The annual biodiversity conservation funding in 2022 was just *** billion U.S. dollars. To reach the targets of the United Nations Biodiversity Conference (COP), approximately five times this amount must be increased.

Managers require quantitative yet tractable tools that can identify areas for restoration yielding effective benefits for targeted wildlife species and the ecosystems they inhabit. A spatially explicit conservation planning tool that guides effective sagebrush restoration for sage-grouse can be made more effective by integrating baseline maps describing existing (pre-restoration) habitat suitability, and the distribution and abundance of breeding sage-grouse. Accordingly, we provide two rasters. The first is a floating point raster file informed by lek data, and derived from: 1) utilization distributions weighted by lek attendance, and 2) a non-linear probability of space-use relative to distance to lek. The second is a floating point raster file of baseline sage-grouse habitat modeled as a resource selection function and then relativized to bracket values between 1.0 (highest modeled suitability) and 0.0 (lowest modeled suitability). Note that this map differs slightly from previous unpublished maps of Bi-State habitat suitability owing to differences in data inputs and modeling methods. These data support the following publication: Ricca, M.A., Coates, P.S., Gustafson, K.B., Brussee, B.E., Chambers, J.C., Espinosa, S.P., Gardner, S.C., Lisius, S., Ziegler, P., Delehanty, D.J., and Casazza, M.L., 2018, A conservation planning tool for greater sage-grouse using indices of species distribution, resilience, and resistance, Ecological Applications, http://dx.doi.org/10.1002/eap.1690

A new 30 meter resolution polygon data layer of the islands of the United States, with associated attributes describing key physical and conservation geography characteristics. Islands were grouped into a three-tiered hierarchy of island provinces (12), island regions (28), and individual islands (a total of 19,023 islands were extracted). Islands were classified as estuarine vs non-estuarine, and nearshore vs. offshore.

Financial overview and grant giving statistics of Washingtonians For Wildlife Conservation

Financial overview and grant giving statistics of Wildlife Conservation and Education Society of South Texas

Wildlife Conservation Society (WCS) is a conservation NGO working globallly and in PNG

The National Conservation Lands Database contains data on the location and nature of private lands protected and/or managed for conservation purposes in Australia. This data set was created as part of a collaborative project between the data contributors and the Australian Government. The project was governed by a Steering Committee with representatives from five of the nine data contributors listed below. These acronyms are used in the remainder of the data. The program that contributed the data is described in the Completeness section of the metadata.DEC: Western Australian Department of Environment and Conservation DECCW: New South Wales Department of Environment, Climate Change and WaterDEH: South Australia Department of Environment and Heritage - now the Department of Climate Change, Energy, the Environment and Water and Natural ResourcesDERM: Queensland Department of Environment and Resource ManagementDPIPWE: Tasmania Department of Primary Industries, Parks, Water and EnvironmentNCT: New South Wales Nature Conservation Trusts Covenanting program NRETAS: Northern Territory Department of Natural Resources, Environment, the Arts and Sport Covenanting ProgramNTA WA: The National Trust of Australia (WA) TFN: Trust For Nature (Victoria) This 2009 (first) version of the database includes the majority of high security mechanisms operating on private land in Australia, where conservation is the sole or key objective. The data set contains all agreements from the inception of the program through which they were delivered to (and including) those established on the 30 June 2009. The department intends to annually update the database.The database contains:- an NCLD_DESC table - that contains descriptions of each agreement- an NCLD_POLY feature class - that contains all the agreement polygons- a NCLD_LABEL layer - that contains one point for each agreement that fits within an agreement polygon- a NCLD_OVERLAP_POLY feature class -that contains all agreement polygons that overlapped higher level agreement polygons. This is explained below.The polygons in this data set represent the land subject to private land conservation agreements. Each agreement is uniquely identified by AGREMT_ID. There are two polygon layers associated with the database. The principle layer is called the NCLD_POLY feature class and contains polygons of the location of the agreements. Where there are overlapping agreements, the most secure agreement is represented in the polygon layer and those agreements that were of lower security and overlapped, have been removed from the agreement polygon layer and stored in the NCLD_OVERLAP_POLY feature class. The NCLD_POLY feature class and the NCLD_OVERLAP_POLY feature class attribute table that the AGREMT_ID and few other fields. The descriptive details of each agreement are stored in the NCLD_DESC table including the GIS_AREA for the convenience of calculating statistics. This text table can be linked to the polygon layers for GIS analysis. The attributes of the NCLD_DESC table are described in the Attribute Accuracy section of this metadata. Many of the attributes are the same as those used in the Collaborative Australian Protected Area Database .In order to facilitate topology checking and analysis the NCLD_POLY feature class does not contain overlapping polygons. Overlapping agreements do occur in practice, where there is more than one agreement legally still in place at a time. To capture this information the polygon data has been processed to represent the highest security agreement at any one location in the agreements polygon layer. Agreements that are completely displaced by a higher security agreement have the value in the OVERLAP field in the text table, a GIS_AREA of 0 (zero) and the entire polygon represented only in the NCLD_OVERLAP_POLY feature class. Agreements that are only partially displaced have the displaced portion of the polygon(s) in the NCLD_OVERLAP_POLY feature class. In the agreements text table the partially displaced agreements will have the reduced value in the OVERLAP field and a GIS_AREA is the area of the remaining polygon in the NCLD_POLY feature class.All the overlaps in this version of the database occurred within the DECCW agreements. A hierarchy of highest to lower level agreements was decided in collaboration with DECCW. TYPE = Conservation Agreements were agreed to have the highest protection value, being in perpetuity, registered on title and as the agreement contains a broader range of conservation measures. TYPE = Registered property agreements were ranked the middle security level as these were registered on title and the agreements had more restricted conservation scope. The lowest level agreements were considered to be TYPE = Wildlife Refuge, as these were not registered on title and of an indefinite term and contained the lowest level of restrictions on activities and conservation measures that had to be adopted as part of the agreement compared to the other two types.The way that agreements were mapped differed between authorities and agreement types. Some types only mapped the high conservation value portion of a land parcel whereas others mapped the entire title or property or nearly the whole title and subdivided into zones with one a conservation or protected zone. For example a WA Department of Environment and Conservation Covenant does not have any zones and only covers the conservation or protected area. In contrast, a Trust for Nature Victoria Conservation Covenant is subdivided into three zones; Modified Land, Domestic Area and Protected Area.Programs that map only the high conservation value portion of a land parcel are those that have AUTHORITY of DEC, NCT, DEH, DPIPWE (most agreements) and DECCW (where TYPE is Conservation Agreement or Registered Property Agreement). In the case of AUTHORITY = NTA WA AND TYPE = National Trust Covenants the entire property is mapped and subdivided into farmland and bushland zones. In the case AUTHORITY = DECCW AND TYPE = Wildlife Refuge , the whole title was mapped (the high conservation value area is delineated on a map filed with the agreement -but not mapped in their spatial database). DERM and VIC TFN map either the entire property or part of the property and use zones to differentiate between the conservation area and areas where less restricted activity can take place. Examples of DERM Nature Refuge zone types are conservation, domestic, infrastructure, agriculture, restoration. A few DPIPWE Conservation Covenants contain zones. This data set only maps the area of the whole agreement, and does not delineate zones. Therefore although the area of the conservation part of the agreement could be the same between a DEC agreement and a NTA WA agreement, the GIS_AREA will be higher for the NTA WA agreement that also includes farmland. GIS_AREA will be higher for programs that map a larger area in addition to the high conservation value area.NOTE: This item refers to a dataset with restricted access. The related metadata is available for download as a Word document as necessary. Additional information about this dataset or requests for access to the data should be directed to geospatial@dcceew.gov.au

The Conservation Practice Effectiveness Database compiles information on the effectiveness of a suite of conservation practices. This database presents a compilation of data on the effectiveness of innovative practices developed to treat contaminants in surface runoff and tile drainage water from agricultural landscapes. Traditional conservation practices such as no-tillage and conservation crop rotation are included in the database, as well as novel practices such as drainage water management, blind inlets, and denitrification bioreactors. This will be particularly useful to conservation planners seeking new approaches to water quality problems associated with dissolved constituents, such as nitrate or soluble reactive phosphorus (SRP), and for researchers seeking to understand the circumstances in which such practices are most effective. Another novel feature of the database is the presentation of information on how individual conservation practices impact multiple water quality concerns. This information will be critical to enabling conservationists and policy makers to avoid (or at least be aware of) undesirable tradeoffs, whereby great efforts are made to improve water quality related to one resource concern (e.g., sediment) but exacerbate problems related to other concerns (e.g., nitrate or SRP). Finally, we note that the Conservation Practice Effectiveness Database can serve as a source of the soft data needed to calibrate simulation models assessing the potential water quality tradeoffs of conservation practices, including those that are still being developed. This database is updated and refined annually. Resources in this dataset:Resource Title: 2019 Conservation Practice Effectiveness (CoPE) Database. File Name: Conservation_Practice_Effectiveness_2019.xlsxResource Description: This version of the database was published in 2019.

File Name: Web Page, url: https://data.mendeley.com/datasets/2jxj4k32m2/1 Khoury, Colin K.; Amariles, Daniel; Soto, Jonatan; Diaz, Maria Victoria; Sotelo, Steven; Sosa, Chrystian C.; Ramírez-Villegas , Julian; Achicanoy, Harold; Castañeda-Álvarez , Nora P.; León, Blanca; Wiersema, John H. (2018), Data for the calculation of an indicator of the comprehensiveness of conservation of useful wild plants, Mendeley Data, v1. http://dx.doi.org/10.17632/2jxj4k32m2.1 The datasets presented here are related to the research article entitled “Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets” (Khoury et al., 2019). The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes.

The Species of Greatest Conservation Need National Database is an aggregation of lists from State Wildlife Action Plans. Species of Greatest Conservation Need (SGCN) are wildlife species that need conservation attention as listed in action plans. In this database, we have validated scientific names from original documents against taxonomic authorities to increase consistency among names enabling aggregation and summary. This database does not replace the information contained in the original State Wildlife Action Plans. The database includes SGCN lists from 56 states, territories, and districts, encompassing action plans spanning from 2005 to 2022. State Wildlife Action Plans undergo updates at least once every 10 years by respective wildlife agencies. The SGCN list data from these action plans have been compiled in partnership with individual wildlife management agencies, the United States Fish and Wildlife Service, and the Association of Fish and Wildlife Agencies. The SGCN ...

This dataset is a compilation of species ranges gathered from various sources. Many of these ranges were created by IDFG using methodologies similar to those employed in the NW ReGAP or the HUC5 observation effort. Species ranges provide a general representation of where a species might occur during its lifetime. It's important to distinguish these from species 'distribution models,' which pinpoint potential habitat within the range.These ranges were constructed using the best available data and can estimate potential occurrences. To use this data effectively, users can apply a definition query in ArcGIS to visualize specific species ranges. For the most straightforward download, viewing, or filtering of the dataset, it's recommended to bring the API REST service into ArcGIS Pro. Keep in mind that due to the dataset's size, the Open Data Site download might experience timeouts, particularly with a large number of ranges. If you opt to use the Open Data Site, follow the directions by clicking on this LINK.Species range models were compiled initially for use within an online map service to depict species range for species within the 'Idaho Species Catalog',https://idfg.idaho.gov/speciesIdaho species range models compiled and/or created by the Idaho Department of Fish and Game, Idaho Fish and Wildlife Information System. Data pulled 18 December 2023, edits are ongoing as needed.

ACE is a CDFW effort to analyze large amounts of map-based data in a targeted, strategic way, and expressed visually, so decisions can be informed around important goals like conservation of biodiversity, habitat connectivity, and climate change resiliency. The ACE maps provide a coarse level view of information for conservation planning purposes, ranging from ecological research and modeling to local land-use planning and conservation decision-making. However, they do not replace the need for site-specific evaluation of biological resources and should not be used for regulatory purposes.

Conservation planning in the Great Plains often depends on understanding the degree of fragmentation of the various types of grasslands and savannas that historically occurred in this region. To define ecological subregions of the Great Plains, we used a revised version of Kuchler’s (1964) map of the potential natural vegetation of the United States. The map was digitized from the 1979 physiographic regions map produced by the Bureau of Land Management, which added 10 physiognomic types. All analyses are based on data sources specific to the United States; hence, we only analyze the portion of the Great Plains occurring in the United States.We sought to quantify the current amount of rangeland in the US Great Plains converted due to 1) woody plant encroachment; 2) urban, exurban, and other forms of development (e.g., energy infrastructure); and 3) cultivation of cropland. At the time of this analysis, the most contemporary measure of land cover across the United States was the 2011 NLCD (Homer et al. 2015). One limitation of the NLCD is that some grasslands with high rates of productivity, such as herbaceous wetlands or grasslands along riparian zones, are misclassified as cropland. A second limitation is the inability to capture cropland conversion occurring after 2011 (Lark et al. 2015). Beginning in 2009 (and retroactively for 2008), the US Department of Agriculture - NASS has annually produced a Cropland Data Layer (CDL) for the United States from satellite imagery, which maps individual crop types at a 30-m spatial resolution. We used the annual CDLs from 2011 to 2017 to map the distribution of cropland in the Great Plains. We merged this map with the 2011 NLCD to evaluate the degree of fragmentation of grasslands and savannas in the Great Plains as a result of conversion to urban land, cropland, or woodland. We produced two maps of fragmentation (best case and worst case scenarios) that quantify this fragmentation at a 30 x 30 m pixel resolution across the US Great Plains, and make them available for download here. Resources in this dataset: Resource title: Data Dictionary for Figure 2 derived land cover of the US portion of the North American Great Plains File name: Figure2_Key for landcover classes.csv Resource title: Figure 1. Potential natural vegetation of US portion of the North American Great Plains, adapted from Kuchler (1964). File name: Figure1_Kuchler_GPRangelands.zip Resource description: Extracted grassland, shrubland, savanna, and forest communities in the US Great Plains from the revised Kuchler natural vegetation map Resource title: Figure 2. Derived land cover of the US portion of the North American Great Plains. File name: Figure2_Key for landcover classes.zip Resource description: The fNLCD-CDL product estimates that 43.7% of the Great Plains still consists of grasslands and shrublands, with the remainder consisting of 40.6% cropland, 4.4% forests, 3.0% UGC, 3.0% developed open space, 2.9% improved pasture or hay fields, 1.2% developed land, 1.0% water, and 0.2% barren land, with important regional and subregional variation in the extent of rangeland loss to cropland, forests, and developed land. Resource title: Figure 3. Variation in the degree of fragmentation of Great Plains measured in terms of distance to cropland, forest, or developed lands. File name: Figure3_bestcase_disttofrag.zip Resource description: This map depicts a “best case” scenario in which 1) croplands are mapped based only on the US Department of AgricultureNational Agricultural Statistics Service Cropland Data Layers (2011e2017), 2) all grass-dominated cover types including hay fields and improved pasture are considered rangelands, and 3) developed open space (as defined by the National Land Cover Database) are assumed to not be a fragmenting land cover type. Resource title: Figure 4. Variation in the degree of fragmentation of Great Plains measured in terms of distances to cropland, forest, or developed lands. File name: Figure4_worstcase_disttofrag.zip Resource description: This map depicts a ‘worst case’ scenario in which 1) croplands are mapped based on the US Department of AgricultureNational Agricultural Statistics Service Cropland Data Layers (2011e2017) and the 2011 National Land Cover Database (NLCD), 2) hay fields and improved pasture are not included as rangelands, and 3) developed open space (as defined by NLCD) is included as a fragmenting land cover type.

Description of the data and file structure

We compiled a suite of existing spatial data sets and converted them into the nested hexagon framework (NHF 2022). Once all files were converted into the hexagon framework, they were read into the program Zonation to run the conservation prioritization analysis. Here, we provide all of the datasets used in the Zonation analysis and the map products from the analysis that identify the landscapes with high conservation potential for the black-tailed prairie dog (BTPD) ecosystem.

https://doi.org/10.5061/dryad.wpzgmsbr5

Input Data

FINE SCALE HABITAT SUITABILITY SPATIAL DATA

File: ensemble_wave_Jan192021_HOTR

Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of an ensemble model of BTPD habitat potential, under current climate. Resolution: 90m. The data is from Davidson et al. 2023.

File: eensemble_Warmwet_90_HOTR

**Descri...

This investigation proceeded along two lines. First, individuals gathered data on 208 HCPs that had been approved by August 1997 in order to obtain basic descriptive information about plans. Second, the group conducted a more comprehensive analysis for a focal subset (43) of these plans. The HCPs in the focal subset range widely in geographic location, size, duration, methods, and approval dates. For this in-depth investigation, we developed two separate data questionnaires: one asked for information on theplans themselves, and the other focused on listed species and their treatment within HCPs. These questionnaires included information about what scientific data were available for use in formulating the HCP, how existing data were used, and the rigor of analysis used in each stage of the HCP process. As a whole, the questions were designed to generate a detailed profile of each HCP and to document the use (or lack thereof) of scientific data and tools. Plans were not judged overall; rather, questionnaires focused on different stages of the planning process, including the HCPs assessment of (1) the status of the species; (2) the take of species under the HCP; (3) the impact of the take on the species; (4) the mitigation for the anticipated take; and (5) the biological monitoring associated with the HCP. All of the data sheets, plan descriptions, and other detailed results from this effort are available on the NCEAS website: http://www.nceas.ucsb.edu/projects/2049 . All descriptions of the data set contents are provided through the HCP report available at the web site above. In general the data sets contain responses to the questionaires from the HCP surveys. Each question is listed by question number in the data set and is described in detail in the HCP report (see web site). In addition, a listing of thecontents of the four final data sets is provided in the file hcp-load.lst.

This data set shows point locations of Bird Conservation Areas. Bird Conservation Areas are New York State lands that have been officially designated for their value to bird conservation. Points are approximate locations and may represent large areas.

This dataset includes the survey data that were collected during the Marine Nature Conservation Review (MNCR) between 1987 and 1998, together with data from surveys commissioned by the Nature Conservancy Council in the 1970s and 1980s and data collected subsequently by JNCC. The MNCR was initiated to provide a comprehensive baseline of information on marine habitats and their associated species around the coast of Britain which would aid coastal zone and sea-use management and to contribute to the identification of areas of marine natural heritage importance. The focus of MNCR work was on benthic habitats (often referred to as 'biotopes') in intertidal and inshore (typically within 3nm) subtidal areas.

The California Conservation Easement Database (CCED) contains lands protected under conservation easements. It is a parallel data set to the California Protected Areas Database (CPAD), which covers protected areas owned in fee. The first version of the CCED database was released in April 2014, the latest update is from June 2025.

CCED is maintained and published by GreenInfo Network (www.greeninfo.org). GreenInfo Network publishes CCED twice annually.

Conservation Authority Administrative Areas are lands under the jurisdiction of a Conservation Authority.

Instructions for downloading this dataset:

• select the link below and scroll down the metadata record page until you find Transfer Options in the Distribution Information section
• select the link beside the Data for download label
• you must provide your name, organization and email address in order to access the dataset.

This product requires the use of GIS software.

*[GIS]: geographic information system