We are studying the species repartition and weight of animals caught in plots in our study area.

Data on the datasets contained in data.wa.gov; useful for assessing trends in subject matter, utilization, and publishers of open data on this site.

Species interactions in food-webs from forested landscapes were extracted from a database online and converted into directed binary interactions.

This summary data set in Microsoft Excel format is from the master relational database for whitebark pine pine tree monitoring starting in 2004 at permanent, long term monitoring plots on federally administered lands throughout the Greater Yellowstone Ecosystem.

This collection of GIS layers was prepared for the report Alaska Arctic Marine Fish Ecology Catalog (U.S. Geological Survey Scientific Investigations Report 2016–5038). The layers display geographic distribution and sampling locations for Arctic marine fish species in the region of United States sectors of the Chukchi and Beaufort Seas. Certain diadromous species (for example, Pacific salmon, char, and whitefishes) are treated as marine fishes (McDowall, 1987) because much of their life cycle is in marine and brackish environments. This synthesis of information is meant to provide current information and understanding of this fauna and its relative vulnerability to changing Arctic conditions. There are 104 species in the collection - some species have both polygon and point data layers. The report (SIR 2016-5038) also describes for each species its names - species, common, and colloquial; ecological role; physical description/attributes; range (geographic); relative abundance; depth range; habitats and life history; behavior; populations or stocks, reproduction, food and feeding, biological interactions, resilience, traditional and cultural importance, commercial fisheries, potential effects of climate change, areas for future research, cited references, and bibliography. The published report has one map for each species showing the polygon and point data as well as land and relevant administrative boundaries. Although some of the species also have an inland water presence, this report was concerned only with their marine conditions; therefore, the land component (from the original sources) has been clipped and removed. The distribution areas may be greater in extent than that shown in the report map bounding box limits. Distributions of marine fishes are shown in adjacent Arctic seas where reliable data are available. The report can be accessed at: https://doi.org/10.3133/sir20165038 This metadata document describes the collection of species data layers. Each species layer file will have its own metadata with details specific to that layer.

WorldClim 2.1 Data https://www.worldclim.org/data/worldclim21.html

Two scales (1km and 10km)

Bioclimatic Variables https://www.worldclim.org/data/bioclim.html

BIO1 = Annual Mean Temperature BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp)) BIO3 = Isothermality (BIO2/BIO7) (×100) BIO4 = Temperature Seasonality (standard deviation ×100) BIO5 = Max Temperature of Warmest Month BIO6 = Min Temperature of Coldest Month BIO7 = Temperature Annual Range (BIO5-BIO6) BIO8 = Mean Temperature of Wettest Quarter BIO9 = Mean Temperature of Driest Quarter BIO10 = Mean Temperature of Warmest Quarter BIO11 = Mean Temperature of Coldest Quarter BIO12 = Annual Precipitation BIO13 = Precipitation of Wettest Month BIO14 = Precipitation of Driest Month BIO15 = Precipitation Seasonality (Coefficient of Variation) BIO16 = Precipitation of Wettest Quarter BIO17 = Precipitation of Driest Quarter BIO18 = Precipitation of Warmest Quarter BIO19 = Precipitation of Coldest Quarter

The second version of the Fine-Root Ecology Database is available for download! Download the full FRED 2.0 data set, user guidance document, map, and list of data sources here. Prior to downloading the data, please read and follow the Data Use Guidelines, and it's worth checking out some tips for using FRED before you begin your analyses. Also, see here for an updating list of corrections to FRED 2.0.

Collections data on ecology of bottom animal of the Southern ocean, compiled by Igor Smirnov, Anastasija Vasiljeva, Tatjana Konina, from the Zoological Institute, St Petersburg, Russia.

Schedule for a one-semester course at Colorado State University (NR 575: Systems Ecology) introducing concepts of model–data assimilation to graduate students in ecology.

Abstract

This study consists of qualitative interviews about birdwatching, citizen science, and the use of the birdwatching data platform eBird in India. Interview partners are birdwatchers, citizen science practitioners, and ecologists who have used eBird data. Some of the main topics covered include: the nature of the birdwatching community and styles of birdwatching in India; the history of the adoption of eBird in India; the value of birdwatching and citizen science; challenges involved in conducting or participating in citizen science; opportunities and limitations of using data from eBird and citizen science; processes of data collection and quality control in eBird; and ecological research, conservation priorities, and environmental activism in India. This study is part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS).

Methods

The data in this study was collected using semi-structured qualitative interviews.

Interview partners were recruited by snowball sampling through their engagement with eBird India and related organisations. There were 17 interview partners, interviewed either once or several times. 19 interviews were conducted in total.

Interview guides/questionnaires were designed for each interviewee depending on their status as birdwatchers, citizen science coordinators, and eBird data users.

Interviews were conducted between April 2022 and June 2023. The interviews took place online using Zoom videoconferencing software. Interviews lasted 35-70 minutes. When participants provided their written consent, interviews were audio-recorded and transcribed smart verbatim using otter.ai and manual proofreading. Sensitive information was removed before publishing transcripts.

Transcripts were analysed using semi-grounded coding. Codes were organised into parent codes using an inductive approach based on emergent categories.

Description of the data and file structure

Documentation files include interview guides, the information sheet and consent form, ethics approval, and the data narrative. Documentation files are named according to the structure: authorname_filename_DOCUMENTATION.

Data files consist of a summary of participants, 17 of the interview transcripts, and a code list. Interview transcript files are named according to the structure: authorname_interviewnumber_date.

A full list of files is provided in the README file.

Notes

This study was conducted as part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS). More information can be found at https://opensciencestudies.eu

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101001145).

For a series of studies on the ecosystem service values of chaparral in Southern California, we developed a raster data layer providing an ecological unit classification of the Southern California landscape. This raster dataset is at a 30 meter pixel resolution and partitions the landscape into 37 different ecological unit types. This dataset was derived through a GIS-based cluster analysis of 10 different physiographic variables, namely soil suborder type, terrain geomorphon type, flow accumulation, slope, solar irradiation, annual precipitation, annual minimum temperature, actual evapotranspiration, and climatic water deficit. This partitioning was based on physiographic variables rather than vegetation types because of the wish to have the ecological units reflect biophysical characteristics rather than the historical land use patterns that may influence vegetation. The cluster analysis was performed across a set of 10,000 points randomly placed on a GIS layer stack for the 10 variables. These random points were grouped into 37 discrete clusters using an algorithm called partitioning around medoids. This assignment of points to clusters was then used to train a random forest classifier, which in turn was run across the GIS stack to produce the output raster layer.

This dataset is described in the following book chapter publication:

Underwood, Emma C., Allan D. Hollander, Patrick R. Huber, and Charlie Schrader-Patton. 2018. "Mapping the Value of National Forest Landscapes for Ecosystem Service Provision." In Valuing Chaparral, 245–70. Springer Series on Environmental Management. Springer, Cham. https://doi.org/10.1007/978-3-319-68303-4_9.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains human population (total) measurements in number units and were aggregated to a yearly timescale.

Collapses of food producer societies are recurrent events in prehistory and have triggered a growing concern for identifying the underlying causes of convergences/divergences across cultures around the world. One of the most studied and used as a paradigmatic case is the population collapse of the Rapa Nui society. Here, we test different hypotheses about by developing explicit population dynamic models that integrate feedbacks between climatic, demographic and ecological factors that underpinned the socio-cultural trajectory of these people. We evaluate our model outputs against a reconstruction of past population size based on archaeological radiocarbon dates from the island. The resulting estimated demographic declines of the Rapa Nui people are linked to the long-term effects of climate change on the island's carrying capacity, and in turn on the "per capita food supply".

These data include 2015 - 2018 eDNA field sample points indicating lab results for presence or absence of bull trout. Sample sites are spaced at a 1 kilometer interval throughout the historical range of bull trout. eDNA stream samples are collected and species presence/absence is determined by analyses at the National Genomics Center. Results are recorded in the feature attribute table of the eDNA sample site shapefile. One point feature in the shapefile was generated for each 1 kilometer sample point in the bull trout eDNA feature class. Where multiple samples were collected at a single eDNA sample site, replicate point features will occur at a single location in the shapefile. The bull trout is an ESA-listed species with a historical range that encompasses many waters across the Northwest. Though once abundant, bull trout have declined in many locations and are at risk from a changing climate, nonnative species, and habitat degradation. Informed conservation planning relies on sound and precise information about the distribution of bull trout in thousands of streams, but gathering this information is a daunting and expensive task. To overcome this problem, we coupled 1) predictions from the range-wide, spatially precise Climate Shield model on the location of natal habitats of bull trout with 2) a sampling template for every 8-digit hydrologic unit in the historical range of bull trout, based on the probability of detecting bull trout presence using environmental DNA (eDNA) sampling (McKelvey et al. 2016). The template consists of a master set of geospatially referenced sampling locations at 1-kilometer intervals within each cold-water habitat. We also identified sampling locations at this same interval based on the U.S. Fish and Wildlife Service's (USFWS) designation of critical spawning and rearing habitat. Based on field tests of eDNA detection probabilities conducted by the National Genomics Center for Wildlife and Fish Conservation, this sampling approach will reliably determine the presence of populations of bull trout, as well as provide insights on non-spawning habitats used by adult and subadult fish. The completed bull trout eDNA survey results are available through an interactive ArcGIS Online Map. The map provides the ability to zoom in and look at an area of interest, as well as to create queries or select an area to download points as a shapefile.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This dataset contains contains the raw benthic/ecological data collected from Cecile Peninsular Reef Flat, Kiritimati Island

Annual changes in the number of dead trees over fifteen years after the introduction of Bursaphelencus xylophilus at seven locations in China.

This depository contains two data sets:1. Collected and analysed field data related to herbivore browsing, and2. The 50 x 50 km fishnet (GIS data) as applied in:Per Angelstam P., Manton M., Pedersen S. and M. Elbakidze 2017. Disrupted trophic interactions affect recruitment of boreal deciduous and coniferous trees in northern Europe. Ecological Applications xxPlease note, other data used in this publication can be sourced from the original data sources (see cited literature for more information).

R data fileR data file containing metadata, OTU table, and other R objects used for publication.PS_urban_eDNA_data.rDataR analysis scriptR script used for analyses resulting in the publication. Calls data from associated Rdata file.PS_urban_eDNA_Analysis_20160707.RBanzai parametersParameters for bioinformatics pipeline used for dataset.analysis_parameters.txtBanzai scriptAnalytical script for bioinformatics (Banzai Pipeline, O'Donnell, available at: https://github.com/jimmyodonnell/banzai/) used in publication.analysis_script.txtmetadata_PugetSound_eDNAMetadata for DNA sequences accessioned in Genbank Bioproject 408172.BioSampleObjectsContains more specific Genbank accession information relevant to the project.

This file contains the data and codes used in the paper: Abrupt shifts govern ecosystem productivity dynamics in global drylands. It has two Rmarkdown files, one for achieving the database and other to perform statistical analyses described in the paper.The files FinalDB3 and MiguelResTypesMD contains the output of the first code. Rest of csv files contain data for plot directly the resulting figures of the paper.The original raw data of NDVI (termed Time series new in the codes) do not belong to the authors and therefore are not available in this public repository. However they can be requested for replication purposes at: mglberdugo@gmail.com.

The dataset is composed of two data tables containing information from boat electrofishing surveys conducted during 2021, 2023, and 2024 in sections of the Mohawk River where it runs separately from the New York State Canal System (i.e., is not canalized). The first table contains fish collection data and the second table contains information on the sampled reaches.