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.
93
datasets available to search
ShareScore release 0.7.1
Dataset results
93 results for “Environmental Predictability”
Mean species responses predict effects of environmental change on coexistence
<p>Environmental change research is plagued by the curse of dimensionality: the number of communities at risk and the number of environmental drivers are both large. This raises the pressing question if a general understanding of ecological effects is achievable. These data show that this is indeed possible. It contains code that calculates the feasibility domain size (a proxy for coexistence) for bi- and tritrophic communities challenged by environmental change. Some of this code simply returns the output of a closed-form expressions (i.e. simple equations), while some rely on simulations that require specific packages.</p> <p>The data also contain presence/absence data of macroinvertebrate taxa and water chemistry variables measured across sites (that are either severely or weakly modified by human activity, quantified via land use) at US streams. With these data, we were able to test if sites that share the same community have similar water chemistry, in other words: how tightly is a community linked to a certain water chemistry? This analysis demonstrates how to apply our theory to the analysis of field data, and lends support to effects of land use change on coexistence in natural invertebrate communities.</p>
Mean species responses predict effects of environmental change on coexistence
Open the record for dataset details and reuse information.
Predicting the breeding distribution of wader species across climatic and environmental gradients
Open the record for dataset details and reuse information.
Data from: Strong bat predation and weak environmental constraints predict longer moth tails
Open the record for dataset details and reuse information.
Data from: Improving inferences and predictions of species environmental responses with occupancy data
Open the record for dataset details and reuse information.
Improving genomic prediction for plant disease using environmental covariates
Open the record for dataset details and reuse information.
The importance of variation in vital rates and environmental resource availability in predicting demography of a rare understory herb
Plant demography is a function of both the vital rate characteristics of a species (i.e., survival, growth, and reproduction) and the environmental factors that interact with them to create population dynamics. A more detailed understanding of how local-scale environmental factors and variation in individual vital rates shape population-level demographic patterns is needed to improve predictions of population responses to environmental change and implement successful plant conservation strategies. In this study, we examined how individual vital rates for Shortia galacifolia, an endangered, evergreen herb endemic to the southern Blue Ridge Mountains, USA, change as a function of individual size and resource availability and how that variation affects Shortia demography at four sites representing natural and introduced populations using integral projection models (IPMs). We found that Shortia population growth is positively related to individual size and soil moisture. Changes in soil moisture availability altered the importance of survival and growth in predicting Shortia demography but did not affect the contribution of asexual reproduction for most sites. Moreover, changes in vital rate contributions under a low soil moisture scenario were limited to introduced populations growing outside Shortia’s natural climate envelope. Our study underscores the importance of quantifying the influence of individual state characteristics and environmental variables on different vital rates among natural and introduced populations and demonstrates how the combination of these factors can contribute to the success or failure of rare plant populations.
Data from: Genome assembly of the ragweed leaf beetle, a step forward to better predict rapid evolution of a weed biocontrol agent to environmental novelties
<p><span>Rapid evolution of weed biological control agents (BCAs) to new biotic and abiotic conditions is poorly understood and so far, only little considered both in pre-release and post-release studies, despite potential major negative or positive implications for risks of non-targeted attacks or for colonizing yet unsuitable habitats, respectively. Provision of genetic resources, such as assembled and annotated genomes, is essential to assess potential adaptive processes by identifying underlying genetic mechanisms. Here, we provide the first sequenced genome of a phytophagous insect used as a BCA, <i>i.e.</i> the leaf beetle <i>Ophraella communa</i>, a promising BCA of common ragweed, recently and accidentally introduced into Europe. A total 33.98 Gb of raw DNA sequences, representing c. 43-fold coverage, were obtained using the PacBio SMRT-Cell sequencing approach. Among the five different assemblers tested, the SMARTdenovo assembly displaying the best scores was then corrected with Illumina short reads. A final genome of 774 Mb containing 7,003 scaffolds was obtained. The reliability of the final assembly was then assessed by benchmarking universal single-copy orthologous genes (> 96.0% of the 1,658 expected insect genes) and by remapping tests of Illumina short reads (average of 98.6% ± 0.7% without filtering). The number of protein-coding genes of 75,642, representing 82% of the published antennal transcriptome, and the phylogenetic analyses based on 825 orthologous genes placing <i>O. communa </i>in the monophyletic group of Chrysomelidae, confirm the relevance of our genome assembly. Overall, the genome provides a valuable resource for studying potential risks and benefits of this BCA facing environmental novelties.</span></p>
Beef database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The beef database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets, and is further differentiated into All Beef, Breeders and Fatteners: BasicFarmType (18 rows), DetailedFarmType (75 rows), ClimateClass+BasicFarmType (270 rows), NUTS+BasicFarmType (2074 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Dairy database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The dairy database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets: BasicFarmType (18 rows), DetailedFarmType (10 rows), ClimateClass+BasicFarmType (100 rows), NUTS+DetailedFarmType (1452 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Data from: Accumulation curves of environmental DNA sequences predict coastal fish diversity in the Coral Triangle
Environmental DNA (eDNA) has the potential to provide more comprehensive biodiversity assessments particularly for vertebrates in species-rich regions. Yet, this method requires the completeness of a reference database, i.e. a list of DNA sequences attached to each species, which is never met. As an alternative, a diversity of Operational Taxonomic Units (OTUs) can be extracted from eDNA metabarcoding. However, the extent to which the diversity of OTUs provided by a limited eDNA sampling effort can predict regional species diversity is unknown. Here, by modelling OTU accumulation curves of eDNA seawater samples across the Coral Triangle, we obtained an asymptote reaching 1,531 fish OTUs while 1,611 fish species are recorded in the region. Besides, we also accurately predict (R² = 0.92) the distribution of species richness among fish families from OTU-based asymptotes. Thus, the multi-model framework of OTU accumulation curves extends the use of eDNA metabarcoding in ecology, biogeography and conservation.
Strong species differences in life-history do not predict oxidative stress physiology or sensitivity to an environmental oxidant
1. Species typically align along a fast-slow life-history continuum, yet it is not clear to what extent oxidative stress physiology can be integrated with this continuum to form a 'pace-of-life syndrome', especially so in invertebrates. This is important, given the assumed role of oxidative stress in mediating life-history trade-offs, and the prediction that species with a faster pace should be more vulnerable to oxidative stress. 2. We tested whether a species' life-history pace, here represented by its growth rate, can predict species-level differentiation in physiology and sensitivity to oxidative stress. Therefore, we exposed four species of Ischnura damselflies that strongly align along a fast-slow life-history continuum to different levels of ultraviolet (UV) radiation. We measured an extended set of physiological traits linked to the pace-of-life: standard metabolic rate (SMR), oxidative stress physiology (antioxidant enzymes and oxidative damage), and defence/condition traits (investment in immune function, energy storage, and structural defence). 3. Despite strong species differences in growth rate and physiology, growth rate did not predict species-level differentiation in physiology. Hence there was no support for the integration of metabolic rate, oxidative stress physiology or defence/condition traits into a species-level syndrome. 4. UV exposure affected nearly all traits: it reduced growth rate and increased metabolic rate, affected all oxidative stress physiology traits and increased the two defence traits (immune function, and melanin content). Nevertheless, the pace-of-life based on growth rate did not predict sensitivity to UV. Instead, the observed pattern of investment in structural UV defence (melanin) might have reduced the need for enzymatic antioxidant defence, this way potentially decoupling the covariation between the life-history pace and oxidative stress physiology. 5. The absence of an integrated axis of life-history and physiological variation indicates no major constraints for the evolution of these traits among the studied damselfly species. Our study highlights that ecological differences between species may decouple covariation between species' life-history pace and their physiology, as well as their sensitivity to environmental stressors. 30-Mar-2020
Comparative and predictive phylogeography in the South American diagonal of open formations: Unravelling the biological and environmental influences on multitaxon demography
<p><span><span>Phylogeography investigates historical drivers of species' geographic distribution. Special attention has been given to ecological, climatic, and geological processes in the diversification of the Neotropical biota. Several species sampled across the dry diagonal of South America (DDSA, comprising the Caatinga, Cerrado, and Chaco biomes) experienced range shifts coincident with Quaternary climatic changes. However, studies across different spatial, temporal, and biological scales on species from South America's dry biomes are still poorly represented. Here, we combine phylogeographic model selection and machine learning predictive frameworks to investigate the influence of Pleistocene climatic changes on both plant and animal species from the DDSA. We assembled mitochondrial/chloroplastic DNA sequences in public repositories and inferred the historical demographic responses of 70 lineages. We then built a random forest model using both biotic and abiotic information to identify potential traits for predicting whether species underwent population expansion, contraction, or stasis during the Pleistocene. Finally, we estimated the temporal synchrony of species demographic responses using hierarchical approximate Bayesian computation (hABC). Biotic variables largely predicted how species responded to Pleistocene climatic changes, and demographic changes were mostly synchronous during the Middle Pleistocene. Although many DDSA species underwent demographic expansion, presumably associated with the spread of aridity during glacial Pleistocene periods, our findings suggest that some species exhibited the opposite response and that species-specific attributes might have been related to these differences.</span></span></p>
Environmental gradients predict the ratio of environmentally acquired carotenoids to self-synthesised pteridine pigments
<p>Carotenoids are important pigments producing integument coloration; however, their dietary availability may be limited in some environments. Many species produce red to yellow hues using a combination of carotenoids and self-synthesised pteridine pigments. A compelling but untested hypothesis is that pteridines replace carotenoids in environments where carotenoid availability is limited. Based on a phylogenetic comparative analysis of pigment concentrations in agamid lizards, we show that environmental gradients predict the ratio of carotenoids to pteridines; carotenoid concentrations are lower and pteridine concentrations higher in arid environments with low vegetation productivity. Both carotenoid and pteridine pigments were present in all species, but only pteridine concentrations explained colour variation among species and there were no correlations between carotenoid and pteridine pigments with similar hue. These results suggest that pteridine pigments replace carotenoids in carotenoid-limited environments, irrespective of skin hue, presumably because it is metabolically cheaper to synthesise pteridines than to acquire and sequester carotenoids when carotenoids are rare.</p>
Species occurrence locations and environmental parameters used for predicting the potential planting regions of Pterocarpus santalinus
<p>This study explores the habitat distribution of <em>Pterocarpus santalinus</em>, a valuable rosewood species, across China, focusing on its response to current and future climate changes. Utilizing the MaxEnt model, we assess its suitable habitat under present conditions and future climate scenarios (SSPs126, SSPs245, and SSPs585). Our findings reveal that the current suitable habitat, spanning approximately 409,600 km², is primarily located in the central and southern parts of Guangdong, Guangxi, Fujian, Yunnan, as well as in the Hainan provinces, along with the coastal regions of Taiwan, and the Sichuan-Chongqing border. The habitat's distribution is significantly influenced by climatic factors such as temperature seasonality (bio4), mean temperature of the wettest quarter (bio8), annual mean temperature (bio1), and annual precipitation (bio12), while terrain and soil factors play a lesser role. Under future climate scenarios, the suitable habitat for <em>P. santalinus</em> is projected to expand, with a northeastward shift in its distribution center. This research not only sheds light on the geoecological characteristics and geographical distribution of <em>P. santalinus</em> in China but also offers a scientific basis for planning its cultivation areas and enhancing cultivation efficiency under changing climate conditions.</p>
Data from: Phenotypically plastic responses to environmental variation are more complex than life history theory predicts
<p>For insects that exhibit wing polyphenic development, abiotic and biotic signals dictate the adult wing morphology of the insect in an adaptive manner such that in stressful environments the formation of a flight-capable morph is favored and in low stress environments a flightless morph is favored. While there is a relatively large amount known about the environmental cues that dictate morph formation in wing polyphenic hemipterans like planthoppers and aphids, whether those cues dictate the same morphs in non-hemipteran (i.e. cricket) wing polyphenic species has not been explicitly investigated. To experimentally test the generality of environmental cue determination of wing polyphenism across taxa with diverse life histories, in this study we tested the importance of food quantity, parasitic infection, and tactile cues on wing morph determination in the wing polyphenic sand field cricket, <em>Gryllus firmus</em>. Our results also show that certain stress cues, such as severe diet quantity limitation and parasitic infection, actually led to an increase in the production of flightless morph. Based on these findings, our results suggest that physiological and genetic constraints are important to an organism's ability to respond to environmental variation in an adaptive manner beyond simple life history trade-offs.</p>
Fig. 1 in Predicting the risk of Alaria alata infestation in wild boar on the basis of environmental factors
Fig. 1. The trend in prevalence of A. alata in provinces with WETLANDS.
Divergence time and environmental similarity predict the strength of morphological convergence in stick and leaf insects
<p>This uploads contains the datasets, phylogenetic tree and associated R code used to generate the results reported in the article: "Divergence time and environmental similarity predict the strength of morphological convergence in stick and leaf insects" published in Proceedings of the National Academy of Sciences USA (2024).<br>A detailed explanation of datasetS1 can be found in the supplementary data of the article. </p>
Fig. 1 in Environmental factors predicting fish community structure in two neotropical rivers in Brazil
Fig. 1. The Iguatemi River basin, showing the sampling sites in the Jogui and Iguatemi rivers.
Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (Capra ibex) as tools for conserving migration
<p># GPS locations of Alpine ibex</p> <p>This dataset contains migratory tracks of Alpine ibex identified using the application Migration Mapper (https://migrationinitiative.org/content/migration-mapper) and used in the work <strong>Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex (<em>Capra ibex</em>) as tools for conserving migration</strong></p> <p># Dataset structure</p> <p>Each row of the dataset represents a GPS location with its coordinates contained in the x (longitude) and y(latitude) columns. Coordinates are given in wgs84 (epsg 4326).<br> The column t1_ informs on the date and time the location was recorded.<br> The id and pop columns provide information about the identity of the animal and the population to which it belongs.</p> <p> </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.