Wild-Time is a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning.

This dataset originally held 5 647 442 total records, where 34% of the records corresponded to germplasm accessions and 66% to herbarium samples. A total of 3 231 286 records had cross-checked coordinates (see Figure 2). 322 735 records were newly georeferenced using The Google Geocoding API and 15 713 new records were obtained after digitizing the information contained in herbaria specimens. Data was gathered from more than 100 data providers, including GBIF (a comprehensive list of institutions and individuals is available here: http://www.cwrdiversity.org/data-sources/ ).

The geographic coverage of the dataset includes 96% of the world countries and also includes records of cultivated plants (1/3 of the dataset). Records of the crop wild relatives of 80 crop gene pools can be queried and visualized in this interactive map: http://www.cwrdiversity.org/distribution-map/

This dataset was assembled as part of the project ‘Adapting Agriculture to Climate Change: Collecting, Protecting and Preparing Crop Wild Relatives’, which is supported by the Government of Norway. The project is managed by the Global Crop Diversity Trust and the Millennium Seed Bank of the Royal Botanic Gardens, Kew, and implemented in partnership with national and international genebanks and plant breeding institutes around the world. For further information, please refer to the project website: http://www.cwrdiversity.org/

For publication to GBIF, all records originally gathered from GBIF have been removed to avoid data duplication.

Citation: Crop Wild Relatives Occurrence data consortia ([year]). A global database for the distributions of crop wild relatives. Centro Internacional de Agricultura Tropical (CIAT). Occurrence dataset https://doi.org/10.15468/jyrthk accessed via GBIF.org on [date].

Understanding the impacts of landscape change on species distributions can help inform decision-making and conservation planning. Unfortunately, empirical data that span large spatial extents across multiple taxa are limited. In this study, we used expert elicitation techniques to develop species distribution models (SDMs) for harvested wildlife species (n = 10) in the New England region of the northeastern United States. We administered an online survey that elicited opinions from wildlife experts on the probability of species occurrence throughout the study region. We collected 3396 probability of occurrence estimates from 46 experts, and used linear mixed-effects methods and landcover variables at multiple spatial extents to develop SDMs. We applied models to rasters (30 × 30 m pixles) of the New England region to map each species’ distribution. Details of the project can be found in the following publication: Pearman-Gillman SB, Katz JE, Mickey R, Murdoch JD, and Donovan TM. 2 ...

Builds on top of recent data collection efforts by domain experts in these applications and provides a unified collection of datasets with evaluation metrics and train/test splits that are representative of real-world distribution shifts.

The v2.0 update adds unlabeled data to 8 datasets. The labeled data and evaluation metrics are exactly the same, so all previous results are directly comparable.

Wild boar is a host of a number of arthropod-vectored diseases and its numbers are on the rise in mainland Europe. The first of the three models provide a European map presenting the probability of presence of Sus scrofa, which can be used to describe the likely geographical distribution of the species. The second and third models provide indices to help describe the likely abundance across the continent. The two indices include “the proportion of suitable habitat where presence is estimated” and a simple classification of boar abundance across Europe using quantiles of existing abundance data and proxies.

Please provide the download link for the shapefile of the distribution map of wild animals from the third forest resources survey, as well as the accompanying interpretation data.

This dataset tracks annual distribution of students across grade levels in Wild Rose High School

Extensive restoration and translocation efforts beginning in the mid-20th century helped to reestablish eastern wild turkeys (Meleagris gallopavo silvestris) throughout their ancestral range. The adaptability of wild turkeys resulted in further population expansion in regions that were considered unfavorable during initial reintroductions across the northern United States. Identification and understanding of species distributions and contemporary habitat associations are important for guiding effective conservation and management strategies across different ecological landscapes. To investigate differences in wild turkey distribution across two contrasting regions, heavily forested northern Wisconsin, USA, and predominately agricultural southeast Wisconsin, we conducted 3,050 gobbling call-count surveys from March–May 2014–2018 and used multiseason correlated-replicate occupancy models to evaluate occupancy-habitat associations and distributions of wild turkeys in each study region. Detection probabilities varied widely and were influenced by sampling period, time of day, and wind speed. Spatial autocorrelation between successive stations was prevalent along survey routes but were stronger in our northern study area. In heavily forested northern Wisconsin, turkeys were more likely to occupy areas characterized by moderate availability of open land cover. Conversely, large agricultural fields decreased the likelihood of turkey occupancy in southeast Wisconsin, but occupancy probability increased as upland hardwood forest cover became more aggregated on the landscape. Turkeys in northern Wisconsin were more likely to occupy landscapes with less snow cover and a higher percentage of row crops planted in corn. However, we were unable to find supporting evidence in either study area that abandonment of turkeys from survey routes was associated with snow depth or with the percentage of agricultural cover. Spatially, model-predicted estimates of patch-specific occupancy indicated turkey distribution was nonuniform across northern and southeast Wisconsin. Our findings demonstrate that the environmental constraints of turkey occupancy varied across the latitudinal gradient of the state with open cover, snow, and row crops being influential in the north, and agricultural areas and hardwood forest cover important in the southeast. These forces contribute to non-stationarity in wild turkey-environmental relationships. Key habitat-occupancy associations identified in our results can be used to prioritize and strategically target management efforts and resources in areas that are more likely to harbor sustainable turkey populations.

Aim: The aim of this dataset is to provide a list of the Crop Wild Relatives (CWR) in the Nordic region that are most important for future food security, and to provide basic data on geographic distribution, gene pool affinity, invasiveness, and threat level. The dataset can serve as a basis for Nordic level, as well as national level, conservation planning and implementation. Method: A comprehensive CWR checklist for all Nordic CWR taxa was developed in 2017 (Fitzgerald et al., 2017). The taxa on this list were prioritized based on socio-economic value of the related crop(s) and potential utilization value of the CWR for breeding, resulting in the first version of the priority dataset. More information on how the prioritization was performed can be found in Fitzgerald et al. (2019). In 2021, an update of the dataset was made. Nordic scientists and plant breeders were contacted and asked if, in their opinion, there were taxa missing from the dataset. All suggestions were considered and evaluated for socio-economic value and utilization potential. The taxa deemed to fulfill the criteria were added to the list. Also, information on national threat category and national invasive category were added, and information on local names and geographic distribution were updated. Results: The result of the analysis is a list/data set of CWR prioritized based on socio-economic value of the related crop(s) and potential utilization value of the CWR. The list includes information on national occurrence (indigenous, naturalized foreign, temporary findings), to which genepool/taxon group the CWR belongs, use category (food/forage), national threat status and national invasiveness classification.

This dataset tracks annual distribution of students across grade levels in Wild Peach Elementary School

Occupied habitat distributions of Nevada small game species. These delineations were deteremined by Nevada Department of Wildlife field biologists and wildlife staff specialists. Species include California quail, chukar, dusky grouse, Gambel's quail, Himalayan snowcock, mountain quail, ruffed grouse, sooty grouse, white-tailed jackrabbit, and wild turkey.

description: Class A streams are streams that support a population of wild (natural reproduction) trout of sufficient size and abundance to support a long-term and rewarding sport fishery. The Commission does not stock these streams. This GIS layer respresents points of streams that are designated as such.; abstract: Class A streams are streams that support a population of wild (natural reproduction) trout of sufficient size and abundance to support a long-term and rewarding sport fishery. The Commission does not stock these streams. This GIS layer respresents points of streams that are designated as such.

Species Distribution Modeling (SDM) and Wildlife Habitat Ratings (WHR) project boundaries contains (study areas) and attributes describing each project (project level metadata), plus links to the locations of other data associated with the project (e.g. reports, WHR polygon datasets, plotfiles). SDM predicts the suitability of different environments for occupation by particular species, and the likelihood that those suitable habitats are occupied. WHR are also known as wildlife habitat interpretations and most commonly use TEM data as a means to identify specific habitats. This layer is derived from the STE_TEI_PROJECT_BOUNDARIES_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: WHR, SDM, PEMWHR, PEMSDM, TEMWHR, TEMSDM, TEMPRW, NEMPRW, TEMSEW, BEIWHR, BEISDM, SEIWHR, SDM, and SOILSW. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

This statistic shows the distribution of seizures of illegal wildlife trade worldwide, by type of wildlife from 2005 to 2014. Thirty-five percent of seizures of wildlife worldwide between 2005 and 2014 were of rosewood.

The dataset consists of 2 data file. File1 A checklist of wild vascular plants in Tianjin and File 2 List of families and genera of wild vascular plants in Tianjin. File1 contains 996 items (rows) and 16 fields (columns) as following: sequence number, main categories of vascular plants, sequence number of family, Chinese family, family, Chinese genus, genus, Chinese name, scientific name, distribution areas, reference or voucher specimen,collector and number , native/introduced, invasive species，rank in the List of National Key Protected Wild Plants 2021，knownledge degree.

Queensland species distributions and densities generalised to a 10 km grid resolution based on the Queensland Government's WildNet database.

Komatsu_2023JJFS_stan_model.stan - Model file for Bayesian analysis with rstan. - Hierarchical Bayesian model with the ordinary logarithm of 137Cs concentration in wild mushrooms as the objective variable and the random effects of the mean concentration for species (r_sp), the error in concentration per species (r_sigma) and the concentration effect per municipality (r_mun). - Consists of eqs. (1)-(5) in RELATED MATERIALS 1. post_stan.RData - RData file to store the results of the posterior distribution obtained by the hierarchical Bayesian model (post: variables in list format) - post$species: species names corresponding to columns in rsp and rsigma - post$rsp: random effect of species - post$rsigma: standard deviation of individuals by species - post$mu_sp: hyper parameter of mean in rsp - post$sigma_sp: hyper parameter of standard deviation in rsp - post$mu_sigma: hyper parameter of mu in rsigma - post$sigma_sigma: hyper parameter of standard deviation in rsigma - post$sigma_mun: hyper parameter of standard deviation in rmun - Posterior results of random effects of municipality (rmun) is excluded.

Distribution of SNPs on all 17 chromosomes in wild-type strains.

This bar chart displays books by publication date using the aggregation count. The data is filtered where the author is Ailsa Wild. The data is about books.

Distribution and observed trends of wild Rangifer populations throughout the circumpolar Arctic (from The Circum Arctic Rangifer Monitoring and Assessment Network, CARMA). Note: Wild boreal forest reindeer have not been mapped by CARMA and thus are not represented here. Published in the Arctic Biodiversity Trends 2010 - Selected indicators of change, INDICATOR #02 - released in 2010