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19 results for “environmental migration”
Dataset for paper "Freihardt (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment. DOI 10.1007/s11111-025-00478-7"
<p>This is the dataset underlying the paper: </p> <p>Freihardt, J. (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment, 47, 6. DOI: 10.1007/s11111-025-00478-7.</p>
Panel of self-reported environmental and non-environmental shocks and migration in Tanzania
<p>This clean dataset was produced by Julia Blocher and Roman Hoffmann using the National Panel Survey - Tanzania: Data on Living Standards conducted by the World Bank and National Bureau of Statistics (https://www.worldbank.org/en/programs/lsms/brief/national-panel-survey-data-on-the-living-standards-of-tanzania). Our data set contains three survey waves (2008/9, 2010/11, 2012/13) and is different from the TZNPS data because it is grouped by a harmonized household and individual identifiers that can be tracked across the three waves. It was used for the paper "The effects of environmental and non-environmental shocks on livelihoods and migration in Tanzania", Population & Environment 46(1). Later waves are not included as the sample differs.</p>
Individual variation in spawning migration timing in a salmonid fish—exploring roles of environmental and social cues
<p><span>Describing and explaining patterns of individual animal behaviors in situ, and their repeatability over the annual cycle, is an emerging field in ecology owing largely to advances in tagging technology. We describe individual movements of adult Sakhalin taimen, <em>Parahucho</em> <em>perryi</em>, an endangered salmonid fish, in the headwaters of a river in northern Japan during the spring spawning season over two years. Migration timing, separated into stages prior to, during, and following the spawning period, was found to be more consistent and repeatable for females than males. We hypothesized that the observed coordinated movement within seasons, and repeatability in migration timing across seasons, could result from 1) individual-specific responsiveness resulting from endogenous, biological traits that are mediated by environmental factors, or 2) social interactions among comigrating individuals. We found that water temperature and water level experienced by fish near the river mouth approximately a week before arrival at the spawning ground explained variability in run timing between years for females but not males. We found no evidence of conspecific attraction or repulsion resulting from social interactions among the spawners and post-spawners. We conclude that individual-specific responsiveness to environmental cues was the likely mechanism underpinning the observed migration timing and movement patterns.</span></p>
Data from: Additive genetic and environmental variation interact to shape the dynamics of seasonal migration in a wild bird population
<p><span>Dissecting joint micro-evolutionary and plastic responses to environmental perturbations requires quantifying interacting components of genetic and environmental variation underlying expression of key traits. This ambition is particularly challenging for phenotypically discrete traits where multiscale decompositions are required to reveal non-linear transformations of underlying genetic and environmental variation into phenotypic variation, and when effects must be estimated from incomplete field observations. We devised a joint multistate capture-recapture and quantitative genetic animal model and fitted this model to full-annual-cycle resighting data from partially-migratory European shags (<em>Gulosus</em> <em>aristotelis</em>) to estimate key components of genetic, environmental and phenotypic variance in the ecologically critical discrete trait of seasonal migration versus residence. We demonstrate non-negligible additive genetic variance in latent liability for migration, resulting in detectable micro-evolutionary responses following two episodes of strong survival selection. Further, liability-scale additive genetic effects interacted with substantial permanent individual and temporary environmental effects to generate complex non-additive effects on expressed phenotypes, causing substantial intrinsic gene-by-environment interaction variance on the phenotypic scale. Our analyses therefore reveal how temporal dynamics of partial seasonal migration arise from combinations of instantaneous micro-evolution and within-individual phenotypic consistency, and highlight how intrinsic phenotypic plasticity could expose genetic variation underlying discrete traits to complex forms of selection.</span></p>
Individual variation in spawning migration timing in a salmonid fish—exploring roles of environmental and social cues
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Data from: Additive genetic and environmental variation interact to shape the dynamics of seasonal migration in a wild bird population
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Data from: Global migration is driven by the complex interplay between environmental and social factors
<p><strong>The datasets were produced in the following article. When using the data, please use the following citation:</strong></p> <p>Niva V, Kallio M, Muttarak R, Taka M, Varis O, Kummu M. 2021. Global migration is driven by the complex interplay between environmental and social factors. Environmental Research Letters. <a href="https://doi.org/10.1088/1748-9326/ac2e86">https://doi.org/10.1088/1748-9326/ac2e86</a></p> <p>The data include the following files:</p> <p><strong>AC.tif </strong></p> <p> Composite index computed based on the four AC variables by taking a mean over the respective variables:</p> <p> <strong>economy.tif</strong></p> <p> Downscaled and min-max normalized income data.</p> <p> <strong>education.tif</strong></p> <p> Min-max normalized education data.</p> <p> <strong>governance.tif</strong></p> <p> Min-max normalized governance data.</p> <p> <strong>health.tif</strong></p> <p> Min-max normalized health data.</p> <p>For all of the above data, 0 and 1 represent the lowest and highest <strong>capacity</strong>, respectively.</p> <p><strong>ES.tif</strong></p> <p> Composite index computed based on the four ES variables by taking a mean over the respective variables:</p> <p> <strong>foodProdScarcityScaled.tif</strong></p> <p> Food production scarcity data based on food production data.</p> <p> <strong>droughtRiskScaled.tif</strong></p> <p> Computed and scaled drought risk based on SPEI index.</p> <p> <strong>waterRiskScaled.tif</strong></p> <p> Computed and scaled water risk data based on three water stress indices.</p> <p>For all of the above data 0 and 1 represent the lowest and highest <strong>stress</strong>, respectively. Kindly note that data for natural hazards is available at its source (please see the list below). </p> <p><strong>class_raster.tif</strong></p> <p> Spatial representation of the classification matrix.</p> <p><strong>cntryID.gpkg</strong></p> <p> Country polygons with country IDs.</p> <p><strong>cntry_raster_masked.tif</strong></p> <p> Country raster with country IDs.</p> <p><strong>countriesRegionsZones.csv</strong></p> <p> Country groups and countries.</p> <p>Dataset specifications:</p> <p>spatial extent: -180, 180, -90, 90</p> <p>spatial resolution: 5 arc-min (0.083333333 degrees)</p> <p>projection: long/lat WGS84</p> <p>no data value: NA</p> <p> </p> <p><strong>Original data to produce the above indicators and to replicate the full analysis is available at the following sources:</strong></p> <p>Net-migration data (30 arc-sec resolution): https://doi.org/10.7927/H4319SVC</p> <p>Natural hazards: https://datadryad.org/stash/dataset/doi:10.5061/dryad.h2v2398</p> <p>Governance effectiveness: https://datadryad.org/stash/dataset/doi:10.5061/dryad.h2v2398</p> <p>Human Development Indicators (income, education, health): <a href="https://doi.org/10.1038/sdata.2019.38">https://doi.org/10.1038/sdata.2019.38</a></p> <p>Water risk indicators: <a href="https://doi.org/10.46830/writn.18.00146">https://doi.org/10.46830/writn.18.00146</a></p> <p>Drought (SPEI index): <a href="https://doi.org/10.1175/2009JCLI2909.1">https://doi.org/10.1175/2009JCLI2909.1</a></p> <p>Food production: <a href="https://doi.org/10.1038/nature11420">https://doi.org/10.1038/nature11420</a></p> <p>Population data: <a href="https://doi.org/10.1177%2F0959683609356587">https://doi.org/10.1177/0959683609356587</a></p>
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>
Wood bison migration metrics and environmental covariates
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Chromosomal evolution, environmental heterogeneity, and migration drive spatial patterns of species richness in Calochortus (Liliaceae)
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Monitoring Atlantic salmon (Salmo salar) smolt migration in a large river system using environmental DNA
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Data from: Using time series analysis to characterize evolutionary and plastic responses to environmental change: a case study of a shift toward earlier migration date in sockeye salmon
Environmental change can shift the phenotype of an organism through either evolutionary or nongenetic processes. Despite abundant evidence of phenotypic change in response to recent climate change, we typically lack sufficient genetic data to identify the role of evolution. We present a method of using phenotypic data to characterize the hypothesized role of natural selection and environmentally driven phenotypic shifts (plasticity). We modeled historical selection and environmental predictors of interannual variation in mean population phenotype using a multivariate state-space model framework. Through model comparisons, we assessed the extent to which an estimated selection differential explained observed variation better than environmental factors alone. We applied the method to a 60-year trend toward earlier migration in Columbia River sockeye salmon Oncorhynchus nerka, producing estimates of annual selection differentials, average realized heritability, and relative cumulative effects of selection and plasticity. We found that an evolutionary response to thermal selection was capable of explaining up to two-thirds of the phenotypic trend. Adaptive plastic responses to June river flow explain most of the remainder. This method is applicable to other populations with time series data if selection differentials are available or can be reconstructed. This method thus augments our toolbox for predicting responses to environmental change.
Data from: Environmental DNA analysis as a non-invasive quantitative tool for reproductive migration of a threatened endemic fish in rivers
Quantitative information regarding reproduction is essential for conserving endangered animals; however, some conventional quantitative methods can be damaging to the target population and their habitats. In the present study, the reproductive migration of a threatened endemic fish, three-lips (Opsariichthys uncirostris uncirostris), was non-invasively monitored by quantitative PCR of species-specific environmental DNA (eDNA), the usefulness of which has been under-studied. Water sampling and from-shore visual inspection were performed weekly along a tributary of Lake Biwa (Japan), where adult fish seasonally migrate upstream to reproduce as well as at lake sites near the river mouth. Species-specific eDNA was collected at all locations at times when the fish were visually observed and at certain sites where the fish were not observed. Log-transformed individual counts from visual inspection were positively correlated with log-transformed eDNA concentration in the river sites, indicating that eDNA analysis can be a reliable quantitative tool for fish abundance in rivers. Furthermore, distance from the lake did not influence eDNA concentration, suggesting that eDNA transport by river flow had a negligible effect on eDNA quantification. eDNA concentration and individual counts both gradually increased from May–July, then both decreased in August. Importantly, eDNA analysis showed that the fish occupied more habitats in the peak reproductive season and stayed for longer time at any given site. An additional underwater survey confirmed unexpected eDNA detections as true positives. eDNA analysis has great potential to quantitatively monitor reproductive fish migrations under certain conditions.
FIG. 2 in Discerning the Environmental Drivers of Annual Migrations in an Endangered Amphibian
FIG. 2. Comparison of arrival dates for male and female flatwoods salamanders. Plots are standardized from 1 October and separated by year, i.e., 10–11 is the fall 2010–spring 2011 breeding season. Within year comparisons are made with Wilcoxon rank-sum tests.
FIG. 1 in Discerning the Environmental Drivers of Annual Migrations in an Endangered Amphibian
FIG. 1. Daily counts of individuals as a proportion of the total number of individuals available for capture for each year. Major tick marks are set at January 1, and minor tick marks are months.
Data from: Using time series analysis to characterize evolutionary and plastic responses to environmental change: a case study of a shift toward earlier migration date in sockeye salmon
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Data from: Environmental DNA analysis as a non-invasive quantitative tool for reproductive migration of a threatened endemic fish in rivers
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Transcriptomics of environmental adaptation and survival in wild adult Pacific Sockeye Salmon (Oncorhynchus nerka) during spawning migration
GEO Series GSE22346. Oncorhynchus mykiss; Oncorhynchus nerka; Salmo salar. 121 samples. Type: Expression profiling by array.
Salmon Spawning Migration: Metabolic Shifts and Environmental Triggers
GEO Series GSE13657. Oncorhynchus mykiss; Oncorhynchus nerka; Salmo salar. 80 samples. Type: Expression profiling by array.
ScienceDex guides
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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.