The predictive performance and stability of six species distribution models

Study on the change of global ecological distribution of Nicotiana tabacum L. based on MaxEnt model

Nicotiana tabacum L. (tobacco) has extremely high economic value, medicinal value, scientific research value and some other uses. Though it has been widely cultivated throughout the world, classification and change of its suitable habitats is not that clear, especially in the context of global warming. In order to achieve rational cultivation and sustainable development of tobacco, current (average from 1970-2000) and future (2070, average from 2061-2080) potential suitable habitats of Nicotiana tabacum L. were forecasted with MaxEnt model and ArcGIS platform based on 854 occurrence data and 22 environmental factors in this study. The results revealed that mean temperature of warmest quarter (bio10), annual precipitation (bio12), solar radiation in September (Srad9), and clay content (CLAY) were the four decisive environment variables for the distribution of Nicotiana tabacum L. Under current climate conditions, suitable habitats of Nicotiana tabacum L. were mainly distributed in south-central Europe, south-central North America, most parts of South America, central Africa, south and southeast Asia, and southeast coast of Australia, and only 13.7% of these areas were highly suitable. By the year 2070, suitable habitats under SSP1-2.6, SSP3-7.0, and SSP5-8.5 climate scenarios would all increase with the largest increase found under SSP3-7.0 scenario, while suitable habitats would reduce under SSP2-4.5 climate scenario. Globally, the center of mass of suitable habitats would migrate to southeast to varying degrees within Libya under four different climate scenarios. The emergence of new habitats and the disappearance of old habitats would all occur simultaneously under each climate scenario, and the specific changes in each area, combined with the prediction results under current climate conditions, will provide an important reference for the adjustment of agronomic practices and rational cultivation of Nicotiana tabacum L. both currently and in the future.

Projecting the impact of climate change on honey bee plant habitat distribution in Northern Ethiopia

Climate change significantly affects the diversity, growth, and survival of indigenous plant species thereby influencing the nutrition, health and productivity of honey bees (Apis mellifera). Hypoestes forskaolii (Vahl) is one of the major honey bee plant species in Ethiopia's Tigray region. It is rich in pollen and nectar that typically provides white honey, which fetches a premium price in both local and inter-national markets. Despite its socio-economic and apicultural significance, the distribution of H. forskaolii has been declining, raising concerns regarding its conservation efforts. However, there is limited knowledge on how environmental and climatic factors affect its current distribution and response to future climate change. The study investigates the current and projected (the 2030s, 2050s, 2070s, and 2090s) habitat distributions of H. forskaolii under three future climate change scenarios (ssp126, ssp245, and ssp585) using the Maximum Entropy Model (MaxEnt). The results show that land use (50.1%), agro-ecology (28%), precipitation during the Driest Quarter (11.2%) and soil texture (6.1%) predominantly influence the distribution of H. forskaolii, collectively explaining 95.4% of the model's predictive power. Habitats rich in evergreen trees and mosaic herbaceous with good vegetation cover are identified as the most suitable for H. forskaolii. The spatial distribution of H. forskaolii is concentrated in the highlands and mid-highlands of the eastern and southern parts of Tigray, characterized by a colder temperature. Across the three climate change scenarios, the size of suitable habitat for H. forskaolii is projected to decrease over the four time periods studied. Predictions under the ssp585 scenario reveal alarming results, indicating a substantial decrease in the suitable habitat for H. forskaolii from 4.26% in the 2030s to 19.09% in the 2090s. Therefore, given the challenges posed by climate change, research efforts should focus on identifying and evaluating new technologies that can help the H. forskaolii species in adapting and mitigating the effects of climate change.

Current and future distribution of Forsythia suspensa in China under climate change adopting the MaxEnt model

This study evaluated the potential impact of climate change on the distribution of Forsythia suspensa, a valuable traditional Chinese medicinal plant, using the MaxEnt model integrated with Geographic Information System (GIS). By analyzing occurrence data from various databases and environmental variables including climate and soil factors, we forecasted the present and future (2050s and 2070s) habitat suitability of F. suspensa under different greenhouse gas emission scenarios (RCP8.5, RCP4.5, RCP2.6). Results indicated that the suitable habitats for F. suspensa were primarily located in North, East, Central, Northwest, and Southwest China, with a significant potential expansion of suitable habitats anticipated by the 2070s, particularly under the high emission scenario. The study identified precipitation and temperature as the primary environmental drivers impacting the distribution of F. suspensa. Furthermore, a northward shift in the centroid of suitable habitats under future climate scenarios suggested a potential migration response to global warming. This work provides crucial insights into the future conservation and cultivation strategies for F. suspensa amidst changing climatic conditions.

Predicting the environmental suitability for Anopheles stephensi under the current conditions in Ghana

Vector-borne diseases emergence, particularly malaria, present a significant public health challenge worldwide. Anophelines are predominant malaria vectors, with varied distribution, and influenced by environment and climate. This study, in Ghana, modelled environmental suitability for Anopheles stephensi, a potential vector that may threaten advances in malaria and vector control. Understanding this vector's distribution and dynamics ensures effective malaria and vector control programmes implementation. We explored the MaxEnt ecological modelling method to forecast An. stephensi's potential hotspots and niches. We analysed environmental and climatic variables to predict spatial distribution and ecological niches of An. stephensi with a spatial resolution of approximately 5 km2. Analysing geospatial and species occurrence data, we identified optimal environmental conditions and important factors for its presence. The model's most important variables guided hotspot prediction across several ecological zones aside from urban and peri-urban regions. Considering the vector's complex bionomics, these areas provide varying and adaptable conditions for the vector to colonise and establish. This is shown by the AUC = 0.943 prediction accuracy of the model, which is considered excellent. Based on our predictions, this vector species would thrive in the Greater Accra, Ashanti Central, Upper East, Northern, and North East regions. Forecasting its environmental suitability by ecological niche modelling supports proactive surveillance and focused malaria management strategies. Public health officials can act to reduce the risk of malaria transmission by identifying areas where mosquitoes may breed, which will ultimately improve health outcomes and disease control.

Distribution and co-occurrence patterns of charophytes and angiosperms in the northern Baltic Sea

The distribution data of 11 soft substrate charophyte and angiosperm species were analyzed. Our study aimed to elucidate the co-occurrence patterns among these sympatric macrophyte species and quantify their distribution areas. The central hypothesis of this study proposed that the observed co-occurrence patterns among the studied species deviate from what would be expected by random chance. Macrophyte occurrence data was derived from an extensive field sampling database. Environmental variables available as georeferenced raster layers including topographical, hydrodynamic, geological, physical, chemical, and biological variables were used as predictor variables in the random forest models to predict the spatial distribution of the species. Permutation tests revealed statistically significant deviations from random co-occurrence patterns. The analysis demonstrated that species tended to co-occur more frequently within their taxonomic groups (i.e., within charophytes and within angiosperms) than between these groups. The most extensive distribution overlap was observed between Chara aspera Willd. and Chara canescens Loisel., while Zostera marina L. exhibited the least overlap with the other species. The mean number of co-occurring species was the highest in Chara baltica (Hartman) Bruzelius while Z. marina had the largest share of single-species occurrences. Based on the distribution models, Stuckenia pectinata (L.) Börner had the largest distribution area.

Impact of climate change on the distribution and predicted habitat suitability of two fruit bats (Rousettus aegyptiacus and Epomophorus labiatus) in Ethiopia: Implications for conservation

Fruit bats serve as crucial bioindicators, seed dispersers, pollinators, and contributors to food security within ecosystems. However, their population and distribution were threatened by climate change and anthropogenic pressures. Understanding the impacts of these pressures through mapping distribution and habitat suitability is crucial for identifying high-priority areas and implementing effective conservation and management plans. We predicted the distribution and extent of habitat suitability for Rousettus aegyptiacus and Epomophorus labiatus under climate change scenarios using average predictions from four different algorithms to produce an ensemble model. Seasonal precipitation, population index, land-use land cover, vegetation, and the mean temperature of the driest quarter majorly contributed to the predicted habitat suitability for both species. The current predicted sizes of suitable habitats for R. aegyptiacus and E. labiatus were varied, on average 60,271.4 and 85,176.1 km2, respectively. The change in species range size for R. aegyptiacus showed gains in suitable areas of 24.4% and 22.8% in 2050 and 2070, respectively. However, for E. labiatus, suitable areas decreased by 0.95% and 2% in 2050 and 2070, respectively. The range size change of suitable areas between 2050 and 2070 for R. aegyptiacus and E. labiatus shows losses of 1.5% and 1.2%, respectively. The predicted maps indicate that the midlands and highlands of southern and eastern Ethiopia harbor highly suitable areas for both species. In contrast, the areas in the northern and central highlands are fragmented. The current model findings show that climate change and anthropogenic pressures have notable impacts on the geographic ranges of two species. Moreover, the predicted suitable habitats for both species are found both within and outside of their historical ranges, which has important implications for conservation efforts. Our ensemble predictions are vital for identifying high-priority areas for fruit bat species conservation efforts and management to mitigate climate change and anthropogenic pressures.

Can Bengal Tiger (Panthera tigris tigris) endure the future climate and land use change scenario in the East Himalayan Region? Perspective from a multiple model framework

Large mammals are susceptible to land use and climate change, unless they are safeguarded within large, protected areas. It is crucial to comprehend the effects of these changes on mammals to develop a conservation plan. We identified ecological hotspots that can sustain an ecosystem for the endangered Bengal tiger (Panthera tigris tigris), an umbrella species. We developed three distinct ensemble species distribution models (SDMs) for the Bengal tiger in the Indian East Himalayan Region (IEHR). The first model served as the baseline and considered habitat type, climate, land cover, and anthropogenic threats. The second model focused on climate, land use, and anthropogenic threats, the third model focused on climate variables. We projected the second and third models onto two future climate scenarios: RCP 4.5 and RCP 8.5. We evaluated the threats possess to protected areas within eco-sensitive zone based on the potential tiger habitat. Finally, we compared the potential habitat with the IUCN tiger range. Our study revealed that the Brahmaputra valley will serve as the primary habitat for tigers in the future. However, considering the projected severe climate scenarios, it is anticipated that tigers will undergo a range shift towards the north and east, especially in high-altitude regions. Very high conservation priority areas, which make up 3.4% of the total area, are predominantly located in the riverine corridor of Assam. High conservation priority areas, which make up 5.5% of total area are located in Assam and Arunachal Pradesh. It is important to note that conservation priority areas outside of protected areas pose a greater threat to tigers. We recommend reassessing the IUCN Red List's assigned range map for tigers in the IEHR, as it is over-predicted. Our study has led us to conclude both land use and climate change possess threats to the future habitat of tigers. The outcomes of our study will provide crucial information on identifying habitat hotspots and facilitate appropriate conservation planning efforts.

Predicting current and future high-risk areas for vectors and reservoirs of cutaneous leishmaniasis in Iran

Climate change will affect the distribution of species in the future. To determine the vulnerable areas relating to CL in Iran, we applied two models, MaxEnt and RF, for the projection of the future distribution of the main vectors and reservoirs of CL. The results of the models were compared in terms of performance, species distribution maps, and the gain, loss, and stable areas. The models provided a reasonable estimate of species distribution. The results showed that the Northern and Southern counties of Iran, which currently do not have a high incidence of CL may witness new foci in the future. The Western, and Southwestern regions of the Country, which currently have high habitat suitability for the presence of some vectors and reservoirs, will probably significantly decrease in the future. Furthermore, the most stable areas are for T. indica and M. hurrianae in the future. So that, this species may remain a major reservoir in areas that are present under current conditions. With more local studies in the field of identifying vulnerable areas to CL, it can be suggested that the national CL control guidelines should be revised to include a section as a climate change adaptation plan.

Cooling-induced expansions of Afromontane forests in the Horn of Africa since the Last Glacial Maximum

Understanding the changing plant ecosystems that existed in East Africa over the past millennia is crucial for identifying links between habitats and past human adaptation and dispersal across the region. In the Horn of Africa, this task is hampered by the scarcity of fossil botanical data. Here we present modelled past vegetation distributions in Ethiopia from the Last Glacial Maximum (LGM) to present at high spatial and temporal resolution. The simulations show that, contrary to long-standing hypotheses, the area covered by Afromontane forests during the Late Glacial was significantly larger than at present. The combined effect of low temperatures and the relative rainfall contribution sourced from the Congo Basin and Indian Ocean, emerges as the mechanism that controlled the migration of Afromontane forests to lower elevations. This process may have enabled the development of continuous forest corridors connecting populations that are currently isolated in mountainous areas over the African continent. Starting with the Holocene, the expansion of forests began to reverse. This decline intensified over the second half of the Holocene leading to a retreat of the forests to higher elevations where they are restricted today. The simulations are consistent with proxy data derived from regional pollen records and provide a key environmental and conceptual framework for human environmental adaptation research.

Present and future distribution of Faidherbia albida in Cabo Verde as revealed by climatic modelling and LULC analysis

Climate change poses one of the most significant challenges to conserve biodiversity, especially in tropical dry islands, as is the case of Cabo Verde (northeast Atlantic Ocean). This archipelago has a low percentage of forest cover and hosts only seven native tree species, among them, Faidherbia albida (Delile) A.Chev. (Fabaceae). Therefore, protective afforestation is extremely important in Cabo Verde, one of the most vulnerable West African countries to climate change. With this work, we aimed to estimate the current distribution and potential shifts in suitable areas for F. albida under climate change, using species distribution models (i.e., random forest, generalized linear and additive models), covering its distribution range in Cabo Verde and mainland Africa. The best model was then projected for the studied area, at two different slice times, using Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Based on current bioclimatic variables, we estimated that almost two thirds of Cabo Verde’s territory is highly suitable for F. albida, which contrasts with its current occurrence. By overlaying the present habitat suitability with land use and land cover data, we concluded that habitat availability and suitability could be constrained by that factor. On average, the predicted suitable habitat for future distributions gradually decreases by 2080 under both scenarios compared with the current, with a smaller effect of RCP4.5 than of RCP8.5. Local authorities can benefit from this research and develop actions to promote sustainable reforestation in Cabo Verde, which should include native tree species that are best adapted to the local climate and could thus contribute to mitigate the effects of climate change.

Multiscale ecological niche modeling exhibits varying climate change impacts on habitat suitability of Madrean Pine-Oak trees

Anthropogenic climate change and increasing greenhouse gas emissions are expected to globally impact the biological function, community structure, and spatial distribution of biodiversity. Many existing studies explore the effect of climate change on biodiversity, generally at a single spatial scale. This study explores the potential effects of climate change on the habitat suitability of seven tree species at two distinct spatial scales: the Coronado National Forest (CNF), a local management area, and the Sierra Madre Occidental (SMO), an ecoregional extent. Habitat suitability was determined by extrapolating Ecological Niche Models (ENMs) based on citizen-science tree occurrence records into future climatic conditions using projected 30-year normals for two anthropogenic emissions scenarios through the end of the century. These ENMs, examined at a spatial resolution of 1 km2, are constructed using a mean average ensemble of three commonly used machine learning algorithms. The results show that habitat suitability is expected to decrease for all seven tree species at varying degrees. Results also show that climate-forcing scenario choice appears to be far less important for understanding changes in species habitat suitability than the spatial scale of modeling extent. Additionally, we observed non-linear changes in tree species habitat suitability within the SMO and CNF dependent on forest community type, latitude, and elevational gradient. The paper concludes with a discussion of the necessary steps to verify the estimated alters of these tree species under climate change. Most importantly, provides a framework for characterizing habitat suitability across spatial scales.

Factors Influencing the Potential Distribution of Globally Endangered Egyptian Vulture Nesting Habitat in Nepal

Habitat suitability is crucial to ensure the long-term persistence of species and can be identified based on relationships between species occurrences and underlying abiotic and biotic factors. We identified potential nesting habitat for the Egyptian vulture (Neophron percnopterus) in Nepal using ecological niche modeling with climatic variables. We estimated the currently suitable nesting habitat for Egyptian vulture in Nepal at 38,204 km². We found a high probability of suitable nesting habitat on east-facing aspects, and the probability of a suitable nesting habitat was greater in more mountainous areas, particularly in central and western regions of Nepal. Precipitation was a major factor for predicting probability of the presence of nest sites for Egyptian vultures. After identifying potentially suitable habitat, we identified environmental factors affecting landscape-level suitable nesting habitat for Egyptian vultures using generalized linear models. For Egyptian vultures, sites near forests and human settlements were most suitable for nesting, roosting, and foraging, especially in central and western Nepal. Based on potentially suitable nesting habitat and previous work on Egyptian vulture foraging and roosting habitat, we recommend protecting forests near water sources and open areas for their long-term conservation.

A multispecies corridor in a fragmented landscape: Evaluating effectiveness and identifying high-priority target areas

While Misiones, Argentina contains one of the largest remnants of Upper Paraná Atlantic Forest ecoregion, one of the world's biodiversity hotspots, only ~50% of this native forest is protected. Each protected area is at risk of becoming an island of native forest surrounded by a matrix of altered habitats due to ongoing land conversion. In an effort to maximize long-term connectivity between existing protected areas, DeMatteo [1] used a multifaceted cost analysis to determine the optimal location for the region's first multispecies corridor using noninvasive data on jaguars (Panthera onca), pumas (Puma concolor), ocelots (Leopardus pardalis), southern tiger cats (Leopardus guttulus), and bush dogs (Speothos venaticus). This work builds on this framework by integrating new field data that broadens the scope of species-specific data across the region's heterogeneous landscape, which varies in vegetation, disturbance, human proximity, and protective status. In addition, two different land use layers are compared across the distributions of the five carnivores, the overlap in their independent distributions, and their relationship to the multispecies corridor. Interpretation of these land use data to species-specific habitat suitability goes beyond DeMatteo [1], with a subdivision of suitability into marginal and optimal areas. This refined scale allows a reanalysis of key areas in the multispecies corridor, where connectivity was previously defined as at highly-at-risk, allowing for a more directed development of management strategies. These analyses and their interpretation extend beyond northern-central Misiones, as the threats are not unique to this region. The need to develop management strategies that balance human-wildlife needs will continue to grow as humans expand their footprint. The techniques applied in this analysis provide a way to identify key areas that require specific management strategies, either through restoration, protection, or a combination of both.

Effects of climate-change scenarios on the distribution patterns of Castanea henryi

Castanea henryi, with edible nuts and timber value, is a key tree species playing essential roles in China's subtropical forest ecosystems. However, natural and human perturbations have nearly depleted its wild populations. The study identified the dominant environmental variables enabling and limiting its distribution and predicted its suitable habitats and distribution. The 212 occurrence records covering the whole distribution range of C. henryi in China and nine main bioclimatic variables were selected for detailed analysis. We applied the maximum entropy model (MaxEnt) and QGIS to predict potentially suitable habitats under the current and four future climate-change scenarios. The limiting factors for distribution were accessed by Jackknife, percent contribution, and permutation importance. We found that the current distribution areas were concentrated in the typical subtropical zone, mainly Central and South China provinces. The modeling results indicated temperature as the critical determinant of distribution patterns, including mean temperature of the coldest quarter, isothermality, and mean diurnal range. Winter low temperature imposed an effective constraint on its spread. Moisture served as a secondary factor in species distribution, involving precipitation seasonality and annual precipitation. Under future climate-change scenarios, excellent habitats would expand and shift northwards, whereas range contraction would occur on the southern edge. Extreme climate change could bring notable range shrinkage. This study provided a basis for protecting the species' germplasm resources. The findings could guide the management, cultivation, and conservation of C. henryi, assisted by a proposed three-domain operation framework: preservation areas, loss areas, and new areas, each to be implemented using tailor-made strategies.

Range shift and loss of genetic diversity under climate change in the red-backed fairywren (Malurus melanocephalus), an Australian endemic bird species

Climatic variability is the most important force influencing the distribution dynamics of common and widespread species, with significant effects on their current biogeographical patterns. In this study, phylogeography was integrated with ecological niche modelling to understand the range dynamics of an Australian bird species, the red-backed fairywren (Malurus melanocephalus), under various climate change scenarios. Specifically, an ecological niche modelling approach with Bayesian-based phylogeographical analysis was used to develop robust inferences regarding the demographic history of the species. The predictions of the model were mostly consistent with the present distribution of the species. However, under the Last Interglacial bioclimatic conditions, the model predicted a significantly narrower distribution than today, indicating the existence of allopatric refugia. Predictions for the Last Glacial Maximum indicated that the species had a wider distribution, extending northwards. Additionally, predictions for the future (2050 and 2070) indicated that the species will probably have a narrower distribution than at present, which will be shifted eastwards. The extended Bayesian skyline plot analysis, which provides a robust analysis of fluctuations in the effective population size throughout the evolutionary history of a species, produced results highly consistent with the ecological niche modelling predictions for the red-backed fairywren. This is the first study to investigate the Late Quaternary history of an endemic avian taxon from Australia using ecological niche modelling and Bayesian-based demographic analysis.

The current and future distribution of the yellow fever mosquito (Aedes aegypti) on Madeira Island

The Aedes aegypti mosquito is the main vector for several diseases of global importance, such as dengue and yellow fever. This species was first identified on Madeira Island in 2005, and between 2012 and 2013 was responsible for an outbreak of dengue that affected several thousand people. However, the potential distribution of the species on the island remains poorly investigated. Here we assess the suitability of current and future climatic conditions to the species on the island and complement this assessment with estimates of the suitability of land use and human settlement conditions. We used four modelling algorithms (boosted regression trees, generalized additive models, generalized linear models and random forest) and data on the distribution of the species worldwide and across the island. For both climatic and non-climatic factors, suitability estimates predicted the current distribution of the species with good accuracy (mean area under the Receiver Operating Characteristic curve = 0.88 ±0.06, mean true skill statistic = 0.72 ±0.1). Minimum temperature of coldest month was the most influential climatic predictor, while human population density, residential housing density and public spaces were the most influential predictors describing land use and human settlement conditions. Suitable areas under current climates are predicted to occur mainly in the warmer and densely inhabited coastal areas of the southern part of the island, where the species is already established. By mid-century (2041–2060), the extent of climatically suitable areas is expected to increase, mainly towards higher altitudes and in the eastern part of the island. Our work shows that ongoing efforts to monitor and prevent the spread of Ae. aegypti on Madeira Island will have to increasingly consider the effects of climate change.

The Aedes aegypti mosquito is an invasive species on Madeira Island and recently responsible for a dengue outbreak that affected more than 2000 people. To help control the activity of this mosquito, the local health authorities have an entomological surveillance program in place throughout the island. However, the full extent of the areas that can be colonized by this species remains unknown. We estimate the current and future potential distribution of Ae. aegypti on Madeira Island accounting for climatic, land use and human settlement conditions. Our results suggest that suitable conditions are predominantly distributed along the southern coast of the island. However, as climate change progresses, climatically suitable areas are expected to increase, particularly at mid-altitudes and in eastern part of the island. Minimum temperature of the coldest month was the most influential predictor variable in climatic suitability models, while human population density, housing density and public spaces were the most influential in models of land use and human settlement suitability. Our work provides valuable insight on the potential distribution of Ae. aegypti on Madeira Island, which can be used to inform ongoing and future monitoring and prevention initiatives.

Modeling climate change impacts on the distribution of an endangered brown bear population in its critical habitat in Iran

Climate change is one of the major challenges to the current conservation of biodiversity. Here, by using the brown bear, Ursus arctos, in the southernmost limit of its global distribution as a model species, we assessed the impact of climate change on the species distribution in western Iran. The mountainous forests of Iran are inhabited by small and isolated populations of brown bears that are prone to extinction in the near future. We modeled the potential impact of climate change on brown bear distribution and habitat connectivity by the years 2050 and 2070 under four representative concentration pathways (RCPs) of two general circulation models (GCMs): BCC-CSM1-1 and MRI-CGCM3. Our projections revealed that the current species' range, which encompasses 6749.8 km² (40.8%) of the landscape, will decline by 10% (2050: RCP2.6, MRI-CGCM3) to 45% (2070: RCP8.5, BCC-CSM1-1). About 1850 km² (27.4%) of the current range is covered by a network of conservation (CAs) and no-hunting (NHAs) areas which are predicted to decline by 0.64% (2050: RCP2.6, MRI-CGCM3) to 15.56% (2070: RCP8.5, BCC-CSM1-1) due to climate change. The loss of suitable habitats falling within the network of CAs and NHAs is a conservation challenge for brown bears because it may lead to bears moving outside the CAs and NHAs and result in subsequent increases in the levels of bear-human conflict. Thus, re-evaluation of the network of CAs and NHAs, establishing more protected areas in suitable landscapes, and conserving vital linkages between habitat patches under future climate change scenarios are crucial strategies to conserve and manage endangered populations of the brown bear.

Predicting the potential distribution of a near-extinct avian predator on the Arabian Peninsula: implications for its conservation management

Species distribution models (SDMs) generate predicted distribution maps which can be used as effective tools for conservation purposes. The persistence of isolated populations at the margin of a large distributional area depends on local threats which may differ from those faced by the main population. Environmental predictors can indicate suitable areas for these species and, indirectly, evaluate the impact on peripheral populations due to fragmentation and isolation. The Lanner Falcon (Falco biarmicus) is an Afro-tropical and Mediterranean polytypic species considered critically endangered (CR) in Arabian Peninsula by IUCN, but a lack of published information about its distribution persists. Here, we model the distribution of the Lanner Falcon in the Arabian Peninsula using nest-site data and map its core area and their habitat suitability using a robust algorithm with good prediction accuracy even at low sample sizes (MaxEnt). The predictive map suggests a potential distribution of the Lanner Falcons that runs from north to south along the eastern coast of the Red Sea. The Terrain Roughness Index contributed the most to the breeding range model predictions (57.6%), followed by isothermality (Bio3, 15.3%). The model suggests a tendency by Lanner Falcons to occupy areas with a low terrain complexity according to their behavioural patterns and breeding strategies. In addition, this falcon is highly sensitive to climate occupying high isothermal regions in order to avoid extreme heating events. Overall, predictive models indicate a narrow range of suitable environmental conditions for breeding as well restricted favourable areas during dispersal and migration. Thus, these small and fragmented populations are more likely prone to anthropogenic factors and must be buffered against them.

Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health

Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that several closely related viruses have been discovered and sarbecovirus-host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modelled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2°C hotter in a fossil-fuelled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.

Seasonality and Ecological Suitability Modelling for Anthrax (Bacillus anthracis) in Western Africa

Simple Summary

Anthrax is a globally distributed, neglected, underreported, soil-borne zoonotic disease. In West Africa, the disease is hyper-endemic, severely affecting the livestock sector. Many challenges exist to control the disease in this region, particularly constraints on financial and human resources. Therefore, methods that can be utilized to improve reporting, guide and prioritize surveillance and control activities and rationalize the allocation of limited resources are crucial. In this study, we showed how to optimize the use of fragmented, heterogeneous and limited precise reporting data of anthrax in Burkina Faso, Ghana, Togo, Benin and Niger to understand risk periods as well as identify and predict risk areas. To achieve this, we used anthrax data from different databases in combination with environmental and climate variables and geospatial remote sensing techniques. Our study demonstrated that the number of anthrax outbreaks by month increase with the increasing monthly rates of change in precipitation and normalized difference vegetation index (NDVI) during the transition period from the dry to the wet season. Livestock density, precipitation, NDVI and alkaline soils were the main predictors of anthrax suitability in the region. Our findings on anthrax seasonality and ecological suitability can inform surveillance, prevention and control programs undertaken by animal and public health authorities and enhance collaborative One Health strategies.

Abstract

Anthrax is hyper-endemic in West Africa affecting wildlife, livestock and humans. Prediction is difficult due to the lack of accurate outbreak data. However, predicting the risk of infection is important for public health, wildlife conservation and livestock economies. In this study, the seasonality of anthrax outbreaks in West Africa was investigated using climate time series and ecological niche modeling to identify environmental factors related to anthrax occurrence, develop geospatial risk maps and identify seasonal patterns. Outbreak data in livestock, wildlife and humans between 2010 and 2018 were compiled from different sources and analyzed against monthly rates of change in precipitation, normalized difference vegetation index (NDVI) and land surface temperature. Maximum Entropy was used to predict and map the environmental suitability of anthrax occurrence. The findings showed that: (i) Anthrax outbreaks significantly (99%) increased with incremental changes in monthly precipitation and vegetation growth and decremental changes in monthly temperature during January–June. This explains the occurrence of the anthrax peak during the early wet season in West Africa. (ii) Livestock density, precipitation seasonality, NDVI and alkaline soils were the main predictors of anthrax suitability. (iii) Our approach optimized the use of limited and heterogeneous datasets and ecological niche modeling, demonstrating the value of integrated disease notification data and outbreak reports to generate risk maps. Our findings can inform public, animal and environmental health and enhance national and regional One Health disease control strategies.

Predicting the suitable habitats of parasitic desert species based on a niche model with Haloxylon ammodendron and Cistanche deserticola as examples

Haloxylon ammodendron, an excellent tree species for sand fixation and afforestation in the desert areas of western China, is threatened by climate change and anthropogenic activities. The suitable habitat of this species is shrinking at a remarkable rate, although conservation measures have been implemented. Cistanche deserticola is an entirely parasitic herb that occurs in deserts, is a source of "desert ginseng" worldwide, and has extremely high medicinal value. Little is known about using niche models to simulate habitat suitability and evaluate important environmental variables related to parasitic species. In this study, we modeled the current suitable habitat of H. ammodendron and C. deserticola by MaxEnt based on occurrence record data of the distributions of these two species in China. We grouped H. ammodendron and C. deserticola into three groups according to the characteristics of parasitic species and modeled them with environmental factors. The results showed that bioclimate was the most important environmental parameter affecting the H. ammodendron and C. deserticola distribution. Precipitations, such as annual precipitation, precipitation seasonality, and precipitation in the driest quarter, were identified as the most critical parameters. The slope, diurnal temperature range, water vapor pressure, ground-frost frequency, and solar radiation also substantially contributed to the distribution of the two species. The proportions of the most suitable areas for Groups 1, 2, and 3 were 1.2%, 1.3%, and 1.7%, respectively, in China. When combined with cultural geography, five hot spot conservation areas were determined within the distribution of H. ammodendron and C. deserticola. The comprehensive analysis indicated that by using MaxEnt to model the suitable habitat of parasitic species, we further improved the accuracy of the prediction and coupled the error of the distribution of a single species. This study provides a useful reference for the protection of H. ammodendron forests and the management of C. deserticola plantations.

Revising the marine range of the endangered black-capped petrel Pterodroma hasitata: occurrence in the northern Gulf of Mexico and exposure to conservation threats

The black-capped petrel Pterodroma hasitata is an Endangered seabird endemic to the western North Atlantic. Although estimated at ~1000 breeding pairs, only ~100 nests have been located at 2 sites in Haiti and 3 sites in the Dominican Republic. At sea, the species primarily occupies waters of the western Gulf Stream in the Atlantic and the Caribbean Sea. Due to limited data, there is currently no consensus on the geographic marine range of the species although no current proposed ranges include the Gulf of Mexico. Here, we report on observations of black-capped petrels during 2 vessel-based survey efforts throughout the northern Gulf of Mexico from 2010-2011 and 2017-2019. During 558 d and ~54700 km of surveys, we tallied 40 black-capped petrels. Most observations occurred in the eastern Gulf, although birds were observed over much of the east-west and north-south footprint of the survey area. Predictive models indicated that habitat suitability for black-capped petrels was highest in areas associated with dynamic waters of the Loop Current. We used the extent of occurrence and area of occupancy concepts to delimit the geographic range of the species within the northern Gulf. We suggest that the marine range for black-capped petrels be modified to include the northern Gulf of Mexico, recognizing that distribution may be more clumped in the eastern Gulf and that occurrence in the southern Gulf remains unknown due to a lack of surveys there. To date, however, it remains unclear which nesting areas are linked to the Gulf of Mexico.

Ecological niche modeling of genetic lineages of the great gerbil, Rhombomys opimus (Rodentia: Gerbillinae)

Great gerbil (Rhombomys opimus Lichtenstein, 1823) is distributed in Central Asia and some parts of the Middle East. It is widely found in central and northeast parts of Iran with two distinct genetic lineages: R. o. sodalis in the northern slopes of the Elburz Mountains and R. o. sargadensis in the southern slopes. This large rodent acts as the main host of natural focal diseases. No study has surveyed the ecological niche of the lineages and how their distribution might be influenced by different climatic variables. To examine the distribution patterns of this murid rodent, we aimed to determine the habitat preferences and effects of environmental variables on the ecological niche. Using a species distribution approach for modeling of regional niche specialization, suitable habitats predicted for R. o. sodalis were mainly located in Golestan province in northern Iran, along the northern slope of Elburz, while R. o. sargadensis, showed great potential distribution along the southern slope of Elburz and around the Kavir Desert and the Lut Desert. Despite the widest potential distribution of R. o. sargadensis from northeast to northwest and through Central Iran, the geographic range of R. o. sodalis was smaller and mostly confined to Golestan province. The results support the presence of the two genetic lineages of Rhombomys in Iran and confirm that there is no significant niche overlap between the two subspecies. Furthermore, it provided several perspectives for future taxonomic studies and prevention hygiene programs for public health.

Species distribution models for invasive Eurasian watermilfoil highlight the importance of data quality and limitations of discrimination accuracy metrics

AIM

Availability of uniformly collected presence, absence, and abundance data remains a key challenge in species distribution modeling (SDM). For invasive species, abundance and impacts are highly variable across landscapes, and quality occurrence and abundance data are critical for predicting locations at high risk for invasion and impacts, respectively. We leverage a large aquatic vegetation dataset comprising point-level survey data that includes information on the invasive plant Myriophyllum spicatum (Eurasian watermilfoil) to: (a) develop SDMs to predict invasion and impact from environmental variables based on presence-absence, presence-only, and abundance data, and (b) compare evaluation metrics based on functional and discrimination accuracy for presence-absence and presence-only SDMs.

LOCATION

Minnesota, USA.

METHODS

Eurasian watermilfoil presence-absence and abundance information were gathered from 468 surveyed lakes, and 801 unsurveyed lakes were leveraged as pseudoabsences for presence-only models. A Random Forest algorithm was used to model the distribution and abundance of Eurasian watermilfoil as a function of lake-specific predictors, both with and without a spatial autocovariate. Occurrence-based SDMs were evaluated using conventional discrimination accuracy metrics and functional accuracy metrics assessing correlation between predicted suitability and observed abundance.

RESULTS

Water temperature degree days and maximum lake depth were two leading predictors influencing both invasion risk and abundance, but they were relatively less important for predicting abundance than other water quality measures. Road density was a strong predictor of Eurasian watermilfoil invasion risk but not abundance. Model evaluations highlighted significant differences: Presence-absence models had high functional accuracy despite low discrimination accuracy, whereas presence-only models showed the opposite pattern.

MAIN CONCLUSION

Complementing presence-absence data with abundance information offers a richer understanding of invasive Eurasian watermilfoil's ecological niche and enables evaluation of the model's functional accuracy. Conventional discrimination accuracy measures were misleading when models were developed using pseudoabsences. We thus caution against the overuse of presence-only models and suggest directing more effort toward systematic monitoring programs that yield high-quality data.

The need for environmental surveillance to understand the ecology, epidemiology and impact of Cryptococcus infection in Africa

Our understanding of the pathogenic yeasts Cryptococcus neoformans and Cryptococcus gattii has been greatly enhanced by use of genome sequencing technologies. Found ubiquitously as saprotrophs in the environment, inhalation of infectious spores from these pathogens can lead to the disease cryptococcosis. Individuals with compromised immune systems are at particular risk, most notably those living with HIV/AIDS. Genome sequencing in combination with laboratory and clinical studies has revealed diverse lineages with important differences in their observed frequency, virulence and clinical outcomes. However, to date, genomic analyses have focused primarily on clinical isolates that represent only a subset of the diversity in the environment. Enhanced genomic surveillance of these yeasts in their native environments is needed in order to understand their ecology, biology and evolution and how these influence the epidemiology and pathophysiology of clinical disease. This is particularly relevant on the African continent from where global cryptococcal diversity may have originated, yet where environmental sampling and sequencing has been sparse despite harbouring the largest population at risk from cryptococcosis. Here, we review what scientifically and clinically relevant insights have been provided by analysis of environmental Cryptococcus isolates to date and argue that with further sampling, particularly in Africa, many more important discoveries await.

A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

Implications of the 2019-2020 megafires for the biogeography and conservation of Australian vegetation

Australia’s 2019–2020 ‘Black Summer’ bushfires burnt more than 8 million hectares of vegetation across the south-east of the continent, an event unprecedented in the last 200 years. Here we report the impacts of these fires on vascular plant species and communities. Using a map of the fires generated from remotely sensed hotspot data we show that, across 11 Australian bioregions, 17 major native vegetation groups were severely burnt, and up to 67–83% of globally significant rainforests and eucalypt forests and woodlands. Based on geocoded species occurrence data we estimate that >50% of known populations or ranges of 816 native vascular plant species were burnt during the fires, including more than 100 species with geographic ranges more than 500 km across. Habitat and fire response data show that most affected species are resilient to fire. However, the massive biogeographic, demographic and taxonomic breadth of impacts of the 2019–2020 fires may leave some ecosystems, particularly relictual Gondwanan rainforests, susceptible to regeneration failure and landscape-scale decline.

Fires triggered by climate change threaten plant diversity in many biomes. Here the authors investigate how the catastrophic fires of 2019–2020 affected the vascular flora of SE Australia. They report that 816 species were highly impacted, including taxa of biogeographic and conservation interest.

Bioclimatic Modelling Identifies Suitable Habitat for the Establishment of the Invasive European Paper Wasp (Hymenoptera: Vespidae) across the Southern Hemisphere

Species distribution models (SDMs) are tools used by ecologists to help predict the spread of invasive species. Information provided by these models can help direct conservation and biosecurity efforts by highlighting areas likely to contain species of interest. In this study, two models were created to investigate the potential range expansion of Polistes dominula Christ (Hymenoptera: Vespidae) in the southern hemisphere. This palearctic species has spread to invade North and South America, South Africa, Australia, and more recently New Zealand. Using the BIOCLIM and MAXENT modelling methods, regions that were suitable for P. dominula were identified based on climate data across four regions in the southern hemisphere. In South America areas of central Chile, eastern Argentina, parts of Uruguay, and southern Brazil were identified as climatically suitable for the establishment of P. dominula. Similarly, southern parts of South Africa and Australia were identified by the model to be suitable as well as much of the North Island and east of the South Island of New Zealand. Based on outputs from both models, significant range expansion by P. dominula is possible across its more southern invaded ranges.

Zeeshan Ali, Dasgpta N, Uniyal VP, Johnson JA, Hussain SA. Evaluating performance of four species distribution models using Blue-tailed Green Darner Anax guttatus (Insecta: Odonata) as model organism from the Gangetic riparian zone

In this paper we evaluated the performance of four species distribution models: generalized linear (GLM), maximum entropy (MAXENT), random forest (RF) and support vector machines (SVM) model, using the distribution of the dragonfly Blue-tailed Green Darner Anax guttatus in the Gangetic riparian zone between Bijnor and Kanpur barrage, Uttar Pradesh, India.  We used forest cover type, land use, land cover and five bioclimatic variable layers: annual mean temperature, isothermality, temperature seasonality, mean temperature of driest quarter, and precipitation seasonality to build the models.  We found that the GLM generated the highest values for AUC, Kappa statistic, TSS, specificity and sensitivity, and the lowest values for omission error and commission error, while the MAXENT model generated the lowest variance in variable importance. We suggest that researchers should not rely on any single algorithm, instead, they should test performance of all available models for their species and area of interest, and choose the best one to build a species distribution model.  

Multiscale consensus habitat modeling for landscape level conservation prioritization

Globally, wide-ranging carnivore populations are imperiled due to human-caused habitat fragmentation. Where populations are fragmented, habitat quantification is often the first step in conservation. Presence-only species distribution models can provide robust results when proper scales and data are considered. We aimed to identify habitat for a fragmented carnivore population at two scales and aid conservation prioritization by identifying potential future habitat fragmentation. We used location data and environmental variables to develop a consensus model using Maxent and Mahalanobis distance to identify black bear (Ursus americanus floridanus) habitat across Florida, USA. We compared areas of habitat to areas of predicted sea level rise, development, and protected areas. Local-scale models performed better than state-scale models. We identified 23,798 km2 of habitat at the local-scale and 45,703 km2 at the state-scale. Approximately 10% of state- and 14% of local-scale habitat may be inundated by 2100, 16% of state- and 7% of local-scale habitat may be developed, and 54% of state- and 15% of local-scale habitat is unprotected. Results suggest habitat is at risk of fragmentation. Lack of focused conservation and connectivity among bear subpopulations could further fragmentation, and ultimately threaten population stability as seen in other fragmented carnivore populations globally.

Machine Learning for Conservation Planning in a Changing Climate

Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others.

Species distribution models for the eastern blacklegged tick, Ixodes scapularis, and the Lyme disease pathogen, Borrelia burgdorferi, in Ontario, Canada

The blacklegged tick, Ixodes scapularis, is established in several regions of Ontario, Canada, and continues to spread into new geographic areas across the province at a rapid rate. This poses a significant public health risk since I. scapularis transmits the Lyme disease-causing bacterium, Borrelia burgdorferi, and other pathogens of potential public health concern. The objective of this study was to develop species distribution models for I. scapularis and B. burgdorferi to predict and compare the potential distributions of the tick vector and the Lyme disease pathogen as well as the ecological factors most important for species establishment. Ticks were collected via tick dragging at 120 sites across southern, central, and eastern Ontario between 2015 and 2018 and tested for tick-borne pathogens. A maximum entropy (Maxent) approach was used to model the potential distributions of I. scapularis and B. burgdorferi. Two independent datasets derived from tick dragging at 25 new sites in 2019 and ticks submitted by the public to local health units between 2015 and 2017 were used to validate the predictive accuracy of the models. The model for I. scapularis showed high suitability for blacklegged ticks in eastern Ontario and some regions along the shorelines of the Great Lakes, and moderate suitability near Algonquin Provincial Park and the Georgian Bay with good predictive accuracy (tick dragging 2019: AUC = 0.898; ticks from public: AUC = 0.727). The model for B. burgdorferi showed a similar predicted distribution but was more constrained to eastern Ontario, particularly between Ottawa and Kingston, and along Lake Ontario, with similarly good predictive accuracy (tick dragging 2019: AUC = 0.958; ticks from public: AUC = 0.863. The ecological variables most important for predicting the distributions of I. scapularis and B. burgdorferi included elevation, distance to deciduous and coniferous forest, proportions of agricultural land, water, and infrastructure, mean summer/spring temperature, and cumulative annual degree days above 0°C. Our study presents a novel application of species distribution modelling for I. scapularis and B. burgdorferi in Ontario, Canada, and provides an up to date projection of their potential distributions for public health knowledge users.

Variation in behavioral traits of two frugivorous mammals may lead to differential responses to human disturbance

Human activities can lead to a shift in wildlife species' spatial distribution. Understanding the specific effects of human activities on ranging behavior can improve conservation management of wildlife populations in human-dominated landscapes. This study evaluated the effects of forest use by humans on the spatial distribution of mammal species with different behavioral adaptations, using sympatric western lowland gorilla and central chimpanzee as focal species. We collected data on great ape nest locations, ecological and physical variables (habitat distribution, permanent rivers, and topographic data), and anthropogenic variables (distance to trails, villages, and a permanent research site). Here, we show that anthropogenic variables are important predictors of the distribution of wild animals. In the resource model, the distribution of gorilla nests was predicted by nesting habitat distribution, while chimpanzee nests were predicted first by elevation followed by nesting habitat distribution. In the anthropogenic model, the major predictors of gorilla nesting changed to human features, while the major predictors of chimpanzee nesting remained elevation and the availability of their preferred nesting habitats. Animal behavioral traits (body size, terrestrial/arboreal, level of specialization/generalization, and competitive inferiority/superiority) may influence the response of mammals to human activities. Our results suggest that chimpanzees may survive in human-encroached areas whenever the availability of their nesting habitat and preferred fruits can support their population, while a certain level of human activities may threaten gorillas. Consequently, the survival of gorillas in human-dominated landscapes is more at risk than that of chimpanzees. Replicating our research in other sites should permit a systematic evaluation of the influence of human activity on the distribution of mammal populations. As wild animals are increasingly exposed to human disturbance, understanding the resulting consequences of shifting species distributions due to human disturbance on animal population abundance and their long-term survival will be of growing conservation importance.

Spatial pattern and determinants of global invasion risk of an invasive species, sharpbelly Hemiculter leucisculus (Basilesky, 1855)

Invasive species have imposed huge negative impacts on worldwide aquatic ecosystems and are generally difficult or impossible to be eradicated once established. Consequently, it becomes particularly important to ascertain their invasion risk and its determinants since such information can help us formulate more effective preventive or management actions and direct these measures to those areas where they are truly needed so as to ease regulatory burdens. Here, we examined the global invasion risk and its determinants of sharpbelly (Hemiculter leucisculus), one freshwater fish which has a high invasive potential, by using species distribution models (SDMs) and a layer overlay method. Specifically, first an ensemble species distribution model and its basal models (developed from seven machine learning algorithms) were explored to forecast the global habitat-suitability and variables importance for this species, and then a global invasion risk map was created by combining habitat-suitability with a proxy for introduction likelihood (entailing propagule pressure and dispersal constraints) of exotic sharpbelly. The results revealed that (1) the ensemble model had the highest predictive power in forecasting sharpbelly's global habitat-suitability; (2) areas with high invasion risk by sharpbelly patchily spread over the world except Antarctica; and (3) the Human Influence Index (HII), rather than any of the bioclimatic variables, was the most important factor influencing sharpbelly' future invasion. Based on these results, the present study also attempted to propose a series of prevention and management strategies to eliminate or alleviate the adverse effects caused by this species' further expansion.

The elephant-livestock interface modulates anthrax suitability in India

Anthrax is a potentially life-threatening bacterial disease that can spread between wild and livestock animals and humans. Transmission typically occurs indirectly via environmental exposure, with devastating consequences for human and animal health, as well as pastoralist economies. India has a high annual occurrence of anthrax in some regions, but a country-wide delineation of risk has not yet been undertaken. The current study modelled the geographical suitability of anthrax across India and its associated environmental features using a biogeographic application of machine learning. Both biotic and abiotic features contributed to risk across multiple scales of influence. The elephant-livestock interface was the dominant feature in delineating anthrax suitability. In addition, water-soil balance, soil chemistry and historical forest loss were also influential. These findings suggest that the elephant-livestock interface plays an important role in the cycling of anthrax in India. Livestock prevention efforts targeting this interface, particularly within anthropogenic ecotones, may yield successes in reducing ongoing transmission between animal hosts and subsequent zoonotic transmission to humans.

Future range dynamics of the red alga Capreolia implexa in native and invaded regions: contrasting predictions from species distribution models versus physiological knowledge

Abstract

Correlative species distribution modelling is a widely used method to predict potential species ranges but can suffer from limitations in integrating species’ fundamental niches. Therefore, they might underestimate suitable ranges, but including physiological information can improve accuracy of predictions and help identify mechanisms of e.g. range limitation. However, approaches using both, results from correlative as well as physiological investigations are rare, especially in research on seaweeds. Here, we provide results from both approaches to predict the suitable habitat range of Capreolia implexa (Rhodophyta) in its native range (Australia and New Zealand) and invaded range (Chile) under present and future climate scenarios (year 2100, rcp 2.6 and rcp 8.5). We used the Maxent modelling technique and physiological knowledge from a temperature tolerance experiment (2–20 °C) for thermal niche estimation. Results from both approaches suggest larger suitable habitat ranges under present day conditions for both regions than currently occupied. Abiotic range limitation in the native range led to underestimation of the suitable temperature range by Maxent (here lower temperature limit = 8.3 °C). Predictions based on the laboratory temperature tolerance experiment suggest additional suitable habitat in colder regions (here lower temperature limit = 6.6 ± 0.4 °C). Under future climate conditions, both native and invaded ranges should shift southward, which will lead to an overall loss of suitable habitat in the native range. Like that, rcp 8.5 conditions should reduce the native range to 50% of the present-day extent. We demonstrate the limitation of correlative SDM modelling for species that live on continental margins and that physiological experiments can help to identify species’ niches beyond correlative analyses, providing valuable information for range projections. Furthermore, we provide valuable insights relevant for both invasion management and conservation.

Graphic abstract

Using species distribution models to predict potential hot-spots for Rift Valley Fever establishment in the United Kingdom

Vector borne diseases are a continuing global threat to both human and animal health. The ability of vectors such as mosquitos to cover large distances and cross country borders undetected provide an ever-present threat of pathogen spread. Many diseases can infect multiple vector species, such that even if the climate is not hospitable for an invasive species, indigenous species may be susceptible and capable of transmission such that one incursion event could lead to disease establishment in these species. Here we present a consensus modelling methodology to estimate the habitat suitability for presence of mosquito species in the UK deemed competent for Rift Valley fever virus (RVF) and demonstrate its application in an assessment of the relative risk of establishment of RVF virus in the UK livestock population. The consensus model utilises observed UK mosquito surveillance data, along with climatic and geographic prediction variables, to inform six independent species distribution models; the results of which are combined to produce a single prediction map. As a livestock host is needed to transmit RVF, we then combine the consensus model output with existing maps of sheep and cattle density to predict the areas of the UK where disease is most likely to establish in local mosquito populations. The model results suggest areas of high suitability for RVF competent mosquito species across the length and breadth of the UK. Notable areas of high suitability were the South West of England and coastal areas of Wales, the latter of which was subsequently predicted to be at higher risk for establishment of RVF due to higher livestock densities. This study demonstrates the applicability of outputs of species distribution models to help predict hot-spots for risk of disease establishment. While there is still uncertainty associated with the outputs we believe that the predictions are an improvement on just using the raw presence points from a database alone. The outputs can also be used as part of a multidisciplinary approach to inform risk based disease surveillance activities.

Niche modeling for the genus Pogona (Squamata: Agamidae) in Australia: predicting past (late Quaternary) and future (2070) areas of suitable habitat

Background

As the climate warms, many species of reptiles are at risk of habitat loss and ultimately extinction. Locations of suitable habitat in the past, present, and future were modeled for several lizard species using MaxEnt, incorporating climatic variables related to temperature and precipitation. In this study, we predict where there is currently suitable habitat for the genus Pogona and potential shifts in habitat suitability in the past and future.

Methods

Georeferenced occurrence records were obtained from the Global Biodiversity Information Facility, climate variables (describing temperature and precipitation) were obtained from WorldClim, and a vegetation index was obtained from AVHRR satellite data. Matching climate variables were downloaded for three different past time periods (mid-Holocene, Last Glacial Maximum, and Last Interglacial) and two different future projections representative concentration pathways (RCPs 2.6 and 8.5). MaxEnt produced accuracy metrics, response curves, and probability surfaces. For each species, parameters were adjusted for the best possible output that was biologically informative.

Results

Model results predicted that in the past, there was little suitable habitat for P. henrylawsoni and P. microlepidota within the areas of their current range. Past areas of suitable habitat for P. barbata were predicted to be similar to the current prediction. Pogona minor and P. nullarbor were predicted to have had a more expansive range of suitable habitat in the past, which has reduced over time. P. vitticeps was predicted to have less suitable habitat in the past when examining the region of their known occurrence; however, there was predicted growth in suitable habitat in Western Australia. Both 2070 models predict a similar distribution of habitat; however, the model produced using the 2070 RCP 8.5 climate change projection showed a larger change, both in areas of suitable habitat gain and loss. In the future, P. henrylawsoni and P. microlepidota might gain suitable habitat, while the other four species could possibly suffer habitat loss.

Discussion

Based on the model results, P. henrylawsoni and P. microlepidota had minimal areas of suitable habitat during the Last Glacial Maximum, possibly due to changes in tolerance or data/model limitations, especially since genetic analyses for these species suggest a much earlier emergence. The predicted late Quaternary habitat results for all species of Pogona are conservative and should be compared to the fossil record which is not possible at the moment due to the current inability to identify fossil Pogona to the species level. P. nullarbor and P. vitticeps future models predict substantial habitat loss. P. nullarbor could potentially be considered vulnerable in the present since it already has a restricted range, and a conservation plan may need to be considered.