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.
127
datasets available to search
ShareScore release 0.9.0
Dataset results
127 results for “predictive ecology”
Gene prediction for: A reference genome for ecological restoration of the sunflower sea star, Pycnopodia helianthoides
Open the record for dataset details and reuse information.
Ecological and life-history traits predict temporal trends in biomass of boreal moths
Open the record for dataset details and reuse information.
Data from: Community ecology in a bottle: Leveraging eDNA metabarcoding data to predict occupancy of co-occurring species
Open the record for dataset details and reuse information.
Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models
Open the record for dataset details and reuse information.
A direct comparison of ecological theories for predicting the relationship between plant traits and growth
Open the record for dataset details and reuse information.
Island biogeography predicts skull gigantism and shape variation in meadow voles (Microtus pennsylvanicus) through ecological release and allometry
Open the record for dataset details and reuse information.
Ecological patterns of root nodule diversity in cultivated and wild rooibos populations: a community prediction approach
Open the record for dataset details and reuse information.
Data for: Ecology and behavior predict an evolutionary trade-off between song complexity and elaborate plumages in antwrens (Aves, Thamnophilidae)
Open the record for dataset details and reuse information.
Multidimensional trait morphology predicts ecology across ant lineages
<p>1. Understanding the link between ecology and morphology is a fundamental goal in biology. Ants are diverse terrestrial organisms, known to exhibit ecologically-driven morphological variation. While relationships between individual traits and ecologies have been identified, multidimensional interactions among traits and their cumulative predictive power remain unknown. Because selective pressures may generate convergent syndromes spanning multiple traits, we applied multivariate analyses across a wide sampling of taxa to assess ecomorphological variation in an integrative context.</p> <p>2. How well does morphology predict ecology? Moreover, are there quantitatively-supported ant ecomorphs? We investigated the links between trait morphology and ecology by assembling a morphometric dataset spanning over 160 species within 110 genera. Because ants occupy a wide range of ecologies, we compiled natural history data on nesting microhabitat, foraging stratum, and functional role into 35 defined niche combinations. This tripartite ecological classification and our morphological dataset were optimized under dimension reduction techniques including Principal Component Analysis, Principal Coordinate Analysis, Linear Discriminant Analysis, and Random Forest supervised machine learning.</p> <p>3. Our results describe ant ecomorphospace as comprising regions of shared, generalized morphology as well as unique phenotypic space associated with specialized ecologies. Dimension reduction and model-based approaches predict ecology with 77-85% accuracy and Random Forest analysis consistently outperforms LDA. While accounting for shared ancestry, we found eye, antennal scape, and leg morphology to be most informative in differentiating among ecologies. We also note some heterogeneity between trait significance in each ecological aspect (nesting niche, foraging niche, functional role). To increase the utility of ecomorphological classification we simplified our 35 observed niche combinations into 10 ecomorph syndromes, which were also predicted by morphology. The predictive power of these machine learning methods underscores the strong role that ecology has in convergently shaping overall body plan morphology across ant lineages. We include a pipeline for predictive ecomorphological modeling using morphometric data, which may be expanded with additional specimen-based and natural history data.</p>
Data from: Predicting ecological responses in a changing ocean: the effects of future climate uncertainty
Predicting how species will respond to climate change is a growing field in marine ecology, yet knowledge of how to incorporate the uncertainty from future climate data into these predictions remains a significant challenge. To help overcome it, this review separates climate uncertainty into its three components (scenario uncertainty, model uncertainty, and internal model variability) and identifies four criteria that constitute a thorough interpretation of an ecological response to climate change in relation to these parts (awareness, access, incorporation, communication). Through a literature review, the extent to which the marine ecology community has addressed these criteria in their predictions was assessed. Despite a high awareness of climate uncertainty, articles favoured the most severe emission scenario, and only a subset of climate models were used as input into ecological analyses. In the case of sea surface temperature, these models can have projections unrepresentative against a larger ensemble mean. Moreover, 91% of studies failed to incorporate the internal variability of a climate model into results. We explored the influence that the choice of emission scenario, climate model, and model realisation can have when predicting the future distribution of the pelagic fish, Electrona antarctica. Future distributions were highly influenced by the choice of climate model, and in some cases, internal variability was important in determining the direction and severity of the distribution change. Increased clarity and availability of processed climate data would facilitate more comprehensive explorations of climate uncertainty, and increase in the quality and standard of marine prediction studies.
Data from: Ecological interactions and coexistence are predicted by gene expression similarity in freshwater green algae
Phenotypic variation controls the species interactions which determine whether or not species coexist. Long-standing hypotheses in ecology and evolution posit that phenotypic differentiation enables coexistence by increasing the size of niche differentiation. This hypothesis has only been tested using macroscopic traits to date, but niche differentiation, particularly of microscopic organisms, also occurs at the molecular and metabolic level. We examined how phenotypic variation that arises at the level of gene expression over evolutionary time affects phytoplankton species interactions and coexistence. We predicted that similarity in gene expression among species would decline with phylogenetic distance, and that reduced similarity in gene expression would weaken competition, increase facilitation and promote coexistence. To test this, we grew eight species of freshwater green algae in monocultures and bicultures for 46 days in a laboratory microcosm experiment. We quantified the strength of species interactions by: (i) fitting Lotka–Volterra models to time-series densities and estimating interaction coefficients, and (ii) calculating relative densities that compare species' steady-state densities in biculture to those in monoculture. We used Illumina high throughput sequencing to quantify the expression of 1253 families of homologous genes, including a set of 17 candidate genes that we hypothesized a priori to be involved in competition or facilitation. Synthesis. We found that closely related species had greater similarity in gene expression than did distantly related species, but as gene expression became more similar, species experienced weaker competition or greater facilitation, and were more likely to coexist. We identified gene functional categories that were uniquely differentially regulated in association with particular species interaction types. Contrary to common thinking in ecology and evolution, similarity in gene expression, and not differentiation, was associated with weaker competition, facilitation and coexistence.
Data from: Ecological niche modeling as a tool for prediction of the potential geographic distribution of Bacillus anthracis spores in Tanzania
Introduction: Anthrax is caused by the spore-forming, Gram-positive bacterium Bacillus anthracis. The aim of this study was to predict the potential distribution of B. anthracis in Tanzania and produce epidemiological evidence for the management of anthrax outbreaks in the country. Methods: The Maxent algorithm was used to predict areas at risk of anthrax outbreaks based on the occurrence and environmental data in Arusha and Kilimanjaro regions; the model was later transferred to predict the entire country. Seventy percent of the occurrence data were used to train the model, while 30% were used for model evaluation. Results: Four regions of northern Tanzania are predicted to have a high risk for anthrax outbreaks, while the southern and western regions had low-risk areas. Soil type (56.5%), soil pH (23.7%), and isothermally (10.4%) were the most important variables for the model prediction, and the most significant soil types were solonetz, fluvisols, and lithosols. Conclusions: A strong risk level across districts of the Tanzania mainland was identified in this study. A total of 18 districts in Tanzania Mainland are predicted to be at very high risk of an anthrax outbreak occurrence. These findings are important for policymakers to effectively mount targeted control measures for anthrax outbreaks in Tanzania.
Data from: Seasonal difference in temporal transferability of an ecological model: near-term predictions of lemming outbreak abundances
Ecological models have been criticized for a lack of validation of their temporal transferability. Here we answer this call by investigating the temporal transferability of a dynamic state-space model developed to estimate season-dependent biotic and climatic predictors of spatial variability in outbreak abundance of the Norwegian lemming. Modelled summer and winter dynamics parametrized by spatial trapping data from one cyclic outbreak were validated with data from a subsequent outbreak. There was a distinct difference in model transferability between seasons. Summer dynamics had good temporal transferability, displaying ecological models' potential to be temporally transferable. However, the winter dynamics transferred poorly. This discrepancy is likely due to a temporal inconsistency in the ability of the climate predictor (i.e. elevation) to reflect the winter conditions affecting lemmings both directly and indirectly. We conclude that there is an urgent need for data and models that yield better predictions of winter processes, in particular in face of the expected rapid climate change in the Arctic.
Data from: How to characterize chemical exposure to predict ecologic effects on aquatic communities?
Reliable characterization of exposure is indispensable for ecological risk assessment of chemicals. To deal with mixtures, several approaches have been developed, but their relevance for predicting ecological effects on communities in the field has not been elucidated. In the present study, we compared nine metrics designed for estimating the total toxicity of mixtures regarding their relationship with an effect metric for stream macroinvertebrates. This was done using monitoring data of biota and organic chemicals, mainly pesticides, from five studies comprising 102 streams in several regions of Europe and South-East Australia. Mixtures of less than 10 pesticides per water sample were most common for concurrent exposure. Exposure metrics based on the 5% fraction of a species sensitivity distribution performed best, closely followed by metrics based on the most sensitive species and Daphnia magna as benchmark. Considering only the compound with the highest toxicity and ignoring mixture toxicity was sufficient to estimate toxicity in predominantly agricultural regions with pesticide exposure. The multisubstance Potentially Affected Fraction (msPAF) that combines concentration and response addition was advantageous in the study where further organic toxicants occurred. We give recommendations on exposure metric selection depending on data availability and the involved compounds.
Data from: Predicting ecological and phenotypic differentiation in the wild: a case of piscivorous fish in a fishless environment
Environmental variation drives ecological and phenotypic change. How predictable is differentiation in response to environmental change? Answering this question requires the development and testing of multifarious a priori predictions in natural systems. We employ this approach using Gobiomorus dormitor populations that have colonized inland blue holes differing in the availability of fish prey. We evaluated predictions of differences in demographics, habitat use, diet, locomotor and trophic morphology, and feeding kinematics and performance between G. dormitor populations inhabiting blue holes with and without fish prey. Populations of G. dormitor independently diverged between prey regimes, with broad agreement between observed differences and a priori predictions. For example, in populations lacking fish prey, we observed male-biased sex ratios, a greater use of shallow-water habitat, and larger population diet breadths as a result of greater individual diet specialization. Furthermore, we found predictable differences in body shape, mouth morphology, suction generation capacity, strike kinematics, and feeding performance on different prey types, consistent with the adaptation of G. dormitor to piscivory when coexisting with fish prey and to feeding on small invertebrates in their absence. The results of the present study suggest great potential in our ability to predict population responses to changing environments, which is an increasingly important capability in a human-dominated, ever-changing world.
Data from: Ecological resistance surfaces predict fine scale genetic differentiation in a terrestrial woodland salamander
Landscape genetics has seen tremendous advances since its introduction, but parameterization and optimization of resistance surfaces still poses significant challenges. Despite increased availability and resolution of spatial data, few studies have integrated empirical data to directly represent ecological processes as genetic resistance surfaces. In our study, we determine the landscape and ecological factors affecting gene flow in the western slimy salamander (Plethodon albagula). We used field data to derive resistance surfaces representing salamander abundance and rate of water loss through combinations of canopy cover, topographic wetness, topographic position, solar exposure, and distance from ravine. These ecologically-explicit composite surfaces directly represent an ecological process or physiological limitation of our organism. Using generalized linear mixed effects models, we optimized resistance using a non-linear optimization algorithm to minimize model AIC. We found clear support for the resistance surface representing the rate of water loss experienced by adult salamanders in the summer. Resistance was lowest at intermediate levels of water loss and higher when the rate of water loss was predicted to be low or high. This pattern may arise from the compensatory movement behavior of salamanders through suboptimal habitat, but also reflects the physiological limitations of salamanders and their sensitivity to extreme environmental conditions. Our study demonstrates that composite representations of ecologically-explicit processes can provide novel insight and can better explain genetic differentiation than ecologically-implicit landscape resistance surfaces. Additionally, our study underscores the fact that spatial estimates of habitat suitability or abundance may not serve as adequate proxies for describing gene flow, as predicted abundance was a poor predictor of genetic differentiation.
Data from: Social-ecological landscape patterns predict woody encroachment from native tree plantings in a temperate grassland
Afforestation is often viewed as the purposeful planting of trees in historically non-forested grasslands, but an unintended consequence is woody encroachment, which should be considered part of the afforestation process. In North America's temperate grassland biome, Eastern redcedar (Juniperus virginiana L.) is a native species used in tree plantings that aggressively invades in the absence of controlling processes. Cedar is a well-studied woody encroacher, but little is known about the degree to which cedar windbreaks, which are advocated for in agroforestry programs, are contributing to woody encroachment, what factors are associated with cedar spread from windbreaks, nor where encroachment from windbreaks is occurring in contemporary social–ecological landscapes. We used remotely sensed imagery to identify the presence and pattern of woody encroachment from windbreaks in the Nebraska Sandhills. We used multimodel inference to compare three classes of models representing three hypotheses about factors that could influence cedar spread: (a) windbreak models based on windbreak structure and design elements; (b) abiotic models focused on local environmental conditions; and (c) landscape models characterizing coupled human-natural features within the broader matrix. Woody encroachment was evident for 22% of sampled windbreaks in the Nebraska Sandhills. Of our candidate models, our inclusive landscape model carried 92% of the model weight. This model indicated that encroachment from windbreaks was more likely near roadways and less likely near farmsteads, other cedar plantings, and waterbodies, highlighting strong social ties to the distribution of woody encroachment from tree plantings across contemporary landscapes. Cedar control efforts are insufficient for nearly one-quarter of windbreaks in the Nebraska Sandhills. Our model findings indicate where additional investments into cedar control can be prioritized to prevent cedar spread from windbreaks. This approach can serve as a model in other temperate regions to identify where woody encroachment resulting from temperate agroforestry programs is emerging.
Data from: SIDER: an R package for predicting trophic discrimination factors of consumers based on their ecology and phylogenetic relatedness
Stable isotope mixing models (SIMMs) are an important tool used to study species' trophic ecology. These models are dependent on, and sensitive to, the choice of trophic discrimination factors (TDF) representing the offset in stable isotope delta values between a consumer and their food source when they are at equilibrium. Ideally, controlled feeding trials should be conducted to determine the appropriate TDF for each consumer, tissue type, food source, and isotope combination used in a study. In reality however, this is often not feasible nor practical. In the absence of species-specific information, many researchers either default to an average TDF value for the major taxonomic group of their consumer, or they choose the nearest phylogenetic neighbour for which a TDF is available. Here, we present the SIDER package for R, which uses a phylogenetic regression model based on a compiled dataset to impute (estimate) a TDF of a consumer. We apply information on the tissue type and feeding ecology of the consumer, all of which are known to affect TDFs, using Bayesian inference. Presently, our approach can estimate TDFs for two commonly used isotopes (nitrogen and carbon), for species of mammals and birds with or without previous TDF information. The estimated posterior probability provides both a mean and variance, reflecting the uncertainty of the estimate, and can be subsequently used in the current suite of SIMM software. SIDER allows users to place a greater degree of confidence on their choice of TDF and its associated uncertainty, thereby leading to more robust predictions about trophic relationships in cases where study-specific data from feeding trials is unavailable. The underlying database can be updated readily to incorporate more stable isotope tracers, replicates and taxonomic groups to further increase the confidence in dietary estimates from stable isotope mixing models, as this information becomes available.
Data and code for: A sensory ecology of fear: Eye size predicts moonlight avoidance responses in Neotropical electric fishes
<p>Data in support of: Eye size predicts moonlight avoidance responses in Neotropical electric fishes</p>
Fine-scale ecological and anthropogenic variables predict the habitat use and detectability of sloth bears in the Churia habitat of east Nepal
<p>Once widespread throughout the tropical forests of the Indian Subcontinent, the sloth bears have suffered a rapid range collapse and local extirpations in the recent decades. A significant portion of their current distribution range is situated outside of the protected areas (PAs). These unprotected sloth bear populations are under tremendous human pressures, but little is known about the patterns and determinants of their occurrence in most of these regions. The situation is more prevalent in Nepal where virtually no systematic information is available for sloth bears living outside of the PAs. We undertook a sign survey-based single-season occupancy study intending to overcome this information gap for the sloth bear populations residing in the Trijuga forest of southeast Nepal. Sloth bear sign detection histories and field-based covariates data were collected between 2nd October to 3rd December 2020 at the 74 randomly chosen 4-km^2 grid cells using a varying number of 400m long transects in each grid cell. From our results, the model-averaged estimate of site use probability (ψ ± SE) was estimated to be 0.432 ± 0.039, which is a 13% increase from the naïve estimate (0.297) not accounting for imperfect detections of sloth bear signs. The presence of termite mound and the distance to the nearest water source were the most important variables affecting the habitat use probability of sloth bears. The average site-level detectability (p ± SE) of sloth bear signs was estimated to be 0.195 ± 0.003 and was significantly determined by the index of human disturbances. We recommend considering the importance of fine-scale ecological and anthropogenic factors in predicting the sloth bear-habitat relationships across their range in the Churia habitat of Nepal, and more specifically in the unprotected areas.</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.