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201 results for “habitat modelling”
Code and data for "Optimising habitat management for amphibians: from simple models to complex decisions"
<p>The zip archive contains code and data to reproduce the analysis contained in the following manuscript:</p> <p>Scroggie, M.P., Preece, K., Nicholson, E., McCarthy, M.A., Parris, K.M. and Heard, G.W. Optimising habitat management for amphibians: from simple models to complex decisions.</p> <p>Included in the archive is the source code of two R packages (METAPOP, and METAPOPPLAN), which must first be installed, along with their various dependencies which include Rcpp, RcppArmadillo, sp, spdep and rgeos. As package METAPOPPLAN contains C++ code, installation requires the presence of the appropriate C++ compilers and other software development tools. These should be available or easily installable on Linux or other Unix based systems, but Microsoft Windows users must first install the appropriate version of Rtools, which can be downloaded from https://cran.r-project.org/bin/windows/Rtools/.</p> <p>With all appropriate packages installed, the analysis can be replicated by running the included Makefile.</p> <p>Total execution time will be quite long, due to the large number of simulations that must be run. On my Windows system, with 12 cores and 4GB of RAM, execution took approximately 10 days. The code will run much faster if the various management scenarios included in the analysis are executed in parallel. This can be done by executing make with a -j argument specifying the number of cores to utilise. For example, if your system has 12 cores, invoke *make* as follows:</p> <p>make -j 12</p> <p>Overall execution time will scale roughly with the number of available cores, up to a maximum of 24 (the total number of management scenarios).<br> </p>
Figure 1 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 1. The distribution of Pica mauritanica throughout North Africa.
Figure 3 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 3. Variable importance (based on correlation metric) of the ensemble model.
Vortex input files -- Linking habitat and population viability analysis models of a metapopulation of Florida scrub-jays
<p>Vortex input files for manuscript "<span>Linking </span><span><span>habitat and population viability analysis models to account for <span>vegetation dynamics, habitat fragmentation, and social behavior of a metapopulation of Florida scrub-jays</span></span></span>" by R. C. Lacy, D. B. Breininger, et al. </p>
Data from: Assessing the usefulness of Citizen Science Data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol
<p><strong>Aim:</strong> To evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements, and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high quality absences from OR based on questionnaires sent to observers.</p> <p><strong>Location:</strong> Both datasets cover the same large area (Sweden) and time period (2000 -2013).</p> <p><strong>Methods:</strong> Species distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data were assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR.</p> <p><strong>Results:</strong> For all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species-environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found.</p> <p><strong>Main Conclusions:</strong> Relatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist.</p>
Multispecies modelling reveals potential for habitat restoration to re-establish boreal vertebrate community dynamics
<p>1. The restoration of habitats degraded by industrial disturbance is essential for achieving conservation objectives in disturbed landscapes. In boreal ecosystems, disturbances from seismic exploration lines and other linear features have adversely affected biodiversity, most notably leading to declines in threatened woodland caribou. Large-scale restoration of disturbed habitats is needed, yet empirical assessments of restoration effectiveness on wildlife communities remain rare.</p> <p>2. We used 73 camera trap deployments from 2015-2019 and joint species distribution models to investigate how habitat use by the larger vertebrate community (>0.2 kg) responded to variation in key seismic line characteristics (line-of-sight, width, density and mounding) following restoration treatments in a landscape disturbed by oil and gas development in northeastern Alberta.</p> <p>3. The proportion of variation explained by line characteristics was low in comparison to habitat type and season, suggesting short-term responses to restoration treatments were relatively weak. However, we predicted that lines with characteristics consistent with restored conditions would support altered community composition, with reduced use by wolf and coyote, thereby indicating that line restoration will result in reduced contact rates between caribou and these key predators.</p> <p>4. Our analysis provides a framework to assess and predict wildlife community responses to emerging restoration efforts. With the growing importance of habitat restoration for caribou and other vertebrate species, we recommend longer-term monitoring combined with landscape-scale comparisons of different restoration approaches to more fully understand and direct these critical conservation investments. Only by combining rigorous multispecies monitoring with large-scale restoration will we effectively conserve biodiversity within rapidly changing environments.</p>
Data from: One model to rule them all: Identifying priority bat habitats from multi‐species habitat suitability models
<p>Bats are important components of global ecosystems, providing essential ecosystem services with substantial economic benefit. Yet North American bat populations have been negatively affected by numerous factors (e.g., disease, habitat loss, and wind energy development) with compounding effects. Bats use habitats at a variety of scales, from small, isolated patches to large, contiguous corridors. Landscape‐level research is necessary to identify important habitats, patches, and corridors to strategically target management interventions. We created habitat suitability models (HSMs) for hoary bats (<em>Lasiurus cinereus</em>), eastern red bats (<em>L. borealis</em>), and tri‐colored bats (<em>Perimyotis subflavus</em>) across Illinois, USA using species-specific landscape and climate variables. With the 3 models from this study and a previously published HSM for Indiana bats (<em>Myotis sodalis</em>), we stacked binary HSMs, thereby identifying priority conservation areas across Illinois. Species exhibited different distributional patterns and habitat preferences across Illinois. Multi‐species HSMs highlight high quality habitat (i.e., ecologically important habitat that provides preferred resources for roosting, foraging, and raising young) in southern Illinois and along river riparian areas. This approach identified priority conservation areas mainly following hydrologic zones, which allows managers to strategically target restoration and conservation measures, invest funds in habitat likely to have high return‐on‐investment, and assist with decisions that affect bats (e.g., siting wind turbines and purchasing mitigation lands). </p>
Data obtained during classification of intertidal habitats using UAV imagery in the Galapagos Archipelago (Orthophotos, digital elevation models (DEM) and orthophoto-draped 3D models)
<p>In the repository 5 folders exist. 1) Digital elevation models (DEMs), 2) Intertidal habitat map, 3) Othophoto draped 3D models, 4) Orthophotos, and 5) Processing reports. The data has been collected in Puerto Ayora at Santa Cruz in August 2017, the most urbanized island of the Galapagos Archipelago. The purpose of this study was to investigate the image classification opportunities for these intertidal habitats using Uncrewed Aerial Vehicle (UAV) imagery. This dataset is cited in an open-access publication: https://doi.org/10.3390/drones7070416. </p>
Supplemental data for "Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission using a Hydrology-based Model"
<p>Supplemental data for "Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission using a Hydrology-based Model"</p>
Fijian habitat and invertebrate species distribution modelling
<p><strong>Aim</strong></p> <p>Spatially explicit protections of coastal habitats determined on the current distribution of species and ecosystems risk becoming obsolete in 100 years if the movement of species ranges outpaces management action. Hence, a critical step of conservation is predicting the efficacy of management actions in future. We aimed to determine how foundational, habitat‐building species will respond to climate change in Fiji.</p> <p><strong>Location</strong></p> <p>The Republic of Fiji.</p> <p><strong>Methods</strong></p> <p>We develop species distribution models (SDMs) using MaxEnt, General Additive Models and Boosted Regression Trees and publicly available data from the Global Biodiversity Information Facility to predict changes in distribution of suitable habitat for mangrove forests, coral habitat, seagrass meadows and critical fisheries invertebrates under several IPCC climate change scenarios in 2070 or 2100. We then overlay predicted distribution models onto existing Fijian protected area network to assess whether today's conservation measures will afford protection to tomorrow's distributions.</p> <p><strong>Results</strong></p> <p>We develop species distribution models (SDMs) using MaxEnt, General Additive Models and Boosted Regression Trees and publicly available data from the Global Biodiversity Information Facility to predict changes in distribution of suitable habitat for mangrove forests, coral habitat, seagrass meadows and critical fisheries invertebrates under several IPCC climate change scenarios in 2070 or 2100. We then overlay predicted distribution models onto existing Fijian protected area network to assess whether today's conservation measures will afford protection to tomorrow's distributions.</p> <p><strong>Main conclusions</strong></p> <p>Species distribution models are a critical tool for conservation managers, as linking spatial distribution data with future climate change scenarios can aid in the creation and resiliency of protected area programmes. New protected area designations should consider the future distribution of species to maximize benefits to those taxa.</p>
Habitat distribution models for pygmy rabbits in Idaho
<p>Environmental relationships can differ across the geographic range of species, especially for widespread generalists. Because habitat specialists are more vulnerable to environmental changes, incorrect assumptions about consistent habitat associations could hinder strategic conservation efforts. We used species distribution models (SDMs) to evaluate intraspecific variation in habitat associations for a habitat specialist of conservation concern, the pygmy rabbit (<em>Brachylagus idahoensis</em>), which is endemic to the sagebrush biome of the western USA. Our goal was to model habitat associations for pygmy rabbits across a portion of their range to evaluate regional variation and contrast predictions with results from a model developed at the rangewide extent. We created inductive SDMs using maximum entropy methods within five ecological regions that encompassed about 20% of the species rangewide distribution and spanned diverse environmental gradients. We included a suite of environmental predictor variables representing topography, vegetation, climate, and soil characteristics. Results of the regional models identified substantial variation in habitat associations across the five regions, with each retaining a unique set of environmental predictors. Bioclimatic variables were the most influential environmental parameters in all five regions, but the specific variables differed. The models developed at regional extents predicted smaller areas of habitat (an average of 15% less for suitable habitat and 80% less for primary habitat) than predictions generated from a model developed at the rangewide extent. Because bioclimatic variables were effective in discriminating areas used by pygmy rabbits, they also provided an opportunity to assess potential changes in habitat distribution by incorporating future climate projections. Distributions modeled under two mid-century emission scenarios projected substantial reductions in suitable habitat for pygmy rabbits across most regions and pronounced variation among regions in the magnitude and direction of the climate effects. Collectively, results of this work underscore the need to incorporate regional variation in habitat associations into planning for current and future conservation and management strategies.</p>
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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Fijian habitat and invertebrate species distribution modelling
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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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Modeling polar bear (Ursus maritimus) snowdrift den habitat on Alaska’s Beaufort Sea coast using SnowDens-3D and ArcticDEM data
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Modelling the potential global distribution of suitable habitat for the biological control agent Heterorhabditis indica
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.