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162 results for “habitat mapping”
Map 4 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 4. Distribution map for Androniscus dentiger.
Map 3 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 3. Distribution map for Ligidium hypnorum.
Map 1 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 1. The ecological regions in Belgium.
Map 2 in Habitat and seasonal activity patterns of the terrestrial isopods (Isopoda: Oniscidea) of Belgium
Map 2. Distribution map for Ligia oceanica.
Mapping the potential habitat suitability, and opportunities of bush encroacher species in Southern Africa: a case study of the SteamBioAfrica project.
<p>Senegalia mellifera (Benth) Seigler & Ebinger. , Dichrostachys cinerea (L.) Wight & Arn. and Terminalia sericea Burch. Ex DC., are three important bush encroacher species that contribute to the well-known ecological process named "thicketization" in Southern Africa. This issue has persisted for many years, impacting species distribution, plant communities, soil, and fauna dynamics. According to climate change projections, Southern Africa is expected to become drier and warmer in future scenarios, creating favourable conditions for proliferation of bush encroacher species.</p> <p>In the paper, we analyze the habitat suitability of these bush encroachers through MaxEnt 3.4.4 under three different future climate models and scenarios. Future projections were made using the shared socio-economic pathways (SSPs) for greenhouse gas concentrations, specifically SSP245-585 across two-time horizons: 2041-2060 and 2061-2180. We utilized <a href="https://www.worldclim.org/data/cmip6/cmip6climate.html">WorldClim 1-km resolution climate data</a> from three global Earth System Models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) that obtained better results than CMIP5 models (Bouramdane 2022; Mmame and Ngongondo 2024): the Max Planck Institute for Meteorology Earth System Model (MPI-ESM1.2-HR), the Hadley Climate Center Earth System Model (UK-ESM1.0-LL) and the Institute Model for Numerical Mathematics (INM-CM5-0). </p>
Habitat Mapping and Crocodile Biomass, Northern Territory, Australia
<p>The data set is used for practical tasks in an Environmental Science unit offered through the Charles Darwin University. The data comprises shapefiles of the Daly and Adelaide Rivers in the Northern Territory of Australia, synthetic reference points for a classification task, and a simulated crocodile biomass data in CSV format. </p>
Precision mapping of snail habitat provides a powerful indicator of human schistosomiasis transmission
Recently, the World Health Organization recognized that efforts to interrupt schistosomiasis transmission through mass drug administration have been ineffective in some regions; one of their new recommended strategies for global schistosomiasis control emphasizes targeting the freshwater snails that transmit schistosome parasites. We sought to identify robust indicators that would enable precision targeting of these snails. At the site of the world's largest recorded schistosomiasis epidemic—the Lower Senegal River Basin in Senegal—intensive sampling revealed positive relationships between intermediate host snails (abundance, density, and prevalence) and human urogenital schistosomiasis reinfection (prevalence and intensity in schoolchildren after drug administration). However, we also found that snail distributions were so patchy in space and time that obtaining useful data required effort that exceeds what is feasible in standard monitoring and control campaigns. Instead, we identified several environmental proxies that were more effective than snail variables for predicting human infection: the area covered by suitable snail habitat (i.e., floating, nonemergent vegetation), the percent cover by suitable snail habitat, and size of the water contact area. Unlike snail surveys, which require hundreds of person-hours per site to conduct, habitat coverage and site area can be quickly estimated with drone or satellite imagery. This, in turn, makes possible large-scale, high-resolution estimation of human urogenital schistosomiasis risk to support targeting of both mass drug administration and snail control efforts.
Data used, summary and codes: A validation standard for Area of Habitat maps for terrestrial birds and mammals
<p>Birds_AOH_Metadata: Logistic modelling and point validation summary along with pixel summary of AOH maps for Birds .</p> <p>Mammals_AOH_Metadata: Logistic modelling and point validation summary along with pixel summary of AOH maps for Mammals.</p> <p>Column_Names_Index_AOH_Metedata: Description of column names of Birds_AOH_Metadata and Mammals_AOH_Metadata</p> <p>Point_localities_ebird: Latitude and longitude of points per species for birds downloaded from eBird used to validate the AOH maps. </p> <p>Point_localities_gbif: Latitude and longitude of points per species for mammals downloaded from GBIF used to validate the AOH maps. </p> <p>Logistic_Regression_Codes_AOH_Validation: R script for logistic modelling.</p> <p>Point_Validation_Codes_AOH_Validation: R script for point validation of AOH maps.</p> <p>Sample_AOH_Maps: Few sample AOH maps which were validated.</p> <p> </p>
Integrating animal tracking datasets at a continental scale for mapping wildlife habitat
<div><em>Aim:</em></div> <div> </div> <div>The increasing availability of animal tracking datasets collected across many sites provides new opportunities to move beyond local assessments to enable detailed and consistent habitat mapping at biogeographic scales. However, integrating wildlife datasets across large areas and study sites is challenging, as species' varying responses to different environmental contexts must be reconciled. Here, we compare approaches for large-area habitat mapping and assess available habitat for a recolonizing large carnivore, the Eurasian lynx (Lynx lynx).</div> <div> </div> <div> <em>Location: </em>Europe</div> <div> </div> <div><em>Methods:</em></div> <div> </div> <div>We use a continental-scale animal tracking database (450 individuals from 14 study sites) to systematically assess modeling approaches, comparing (1) global strategies that pool all data for training vs. building local, site-specific models and combining them, (2) different approaches for incorporating regional variation in habitat selection, and (3) different modeling algorithms, testing nonlinear mixed effects models as well as machine-learning algorithms.</div> <div> </div> <div><em>Results:</em></div> <div> </div> <div>Both global and local modeling strategies allowed building transferable habitat models with overall similar predictive performance. Model performance was the highest using flexible machine-learning algorithms and when incorporating variation in habitat selection as a function of environmental variation. Our best-performing model used a weighted combination of local, site-specific habitat models. Our habitat maps identified large areas of suitable, but currently unoccupied lynx habitat, with many of the most suitable unoccupied areas located in regions that could foster connectivity between currently isolated populations.</div> <div> </div> <div><em>Main conclusions:</em></div> <div> </div> <div>We demonstrate that global and local modeling strategies can achieve robust habitat models at the continental scale and that considering regional variation in habitat selection improves broad-scale habitat mapping. More generally, we highlight the promise of large wildlife tracking databases for large-area habitat mapping. Our maps provide the first high-resolution, yet continental assessment of lynx habitat across Europe, providing a consistent basis for conservation planning for restoring the species within its former range.</div>
Data from: Ultra-fine scale spatially-integrated mapping of habitat and occupancy using structure-from-motion
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Data from: Habitat structure modifies microclimate: an approach for mapping fine-scale thermal refuge
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Reef cover classification (v1): internal coral reef class descriptors for global coral reef habitat mapping
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Precision mapping of snail habitat provides a powerful indicator of human schistosomiasis transmission
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Global maps of current (1979-2013) and future (2061-2080) habitat suitability probability for 1,485 European endemic plant species
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SDMapCH (v1.3): a Comprehensive database of modelled species habitat suitability maps for Switzerland
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Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat
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Data from: Mapping Tasmania's cultural landscapes: using habitat suitability modelling of archaeological sites as a landscape history tool
Aim: Understanding past distributions of people across the landscape is key to understanding how people used, affected and related to the natural environment. Here we use habitat suitability modelling to represent the landscape distribution of Tasmanian Aboriginal archaeological sites and assess the implications for patterns of past human activity. Location: Tasmania, Australia Methods: We developed a RandomForest 'habitat suitability' model of site records in the Tasmanian Aboriginal Heritage Register. We applied a best-effort bias correction, considered 31 predictor variables relating to climate, topography and resource proximity, and used a variable selection procedure to optimise the final model. Model uncertainty was assessed via bootstrapping and we ran an analogous MAXENT model as a cross-validation exercise. Results: The results from the RandomForest and MAXENT models are highly congruent. The strongest environmental predictors of site occurrence include distance to coast, elevation, soil clay content, topographic roughness and distance to inland water. The highest habitat suitability scores are distributed across a wide range of environments in central, northern and eastern Tasmania, including coastal areas, inland water body margins, and forests and savannas in the drier parts of Tasmania. With the exception of coastal areas much of western Tasmania has low habitat suitability scores, consistent with theories of low-density Holocene Tasmanian Aboriginal settlement in this region. Main conclusions: Our modelling suggests Tasmanian Aboriginal people occupied a heterogeneity of habitats but targeted coastal areas around the whole island, and drier, less steep, and/or open forest and savanna environments in the central lowlands. The western interior was identified as being rarely used by Aboriginal people in the Holocene, with the exception of isolated pockets of habitat; yet whether this is a true reflection of Aboriginal resource use demands increased archaeological surveys, particularly in the Tasmanian Wilderness World Heritage Area.
High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)
<p>On the Caribbean island of St. Eustatius, Coralita (<i>Antigonon leptopus</i>)<i> </i>is an aggressive invasive vine posing major biodiversity conservation concerns. The generation of distribution maps can address these conservation concerns by helping to elucidate the drivers of invasion. We test the use of support vector machines to map the distribution of Coralita on St. Eustatius at high spatial resolution and use this map to identify potential landscape and geomorphological factors associated with Coralita presence. This latter step was performed by comparing the actual distribution of Coralita patches to a random distribution of patches. To train the support vector machine algorithm, we used three vegetation indices and seven texture metrics derived from a 2014 WorldView-2 image. The resulting map shows that Coralita covered 3.18% of the island in 2014, corresponding to an area of 64 ha. The mapped distribution was highly accurate, with 93.2% overall accuracy (Coralita class producer's accuracy: 76.4%, user's accuracy: 86.2%). Using this classification map, we found that Coralita is not randomly distributed across the landscape, occurring significantly closer to roads and drainage channels, in areas with higher accumulated moisture, and on flatter slopes. Coralita was found more often than expected in grasslands, disturbed forest and urban areas, but was relatively rare in natural forest. These results highlight the ability of high spatial resolution data from sensors such as WorldView-2 to produce accurate invasive species, providing valuable information for predicting current and future spread risks and for early detection and removal plans.</p>
Data from: Topographic mapping of the interfaces between human and aquatic mosquito habitats to enable barrier-targeting of interventions against malaria vectors
Geophysical topographic metrics of local water-accumulation potential are freely available and have long been known as high resolution predictors of where aquatic habitats for immature Anopheles mosquitoes are most abundant, resulting in elevated densities of adult malaria vectors and human infection burden. Using existing entomological and epidemiological survey data, here we illustrate how topography can also be used to map out the interfaces between wet, unoccupied valleys and dry, densely populated uplands, where malaria vector densities and infection risk are focally exacerbated. These topographically identifiable geophysical boundaries experience disproportionately high vector densities and malaria transmission risk because this is where Anopheles mosquitoes first encounter humans when they search for blood after emerging or ovipositing in the valleys. Geophysical topographic indicators accounted for 67% of variance for vector density but only 43% for infection prevalence, so they could enable very selective targeting of interventions against the former but not the latter (targeting ratios of 9.0 versus 1.5 to 1, respectively). So in addition to being useful for targeting larval source management to wet valleys, geophysical topographic indicators may also be used to selectively target adult Anopheles mosquitoes with insecticidal residual sprays, fencing, vapour emanators or space sprays to barrier areas along their fringes.
Data from: Ensemble approach for potential habitat mapping of invasive Prosopis in Turkana, Kenya
Aim: Prosopis spp. are an invasive alien plant species native to the Americas and well adapted to thrive in arid environments. In Kenya, several remote‐sensing studies conclude that the genus is well established throughout the country and is rapidly in‐ vading new areas. This research aims to model the potential habitat of Prosopis spp. by using an ensemble model consisting of four species distribution models. Furthermore, environmental and expert knowledge‐based variables are assessed. Location: Turkana County, Kenya. Methods: We collected and assessed a large number of environmental and expert knowl‐ edge‐based variables through variable correlation, collinearity, and bias tests. The varia‐ bles were used for an ensemble model consisting of four species distribution models: (a) logistic regression, (b) maximum entropy, (c) random forest, and (d) Bayesian networks. The models were evaluated through a block cross‐validation providing statistical measures. Results: The best predictors for Prosopis spp. habitat are distance from water and built‐up areas, soil type, elevation, lithology, and temperature seasonality. All species distribution models achieved high accuracies while the ensemble model achieved the highest scores. Highly and moderately suitable Prosopis spp. habitat covers 6% and 9% of the study area, respectively. Main conclusions: Both ensemble and individual models predict a high risk of continued invasion, confirming local observations and conceptions. Findings are valuable to stake‐ holders for managing invaded area, protecting areas at risk, and to raise awareness.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.