Spatial and temporal patterns of haemorrhagic fever with renal syndrome (HFRS) and the impact of environmental drivers in a border area of the Russian Far East

AIMS

Haemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease transmitted by rodents. The distribution of HFRS in the European part of Russia has been studied quite well; however, much less is known about the endemic area in the Russian Far East. The mutual influence of the epidemic situation in the border regions and the possibility of cross-border transmission of infection remain poorly understood. This study aims to identify the spatiotemporal hot spots of the incidence and the impact of environmental drivers on the HFRS distribution in the Russian Far East.

METHODS AND RESULTS

A two-scale study design was performed. Kulldorf's spatial scan statistic was used to conduct spatiotemporal analysis at a regional scale from 2000 to 2020. In addition, an ecological niche model based on maximum entropy was applied to analyse the contribution of various factors and identify spatial favourability at the local scale. One spatiotemporal cluster that existed from 2002 to 2011 and located in the border area and one pure temporal cluster from 2004 to 2007 were revealed. The best suitability for orthohantavirus persistence was found along rivers, including those at the Chinese-Russian border, and was mainly explained by land cover, NDVI (as an indicator of vegetation density and greenness) and elevation.

CONCLUSIONS

Despite the stable incidence in recent years in, targeted prevention strategies are still needed due to the high potential for HRFS distribution in the southeast of the Russian Far East.

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.

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data

Comprehensive biodiversity data is crucial for ecosystem protection. The Biome mobile app, launched in Japan, efficiently gathers species observations from the public using species identification algorithms and gamification elements. The app has amassed >6 million observations since 2019. Nonetheless, community-sourced data may exhibit spatial and taxonomic biases. Species distribution models (SDMs) estimate species distribution while accommodating such bias. Here, we investigated the quality of Biome data and its impact on SDM performance. Species identification accuracy exceeds 95% for birds, reptiles, mammals, and amphibians, but seed plants, molluscs, and fishes scored below 90%. Our SDMs for 132 terrestrial plants and animals across Japan revealed that incorporating Biome data into traditional survey data improved accuracy. For endangered species, traditional survey data required >2000 records for accurate models (Boyce index ≥ 0.9), while blending the two data sources reduced this to around 300. The uniform coverage of urban-natural gradients by Biome data, compared to traditional data biased towards natural areas, may explain this improvement. Combining multiple data sources better estimates species distributions, aiding in protected area designation and ecosystem service assessment. Establishing a platform for accumulating community-sourced distribution data will contribute to conserving and monitoring natural ecosystems.

The use of an integrated framework combining eco-evolutionary data and species distribution models to predict range shifts of species under changing climates

Species distribution models (SDMs) are powerful tools that can predict potential distributions of species under climate change. However, traditional SDMs that rely on current species occurrences may underestimate their climatic tolerances and potential distributions. To address this limitation, we developed an integrated framework that incorporates eco-evolutionary data into SDMs. In our approach, the fundamental niches of species are constructed by their realized niches in different periods, and those fundamental niches are used to predict potential distributions of species. Our framework includes multiple phylogenetic analyses, such as niche evolution rate estimation and ancestral area reconstruction. These analyses provide deeper insights into the responses of species to climate change. We applied our approach to the Chrysanthemum zawadskii species complex to evaluate its efficacy through comprehensive performance evaluations and validation tests. Our framework can be applied broadly to species with available phylogenetic data and occurrence records, making it a valuable tool for understanding species adaptation in a rapidly changing world.•Integrating the niches of species in different periods estimates more complete climatic envelopes for them.•Combining eco-evolutionary data with SDMs predicts more comprehensive potential distributions of species under climate change.•Our framework provides a general procedure for species with phylogenetic data and occurrence records.

The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest-dwelling bats in Germany

In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland-Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land-cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.

Future changes in society and climate may strongly shape wild large-herbivore faunas across Europe

Restoring wild communities of large herbivores is critical for the conservation of biodiverse ecosystems, but environmental changes in the twenty-first century could drastically affect the availability of habitats. We projected future habitat dynamics for 18 wild large herbivores in Europe and the relative future potential patterns of species richness and assemblage mean body weight considering four alternative scenarios of socioeconomic development in human society and greenhouse gas emissions (SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0, SSP5-RCP8.5). Under SSP1-RCP2.6, corresponding to a transition towards sustainable development, we found stable habitat suitability for most species and overall stable assemblage mean body weight compared to the present, with an average increase in species richness (in 2100: 3.03 ± 1.55 compared to today's 2.25 ± 1.31 species/area). The other scenarios are generally unfavourable for the conservation of wild large herbivores, although under the SSP5-RCP8.5 scenario there would be increase in species richness and assemblage mean body weight in some southern regions (e.g. + 62.86 kg mean body weight in Balkans/Greece). Our results suggest that a shift towards a sustainable socioeconomic development would overall provide the best prospect of our maintaining or even increasing the diversity of wild herbivore assemblages in Europe, thereby promoting trophic complexity and the potential to restore functioning and self-regulating ecosystems. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

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.

Risk of spread of the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Ghana

The impact of invasive species on biodiversity, food security and economy is increasingly noticeable in various regions of the globe as a consequence of climate change. Yet, there is limited research on how climate change affects the distribution of the invasive Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera:Liviidae) in Ghana. Using maxnet package to fit the Maxent model in R software, we answered the following questions; (i) what are the main drivers for D. citri distribution, (ii) what are the D. citri-specific habitat requirements and (iii) how well do the risk maps fit with what we know to be correctly based on the available evidence?. We found that temperature seasonality (Bio04), mean temperature of warmest quarter (Bio10), precipitation of driest quarter (Bio17), moderate resolution imaging spectroradiometer land cover and precipitation seasonality (Bio15), were the most important drivers of D. citri distribution. The results follow the known distribution records of the pest with potential expansion of habitat suitability in the future. Because many invasive species, including D. citri, can adapt to the changing climates, our findings can serve as a guide for surveillance, tracking and prevention of D. citri spread in Ghana.

Future climatically suitable areas for bats in South Asia

Climate change majorly impacts biodiversity in diverse regions across the world, including South Asia, a megadiverse area with heterogeneous climatic and vegetation regions. However, climate impacts on bats in this region are not well-studied, and it is unclear whether climate effects will follow patterns predicted in other regions. We address this by assessing projected near-future changes in climatically suitable areas for 110 bat species from South Asia. We used ensemble ecological niche modelling with four algorithms (random forests, artificial neural networks, multivariate adaptive regression splines and maximum entropy) to define climatically suitable areas under current conditions (1970-2000). We then extrapolated near future (2041-2060) suitable areas under four projected scenarios (combining two global climate models and two shared socioeconomic pathways, SSP2: middle-of-the-road and SSP5: fossil-fuelled development). Projected future changes in suitable areas varied across species, with most species predicted to retain most of the current area or lose small amounts. When shifts occurred due to projected climate change, new areas were generally northward of current suitable areas. Suitability hotspots, defined as regions suitable for >30% of species, were generally predicted to become smaller and more fragmented. Overall, climate change in the near future may not lead to dramatic shifts in the distribution of bat species in South Asia, but local hotspots of biodiversity may be lost. Our results offer insight into climate change effects in less studied areas and can inform conservation planning, motivating reappraisals of conservation priorities and strategies for bats in South Asia.

Data-centric species distribution modeling: Impacts of modeler decisions in a case study of invasive European frog-bit

Premise

Species distribution models (SDMs) are widely utilized to guide conservation decisions. The complexity of available data and SDM methodologies necessitates considerations of how data are chosen and processed for modeling to enhance model accuracy and support biological interpretations and ecological applications.

Methods

We built SDMs for the invasive aquatic plant European frog-bit using aggregated and field data that span multiple scales, data sources, and data types. We tested how model results were affected by five modeler decision points: the exclusion of (1) missing and (2) correlated data and the (3) scale (large-scale aggregated data or systematic field data), (4) source (specimens or observations), and (5) type (presence-background or presence-absence) of occurrence data.

Results

Decisions about the exclusion of missing and correlated data, as well as the scale and type of occurrence data, significantly affected metrics of model performance. The source and type of occurrence data led to differences in the importance of specific explanatory variables as drivers of species distribution and predicted probability of suitable habitat.

Discussion

Our findings relative to European frog-bit illustrate how specific data selection and processing decisions can influence the outcomes and interpretation of SDMs. Data-centric protocols that incorporate data exploration into model building can help ensure models are reproducible and can be accurately interpreted in light of biological questions.

Rising Temperatures, Falling Leaves: Predicting the Fate of Cyprus's Endemic Oak under Climate and Land Use Change

Endemic island species face heightened extinction risk from climate-driven shifts, yet standard models often underestimate threat levels for those like Quercus alnifolia, an iconic Cypriot oak with pre-adaptations to aridity. Through species distribution modelling, we investigated the potential shifts in its distribution under future climate and land-use change scenarios. Our approach uniquely combines dispersal constraints, detailed soil characteristics, hydrological factors, and anticipated soil erosion data, offering a comprehensive assessment of environmental suitability. We quantified the species' sensitivity, exposure, and vulnerability to projected changes, conducting a preliminary IUCN extinction risk assessment according to Criteria A and B. Our projections uniformly predict range reductions, with a median decrease of 67.8% by the 2070s under the most extreme scenarios. Additionally, our research indicates Quercus alnifolia's resilience to diverse erosion conditions and preference for relatively dry climates within a specific annual temperature range. The preliminary IUCN risk assessment designates Quercus alnifolia as Critically Endangered in the future, highlighting the need for focused conservation efforts. Climate and land-use changes are critical threats to the species' survival, emphasising the importance of comprehensive modelling techniques and the urgent requirement for dedicated conservation measures to safeguard this iconic species.

The influence of climate change on the future distribution of two Thymus species in Iran: MaxEnt model-based prediction

Within a few decades, the species habitat was reshaped at an alarming rate followed by climate change, leading to mass extinction, especially for sensitive species. Species distribution models (SDMs), which estimate both present and future species distribution, have been extensively developed to investigate the impacts of climate change on species distribution and assess habitat suitability. In the West Asia essential oils of T. daenensis and T. kotschyanus include high amounts of thymol and carvacrol and are commonly used as herbal tea, spice, flavoring agents and medicinal plants. Therefore, this study aimed to model these Thymus species in Iran using the MaxEnt model under two representative concentration pathways (RCP 4.5 and RCP 8.5) for the years 2050 and 2070. The findings revealed that the mean temperature of the warmest quarter (bio10) was the most significant variable affecting the distribution of T. daenensis. In the case of T. kotschyanus, slope percentage was the primary influencing factor. The MaxEnt modeling also demonstrated excellent performance, as indicated by all the Area Under the Curve (AUC) values exceeding 0.9. Moreover, based on the projections, the two mentioned species are expected to undergo negative area changes in the coming years. These results can serve as a valuable achievement for developing adaptive management strategies aimed at enhancing protection and sustainable utilization in the context of global climate change.

The Neanderthal niche space of Western Eurasia 145 ka to 30 ka ago

Neanderthals occupied Western Eurasia between 350 ka and 40 ka ago, during the climatically volatile Pleistocene. A key issue is to what extent Neanderthal populations expanded into areas of Western Eurasia and what conditions facilitated such range expansions. The range extent of Neanderthals is generally based on the distribution of Neanderthal material, but the land-altering nature of glacial periods has erased much of the already sparse material evidence of Neanderthals, particularly in the northern latitudes. To overcome this obstacle species distribution models can estimate past distributions of Neanderthals, however, most implementations are generally constrained spatially and temporally and may be artificially truncating the Neanderthal niche space. Using dated contexts from Neanderthal sites from across Western Eurasia, millennial-scale paleoclimate reconstructions, and a spatiotemporal species distribution model, we infer the fundamental climatic niche space of Neanderthals and estimate the extent of Neanderthal occupation. We find that (a.) despite the long timeframe, Neanderthals occupy a relatively narrow fundamental climatic niche space, (b.) the estimated projected potential Neanderthal niche space suggests a larger geographic range than the material record suggests, and (c.) that there was a general decline in the size of the projected potential Neanderthal niche from 145 ka ago onward, possibly contributing to their extinction.

Niche analyses and the potential distribution of four invasive bumblebees worldwide

The introduction of bees for agricultural production in distinct parts of the world and poor management have led to invasion processes that affect biodiversity, significantly impacting native species. Different Bombus species with invasive potential have been recorded spreading in different regions worldwide, generating ecological and economic losses. We applied environmental niche and potential distribution analyses to four species of the genus Bombus to evaluate the similarities and differences between their native and invaded ranges. We found that B. impatiens has an extended environmental niche, going from dry environmental conditions in the native range to warmer and wetter conditions in the invaded range. Bombus ruderatus also exhibited an extended environmental niche with drier and warmer conditions in the invaded range than in its native range. Bombus subterraneus expanded its environmental niche from cooler and wetter conditions in the native range to drier and warmer conditions in the invaded range. Finally, B. terrestris showed the most significant variation in the environmental niche, extending to areas with similar and different environmental conditions from its native range. The distribution models agreed with the known distributions for the four Bombus species, presenting geographic areas known to be occupied by each species in different regions worldwide. The niche analysis indicate shifts in the niches from the native to the invaded distribution area of the bee species. Still, niche similarities were observed in the areas of greatest suitability in the potential distribution for B. ruderatus, B. subterraneus, and B. terrestris, and to a lesser degree in the same areas with B. impatiens. These species require similar environmental conditions as in their native ranges to be established in their introduced ranges. Still, they can adapt to changes in temperature and humidity, allowing them to expand their ranges into new climatic conditions.

Ecological forecasts of insect range dynamics: a broad range of taxa includes winners and losers under future climate

Species distribution models are the primary tools to project future species' distributions, but this complex task is influenced by data limitations and evolving best practices. The majority of the 53 studies we examined utilized correlative models and did not follow current best practices for validating retrospective or future environmental data layers. Despite this, a summary of results is largely unsurprising: shifts toward cooler regions, but otherwise mixed dynamics emphasizing winners and losers. Harmful insects were more likely to show positive outcomes compared with beneficial species. Our restricted ability to consider mechanisms complicates interpretation of any single study. To improve this area of modeling, more classic field and lab studies to uncover basic ecology and physiology are crucial.

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.

Unravelling the role of tropical cyclones in shaping present species distributions

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

Mapping queen snapper (Etelis oculatus) suitable habitat in Puerto Rico using ensemble species distribution modeling

Queen snapper (Etelis oculatus) is of interest from an ecological and management perspective as it is the second most landed finfish species (by total pounds) as determined by Puerto Rico commercial landings (2010-2019). As fishing activities progressively expand into deeper waters, it is critical to gather data on deep-sea fish populations to identify essential fish habitats (EFH). In the U.S. Caribbean, the critically data-deficient nature of this species has made this challenging. We investigated the use of ensemble species distribution modeling (ESDM) to predict queen snapper distribution along the coast of Puerto Rico. Using occurrence data and terrain attributes derived from bathymetric datasets at different resolutions, we developed species distribution models unique to each sampling region (west, northeast, and southeast Puerto Rico) using seven different algorithms. Then, we developed ESDM models to analyze fish distribution using the highest-performing algorithms for each region. Model performance was evaluated for each ensemble model, with all depicting 'excellent' predictive capability (AUC > 0.8). Additionally, all ensemble models depicted 'substantial agreement' (Kappa > 0.7). We then used the models in combination with existing knowledge of the species' range to produce binary maps of potential queen snapper distributions. Variable importance differed across spatial resolutions of 30 m (west region) and 8 m (northeast and southeast region); however, bathymetry was consistently one of the best predictors of queen snapper suitable habitat. Positive detections showed strong regional patterns localized around large bathymetric features, such as seamounts and ridges. Despite the data-deficient condition of queen snapper population dynamics, these models will help facilitate the analysis of their spatial distribution and habitat preferences at different spatial scales. Our results therefore provide a first step in designing long-term monitoring programs targeting queen snapper, and determining EFH and the general distribution of this species in Puerto Rico.

Incorporating eco-evolutionary information into species distribution models provides comprehensive predictions of species range shifts under climate change

As climate changes increasingly influence species distributions, ecosystem functions, and biodiversity, the urgency to understand how species' ranges shift under those changes is great. Species distribution models (SDMs) are vital approaches that can predict species distributions under changing climates. However, SDMs based on the species' current occurrences may underestimate the species' climatic tolerances. Integrating species' realized niches at different periods, also known as multi-temporal calibration, can provide an estimation closer to its fundamental niche. Based on this, we further proposed an integrated framework that combines eco-evolutionary data and SDMs (phylogenetically-informed SDMs) to provide comprehensive predictions of species range shifts under climate change. To evaluate our approach's performance, we applied it to a group of related species, the Chrysanthemum zawadskii species complex (Anthemidae, Asteracee). First, we investigated the niche differentiation between species and intraspecific lineages of the complex and estimated their rates of niche evolution. Next, using both standard SDMs and our phylogenetically-informed SDMs, we generated predictions of suitability areas for all species and lineages and compared the results. Finally, we reconstructed the historical range dynamics for the species of this complex. Our results showed that the species and intraspecific lineages of the complex had varying degrees of niche differentiation and different rates of niche evolution. Lineage-level SDMs can provide more realistic predictions for species with intraspecific differentiation than species-level models can. The phylogenetically-informed SDMs provided more complete environmental envelopes and predicted broader potential distributions for all species than the standard SDMs did. Range dynamics varied among the species that have different rates of niche evolution. Our framework integrating eco-evolutionary data and SDMs contributes to a better understanding of the species' responses to climate change and can help to make more targeted conservation efforts for the target species under climate change, particularly for rare species.

Simulation of mangrove suitable habitat in the Guangdong-Hong Kong-Macao Area under the background of climate change

Climate change has resulted in great influence on the geographical distribution of species. Mangrove forests are one of the most precious ecosystems on the planet, yet they are being threatened by the habitat destruction and degradation under the situation of global warming. Seeking suitable areas for planting mangroves to tackle climate change has been gradually popular in ecological restoration. In this study, we applied the Maximum Entropy algorithm to assess the contribution of environmental factors on mangrove distribution, simulated mangrove suitable habitat for present and future (scenario of SSP245-2070s), and used kernel density analysis for identifying priority of mangrove reserve construction. Results indicate that mean diurnal range and elevation made the highest contribution on mangrove distribution. At present, the mangrove habitat suitability along the western coast of the Guangdong-Hong Kong-Macao Area (GHMA) was the highest while that along the eastern coast was the lowest. By 2070s, mangrove suitable areas would show a decreasing trend under SSP245 scenario. High suitable areas (HSAs) would change fastest and shift to northeast in the same direction as dominant environmental factors. For further mangrove restoration, it is advisable to select sites with high suitability density in the future but low reclamation density at present as prior mangrove reserves, and these sites distribute along the northeastern and northwestern coast of Zhanjiang, Yangjiang and Jiangmen, the Pearl River Estuary and Honghai Bay of Shanwei. Meanwhile, regions with lower suitability density but higher reclamation density could be listed as secondary mangrove reserves.

Habitat suitability maps for Australian flora and fauna under CMIP6 climate scenarios

BACKGROUND

Spatial information about the location and suitability of areas for native plant and animal species under different climate futures is an important input to land use and conservation planning and management. Australia, renowned for its abundant species diversity and endemism, often relies on modeled data to assess species distributions due to the country's vast size and the challenges associated with conducting on-ground surveys on such a large scale. The objective of this article is to develop habitat suitability maps for Australian flora and fauna under different climate futures.

RESULTS

Using MaxEnt, we produced Australia-wide habitat suitability maps under RCP2.6-SSP1, RCP4.5-SSP2, RCP7.0-SSP3, and RCP8.5-SSP5 climate futures for 1,382 terrestrial vertebrates and 9,251 vascular plants vascular plants at 5 km2 for open access. This represents 60% of all Australian mammal species, 77% of amphibian species, 50% of reptile species, 71% of bird species, and 44% of vascular plant species. We also include tabular data, which include summaries of total quality-weighted habitat area of species under different climate scenarios and time periods.

CONCLUSIONS

The spatial data supplied can help identify important and sensitive locations for species under various climate futures. Additionally, the supplied tabular data can provide insights into the impacts of climate change on biodiversity in Australia. These habitat suitability maps can be used as input data for landscape and conservation planning or species management, particularly under different climate change scenarios in Australia.

Niche differentiation, reproductive interference, and range expansion

Understanding species distributions and predicting future range shifts requires considering all relevant abiotic factors and biotic interactions. Resource competition has received the most attention, but reproductive interference is another widespread biotic interaction that could influence species ranges. Rubyspot damselflies (Hetaerina spp.) exhibit a biogeographic pattern consistent with the hypothesis that reproductive interference has limited range expansion. Here, we use ecological niche models to evaluate whether this pattern could have instead been caused by niche differentiation. We found evidence for climatic niche differentiation, but the species that encounters the least reproductive interference has one of the narrowest and most peripheral niches. These findings strengthen the case that reproductive interference has limited range expansion and also provide a counterexample to the idea that release from negative species interactions triggers niche expansion. We propose that release from reproductive interference enables species to expand in range while specializing on the habitats most suitable for breeding.

Assessing the invasive potential of different source populations of ragweed (Ambrosia artemisiifolia L.) through genomically informed species distribution modelling

The genetic composition of founding populations is likely to play a key role in determining invasion success. Individual genotypes may differ in habitat preference and environmental tolerance, so their ability to colonize novel environments can be highly variable. Despite the importance of genetic variation on invasion success, its influence on the potential distribution of invaders is rarely investigated. Here, we integrate population genomics and ecological niche models (ENMs) into a single framework to predict the distribution of globally invasive common ragweed (Ambrosia artemisiifolia) in Australia. We identified three genetic clusters for ragweed and used these to construct cluster-specific ENMs and characterize within-species niche differentiation. The potential range of ragweed in Australia depended on the genetic composition and continent of origin of the introduced population. Invaders originating from warmer, wetter climates had a broader potential distribution than those from cooler, drier ones. By quantifying this change, we identified source populations most likely to expand the ragweed distribution. As prevention remains the most effective method of invasive species management, our work provides a valuable way of ranking the threat posed by different populations to better inform management decisions.

Projected Impacts of Climate Change on the Range Expansion of the Invasive Straggler Daisy (Calyptocarpus vialis) in the Northwestern Indian Himalayan Region

Human-induced climate change modifies plant species distribution, reorganizing ecologically suitable habitats for invasive species. In this study, we identified the environmental factors that are important for the spread of Calyptocarpus vialis, an emerging invasive weed in the northwestern Indian Himalayan Region (IHR), along with possible habitats of the weed under current climatic scenarios and potential range expansion under several representative concentration pathways (RCPs) using MaxEnt niche modeling. The prediction had a high AUC (area under the curve) value of 0.894 ± 0.010 and a remarkable correlation between the test and expected omission rates. BIO15 (precipitation seasonality; 38.8%) and BIO1 (annual mean temperature; 35.7%) had the greatest impact on the probable distribution of C. vialis, followed by elevation (11.7%) and landcover (6.3%). The findings show that, unlike the current situation, "high" and "very high" suitability areas would rise while less-suited habitats would disappear. All RCPs (2.6, 4.5, 6.0, and 8.5) indicate the expansion of C. vialis in "high" suitability areas, but RCP 4.5 predicts contraction, and RCPs 2.6, 6.0, and 8.5 predict expansion in "very high" probability areas. The current distribution of C. vialis is 21.59% of the total area of the state, with "medium" to "high" invasion suitability, but under the RCP 8.5 scenario, it might grow by 10% by 2070. The study also reveals that C. vialis may expand its niche at both lower and higher elevations. This study clarifies how bioclimatic and topographic factors affect the dispersion of invasive species in the biodiverse IHR. Policymakers and land-use managers can utilize the data to monitor C. vialis hotspots and develop scientifically sound management methods.

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.

Modelling the historical distribution of schistosomiasis-transmitting snails in South Africa using ecological niche models

Schistosomiasis is a vector-borne disease transmitted by freshwater snails and is prevalent in rural areas with poor sanitation and no access to tap water. Three snail species are known to transmit schistosomiasis in South Africa (SA), namely Biomphalaria pfeifferi, Bulinus globosus and Bulinus africanus. In 2003, a predicted prevalence of 70% was reported in tropical climates in SA. Temperature and rainfall variability can alter schistosomiasis-transmitting snails' development by increasing or decreasing their abundance and geographical distribution. This study aimed to map the historical distribution of schistosomiasis from 1950 to 2006 in SA. The snail sampling data were obtained from the historical National Snail Freshwater Collection (NFSC). Bioclimatic variables were extracted using ERA 5 reanalysis data provided by the Copernicus Climate Change Service. In this study, we used 19 bioclimatic and four soil variables. The temporal aggregation was the mean climatological period pre-calculated over the 40-year reference period with a spatial resolution of 0.5° x 0.5°. Multicollinearity was reduced by calculating the Variance Inflation Factor Core (VIF), and highly correlated variables (> 0.85) were excluded. To obtain an "ensemble" and avoid the integration of weak models, we averaged predictions using the True Skill Statistical (TSS) method. Results showed that the ensemble model achieved the highest Area Under the Curve (AUC) scores (0.99). For B. africanus, precipitation-related variables contributed to determining the suitability for schistosomiasis. Temperature and precipitation-related variables influenced the distribution of B. globosus in all three models. Biomphalaria pfeifferi showed that Temperature Seasonality (bio4) contributed the most (47%) in all three models. According to the models, suitable areas for transmitting schistosomiasis were in the eastern regions of South Africa. Temperature and rainfall can impact the transmission and distribution of schistosomiasis in SA. The results will enable us to develop future projections for Schistosoma in SA based on climate scenarios.

Gridded livestock density database and spatial trends for Kazakhstan

Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSKD) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSKD database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000-2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.

Potentially suitable distribution areas of Populus euphratica and Tamarix chinensis by MaxEnt and random forest model in the lower reaches of the Heihe River, China

Populus euphratica and Tamarix chinensis play a vital role in windbreak and sand fixation, maintaining species diversity and ensuring community stability. Managing and protecting the P. euphratica and T. chinensis forests in the Heihe River's lower reaches is an urgent issue to maintain the desert region's ecological balance. In this study, based on the distribution points of P. euphratica and T. chinensis species and environmental data, MaxEnt and random forest (RF) models were used to characterize the potential distribution areas of P. euphratica and T. chinensis in the lower reaches of the Heihe River. The results showed that the accuracy of the RF model was much higher than that of the MaxEnt model. Both the RF and MaxEnt models showed that the distance to the river greatly influenced the distribution of P. euphratica and T. chinensis. Furthermore, the RF model predicted significantly larger highly suitable areas for both P. euphratica and T. chinensis than the MaxEnt model. Our study enhances the understanding of the species' spatial distribution, offering valuable insights for practical management and conservation strategies.

Impact of Holocene environmental change on the evolutionary ecology of an Arctic top predator

The Arctic is among the most climatically sensitive environments on Earth, and the disappearance of multiyear sea ice in the Arctic Ocean is predicted within decades. As apex predators, polar bears are sentinel species for addressing the impact of environmental variability on Arctic marine ecosystems. By integrating genomics, isotopic analysis, morphometrics, and ecological modeling, we investigate how Holocene environmental changes affected polar bears around Greenland. We uncover reductions in effective population size coinciding with increases in annual mean sea surface temperature, reduction in sea ice cover, declines in suitable habitat, and shifts in suitable habitat northward. Furthermore, we show that west and east Greenlandic polar bears are morphologically, and ecologically distinct, putatively driven by regional biotic and genetic differences. Together, we provide insights into the vulnerability of polar bears to environmental change and how the Arctic marine ecosystem plays a vital role in shaping the evolutionary and ecological trajectories of its inhabitants.

30×30 biodiversity gains rely on national coordination

Global commitments to protect 30% of land by 2030 present an opportunity to combat the biodiversity crisis, but reducing extinction risk will depend on where countries expand protection. Here, we explore a range of 30×30 conservation scenarios that vary what dimension of biodiversity is prioritized (taxonomic groups, species-at-risk, biodiversity facets) and how protection is coordinated (transnational, national, or regional approaches) to test which decisions influence our ability to capture biodiversity in spatial planning. Using Canada as a model nation, we evaluate how well each scenario captures biodiversity using scalable indicators while accounting for climate change, data bias, and uncertainty. We find that only 15% of all terrestrial vertebrates, plants, and butterflies (representing only 6.6% of species-at-risk) are adequately represented in existing protected land. However, a nationally coordinated approach to 30×30 could protect 65% of all species representing 40% of all species-at-risk. How protection is coordinated has the largest impact, with regional approaches protecting up to 38% fewer species and 65% fewer species-at-risk, while the choice of biodiversity incurs much smaller trade-offs. These results demonstrate the potential of 30×30 while highlighting the critical importance of biodiversity-informed national strategies.

Potential changes in the extent of suitable habitats for geladas (Theropithecus gelada) in the Anthropocene

BACKGROUND

Climate change coupled with other anthropogenic pressures may affect the extent of suitable habitat for species and thus their distributions. This is particularly true for species occupying high-altitude habitats such as the gelada (Theropithecus gelada) of the Ethiopian highlands. To explore the impact of climate change on species distributions, Species Distribution Modelling (SDM) has been extensively used. Here we model the current and future extent of sutibale habitat for geladas. Our modelling was based on 285 presence locations of geladas, covering their complete current distribution. We used different techniques to generate pseudoabsence datasets, MaxEnt model complexities, and cut-off thresholds to map the potential distribution of gelada under current and future climates (2050 and 2070). We assembled maps from these techniques to produce a final composite map. We also evaluated the change in the topographic features of gelada over the past 200 years by comparing the topography in current and historical settings.

RESULTS

All model runs had high performances, AUC = 0.87-0.96. Under the current climate, the suitable habitat predicted with high certainty was 90,891 km2, but it decreased remarkably under future climates, -36% by 2050 and - 52% by 2070. However, since the habitats of geladas already extend to mountaintop grasslands, no remarkable range shifts across elevation gradients were predicted under future climates.

CONCLUSIONS

Our findings indicated that climate change most likely results in a loss of suitable habitat for geladas, particularly south of the Rift Valley. Currently geladas are confined to higher altitudes and steep slopes compared to historical sightings, probably qualifying geladas as refugee species. The difference in topography is potentially associated with anthropogenic pressures that drove niche truncation to higher altitudes, undermining the climatic and topographic niche our models predicted. We recommend protecting the current habitats of geladas even when they are forecasted to become climatically unsuitable in the future, in particular for the population south of the Rift Valley.

A global examination of ecological niche modeling to predict emerging infectious diseases: a systematic review

Introduction

As emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species' spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types. We conducted a systematic review (PROSPERO: CRD42021251968) with the research question: What is the state of the science and practice of estimating ecological niches via ENM to predict the emergence and spread of vector-borne and/or zoonotic diseases?

Methods

We searched five research databases and eight widely recognized One Health journals between 1995 and 2020. We screened 383 articles at the abstract level (included if study involved vector-borne or zoonotic disease and applied ENM) and 237 articles at the full-text level (included if study described ENM features and modeling processes). Our objectives were to: (1) describe the growth and distribution of studies across the types of infectious diseases, scientific fields, and geographic regions; (2) evaluate the likely effectiveness of the studies to represent ecological niches based on the biotic, abiotic, and mobility framework; (3) explain some potential pitfalls of ENM algorithms and techniques; and (4) provide specific recommendation for future studies on the analysis of ecological niches to predict EIDs.

Results

We show that 99% of studies included mobility factors, 90% modeled abiotic factors with more than half in tropical climate zones, 54% modeled biotic conditions and interactions. Of the 121 studies, 7% include only biotic and mobility factors, 45% include only abiotic and mobility factors, and 45% fully integrated the biotic, abiotic, and mobility data. Only 13% of studies included modifying social factors such as land use. A majority of studies (77%) used well-recognized ENM algorithms (MaxEnt and GARP) and model selection procedures. Most studies (90%) reported model validation procedures, but only 7% reported uncertainty analysis.

Discussion

Our findings bolster ENM to predict EIDs that can help inform the prevention of outbreaks and future epidemics.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier (CRD42021251968).

Veni, vidi, vici? Future spread and ecological impacts of a rapidly expanding invasive predator population

Economic and ecological consequences of invasive species make biological invasions an influential driver of global change. Monitoring the spread and impacts of non-native species is essential, but often difficult, especially during the initial stages of invasion. The Jorō spider, Trichonephila clavata (L. Koch, 1878, Araneae: Nephilidae), is a large-bodied orb weaver native to Asia, likely introduced to northern Georgia, U.S. around 2010. We investigated the nascent invasion of T. clavata by constructing species distribution models (SDMs) from crowd-sourced data to compare the climate T. clavata experiences in its native range to its introduced range. We found evidence that the climate of T. clavata's native range differs significantly from its introduced range. Species distribution models trained with observations from its native range predict that the most suitable habitats in North America occur north of its current introduced range. Consistent with SDM predictions, T. clavata appears to be spreading faster to the north than to the south. Lastly, we conducted surveys to investigate potential ecological impacts of T. clavata on the diversity of native orb weaving spiders. Importantly, Trichonephila clavata was the most common and abundant species observed in the survey, and was numerically dominant at half of the sites it was present in. Our models also suggest that there is lower native orb weaver species richness and diversity closer to where T. clavata was initially found and where it has been established the longest, though human population density complicates this finding. This early study is the first to forecast how widely this spider may spread in its introduced range and explore its potential ecological impacts. Our results add evidence that T. clavata is an invasive species and deserves much more ecological scrutiny.

Biogeography and environmental preferences of Butia yatay (Mart.) Becc

During the Quaternary, Chaco Phytogeographic Domain (Chaco) flora in subtropical South America experienced temperature and humidity fluctuations, primarily driven by wind dynamics, leading to significant shifts in species distribution. The palm Butia yatay is endemic to the Chaco and thrives in areas characterized by a warm-rainy climate and mostly restricted to sandy soils. To investigate the current geographic distribution of suitable habitat for B. yatay while assessing the significance of soil variables, we employed two distinct algorithms in species distribution modeling (SDM). We also determined whether the distribution of B. yatay has changed since the Pleistocene and whether these changes align with previously proposed Pleistocene refugia. In the present SDMs, we considered two separate sets of predictors, one set with bioclimatic variables only and the other set with bioclimatic topographic and soil variables. Additionally, we reconstructed the historical geographic distribution of suitable habitats using bioclimatic data. Our results suggested that the primary determinants of B. yatay's current distribution include precipitation and temperature of the driest month and soil cation exchange capacity. Incorporating soil variables affected the estimated size and range of suitable areas. Projections into the past indicated similar suitable habitat distributions during interglacial periods compared with the present. During the Last Glacial Maximum, climatically suitable habitat may have shifted northward, partially overlapping with previously suggested Pleistocene refugia located between the Paraná and Uruguay Rivers. These findings indicate the main factors driving the distribution and ecology of B. yatay and enhance understanding of subtropical flora shifts during the Quaternary. The approach also may prove valuable for other studies within the Chaco.

Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches

BACKGROUND

The ticks Ixodes ricinus and Dermacentor reticulatus are two of the most important vectors in Europe. Climate niche modelling has been used in many studies to attempt to explain their distribution and to predict changes under a range of climate change scenarios. The aim of this study was to assess the ability of different climate niche modelling approaches to explain the known distribution of I. ricinus and D. reticulatus in Europe.

METHODS

A series of climate niche models, using different combinations of input data, were constructed and assessed. Species occurrence records obtained from systematic literature searches and Global Biodiversity Information Facility data were thinned to different degrees to remove sampling spatial bias. Four sources of climate data were used: bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived data. Eight different model training extents were examined and three modelling frameworks were used: maximum entropy, generalised additive models and random forest models. The results were validated through internal cross-validation, comparison with an external independent dataset and expert opinion.

RESULTS

The performance metrics and predictive ability of the different modelling approaches varied significantly within and between each species. Different combinations were better able to define the distribution of each of the two species. However, no single approach was considered fully able to capture the known distribution of the species. When considering the mean of the performance metrics of internal and external validation, 24 models for I. ricinus and 11 models for D. reticulatus of the 96 constructed were considered adequate according to the following criteria: area under the receiver-operating characteristic curve > 0.7; true skill statistic > 0.4; Miller's calibration slope 0.25 above or below 1; Boyce index > 0.9; omission rate < 0.15.

CONCLUSIONS

This comprehensive analysis suggests that there is no single 'best practice' climate modelling approach to account for the distribution of these tick species. This has important implications for attempts to predict climate-mediated impacts on future tick distribution. It is suggested here that climate variables alone are not sufficient; habitat type, host availability and anthropogenic impacts, not included in current modelling approaches, could contribute to determining tick presence or absence at the local or regional scale.

Utilising random forests in the modelling of Eragrostis curvula presence and absence in an Australian grassland system

Eragrostis curvula is an agronomically and ecologically undesirable perennial tussock grass dispersed across Australia. The objective of this study is to investigate relationships of ecologically relevant abiotic variables with the presence of E. curvula at a landscape scale in the Snowy Monaro region, Australia. Through vegetation surveys across 21 privately owned properties and freely available ancillary data on E. curvula presence, we used seven predictor variables, including Sentinel 2 NDVI reflectance, topography, distance from roads and watercourses and climate, to predict the presence or absence of E. curvula across its invaded range using a random forest (RF) algorithm. Assessment of performance metrics resulted in a pseudo-R squared of 0.96, a kappa of 0.97 and an R squared for out-of-bag samples of 0.67. Temperature had the largest influence on the model's performance, followed by linear features such as highways and rivers. Highways' high importance in the model may indicate that the presence or absence of E. curvula is related to the density of human transit, thus as a vector of E. curvula propagule dispersal. Further, humans' tendency to reside adjacent to rivers may indicate that E. curvula's presence or absence is related to human density and E. curvula's potential to spread via water courses.

spatialMaxent: Adapting species distribution modeling to spatial data

Conventional practices in species distribution modeling lack predictive power when the spatial structure of data is not taken into account. However, choosing a modeling approach that accounts for overfitting during model training can improve predictive performance on spatially separated test data, leading to more reliable models. This study introduces spatialMaxent (https://github.com/envima/spatialMaxent), a software that combines state-of-the-art spatial modeling techniques with the popular species distribution modeling software Maxent. It includes forward-variable-selection, forward-feature-selection, and regularization-multiplier tuning based on spatial cross-validation, which enables addressing overfitting during model training by considering the impact of spatial dependency in the training data. We assessed the performance of spatialMaxent using the National Center for Ecological Analysis and Synthesis dataset, which contains over 200 anonymized species across six regions worldwide. Our results show that spatialMaxent outperforms both conventional Maxent and models optimized according to literature recommendations without using a spatial tuning strategy in 80 percent of the cases. spatialMaxent is user-friendly and easily accessible to researchers, government authorities, and conservation practitioners. Therefore, it has the potential to play an important role in addressing pressing challenges of biodiversity conservation.

Beyond protected areas: The importance of mixed-use landscapes for the conservation of Sumatran elephants (Elephas maximus sumatranus)

Elephants were once widely distributed across the Indonesian island of Sumatra but now exist in small, isolated populations. Using the best data available on elephant occurrence, we aimed to (a) predict potential habitat suitability for elephants (Elephas maximus sumatranus) across the island of Sumatra and (b) model landscape connectivity among the extant elephant populations. We used direct sightings and indirect observations of elephant signs, as well as six remotely sensed proxies of surface ruggedness, vegetation productivity and structure, and human land use and disturbance, to model habitat suitability in a Google Earth Engine (GEE) environment. We validated the habitat suitability prediction using 10-fold spatial block cross validation and by calculating the area under the precision-recall curve (AUC-PR), sensitivity, and specificity for each model iteration. We also used a geolocation dataset collected from global positioning system (GPS) collars fitted on elephants as an independent validation dataset. Models showed good predictive performance with a mean AUC-PR of 0.73, sensitivity of 0.76, and specificity of 0.68. Greater than 83% of the independent GPS collar geolocations were located in predicted suitable habitat. We found human modification, surface ruggedness, and normalized difference vegetation index to be the most important variables for predicting suitable elephant habitat. Thirty-two percent, or 135,646 km2, of Sumatra's land area was predicted to be suitable habitat, with 43 patches of suitable habitat located across Sumatra. Areas with high connectivity were concentrated in the Riau and North Sumatra provinces. Though our analysis highlights the need to improve the quality of data collected on Sumatran elephants, more suitable habitat remains on Sumatra than is used by known populations. Targeted habitat conservation, especially of the suitable habitat in and around the Lamno, Balai Raja, Tesso Tenggara, Tesso Utara, Bukit Tigapuluh, Seblat, Padang Sugihan, and Bukit Barisan Selatan ranges, may improve the long-term viability of this critically endangered species.

Areas of spatial overlap between common bottlenose dolphin, recreational boating, and small-scale fishery: management insights from modelling exercises

Background

Sustainable management requires spatial mapping of both species distribution and human activities to identify potential risk of conflict. The common bottlenose dolphin (Tursiops truncatus) is a priority species of the European Union Habitat Directive, thus, to promote its conservation, the understanding of habitat use and distribution, as well as the identification and spatial trend of the human activities which may directly affect populations traits, is pivotal.

Methods

A MaxEnt modeling approach was applied to predict the seasonal (from April to September) habitat use of a small population of bottlenose dolphins in the north-western Sardinia (Mediterranean Sea) in relation to environmental variables and the likelihoods of boat and fishing net presence. Then, the overlapping areas between dolphin, fishing net and boat presence were identified to provide insights for the marine spatial management of this area.

Results

Three of the main factors influencing the seasonal distribution of bottlenose dolphins in the area are directly (boating and fishing) or indirectly (ocean warming) related to human activities. Furthermore, almost half of the most suitable area for dolphins overlapped with areas used by fishing and boating. Finally, relying on fishing distribution models, we also shed light on the potential impact of fishing on the Posidonia oceanica beds, a protected habitat, which received higher fishing efforts than other habitat types.

Discussion

Modelling the spatial patterns of anthropogenic activities was fundamental to understand the ecological impacts both on cetacean habitat use and protected habitats. A greater research effort is suggested to detect potential changes in dolphin habitat suitability, also in relation to ocean warming, to assess dolphin bycatch and the status of target fish species, and to evaluate sensitive habitats conditions, such as the Posidonia oceanica meadow.

Predicting climate heating impacts on riverine fish species diversity in a biodiversity hotspot region

Co-occurring biodiversity and global heating crises are systemic threats to life on Earth as we know it, especially in relatively rare freshwater ecosystems, such as in Iran. Future changes in the spatial distribution and richness of 131 riverine fish species were investigated at 1481 sites in Iran under optimistic and pessimistic climate heating scenarios for the 2050s and 2080s. We used maximum entropy modeling to predict species' potential distributions by hydrologic unit (HU) occupancy under current and future climate conditions through the use of nine environmental predictor variables. The most important variable determining fish occupancy was HU location, followed by elevation, climate variables, and slope. Thirty-seven species were predicted to decrease their potential habitat occupancy in all future scenarios. The southern Caspian HU faces the highest future species reductions followed by the western Zagros and northwestern Iran. These results can be used by managers to plan conservational strategies to ease the dispersal of species, especially those that are at the greatest risk of extinction or invasion and that are in rivers fragmented by dams.

Modelling the effects of topographic heterogeneity on distribution of Nitraria tangutorum Bobr. species in deserts using LiDAR-data

Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species Distribution Models (SDMs) can be used to understand these relationships. We used data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light Detection and Ranging) data of one square kilometer in the inner Mongolia region of China to develop SDMs. We evaluated the performance of SDMs developed with a variety of both the parametric and nonparametric algorithms (Bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, Generalized Linear Model, Generalized Additive Model, Random Forest (RF), and Support Vector Machine). The area under the receiver operating characteristic curve was used to evaluate these algorithms. The SDMs developed with RF showed the best performance based on the area under curve (0.7733). We also produced the Nitraria tangutorum Bobr. distribution maps with the best SDM and suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to southern part with 0-20 degree slopes at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on the desert species distribution on a small scale. The presented SDMs can have important applications for predicting species distribution and will be useful for preparing conservation and management strategies for desert ecosystems on a small scale.

Multitemporal Land Use and Cover Analysis Coupled with Climatic Change Scenarios to Protect the Endangered Taxon Asphodelus bento-rainhae subsp. bento-rainhae

Climate change and land use and land cover (LULC) change are impacting the species' geographic distribution, causing range shifts and reducing suitable habitats. Asphodelus bento-rainhae subsp. bento-rainhae (AbR) is an endangered endemic plant restricted to Serra da Gardunha (Portugal), and knowledge of those changes will help to design conservation measures. MaxEnt was used to model AbR's current distribution and project it into the future, 2050, using the Shared Socioeconomic Pathway SSP3-7. The Portuguese LULC maps from 1951-1980, 1995, 2007, and 2018 were used to assess and quantify LULC changes over time. The results showed that the AbR current predicted distribution matches its actual known distribution, which will not be affected by future predicted climate change. The significant LULC changes were observed during the study periods 1951-1980 to 2018, particularly between 1951-1980 and 1995. Scrubland and Agriculture decreased by 5% and 2.5%, respectively, and Forests increased by 4% in the study area. In the occurrence area, Agriculture increased, and Forests decreased between 1980 and 2018, due to Orchard expansion (34%) and declines in Chestnut (16.9%) and Pine (11%) areas, respectively. The use of species distribution models and the LULC change analysis contributed to understanding current and future species distribution. The LULC changes will have a significant impact on future species distribution. To prevent the extinction of this endemic species in the future, it is crucial to implement conservation measures, namely species monitoring, replantation, and germplasm conservation, in addition to guidelines for habitat conservation.

Giant pandas are losing their edge: Population trend and distribution dynamic drivers of the giant panda

Comprehending the population trend and understanding the distribution range dynamics of species are necessary for global species protection. Recognizing what causes dynamic distribution change is crucial for identifying species' environmental preferences and formulating protection policies. Here, we studied the rear-edge population of the flagship species, giant pandas (Ailuropoda melanoleuca), to (1) assess their population trend using their distribution patterns, (2) evaluate their distribution dynamics change from the second (1988) to the third (2001) survey (2-3 Interval) and third to the fourth (2013) survey (3-4 Interval) using a machine learning algorithm (eXtremely Gradient Boosting), and (3) decode model results to identify driver factors in the first known use of SHapley Additive exPlanations. Our results showed that the population trends in Liangshan Mountains were worst in the second survey (k = 1.050), improved by the third survey (k = 0.97), but deteriorated by the fourth survey (k = 0.996), which indicates a worrying population future. We found that precipitation had the most significant influence on distribution dynamics among several potential environmental factors, showing a negative correlation between precipitation and giant panda expansion. We recommend that further research is needed to understand the microenvironment and animal distribution dynamics. We provide a fresh perspective on the dynamics of giant panda distribution, highlighting novel focal points for ecological research on this species. Our study offers theoretical underpinnings that could inform the formulation of more effective conservation policies. Also, we emphasize the uniqueness and importance of the Liangshan Mountains giant pandas as the rear-edge population, which is at a high risk of population extinction.

Association between the potential distribution of Lutzomyia longipalpis and Nyssomyia whitmani and leishmaniasis incidence in Piauí State, Brazil

BACKGROUND

Leishmaniases are vector borne diseases caused by Leishmania spp. parasites transmitted by female sandflies (Diptera: Psychodidae) whose geographic distribution is influenced by environmental factors. Among the main tools for studying the distribution of vector species, modeling techniques are used to analyze the influence of climatic and environmental factors on the distribution of these insects and their association with human cases of the disease.

METHODOLOGY/PRINCIPAL FINDINGS

Here, we used a multiscale ecological niche modeling approach to assess the environmental suitability of sandfly vectors of the etiological agents of Visceral (VL) and American Cutaneous Leishmaniasis (ACL) in Piauí state, northeastern Brazil, and then evaluated their relationship with human disease incidence. For this, we obtained the geographic coordinates of the vector species Lutzomyia longipalpis and Nyssomyia whitmani through literature review, online databases and unpublished records. These data were used for the development of predictive models of the distribution of both sandflies species based on climatic and environmental variables. Finally, the environmental suitability for the presence of these vectors was compared with the incidence of both the diseases at the municipality level. The final models for each sandfly species showed good predictive powers with performance metric values of 0.889 for Lu. longipalpis and 0.776 for Ny. whitmani. The areas with greater environmental suitability for the presence of these species were concentrated in the central-north region of Piauí and coincide with the location of those municipalities presenting higher incidences of VL and ACL, situated in the central-north and extreme north of the state, respectively. The south and southeast regions of Piauí state have low incidence of these diseases and presented low environmental suitability for the presence of both vectors.

CONCLUSIONS/SIGNIFICANCE

We discuss how predictive modeling can guide entomological and epidemiological surveillances and recommend an increased supervision and control activities in Teresina (capital of the state of Piaui), Altos and Pedro II, in addition to other municipalities with similar social and environmental characteristics.

Leveraging Public Data to Predict Global Niches and Distributions of Rhizostome Jellyfishes

As climate change progresses rapidly, biodiversity declines, and ecosystems shift, it is becoming increasingly difficult to document dynamic populations, track fluctuations, and predict responses to climate change. Concurrently, publicly available databases and tools are improving scientific accessibility, increasing collaboration, and generating more data than ever before. One of the most successful projects is iNaturalist, an AI-driven social network doubling as a public database designed to allow citizen scientists to report personal biodiversity reports with accuracy. iNaturalist is especially useful for the research of rare, dangerous, and charismatic organisms, but requires better integration into the marine system. Despite their abundance and ecological relevance, there are few long-term, high-sample datasets for jellyfish, which makes management difficult. To provide some high-sample datasets and demonstrate the utility of publicly collected data, we synthesized two global datasets for ten genera of jellyfishes in the order Rhizostomeae containing 8412 curated datapoints from both iNaturalist (n = 7807) and the published literature (n = 605). We then used these reports in conjunction with publicly available environmental data to predict global niche partitioning and distributions. Initial niche models inferred that only two of ten genera have distinct niche spaces; however, the application of machine learning-based random forest models suggests genus-specific variation in the relevance of abiotic environmental variables used to predict jellyfish occurrence. Our approach to incorporating reports from the literature with iNaturalist data helped evaluate the quality of the models and, more importantly, the quality of the underlying data. We find that free, accessible online data is valuable, yet subject to biases through limited taxonomic, geographic, and environmental resolution. To improve data resolution, and in turn its informative power, we recommend increasing global participation through collaboration with experts, public figures, and hobbyists in underrepresented regions capable of implementing regionally coordinated projects.

Glacier retreat reorganizes river habitats leaving refugia for Alpine invertebrate biodiversity poorly protected

Alpine river biodiversity around the world is under threat from glacier retreat driven by rapid warming, yet our ability to predict the future distributions of specialist cold-water species is currently limited. Here we link future glacier projections, hydrological routing methods and species distribution models to quantify the changing influence of glaciers on population distributions of 15 alpine river invertebrate species across the entire European Alps, from 2020 to 2100. Glacial influence on rivers is projected to decrease steadily, with river networks expanding into higher elevations at a rate of 1% per decade. Species are projected to undergo upstream distribution shifts where glaciers persist but become functionally extinct where glaciers disappear completely. Several alpine catchments are predicted to offer climate refugia for cold-water specialists. However, present-day protected area networks provide relatively poor coverage of these future refugia, suggesting that alpine conservation strategies must change to accommodate the future effects of global warming.

Soundscape analysis using eco-acoustic indices for the birds biodiversity assessment in urban parks (case study: Isfahan City, Iran)

Biophony and anthrophony analysis as part of the urban soundscape is an efficient approach to bird biodiversity monitoring and to studying the impact of noise pollution in urban parks. Here, we analyzed the soundscape composition to monitor the diversity of birds using acoustic indices and machine learning in 21 urban parks of Isfahan, Iran, in spring 2019. To achieve this purpose four-step method was considered: (i) choosing parks and sampling of sound and bird species; (ii) calculated the six acoustic indices; (iii) calculated the six biodiversity indices; and (iv) statistical analysis for predicting biodiversity index from acoustic indices. Three regression models including support vector machine (SVM), random forest (RF), and elastic net regularization (GLMNET) applied the acoustic indices with minimum and maximum recorded thresholds to feature extraction to measure biodiversity indicators. The optimization model was applied to reduce the independent variables. Generally, more than 18,000 samples were modeled for the dependent variables in each model. The regression results demonstrated that the highest R square was related to the songbird (0.93), evenness (0.92), and richness (0.9) indecies in the SVM model and the Shannon index (0.86) in the RF model. The results of acoustics analysis demonstrated that the Acoustic Entropy Index (H), Normalized Difference Soundscape Index (NDSI), Bioacoustics Index (BI), and Acoustic Complexity Index (ACI) indices were suitable because they could serve as proxies for bird richness and activity that reflect differences in habitat quality. Our findings offer using acoustic indicators as an efficient approach for monitoring bird biodiversity in urban parks.

Streams in the Mediterranean Region are not for mussels: Predicting extinctions and range contractions under future climate change

Climate change is becoming the leading driver of biodiversity loss. The Mediterranean region, particularly southwestern Europe, is already confronting the consequences of ongoing global warming. Unprecedented biodiversity declines have been recorded, particularly within freshwater ecosystems. Freshwater mussels contribute to essential ecosystem services but are among the most threatened faunal groups on Earth. Their poor conservation status is related to the dependence on fish hosts to complete the life cycle, which also makes them particularly vulnerable to climate change. Species Distribution Models (SDMs) are commonly used to predict species distributions, but often disregard the potential effect of biotic interactions. This study investigated the potential impact of future climate on the distribution of freshwater mussel species while considering their obligatory interaction with fish hosts. Specifically, ensemble models were used to forecast the current and future distribution of six mussel species in the Iberian Peninsula, including environmental conditions and the distribution of fish hosts as predictors. We found that climate change is expected to severely impact the future distribution of Iberian mussels. Species with narrow ranges, namely Margaritifera margaritifera and Unio tumidiformis, were predicted to have their suitable habitats nearly lost and could potentially be facing regional and global extinctions, respectively. Anodonta anatina, Potomida littoralis, and particularly Unio delphinus and Unio mancus, are expected to suffer distributional losses but may gain new suitable habitats. A shift in their distribution to new suitable areas is only possible if fish hosts are able to disperse while carrying larvae. We also found that including the distribution of fish hosts in the mussels' models avoided the underprediction of habitat loss under climate change. This study warns of the imminent loss of mussel species and populations and the urgent need of management actions to reverse current trends and mitigate irreversible damage to species and ecosystems in Mediterranean regions.

Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology

BACKGROUND

Although malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts.

METHODS

A workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m.

RESULTS

The identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation.

CONCLUSIONS

This study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.

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

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