Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
131
datasets available to search
ShareScore release 0.9.0
Dataset results
131 results for “habitat prediction”
Data for: Predicting berry plant habitat under climate change in Bristol Bay, AK
<p>Aim: Climate change is altering suitable habitat distributions of many species in high latitudes. Fleshy fruit-producing plants (hereafter "berry plants"), important in arctic food webs and as subsistence resources for human communities, may be impacted, but their response to a warming and increasingly variable climate at a landscape scale has not yet been examined. Here, we identified influential environmental determinants of berry plant distribution and produced predictions on how climate change might shift these distributions.</p> <p>Location: Bristol Bay and Togiak NRCS Survey Areas, Alaska.</p> <p>Methods: We built species distribution models using the Random Forests algorithm to identify key characteristics and predict the spatial distribution of habitats suitable for five berry plant species: <em>Vaccinium uliginosum</em> L., <em>Empetrum nigrum</em> L., <em>Rubus chamaemorus</em> L., <em>Vaccinium vitis-idaea</em> L., and <em>Viburnum edule</em> (Michx.) Raf. Then, we used future climate projections (2081-2100; representative concentration pathways 4.5, 6.0, & 8.5) to predict shifts in species' suitable habitat distributions based on future climate conditions.</p> <p>Results: The predicted amount and spatial patterns of suitable habitat for the current time period were variable among species, consistent with species' diverse life history attributes and habitat preferences. Future climate models predicted both positive and negative changes to suitable habitat probability for all species; future binary classification maps predicted net declines in suitable habitat area for all species and climate scenarios tested. Models identified elevation, soil characteristics, and January and July temperatures as important drivers of suitable habitat distributions.</p> <p>Main conclusions: Our work contributes to understanding the response of important berry plant species to climate change at a landscape scale. Shifting and retracting distributions may alter where communities have access to harvesting areas, suggesting that access to these resources may become restricted in the future. Our prediction maps may help inform climate adaptation planning as communities anticipate shifting access to harvesting locations.</p>
Predicting the Habitat Suitability of Asian Elephants in Madhesh Landscape of Southern Nepal
<p>This is the dataset used during my thesis entitled " Predicting the Habitat Suitability of Asian Elephants (<em>Elephas maximas</em>) in Madhesh landscape of southern Nepal.</p>
Data from: Semi-natural habitat, but not aphid amount or continuity, predicts lady beetle abundance across agricultural landscapes
Open the record for dataset details and reuse information.
Data from: Habitat edge responses of generalist predators are predicted by prey and structural resources
Open the record for dataset details and reuse information.
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
Open the record for dataset details and reuse information.
Data for: Predicting habitat suitability for Townsend’s big-eared bats across California in relation to climate change
Open the record for dataset details and reuse information.
Data for: Predicting berry plant habitat under climate change in Bristol Bay, AK
Open the record for dataset details and reuse information.
Habitat association predicts population connectivity and persistence in flightless beetles: a population genomics approach within a dynamic archipelago
Open the record for dataset details and reuse information.
Expanded distribution and predicted suitable habitat for the critically endangered yellow-tailed woolly monkey (Lagothrix flavicauda) in Peru
Open the record for dataset details and reuse information.
Airflow modelling predicts seabird breeding habitat across islands
Open the record for dataset details and reuse information.
Dataset of habitat quality does not predict animal population abundance on frequently disturbed landscapes
Open the record for dataset details and reuse information.
Future seasonal changes in habitat for Arctic whales during predicted ocean warming
Open the record for dataset details and reuse information.
Data from: Habitat-based predictions of bridle shiner (<em>Notropis bifrenatus</em>) in the northeastern United States
Open the record for dataset details and reuse information.
Vertebrate-habitat relationships: Logistic regression models predict probability of occurrence of bird and small mammal species in western Oregon
Logistic regression models predicting probability of occurrence of bird and of small-mammal species were produced using animal-habitat data sets from throughout western Oregon (Garman and Cole 1999 - Vertebrate Habitat Relationships Data Bank (VHRDB), Report to Coastal Landscape Analysis and Modeling Study). Regression coefficients, variables, and metrics related to model predictions are provided here under Entity 1, and in VHRDB as VERTLOGR.
Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Data from: Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds
<p>This data repository contains the processed and extracted metrics from the Dutch land cover, country wide airborne laser scanning and Sentinel-1 and 2 datasets used as input predictor variables in the species distribution modelling step. The study area within the Netherlands comprised five Dutch provinces (Groningen, Drenthe, Overijssel, Gelderland, and Flevoland) for which both ALS and Sentinel data were available for the same year. The land cover metrics were derived using the Dutch land cover map from 2018 (LGN2018 or LGN8). The country-wide LiDAR point clouds were derived from the third Dutch national ALS flight campaign (AHN3, Actueel Hoogtebestand Nederland). The AHN3 dataset is openly accessible data available from (<a href="https://ahn.arcgisonline.nl/ahnviewer/">https://ahn.arcgisonline.nl/ahnviewer/</a>). The Sentinel datasets were processed using Google Earth Engine. </p>
Data from: Predicting habitat suitability and connectivity for management and conservation of urban wildlife: A real-time web application for grassland water voles
<ol> <li>Natural habitats in urban areas provide benefits for both humans and biodiversity. However, to achieve biodiversity gains we require new techniques to determine habitat suitability and ecological connectivity that will inform urban planning and development.</li> <li>Using an example of an urban population of water voles (<i>Arvicola amphibius</i>) we developed a habitat suitability model and a resistance-surface-based model of landscape connectivity to identify potential connectivity between areas of suitable habitat. We then updated the environmental variables according to new urban development plans and used our models to generate spatially explicit predictions of both habitat suitability and connectivity.</li> <li>To make models accessible to urban and conservation planners we developed an interactive mapping tool that provided users with a graphical user interface (GUI) to inform conservation planning for this species.</li> <li>The model found that habitat suitability for water voles was related to distance from key environmental variables, such as built-up areas and urban green spaces, while the connectivity model identified important corridors connecting areas of potential distribution for this species.</li> <li>Future development plans altered the potential spatial distribution of the water vole population, reducing the extent of suitable habitat in some core areas. The interactive mapping tool made available suitable habitat and connectivity maps for conservation managers to assess new planning applications and for the development of a conservation action plan for water voles.</li> <li>Synthesis and applications: We believe this approach provides a framework for future development of nature conservation tools that can be used by planners to inform ecological decision making, increase biodiversity and reduce human-wildlife conflict in urban environments.</li> </ol>
Habitat structural complexity predicts cognitive performance and behavior in western mosquitofish
<p>Urbanization and stream order alter freshwater habitat complexity (defined as the degree of variation in physical habitat structure). More complex habitats have more variation in habitat structure. Habitat complexity affects species composition and shapes animal ecology, behavior, and cognition. We used a delayed detour test to measure whether motor self-regulation and behavior of Western mosquitofish, <em>Gambusia affinis, </em>varied with habitat structural complexity that was quantified for nine populations. We predicted that motor self-regulation, motivation, and risk-taking behavior would increase with increasing habitat complexity, yet we found the opposite relationship. Lower complexity habitats offer less refuge which could increase predation pressure and select for greater risk-taking by fish with greater motor self-regulation. Our findings provide insight into how habitat complexity is related to cognitive processes and behavioral outcomes, and provide an explanation for why some species have a higher tolerance for urbanized environments.</p>
Spatiotemporal predictions of the alternative prey hypothesis: Predator habitat use during decreasing prey abundance
<p>The alternative prey hypothesis supposes that predators supported by a primary prey species will shift to consume alternative prey during a decrease in primary prey abundance. The hypothesis implies that during declines of one prey species, a predator modifies their behavior to exploit a secondary, or alternative, species. Despite occurring in many systems, the behavioral mechanisms (e.g., habitat selection) allowing predators to shift toward alternative prey during declines in the abundance of their primary prey are poorly understood. We evaluated habitat selection and use by a generalist predator with respect to two prey species during a dramatic decrease in the abundance of primary prey. Further, we evaluated how spatial variation in access to primary prey affected habitat selection and assessed similarity and overlap between habitats used by each prey species. Coyotes (<em>Canis</em> <em>latrans</em>) exhibited decreasing selection for cottontail rabbits (<em>Sylvilagus</em> spp.; primary prey) during population decreases but did not shift habitat selection toward neonate mule deer (<em>Odocoileus</em> <em>hemionus</em>; alternative prey). Use of rabbit habitat remained high even during historically low rabbit abundance, while mule deer habitat was used in proportion to its availability. Coyotes seemingly do not make large shifts in habitat selection toward alternative prey following spatial and temporal decreases in the abundance of primary prey, but instead, take advantage of habitat overlap to facilitate prey-switching behavior. Our work extends previous research conducted under the alternative prey hypothesis by explicitly evaluating the influence of habitat overlap between prey species and variation in access to prey habitat as factors affecting prey-switching behaviors in predators.</p>
ScienceDex guides
Understand access before you commit
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.