The Wildlife Action Network (WAN) was developed as part of the 2015-2025 MN Wildlife Action Plan revision. The WAN is made up of ten GIS layers representing quality aquatic and terrestrial habitats across the state of Minnesota. The layers include composite population viability/persistence maps of Species In Greatest Conservation Need (SGCN), species richness hotspots of SGCN, spatially prioritizied MBS Sites of Biodiversity Significance, cores and corridors of the MN Prairie Conservation Plan, High Conservation Value Forests, Lakes of Biological Significance, and Rivers/Streams with an exceptional index of biological integrity (IBI) score. Five of these layers, chosen because they were entirely or almost entirely statewide in extent and had a score or rank, were used to then rank the areas within the WAN on a 5-level scale from poor to excellent. The layers used for scoring were a) Composite SGCN Population Scores, b) SGCN richness grid used in the hotspot analysis, c) Marxan scores of prioritized Sites of Biodiversity Significance, d) Stream Index of Biological Integrity, and e) Lakes of Biological Significance.

As of October 16, 2024, there were 76 insect species in the United States in danger of becoming extinct throughout all or a significant part of their natural range. Many animals native to the U.S. share this fate, but plants are the most affected, with 764 species on the endangered list as of October 2024.

The illegal import of wildlife and wildlife products is a growing concern, and the U.S. is one of the world’s leading countries in the consumption and transit of illegal wildlife and their derivatives. Yet, few U.S. studies have analyzed the illegal wildlife trade (IWT) on a national or local scale. Moreover, few studies have examined the trends associated with IWT moving through personal baggage. This work aimed to better understand the magnitude of illegal wildlife importation into U.S. ports of entry by determining trends associated with illegal wildlife products from personal baggage seizures in the Pacific Northwest (PNW). To identify the most influential factors in determining the numbers and types of personal baggage seizures into PNW, we analyzed 1,731 records between 1999 and 2016 from the Fish and Wildlife Service’s (FWS) Law Enforcement Management Information System (LEMIS) database. We found five significant contributors: taxonomic Class of wildlife, categorical import date, wildlife product, source region, and the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) status. While wildlife seizures across taxonomic categories have decreased in the PNW since 2008, other findings provide a reason for concern. Three main findings of this study include: (1) mammals make up the majority of seizures (2) temporal trends of wildlife seizures point to increases in seizures in many taxonomic groupings and (3) the majority of seizures originate from six regions, of which East Asia is the largest source. This work adds to the growing understanding of IWT through large-scale geographical seizure data using a highly important global port as our case study.

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WDFW cartography staff create map content designed to inform map viewers where certain types of recreation opportunities are promoted on WDFW Wildlife Areas. This layer is created from WDFW parcel data using parcel attributes to define where these targeted recreation opportunities exist. There are currently two focused map content areas, one is to support the GoHunt application where hunting opportunities are promoted. The other is used to identify WDFW lands where a Washington Discover Pass is required. The Recreation Access Code, managed in the WDFW_Lands feature class, is used to define which parcels are dissolved into this feature class. Recreation Access Code values that are brought across as a result of a standard definition query are: 1 - Parcels managed within a designated Wildlife Area and not restricted in any way for being displayed on GoHunt or Discover Pass maps; 4 - Parcels designated by the Wildlife Program for exclusion from GoHunt activities; 5 - Parcels designated by the Wildlife Program for exclusion from the Discover Pass. Users of this feature class can use ArcMap definition queries to appropriately display either GoHunt or Discover Pass map content. This feature class displays the finest scale of the Wildlife Area administrative hierarchy that consists of Widlife Area Complexes, Wildlife Areas and Wildlife Area Units. There are several fields in this data that can be used to label maps with the Wildlife Area Unit name.

A wildlife dataset.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

Management units as used by the Iowa DNR Wildlife Bureau.

Wildlife Habitats (HAFA) contains data for the 11 legal wildlife habitats located on land under the domain of the State and is protected under the Wildlife Habitat Regulations (RHF). There are also HAFAs located on mixed and private lands for information purposes. Since they are essential environments for wildlife, the eleven habitats benefit from legal protection in Quebec. The conservation of wildlife species and their habitats is beneficial for biodiversity. Each of these species plays an important role in our ecosystems. ### #Mise on guard: The digital version of geo-descriptive data describing wildlife habitats is produced from a legal perspective of location, protection and management of habitats. In fact, only the digital version that has been published in the Official Gazette of Quebec is recognized as legal. Last publication of wildlife habitats: November 17, 2022.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Vehicle-caused wildlife mortality and wildlife management destruction data for Yoho National Park between the years 2005 and 2017. Each species mortality is recorded by date, location, count, age, gender, and cause. For more recent records please refer to “Human-wildlife coexistence incidents in selected national parks from 2010 to 2021 - Open Government Portal (canada.ca)”: https://open.canada.ca/data/en/dataset/cc5ea139-c628-46dc-ac55-a5b3351b7fdf

Brisbane City Council has decided to partner with the Atlas of Living Australia (ALA). The ALA will be used to capture and manage Council’s flora and fauna data. Council will be progressively loading data onto the site. ALA is a collaborative national project that aggregates biodiversity data from multiple sources and makes it freely available and usable online. This dataset provides a link to download wildlife (flora and fauna) data for the Brisbane region from ALA. An ALA account is needed to download data. This data could be from many data providers. Any use of the data downloaded is governed by the terms and conditions specified on ALA, please read these to confirm attribution for any data used. To search and view data use the link in the Data and resources section below.

Through a 2021 AFWA MultiState Conservation Grant, Virginia Tech and the AFWA Wildlife Viewing and Nature Tourism Working Group conducted national and state level surveys to gather more data on wildlife viewers. This dataset is from the survey conducted in New Mexico.

It contains: 1. README.rtf: an overview of the datasheets and files. 2. New Mexico Wildlife Viewer Survey.pdf: a pdf version of the survey instrument 3. NewMexico_WildlifeViewerSurvey.csv: a csv (comma-separated values) file of the dataset 4. NewMexico_WildlifeViewerSurvey.sav: a sav (compatible with SPSS, the Statistical Package for Social Science) file of the dataset 5. WildlifeViewerSurveyData_VariableGuide: a guide to each variable name in the datasets.

The DCP ESA Listed Terrestrial Wildlife Species California Wildlife Habitat Relationships Study is one of many relicensing documents for the Devil Canyon Project (DCP) Hydropower Project Number 14797. The California Department of Water Resources applied to the Federal Energy Regulatory Commission for a new license of the DCP Project located in San Bernardino County, California along the East Branch of the State Water Project (SWP). The SWP provides southern California with many benefits, including an affordable water supply, reliable regional clean energy, opportunities to integrate green energy, accessible public recreation opportunities, and environmental benefits.

CDFW divides the state into six administrative Regions. CDFW staff in each Region identified linear segments of infrastructure that currently present barriers to wildlife populations in their jurisdiction. In doing so, the Regions used all available empirical information in their possession, including existing connectivity and road crossing studies, collared-animal movement data, roadkill observations, and professional expertise. This dataset represents the ten highest priority barriers identified in each Region and the twelve top priority barriers statewide. This dataset represents CDFWs ongoing effort to identify priority wildlife movement barriers across the state. Currently, increasing attention is being directed toward wildlife habitat connectivity as a mechanism of maintaining biodiversity in the face of population growth and climate change. Listing priority wildlife barrier locations will help focus limited financial resources where the highest need has been identified to improve wildlife movement. This is complementary to CDFW''s fish passage barrier priorities that have been identified for anadromous fish. Like the fish passage priorities, the wildlife barrier priorities list will be periodically updated to reflect new information and barrier removal successes.Most of the barriers identified are highway segments, but the list also includes a local road and two high speed rail alignments.Additional information can be found in this report: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=204648. Wildlife Movement Barriers - CDFW [ds2867] represents a comprehensive dataset of all barriers identified to date, including those which have been remediated since 2020.

Here we tested the accuracy of utilizing the automatic application, Wildlife Insight, in accurately detecting and identifying observed animal species. We compared over 1500 processed images fro wildlife insight with the same 1500 images visually processed by observers to determine the accuracy of this application.

As of July 2023, 17 insect species in the United States are likely to become endangered in the foreseeable future. Additionally, 174 plant species and 78 species of fish were threatened in the United States.

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

Wildlife viewing, defined as intentionally observing, feeding, or photographing wildlife, or visiting or maintaining natural areas because of wildlife, is one of the most popular outdoor recreation activities in the United States. The 2016 National Survey of Hunting, Fishing, and Wildlife-Associated Recreation reported that there are approximately 86 million wildlife viewers aged 16 or older in the U.S. ‒ more than one-third of the adult population ‒ and participation in wildlife viewing has been increasing since the mid-1990s (USDOI et al. 2016). Consistent with national trends, in 2016, about 35% of Virginia’s population viewed wildlife, amounting to 2.1 million wildlife viewers in the state (Rockville Institute, 2020). A growing body of literature shows that wildlife viewers contribute to habitat and wildlife conservation financially, politically, and through participation in other conservation activities (Cooper et al., 2015; Hvenegaard, 2002; McFarlane & Boxall, 1996). In 2016, Virginia wildlife viewers spent over $3.2 billion for their wildlife viewing activities, both in and out of state, on equipment purchases, membership dues and contributions, and trip-related expenses, including food and lodging, transportation, and access fees for public and private lands (Rockville Institute, 2020). Beyond its direct conservation potential, wildlife viewing is also a means of connecting more people to nature (Kellert et al., 2017).