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.
22,710
datasets available to search
ShareScore release 0.9.0
Dataset results
22,710 results for “Plants for planting”
Data for: Predicting berry plant habitat under climate change in Bristol Bay, AK
<p>Aim: Climate change is altering suitable habitat distributions of many species in high latitudes. Fleshy fruit-producing plants (hereafter "berry plants"), important in arctic food webs and as subsistence resources for human communities, may be impacted, but their response to a warming and increasingly variable climate at a landscape scale has not yet been examined. Here, we identified influential environmental determinants of berry plant distribution and produced predictions on how climate change might shift these distributions.</p> <p>Location: Bristol Bay and Togiak NRCS Survey Areas, Alaska.</p> <p>Methods: We built species distribution models using the Random Forests algorithm to identify key characteristics and predict the spatial distribution of habitats suitable for five berry plant species: <em>Vaccinium uliginosum</em> L., <em>Empetrum nigrum</em> L., <em>Rubus chamaemorus</em> L., <em>Vaccinium vitis-idaea</em> L., and <em>Viburnum edule</em> (Michx.) Raf. Then, we used future climate projections (2081-2100; representative concentration pathways 4.5, 6.0, & 8.5) to predict shifts in species' suitable habitat distributions based on future climate conditions.</p> <p>Results: The predicted amount and spatial patterns of suitable habitat for the current time period were variable among species, consistent with species' diverse life history attributes and habitat preferences. Future climate models predicted both positive and negative changes to suitable habitat probability for all species; future binary classification maps predicted net declines in suitable habitat area for all species and climate scenarios tested. Models identified elevation, soil characteristics, and January and July temperatures as important drivers of suitable habitat distributions.</p> <p>Main conclusions: Our work contributes to understanding the response of important berry plant species to climate change at a landscape scale. Shifting and retracting distributions may alter where communities have access to harvesting areas, suggesting that access to these resources may become restricted in the future. Our prediction maps may help inform climate adaptation planning as communities anticipate shifting access to harvesting locations.</p>
Plant FUnctional COnservation DB (Plant FUNCO) Download
<p>Zenodo repository hosting main resources generated in the database.</p>
Data from: How important are functional and developmental constraints on phenotypic evolution? An empirical test with the stomatal anatomy of flowering plants
<p>Quantifying the relative contribution of functional and developmental constraints on phenotypic variation is a longstanding goal of macroevolution, but it is often difficult to distinguish different types of constraints. Alternatively, selection can limit phenotypic (co)variation if some trait combinations are generally maladaptive. The anatomy of leaves with stomata on both surfaces (amphistomatous) presents a unique opportunity to test the importance of functional and developmental constraints on phenotypyic evolution. The key insight is that stomata on each leaf surface encounter the same functional and developmental constraints, but potentially different selective pressures because of leaf asymmetry in light capture, gas exchange, and other features. Independent evolution of stomatal traits on each surface implies that functional and developmental constraints alone likely do not explain trait covariance. Packing limits on how many stomata can fit into a finite epidermis and cell-size-mediated developmental integration are hypothesized to constrain variation in stomatal anatomy. The simple geometry of the planar leaf surface and knowledge of stomatal development makes it possible to derive equations for phenotypic (co)variance caused by these constraints and compare them with data. We analyzed evolutionary covariance between stomatal density and length in amphistomatous leaves from 236 phylogenetically independent contrasts using a robust Bayesian model. Stomatal anatomy on each surface diverges partially independently, meaning that packing limits and developmental integration are not sufficient to explain phenotypic (co)variation. Hence, (co)variation in ecologically important traits like stomata arises in part because there is a limited range of evolutionary optima. We show how it is possible to evaluate the contribution of different constraints by deriving expected patterns of (co)variance and testing them using similar but separate tissues, organs, or sexes.</p>
Figure S50 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S50. Optimisation of tropical and temperate niches across the Mimosoid phylogeny. Ancestral niches were estimated using a complete metachronogram for Caesalpinioideae, including non-Mimosoid Caesalpinioideae taxa, but only the Mimosoid clade is shown here.
Figure S48 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S48. Speciation rates estimated across the Caesalpinioideae metachronogram under eight scenarios with different fixed extinction rates. Extinction rates are shown above each subfigure, while speciation rates are indicated by branch colours.
Figure S49 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S49. Top: Speciation rates in the Mimosoid clade through time, estimated under different extinction rate scenarios using BAMM. Middle: Paleotemperature inferred from delta O18 measurements, using data from Zachos et al. (179). Bottom: Phenogram of mean annual precipitation in the Mimosoid clade through time. Coloured lines with dots show the median, wettest, and driest reconstructed rainfall niche of all nodes in the phylogeny per time bin of one million years.
Figure S47 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S47. Ancestral range estimation of Caesalpinioideae, performed using BioGeoBEARS with the best-fitting model (i.e., DEC+J). Trans-oceanic dispersal events in the Mimosoid clade, based on a model with seven regions, are indicated with numbered green circles.
Figure S46 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S46. Optimisation of dry season length across the Mimosoid phylogeny. Inset shows the fraction of dry season length niche shifs per speciation event through time. See caption Figure 1 for explanation.
Figure S45 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S45. Variation partitioning results obtained using the genus-level Mimosoid phylogeny (rather than the metachronogram). See caption Figure 2 for explanation.
Figure S42 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S42. Phyloregionalization per continent using the metachronogram, showing global distribution of isohyets. Caption otherwise as for Figure 3.
Figure S30 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S30 (right). Conflict and concordance among the 821 single-copy gene trees for each bipartition mapped onto the single-copy genes ASTRAL species tree (Figure S14). Pie charts show the fraction of gene trees supporting that bipartition in blue, the fraction of gene trees supporting the most likely alternative configuration in green, the fraction of gene trees supporting additional conflicting configurations in red, and the fraction of uninformative gene trees in grey. Numbers above and below the pie charts indicate the total number of gene trees supporting and conflicting the bipartition, respectively. Branch lengths are set equal for easier visualisation.
Figure S22 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S22. Phylogeny of Caesalpinioideae. RAxML species tree based on the amino acid alignment of all genes with orthology assessment. Bootstrap support values are only shown for nodes with <100% bootstrap support.
Figure S27 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S27. Tanglegram comparing the PhyloBayes phylogeny (Figure S23) with the RAxML amino acid single-copy genes phylogeny (Figure S20).
Figure S37 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S37. Phyloregionalization of South America using the metachronogram. Subfigures show clustering results with two to eight phyloregions, as well as the results of phyloregionalization analyses using the geographic residuals of phylogenetic turnover, and ancient phylogenetic turnover with a cut-off of 5, 10, and 20 million years.
Figure S21 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S21. Phylogeny of Caesalpinioideae. RAxML species tree based on the amino acid alignment of all genes without orthology assessment. Bootstrap support values are only shown for nodes with <100% bootstrap support.
Figure S38 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S38. Phyloregionalization of Africa using the metachronogram. Subfigures show clustering results with two to eight phyloregions, as well as the results of phyloregionalization analyses using the geographic residuals of phylogenetic turnover, and ancient phylogenetic turnover with a cut-off of 5, 10, and 20 million years.
Figure S43 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S43. Climatic distinctiveness of phyloregions. Each subplot shows, for phyloregionalization analyses with two to eight phyloregions performed using the metachronogram, how many of the resulting phyloregions have statistically significant different climatic values from the other regions based on mean annual precipitation (P), precipitation seasonality (Pseas), and dry season length.
Figure S34 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S34. Metachronogram with the names and locations of all subtrees (coloured branches) that were grafed onto the phylogenomic backbone (black branches).
Figure S19 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S19. Phylogeny of Caesalpinioideae. RAxML species tree based on the nucleotide alignment of all genes with orthology assessment. Bootstrap support values are only shown for nodes with <100% bootstrap support.
Figure S24 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S24. Tanglegram comparing the ASTRAL single-copy genes phylogeny (Figure S14) with the RAxML nucleotide single-copy genes phylogeny (Figure S17).
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.