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248 results for “landscape dynamics”
Social-Ecological Dynamics of Recreational Fishery Landscapes: Recreational angler catch and satisfaction in the Midwest, 2018 to 2024
The goal of this dataset is to understand angler information gathering and sharing, technology use, expectation-setting, site choice, and the factors influencing angler satisfaction in rural and urban environments. These data were collected through on-site creel surveys and instantaneous counts of fishing effort and associated angler vehicles. Traditional creel surveys are completed only at the end of an angler's fishing trip. These dual-intercept creel survey data are novel because anglers were intercepted both before and after their fishing trip. This survey design allowed us to collect unbiased estimates of anglers' expected catch. These data can be used to understand the role of angler expectations in angler satisfaction and behavior. Data collection were conducted in two counties in Wisconsin, USA. Vilas County is a primarily rural, forested, and glaciated region with many relatively small lakes. Dane County has a larger urban center (Madison, WI) that is surrounded by agricultural areas. Most of the Dane County lakes surveyed were part of the Yahara chain of lakes: Lakes Mendota, Monona, Wingra, Waubesa, and Kegonsa. Dane county has fewer, larger lakes with greater connectivity to each other. Vilas County was sampled in the summer of 2018, the summer of 2019, and the summer and winter of 2022. Dane County was sampled in the winter and summer of 2022.
Landscape-Scale Forest Dynamics in the Luquillo Experimental Forest in Puerto Rico 1936-1989
This study examined landscape-scale forest dynamics in the Luquillo Experimental Forest (Puerto Rico). The analysis was based on vegetation maps created from aerial photographs taken in 1936 and 1989. For details on methods and results, please see the published paper (Foster, D. R., M. Fluet and E. R. Boose. 1999. Human or natural disturbance: landscape-scale dynamics of the tropical forests of Puerto Rico. Ecological Applications 9: 555-572). The Abstract from the paper is reproduced below. "Increasingly ecologists are recognizing that human disturbance has played an important role in tropical forest history and that many assumptions concerning the relative importance of natural processes warrant re-examination. To assess the historical role of broad-scale human versus natural disturbance on an intensively studied tropical forest we undertook a landscape-level analysis of forest dynamics in the Luquillo Experimental Forest (LEF; 10,871 ha) in eastern Puerto Rico. Using aerial photographs (1936 and 1989), GIS, a model of topographic exposure to hurricane winds, and historical data, we sought to: (1) document historical changes in extent, cover and type of forest vegetation, (2) evaluate the distribution of land-use and hurricane impacts, (3) assess the contributions of these processes in controlling current vegetation patterns, and (4) relate these results to ongoing ecological, conservation and natural resource discussions. "With over 1000 m of relief in the LEF, the broad vegetation zones of Tabonuco (below 600 m a.s.l.), Colorado (600-900 m), Dwarf (above 900 m), and Palm forest are determined by environmental gradients. However, over the past 60-100 years forest extent, cover, and type have been transformed: in 1936, 40% of the LEF was unforested or secondary forest and less than 50% had continuous canopy (more than 80% cover); in 1989, less than 97% was continuous forest. Secondary forest and agricultural lands in 1936 were replaced largely by Tabonuco and Colo
Dynamic landscapes of fear and safety alter prey refuge use in freshwater habitats
The non-consumptive effects of predators on prey behavior have been studied in many different systems. However, predator-prey ecology has placed a bulk of emphasis on how fear alters prey behavior, and new studies have begun to shift focus to the importance that safety in the form of refuges has in structuring prey behavioral responses. This project focuses on changes in the safety landscape as well as changes in the fear landscape and how these changes impact crayfish behavior. Using an established bass-crayfish predator prey system, we altered shelter quality and location in relation to the presence of bass odor signals. We measured shelter use by the crayfish in response to this changing landscape.
Data for Table S10 of the article "Source-to-sink aeolian fluxes from arid landscape dynamics in the Lut Desert"
<p>Exhaustive list of the 227 individual denudation rates in arid areas compiled to estimate median denudation rate and sediment discharge for the internal river system of the Lut watershed.</p>
Simulated forest dynamics (2016-2100) for six future climate-fire scenarios and five representative landscapes in Greater Yellowstone, USA
We simulated fire (incorporating fuels feedbacks) and forest dynamics on five landscapes spanning the Greater Yellowstone Ecosystem (GYE) to ask: (1) How and where are forest landscapes likely to change with 21st-century warming and fire activity? (2) Are future forest changes gradual or abrupt, and do forest attributes change synchronously or sequentially? (3) Can forest declines be averted by mid-21st-century stabilization of atmospheric greenhouse gas (GHG) concentrations? We used the spatially explicit individual-based forest model iLand to track multiple attributes (forest extent, stand age, tree density, basal area, aboveground carbon stocks, dominant forest types, species occupancy) through 2100 for six climate scenarios. The five study landscapes are representative of dominant forest types and environmental gradients of the Northern Rockies; collectively, they encompass nearly 300,000 ha, of which 279,488 ha are potentially stockable with trees. This data set contains annual landscape-level output data for simulations to 2100 with 6 climate scenarios (3 general circulation models x 2 representative concentration pathways) x 5 landscapes x 20 iterations of simulated fires. We include the data and R scripts used for the analyses of abrupt change in the publication associated with these data; all other analyses used standard functions in R.
Dataset from paper "Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest"
<h3><strong><span>Dataset from the paper “Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest”</span></strong></h3> <p><span> </span><span>This repository contains:</span></p> <ul> <li><span>Dataset Description: “raster_labels.xlsx” (an Excel spreadsheet detailing raster pixel values and their respective fragmentation classes).</span></li> <li><span>Fragmentation Raster Files: “forest_fragmentation_mspa_2020.tif” and “forest_fragmentation_mspa_2020.tif” (GeoTIFF files of landscape forest fragmentation for 1985 and 2020).</span></li> </ul> <p><span> </span><span>If you need anything else, please contact the corresponding author, Igor Broggio (<a href="mailto:isbbroggio@gmail.com">isbbroggio@gmail.com</a>).</span></p> <p><span> </span></p> <p><span>If you use these data, please cite the paper: </span><span>[Citation]</span></p>
Morpho-sedimentary outlines displayed in Figures 1, S1, and S6-S15 of the article "Source-to-sink aeolian fluxes from arid landscape dynamics in the Lut Desert"
<p>Morpho-sedimentary outlines of the aeolian landforms in the Lut Desert.</p>
Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"
<p>City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics.</p> <p>To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: <strong><em><span>Escherichia coli</span></em></strong>, the emerging pathogen <strong><em><span>Streptococcus suis</span></em></strong>, and the sewer-inhabiting <strong><em><span>Arcobacter cryaerophilus</span></em></strong>. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin).<span> Using</span> these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.</p> <p> </p> <p><strong>Usage</strong></p> <p>Users can perform the analysis by running the R script (TC3D.R) after the installation of all</p> <p>package mentioned in the preamble,<span> </span></p> <p>This folder contains:</p> <p>- 3 datasets with OD measures for the 3 species:</p> <p><span> </span>* data_acrya.xlsx</p> <p><span> </span>* data_ecoli.xlsx</p> <p><span> </span>* data_ssuis.xlsx</p> <p>- 1 excel files with metadata (plate, well, species, environmental conditions)</p> <p><span> </span>* map_plate_all.xlsx</p> <p>- 4 datasets giving time series of the flow and several measures including <span> </span>conductivity and <span> </span>temperaturefor 2 sewers of Barcelona obtained from sample cabines <span> </span>set during the implementation of SCOREWATER (ID:820751)</p> <p><span> </span>* carmel_flow.csv</p> <p><span> </span>* carmel_quality.csv</p> <p><span> </span>* poblenou_flow.csv</p> <p><span> </span>* poblenou_quality.csv</p> <p>- 1 C++ script compiled and run with the R TMB package:</p> <p><span> </span>* fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.</p> <p><br><br></p>
Virgin Islands National Park: Coral Reef: Population Dynamics: Landscape-scale Variation in Scleractinian Corals
This study provides a landscape-scale context to a decadal-scale analysis of community structure on shallow reefs along 4 km of the south shore of St. John, US Virgin Islands. By focusing on 12-14 sites along ~100 km of the shores of St. John and St. Thomas, surveys conducted in 2011 were used to contrast: (1) a local-scale with a landscape-scale analysis on two islands, (2) reefs around St. John and St. Thomas, and (3) reefs on north and south shores. Reefs were censused using photoquadrats that were analyzed for percentage cover first by functional groups (coral, macraolagae and CTB), and then by coral genus. In general, among-site variation for the coarse-resolution analysis eclipsed shore and island effects, but the fine-resolution analysis revealed strong site-specific differences for multiple coral genera that could be the product of priority effects in community succession. Over the next decade these differences probably will create unique community trajectories at each site.
Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'
<p>This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'.</p> <p><strong>Metrics calculated for each of the 4269 50 km by 50 km regions</strong></p> <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc<br>y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> <p>ifpa_count.nc<br>ifpa_count_max_20.nc<br>ifpa_count_max_100.nc<br>ifpa_count_max_250.nc</p> </td> <td>bedmach_count.nc<br>bedmach_count_max_20.nc<br>bedmach_count_max_100.nc<br>bedmach_count_max_250.nc</td> <td> </td> <td> <p>The number of hills with a 50 m prominence within a 5 km neighbourhood <br>(or 20 m, 100 m, 250 m respectively)</p> </td> </tr> <tr> <td>ifpa_b1_5km.nc<br>ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc<br>bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc<br>i_std_l.nc</td> <td>bedmach_std_deslope.nc<br>b_std_l.nc</td> <td> </td> <td>The standard deviation:<br>- with the best fit slope removed<br>- of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc<br>i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc<br>b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of:<br>- the bed elevation<br>- some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table> <p> </p> <p><strong>Datasets required for plotting</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> <p>IFPA_bed_C50.nc</p> </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> <p><strong>To plot the figures, you will either require the following datasets: </strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> <p>HA_data.csv<br>HB_data.csv<br>RSB_data.csv</p> </td> <td>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> <p>HA_data_ifpa.csv<br>HB_data_ifpa.csv<br>RSB_data_ifpa.csv</p> </td> <td> <p>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile</p> </td> </tr> </tbody> </table> <p><strong>or, the figures can be regenerated using the following datasets, which are available at the listed DOIs</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table> <p><strong> </strong></p>
Regional and local factors interact to shape colonization and extinction dynamics of invasive Hydrilla verticillata in a patchy landscape
<p>Understanding the response of species to global change requires disentangling the drivers of their distributions across landscapes. Colonization and extinction processes, shaped by the interplay of landscape-level and local patch-level factors, are key determinants of these distributions. However, disentangling the influence of these factors, when larger-scale processes manifest at local scales, remains a challenge. We addressed this challenge by investigating the colonization and extinction dynamics of the aquatic plant, <em>Hydrilla verticillata</em>, in a complex riverine rock pool system. This system, with hundreds of rock pools experiencing varying flooding frequencies, provided a natural laboratory to examine how a single landscape-level disturbance can differentially impact colonization and extinction depending on local patch characteristics to shape species distributions. Using five years of data across over 500 sites and more than 5,000 surveys, we employed dynamic occupancy models to model colonization, extinction, and changes in <em>Hydrilla</em> patch occupancy while accounting for imperfect detection. Our results revealed that larger, infrequently flooded pools closer to the river were more likely to be colonized. In contrast, local extinction of Hydrilla was more likely in smaller pools closer to the river that flooded frequently. These findings underscore the importance of considering context-dependence in species distribution models. The same landscape-level disturbance (flooding) had opposing effects on colonization and extinction, with the direction and magnitude of these effects varying with local patch characteristics. Our study highlights the need for integrating local and landscape-level factors, and considering how larger-scale processes play out at the patch level, to understand the complex dynamics that shape species distributions.</p>
Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
<p>Unraveling the intricate mechanisms that govern community coexistence remains a daunting challenge, particularly amidst ongoing environmental change. To understand the response of individual animals to environmental change, physiology and individual metabolism are often studied. However, this perspective is currently largely lacking in community ecology. We argue that the integration of individual metabolism into community theory can offer new insights into coexistence. We present the first individual-based metabolic community model for a terrestrial mammal community to simulate energy dynamics and home range behavior in different environments. Using this model, we investigate how ecologically similar species coexist and maintain their energy balance under food competition. Only if individuals of different species are able to balance their incoming and outgoing energy over the long-term will they be able to coexist. After thoroughly testing and validating the model against real-world patterns such as of home range dynamics and field metabolic rates, we applied it as a case study to scenarios of habitat fragmentation - a widely discussed topic in biodiversity research. First, comparing single-species simulations with community simulations, we find that the effect of habitat fragmentation on populations is strongly context-dependent. While populations of species living alone in the landscape were mostly positively affected by fragmentation, the diversity of a community of species was highest under medium fragmentation scenarios. Under medium fragmentation, energy balance and reproductive investment were also most similar among species. We therefore suggest that similarity in energy balance among species promotes coexistence. We argue that energetics should be part of community ecology theory, as the relative energetic status and reproductive investment can reveal why and under what environmental conditions coexistence is likely to occur. As a result, landscapes can potentially be protected and designed to maximize coexistence. The metabolic community model presented here can be a promising tool to investigate other scenarios of environmental change or other species communities to further disentangle global change effects and preserve biodiversity.</p>
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>
Ecological Dynamics and Coexistence Patterns of Wild and Domestic Mammals in an Abandoned Landscape
<p>This repository contains all scripts and data used for analysis and figures for the paper <strong>Zuleger, Annika M., Perino, Andrea, Pereira, Henrique M. (2024): <em>Ecological Dynamics and Coexistence Patterns of Wild and Domestic Mammals in an Abandoned Landscape</em>. Wildlife Biology. DOI: 10.1002/wlb3.01319</strong></p> <p><strong>Full Changelog</strong>: https://github.com/AMZuleger/Dynamics_Coexistence_Peneda/commits/v1.0.0</p> <h2><strong>Abstract</strong></h2> <p>The issue of agricultural land abandonment in Southern Europe has raised concerns about its impact on biodiversity. While abandoned areas can lead to positive developments like creating new habitats and restoring native vegetation, they can also result in human-wildlife conflicts, particularly in areas with extensive farming and free-ranging livestock. To understand habitat selection and use of livestock and wild ungulates, it is essential to study their spatial and temporal distribution patterns. In this context, we conducted a long-term large mammal monitoring project using camera traps in the Peneda-Gerês National Park in Northern Portugal. Our primary focus was on exploring habitat preferences, occupancy dynamics, and potential spatial use correlations between domestic and wild species, utilizing dynamic occupancy models. Most wild species exhibited stable area use patterns, while domestic species experienced marginal declines, and the Iberian ibex displayed signs of repopulation. We observed distinct effects of habitat variables on occupancy, colonization, and extinction, revealing species-specific patterns of habitat utilization. Human disturbance had a notable impact on domestic species but did not affect wild ones. Camera sensitivity emerged as a critical factor, enhancing detection probability for all species. Additionally, habitat and weather variables exerted varying effects on detection probabilities, underscoring the necessity of accounting for these factors in modeling the detection process. We found shared habitat preferences between cattle and horses, both positively correlated with wolves, suggesting potential human-wildlife conflicts. Despite extensive spatial overlap, domestic and wild species seem to exhibit ecological independence due to distinct strategies and low predation pressure. Overall, the study emphasizes the multifaceted factors influencing habitat use. The observed species associations contribute to understanding ecological relationships and potential resource competition, emphasizing the importance of considering environmental variables for effective wildlife conservation and management.</p> <div> <h2><strong>Structure</strong></h2> </div> <ul> <li>R_Ecological_Dynamics_and_Coexistence_Patterns_rv.R --> R Script to perform all analysis from the publication <ul> <li>This is the only file needed to perform the entire analysis. Code will automaticall download data from GitHub and produce all Results and Figures.</li> </ul> </li> </ul> <div> <h3><strong>Data</strong></h3> </div> <ul> <li>Presence-absence tables for each species (per grid cell and week) from 2015 to 2022 (e.g. Domestic cattle.csv)</li> <li>SiteCovs_2015_grid.csv --> intial site covariates for the first primary sampling period</li> <li>yearlySiteCovs_grid.rds --> yearly site covariates for each primary sampling period</li> <li>ObsCovs.rds --> observation covariates for each secondary sampling period</li> </ul> <h2><strong>Authors</strong></h2> <p>Annika M. Zuleger*, Andrea Perino and Henrique M. Pereira</p> <p>*Corresponding author: Annika Mikaela Zuleger, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany Email: <a href="mailto:annika_mikaela.zuleger@idiv.de">annika_mikaela.zuleger@idiv.de</a></p>
Data and scripts belonging to "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape"
<p>Data and scripts belonging to Kaandorp et al., 2021 "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape". Hydrology and Earth System Sciences. </p>
Sampling Rare Event Energy Landscapes via Birth-Death Augmented Dynamics
<p>Archive with data supporting the paper "Sampling Rare Event Energy Landscapes via Birth-Death Augmented Dynamics" and the related PhD thesis by B. Pampel</p>
Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase.
<p>The data deposited here accompany the manuscript "Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase" and include the molecular dynamics trajectories and the AMBER topology (parm) files. Detailed file contents are summarized in the README file.</p>
Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
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Data from: When beggars are choosers-How nesting of a solitary bee is affected by temporal dynamics of pollen plants in the landscape
Open the record for dataset details and reuse information.
Regional and local factors interact to shape colonization and extinction dynamics of invasive Hydrilla verticillata in a patchy landscape
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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.