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”
Predicting geographic distribution and habitat suitability of Opuntia streptacantha in paleoclimatic, current, and future scenarios in Mexico
<p>Geographical records. A total of 825 records (Figure 1), representing the natural distribution historically recognized for <em>O</em>. <em>streptacantha.</em></p> <p>Maps for past, current, and future models in QGIS format</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>
Habitat structural complexity predicts cognitive performance and behavior in western mosquitofish
Open the record for dataset details and reuse information.
Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models
Open the record for dataset details and reuse information.
Habitat suitability modeling to predict the spatial distribution of cold-water coral communities affected by the Deepwater Horizon oil spill
Open the record for dataset details and reuse information.
Spatiotemporal predictions of the alternative prey hypothesis: Predator habitat use during decreasing prey abundance
Open the record for dataset details and reuse information.
Climate, habitat and demography predict dispersal by an endangered bird in a fragmented landscape
Open the record for dataset details and reuse information.
Landscape features and seasonal habitat predicts lek-site selection and lek size of a <em>Tympanuchus</em> grouse
Open the record for dataset details and reuse information.
Data from: Predicting habitat suitability and connectivity for management and conservation of urban wildlife: A real-time web application for grassland water voles
Open the record for dataset details and reuse information.
Data from: Combined mechanistic modelling predicts changes in species distribution and increased co-occurrence of a tropical urchin herbivore and a habitat-forming temperate kelp
Open the record for dataset details and reuse information.
Habitat openness and edge avoidance predict saltmarsh sparrow abundance better than habitat area
Open the record for dataset details and reuse information.
Data from: The importance of using topographic features to predict climate-resilient habitat for migratory forest landbirds: an example for the Rusty Blackbird, Olive-sided Flycatcher, and Canada Warbler
<p>Maintaining a functionally-connected network of high-quality habitat is one of the most effective responses to biodiversity loss. However, the spatial distribution of suitable habitat may shift over time in response to climate change. Taxa such as migratory forest landbirds are already undergoing climate-driven range shifts. Therefore, patches of climate-resilient habitat (also known as "climate refugia") are especially valuable from a conservation perspective. Here, we performed maximum entropy (Maxent) species distribution modeling to predict suitable and potentially climate-resilient habitat in Nova Scotia, Canada, for three migratory forest landbirds: Rusty Blackbird (Euphagus carolinus), Olive-sided Flycatcher (Contopus cooperi), and Canada Warbler (Cardellina canadensis). We used a reverse stepwise elimination technique to identify covariates that influence habitat suitability for the target species at broad scales, including abiotic (topographic control of moisture and nutrient accumulation) and biotic (forest characteristics) covariates. As topography should be relatively unaffected by a changing climate and helps regulate the structure and composition of forest habitat, we posit that the inclusion of appropriate topographic features may support the identification of climate-resilient habitat. Of all covariates, Depth to water table was the most important predictor of relative habitat suitability for the Rusty Blackbird and Canada Warbler, with both species showing a strong association with wet areas. Mean canopy height was the most important predictor for the Olive-sided Flycatcher, whereby the species was associated with taller trees. Our models, which comprise the finest scale species distribution models available for these species in this region, further indicated that, for all species, habitat (1) remains relatively abundant and well distributed in Nova Scotia and (2) is often located in wet lowlands (a climate-resilient topographic landform). These findings suggest that opportunities remain to conserve breeding habitat for these species despite changing temperature and precipitation regimes.</p>
Remotely sensed forest understory density and nest predator occurrence interact to predict suitable breeding habitat and the occurrence of a resident boreal bird species
<p>Habitat suitability models (HSM) based on remotely sensed data are useful tools in conservation work. However, they typically use species occurrence data rather than robust demographic variables, and their predictive power is rarely evaluated. These shortcomings can result in misleading guidance for conservation. Here, we develop and evaluate a HSM based on correlates of long term breeding success of an open nest building boreal forest bird, the Siberian jay. In our study site in northern Sweden, nest failure of this permanent resident species is driven mainly by visually hunting corvids that are associated with human settlements. Parents rely on understory nesting cover as protection against these predators. Accordingly, our HSM includes a light detection and ranging (LiDAR) based metric of understory density around the nest and the distance of the nest to the closest settlement to predict breeding success. It reveals that a high understory density 15-80 m around nests is associated with increased breeding success in territories close to settlements (<1.5 km). Farther away from human settlements breeding success is highest at nest sites with a more open understory providing a favourable warmer microclimate. We validated this HSM by comparing the predicted breeding success with landscape-wide census data on Siberian jay occurrence. The correlation between breeding success and occurrence was strong up to 40 km around the study site. However, the HSM appears to overestimate breeding success in regions with a milder climate, and therefore higher corvid numbers. Our findings suggest that maintaining patches of small diameter trees may provide a cost-effective way to restore the breeding habitat for Siberian jays up to 1.5 km from human settlements. This distance is expected to increase in the warmer, southern, and coastal range of the Siberian jay where the presence of other corvids is to a lesser extent restricted to settlements.</p>
Data from: Density-dependent habitat selection predicts fitness and abundance in a small lizard
Density-dependent habitat selection has been used to predict and explain patterns of abundance of species between habitats. Thermal quality, a density-independent component of habitat suitability, is often the most important factor for habitat selection in ectotherms which comprise the vast majority of animal species. Ectotherms may reach high densities such that individual fitness is reduced in a habitat due to increased competition for finite resources. Therefore, density and thermal quality may present conflicting information about which habitat will provide the highest fitness reward and ectotherm habitat selection may be density-independent. Using ornate tree lizards Urosaurus ornatus at 10 sites each straddling two adjacent habitats (wash and upland), we tested the hypothesis that habitat selection is density-dependent even when thermal quality differs between habitats. We first tested that fitness proxies decline with density in each habitat, indicating density-dependent effects on habitat suitability. We also confirmed that the two habitats vary in suitability (quantified by food abundance and thermal quality). Next, we tested the predictions that habitat selection depends on density with isodar analyses and that fitness proxies are equal in the two habitats within a site. We found that monthly survival rates decreased with density, and that the wash habitat had more prey and higher thermal quality than the upland habitat. Lizards preferred the habitat with more food and higher thermal quality, lizard densities in the two habitats were positively correlated, and fitness proxies of lizards did not differ between habitats. These patterns are consistent with density-dependent habitat selection, despite differences in thermal quality between habitats. We expect that density-dependent habitat selection is widespread in terrestrial ectotherms when densities are high and temperatures are close to their optimal performance range. In areas where thermal quality is low, however, we expect that depletable resources, such as food, become less limiting because assimilating resources is more difficult.
Data from: Habitat selection predicts genetic relatedness in an alpine ungulate
Landscape heterogeneity plays an integral role in shaping ecological and evolutionary processes. Despite links between the two disciplines, ecologists and population geneticists have taken different approaches to evaluating habitat selection, animal movement, and gene flow across the landscape. Ecologists commonly use statistical models such as resource selection functions (RSFs) to identify habitat features disproportionately selected by animals, while population genetic approaches model genetic differentiation according to the distribution of habitat variables. We combined ecological and genetic approaches by using RSFs and step-selection functions (SSFs) to predict genetic relatedness across a heterogeneous landscape. We constructed sex and season-specific resistance surfaces based on RSFs and SSFs estimated using data from 102 GPS radiocollared mountain goats (Oreamnos americanus) in southeast Alaska. Based on mountain goat ecology, we hypothesized that summer and male surfaces would be the best predictors of relatedness. All individuals were genotyped at 22 microsatellite loci, which we used to estimate genetic relatedness. Summer resistance surfaces derived from RSFs were the best predictors of genetic relatedness, and winter models the poorest. Male and female specific surfaces were similar, except for winter where male habitat selection better predicted genetic relatedness. The null models of isolation-by-distance and barrier only outperformed the winter models. This study merges high-resolution individual locations through GPS telemetry and genetic data, that can be used to validate and parameterize landscape genetics models, and further elucidates the relationship between landscape heterogeneity and genetic differentiation.
Data from: The influence of data resolution on predicted distribution and estimates of extent of current protection of three 'listed' deep-sea habitats
Modelling approaches have the potential to significantly contribute to the spatial management of the deep-sea ecosystem in a cost effective manner. However, we currently have little understanding of the accuracy of such models, developed using limited data, of varying resolution. The aim of this study was to investigate the performance of predictive models constructed using non-simulated (real world) data of different resolution. Predicted distribution maps for three deep-sea habitats were constructed using MaxEnt modelling methods using high resolution multibeam bathymetric data and associated terrain derived variables as predictors. Model performance was evaluated using repeated 75/25 training/test data partitions using AUC and threshold-dependent assessment methods. The overall extent and distribution of each habitat, and the percentage contained within an existing MPA network were quantified and compared to results from low resolution GEBCO models. Predicted spatial extent for scleractinian coral reef and Syringammina fragilissima aggregations decreased with an increase in model resolution, whereas Pheronema carpenteri total suitable area increased. Distinct differences in predicted habitat distribution were observed for all three habitats. Estimates of habitat extent contained within the MPA network all increased when modelled at fine scale. High resolution models performed better than low resolution models according to threshold-dependent evaluation. We recommend the use of high resolution multibeam bathymetry data over low resolution bathymetry data for use in modelling approaches. We do not recommend the use of predictive models to produce absolute values of habitat extent, but likely areas of suitable habitat. Assessments of MPA network effectiveness based on calculations of percentage area protection (policy driven conservation targets) from low resolution models are likely to be fit for purpose.
Data from: Predictive modelling of habitat selection by marine predators with respect to the abundance and depth distribution of pelagic prey
1. Understanding the ecological processes that underpin species distribution patterns is a fundamental goal in spatial ecology. However, developing predictive models of habitat use is challenging for species that forage in marine environments, as both predators and prey are often highly mobile and difficult to monitor. Consequently, few studies have developed resource selection functions for marine predators based directly on the abundance and distribution of their prey. 2. We analysed contemporaneous data on the diving locations of two seabird species, the shallow-diving Peruvian Booby (Sula variegata) and deeper diving Guanay Cormorant (Phalacrocorax bougainvilliorum), and the abundance and depth distribution of their main prey, Peruvian anchoveta (Engraulis ringens). Based on this unique data set, we developed resource selection functions to test the hypothesis that the probability of seabird diving behaviour at a given location is a function of the relative abundance of prey in the upper water column. 3. For both species, we show that the probability of diving behaviour is mostly explained by the distribution of prey at shallow depths. While the probability of diving behaviour increases sharply with prey abundance at relatively low levels of abundance, support for including abundance in addition to the depth distribution of prey is weak, suggesting that prey abundance was not a major factor determining the location of diving behaviour during the study period. 4. The study thus highlights the importance of the depth distribution of prey for two species of seabird with different diving capabilities. The results complement previous research that points towards the importance of oceanographic processes that enhance the accessibility of prey to seabirds. The implications are that locations where prey is predictably found at accessible depths may be more important for surface foragers, such as seabirds, than locations where prey is predictably abundant. 5. Analysis of the relative importance of abundance and accessibility is essential for the design and evaluation of effective management responses to reduced prey availability for seabirds and other top predators in marine systems.
Data from: Predicting species occurrences with habitat network models
1. Biodiversity conservation requires modelling tools capable of predicting the presence or absence (i.e. occurrence-state) of species in habitat patches. Local habitat characteristics of a patch (lh), the cost of traversing the landscape matrix between patches (weighted connectivity; (wc), and the position of the patch in the habitat network topology (nt) all influence occurrence-state. Existing models are data demanding or consider only local habitat characteristics. We address these shortcomings and present a network-based modelling approach, which aims to predict species occurrence-state in habitat patches using readily available presence-only records. 2. For the tree frog Hyla arborea on the Swiss Plateau, we delineated habitat network nodes from an ensemble habitat suitability model, and used different cost surfaces to generate the edges of three networks: one limited only by dispersal distance (Uniform), another incorporating traffic, and a third based on inverse habitat suitability. For each network, we calculated explanatory variables representing the three categories (lh, wc and nt). The response variable, occurrence-state, was parametrized by a sampling-intensity procedure assessing observations of comparable species over a threshold of patch visits. The explanatory variables from the three networks and an additional non-topological model were related to the response variable with boosted regression trees. 3. The habitat network models had a similar fit; they all outperformed the non-topological model. Habitat suitability index ((lh) was the most important predictor in all networks, followed by third-order neighborhood (nt). Patch size (lh) was unimportant in all three networks. 4. We found that topological variables of habitat networks are relevant for the prediction of species occurrence-state, a step-forward from models considering only local habitat characteristics. For any habitat patch, occurrence-state is most prominently influenced by its habitat suitability, and then by the number of patches in a wide neighborhood. Our approach is generic and can be applied to multiple species in different habitats.
Data from: Habitat niche breadth predicts invasiveness in solitary ascidians
A major focus of invasion biology is understanding the traits associated with introduction success. Most studies assess these traits in the invaded region, while only few compare non-indigenous species to the pool of potential invaders in their native region. We focused on the niche-breadth hypothesis, commonly evoked but seldom tested, which states that generalist species are more likely to become introduced since they are capable of thriving under a wide set of conditions. Based on the massive introduction of tropical species into the Mediterranean via the Suez Canal (Lessepsian migration), we defined ascidians in the Red Sea as the pool of potential invaders. We constructed unique settlement plates, each representing six different niches, to assess ascidian niche breadth, and deployed them in similar habitats in the native and invaded regions. For each species found on plates, we evaluated its abundance, relative abundance across successional stages and niche breadth, and then compared (1)species in the Red Sea known to have been introduced into the Mediterranean (Lessepsian species) and those not known from the Mediterranean (non-Lessepsian); and (2)non-indigenous and indigenous species in the Mediterranean. Lessepsian ascidians identified on plates in the Red Sea demonstrated wider niche breadth than non-Lessepsian ascidians, supporting the niche-breadth hypothesis within the native region. No differences were found between Lessepsian and non-Lessepsian species in species abundance and successional stages. In the Mediterranean, non-indigenous species numerically dominated the settlement plates. This precluded robust comparisons of niche breadth between non-indigenous and indigenous species in the invaded region. In conclusion, using Red Sea ascidians as the pool of potential invaders, we found clear evidence supporting the niche-breadth hypothesis in the native region. We suggest that such patterns may often be obscured when conducting trait-based studies in the invaded regions alone. Our findings indicate that quantifying the niche breadth of species in their native regions will improve estimates of invasiveness potential.
Data from: Use of opportunistic sightings and expert knowledge to predict and compare Whooping Crane stopover habitat
Predicting a species' distribution can be helpful for evaluating management actions such as critical habitat designations under the U.S. Endangered Species Act or habitat acquisition and rehabilitation. Whooping Cranes (Grus americana) are one of the rarest birds in the world, and conservation and management of habitat is required to ensure their survival. We developed a species distribution model (SDM) that could be used to inform habitat management actions for Whooping Cranes within the state of Nebraska (U.S.A.). We collated 407 opportunistic Whooping Crane group records reported from 1988 to 2012. Most records of Whooping Cranes were contributed by the public; therefore, developing an SDM that accounted for sampling bias was essential because observations at some migration stopover locations may be under represented. An auxiliary data set, required to explore the influence of sampling bias, was derived with expert elicitation. Using our SDM, we compared an intensively managed area in the Central Platte River Valley with the Niobrara National Scenic River in northern Nebraska. Our results suggest, during the peak of migration, Whooping Crane abundance was 262.2 (90% CI 40.2−3144.2) times higher per unit area in the Central Platte River Valley relative to the Niobrara National Scenic River. Although we compared only 2 areas, our model could be used to evaluate any region within the state of Nebraska. Furthermore, our expert-informed modeling approach could be applied to opportunistic presence-only data when sampling bias is a concern and expert knowledge is available.
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.