Habitat Suitability Map Research Articles

Predicting the habitat suitability and species richness of plants of Great Himalayan National Park under different climate change scenarios.

This study elucidates the distribution of plants in Great Himalayan National Park (GHNP), India, in current and different future climate change scenarios. The distribution of plants and habitat suitability in GHNP due to climate change was analyzed by MaxEnt, species distribution model (SDM) algorithm. In this study, species presence records were retrieved through field survey and published literature. We have projected the distribution of 44 plant species using MaxEnt and tested whether GHNP is performing well in conserving the plant species. Initially, we have constructed a model for each species and created the habitat suitability map from average of ascii files and later we added the maps of all species in order to make binary map to show the species richness in the selected climate scenarios. The model was created using the HADGEM-2 global circulation model in 2050 and 2070years by using climate change situations of RCP 2.6 and RCP 8.5. The area under curve (AUC) values in the final models of 44 plant species were in the range 0.70-0.97 that indicates statistically significant results. The model identified precipitation of driest month followed by altitude and annual mean temperature as most determining variables in the distribution of plants of GHNP in selected climate scenarios. In the present study, we found that overall suitable habitat increased for nine species, decreased for thirty-four, and unchanged for one plant species in terms of percent area change from current to future scenarios. So these nine species were found to be more adaptable towards changing climate than the other plant species in this study. The species richness was high in western and southwestern parts of GHNP in the current scenario, however under future climatic scenarios, species richness shows a decreasing trend. Based on our findings, it can be concluded that GHNP should be prioritized for conserving the plant species.

Modeling the seasonal wildfire cycle and its possible effects on the distribution of focal species in Kermanshah Province, western Iran.

Predicting environmental disturbances and evaluating their potential impacts on the habitats of various plant and animal species is a suitable strategy for guiding conservation efforts. Wildfires are a type of disturbance that can affect many aspects of an ecosystem and its species. Therefore, through the integration of spatial models and species distribution models (SDMs), we can make informed predictions of the occurrence of such phenomena and their potential impacts. This study focused on five focal species, namely, the brown bear (Ursus arctos), wild goat (Capra aegagrus), wild sheep (Ovis orientalis), wildcat (Felis silvestris), and striped hyena (Hyaena hyaena). This study used MODIS active fire data and ensemble machine learning methods to model the risk of wildfire occurrence in 2023 for spring, summer, and autumn separately. This study also investigated the suitability of habitats for focal species via SDMs. The predicted probability maps for wildfire risk and habitat suitability were converted to binary values via the true skill statistic (TSS) threshold. The overlap of the habitat suitability map and wildfire occurrence areas was analyzed via GAP analysis. The area prone to fire in spring, summer and winter is equal to 9077.32; 10,199.83 and 13,723.49 KM2 were calculated, which indicates an increase in wildfire risk. Proximity to roads is one of the most important factors affecting the possible effects of wildfires in all seasons. Most fire occurrences are concentrated on agricultural lands, which, when integrated with other land use types, have wildfire potential in all seasons. The use of fire to destroy agricultural residues is a critical factor in the occurrence of wildfires. The distribution range of each focal species is considered the most important component of fire susceptibility. Hence, the suitable habitat for Hyaena hyaena in spring, summer, and autumn, with areas of 5.257, 5.856, and 6.889 km2 respectively, is the most affected by the possibility of fire. In contrast, these areas have the lowest values for Ovis orientalis, with 162, 127, and 396 km2 respectively. Therefore, species that are dependent on human-based ecosystems have the highest vulnerability to wildfire. Conservation efforts should focus on familiarizing farmers with methods of destroying agricultural residues as well as the consequences of intentional fires. The findings of this study can be used to mitigate the negative impacts of wildfire and protect the habitat of focal species.

A New SDM-Based Approach for Assessing Climate Change Effects on Plant–Pollinator Networks

Current methods for studying the effects of climate change on plants and pollinators can be grouped into two main categories. The first category involves using species distribution models (SDMs) to generate habitat suitability maps, followed by applying climate change scenarios to predict the future distribution of plants and pollinators separately. The second category involves constructing interaction matrices between plants and pollinators and then either randomly removing species or selectively removing generalist or specialist species, as a way to estimate how climate change might affect the plant–pollinator network. The primary limitation of the first approach is that it examines plant and pollinator distributions separately, without considering their interactions within the context of a pollination network. The main weakness of the second approach is that it does not accurately predict climate change impacts, as it arbitrarily selects species to remove without knowing which species will truly shift, decline, or increase in distribution due to climate change. Therefore, a new approach is needed to bridge the gap between these two methods while avoiding their specific limitations. In this context, we introduced an innovative approach that first requires the creation of binary climate suitability maps for plants and pollinators, based on SDMs, for both the current and future periods. This step aligns with the first category of methods mentioned earlier. To assess the effects of climate change within a network framework, we consider species co-overlapping in a geographic matrix. For this purpose, we developed a Python program that overlays the binary distribution maps of plants and pollinators, generating interaction matrices. These matrices represent potential plant–pollinator interactions, with a ‘0’ indicating no overlap and a ‘1’ where both species coincide in the same cell. As a result, for each cell within the study area, we can construct interaction matrices for both the present and future periods. This means that for each cell, we can analyze at least two pollination networks based on species co-overlap. By comparing the topology of these matrices over time, we can infer how climate change might affect plant–pollinator interactions at a fine spatial scale. We applied our methodology to Chile as a case study, generating climate suitability maps for 187 plant species and 171 pollinator species, resulting in 2906 pollination networks. We then evaluated how climate change could affect the network topology across Chile on a cell-by-cell basis. Our findings indicated that the primary effect of climate change on pollination networks is likely to manifest more significantly through network extinctions, rather than major changes in network topology.

A computer model of the distribution of the tapeworm Echinococcus multilocularis in Ukraine

Alveolar echinococcosis, caused by the larval form of the parasitic tapeworm Echinococcus multilocularis, aff ects a wide range of animal species, including domestic and wild livestock, and is a highly pathogenic helminthic disease for humans. Recently developed for the species habitat suitability maps are restricted to the border of the European Union and have left Ukraine outside the scope of this study. Our goal was to model and characterize the large-scale ecological niche of E. multilocularis in Europe, placing a particular emphasis on Ukraine, and to justify an assessment of the risk of E. multilocularis transmission in Ukraine under current environmental conditions. Using the CMCC-BioClimInd dataset and a Bayesian modeling approach, we developed a habitat suitability map for the species in Ukraine and distinguished corresponding risk areas. We anticipate the model will enhance future surveillance eff orts and help to understand the epidemiological situation of the disease, especially under wartime conditions.

Mapping habitat suitability of invasive crayfish in aridland riverscapes: Virile crayfish (Faxonius virilis) in the Lower Colorado River Basin, USA

Correlative species distribution modeling (SDM) is an important tool to predict distributions of invasive species. A unique challenge of implementing SDMs in aridland stream networks is identifying which streams are perennial and which of those offer suitable habitat for obligate aquatic species. Here, we map perennial streams and habitat suitability of invasive virile crayfish (Faxonius virilis) in the Lower Colorado River Basin (LCRB) in the southwestern USA, and quantify drivers of uncertainty in these projections. First, we surveyed F. virilis at 122 stream sites in May–July of 2021, 2022, and 2023. We then implemented an ensemble of SDMs fit using combinations of randomly drawn sites, categories of environmental covariates measured at different spatial scales, survey years, and statistical algorithms. Next, we used open-source street view images from May–July of 2022 and 2023 to assess dry-wet status at 326 road-stream intersections and the same ensemble framework to map perennial stream reaches. Lastly, we quantified drivers of variation in predictive accuracy and mapped habitat suitability across replicates. Median true skill statistic (TSS) across F. virilis replicates was 0.613 and habitat suitability was highest in mountain ecoregions and lowest in desert ecoregions. Of the 130,847 stream km in the LCRB, we estimate 29,078 km (22.2 %) have wetted channels during the May–June dry season and median 50.3 % of these perennial streams provide suitable habitat for F. virilis. The statistical algorithm was the strongest driver of TSS variation across replicates, whereas the spatial scale at which covariates were measured was the strongest driver of variation in mapped habitat suitability across replicates. We confirm the widespread invasion of F. virilis throughout the LCRB, particularly in perennial mountain streams. The modest predictive accuracy highlights the generalistic niche of F. virilis. Overall, we demonstrate that spatiotemporally comprehensive datasets combined with ensemble modeling can guide management at regional extents.

Species Distribution Models for Mesopelagic Mesozooplankton Community

ABSTRACTAimWe aimed to enhance our understanding of the distribution of mesopelagic mesozooplankton (MM) using species distribution models, assess the performance of various modelling techniques, identify key environmental predictors for MM distribution and compute their habitat suitability indices.LocationOur study focused on the mesopelagic zone globally, with data analysed from different oceans.TaxonOur focus was primarily on mesopelagic mesozooplankton, gathering data on 861 different species from the Mesopelagic Mesozooplankton and Micronekton (MMM) Database.MethodsWe used an ensemble of species distribution models, applying 10 different modelling algorithms and three multi‐model ensemble approaches. We explored two important factors that can affect model performance: subsampling and the choice of background points. We also estimated the relative importance of various environmental conditions such as mixed layer depth, temperature, salinity, net primary productivity, euphotic zone depth and dissolved nitrate concentration on the distribution of these species.ResultsEuphotic zone depth, salinity and dissolved nitrate concentration were identified as the most important variables for explaining the distribution of mesopelagic mesozooplankton. The ensemble modelling results were robust in areas with abundant observational records, but high uncertainty was observed in data‐limited regions. We found a patchy habitat suitability map for zooplankton when modelled within their native range, largely due to uneven sampling. Unrestricted range models yielded smoother patterns but could inaccurately project species in areas where they do not occur.Main ConclusionsOur study highlights the need for increased sampling effort in data‐limited regions to improve the accuracy of mesopelagic species distribution models. Despite some inaccuracies, unrestricted range models, assuming ecological equivalence (where different species occupying a similar ecological niche in different geographical regions or different ecosystems exhibit similar adaptations and behaviours), provide a reasonable comparison for habitat suitability maps and model performance. It also confirms the significant impact of certain environmental conditions on mesozooplankton distribution.

Integrating fine-scale habitat suitability and hydrological structures to assess population connectivity for the Kaiser's mountain newt (Neurergus kaiseri) in southwest of Iran

The unique life cycle of amphibians, encompassing both aquatic and terrestrial stages, poses challenges for examining landscape connectivity within meta-populations. Their heavy reliance on water and limited mobility exacerbate these challenges. Fine-scale investigations of environmental factors contribute to understanding the distribution range and landscape connectivity of amphibian meta-populations. The present study examines the habitat suitability and connectivity among population cores of the Luristan newt (Neurergus kaiseri), a vulnerable (VU) species in southwest Iran. Using the Digital Elevation Model (DEM) and hydrological tools, the boundary of the study area was defined based on drainage basins and Maximum Entropy (MaxEnt), incorporating eight habitat variables to investigate the distribution range. Given that amphibians rely on moisture to move, DEM was used to calculate the Sediment Transport Index (STI) as a surrogate for suitable movement contexts. Using this index, the potential movement of sediment within streams across the landscape will be assessed. A high STI value can limit amphibian movement and shelter and therefore threaten the survival of larvae. Here, the STI was fuzzified based on the presence points using the s-shaped method and the symmetric membership function. The fuzzy overlay technique was employed using the habitat suitability map and OR logic, and finally, the inverse of the fuzzy map was used as landscape resistance. Sub-basins with at least two population cores in the drainage basin were identified and their linkage was established using the Linkage Mapper. Also, the depth and number of sinks along the linkage path of the population cores in each sub-basin were identified. The MaxEnt analysis highlighted the variables of elevation, distance from streams, and proximity to the forest as the most important factors influencing N. kaiseri. All Iranian populations of N. kaiseri were located within a single drainage basin. In this drainage basin, there were five sub-basins with more than two populations. Sub-basin 4 encompassed the largest population and exhibited the highest connectivity quality (29 flow rate). The maximum depth of the links between the population cores in this sub-basin reached 100 m from the surface, and a large segment of the population was located in the upper part of the sub-basin. 432 sinks were located between the population cores that helped conserve water, create microclimates, and supply moisture.The arrangement of the populations within the sub-basin, along with the presence of sinks and variations in linkage path depths created distinct conditions for population cores. These conditions significantly influenced the quality of the linkages. The results of the present study can serve as a valuable tool to identify and prioritize population cores and sub-basins for targeted conservation efforts.

Will cinereous vulture (Aegypius monachus L.) become extinct in the forests of Türkiye in the future?

Changing climate conditions and different climate scenarios on a global scale are associated with the shrinkage, fragmentation and even extinction of habitats of important bird species. Based on this, the aim is to conduct habitat suitability modelling and mapping of the cinereous vulture (Aegypius monachus), which has the largest body among the four different vulture species in the world, under the circumstances of climate change in Türkiye. MaxEnt method was performed to reveal the current habitat suitability model of the cinereous vulture, which is an indicator of old and high-quality black pine forests in terms of biological diversity in Türkiye. It was determined that the variables contributing to the current habitat suitability model of the cinereous vulture were bedrock, isothermality, landform classification and seasonal precipitation. Chelsa climate scenarios (SSP126-SSP370-SSP585) for the year 2100 were used to reveal the effects of changing climate conditions on the cinereous vulture. Mapping results according to different scenarios were classified as 0.5 – unsuitable, 0.51-0.8 – suitable and 0.81-1.0 – the most suitable habitat. According to the mapping results based on different year and scenarios, cinereous vulture has suitable habitat in a minimum of 16.13% of the study area in the present state, 13.95% in the year 2100 in the SPP126 climate scenario, 10.11% in the SPP370 climate scenario and 7.36% in the SPP585climate scenario. As a result, when the 2100 SSP585 climate scenario mapping was compared to the current habitat suitability mapping, it was determined that habitat suitability for the cinereous vulture decreased by approximately 55%. Therefore, these results will be a source of information to prevent the extinction of the cinereous vulture, in order to protect its current and potential distributions in advance, and to reduce the impact of changing climate conditions.

Dataspace Integration for Agrobiodiversity Digital Twins with RO-Crate

A severe impact of human-induced climate change is the global decrease in crop yields, threatening the United Nations sustainable development goal (SDG) to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture (SDG 2: “Zero Hunger Goal”). Decision-making on appropriate mitigation strategies requires data integration from heterogeneous data-rich contexts including Earth observation (EO), biodiversity monitoring, gene banks as well as socio-economic data. Accordingly, large-scale initiatives for thematic data spaces, such as the European projects Destination Earth (DestinE) and the Green Deal Data Space, have been launched. Underutilized crops and crop wild relatives (CWR) can contribute to mitigating the impacts of climate change since their greater genetic diversity allows adaptation of agro-food sources to extreme weather events such as flooding and drought or emerging crop pests. Within the framework of Biodiversity Digital Twin (BioDT), a prototype digital twin for crop genetic resources modeling is under development. This prototype combines the scanning of genetic resources of CWRs and traditional cultivars for promising pest resistance and abiotic tolerance traits with advanced species distribution modeling and actual climate scenarios from EO. The aim is to generate habitat suitability maps and identify potential areas where relevant populations are potentially growing (Chala et al. 2024). The talk gives an overview of data models used to facilitate the required interoperability of both Earth models and environmental data within and across different data spaces such as the DestinE Data Lake (Duatis Juarez et al. 2023). Based on our findings from the use case of agrobiodiversity digital twins, we will discuss approaches for processing machine-actionable digital objects (Fig. 1) containing workflow information (i.e., in the Common Workflow Language format, Crusoe et al. 2022), and for sharing resulting research data in a FAIR (Findable, Accessible, Interoperable, and Reusable) manner by leveraging on so-called webby FAIR Digital Objects involving Research Object Crate (RO-Crate, Soiland-Reyes et al. 2022), FAIR Signposting (Soiland-Reyes et al. 2024) and schema.org and its Bioschemas extension respectively (Castro et al. 2020),

Graph-theoretic modeling reveals connectivity hotspots for herbivorous reef fishes in a restored tropical island system

ContextSeascape connectivity refers to how the spatial configuration of marine habitats facilitates or hinders the movement of organisms, nutrients, materials or energy. Predicting and ranking potential connectivity among habitat patches for coral reef fishes helps to understand how reef fishes could utilize and connect multiple habitat types through the flow of nutrients, energy and biomass across the wider seascape during foraging movements.ObjectivesTo advance a spatially explicit understanding of connectivity linkages within a tropical atoll system by modeling, mapping and quantifying potential seascape connectivity for two locally abundant herbivorous reef fish species, the parrotfish, Chlorurus spilurus (pahoro hohoni or pa’ati pa’apa’a auahi), and the surgeonfish, Acanthurus triostegus (manini).MethodsWe applied a two-step modeling approach by first mapping habitat suitability for the focal species. A graph-theoretic modeling technique was then applied to model and measure the contribution of benthic habitat patches to species-specific potential connectivity within the seascape.ResultsHabitat suitability was higher and less fragmented for C. spilurus than for A. triostegus. Potential ecological connectivity estimates for C. spilurus were higher across the entire seascape, with differences between species likely driven by local-scale benthic habitat patch configuration and species home ranges. Hotspots of ecological connectivity across the atoll were mapped for both species.ConclusionsDespite advances in the application of graph-theoretic techniques in the coastal environment, few marine conservation and restoration measures currently integrate spatial information on ecological connectivity. This two-step spatial modeling approach holds great potential for rapid application of connectivity modeling at multiple spatial scales, which may predict ecological responses to conservation actions including active habitat restoration.

Predicting habitat suitability of the critically endangered Be'er Sheva fringe-fingered lizard.

Anthropogenic changes, such as land use, are the main drivers causing climate change and biodiversity loss, with hundreds of thousands of species lacking sufficient habitats for their populations to persist and likely to go extinct within decades. Endemic species are more susceptible to habitat changes and are at the forefront of the biodiversity crisis. We used species distribution models to generate a relative habitat suitability map and identified the habitat requirements of the critically endangered and endemic Be'er Sheva fringe-fingered lizard (Acanthodactylus beershebensis). The model showed that the species' suitable habitats are associated with arid loess plains characterized by scattered, low vegetation cover, primarilyon north-facing aspects, suggesting that these species-specific habitat requirements limit its distribution. The size of the potentially suitable area within the species' historical range is 1350.73 km2. However, anthropogenic changes decreased the remaining suitable habitat to 995.04 km2. Most of this area is unprotected and at risk of further adverse anthropogenic effects. Only 91.72 km2 of this area is protected by the Israel Nature and Parks Authority, and 587.11 km2 may be considered indirectly protected because it is within military firing zones. Our study is the first attempt to map the remaining suitable habitat of A. beershebensis based on the results of a species distribution model. The results of this model can assist in prioritizing the protection of areas needed for the conservation of this critically endangered and endemic lizard species.

Mapping Avian Habitat Suitability Using Linear and Non-Linear Techniques in the Case of Wetland Landscapes

Habitat quality is crucial for wildlife management that impacts the conservation of sensitive landscapes such as wetlands. With advancements in GIS, habitat modelling now effectively predicts species occurrences and habitat suitability. This study aims to model and map habitat suitability for case bird species of Kentish plover in Tuzla Lagoon using multiple techniques. Kentish plover nesting data were collected from 293 nests, and reproductive success measures such as lay date, egg volume, and nest fate were analysed. Spatial habitat modelling techniques, including regression, co-kriging, artificial neural networks, and decision trees, were used with IKONOS imagery and ground data. The overall prediction accuracies were poor for lay date across all techniques, with the decision tree being the most accurate, while egg volume was best predicted by co-kriging, egg success by linear regression, and nest fate by both binomial logistic regression and ANN with 75% accuracy.

Wildlife habitat suitability analysis and mapping the former dhidhessa wildlife sanctuary using GIS-based analytical hierarchal process and weighted linear combination methods

Management of wildlife populations and the creation of conservation programs depend on the evaluation of wildlife habitats. Habitat suitability mapping is a technique typically used to map appropriate environmental factors and assess species existence in different areas. This study aims to map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, Ethiopia, using GIS-based Analytical Hierarchal Process and Weighted Linear Combination Methods. This study used both primary and secondary data sources. Datasets used to collect data include Digital Elevation Model (DEM), Landsat 9 (OLI/TIRS) and population data. Beside, large mammalian species occurrence data obtained from field survey was used. To map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, environmental factors such as proximity of road network, distance to surface water, land use land cover types, slope, population density and topography were used with the integration of species occurrence data recorded from the study area. These environmental factors scaled to common ranges, and assigned appropriate weights. The quantile classification method was utilized to classify suitability index into five zones (unsuitable, less suitable, moderately suitable, suitable, and highly suitable) to produce the map. Accordingly, the model revealed that 18.9 % of the study area is highly suitable, 19.5 % is suitable, 19.9 % is moderately suitable, 19.5 % is less suitable, and 22.2 % is unsuitable for wildlife. About 58.3 % of the study area is currently identified as suitable for wildlife whereas 41.7 % is unsuitable. This showed that the former Dhidhessa wildlife sanctuary is still having large suitable habitats that can support wide ranges of wildlife. Hence, based on the developed preliminary habitat suitability indices and maps, the federal and local governments shall reevaluate the status of former Dhidhessa wildlife sanctuary and develop future conservation and management plans to enhance the conservation of wildlife and their habitats in the area.

Mapping habitat suitability and connectivity for the Sunda clouded leopard (Neofelis diardi) in Batutegi Protected Forest, Lampung, Indonesia

Abstract. Malik HN, Rahman DA, Setiawan Y, Huda R. 2024. Mapping habitat suitability and connectivity for the Sunda clouded leopard (Neofelis diardi) in Batutegi Protected Forest, Lampung, Indonesia. Biodiversitas 25: 2249-2256. One of the rarest and least understood species of carnivore, the Sunda clouded leopard (Neofelis diardi Cuvier, 1823), is facing considerable threats from continued habitat loss and fragmentation. The Batutegi Protected Forest, Lampung, Indonesia, which was recently identified as an existing but isolated habitat of this vulnerable felid, is also experiencing fragmentation and encroachment. These combined threats have led to a reduction in the availability of suitable habitats for this isolated population and created barriers restricting its movement between forested areas. Understanding the factors that influence habitat selection and developing ecological corridors to improve its connectivity is critical to the conservation of the Sunda clouded leopard in Batutegi Protected Forest. We examined camera trap data and used maximum entropy modeling to construct a habitat suitability map, which was used to develop the resistance surface. We then performed habitat connectivity analysis using least-cost modeling. Our results indicated that there was a moderate potential in some areas of the study region to support the occurrence of Sunda clouded leopard, whose presence was mainly driven by the presence of forested landscapes and higher elevations. The predicted connectivity network revealed that the most suitable corridor could facilitate the movement of Sunda clouded leopards between forested areas by avoiding the monoculture plantations and mainly crossing through a matrix of forested patches and mixed plantation areas. This study contributes important insights to assisting in the prioritization of habitat conservation for the protection of Sunda clouded leopard and overall forest biodiversity due to its capacity as an umbrella species.

Assessment of American Bullfrog (Lithobates catesbeianus) spreading in the Republic of Korea using rule learning of elementary cellular automata

The spread of American Bullfrog has a significant impact on the surrounding ecosystem. It is important to study the mechanisms of their spreading so that proper mitigation can be applied when needed. This study analyzes data from national surveys on bullfrog distribution. We divided the data into 25 regional clusters. To assess the spread within each cluster, we constructed temporal sequences of spatial distribution using the agglomerative clustering method. We employed Elementary Cellular Automata (ECA) to identify rules governing the changes in spatial patterns. Each cell in the ECA grid represents either the presence or absence of bullfrogs based on observations. For each cluster, we counted the number of presence location in the sequence to quantify spreading intensity. We used a Convolutional Neural Network (CNN) to learn the ECA rules and predict future spreading intensity by estimating the expected number of presence locations over 400 simulated generations. We incorporated environmental factors by obtaining habitat suitability maps using Maxent. We multiplied spreading intensity by habitat suitability to create an overall assessment of bullfrog invasion risk. We estimated the relative spreading assessment and classified it into four categories: rapidly spreading, slowly spreading, stable populations, and declining populations.

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.

Mapping seabird vulnerability to offshore wind farms in Norwegian waters

IntroductionOffshore wind energy development (OWED) has been identified as a major contributor to the aspired growth in Norwegian renewable energy production. Spatially explicit vulnerability assessments are necessary to select sites that minimize the harm to biodiversity, including seabird populations. Distributional data of seabirds in remote areas are scarce, and to identify vulnerable areas, species, and seasons it is necessary to combine data sets and knowledge from different sources.MethodsIn this study, we combined seabird tracking data, data from dedicated coastal and seabird at-sea surveys, and presence-only data from citizen science databases to develop habitat suitability maps for 55 seabird species in four seasons throughout the Norwegian exclusive economic zone; in total 1 million km2 in the Northeast Atlantic. The habitat suitability maps were combined with species-specific vulnerability indicators to yield maps of seabird vulnerability to offshore wind farms (OWFs). The resulting map product can be used to identify the relative vulnerability of areas prospected for OWED with respect to seabird collision and habitat displacement. More detailed assessments can be done by splitting the spatial indicators into seasonal and species-specific components.Results and discussionAssociated with higher diversity of seabirds near the coast, the cumulative vulnerability indicator showed a strong declining gradient from the coast to offshore waters while the differences in vulnerability between ocean areas and seasons were negligible. Although the present map product represents the best currently available knowledge, the indicators are associated with complex uncertainties related to known and unknown sampling biases. The indicators should therefore be used cautiously, they should be updated regularly as more data become available, and we recommend that more detailed environmental impact assessments based on dedicated seabird surveys, tracking of birds from potentially affected populations and population viability analyses are conducted in areas ultimately selected for OWED.

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.

Using an ecosystem service model to inform restoration planning: A spatially explicit oyster filtration model for Pensacola Bay, Florida.

The development of science-based restoration goals that reflect the primary motivation of stakeholders is a key factor leading to large-scale, long-term restoration successes. The ability to predict the potential ecosystem service delivery from restoration can inform the setting of appropriate goals and facilitate the strategic planning of restoration activities. While recovery of the ecosystem services provided by oyster reefs is a regularly cited reason for undertaking restoration, few examples exist where large-scale oyster habitat restoration plans have been informed using ecosystem service functions. Such an approach is currently being implemented in the Pensacola Bay System, Florida, where a broad coalition of partners and community stakeholders are utilizing a watershed approach to restoring oysters with the aim of restoring oysters for multiple objectives including habitat, ecosystem services, and wild harvest and aquaculture. Through the process of developing a habitat management plan, water filtration was identified as a key ecosystem service by the stakeholders. To support restoration planning we derived a spatially explicit estimate of water filtration services provided by the eastern oyster in the Pensacola Bay system by linking an oyster habitat suitability map to a hydrodynamic-oyster filtration model. This spatially explicit model allowed us to identify the areas where restored oyster reefs have the potential to provide the greatest increase in filtration service as well as provide spatially explicit estimates of the potential filtration provided by oyster habitat restored. Such information is useful in restoration planning and management and for stakeholder engagement, outreach, and education programs.

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

Spatial information about the location and suitability of areas for native plant and animal species under different climate futures is an important input to land use and conservation planning and management. Australia, renowned for its abundant species diversity and endemism, often relies on modeled data to assess species distributions due to the country's vast size and the challenges associated with conducting on-ground surveys on such a large scale. The objective of this article is to develop habitat suitability maps for Australian flora and fauna under different climate futures. Using MaxEnt, we produced Australia-wide habitat suitability maps under RCP2.6-SSP1, RCP4.5-SSP2, RCP7.0-SSP3, and RCP8.5-SSP5 climate futures for 1,382 terrestrial vertebrates and 9,251 vascular plants vascular plants at 5 km2 for open access. This represents 60% of all Australian mammal species, 77% of amphibian species, 50% of reptile species, 71% of bird species, and 44% of vascular plant species. We also include tabular data, which include summaries of total quality-weighted habitat area of species under different climate scenarios and time periods. The spatial data supplied can help identify important and sensitive locations for species under various climate futures. Additionally, the supplied tabular data can provide insights into the impacts of climate change on biodiversity in Australia. These habitat suitability maps can be used as input data for landscape and conservation planning or species management, particularly under different climate change scenarios in Australia.