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56 results for “habitat suitability model”
Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models
<p>Sowing flower strips along field edges is a widely adopted method for conserving pollinating insects in agricultural landscapes. To maximize the effect of flower strips given limited resources, we need spatially explicit tools that can prioritize their placement, and for identifying plant species to include in seed mixtures.</p> <p>We sampled bees and plant species as well as their interactions in a semi-controlled field experiment with roadside/field edge pairs with/without a sown flower strip at 31 sites in Norway and used a regional spatial model of solitary bee species richness to test if the effect of flower strips on bee species richness was predictable from the modelled solitary bee species richness.</p> <p>We found that sites with flower strips were more bee species rich compared to sites without flower strips and that this effect was greatest in areas that the regional solitary bee species richness model had identified to be particularly important for bees. Spatial models revealed that even within small landscapes there were pronounced differences between field edges in the predicted effect of sowing flower strips.</p> <p>Of the plant species that attracted the most bee species, the majority mainly attracted bumblebees and only few species also attracted solitary bees. Considering both the taxonomic diversity of bees and the species richness of bees attracted by plants we suggest that seed mixes containing <em>Hieracium </em>spp. such as <em>Hieracium umbellatum </em>and <em>Pilosella officinarum</em>; <em>Taraxacum</em> spp; <em>Trifolium repens</em>;<em> Lotus corniculatus</em>; S<em>tellaria graminea</em>; and <em>Achillea millefolium</em> would provide resources for diverse bee communities in our region.</p> <p>Spatial prediction models of bee diversity can be used to identify locations where flower strips are likely to have the largest effect and can thereby provide managers with an important tool for prioritizing how funding for agri-environmental schemes such as flower strips should be allocated. Such flower strips should contain plant species that are attractive to both solitary and bumblebees, and do not need to be particularly plant species rich as long as the selected plants complement each other.</p>
Data from: Sampling methodology influences habitat suitability modeling for Chiropteran species
<p>Technological advances increase opportunities for novel wildlife survey methods. With increased detection methods, many organizations and agencies are creating habitat suitability models (HSMs) to identify critical habitats and prioritize conservation measures. However, multiple occurrence data types are utilized independently to create these HSMs with little understanding of how biases inherent to those data might impact HSM efficacy.</p> <p>We sought to understand how different data types can influence HSMs using three bat species (<em>L. borealis</em>, <em>L. cinereus</em>, and <em>P. subflavus</em>). We compared the overlap of models created from passive-only (acoustics), active-only (mist-netting and wind turbine mortalities), and combined occurrences to identify the effect of multiple data types and detection bias.</p> <p>For each species, the active-only models had the highest discriminatory ability to tell occurrence from background points and for two of the three species, active-only models performed best at maximizing the discrimination between presence and absence values. By comparing the niche overlaps of HSMs between data types, we found a high amount of variation with no species having over 45% overlap between the models. Passive models showed more suitable habitat in agricultural lands, while active models showed higher suitability in forested land, reflecting sampling bias.</p> <p>Overall, our results emphasize the need to carefully consider the influences of detection and survey biases on modeling, especially when combining multiple data types or using single data types to inform management interventions. Biases from sampling, behavior at the time of detection, false positive rates, and species life history intertwine to create striking differences among models. The final model output should consider biases of each detection type, particularly when the goal is to inform management decisions, as one data type may support very different management strategies than another. </p>
Environmental drivers and distribution of cold-water corals in the global ocean - Habitat Suitability Models
<p><strong>Publication Abstract</strong></p> <p>Species distribution models (SDMs) are useful tools for identifying the distribution of marine species in data limited environments. Outputs from SDMs have been used to identify areas for spatial management, analyzing trawl closures, quantitatively measuring the risk of bottom trawling, and evaluating protected areas for improving conservation management. Cold-water corals are globally distributed habitat forming organisms that are vulnerable to anthropogenic impacts and climate change, but data deficiency remains an ongoing issue for the effective spatial management of these important ecosystem engineers. In this study, we constructed 11 environmental seabed variables at 500m resolution based on the latest multi-depth global datasets and high-resolution bathymetry. Ensemble modeling methods were used to predict the global habitat suitability for ten widespread cold-water coral species, including six reef Scleractinian framework-forming species and four large gorgonian species. Temperature, depth, salinity, terrain ruggedness index, carbonate saturation state and chlorophyll were the most important factors in determining the global distributions of these species. The Scleractinian species <em>Madrepora oculata</em> showed the widest niche breadth, whilst most other species demonstrated somewhat limited niche breadth. The shallowest study species, <em>Oculina varicosa</em>, had the most distinctive niche of the group. The model outputs from this study represent the highest resolution global predictions for these species to date and are valuable in aiding the management, conservation and continued research into cold-water coral species.</p> <p><strong>Data description</strong></p> <p>These datasets (compressed Zip archives) contain the habitat suitability model outputs generated for the publication Tong et al., (2023) doi: 10.3389/fmars.2023.1217851, please refer to the manuscript for methodological details. These files are provided in an ArcGIS compatible TIFF format that is readable by various GIS packages and can be imported to R. </p> <p>AA.zip = <em>Acanella arbuscula</em><br> DP.zip = <em>Desmophyllum pertusum</em> (former and now unaccepted synonym <em>Lophelia pertusa</em>)<br> ER.zip = <em>Enallopsammia rostrata</em><br> GD.zip = <em>Goniocorella dumosa</em><br> MO.zip = <em>Madrepora oculata</em><br> OV.zip = <em>Oculina varicosa</em><br> PA.zip = <em>Paragorgia arborea</em><br> PP.zip = <em>Paramuricea placomus</em><br> PR.zip = <em>Primnoa resedaeformis</em><br> SV.zip = <em>Solenosmilia variabilis</em></p>
Data from: One model to rule them all: Identifying priority bat habitats from multi‐species habitat suitability models
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Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models
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Habitat suitability modeling to predict the spatial distribution of cold-water coral communities affected by the Deepwater Horizon oil spill
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Modelling the potential global distribution of suitable habitat for the biological control agent Heterorhabditis indica
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Climate change effects on deep-water corals – habitat suitability model input data
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Supporting reintroduction planning: A framework integrating habitat suitability, connectivity and individual-based modelling. A case study with the Eurasian lynx in the Apennines
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Data from: Assessing the usefulness of Citizen Science Data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol
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SDMapCH (v1.3): a Comprehensive database of modelled species habitat suitability maps for Switzerland
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Data from: Sampling methodology influences habitat suitability modeling for Chiropteran species
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Modelling Avian Habitat Suitability in Boreal Forest using Structural and Spectral Remote Sensing Data
<p>Data used in research regarding avian habitat suitability models in Harry's River Watershed in Newfoundland, Canada</p>
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.
Support for a relationship between demography and modeled habitat suitability is scale dependent for the Purple Martin, Progne subis
<p>1. Species distribution models (SDMs) estimate habitat suitability for species in geographic space. They are extensively used in conservation under the assumption that there is a positive relationship between habitat suitability and species success and stability.</p> <p>2. Given the difficulties in obtaining demographic data across a species' range, this assumption is rarely tested. Here we provide a range-wide test of this relationship for the eastern subspecies of purple martin (<i>Progne subis subis</i>).</p> <p>3. We build a well-supported SDM for the breeding range of the purple martin, and pair it with an unparalleled demographic dataset of nest success and local and regional abundance data for the species to test the proposed link between habitat suitability and fecundity and demography.</p> <p>4. We find a positive relationship between regional abundance and habitat suitability but no relationship between local abundance or fecundity and habitat suitability.</p> <p>5. Our data suggest that local success is driven largely by biotic and stochastic factors and raise the possibility that purple martins are experiencing a time lag in their distribution. More broadly our results call for caution in how we interpret SDMs and do not support the assumption that areas of high habitat suitability are the best areas for species persistence.</p>
Data from: Development and validation of an expert-based habitat suitability model to support boreal caribou conservation
The long-term persistence of boreal caribou (Rangifer tarandus caribou) is threatened by the negative impacts of human activities, including industrial development. The vast geographical distribution and behavioural variability of caribou warranted the development and validation of an appropriate conservation tool for managers in eastern Canada. We developed a habitat suitability model for boreal caribou in Québec by integrating expert knowledge into a hierarchical analysis. We elicited responses from 14 experts on caribou ecology to determine the best spatiotemporal scales of application, the relative importance of habitat variables, the zone of influence of human infrastructure, and the parameterization of the model. Based on this input, we built the model using 8 habitat categories and 3 human infrastructure variables. Experts identified mature conifer-dominated forests and open lichen woodlands as the most important vegetation categories for boreal caribou, whereas density of and proximity to paved roads, forest roads, and mines decreased habitat quality. We mapped the resulting model over the entire province of Québec (up to the northern forest allocation limit), and validated it using independent GPS telemetry datasets acquired in 3 distinct regions. Our model predicted that habitat suitability was highest in the northeastern part of our study area, where timber harvest activities and roads were virtually absent. Conversely, southern parts of Québec were generally unsuitable for boreal caribou. Our habitat suitability model is among the first tools for boreal caribou conservation available to wildlife and land managers in Québec.
Data from: Combining human acceptance and habitat suitability in a unified socio-ecological suitability model: a case study of the wolf in Switzerland
Habitat suitability models (HSMs) are commonly used in conservation practise to assess the potential of an area to be occupied and colonised. A major limitation of these models, however, is the omission of spatially explicit understanding of human acceptance towards the focal species. As wildlife is more and more subject to human-dominated landscapes, ignoring the sociological component will result in misrepresentation of the observed processes and inappropriate management. We distributed 10 000 questionnaires across Switzerland and identified key socio-demographical factors correlated with human acceptance of the wolf. We then created a spatially explicit acceptance model based on geo-referenced socio-demographical, social and geographical information. Finally, we combined our acceptance model with a HSM to obtain a unified socio-ecological suitability model, which included human and ecological components. We showed that the key factors associated with human acceptance were perception of how harmful the wolf is, interest in wolf-related issues, need for livestock protection, and fear of the wolf. Perceived harmfulness was in turn correlated with direct and indirect experience with the wolf, and level of education. Our acceptance map predicted decreasing acceptance with increasing altitude of residency and proximity to locations of confirmed wolf presence. This resulted in the overall opposition to the wolf for the Alpine region, albeit substantial regional differences. We found little spatial overlap (6% of Switzerland) between areas where the wolf was accepted and areas of suitable habitat. These areas of socio-ecological suitability were concentrated in the Jura Mountains and in the eastern and southern Alps, and were absent in the western and central Alps. Particularly in the Jura region, which is yet to be colonised, management of human acceptance will be a crucial conservation target. Synthesis and applications. We developed an integrative, socio-ecological approach that allowed us to accurately reproduce recent wolf recolonisation. We anticipate our framework to be a powerful tool to reliably evaluate overall suitable habitats and predict short to medium-term range expansion for species whose distribution is also dependent on human attitudes. Because our approach is sensitive to both the ecological and human component, it is ideally suited to identify key regions where proactive and targeted socio-ecological management plans are needed.
Worldclim 2.1 versus Worldclim 1.4: climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe
<p>The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs-based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between-version differences). To this aim, we simulated occurrence probability and presence-absence across Europe of four virtual species (VS) with differing climate-occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate-occurrence relationships for the climate-tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between-version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between-version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between-version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re-evaluating previous biodiversity-related works relying on geographical predictions from Worldclim-based HSMs.</p>
Data from: Development and validation of an expert-based habitat suitability model to support boreal caribou conservation
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Data from: Can opportunistically-collected Citizen Science data fill a data gap for habitat suitability models of less common species?
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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)
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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.