Predicting species distribution: offering more than simple habitat models

Pushing the envelope: do narrowly and widely distributed Eucalyptus species differ in response to climate warming?

Contemporary climate change will push many tree species into conditions that are outside their current climate envelopes. Using the Eucalyptus genus as a model, we addressed whether species with narrower geographical distributions show constrained ability to cope with warming relative to species with wider distributions, and whether this ability differs among species from tropical and temperate climates. We grew seedlings of widely and narrowly distributed Eucalyptus species from temperate and tropical Australia in a glasshouse under two temperature regimes: the summer temperature at seed origin and +3.5°C. We measured physical traits and leaf-level gas exchange to assess warming influences on growth rates, allocation patterns, and physiological acclimation capacity. Warming generally stimulated growth, such that higher relative growth rates early in development placed seedlings on a trajectory of greater mass accumulation. The growth enhancement under warming was larger among widely than narrowly distributed species and among temperate rather than tropical provenances. The differential growth enhancement was primarily attributable to leaf area production and adjustments of specific leaf area. Our results suggest that tree species, including those with climate envelopes that will be exceeded by contemporary climate warming, possess capacity to physiologically acclimate but may have varying ability to adjust morphology.

Modeling the pest-pathogen threats in a warming world for the red turpentine beetle (Dendroctonus valens) and its symbiotic fungus (Leptographium procerum)

BACKGROUND

Dendroctonus valens along with its symbiotic fungi have caused unprecedented damage to pines in China. Leptographium procerum, its primary symbiotic fungus, facilitates the invasion and colonization of the pest, thereby aggravating ecological threats. Assessing shifts in the niches and ranges of D. valens and its symbiotic fungus could provide a valuable basis for pest control. Here, we conducted niche comparisons between native and invasive populations of D. valens. Then, we employed standard ecological niche models and ensembles of small models to predict the potential distributions of D. valens and L. procerum under climate change conditions and to estimate areas of overlap.

RESULTS

The niche of invasive population of D. valens in Chinese mainland only occupied a limited portion of the niche of native population in North America, leaving a substantial native niche unfilled and without any niche expansion. The suitable regions for D. valens are predicted in central and southern North America and central and northeastern Chinese mainland. The overlap with the suitable regions of L. procerum included eastern North America and the central and northeastern Chinese mainland under historical climatic scenarios. The regions susceptible to their symbiotic damage will shift northward in response to future climate change.

CONCLUSIONS

Projected distributions of D. valens and its symbiotic fungus, along with areas vulnerable to their symbiotic damage, provide essential insights for devising strategies against this association. Additionally, our study contributes to comprehending how biogeographic approaches aid in estimating potential risks of pest-pathogen interactions in forests within a warming world. © 2024 Society of Chemical Industry.

Distribution patterns of Phytoseiulus persimilis in response to climate change

BACKGROUND

The biological control agent Phytoseiulus persimilis is a commercialized specialist predator of two agricultural pest mite species Tetranychus urticae and Tetranychus evansi. Biocontrol of these pest species by P. persimilis has achieved success in biological control in some areas. However, the lack of precise information about the influence of global climate change on the worldwide distribution of this biocontrol agent hampers international efforts to manage pest mites with P. persimilis. With 276 occurrence records and 19 bioclimatic variables, this study investigated the potential global distribution of P. persimilis.

RESULTS

The results demonstrated that the Maximum Entropy (MaxEnt) model performed well, with the area under the curve being 0.956, indicating the high accuracy of this model. Two variables, the minimum temperature of the coldest month (Bio_6) and precipitation of the coldest quarter (Bio_19) were the most important environmental variables that influenced the distribution of P. persimilis, contributing more than 30% to the model, respectively. The suitable area currently occupies 21.67% of the world's land area, spanning latitudes between 60°S and 60°N. Under shared socio-economic pathway (SSP) 5-8.5 (high-carbon emissions), the low suitable area would increase by 1.31% until the 2050s.

CONCLUSION

This study successfully identified that south-eastern China, parts of countries in the Mediterranean coastal regions, including Libya, Algeria, Portugal, Spain, and France, are climatically favorable regions for P. persimilis, providing valuable information about the potential areas where it can be effectively exploited as biocontrol agents in classical biological control programs to manage pest spider mites environmentally friendly. © 2024 Society of Chemical Industry.

Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions

Reliable maps of species distributions are fundamental for biodiversity research and conservation. The International Union for Conservation of Nature (IUCN) range maps are widely recognized as authoritative representations of species' geographic limits, yet they might not always align with actual occurrence data. In recent area of habitat (AOH) maps, areas that are not habitat have been removed from IUCN ranges to reduce commission errors, but their concordance with actual species occurrence also remains untested. We tested concordance between occurrences recorded in camera trap surveys and predicted occurrences from the IUCN and AOH maps for 510 medium- to large-bodied mammalian species in 80 camera trap sampling areas. Across all areas, cameras detected only 39% of species expected to occur based on IUCN ranges and AOH maps; 85% of the IUCN only mismatches occurred within 200 km of range edges. Only 4% of species occurrences were detected by cameras outside IUCN ranges. The probability of mismatches between cameras and the IUCN range was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya and in areas with shorter canopy forests. Our findings suggest that range and AOH maps rarely underrepresent areas where species occur, but they may more often overrepresent ranges by including areas where a species may be absent, particularly at range edges. We suggest that combining range maps with data from ground-based biodiversity sensors, such as camera traps, provides a richer knowledge base for conservation mapping and planning.

Projection of Interspecific Competition (PIC) Matrices: A Conceptual Framework for Inclusion in Population Risk Assessments

Accounting for intraspecific and interspecific competition when assessing the effects of chemical and nonchemical stressors is an important uncertainty in ecological risk assessments. We developed novel projection of interspecific competition (PIC) matrices that allow for analysis of population dynamics of two or more species exposed to a given stressor(s) that compete for shared resources within a landscape. We demonstrate the application of PIC matrices to investigate the population dynamics of two hypothetical fish species that compete with one another and have differences in net reproductive rate and intrinsic rate of population increase. Population status predictions were made under scenarios that included exposure to a chemical stressor that reduced fecundity for one or both species. The results of our simulations demonstrated that measures obtained from the life table and Leslie matrix of an organism, including net reproductive rate and intrinsic rate of increase, can result in erroneous conclusions of population status and viability in the absence of a consideration of resource limitation and interspecific competition. This modeling approach can be used in conjunction with field monitoring efforts and/or laboratory testing to link effects due to stressors to possible outcomes within an ecosystem. In addition, PIC matrices could be combined with adverse outcome pathways to allow for ecosystem projection based on taxonomic conservation of molecular targets of chemicals to predict the likelihood of relative cross-species susceptibility. Overall, the present study shows how PIC matrices can integrate effects across the life cycles of multiple species, provide a linkage between endpoints observed in individual and population-level responses, and project outcomes at the community level for multiple generations for multiple species that compete for limited resources. Environ Toxicol Chem 2024;43:1406-1422. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.

Identifying Areas of Invasion Risk and Changes in the Ecological Niche Occupied by the Coffee Leaf Miner Leucoptera coffeella (Lepidoptera: Lyonetiidae)

Insects of economic importance such as Leucoptera coffeella can cause high defoliation in plants and reduce crop yields. We aimed to identify changes in the ecological niche and potential zones of the invasion. Occurrence records were obtained from databases and bibliography. WorldClim V2.0 bioclimatic layers were used. For the modeling of the potential distribution, the kuenm R package was used by executing the Maxent algorithm. The potential distribution models suggested greatest environmental suitability extends from Europe, South Asia, and Central and South Africa, showing the "tropical and subtropical moist broadleaf forests" as the ecoregion that presents the greatest probability of the presence of L. coffeella. The potential distribution model projected in the invaded area agrees with the known distribution in the region (America), although the results show that it is occupying environmental spaces not present in the area of origin. This species presented a large proportion of the invaded niche that overlaps the native niche and is colonizing new environmental conditions in the invaded area relative to its native distribution (Africa). This information could be used in the planning of coffee crops on the American continent.

Niche conservatism and spread explain introgression between native and invasive fish

Hybridisation can be an important driver of evolutionary change, but hybridisation with invasive species can have adverse effects on native biodiversity. While hybridisation has been documented across taxa, there is limited understanding of ecological factors promoting patterns of hybridisation and the spatial distribution of hybrid individuals. We combined the results of ecological niche modelling (ENM) and restriction site-associated DNA sequencing to test theories of niche conservatism and biotic resistance on the success of invasion, admixture, and extent of introgression between native and non-native fishes. We related Maxent predictions of habitat suitability based on the native ranges of invasive Eastern Banded Killifish (Fundulus diaphanus diaphanus Lesueur 1817) and native Western Banded Killifish (Fundulus diaphanus menona Jordan and Copeland 1877) to admixture indices of individual Banded Killifish. We found that Eastern Banded Killifish predominated at sites predicted as suitable from their ENM, consistent with niche conservatism. Admixed individuals were more common as Eastern Banded Killifish habitat suitability declined. We also found that Eastern Banded Killifish were most common at sites closest to the presumed source of this invasion, whereas the proportion of admixed individuals increased with distance from the source of invasion. Lastly, we found little evidence that habitat suitability for Western Banded Killifish provides biotic resistance from either displacement by, or admixture with, invasive Eastern Banded Killifish. Our study demonstrates that ENMs can inform conservation-relevant outcomes between native and invasive taxa while emphasising the importance of protecting isolated Western Banded Killifish populations from invasive conspecifics.

Fitting individual-based models of spatial population dynamics to long-term monitoring data

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Climate change may reveal currently unavailable parts of species' ecological niches

The ability of climatic niche models to predict species extinction risks can be hampered if niches are incompletely quantified. This can occur when niches are estimated considering only currently available climatic conditions, disregarding the fact that climate change can open up portions of the fundamental niche that are currently inaccessible to species. Using a new metric, we estimate the prevalence of potential situations of fundamental niche truncation by measuring whether current ecological niche limits are contiguous to the boundaries of currently available climatic conditions for 24,944 species at the global scale in both terrestrial and marine realms and including animals and plants. We show that 12,172 (~49%) species are showing niche contiguity, particularly those inhabiting tropical ecosystems and the marine realm. Using niche expansion scenarios, we find that 86% of species showing niche contiguity could have a fundamental niche potentially expanding beyond current climatic limits, resulting in lower-yet still alarming-rates of predicted biodiversity loss, particularly within the tropics. Caution is therefore advised when forecasting future distributions of species presenting niche contiguity, particularly towards climatic limits that are predicted to expand in the future.

Using ancient sedimentary DNA to forecast ecosystem trajectories under climate change

Ecosystem response to climate change is complex. In order to forecast ecosystem dynamics, we need high-quality data on changes in past species abundance that can inform process-based models. Sedimentary ancient DNA (sedaDNA) has revolutionised our ability to document past ecosystems' dynamics. It provides time series of increased taxonomic resolution compared to microfossils (pollen, spores), and can often give species-level information, especially for past vascular plant and mammal abundances. Time series are much richer in information than contemporary spatial distribution information, which have been traditionally used to train models for predicting biodiversity and ecosystem responses to climate change. Here, we outline the potential contribution of sedaDNA to forecast ecosystem changes. We showcase how species-level time series may allow quantification of the effect of biotic interactions in ecosystem dynamics, and be used to estimate dispersal rates when a dense network of sites is available. By combining palaeo-time series, process-based models, and inverse modelling, we can recover the biotic and abiotic processes underlying ecosystem dynamics, which are traditionally very challenging to characterise. Dynamic models informed by sedaDNA can further be used to extrapolate beyond current dynamics and provide robust forecasts of ecosystem responses to future climate change. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Potential range shift of a long-distance migratory rice pest, Nilaparvata lugens, under climate change

The biogeographical range shift of insect pests is primarily governed by temperature. However, the range shift of seasonal long-distance migratory insects may be very different from that of sedentary insects. Nilaparvata lugens (BPH), a serious rice pest, can only overwinter in tropical-to-subtropical regions, and some populations migrate seasonally to temperate zones with the aid of low-level jet stream air currents. This study utilized the CLIMEX model to project the overwintering area under the climate change scenarios of RCP2.6 and RCP8.5, both in 2030s and 2080s. The overwintering boundary is predicted to expand poleward and new overwintering areas are predicted in the mid-latitude regions of central-to-eastern China and mid-to-southern Australia. With climate change, the habitable areas remained similar, but suitability decreased substantially, especially in the near-equatorial regions, owing to increasing heat stress. The range shift is similar between RCP2.6-2030s, RCP2.6-2080s, and RCP8.5-2030s, but extreme changes are projected under RCP8.5-2080s with marginal areas increasing from 27.2 to 38.8% and very favorable areas dropping from 27.5 to 3.6% compared to the current climate. These findings indicate that climate change will drive range shifts in BPH and alter regional risks differently. Therefore, international monitoring programs are needed to effectively manage these emerging challenges.

A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm

Neural networks are frequently employed to model species distribution through backpropagation methods, known as backpropagation neural networks (BPNN). However, the complex structure of BPNN introduces parameter settings challenges, such as the determination of connection weights, which can affect the accuracy of model simulation. In this paper, we integrated the Grey Wolf Optimizer (GWO) algorithm, renowned for its excellent global search capacity and rapid convergence, to enhance the performance of BPNN. Then we obtained a novel hybrid algorithm, the Grey Wolf Optimizer algorithm optimized backpropagation neural networks algorithm (GNNA), designed for predicting species' potential distribution. We also compared the GNNA with four prevalent species distribution models (SDMs), namely the generalized boosting model (GBM), generalized linear model (GLM), maximum entropy (MaxEnt), and random forest (RF). These models were evaluated using three evaluation metrics: the area under the receiver operating characteristic curve, Cohen's kappa, and the true skill statistic, across 23 varied species. Additionally, we examined the predictive accuracy concerning spatial distribution. The results showed that the predictive performance of GNNA was significantly improved compared to BPNN, was significantly better than that of GLM and GBM, and was even comparable to that of MaxEnt and RF in predicting species distributions with small sample sizes. Furthermore, the GNNA demonstrates exceptional powers in forecasting the potential non-native distribution of invasive plant species.

Socio-economic variables improve accuracy and change spatial predictions in species distribution models

In an era marked by increasing anthropogenic pressure, understanding the relations between human activities and wildlife is crucial for understanding ecological patterns, effective conservation, and management strategies. Here, we explore the potential and usefulness of socio-economic variables in species distribution modelling (SDM), focusing on their impact on the occurrence of wild mammals in Poland. Beyond the environmental factors commonly considered in SDM, like land-use, the study tests the importance of socio-economic characteristics of local human societies, such as age, income, working sector, gender, education, and village characteristics for explaining distribution of diverse mammalian groups, including carnivores, ungulates, rodents, soricids, and bats. The study revealed that incorporating socio-economic variables enhances the predictive power for >60 % of species and overall for most groups, with the exception being carnivores. For all the species combined, among the 10 predictors with highest predictive power, 6 belong to socio-economic group, while for specific species groups, socio-economic variables had similar predictive power as environmental variables. Furthermore, spatial predictions of species occurrence underwent changes when socio-economic variables were included in the model, resulting in a substantial mismatch in spatial predictions of species occurrence between environment-only models and models containing socio-economic variables. We conclude that socio-economic data has potential as useful predictors which increase prediction accuracy of wildlife occurrence and recommend its wider usage. Further, to our knowledge this is a first study on such a big scale for terrestrial mammals which evaluates performance based on presence or absence of socio-economic predictors in the model. We recognise the need for a more comprehensive approach in SDMs and that bridging the gap between human socio-economic dynamics and ecological processes may contribute to the understanding of the factors influencing biodiversity.

Spatial distribution of Triatoma dimidiata peridomestic colonies modulated by distance between susceptible microhabitat patches

Triatoma dimidiata is a vector of the hemoparasite Trypanosoma cruzi, the causal agent of Chagas disease. It settles reproductive colonies in the peridomicile of the premises. The peridomicile is comprised of a random set of artificial and natural features that overlap and assemble a network of microenvironmental suitable sites (patches) that interact with each other and favor the structure and proliferation of T. dimidiata colonies. The heterogeneity of patch characteristics hinders the understanding and identification of sites susceptible to colonization. In this study, a classification system using a random forest algorithm was used to identify peridomiciles susceptible to colonization to describe the spatial distribution of these sites and their relationship with the colonies of T. dimidiata in ten localities of Yucatan. From 1,000 peridomiciles reviewed, the classification showed that 13.9 % (139) of the patches were highly susceptible (HSP), and 86.1 % (861) were less susceptible (LSP). All localities had at least one HSP. The occupancy by patch type showed that the percentage of total occupancy and by colonies was higher in the HSP, while the occupancy by adult T. dimidiata without evidence of nymphs or exuviae (propagules) was higher in the LSP. A generalized additive model (GAM) revealed that the percentage of occupied patches increases as the abundance of individuals in the localities increases however, the percentage of occupied patches in LSP is lower than occupied in HSP. Distance analyses revealed that colonies and propagules were located significantly closer (approximately 200 m) to a colony in a HSP than any colony in a LSP. The distribution of T. dimidiata in the localities was defined by the distribution of patch type; as the occupancy in these patches increased, a network of peridomestic populations was configured, which may be promoted by a greater abundance of insects inside the localities. These results reveal that the spatial distribution of T. dimidiata individuals and colonies in the peridomicile at the locality scale corresponds to a metapopulation pattern within the localities through a system of patches mediated by distance and level of the vectors' occupancy.

Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area

Correlative species distribution models (SDMs) are important tools to estimate species' geographic distribution across space and time, but their reliability heavily relies on the availability and quality of occurrence data. Estimations can be biased when occurrences do not fully represent the environmental requirement of a species. We tested to what extent species' physiological knowledge might influence SDM estimations. Focusing on the Japanese sea cucumber Apostichopus japonicus within the coastal ocean of East Asia, we compiled a comprehensive dataset of occurrence records. We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs: a naïve model that solely depends on environmental correlates, and a physiologically informed model that further incorporates physiological information as priors. We further tested the models' sensitivity to calibration area choices by fitting them with different buffered areas around known presences. Compared with naïve models, the physiologically informed models successfully captured the negative influence of high temperature on A. japonicus and were less sensitive to the choice of calibration area. The naïve models resulted in more optimistic prediction of the changes of potential distributions under climate change (i.e., larger range expansion and less contraction) than the physiologically informed models. Our findings highlight benefits from incorporating physiological information into correlative SDMs, namely mitigating the uncertainties associated with the choice of calibration area. Given these promising features, we encourage future SDM studies to consider species physiological information where available.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-024-00226-0.

Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol

Blacklegged ticks (Ixodes scapularis Say) pose an enormous public health risk in eastern North America as the vector responsible for transmitting 7 human pathogens, including those causing the most common vector-borne disease in the United States, Lyme disease. Species distribution modeling is an increasingly popular method for predicting the potential distribution and subsequent risk of blacklegged ticks, however, the development of such models thus far is highly variable and would benefit from the use of standardized protocols. To identify where standardized protocols would most benefit current distribution models, we completed the "Overview, Data, Model, Assessment, and Prediction" (ODMAP) distribution modeling protocol for 21 publications reporting 22 blacklegged tick distribution models. We calculated an average adherence of 73.4% (SD ± 29%). Most prominently, we found that authors could better justify and connect their selection of variables and associated spatial scales to blacklegged tick ecology. In addition, the authors could provide clearer descriptions of model development, including checks for multicollinearity, spatial autocorrelation, and plausibility. Finally, authors could improve their reporting of variable effects to avoid undermining the models' utility in informing species-environment relationships. To enhance future model rigor and reproducibility, we recommend utilizing several resources including the ODMAP protocol, and suggest that journals make protocol compliance a publication prerequisite.

Geographical distribution and ecological niche dynamics of Crassostrea sikamea (Amemiya, 1928) in China's coastal regions under climate change

Global climate change drives species redistribution, threatening biodiversity and ecosystem heterogeneity. The Kumamoto oyster, Crassostrea sikamea (Amemiya, 1928), one of the most promising aquaculture species because of its delayed reproductive timing, was once prevalent in southern China. In this study, an ensemble species distribution model was employed to analyze the distribution range shift and ecological niche dynamics of C. sikamea along China's coastline under the current and future climate scenarios (RCP 2.6-8.5 covering 2050 s and 2100 s). The model results indicated that the current habitat distribution for C. sikamea consists of a continuous stretch extending from the coastlines of Hainan Province to the northern shores of Jiangsu Province. By the 2050 s, the distribution range will stabilize at its southern end along the coast of Hainan Province, while expanding northward to cover the coastal areas of Shandong Province, showing a more dramatic trend of contraction in the south and invasion in the north by the 2100 s. In RCP8.5, the southern end retracts to the coasts of Guangdong, whereas the northern end covers all of China's coastal areas north of 34°N. C. sikamea can maintain relatively stable ecological niche characteristics, while it may occupy different ecological niche spaces under future climate conditions. Significant niche expansion will occur in lower temperature. We concluded C. sikamea habitats are susceptible to climate change. The rapid northward expansion of C. sikamea may open new possibilities for oyster farming in China, but it will also have important consequences for the ecological balance and biodiversity of receiving areas. It's imperative that we closely examine and strategize to address these repercussions for a win-win situation.

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'.

Temperature-dependent dispersal and ectotherm species' distributions in a warming world

Dispersal is a crucial component of species' responses to climate warming. Warming-induced changes in species' distributions are the outcome of how temperature affects dispersal at the individual level. Yet, there is little or no theory that considers the temperature dependence of dispersal when investigating the impacts of warming on species' distributions. Here I take a first step towards filling this key gap in our knowledge. I focus on ectotherms, species whose body temperature depends on the environmental temperature, not least because they constitute the majority of biodiversity on the planet. I develop a mathematical model of spatial population dynamics that explicitly incorporates mechanistic descriptions of ectotherm life history trait responses to temperature. A novel feature of this framework is the explicit temperature dependence of all phases of dispersal: emigration, transfer and settlement. I report three key findings. First, dispersal, regardless of whether it is random or temperature-dependent, allows both tropical and temperate ectotherms to track warming-induced changes in their thermal environments and to expand their distributions beyond the lower and upper thermal limits of their respective climate envelopes. In the absence of dispersal mortality, warming does not alter these new distributional limits. Second, an analysis based solely on trait response data predicts that tropical ectotherms should be able to expand their distributions polewards to a greater degree than temperate ectotherms. Analysis of the dynamical model confirms this prediction. Tropical ectotherms have an advantage when moving to cooler climates because they experience lower within-patch and dispersal mortality, and their higher thermal optima and maximal birth rates allow them to take advantage of the warmer parts of the year. Previous theory has shown that tropical ectotherms are more successful in invading and adapting the temperate climates than vice versa. This study provides the key missing piece, by showing how temperature-dependent dispersal could facilitate both invasion and adaptation. Third, dispersal mortality does not affect the poleward expansion of ectotherm distributions. But, it prevents both tropical and temperate ectotherms from maintaining sink populations in localities that are too warm to be viable in the absence of dispersal. Dispersal mortality also affects species' abundance patterns, causing a larger decline in abundance throughout the range when species disperse randomly rather than in response to thermal habitat suitability. In this way, dispersal mortality can facilitate the evolution of dispersal modes that maximize fitness in warmer thermal environments.

A new threshold selection method for species distribution models with presence-only data: Extracting the mutation point of the P/E curve by threshold regression

Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers' decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point. To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.

The native distribution of a common legume shrub is limited by the range of its nitrogen-fixing mutualist

Plant-microbe mutualisms, such as the legume-rhizobium symbiosis, are influenced by the geographical distributions of both partners. However, limitations on the native range of legumes, resulting from the absence of a compatible mutualist, have rarely been explored. We used a combination of a large-scale field survey and controlled experiments to determine the realized niche of Calicotome villosa, an abundant and widespread legume shrub. Soil type was a major factor affecting the distribution and abundance of C. villosa. In addition, we found a large region within its range in which neither C. villosa nor Bradyrhizobium, the bacterial genus that associates with it, were present. Seedlings grown in soil from this region failed to nodulate and were deficient in nitrogen. Inoculation of this soil with Bradyrhizobium isolated from root nodules of C. villosa resulted in the formation of nodules and higher growth rate, leaf N and shoot biomass compared with un-inoculated plants. We present evidence for the exclusion of a legume from parts of its native range by the absence of a compatible mutualist. This result highlights the importance of the co-distribution of both the host plant and its mutualist when attempting to understand present and future geographical distributions of legumes.

On the elephant trails: habitat suitability and connectivity for Asian elephants in eastern Indian landscape

Identifying suitable habitats and conserving corridors are crucial to the long-term conservation of large and conflict-prone animals. Being a flagship species, survival of Asian elephants is threatened by human-induced mortality and habitat modification. We aimed to assess the habitat suitability and connectivity of the Asian elephant Elephas maximus Linnaeus, 1758 habitat in the state of Odisha in eastern India. We followed the ensemble of spatial prediction models using species presence data and five environmental variables. We used least-cost path and circuit theory approaches to identify the spatial connectivity between core habitats for Asian elephants. The results revealed that normalized difference vegetation index (NDVI; variable importance 42%) and terrain ruggedness (19%) are the most influential variables for predicting habitat suitability of species within the study area. Our habitat suitability map estimated 14.6% of Odisha's geographical area (c. 22,442 km2) as highly suitable and 13.3% (c. 20,464 km2) as moderate highly suitable. We identified 58 potential linkages to maintain the habitat connectivity across study area. Furthermore, we identified pinch points, bottlenecks, and high centrality links between core habitats. Our study offers management implications for long-term landscape conservation for Asian elephants in Odisha and highlights priority zones that can help maintain spatial links between elephant habitats.

A super SDM (species distribution model) 'in the cloud' for better habitat-association inference with a 'big data' application of the Great Gray Owl for Alaska

The currently available distribution and range maps for the Great Grey Owl (GGOW; Strix nebulosa) are ambiguous, contradictory, imprecise, outdated, often hand-drawn and thus not quantified, not based on data or scientific. In this study, we present a proof of concept with a biological application for technical and biological workflow progress on latest global open access 'Big Data' sharing, Open-source methods of R and geographic information systems (OGIS and QGIS) assessed with six recent multi-evidence citizen-science sightings of the GGOW. This proposed workflow can be applied for quantified inference for any species-habitat model such as typically applied with species distribution models (SDMs). Using Random Forest-an ensemble-type model of Machine Learning following Leo Breiman's approach of inference from predictions-we present a Super SDM for GGOWs in Alaska running on Oracle Cloud Infrastructure (OCI). These Super SDMs were based on best publicly available data (410 occurrences + 1% new assessment sightings) and over 100 environmental GIS habitat predictors ('Big Data'). The compiled global open access data and the associated workflow overcome for the first time the limitations of traditionally used PC and laptops. It breaks new ground and has real-world implications for conservation and land management for GGOW, for Alaska, and for other species worldwide as a 'new' baseline. As this research field remains dynamic, Super SDMs can have limits, are not the ultimate and final statement on species-habitat associations yet, but they summarize all publicly available data and information on a topic in a quantified and testable fashion allowing fine-tuning and improvements as needed. At minimum, they allow for low-cost rapid assessment and a great leap forward to be more ecological and inclusive of all information at-hand. Using GGOWs, here we aim to correct the perception of this species towards a more inclusive, holistic, and scientifically correct assessment of this urban-adapted owl in the Anthropocene, rather than a mysterious wilderness-inhabiting species (aka 'Phantom of the North'). Such a Super SDM was never created for any bird species before and opens new perspectives for impact assessment policy and global sustainability.

Habitat Suitability and Determinants for Anatidae in Multi-Watershed Composite Wetlands in Anhui, China

Habitat suitability analysis is essential in habitat and species conservation. Anatidae are known for their migratory behaviour, high population density, and wide distribution range. Understanding their habitat utilzation and influencing factors is crucial in targeted conservation and management. In this study, we collected Anatidae diversity data, including the number of species, through field surveys from October 2021 to March 2022 and thirty habitat variables through an online database in Anhui Province, China. By using MaxEnt, we simulated the habitat suitability of twenty-one Anatidae species, revealing potential distribution sites in Anhui Province. Generalized linear mixed models (GLMM) were employed to identify factors affecting the distribution of geese and ducks. The results showed that high-suitability habitats were predominantly located in the large lakes of the Yangtze River floodplain. The GLMM analysis showed significant correlations between Anatidae richness and altitude, distribution of farmland, and human footprint. In addition, ducks were more sensitive to the human interference factor than geese. In summary, the lakes in the Yangtze River floodplain emerged as the most important Anatidae habitats in Anhui Province due to their abundant wetland resources, flat terrain, and high distribution of farmlands. These findings provide a scientific basis for the development of relevant conservation strategies and measures, aiding in wildlife epidemic monitoring, prevention, and control.

Geographic patterns of distribution and ecological niche of the snake-necked turtle genus Hydromedusa

Biotic and abiotic factors play a crucial role in determining the distribution of species. These factors dictate the conditions that must be met for a species to thrive in a particular area. Sister species that present some degree of niche overlap can shed light on how they are distributed and coexist in their environment. This study aims to investigate the geographical distribution and ecological niche of the sister species of snake-necked turtles Hydromedusa maximiliani and H. tectifera. By analyzing their niche overlap, we aim to obtain a better understanding of how these two species coexist and which variables are determining their occurences. We applied species distribution modeling and compared the niches using the niche equivalence and similarity tests. Our findings show that the distribution of H. maximiliani is most influenced by temperature seasonality and isothermality, while H. tectifera is most affected by the temperature seasonality, precipitation of warmest quarter and mean diurnal range. In addition, our results suggest that the niche expressed by H. maximiliani retained ecological characteristics that can accurately predict the H. tectifera distribution, but the inverse is not true. In this sense, differences are not solely due to the geographic availability of environmental conditions but can reflect niche restrictions, such as competition.

Predicting future climate change impacts on the potential distribution of the black howler monkey (Alouatta pigra): an endangered arboreal primate

Climate change is one of the main factors affecting biodiversity worldwide at an alarming rate. In addition to increases in global extreme weather events, melting of polar ice caps, and subsequent sea level rise, climate change might shift the geographic distribution of species. In recent years, interest in understanding the effects of climate change on species distribution has increased, including species which depend greatly on forest cover for survival, such as strictly arboreal primates. Here, we generate a series of species distribution models (SDMs) to evaluate future projections under different climate change scenarios on the distribution of the black howler monkey (Alouatta pigra), an endemic endangered primate species. Using SDMs, we assessed current and future projections of their potential distribution for three Social Economic Paths (SSPs) for the years 2030, 2050, 2070, and 2090. Specifically, we found that precipitation seasonality (BIO15, 30.8%), isothermality (BIO3, 25.4%), and mean diurnal range (BIO2, 19.7.%) are the main factors affecting A. pigra distribution. The future climate change models suggested a decrease in the potential distribution of A. pigra by projected scenarios (from - 1.23 to - 12.66%). The highly suitable area was the most affected above all in the more pessimist scenario most likely related to habitat fragmentation. Our study provides new insights into the potential future distribution and suitable habitats of Alouatta pigra. Such information could be used by local communities, governments, and non-governmental organizations for conservation planning of this primate species.

Mapping multi-seasonal habitats of giant pandas to identify seasonal shifts

As a flagship species of biodiversity conservation globally, the giant panda has seasonal migration to cope with seasonal changes in available resources. Here, we have mapped the spatial distribution of multi-seasonal habitats of the giant panda across the Baishuijiang reserve in China. Results show that the spatial patterns are different in different seasons, generally, large patches are observed in the western part, while staggered clusters occur in the middle and eastern parts. That is, suitable habitats for giant pandas are mostly distributed in the west part. More than 75% of the predicted suitable habitats are within the core zone of the reserve year-round, indicating the core zone essentially meet giant panda's ecological needs, although this range could potentially be expanded. This study provides valuable insights into the spatiotemporal migration patterns of endangered species and helps to guide conservation planning.

Potential habitat areas and priority protected areas of Tilia amurensis Rupr in China under the context of climate change

Introduction

Tilia amurensis Rupr (T. amurensis) is one endangered and national class II key protected wild plant in China. It has ornamental, material, economic, edible and medicinal values. At present, the resources of T. amurensis are decreasing, and the prediction of the distribution of its potential habitat in China can provide a theoretical basis for the cultivation and rational management of this species.

Methods

In this study, the R language was used to evaluate 358 distribution records and 38 environment variables. The MaxEnt model was used to predict the potential distribution areas of T. amurensis under the current and future climate scenarios. The dominant environmental factors affecting the distribution of T. amurensis were analyzed and the Marxan model was used to plan the priority protected areas of this species.

Results

The results showed that Bio18, Slope, Elev, Bio1, Bio9 and Bio2 were the dominant environmental factors affecting the distribution of T. amurensis. Under the future climatic scenarios, the potential suitable areas for T. amurensis will mainly distribute in the Northeast China, the total suitable area will reduce compared with the current climate scenarios, and the general trend of the centroid of suitable habitat will be towards higher latitudes. The SPF value of the best plan obtained from the priority conservation area planning was 1.1, the BLM value was 127,616, and the priority conservation area was about 57.61×104 km2. The results suggested that climate, soil and topographic factors jointly affected the potential geographical distribution of T. amurensis, and climate and topographic factors had greater influence than soil factors.

Discussion

The total suitable area of T. amurensis in China under different climate scenarios in the future will decrease, so more effective protection should be actively adopted.

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.

Spatial-temporal shifting patterns and in situ conservation of spotted seal (Phoca largha) populations in the Yellow Sea ecoregion

Understanding the habitat shifting pattern is a prerequisite for implementing in situ conservation of migratory species. Spotted seals (Phoca largha) inhabiting the Yellow Sea ecoregion (YSE) comprise a small population with independent genes and represent a charismatic flagship species in this region. However, this population has declined by 80% since the 1940s, and increased support from the countries around the YSE is urgently needed to address the potential local extinction risk. A time-series niche model and life-history weighted systematic conservation planning were designed on the basis of a satellite beacon tracking survey (2010-2020) of the YSE population. The results showed clustering and spreading shifting patterns during the breeding and migratory seasons, respectively. The closed-loop migration route formed in the YSE indicated that this population might be geographically isolated from populations in other breeding areas around the world. The conservation priority area (CPA), with an area of 19 632 km2 (3.58% of the total YSE area), was the most effective response to the potential in situ risk. However, nearly 80% of the CPA was exposed outside the existing marine protected areas (MPAs). Future establishment of MPAs in China should strategically consider the conservation gap identified herein, and it is recommended for Korea's closed fishing season to be spatially set in the western Korean Peninsula from May to August. This study also exemplified that the lack of temporal information would lead to the dislocation of niche modeling for migratory species represented by spotted seals. Attention should be paid to protecting small and migratory populations in marine biodiversity conservation planning.

Effects of diluted effluent on aquatic macroinvertebrate communities at the McClean Lake uranium operation in northern Saskatchewan

Diluted treated effluent from the McClean Lake uranium mill in northern Saskatchewan is released into Vulture Lake, which flows into the east basin of McClean Lake; this input could potentially cause a variety of disturbances to the aquatic systems. This study aimed to determine the potential effects of diluted effluent exposure (metals and major ions) on benthic macroinvertebrates in Vulture Lake and McClean Lake. Two monitoring locations located in Vulture Lake and eight in McClean Lake were used for collection water, sediment, and benthic macroinvertebrates. Complementary surface water bioassays were performed with larvae of the midge Chironomus dilutus using lake water from selected sites. Results indicated that total macroinvertebrate abundance and Margalef index (MI) did not follow the diluted effluent pattern. In addition, while the MI from artificial substrate samplers showed higher values in Vulture Lake and lower values at McClean Lake sites 4 and 5 (closer to effluent diffuser), the values recorded for sediment grab samples registered lower indices in Vulture Lake and higher values for sites 4 and 5. The final model from a Generalized Additive Modelling (GAM) approach suggested that electrical conductivity (EC), selenium (Se), and chloride (Cl) in water, and total organic carbon (TOC) and cadmium (Cd) in sediment are key variables that collectively may have influenced macroinvertebrate community composition at the study sites. Finally, across all test endpoints in the bioassays, exposure to lake water from Vulture Lake and McClean Lake had no statistically significant effects on C. dilutus.

Climate change increases threat to plant diversity in tropical forests of Central America and southern Mexico

Global biodiversity is negatively affected by anthropogenic climate change. As species distributions shift due to increasing temperatures and precipitation fluctuations, many species face the risk of extinction. In this study, we explore the expected trend for plant species distributions in Central America and southern Mexico under two alternative Representative Concentration Pathways (RCPs) portraying moderate (RCP4.5) and severe (RCP8.5) increases in greenhouse gas emissions, combined with two species dispersal assumptions (limited and unlimited), for the 2061-2080 climate forecast. Using an ensemble approach employing three techniques to generate species distribution models, we classified 1924 plant species from the region's (sub)tropical forests according to IUCN Red List categories. To infer the spatial and taxonomic distribution of species' vulnerability under each scenario, we calculated the proportion of species in a threat category (Vulnerable, Endangered, Critically Endangered) at a pixel resolution of 30 arc seconds and by family. Our results show a high proportion (58-67%) of threatened species among the four experimental scenarios, with the highest proportion under RCP8.5 and limited dispersal. Threatened species were concentrated in montane areas and avoided lowland areas where conditions are likely to be increasingly inhospitable. Annual precipitation and diurnal temperature range were the main drivers of species' relative vulnerability. Our approach identifies strategic montane areas and taxa of conservation concern that merit urgent inclusion in management plans to improve climatic resilience in the Mesoamerican biodiversity hotspot. Such information is necessary to develop policies that prioritize vulnerable elements and mitigate threats to biodiversity under climate change.

Spatial and temporal patterns of phytoplankton community succession and characteristics of realized niches in Lake Taihu, China

Understanding the dynamics and succession of phytoplankton in large lakes can help inform future lake management. The study analyzed phytoplankton community variations in Lake Taihu over a 21-year period, focusing on realized niches and their impact on succession. The study developed a niche periodic table with 32 niches, revealing responses to environmental factors and the optimal number of niches. Results showed that the phytoplankton in Lake Taihu showed significant spatial and temporal heterogeneity, with biomass decreasing as one moved from the northwest to the southeast and expanding towards central lake area, and towards autumn and winter. Different phytoplankton groups in Lake Taihu occupied realized niches shaped by temperature, nitrate, and phosphate. To predict the response of eutrophic freshwater lake ecosystems to human activities and climate change, it is critical to interpret the law of phytoplankton bloom and niche succession.

Predicting current and future potential distributions of the greater bandicoot rat (Bandicota indica) under climate change conditions

BACKGROUND

Rodent infestation is a global problem. Rodents cause huge harm to agriculture, forestry, and animal husbandry around the world and spread various zoonoses. In this study, we simulated the potentially suitable habitats of Bandicota indica and predicted the impact of future climate change on its distribution under different socio-economic pathway scenarios of CMIP6 using a parameter-optimized maximum entropy (MaxEnt) model.

RESULTS

The average area under the receiver operating characteristic curve (AUC) value (0.958 ± 0.006) after ten repetitions proved the high accuracy of the MaxEnt model. Model results show that the annual mean temperature (≥ 15.93 °C), isothermality (28.52-80.49%), annual precipitation (780.13-3863.13 mm), precipitation of the warmest quarter (≥ 204.37 mm), and nighttime light (≥ 3.38) were important limiting environmental variables for the distribution of B. indica. Under current climate conditions, the projected potential suitable habitats for B. indica were mainly in India, China, Myanmar, Thailand, and Vietnam, which cover a total area of 301.70 × 104  km2 . The potentially suitable areas of B. indica in the world will expand under different future climate change scenarios by 1.61-17.65%.

CONCLUSIONS

These results validate the potential influence of climate change on the distribution of B. indica and aid in understanding the linkages between B. indica niches and the relevant environment, thereby identifying urgent management areas where interventions may be necessary to develop feasible early warning and prevention strategies to protect against this rodent's spread. © 2023 Society of Chemical Industry.

Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients

Mangrove is one of the most productive and sensitive ecosystems in the world. Due to the complexity and specificity of mangrove habitat, the development of mangrove is regulated by several factors. Species distribution models (SDMs) are effective tools to identify the potential habitats for establishing and regenerating the ecosystem. Such models usually include exclusively environmental factors. Nevertheless, recent studies have challenged this notion and highlight the importance of including biotic interactions. Both factors are necessary for a mechanistic understanding of the mangrove distribution in order to promote the protection and restoration of mangroves. Thus, we present a novel approach of combining environmental factors and interactions with salt marsh for projecting mangrove distributions at the global level and within latitudinal zones. To test the salt marsh interaction, we fit the MaxEnt model with two predicting sets: (1) environments only and (2) environments + salt marsh interaction index (SII). We found that both sets of models had good predictive ability, although the SII improved model performance slightly. Potential distribution areas of mangrove decrease with latitudes, and are controlled by biotic and abiotic factors. Temperature, precipitation and wind speed are generally critical at both global scale and ecotones along latitudes. SII is important on global scale, with a contribution of 5.9%, ranking 6th, and is particularly critical in the 10-30°S and 20-30°N zone. Interactions with salt marsh, including facilitation and competition, are shown to affect the distribution of mangroves at the zone of coastal ecotone, especially in the latitudinal range from 10° - 30°. The contribution of SII to mangrove distribution increases with latitudes due to the difference in the adaptive capacity of salt marsh plants and mangroves to environments. Totally, this study identified and quantified the effects of salt marsh on mangrove distribution by establishing the SII. The results not only facilitate to establish a more accurate mangrove distribution map, but also improve the efficiency of mangrove restoration by considering the salt marsh interaction in the mangrove management projects. In addition, the method of incorporating biotic interaction into SDMs through establish the biotic interaction index has contributed to the development of SDMs.

Range map data of marine ecosystem structuring species under global climate change

Data on contemporary and future geographical distributions of marine species are crucial for guiding conservation and management policies in face of climate change. However, available distributional patterns have overlooked key ecosystem structuring species, despite their numerous ecological and socioeconomic services. Future range estimates are mostly available for few species at regional scales, and often rely on the outdated Representative Concentration Pathway scenarios of climate change, hindering global biodiversity estimates within the framework of current international climate policies. Here, we provide range maps for 980 marine structuring species of seagrasses, kelps, fucoids, and cold-water corals under present-day conditions (from 2010 to 2020) and future scenarios (from 2090 to 2100) spanning from low carbon emission scenarios aligned with the goals of the Paris Agreement (Shared Socioeconomic Pathway 1-1.9), to higher emissions under reduced mitigation strategies (SSP3-7.0 and SSP5-8.5). These models were developed using state-of-the-art and advanced machine learning algorithms linking the most comprehensive and quality-controlled datasets of occurrence records with high-resolution, biologically relevant predictor variables. By integrating the best aspects of species distribution modelling over key ecosystem structuring species, our datasets hold the potential to enhance the ability to inform strategic and effective conservation policy, ultimately supporting the resilience of ocean ecosystems.

Variation of floristic diversity, community composition, endemism, and conservation status of tree species in tropical rainforests of Sri Lanka across a wide altitudinal gradient

Tropical rainforests in Sri Lanka are biodiversity hotspots, which are sensitive to anthropogenic disturbance and long-term climate change. We assessed the diversity, endemism and conservation status of these rainforests across a wide altitudinal range (100-2200 m above sea level) via a complete census of all trees having ≥ 10 cm diameter at breast height in ten one-hectare permanent sampling plots. The numbers of tree families, genera and species and community-scale tree diversity decreased with increasing altitude. Tree diversity, species richness and total basal area per ha across the altitudinal range were positively associated with long-term means of maximum temperature, annual rainfall and solar irradiance. Percentage of endangered species increased with increasing altitude and was positively associated with cumulative maximum soil water deficit, day-night temperature difference and high anthropogenic disturbance. Percentage of endemic species was greater in the lowland rainforests than in high-altitude montane forests. Nearly 85% of the species were recorded in three or less plots, which indicated substantial altitudinal differentiation in their distributions. Less than 10 individuals were recorded in 41% of the endemic species and 45% of the native species, which underlined the need for urgent conservation efforts across the whole altitudinal range.

The global distribution of plants used by humans

Plants sustain human life. Understanding geographic patterns of the diversity of species used by people is thus essential for the sustainable management of plant resources. Here, we investigate the global distribution of 35,687 utilized plant species spanning 10 use categories (e.g., food, medicine, material). Our findings indicate general concordance between utilized and total plant diversity, supporting the potential for simultaneously conserving species diversity and its contributions to people. Although Indigenous lands across Mesoamerica, the Horn of Africa, and Southern Asia harbor a disproportionate diversity of utilized plants, the incidence of protected areas is negatively correlated with utilized species richness. Finding mechanisms to preserve areas containing concentrations of utilized plants and traditional knowledge must become a priority for the implementation of the Kunming-Montreal Global Biodiversity Framework.

FSC forest certification effects on biodiversity: A global review and meta-analysis

FSC is a worldwide recognized forest certification scheme, that aims to promote the environmentally responsible management and conservation of the world's forests. Despite its broad application, there is little evidence of its effect on biodiversity. To address this important knowledge gap, here we conducted a systematic review and a hierarchical meta-analysis of the effects of FSC on biodiversity worldwide. Our review yielded 57 studies spanning 2004-2022. Most studies were in the Americas and Europe (31 % and 28 %, respectively), and largely focused on vascular plants (41 %). Half (51 %) of the studies aimed to determine the effect of FSC certification on biodiversity. There were 15 studies with sufficient information for meta-analysis, resulting in 231 effect sizes for mammal, bird, and vascular plant abundance and 10 for vascular plant richness. Overall, there is a neutral effect of certification on taxa abundance, with only a positive effect on mammal assemblages. Responses varied considerably between mammals' traits. Threatened species, individuals with reduced body weight, and omnivorous species benefit from management under the FSC scheme. Vascular plant richness exhibited significantly higher values in FSC-certified areas. Moreover, the abundance of vascular plants also differs among traits, with shrubs and adult trees benefiting from FSC certification. Our systematic review and meta-analysis revealed strong variation in biodiversity responses to FSC, and major geographic and taxonomic knowledge gaps. The overall neutral effect and the divergent responses of taxa and species traits suggest that taxa/species-specific management and improvement of FSC criteria are required.

Distribution of global sea turtle nesting explained from regional-scale coastal characteristics

Climate change and human activity threaten sea turtle nesting beaches through increased flooding and erosion. Understanding the environmental characteristics that enable nesting can aid to preserve and expand these habitats. While numerous local studies exist, a comprehensive global analysis of environmental influences on the distribution of sea turtle nesting habitats remains largely unexplored. Here, we relate the distribution of global sea turtle nesting to 22 coastal indicators, spanning hydrodynamic, atmospheric, geophysical, habitat, and human processes. Using state-of-the-art global datasets and a novel 50-km-resolution hexagonal coastline grid (Coastgons), we employ machine learning to identify spatially homogeneous patterns in the indicators and correlate these to the occurrence of nesting grounds. Our findings suggest sea surface temperature, tidal range, extreme surges, and proximity to coral and seagrass habitats significantly influence global nesting distribution. Low tidal ranges and low extreme surges appear to be particularly favorable for individual species, likely due to reduced nest flooding. Other indicators, previously reported as influential (e.g., precipitation and wind speed), were not as important in our global-scale analysis. Finally, we identify new, potentially suitable nesting regions for each species. On average, [Formula: see text] of global coastal regions between [Formula: see text] and [Formula: see text] latitude could be suitable for nesting, while only [Formula: see text] is currently used by turtles, showing that the realized niche is significantly smaller than the fundamental niche, and that there is potential for sea turtles to expand their nesting habitat. Our results help identify suitable nesting conditions, quantify potential hazards to global nesting habitats, and lay a foundation for nature-based solutions to preserve and potentially expand these habitats.

Temporal trends of land-use favourability for the strongly declining little bustard: assessing the role of protected areas

The little bustard (Tetrax tetrax) is a steppe bird strongly and negatively influenced by agricultural intensification in Europe. Here, we use the little bustard as a model species to examine how favourability (relative occurrence likelihood of a species based on environmental characteristics, such as habitat availability) varies regionally with degree of protection in north-western Spain. The Natura2000 network is one of the main biodiversity conservation tools of the European Union, aiming to protect areas hosting species of conservation concern from unfavourable land-use changes. The network covers many landscapes across the continent, including farmland. Additionally, we examine the relationship between trends in land-use favourability and little bustard population trends over a decade in the Nature Reserve of Lagunas de Villafáfila, a protected area also in the Natura2000 network where active and intense management focused on steppe bird conservation is carried out. Favourability was much greater in Villafáfila than in both protected areas with lower degree of protection and in non-protected areas. Land-use favourability increased slightly between 2011 and 2020 both in and out of protected areas, whereas little bustard populations declined sharply in that period, even in Villafáfila. Spatial variations in little bustard abundance within Villafáfila depended on social attraction (increasing with the number of neighbouring males) but not significantly on small-scale variations in land-use favourability. These results suggest that land-use management in Natura2000 areas needs to be more conservation-focused, favouring natural and seminatural habitats and traditional farming practices to improve land-use favourability for little bustards and other steppe birds. Additional factors, such as field-level agricultural management or social interaction variables that may cause an Allee effect, should be incorporated in little bustard favourability models to improve their use in conservation planning.

Projection of current and future distribution of adaptive genetic units in an alpine ungulate

Climate projections predict major changes in alpine environments by the end of the 21st century. To avoid climate-induced maladaptation and extinction, many animal populations will either need to move to more suitable habitats or adapt in situ to novel conditions. Since populations of a species exhibit genetic variation related to local adaptation, it is important to incorporate this variation into predictive models to help assess the ability of the species to survive climate change. Here, we evaluate how the adaptive genetic variation of a mountain ungulate-the Northern chamois (Rupicapra rupicapra)-could be impacted by future global warming. Based on genotype-environment association analyses of 429 chamois using a ddRAD sequencing approach, we identified genetic variation associated with climatic gradients across the European Alps. We then delineated adaptive genetic units and projected the optimal distribution of these adaptive groups in the future. Our results suggest the presence of local adaptation to climate in Northern chamois with similar genetic adaptive responses in geographically distant but climatically similar populations. Furthermore, our results predict that future climatic changes will modify the Northern chamois adaptive landscape considerably, with various degrees of maladaptation risk.

Ecological niche contributes to the persistence of the Western x Glaucous-winged Gull hybrid zone

Hybrid zones occur in nature when populations with limited reproductive barriers overlap in space. Many hybrid zones persist over time, and different models have been proposed to explain how selection can maintain hybrid zone stability. More empirical studies are needed to elucidate the role of ecological adaptation in maintaining stable hybrid zones. Here, we investigated the role of exogenous factors in maintaining a hybrid zone between western gulls (Larus occidentalis) and glaucous-winged gulls (L. glaucescens). We used ecological niche models (ENMs) and niche similarity tests to quantify and examine the ecological niches of western gulls, glaucous-winged gulls, and their hybrids. We found evidence of niche divergence between all three groups. Our results best support the bounded superiority model, providing further evidence that exogenous selection favoring hybrids may be an important factor in maintaining this stable hybrid zone.

Temperature dependence of mosquitoes: comparing mechanistic and machine learning approaches

Mosquito vectors of pathogens (e.g., Aedes , Anopheles , and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically-plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.90). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming.

The challenge in detecting risk areas of snakebite when case rates are low: the case of Amazonian coral snakes

Identifying risk areas for envenomation by animals is relevant for public health, such as strategic distribution of antivenoms. Coral snakes are highly diverse in the Amazon, inhabit natural and human-modified environments, and the outcome of the cases tends to be serious and potentially lethal due to their neurotoxic venom. By integrating species' geographical records and environmental variables, we used species distribution modeling to predict the distribution of coral snake species in the Brazilian Amazonia. We analyzed the relationship between the predicted distribution of coral snake species, along with envenomation data in the region, to propose actions to reduce the number of cases and to provide tools for a better policy of public health. We conclude that the entire Amazon shows high environmental suitability for coral snakes, and such suitability explains little about the incidence of cases. This is probably due to the low human density in the Amazon and to coral snake traits such as secretive habits and non-agressive behavior. Differently from other venomous snakes, the scenario regarding coral snakebites precludes the detection of prominent geographical areas of concern and demands a broad and equitable availability of health centers throughout Amazonia and along other areas of occurrence of the genus Micrurus.

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.

Environmental DNA (eDNA) removal rates in streams differ by particle size under varying substrate and light conditions

The use of environmental DNA (eDNA) as a sampling tool offers insights into the detection of invasive and/or rare aquatic species and enables biodiversity assessment without traditional sampling approaches, which are often labor-intensive. However, our understanding of the environmental factors that impact eDNA removal (i.e., how rapidly eDNA is removed from the water column by the combination of decay and physical removal) in flowing waters is limited. This limitation constrains predictions about the location and density of target organisms after positive detection. To address this question, we spiked Common Carp (Cyprinus carpio) eDNA into recirculating mesocosms (n = 24) under varying light (shaded versus open) and benthic substrate conditions (no substrate, bare substrate, and biofilm-colonized substrate). We then collected water samples from each mesocosm at four time points (40 min, 6 h, 18 h, and 48 h), and sequentially filtered the samples through 10, 1.0, and 0.2 μm filters to quantify removal rates for different eDNA particle sizes under varying light and substrate conditions. Combining all size classes, total eDNA removal rates were higher for mesocosms with biofilm-colonized substrate compared to those with no substrate or bare (i.e., no biofilm) substrate, which is consistent with previous findings linking biofilm colonization with increased eDNA removal and degradation. Additionally, when biofilm was present, light availability increased eDNA removal; eDNA levels fell below detection after 6-18 h for open mesocosms versus 18-48 h for shaded mesocosms. Among size classes, larger particles (>10 μm) were removed faster than small particles (1.0-0.2 μm). These results suggest that changes in the distribution of eDNA size classes over time (e.g., with downstream transport) and with differing environmental conditions could be used to predict the location of target organisms in flowing waters, which will advance the use of eDNA as a tool for species monitoring and management.

Utilizing a top predator to prioritize site protection for biodiversity conservation

Ongoing global change makes it ever more urgent to find creative solutions for biodiversity preservation, but prioritizing sites for protection can be challenging. One shortcut lies in mapping the habitat requirements of well-established biodiversity indicators, such as top predators, to identify high-biodiversity sites. Here, we planned site protection for biodiversity conservation by developing a multi-scale species distribution model (SDM) for the raptorial Northern Goshawk (Accipiter gentilis; goshawk) breeding in an extensive megacity region of Japan. Specifically, we: (1) examined the determinants of top predator occurrence and thus of high-biodiversity value in this megacity setting, (2) identified the biodiversity hotspots, (3) validated whether they actually held higher biodiversity through an independent dataset, and (4) evaluated their current protection by environmental laws. The SDM revealed that goshawks preferred secluded sites far from roads, with abundant forest within a 100 m radius and extensive forest ecotones suitable for hunting within a 900 m radius. This multi-scale landscape configuration was independently confirmed to hold higher biodiversity, yet covered only 3.2% of the study area, with only 44.0% of these sites legally protected. Thus, a rapid biodiversity assessment mediated by a top predator quickly highlighted: (1) the poor development of biodiversity-friendly urban planning in this megacity complex, an aspect overlooked for decades of rapid urban sprawl, and (2) the extreme urgency of extending legal protection to the sites missed by the current protected area network. Exigent biodiversity indicators, such as top predators, could be employed in the early or late stages of anthropogenic impacts in order to proactively incorporate biodiversity protection into planning or flag key biodiversity relics. Our results confirm and validate the applied reliability of top predatory species as biodiversity conservation tools.

A field-validated ensemble species distribution model of Eriogonum pelinophilum, an endangered subshrub in Colorado, USA

Understanding the suitable habitat of endangered species is crucial for agencies such as the Bureau of Land Management to plan management and conservation. However, few species distribution models are directly validated, potentially limiting their application in management. In preparation for a Species Status Assessment of clay-loving wild buckwheat (Eriogonum pelinophilum), an endangered subshrub found in southwest Colorado, we ran a series of species distribution models to estimate the species' potential occupied habitat and validated these models in the field. A 1-meter resolution digital elevation model derived from LiDAR and a high-resolution geology mapping helped identify biologically relevant characteristics of the species' habitat. We employed a weighted ensemble model based on two Random Forest and one Boosted Regression Tree model, and discrimination performance of the ensemble model was high (AUC-PR = 0.793). We then conducted a systematic field survey of model habitat suitability predictions, during which we discovered 55 new subpopulations of the species and demonstrated that new species observations were strongly associated with model predictions (p < .0001, Cliff's delta = 0.575). We further refined our original models by incorporating the additional species occurrences collected in the field survey, a new explanatory variable, and a more diverse set of models. These iterative changes marginally improved performance of the ensemble model (AUC-PR = 0.825). Direct validation of species distribution models is extremely rare, and our field survey provides strong validation of our model results. This helps increase confidence to utilize predictions in planning. The final model predictions greatly improve the Bureau of Land Management's understanding of the species' habitat and increase our ability to consider potential habitat in planning land use activities such as road development and travel management.

Space-for-time substitutions in climate change ecology and evolution

In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate-biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These 'space-for-time substitutions' (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate-focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable - population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate-biotic relationships and (ii) the transferability of these relationships, i.e. whether climate-biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.