Predicted Habitat Suitability Research Articles

Habitat suitability of Aotearoa New Zealand for the recently invaded gold clam (Corbicula fluminea)

ABSTRACT The gold clam (Corbicula fluminea) is a highly invasive freshwater mollusc, which was detected in Aotearoa New Zealand in 2023. Currently, C. fluminea has only been observed in the Waikato catchment, but there is significant concern it will spread across the country. There is an urgent need to identify suitable habitat for the species beyond the Waikato, given the high probability of spread. Here, we used a Maximum Entropy (MaxEnt) model to predict habitat suitability across Aotearoa for C. fluminea. The model was parametrised with 9,544 observations from across the species’ native and invaded ranges and seven climate, environmental and habitat variables associated with the species’ distribution. The highest risk area are mostly located in Te Ika-a-Māui (North Island), in particular, northern Waikato, Auckland, Northland, Bay of Plenty, Hawke’s Bay and in Te Waipounamu (South Island) areas with the highest habitat suitability included Marlborough, North Canterbury and Christchurch. Our findings suggest there are many invadable areas beyond the Waikato and a strategy of containment and suppression of existing C. fluminea populations should now be a priority, given the difficulty of eradication once further spread has occurred and new populations are well established.

Range‐wide prediction of habitat suitability for king cobras under current and future scenarios

Abstract Ophiophagus hannah, commonly known as the king cobra, is listed as vulnerable by the International Union for Conservation of Nature (IUCN) and is protected under national laws in most countries. This charismatic species faces multiple threats, including habitat loss, human persecution, illegal trafficking, and climate change. Due to the king cobra's sensitivity to environmental conditions, its population status and trends are barely understood. This study used the MaxEnt algorithm to predict the potential distribution of king cobras across Asia, a method that has been successfully implemented in modeling distributions of various species in the region. The findings showed that Evergreen Broadleaf Trees emerged as the most influential variable for the distribution of Ophiophagus hannah with a 27.3% contribution, followed by the Mean Diurnal Range and Urban/Built‐up areas. Jackknife analysis identified the Mean Diurnal Range as having the highest testing gain. Approximately 413,268 km2 were found to have the most suitable climatic conditions for sustaining this species. Countries such as India, Myanmar, Bangladesh, Vietnam, Cambodia, Indonesia, and Malaysia were identified as having favorable conditions. Under the future climate scenario SSP5‐8.5, the extent of suitable habitats (maximum) for the king cobra is projected to decrease in the periods 2041–2060 and 2081–2100. This report provides valuable insights that could inform conservation strategies for O. hannah in these regions.

Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models

Rewilding is an ambitious approach to conservation aiming at restoring and protecting natural processes. As the world is rapidly transitioning into conditions that have not been observed before, we need to be able to extrapolate and predict how natural processes would act under new conditions. Species distribution models have a good potential to inform rewilding decisions by the predictive modeling of potential species presence under various habitat conditions. A critical requirement when utilizing these models is to be able to express the uncertainty in the environment or its predictions. This study demonstrates the use of Bayesian statistical models to address this challenge. As a case study, we explore Bayesian logistic regression and Bayesian generalized additive models in order to predict suitable habitats for Asian elephants (Elephas maximus) until the year 2070 under the worst case working scenario of climate change. In this comparative study predictions of habitat suitability are solely based on climatic conditions. The results of the two Bayesian models are compared to two benchmark models, maximum-likelihood estimated logistic regression and random forest. We analyze and discuss trade-offs, relative advantages, and limitations of these modeling choices. The results of our analysis suggest that one configuration of Bayesian logistic regression gives the most robust predictions in this setting, which tend to correspond with the distribution of woodland biomes broadly similar to those in the species' historical range.

Population ecology and habitat suitability modelling of an endangered and endemic medicinal plant Meconopsis aculeata Royle under projected climate change in the Himalaya

The Himalayan region is home to the endangered and endemic medicinal plant, Meconopsis aculeata. Habitat degradation due to overexploitation and unscientific collection of the species in recent years has caused the restricted distribution range of species across its range. Therefore, the present study was conducted in the Hindu Kush Himalayan region (4.5 million km2 area) to study population ecology and identify suitable habitats for the habitat distribution modelling under projected climate change to monitor and restore the dwindling populations. Ten sites/populations (20 × 20 m plot) of M. aculeata have been studied. East aspect (5 sites) and rocky habitat (04 sites) represented the maximum number of sites. The density of M. aculeata has a significant negative correlation with slope and available potassium in soil. The MaxEnt algorithm, species occurrence data and environmental variables were used in the current study. CMIP6 project's MIROC6 and BCC-CSM2-MR climate change models were used to identify the potential distribution area for the future under the SSP245 and SSP370 scenarios for the period 2081–2100. The results showed the satisfactory quality of models based on the True-Skill-Statistic (0.838 ±0.149), Area-Under-Curve values (0.958±0.038), and the Receiver-Operating-Characteristic curves. MaxEnt predicted that 1,78,993 km2 (4.10 %) area is favourable for the occurrence of the species. The precipitation of the driest quarter, precipitation of the coldest quarter and elevation were the most influential variables for habitat suitability prediction. The distribution area with an increase in greenhouse gases shows a decrease in the period 2081–2100 in both SSPs and GCMs. The study predicted highly suitable areas that would play a vital role in in situ conservation and improving the status of species. The predicted distribution maps of the species would help policymakers and decision-makers in developing suitable management plans, which will ultimately help in the conservation of species.

Prediction of Suitable Habitat of Alien Invasive Plant Ambrosia trifida in Northeast China under Various Climatic Scenarios

Ambrosia trifida is an invasive alien plant species, which has very high reproductive and environmental adaptability. Through strong resource acquisition ability and allelopathy, it could inhibit the growth and reproduction of surrounding plants and destroy the stability of an invasive ecosystem. It is very important to predict the change of suitable distribution area of A. trifida with climate change before implementing scientific control measures. Based on 106 A. trifida distribution data and 14 points of environmental data, the optimal parameter combination (RM = 0.1, FC = LQ) was obtained using the MaxEnt (version 3.4.1) model optimized by Kuenm package, and thus the potential suitable areas of A. trifida in Northeast China under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) with different emission intensities in the future (2050, 2070) were predicted. The changes of A. trifida suitable area in Northeast China under three climate scenarios were compared, and the relationship between the change of suitable area and emission intensity was analyzed. In general, the suitable area of A. trifida in Northeast China will expand gradually in the future, and the area of its highly suitable area will also increase with the increasing emission intensity, which is unfavorable to the control of A. trifida.

Exploring a critically endangered pitcher plant Nepenthes rigidifolia and predicting its distribution habitat in North Sumatra, Indonesia

Nepenthes rigidifolia Akhriadi, Hernawati & Tamin is a critically endangered pitcher plant endemic to Sumatra, Indonesia. This species is at risk of extinction due to anthropogenic activities such as land use and illegal harvesting. Immediate conservation efforts are necessary to prevent the extinction of N. rigidifolia. This study aimed to investigate the occurrence of N. rigidifolia and predict its habitat as a part of conservation efforts. Several forests in four regencies of North Sumatra Province, Indonesia, namely Karo, Dairi, Pakpak Bharat, and Simalungun, were explored. The area of occupancy (AOO) and extent of occurrence (EOO) of this species were analyzed using the GeoCAT site. In addition, the prediction of habitat suitability was modeled using MaxEnt. A population of N. rigidifolia was found only in Lae Pondom Protected Forest, Karo Regency, which added two new records for this species. The AOO and EOO values are 12 km2 and 4.17 km2, respectively, located in Karo and Dairi Regencies. According to EOO value, population size, and threat level, we suggest that the new current conservation status of N. rigidifolia is Critically Endangered A2ad + 3d; B1ab(iv,v); C2a(i,ii); D. The AUC value of the MaxEnt model is over 0.9, indicating an excellent performance. An area of 364.18 km2 around Lake Toba is estimated to be a highly suitable area for N. rigidifolia. This area, which overlaps with the EOO, is recommended as a priority area for N. rigidifolia conservation.

Machine learning allows for large-scale habitat prediction of a wide-ranging carnivore across diverse ecoregions

ContextResource selection functions are powerful tools for predicting habitat selection of animals. Recently, machine-learning methods such as random forest have gained popularity for predicting habitat selection due to their flexibility and strong predictive performance.ObjectivesWe tested two methods for predicting continental-scale, second-order habitat selection of a wide-ranging large carnivore, the mountain lion (Puma concolor), to support continent-wide conservation management, including estimating abundance, and to predict habitat suitability for recolonizing or reintroduced animals.MethodsWe compared a generalized linear model (GLM) and a random forest model using GPS location data from 476 individuals across 20 study sites in the western USA and Canada and remotely-sensed landscape data. We internally validated models and examined their ability to correctly classify used and available points by calculating area under the receiver operating characteristics (AUC). We performed leave-one-out (LOO) out-of-sample tests of predictive strength on both models.ResultsBoth models suggested that mountain lions select for steeper slopes, areas closer to water, and with higher normalized difference vegetation index (NDVI), and against variables associated with human impact. The random forest model (AUC = 0.94) demonstrated that mountain lion habitat can be accurately predicted at continental scales, outperforming the traditional GLM model (AUC = 0.68). Our LOO validation provided similar results (x̄ = 0.93 for the random forest and x̄ = 0.65 for the GLM).ConclusionsWe found that the added flexibility of the random forest model provided deeper insights into how individual covariates impacted habitat selection across diverse ecosystems. Our LOO analyses suggested that our model can predict mountain lion habitat selection in unoccupied areas or where local data are unavailable. Our model thus provides a tool to support discussions and analyses relevant to continent-wide mountain lion conservation and management including estimating metapopulation abundance.

Predicting habitat suitability for the Australian cycad-attacking weevil (Siraton internatus) under climate change

AbstractCycads hold important economic and conservation value. Some species are extensively used in landscaping, while others are endangered and legally protected. The Australian cycad-attacking weevil, Siraton internatus, is notably destructive, occasionally causing infestations and invasions across various countries. This study simulated habitat suitability for S. internatus to assess its potential invasion and the impact of climate change. Habitat suitability was evaluated under current climate and four climate change scenarios over two time frames (2050 and 2090). Furthermore, we investigated the threat posed by S. internatus to cycad reserves, using Taiwanese reserves as a representative case. Our MaxEnt predictions demonstrated high accuracy, meeting multiple evaluation criteria. We explored the potential distribution of S. internatus within Australia and internationally, identifying suitable habitats in Africa, the Americas, Asia, and Europe. The case study highlighted the low habitat suitability within the two Taiwanese cycad reserves, which is projected to decrease to unsuitable levels under future climate change scenarios for this weevil species. Moreover, our results revealed that suitable habitat for S. internatus is projected to contract globally under all climate scenarios and time periods, but expansion in Chile and the southern Himalaya (e.g., Nepal). This study provides valuable insights into cycad conservation and pest invasion risks. The results support both global and local efforts to manage the invasion threats from this destructive Australian cycad-attacking weevil species. It also accentuates the urgency for continuous biosecurity inspections and prevention of exporting mature cycad caudexes from Australia.

Fire suppression and land-use strategies drive future dynamics of an invasive plant in a fire-prone mountain area under climate change

Woody invasive alien species can have profound impacts on ecosystem processes and functions, including fire regulation, which can significantly affect landscape resilience. Acacia dealbata, a widespread invasive alien plant in the Iberian Peninsula, holds well-known fire-adaptation traits (e.g., massive soil seed banks and heat-stimulated seed germination). In this study, we assess to what extent fire suppression and land-use strategies could affect the potential distribution of A. dealbata in a fire-prone transboundary protected mountain area of Portugal and Spain, using Habitat Suitability Models. Specifically, we predicted changes in habitat suitability for A. dealbata between years 2010 and 2050. We explored the potential impacts of two land-use strategies ('Business-as-usual' or 'High Nature Value farmlands') combined with three levels of fire suppression effectiveness using the biomod2 package in R. We also considered the potential effects of two climate change scenarios (RCP4.5 and RCP8.5). Our modeling approach demonstrated a strong capacity to predict habitat suitability using either climate or land-cover information alone (AUC climate = 0.947; AUC LC = 0.957). According to climate-based models, A. dealbata thrives under conditions characterized by higher precipitation seasonality, higher precipitation in the warmest month, and higher minimum temperature in the coldest month. Regarding land cover, A. dealbata thrives mainly in landscapes dominated by urban areas and evergreen forest plantations. Our models forecasted that habitat suitability by 2050 could either increase or decrease depending on the specific combinations of fire suppression, land-use, and climate scenarios. Thus, a combination of business-as-usual and fire-exclusion strategies would enhance habitat suitability for the species. Conversely, management promoting High Nature Value farmlands would decrease the available suitable habitat, particularly under low fire suppression efforts. These findings suggest that promoting sustainable farming activities could impede the spread of A. dealbata by reducing habitat availability, while strategies aiming at fire-exclusion could facilitate its expansion, likely by enabling establishment and large seed production. This study highlights the complex interplay between fire-prone invasive species, fire and land-use strategies, and climate change; and thus the need to consider the interactions between land-use and fire management to promote invasive species control and landscape resilience.

Niche conservatism and spread explain introgression between native and invasive fish.

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

Improving species distribution forecasts by measuring and communicating uncertainty: An invasive species case study.

Forecasting invasion risk under future climate conditions is critical for the effective management of invasive species, and species distribution models (SDMs) are key tools for doing so. However, SDM-based forecasts are uncertain, especially when correlative statistical models extrapolate to nonanalog environmental domains, such as future climate conditions. Different assumptions about the functional form of the temperature-suitability relationship can impact predicted habitat suitability under novel conditions. Hence, methods to understand the sources of uncertainty are critical when applying SDMs. Here, we use high-resolution predictions of lake water temperatures to project changes in habitat suitability under future climate conditions for an invasive macrophyte (Myriophyllym spicatum). Future suitability was predicted using five global circulation models and three statistical models that assumed different species-temperature functional responses. The suitability of lakes for M. spicatum was overall predicted to increase under future climate conditions, but the magnitude and direction of change in suitability varied greatly among lakes. Variability was most pronounced for lakes under nonanalog temperature conditions, indicating that predictions for these lakes remained highly uncertain. Integrating predictions from SDMs that differ in their species-environment response function, while explicitly quantifying uncertainty across analog and nonanalog domains, can provide a more robust and useful approach to forecasting invasive species distribution under climate change.

Mesoscale activity drives the habitat suitability of yellowfin tuna in the Gulf of Mexico

Yellowfin tuna, Thunnus albacares, represents an important component of commercial and recreational fisheries in the Gulf of Mexico (GoM). We investigated the influence of environmental conditions on the spatiotemporal distribution of yellowfin tuna using fisheries’ catch data spanning 2012–2019 within Mexican waters. We implemented hierarchical Bayesian regression models with spatial and temporal random effects and fixed effects of several environmental covariates to predict habitat suitability (HS) for the species. The best model included spatial and interannual anomalies of the absolute dynamic topography of the ocean surface (ADTSA and ADTIA, respectively), bottom depth, and a seasonal cyclical random effect. High catches occurred mainly towards anticyclonic features at bottom depths > 1000 m. The spatial extent of HS was higher in years with positive ADTIA, which implies more anticyclonic activity. The highest values of HS (> 0.7) generally occurred at positive ADTSA in oceanic waters of the central and northern GoM. However, high HS values (> 0.6) were observed in the southern GoM, in waters with cyclonic activity during summer. Our results highlight the importance of mesoscale features for the spatiotemporal distribution of yellowfin tunas and could help to develop dynamic fisheries management strategies in Mexico and the U.S. for this valuable resource.

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

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

Habitat suitability and connectivity modelling predict a latitudinal-driven expansion in the Mediterranean basin for a historically introduced reptile

Species introductions outside their native ranges, often driven by trade and other anthropogenic activities, present significant ecological challenges. Reptiles, frequently traded as pets for their attractiveness, are particularly susceptible to such introductions, leading to shifts in distribution patterns and potential ecological impacts. The common chameleon (Chamaeleo chamaeleon), which has been historically introduced in several European countries, is such an example, yet no overall assessments are available to date for this species. In this study, we used ecological niche models to assess habitat suitability for the common chameleon in the Mediterranean basin for current and future scenarios. Concurrently, circuit theory techniques were employed to evaluate habitat connectivity in two historically introduced areas. We identified areas of high habitat suitability and dispersal corridors in introduced regions. Our results reveal a latitudinal gradient in habitat suitability changes, with the species facing both expansion and decline in different parts of its range, depending on the ecozone considered. Severe declines are noted in southeastern Spain, Tunisia, and Israel, while habitat suitability increases westwards in Portugal, Morocco, and Southern Italy. These insights contribute to a better understanding of the common chameleon’s ecological dynamics, providing a foundation for targeted management and conservation efforts. Our study highlights the importance of integrating ecological niche modelling and circuit theory techniques to predict habitat suitability and identify critical dispersal corridors for effective conservation strategies. Considering the ongoing challenges posed by human-mediated dispersals for the common chameleon, our research establishes a foundation for future studies to enhance our understanding of this elusive species.

Species distribution modeling of northern sea otters (Enhydra lutris kenyoni) in a data-limited ecosystem.

Species distribution models (SDMs) are used to map and predict the geographic distributions of animals based on environmental covariates. Typically, SDMs require high-resolution habitat data and time series information on animal locations. For data-limited regions, defined as having scarce habitat or animal survey data, modeling is more challenging, often failing to incorporate important environmental attributes. For example, for sea otters (Enhydra lutris), a federally protected keystone species with variable population trends across the species' range, predictive modeling of distributions has been successfully conducted in areas with robust sea otter population and habitat data. We used open-access data and employed a presence-only model, maximum entropy (MaxEnt), to investigate subtidal habitat associations (substrate and algal cover, bathymetry, and rugosity) of northern sea otters (E. lutris kenyoni) for a data-limited ecosystem, represented by Kachemak Bay, Alaska. Habitat association results corroborated previous findings regarding the importance of bathymetry and understory kelp as predictors of sea otter presence. Novel associations were detected as filamentous algae and shell litter were positively and negatively associated with northern sea otter presence, respectively, advancing existing knowledge of sea otter benthic habitat associations useful for predicting habitat suitability. This study provides a quantitative framework for conducting species distribution modeling with limited temporal and spatial animal distribution and abundance data. Utilizing drop camera information, our novel approach allowed for a better understanding of habitat requirements for a stable northern sea otter population, including bathymetry, understory kelp, and filamentous algae as positive predictors of sea otter presence in Kachemak Bay, Alaska.

Evaluation of Environmental Factor Effects on the Polyphenol and Flavonoid Content in the Leaves of Chrysanthemum indicum L. and Its Habitat Suitability Prediction Mapping.

The leaves of Chrysanthemum indicum L. are known to have various bioactive compounds; however, industrial use is extremely limited. To overcome this situation by producing high-quality leaves with high bioactive content, this study examined the environmental factors affecting the phytochemical content and antioxidant activity using C. indicum leaves collected from 22 sites in Kochi Prefecture, Japan. Total phenolic and flavonoid content in the dry leaves ranged between 15.0 and 64.1 (mg gallic acid g-1) and 2.3 and 11.4 (mg quercetin g-1), while the antioxidant activity (EC50) of the 50% ethanol extracts ranged between 28.0 and 123.2 (µg mL-1) in 1,1-Diphenyl-2-picrylhydrazyl radical scavenging assay. Among the identified compounds, chlorogenic acid and 1,5-dicaffeoylquinic acid were the main constituents in C. indicum leaves. The antioxidant activity demonstrated a positive correlation with 1,5-dicaffeoylquinic acid (R2 = 0.62) and 3,5-dicaffeoylquinic acid (R2 = 0.77). The content of chlorogenic acid and dicaffeoylquinic acid isomers varied significantly according to the effects of exchangeable magnesium, cation exchange capacity, annual temperature, and precipitation, based on analysis of variance. The habitat suitability map using the geographical information system and the MaxEnt model predicted very high and high regions, comprising 3.2% and 10.1% of the total area, respectively. These findings could be used in future cultivation to produce high-quality leaves of C. indicum.

High-resolution species distribution modelling reveals spatio-temporal variability of habitat suitability in a declining grassland bird

Abstract Context Species distribution models (SDMs) may provide accurate predictions of species occurrence across space and time, being critical for effective conservation planning. Objectives Focusing on the little bustard (Tetrax tetrax), an endangered grassland bird, we aimed to: (i) characterise the drivers of the species distribution along its key phenological phases (winter, breeding, and post-breeding); and (ii) quantify spatio-temporal variation in habitat suitability across phenological phases and over the years 2005–2021. Methods Combining remotely sensed metrics at high temporal resolution (MODIS) with long-term (> 12 years) GPS telemetry data collected for 91 individuals at one of the species’ main strongholds within the Iberian Peninsula, we built SDMs (250 m resolution) for the species key phenological phases. Results The use of both dynamic and static predictors unveiled previously unknown ecological responses by little bustards, revealing a marked change in the spatial distribution of suitable habitat among phenological phases. Long-term habitat suitability trends showed considerable fluctuations, mainly in the breeding and post-breeding phases. Overall, SDM projections into the past revealed that while the species’ winter and post-breeding habitats apparently increased since 2005, suitable habitat during the species’ most critical phenological phase, breeding, apparently reduced in area over time. Conclusions Our findings show that matching remotely sensed data with GPS tracking data results in accurate habitat suitability predictions throughout the yearly cycle. Additionally, our findings stress the importance of quantifying habitat loss and its potential impact on little bustard decline over nearly 20 years. Spatio-temporal variations in habitat suitability are also identified in this work, which can help prioritize conservation areas, particularly the breeding areas that have remained stable over time, as this is a key requirement for little bustard lek breeding system.

Habitat suitability of Opuntia ficus-indica (L.) MILL. (CACTACEAE): a comparative temporal evaluation using diverse bio-climatic earth system models and ensemble machine learning approach.

A comprehensive evaluation of the habitat suitability across the India was conducted for the introduced species Opuntia ficus-indica. This assessment utilized a newly developed model called BioClimInd, takes into account five Earth System Models (ESMs). These ESMs consider two different emission scenarios known as Representative Concentration Pathways (RCP), specifically RCP 4.5 and RCP 8.5. Additionally, the assessment considered two future time frames: 2040-2079 (60) and 2060-2099 (80). Current study provided the threshold limit of different climatic variables in annual, quarter and monthly time slots like temperature annual range (26-30°C), mean temperature of the driest quarter (25-28°C); mean temperature of the coldest month (22-25°C); minimum temperature of coldest month (13-17°C); precipitation of the wettest month (250-500mm); potential evapotranspiration Thronthwaite (1740-1800mm). Predictive climatic habitat suitability posits that the introduction of this exotic species is deemed unsuitable in the Northern as well as the entirety of the cooler eastern areas of the country. The states of Rajasthan and Gujarat exhibit the highest degree of habitat suitability for this particular species. Niche hypervolumes and climatic variables affecting fundamental and realized niches were also assessed. This study proposes using multi-climatic exploration to evaluate habitats for introduced species to reduce modeling uncertainties.

Geospatial modelling of dry season habitats of the malaria vector, Anopheles funestus, in south-eastern Tanzania

BackgroundAnopheles funestus is a major malaria vector in Eastern and Southern Africa and is currently the dominant malaria-transmitting vector in many parts of Tanzania. Previous research has identified its preference for specific aquatic habitats, especially those that persist in dry months. This observation suggests the potential for targeted control through precise habitat mapping and characterization. In this study, we investigated the influence of habitat characteristics, land cover and human population densities on An. funestus distribution during dry seasons. Based on the results, we developed a habitat suitability model for this vector species in south-eastern Tanzania.MethodsEighteen villages in south-eastern Tanzania were surveyed during the dry season from September-December 2021. Water bodies were systematically inspected for mosquito larvae and characterized by their physico-chemical characteristics and surrounding environmental features. A generalized linear model was used to assess the presence of An. funestus larvae as a function of the physico-chemical characteristics, land use and human population densities. The results obtained from this model were used to generate spatially explicit predictions of habitat suitability in the study districts.ResultsOf the 1466 aquatic habitats surveyed, 440 were positive for An. funestus, with river streams having the highest positivity (74%; n = 322) followed by ground pools (15%; n = 67). The final model had an 83% accuracy in predicting positive An. funestus habitats, with the most important characteristics being permanent waters, clear waters with or without vegetation or movement and shading over the habitats. There was also a positive association of An. funestus presence with forested areas and a negative association with built-up areas. Human population densities had no influence on An. funestus distribution.ConclusionsThe results of this study underscore the crucial role of both the specific habitat characteristics and key environmental factors, notably land cover, in the distribution of An. funestus. In this study area, An. funestus predominantly inhabits river streams and ground pools, with a preference for clear, perennial waters with shading. The strong positive association with more pristine environments with tree covers and the negative association with built-up areas underscore the importance of ecological transitions in vector distribution and malaria transmission risk. Such spatially explicit predictions could enable more precise interventions, particularly larval source management, to accelerate malaria control.Graphical

Predicted shifts in suitable habitat of interacting benthic species in a warmer and invaded Canadian Arctic

Climate change and related expanding shipping activity are predicted to increase the risk of aquatic invasive species arriving in the Arctic. The goal of this study was to predict the distribution of an interconnected set of native and non-native primary producers and primary and secondary consumers in this changing context. Groups of species were selected to represent a benthic coastal Arctic food web in Hudson Bay, including kelps and eelgrass as primary producers (Alaria esculenta, Agarum clathratum, Saccharina latissima, Laminaria solidungula, and Zostera marina), amphipods as primary consumers (Gammarus oceanicus and G. setosus), and fish as secondary consumers (sculpins Gymnacanthus tricuspis, Myoxocephalus scorpius, M. scorpioides, and M. quadricornis). Ensemble models were used to predict the distribution of these native and several analogue non-native species (species known to be invasive elsewhere that can be considered analogues to Hudson Bay species): Dumontia contorta, Undaria pinnatifida, Sargassum muticum, and Codium fragile (primary producers); Gammarus tigrinus (primary consumer); and Artediellus atlanticus and A. uncinatus (secondary consumers). Predicted habitat suitability of trophic groups and analogue non-native species were overlaid under current and future climate change scenarios to assess areas of change through time. The predicted direction of potential distribution shifts varies by species identity (species composition) but not trophic group. Overall trophic relationships and roles in the ecosystem are likely to be maintained over time because while some species are predicted to decrease their potential ranges (e.g., M. quadricornis), others in the same trophic groups are predicted to increase (e.g., M. scorpius). Overlap (or lack thereof) between native and analogue non-native species pairs are expected to vary through time enabling novel interactions (e.g., competition) in space and time. This approach will help to identify current and future high-risk areas for trophic level changes and interactions with invasive species in response to global warming.