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This study presents updated observation data and mapping of habitat suitability for the endemic snake species Platyceps gracilis (GÜNTHER, 1862). The objective is to improve understanding of its distribution patterns and conservation needs. Observations from field surveys supplemented by literature reviews and citizen science contributions were used in mapping the habitat suitability using Maxent software across Indian subcontinent. Our findings enhance the extent of its known distribution and provide insight into its preferred habitat. regions with a high potential for P. gracilis occurrence were identified by habitat suitability mapping, emphasizing priority regions for conservation efforts.

Invasive alien plant species (IPS) are one of the major threats to biodiversity and ecosystem services. As the dynamics of biological invasions by non-native plant species are expected to intensify with climate change, there is an increasing need to provide accessible information on the distribution of IPS to improve environmental management programmes. Monitoring the probability of occurrence of IPS is therefore essential to limit their spread, as control measures are most effective in the early stages of invasion. This article presents IPS Monitor, a tool developed to monitor habitat suitability for IPS in Germany under current and projected climate conditions. Developed from previous research on IPS impacts and habitat modelling, the tool facilitates the visualisation of habitat suitability for 45 IPS through digital web maps and fact sheets. IPS Monitor acts as a bridge between scientific research and its application, aiming to support decision-making by conservationists, policy-makers and other stakeholders. It provides a scientific basis for developing targeted strategies against the spread of IPS and enables integrated management approaches by providing access to synthesised research and predictive modelling.

Bambusa teres Buch.-Ham. ex Munro (syn. Bambusa nutans subsp. capulata Stapleton) is a fast-growing perennial bamboo that has ecological, economic, cultural and climate change mitigation benefits. However, information on its current and future potential distribution outside forest areas across Nepal and the key factors affecting its growth and distribution are little known. We used a total of 298 occurrence points obtained from the National Bamboo Resource Assessment and 23 environmental variables to project the distribution of B. teres throughout its potential range in Nepal. Maximum entropy model (MaxEnt) was utilized for this study. We assessed the performance of the model using a receiver operating characteristic curve and evaluated the relative importance of predictor variables through a Jackknife procedure. The model achieved a high level of performance with an area under the curve value of 0.928. Precipitation of the coldest quarter (bio_19), temperature seasonality (bio_4) and precipitation seasonality (bio_15) were the significant contributing variables for the distribution of B. teres. The most suitable habitat for B. teres, with a suitability index >0.6, covered 9264.6 km2, with large sections in Eastern and Central Nepal. However, under future climate change scenarios, the area of suitable habitat for the species is projected to increase across Nepal. This study serves as a baseline for assessing potential climate change impacts on B. teres and will enable the development of adaptive measures to protect and establish various bamboo populations outside forest areas in Nepal and globally.

The genus Vigna has several crop species that could be used to feasibly address nutritional security challenges in the subtropical and tropical regions of the world, particularly in climate-changing scenarios. Wild taxa of Vigna are a source of economically important traits and need to be studied. Out of the 34 wild Vigna species reported in India, 928 indigenous accessions belonging to 19 wild Vigna are conserved in India's National Genebank (INGB) housed at the National Bureau of Plant Genetic Resources, New Delhi. Geospatial mapping has identified diversity-rich areas and the Western Ghats region exhibits the highest Shannon diversity values (H = 1.65–3.0). Using the complementarity procedure, six diversity hotspots were identified for the 34 wild Vigna, and these require utmost priority for exploration and germplasm collection. Due to the meagre amount of information available for wild Vigna, the BioClim model was used to successfully predict the Idukki district of Kerala as a suitable site for germplasm-collecting expeditions. Coastal areas identified as rich in twelve wild taxa, V. bourneae, V. dalzelliana, V. marina, V. sublobata, V. subramaniana, V. vexillata, V. stipulacea, V. trilobata, and V. trinervia, require immediate attention to protect hotspots as well as to collect accessions from these areas for ex situ conservation. A hotspot in the protected forest of Anshi National Park and Bhagwan Mahavira Wildlife Sanctuary was identified as an ideal spot for possible in situ conservation of V. konkanensis, V silvestris, and V. sublobata. The 15 wild Vigna species do not have representation in the INGB, and 11 Vigna species have been identified as endemic species to India. Priority needs to be given to these species for focussed exploration and germplasm collection. This paper discusses the future focus on explorations to be carried out for the collection of the germplasm of wild Vigna species. [ABSTRACT FROM AUTHOR]

The tick Ixodes ricinus is widely distributed across Europe and is responsible for the transmission of several pathogens to humans and animals. In this study, we used a knowledge-based method to map variations in habitat suitability for I. ricinus ticks throughout continental France and Corsica. The multi-criteria decision analysis (MCDA) integrated four major biotic and abiotic factors known to influence tick populations: climate, land cover, altitude and the density of wild ungulates. For each factor, habitat suitability index (HSI) values were attributed to different locations based on knowledge regarding its impact on tick populations. For the MCDA, two methods of factor combination were tested, additive and multiplicative, both which were evaluated at the spatial scales of departments and local municipalities. The resulting habitat suitability maps (resolution=100x100 m) revealed that conditions are suitable for I. ricinus over most of France and Corsica. Particularly suitable habitats were located in central, north-eastern and south-western France, while less-suitable habitats were found in the Mediterranean and mountainous regions. To validate the approach, the HSI scores were compared to field data of I. ricinus nymph abundance. Regardless of scale, the correlation between abundance indicator and HSI score was stronger for the additive than for the multiplicative approach. Overall, this study demonstrates the value of MCDA for estimating habitat suitability maps for I. ricinus abundance, which could be especially useful in highlighting areas of the tick’s distribution where preventive measures should be prioritised.

Determining suitable habitats is important for the successful management and conservation of plant and wildlife species. Teucrium polium L. is a wild plant species found in Iran. It is widely used to treat numerous health problems. The range of this plant is shrinking due to habitat destruction and overexploitation. Therefore, habitat suitability (HS) modeling is critical for conservation. HS modeling can also identify the key characteristics of habitats that support this species. This study models the habitats of T. polium using five data mining models: random forest (RF), flexible discriminant analysis (FDA), multivariate adaptive regression splines (MARS), support vector machine (SVM), and generalized linear model (GLM). A total of 119 T. poliumlocations were identified and mapped. According to the RF model, the most important factors describing T. polium habitat were elevation, soil texture, and mean annual rainfall. HS maps (HSMs) were prepared, and habitat suitability was classified as low, medium, high, or very high. The percentages of the study area assigned high or very high suitability ratings by each of the models were 44.62% for FDA, 43.75% for GLM, 43.12% for SVM, 38.91% for RF, 28.72% for MARS, and 39.16% for their ensemble. Although the six models were reasonably accurate, the ensemble model had the highest AUC value, demonstrating a strong predictive performance. The rank order of the other models in this regard is RF, MARS, SVM, FDA, and GLM. HSMs can provide useful output to support the sustainable management of rangelands, reclamation, and land protection. [ABSTRACT FROM AUTHOR]

There is increasing focus on the frequency of jellyfish blooms in the Northeast Atlantic because of negative interactions with humans. However, uncertainty exists as to whether perceptions of increasing bloom frequencies reflect reality due to limitations within long-term population trend data. This study, therefore, developed and applied a semi-quantitative mapping approach to visualise bloom suitability based on the physiological tolerances of seven jellyfish to ocean temperature, salinity and a prey index across the Northeast Atlantic. A 10% increase and a 10% decrease in the environmental parameters was then applied to the maps to assess model sensitivity and the potential influence of environmental change on bloom suitability. The study found that optimal physiological temperatures and salinities combined with peaks in prey abundance drove higher bloom suitability and determined distribution. Several locations predicted to be at high risk of bloom occurrence off British and Irish coasts were found to coincide with areas of high anthropogenic activity that could be impacted by blooms. In the absence of long-term datasets on jellyfish population dynamics, the results and methods developed in this study allow an understanding of historic bloom events and predictions of future populations that will be useful in informing monitoring and management. [ABSTRACT FROM AUTHOR]

This research paper aimed at modeling the relations between mammal wildlife species, which spread around Burdur Lake Basin, with environmental factors and getting habitat suitability maps on the basis of native species by generalizing acquired models. Field survey was carried out on 328 sample plots and 3280 sub-sample plots. Traces, feces and signs belonging to wild boar (Sus scrofa), european hare (Lepus europaeus), beech marten (Martes foina) and red fox (Vulpes vulpes) were identified using the transect counts in the Basin. Data from these species was tested through Jacknife Method by being analyzed with Maximum Entropy (Maxent) method. Base maps belonging to 20 different environmental variances were used for wildlife suitability modeling. Habitat suitability maps were prepared for wild boar (0.77;0,71), brown hare (0.80; 0.74), beech marten (0.86; 0,80), and red fox (0.87; 0.77) by taking into account of obtained models' education data set and test data set ROC values. According to obtained models' results; the relation between wildlife animal species on the study area and topography, distance to forest village road, elevation, bedrock, smoothness, solar lightening index, distance to settlement, distance to lake, distance to runnel and radiation index was detected. In this study, habitat suitability maps based on relation between wild animals and environmental factors were created. These maps will be important bases for mammals species to protect and hunting plans preperation.

Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy. [ABSTRACT FROM AUTHOR]

Sponge aggregations have been recognised as key component of shallow benthic ecosystems providing several important functional roles including habitat building and nutrient recycling. Within the deep-sea ecosystem, sponge aggregations may be extensive and available evidence suggests they may also play important functional roles, however data on their ecology, extent and distribution in the North Atlantic is lacking, hampering conservation efforts. In this study, we used Maximum Entropy Modelling and presence data for two deep-sea sponge aggregation types, Pheronema carpenteri aggregations and ostur aggregations dominated by geodid sponges, to address the following questions: 1) What environmental factors drive the broad-scale distribution of these selected sponge grounds? 2) What is the predicted distribution of these grounds in the northern North Atlantic, Norwegian and Barents Sea? 3) How are these sponge grounds distributed between Exclusive Economic Zones (EEZs) and High Seas areas? 4) What percentage of these grounds in High Seas areas are protected by the current High Seas MPA network? Our results suggest that silicate concentration, temperature, depth and amount of particulate organic carbon are the most important drivers of sponge distribution. Most of the sponge grounds are located within national EEZs rather than in the High Seas. Coordinated conservation planning between nations with significant areas of sponge grounds such as Iceland, Greenland and Faroes (Denmark), Norway (coastal Norway and Svalbard), Portugal and the UK, should be implemented in order to effectively manage these communities in view of the increasing level of human activity within the deep-sea environment. [ABSTRACT FROM AUTHOR]

Bayesian networks (BN) have been increasingly used for habitat suitability modeling of threatened species due to their potential to construct robust models with limited survey data. However, previous applications of this approach have only occurred in countries where human and budget resources are highly available, but the highest concentrations of threatened vertebrates globally are located in the tropics where resources are much more limited. We assessed the effectiveness of Bayesian networks in generating habitat suitability models in Thailand, a biodiversity-rich country where the knowledge base is typically sparse for a wide range of threatened species. The Bayesian network approach was used to generate habitat suitability maps for 52 threatened vertebrate species in Thailand, using a range of evidence types, from relatively well-documented species with good local knowledge to poorly documented species, with few local experts. Published information and expert knowledge were used to define habitat requirements. Focal species were categorized into 22 groups based on known habitat preferences, and then habitat suitability models were constructed with outcomes represented spatially. Models had a consistent structure with three major components: potential habitat, known range, and threat level. Model classification sensitivity was tested using presence-only field data for 21 species. Habitat models for 12 species were relatively sensitive (> 70% congruency between observed and predicted locations), three were moderately congruent, and six were poor. Classification sensitivity tended to be high for bird models and moderate for mammals, whereas sensitivity for reptiles was low, presumably reflecting the relatively poor knowledge base for reptiles in the region. Bayesian network models show significant potential for biodiversity-rich regions with scarce resources, although they require further refinement and testing. It is possible that one detailed ecological study is sufficient to develop a model with reasonable sensitivity, but BN models for species groups with no quantitative data continue to be problematic. [ABSTRACT FROM AUTHOR]

Highlights: [•] We considered habitat suitability indices and mapping for wildlife in Malayan rainforests. [•] The focus was on four large mammals, i.e., tapir, sun bear, tiger and sambar deer. [•] All maps were combined as a habitat suitability map for wildlife/large mammals. [•] Habitat suitability indices and maps of large mammals are useful for protected area management and conservation planning. [ABSTRACT FROM AUTHOR]

Abstract: This study attempts to model the probability of occurrence of some characteristic macro-invertebrate species of the Baltic Sea from different functional groups (i.e. grazers, deposit and suspension feeders, and predators) in response to major environmental forcing factors (salinity, water depth and seabed substrate type). Analyses were based on the inventory data set compiled by revising the data on macrobenthic species for over 12,000 sampling events in the Baltic Sea. In addition, data on environmental variables are retrieved from the results of modelling and large-scale mapping efforts. A simple logistic regression based modelling technique was applied and the candidate model with highest discriminatory power was selected for habitat suitability mapping. Habitat suitability models allowed to satisfactorily predict the potential distribution of macrofaunal species based solely on modelled salinity, bathymetry and rough sediment class information. Our results indicated that salinity, depth and substrate type are all important in determining the distribution of most characteristic macrobenthic species on the large-scale of the whole Baltic Sea. The present exercise is only a first step. Implementation of other variables (e.g. characterizing oxygen and temperature fluctuations, total organic content, and nutrient supply) would obviously increase the model applicability. Information on the ecological potential of habitat suitability can serve as the utmost important basis for scientifically sound marine spatial planning. [ABSTRACT FROM AUTHOR]

We developed a methodology integrating several forms of remotely sensed data into a Geographic Information Systems (GIS) model that identifies suitable sites for riparian conifer restoration at the Cedar River Municipal Watershed in western Washington, U.S.A. The model integrates vegetative and geomorphic variables with information on the habitat preferences of anadromous fishes to identify riparian stands where conifer restoration would have the greatest biological benefit for salmon recovery. The high-resolution raster datasets used in our analysis were capable of characterizing the biophysical attributes of riparian areas at finer spatial scales than was previously possible. This model is intended to serve as a screening tool to identify candidate sites for riparian area restoration. The assessment approach described in this study can be applied not only to model salmonid habitat at the watershed scale but also to assess landscape patterns relevant to a wide range of restoration goals. This methodological framework offers several advantages over other approaches to restoration site selection and planning. First, the fine-scale spatial resolution of the GIS datasets (pixels ≤5 m) used in the model provides a more accurate representation of the habitat conditions than has been possible with coarser-scale data (pixels ≥5 m). Therefore, the accuracy of site identification is greatly improved. Second, the quantitative nature of the model exercises greater objectivity than some other landscape-scale planning approaches. This regional planning tool could be replicated in other watersheds with comparable datasets and could be applied to identify habitat restoration sites for other species or guilds of species by simply altering the model criteria to match the habitat needs of the target organisms. [ABSTRACT FROM AUTHOR]

• A framework for integrating multi-source wildlife data for habitat mapping. • A case study of Rhinopithecus bieti habitat mapping was conducted in Yunnan, China. • Sightings from volunteer villagers and official patrol records were integrated. • Data integration improved habitat mapping accuracy. • The integration framework can support biodiversity monitoring and conservation. Wildlife habitat mapping is a widely used tool for supporting decision making in conservation. It requires data indicating wildlife habitat use to model and map habitat suitability. Collecting wildlife data, however, requires much effort, especially for species in remote mountainous regions of limited accessibility. Such circumstances often necessitate the integration of limited amounts of data available from multiple sources for habitat mapping. To that end, this study presents a framework for integrating multi-source wildlife data for habitat mapping. For evaluating the integration framework, a case study of mapping habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) by integrating sightings elicited from local volunteer villagers and obtained from official patrol records was conducted in Yunnan, China. The integration was explored at three levels: data-, knowledge- and model-level following disparate principles. The predicted habitat suitability maps were validated against monkey occurrence data independently collected though field-tracking. Results show the suitability maps predicted based on data integration were more accurate compared to maps predicted based on individual data sources. Data- and model-level integration achieved higher accuracy compared to knowledge-level integration. Further, data- and model-level integration following a conservative principle, i.e., the 'minimum' operator, led to higher mapping accuracy. The integration framework is generally applicable for integrating data from multiple sources for habitat mapping. It is also easy to implement and thus can be conveniently adopted by practitioners. Habitat suitability maps generated based on integrated data from multiple sources could better supporting decision making in biodiversity monitoring and conservation. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model. [ABSTRACT FROM AUTHOR]