-fitting model. Approximately 12,360 km2 of the study area was identified as suitable habitat for the huemul, of which 30% is included in the national protected area systems of Chile and Argentina. The map of potential distribution produced by our model will facilitate prioritization of future survey efforts in other remote and unexplored areas in which huemul have not been recorded since the 1980s but where there is a high probability of their occurrence.

Conservation managers and policy makers require models that can rank the impacts of multiple, interacting threats on biodiversity so that actions can be prioritized. An integrated modelling framework was used to predict the viability of plant populations for five species in southern California's Mediterranean-type ecosystem. The framework integrates forecasts of land-use change from an urban growth model with projections of future climatically-suitable habitat from climate and species distribution models, which are linked to a stochastic population model. The population model incorporates the effects of disturbance regimes and management actions on population viability. This framework: (1) ranks threats by their relative and cumulative impacts on population viability, such as land-use change, climate change, altered disturbance regimes or invasive species, and (2) ranks management responses in terms of their effectiveness for land protection, assisted dispersal, fire management and invasive species control. Too-frequent fire was often the top threat for the species studied, thus fire reduction was ranked the most important management option. Projected changes in suitable habitat as a result of climate change were generally large, but varied across species and climate scenarios; urban development could exacerbate loss of suitable habitat.

Species distribution models (SDMs), which are well established in many fields of biological research, are still uncommon in the agricultural risk analysis of pest insects. To exemplify the use of SDMs, we investigated the influence of environmental factors on the occurrence of Hyalesthes obsoletus Signoret (Hemiptera: Cixiidae). The planthopper is the only known vector of the grapevine yellows disease ‘bois noir’. The study was conducted in 145 locations in the Baden region of southwest Germany. The planthopper was surveyed on host plant patches, consisting of stinging nettle and/or bindweeds. We used a stratified modelling framework where (1) species presence–absence data were related to an extensive environmental dataset using logistic regressions; and (2) different types of average models were developed based on an information theoretic method. The results show that the incidence of H. obsoletus is associated to above- as well as below-ground environmental factors, particularly to the amount of fine soil and average annual precipitation. This result was consistent across all average models. The relative importance of other environmental variables was dependent upon the average model under consideration and thus may vary according to their intended use, either the explanation of habitat requirements or the prediction and mapping of occurrence risks. The study showed that SDMs offer a quantification of species’ habitat requirements and thus, could represent a valuable tool for pest management purposes. By providing examples of current issues of grapevine pests in viticulture, we discuss the use of SDMs in agricultural risk analysis and highlight their advantages and caveats.