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.
1,042
datasets available to search
ShareScore release 0.9.0
Dataset results
1,042 results for “model species”
Data from: Wallace 2: A shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions
<p>These are the occurrence locality datasets used in the example provided in "<em>wallace</em> 2: a <em>shiny</em> app for modeling species niches and distributions redesigned to facilitate expansion via module contributions" published in Ecography (DOI: 10.1111/ecog.06547). The analysis workflow is displayed in the Supporting information of the paper (Fig. S1), and these data are also used in the <em>wallace</em> 2 vignette (<a href="https://wallaceecomod.github.io/wallace/articles/tutorial-v2.html">https://wallaceecomod.github.io/wallace/articles/tutorial-v2.html</a>).</p>
Dataset for the manuscript: "Three-dimensional species distribution modeling reveals the realized spatial niche for coral recruitment on contemporary Caribbean reefs"
<p>Whether the three-dimensional (3D) structure of habitats influences and partition recruitment niches of corals is unknown. We developed a new method that combined Species Distribution Modeling and Structure from Motion to characterize and map the three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and octocorals. </p> <p>In this repository, we include 48 3D models of small areas of the reef (i.e., within ~ 0.25 m<sup>2</sup> quadrats) reconstructed with Structure-from-Motion, as well as the geospatial data used to characterize and map the realized recruitment niche for scleractinian corals and octocorals on Caribbean coral reefs. We conducted the study at two shallow, fringing reefs off the south shore of St. John, US Virgin Islands, named Grootpan and Europa Bays (18° 18.360’N, 64° 43.140’W, and 18° 19.016’N, 64° 43.798’W, respectively). Within each 0.25 m<sup>2</sup> quadrat, we counted and marked all recruits (octocorals ≤ 5 cm height, and scleractinians ≤ 4 cm wide).</p> <p><em>DATASET DESCRIPTIONS:</em></p> <ul> <li><strong>"Quadname_data.zip":</strong> In each of this folders we included all the data calculated within a quadrat: <ul> <li>ASCII files (.txt).</li> <li>The annotated dense point cloud (.las) for each quadrat.</li> <li>The quadrat 3D model texture (.jpg).</li> <li>The quadrat 3D polygon mesh (.ply).</li> <li>The quadrat 2.5D Digital Elevation Model (i.e., DEM; .tif).</li> <li>Shape files with recruits local coordinates within each quadrat (.dbf, .prj, .shp, .shx).</li> </ul> </li> <li><strong>"datawide.rds": </strong>This is the file needed to run the analyses performed in Martínez-Quintana et al., 2023. This file is obtained after processing all the raw data calculated within each quadrat. All code associated with the workflow used to obtain the datawide.rds file and run the analyses performed in Martínez-Quintana et al., 2023 is available at <a href="https://github.com/AdamWilsonLab/meshSDM">github.com/AdamWilsonLab/meshSDM</a>.</li> </ul> <p><strong>IMPORTANT NOTES: </strong></p> <ul> <li>Quadrat names starting with the letters “eu” indicate the data were collected at Europa Bay, whereas those starting with the letters “ec” indicate that data were collected at Grootpan Bay (commonly named East Cabritte).</li> <li>Each ASCII file (quadname_ASCII_subsampled_X.txt) contains the slope and roughness of the quadrat calculated on the point cloud at 5, 10, 20, and 100 mm scales, and the smooth point cloud used to calculate the topographic exposure index (TEI) described in Martínez-Quintana et al., 2023. Calculations were performed and ASCII files were created with CloudCompare.</li> <li>Each dense point cloud, mesh, texture, and DEM were calculated with Agisoft Metashape.</li> <li>Agisoft Metashape allows the user to classify and annotate groups of points in the dense point cloud. However, the list of classes provided by the software corresponds to the standard list used for terrestrial LiDAR data; these classes cannot be renamed within the software. Thus, for the present study, we coded the automatic semantic classifications available in Metashape as follows: <ul> <li>Ground = Calcareous rock.</li> <li>Building = Igneous rock.</li> <li>High noise = Sand.</li> <li>Low vegetation = Adult Scleractinian corals.</li> <li>Medium vegetation = Adult Octocoral base.</li> <li>High vegetation = Sponge.</li> <li>Water = Octocoral recruit (named also ocr).</li> <li>Road Surface = Scleractinian recruit (named also scr).</li> <li>Unclassified = created points but never classified (excluded from the analyses).</li> <li>Low Point = noise (unreliable points).</li> <li>Transmission tower and Rail = Points outside the quadrat and excluded from the analysis.</li> </ul> </li> </ul>
Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study
<p>Data and R codes necessary to replicate the analyses presented in the paper entitled "Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study", published in Peer Community in Ecology (<a href="https://doi.org/10.24072/pcjournal.263">10.24072/pcjournal.263</a>).</p>
A big data–model integration approach for predicting epizootics and population recovery in a keystone species
<p>Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex non-linear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a non-parametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, <em>Cynomys ludovicianus</em>) and sylvatic plague as an example. We synthesized colony data between 2001–2020 from eight USDA Forest Service National Grasslands across the range of BTPD in central North America. We then modeled extinctions due to plague and colony recovery of BTPD in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summer/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally > 0.80). Thus, these spatially-explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially-explicit forecasting of disease-induced population fluctuations, for use in natural resource management decision-making.</p>
Data from: Whole-body variational modularity in the zebrafish: An inside-out story of a model species
<p><span>Actinopterygians are the most diversified clade of extant vertebrates. Their impressive morphological disparity bears witness to tremendous ecological diversity. Modularity, the organization of biological systems into quasi-independent anatomical/morphological units, is thought to increase evolvability of organisms and facilitate morphological diversification. Our study aims to quantify patterns of variational modularity in a model actinopterygian, the zebrafish (</span><em>Danio rerio</em>), using 3D-geometric morphometrics on osteological structures isolated from micro-CT scans. 72 landmarks were digitised along cranial and postcranial ossified regions of 30 adult zebrafishes. Two methods were used to test modularity hypotheses, the covariance ratio and the distance matrix approach. We find strong support for two modules, one comprised of paired fins and the other comprised of median fins, that are best explained by functional properties of subcarangiform swimming. While the skull is tightly integrated with the rest of the body, its intrinsic integration is relatively weak supporting previous findings that the fish skull is a modular structure. Our results provide additional support for the recognition of similar hypotheses of modularity identified based on external morphology in various teleosts, and at least two variational modules are proposed. Thus, our results hint at the possibility that internal and external modularity patterns may be congruent.</p>
Model outputs and species-level data for "Functional traits and climate drive interspecific differences in disturbance-induced tree mortality"
<p>This repository is divided in three sub-directories: </p> <ul> <li><em><strong>sensitivity </strong></em>contains the posterior of each parameter estimated by the bayesian mortality model in a rdata file. This file was generated by the script https://github.com/jbarrere3/SalvageModel/tree/withFinland</li> <li><em><strong>climate </strong></em>contains for each tree species the climatic variables (mean annual temperature, minimum annual temperature and annual precipitation) extracted from CHELSA and the disturbance-related climatic indices (Fire Weather Index, Snow Water Equivalent and Gust Wind Speed)</li> <li><em><strong>traits </strong></em>contains the traits calculated directly with NFI data (bark thickness, height to dbh ratio, maximum growth), and a text file with the Species and Trait ID to request to TRY database. </li> </ul> <p>The content of this repository can be used to reproduce the analyses of the paper, with the script stored in in https://github.com/jbarrere3/DisturbancePaper</p> <p><strong>Edit (19/09/2023):</strong> A minor coding error was found in the pre-formatted data of the paper, which did not affect the main results but led to minor change in the value of the posterior estimates. An updated version of the posterior estimates of this dataset was made available at https://zenodo.org/record/8358921. </p>
Revealing the drivers of parasite community assembly: using avian haemosporidians to model global dynamics of parasite species turnover
<p>Why do some regions share more or fewer species than others? Community assembly relies on the ability of individuals to disperse, colonize, and thrive in new regions. Therefore, many distinct factors, such as geographic distance and environmental features, can determine the odds of a species colonizing a new environment. For parasites, host community composition (i.e., resources) also plays a key role in their ability to colonize a new environment as they rely on their hosts to complete their life cycle. Thus, variation in host community composition and environmental conditions should determine parasite turnover among regions. Here, we explored the global drivers of parasite turnover using avian malaria and malaria-like (haemosporidian) parasites. We compiled global databases on avian haemosporidian lineages distributions, environmental conditions, avian species distributions and functional traits and ran generalized dissimilarity models to uncover the main drivers of parasite turnover. We demonstrated that haemosporidian parasite turnover is mainly driven by geographic distance followed by host functional traits, environmental conditions, and host distributions. The main host functional traits associated with high parasite turnover were the predominance of resident (i.e., non-migratory) species and strong territoriality while the most important climatic drivers of haemosporidian turnover were mean temperature and temperature seasonality. Overall, we establish the importance of geographic distance as a key predictor of ecological dissimilarity and show that host resources influence parasite turnover more strongly than environmental conditions. We also evidenced that parasite turnover is most pronounced among tropical and less interconnected regions (i.e., regions with mostly territorial and non-migratory hosts). Our findings provide a robust foundation for the prediction of avian pathogen spread and the emergence of infectious diseases.</p>
Making virtual species less virtual by reverse engineering of spatiotemporal ecological models v3
<p>The most up-to-date version of the archive that contains all the necessary data to perform analyses supporting the paper <em>Making virtual species less virtual by reverse engineering of spatiotemporal ecological models </em>(<a href="https://doi.org/10.1111/2041-210X.14176">https://doi.org/10.1111/2041-210X.14176</a>) .</p> <p>The research was supported by the National Science Centre, Poland (grant no. 2018/29/B/NZ8/00066) and Poznań Supercomputing and Networking Centre (grant no. 403). </p> <p> </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>
UAV-Based Height Measurement and Height-Diameter Model integrating Taxonomic Effects: Exploring Vertical Structure of Aboveground Biomass and Species Diversity in a Malaysian Tropical Forest
<p>These Excel files are the dataset used for the analysis in the submitted paper</p> <p>Dataset S1: Data for 6-ha pot in Pasoh Forest Researve</p> <p>Dataset S2: Data for height–diameter (HD) models</p>
Virulence of three Aspergillus species to the model insect Galleria mellonella and the contribution of ergot alkaloids to the pathogenic potential of Aspergillus leporis
<p>Opportunistically pathogenic fungi have varying potential to cause disease in animals. Factors contributing to their virulence include specialized metabolites, which is some cases evolved in contexts unrelated to pathogenesis. Specialized metabolites that increase fungal virulence in the model insect <em>Galleria mellonella</em> include the ergot alkaloids fumigaclavine C in <em>Aspergillus fumigatus</em> (syn. <em>Neosartorya fumigata</em>) and lysergic acid α-hydroxyethylamide (LAH) in the entomopathogen <em>Metarhizium brunneum</em>. Three species of <em>Aspergillus</em> recently found to accumulate high concentrations of LAH were investigated for their pathogenic potential in <em>G. mellonella</em>. A<em>spergillus leporis</em> was most virulent, <em>A. hancockii </em>was intermediate, and <em>A. homomorphus</em> had very little pathogenic potential. <em>Aspergillus leporis</em> and <em>A. hancockii </em>emerged from and sporulated on dead insects, thus completing their asexual life cycles. Inoculation by injection resulted in more lethal infections than did topical inoculation, indicating <em>A. leporis</em> and <em>A. hancockii</em> were pre-adapted for insect pathogenesis but lacked an effective means to breach the insect's cuticle. All three species accumulated LAH in infected insects, with <em>A. leporis</em> accumulating the most. Concentrations of LAH in <em>A. leporis</em> were similar to those observed in the entomopathogen <em>M. brunneum</em>. LAH was eliminated from <em>A. leporis</em> through a CRISPR/Cas9-based gene knockout, and the resulting strain had reduced virulence to <em>G. mellonella</em>. The data indicate <em>A. leporis</em> and <em>A. hancockii</em> have considerable pathogenic potential and that LAH increases the virulence of <em>A. leporis</em>.</p>
Data for: Using distribution models to identify range shifts of four Acroneuria Pictet, 1841 (Plecoptera: Perlidae) species in the Midwest USA
<p><span></span></p> <p>Regional faunal assessments of stoneflies in the United States Midwest (herein defined as Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio, and Wisconsin) indicate increasing imperilment resulting from human disturbance and climate change. Large-bodied Perlid stoneflies with multivoltine life cycles are among the most at risk for regional extirpation, with losses reported in several midwestern states. Species distribution modeling was undertaken to describe distribution shifts for four widespread riverine species: <em>Acroneuria</em> <em>abnormis</em> (Newman, 1838), <em>A. frisoni </em>Stark & Brown, 1991, <em>A. internata </em>(Walker, 1852) and <em>A. lycorias</em> (Newman, 1839). The distribution modeling algorithm MaxEnt was selected to predict both the historical (i.e., pre-1960) and contemporaneous distributions for each species using separate occurrence datasets. These models permit the identification of suitable habitat loss through range contractions associated with human disturbance. Predictions of suitable habitat losses were recorded for multiple species but were greatest for<em> A. abnormis</em> and <em>A</em>. <em>internata</em>. These models serve to guide future collection efforts and to further describe patterns of regional biodiversity loss. The data presented within this dataset contain the occurrence data used for modeling with distinction between temporal periods modeled.</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>
Low- and high-intensity fire in the riparian savanna: demographic impacts in an avian model species and implications for ecological fire management
<ol> <li><span>Climate change is driving changes in fire frequency and intensity, making it more urgent for conservation managers to understand how species and ecosystems respond. In tropical monsoonal savannas – Earth's most fire-prone landscapes – ecological fire management aims to prevent intense wildfires late in the dry season through prescribed low-intensity burns early in the dry season. Riparian habitats embedded within tropical savannas represent critical refuges for biodiversity, yet are particularly fire-sensitive. Better understanding of the impact of fire – including prescribed burns – on riparian habitats is therefore key but requires long-term detailed post-fire monitoring of species' demographic rates, as effects may persist and/or be delayed.</span></li> <li><span>We analyse impacts of (prescribed) low-intensity and (prescribed but escaped) high-intensity fire in northern Australian riparian and adjacent savanna habitat. We quantify multi-year impacts on density, survival, reproduction and dispersal of an Endangered riparian bird, the western purple-crowned fairy-wren (<em>Malurus coronatus coronatus</em>), in a well-studied individually-marked population.</span></li> <li><span>Following low-intensity fire, bird density was reduced by >20% in burnt compared to adjacent unburnt riparian habitat for ≥2.5 years. This was a result of reduced breeding success and recruitment for two years immediately following fire, rather than mortality or dispersal of adults.</span></li> <li><span>In contrast, high-intensity fire (in a dry year) resulted in a sharp decline in population density by 50% 2–8 months after fire, with no signs of recovery after 2.5 years. The decline in density was due to post-fire adult mortality, rather than dispersal. Breeding success of the (few) remaining individuals was low but not detectably lower than in unburnt areas, likely because breeding success was poor overall due to prevailing dry conditions.</span></li> <li> <span><em>Synthesis and applications</em>. </span><span>Even if there is no or very low mortality during fire, and no movement of birds away from burnt areas, both low- and high-intensity fire in the riparian zone reduce population density. However, the mechanism by which this occurs, and recovery time, differs with fire intensity. To minimise impacts of fire on riparian zones in tropical savannas, we suggest employing low-intensity prescribed burns under optimal conditions shortly after the breeding season.</span> </li> </ol>
Multi‐species occupancy modeling reveals methodological and environmental effects on eDNA detection of amphibians in temporary ponds
<p>Aquatic environmental DNA is increasingly used for biodiversity monitoring, such as surveying threatened and invasive species. Mainstreaming these methods in practical applications, however, still requires significant standardisation and optimisation, namely regarding DNA capture methods. Here we evaluated how filter type (standard disc filters vs high-capacity capsules), number of sampling sites, volume of water filtered and environmental factors affected amphibian detection in Mediterranean temporary ponds. The study involved water filtering until clogging at one (capsules) and five (discs) sites from 16 small and shallow ponds, where three urodele and seven anuran species were recorded through sweep-netting and adult observations. Detection probabilities were estimated from site occupancy models based on replicate sampling and from an adaptation of time-to-detection models relating detection probability to volume of water filtered. Discs filtered relatively small volumes (15–1250 mL), with detection probabilities of the two abundant species (<em>Pelobates</em> <em>cultripes</em>, <em>Hyla</em> <em>meridionalis</em>) increasing rapidly with sample size and water volume, reaching almost perfect detection (0.95) at four and seven discs, and 420 mL and 1860 mL, respectively. However, reaching high detection probabilities for rare species (<em>Pelodytes</em> <em>atlanticus</em>, <em>Pleurodeles</em> <em>waltl</em>, <em>Triturus</em> <em>pygmaeus</em>) would require larger sampling effort than that used in our study. Despite filtering much larger volumes (600–5300 mL), filtering with capsules at a single site per pond provided lower detection probabilities for abundant species than filtering with discs at five sites. Rarer species showed no difference between methods, which may be due to small sample sizes and reduced statistical power for species with few detection. The effect of conductivity on species detectability was largely negative, while the influence of water clarity varied across species, and pH had no effects. Overall, our results suggest that eDNA amphibian surveys in Mediterranean temporary ponds need to consider filter clogging, heterogeneous DNA distribution, and highly conductive waters.</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>
Joint species distribution modeling reveals a changing prey landscape for North Pacific right whales on the Bering shelf
<p>The eastern North Pacific right whale (NPRW) is the most endangered population of whale and has been observed north of its core feeding ground in recent years with low sea ice extent. Sea ice and water temperature are important drivers for zooplankton dynamics within the whale's core feeding ground in the southeastern Bering Sea, seasonally forming stable fronts along the shelf that give rise to distinct zooplankton communities. A northward shift in NPRW distribution driven by changing distribution of prey resources could put this species at increased risk of entanglement and vessel strikes. We modeled the abundance of NPRW prey, <em>Calanus glacialis</em>, <em>Neocalanus</em>, and <em>Thysanoessa</em> species, using a dynamic biophysical food web model of nine zooplankton guilds in the Bering shelf zooplankton community during a period of warming (2006–2016). This model is unique from prior zooplankton studies from the region in that it includes density dependence, thereby allowing us to ask whether species interactions influence zooplankton dynamics. Modeling confirmed the importance of sea ice and ocean temperature to zooplankton dynamics in the region. Density-independent growth drove community dynamics while dependent factors were comparatively minimal. Overall, <em>Calanus</em> responded to environmental terms, with the strength and direction of response driven by copepodite stage. <em>Neocalanus</em> and <em>Thysanoessa</em> responses were weaker, likely due to their primary occurrence on the outer shelf. We also modeled the steady-state (equilibrium) abundance of <em>Calanus</em> in conditions with and without wind gusts to test whether advection of outer shelf species might disrupt steady-state dynamics of <em>Calanus</em> abundance; results did not support disruption. Given the annual fall sampling design, we interpret our results as follows: low ice-extent winters induced stronger spring winds and weakened fronts on the shelf, thereby advecting some outer shelf species into the study region; increased development rates in these warm conditions influenced the proportion of <em>C. glacialis</em> copepodite stages over the season. Residual correlation suggests missing drivers, possibly predators and phytoplankton bloom composition. Given the continued loss of sea ice in the region and projected continued warming, our findings suggest that <em>C. glacialis</em> will move northward, and thus, whales may move northward to continue targeting them.</p>
Data from: Too much of a good thing? Supplementing current species observations with fossil data to assess climate change vulnerability via ecological niche models
Open the record for dataset details and reuse information.
Data from: Hindcast-validated species distribution models reveal future vulnerabilities of mangroves and salt marsh species
Open the record for dataset details and reuse information.
Data from: One model to rule them all: Identifying priority bat habitats from multi‐species habitat suitability models
Open the record for dataset details and reuse information.
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.