A Moodle Backup FIle (.mbz) of a course (SB33102 version Semester 1, 2018/19) is a compressed archive of a Moodle course that can be used to restore a course within Moodle. The file preserves course contents, structure and settings, but does not include student work or grades.

[Metadata] Description: Conservation District Subzones as of 2011. Source - DLNR/DOFAW, State Land Use CommissionSource: The Conservation District Subzones were extracted from the LUD95 layers. Subzones are administered by the Department of Land and Natural Resources Office of Conservation and Coastal Lands (OCCL). The Conservation Districts are administered by the State Land Use Commission. The Conservation District Subzone boundaries depicted in these files are not official and are representations for presentation purposes only. A determination of the official subzone boundaries should be obtained through the Dept. of Land and Natural Resources. Revised, Feb. 2011 by the State Land Use Commission.Apr. 2024: Hawaii Statewide GIS Program staff removed extraneous fields that had been added as part of the 2016 GIS database conversion and were no longer needed.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/cdsubzn.txt or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

An RCIS is a voluntary, non-regulatory, and non-binding conservation assessment that includes information and analyses relating to the conservation of focal species, habitats, and other conservation elements in the RCIS area. Any public agency may develop an RCIS. An RCIS establishes biological goals and objectives at the species level and describes conservation actions and habitat enhancement actions that, if implemented, will contribute to those goals and objectives. Those actions will benefit the conservation of focal species, habitats, and other natural resources, and they may be used as a basis to provide advance mitigation through the development of credits or to inform other conservation investments.

A platform-agnostic and living geographic information data dictionary for trafficking of wild flora and fauna based on diverse stakeholder input and with the potential to accelerate convergence of information and increase efficacy of interventions.

Conservationists often have difficulty obtaining financial and social support for protected areas that do not demonstrate their benefits for society. Therefore, ecosystem services have gained importance in conservation science in the last decade, as these services provide further justification for appropriate management and conservation of natural systems. We used InVEST software and a set of GIS procedures to quantify, spatialize and evaluated the overlap between ecosystem services—carbon stock and sediment retention—and a biodiversity proxy–habitat quality. In addition, we proposed a method that serves as an initial approach of a priority areas selection process. The method considers the synergism between ecosystem services and biodiversity conservation. Our study region is the Iron Quadrangle, an important Brazilian mining province and a conservation priority area located in the interface of two biodiversity hotspots, the Cerrado and Atlantic Forest biomes. The resultant priority area for the maintenance of the highest values of ecosystem services and habitat quality was about 13% of the study area. Among those priority areas, 30% are already within established strictly protected areas, and 12% are in sustainable use protected areas. Following the transparent and highly replicable method we proposed in this study, conservation planners can better determine which areas fulfill multiple goals and can locate the trade-offs in the landscape. We also gave a step towards the improvement of the habitat quality model with a topography parameter. In areas of very rugged topography, we have to consider geomorfometric barriers for anthropogenic impacts and for species movement and we must think beyond the linear distances. Moreover, we used a model that considers the tree mortality caused by edge effects in the estimation of carbon stock. We found low spatial congruence among the modeled services, mostly because of the pattern of sediment retention distribution.

CDFW BIOS GIS Dataset, Contact: Dick Cameron, Description: This is the summarized output of priority rangeland conservation areas developed by the California Rangeland Conservation Coaltion. The Coalition is a partnership between ranchers, environmentalists, and land management agencies to develop approaches to protect rangeland resources in the State.

CDFW BIOS GIS Dataset, Contact: Diane Mastalir, Description: This data set is of locations for opan mitigation and conservation banks for which the California Department of Fish and Wildlife is a signatory. It does not include locations for banks which are approved only Federally or for credits for species for which the Department does not require mitigation. All data, including species and habitats covered, are draft and should be verified with the bank sponsor prior to making any decisions based on this data set.

Wildlands in New England is the first U.S. study to map and characterize within one region all conserved lands that, by design, allow natural processes to unfold with no active management or intervention. These “forever wild lands” include federal Wilderness areas along with diverse public and private natural areas and reserves. Knowing the precise locations of Wildlands, their characteristics, and their protection status is important as both a baseline for advancing conservation initiatives and an urgent call to action for supporting nature and society. Wildlands play a unique role in the integrated approach to conservation and land planning advanced by the Wildlands, Woodlands, Farmlands & Communities (WWF&C) initiative, which calls for: at least 70 percent of the region to be protected forest; Wildlands to occupy at least 10 percent of the land; and all existing farmland to be permanently conserved. This research was conducted by WWF&C partners Harvard Forest (Harvard University), Highstead Foundation, and Northeast Wilderness Trust, in collaboration with over one hundred conservation organizations and municipal, state, and federal agencies. This dataset contains the Geographical Information System (GIS) polygon layer of Wildlands created by this project and used in all analyses for the 2023 report. Another GIS layer will be updated as new Wildlands are brought to our attention or created and will be available at https://wildlandsandwoodlands.org/ for researchers.

This data is intended for read-only use. Land and Water Conservation Fund (LWCF) data from surface ownership fund table is attached to surface ownership to create a base layer that is used in Forest Service business functions, as well as by other entities such as states, counties, other agencies, and partners. This layer depicts only the Forest Service lands that are acquired through purchase, exchange, donation, and transfer that used LWCF-designated funds. It is not a complete representation of all Forest Service land acquisitions; only those that used LWCF-designated funds. Metadata and Downloads

Financial overview and grant giving statistics of Society for Conservation Gis Inc.

This hosted feature layer has been published in RI State Plane Feet NAD 83. Approximate edges of Conservation Lands protected by the State of Rhode Island through Fee Title Ownership, Conservation Easement, or Deed Restriction. Includes: Wildlife Management Areas, Drinking Water Supply Watersheds, State Parks, Beaches, Bike Paths, Fishing Access Areas, Local Parks and Recreation Facilities that have been developed with State Grant Funds.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from workshops that were conducted on February 19-21 and October 6-7, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

This is a repository of the raw GIS data files that constitute the network mapping taken forward within the project.

GIS shapefiles for indices related to the publication: Siciliano, 2025, "Prioritizing Ecological Values with Sequential Hierarchical Intersection Layers (SHIL): A Case Study from a Mediterranean Biodiversity Hotspot".

nHQ - Normalised Habitat Quality nHWV - Normalized Habitat Weight Value nBS - Normalized Biota Score nCIS - Normalized Connectivity Importance Score nCEVI - Normalized Composite Ecological Value Index SHIL data - Full dataset in Excel format

When a species is proposed for listing as endangered or threatened under the Endangered Species Act, the U.S. Fish and Wildlife Service must consider whether there are areas of habitat believed to be essential the species’ conservation. Those areas may be proposed for designation as “critical habitat.” Critical habitat is a term defined and used in the Act. It is a specific geographic area(s) that contains features essential for the conservation of a threatened or endangered species and that may require special management and protection. Critical habitat may include an area that is not currently occupied by the species but that will be needed for its recovery. An area is designated as “critical habitat” after the Service publishes a proposed Federal regulation in the Federal Register and receives and considers public comments on the proposal. The final boundaries of the critical habitat are also published in the Federal Register. Critical habitat are areas considered essential for the conservation of a listed species. Federal agencies are required to consult with the U.S. Fish and Wildlife Service on actions they carry out, fund, or authorize to ensure that their actions will not destroy or adversely modify critical habitat. These areas provide notice to the public and land managers of the importance of these areas to the conservation of a listed species. Special protections and/or restrictions are possible in areas where Federal funding, permits, licenses, authorizations, or actions occur or are required.

CDFW BIOS GIS Dataset, Contact: Jeanette Howard, Description: We applied current approaches in conservation planning to prioritize California watersheds for management of biodiversity using the Zonation software. For all watersheds, we compiled data on the presence/absence of herpetofauna and fishes; observations of freshwater-dependent mammals, selected invertebrates, and plants; maps of freshwater habitat types; measures of habitat condition and vulnerability; and current management status.

The schema of this dataset pretty much follows that of MassGIS/EOEEA. Not all data represented here is protected in perpetuity. It is important to view the attribute table and review the MassGIS website documentation to fully understand this dataset.A departure from the MassGIS schema is a related table (tbl_info4_ICP). This table has 1-to-1 relationship with the primary feature class. The tbl_info4_ICP contains a lot of funny codes & IDs for the purposes of utilizing these data on the TrailsMV App and the Martha's Vineyard Land Bank website map.Look for other 'views' of this feature layer to see the data symbolized according to various attribute categories.

Cave-limited species display patchy and restricted distributions, but are challenging to study in-situ because of the difficulty of sampling. It is often unclear whether the observed distribution is a sampling artifact or a true restriction in range. Further, the drivers of the distribution could be local environmental conditions, such as cave humidity, or they could be associated with surface features that are surrogates for cave conditions. If surface features can be used to predict the distribution of important cave taxa, then conservation management goals can be more easily obtained. These GIS data represent the input and results of a spatial statistical model used to examine the hypothesis that the presence of major faunal groups of cave obligate species could be predicted based on features of the earth surface. Georeferenced records of cave obligate amphipods, crayfish, fish, isopods, beetles, millipedes, pseudoscorpions, spiders, and springtails within the area of Appalachian Landscape Conservation Cooperative (LCC) in the eastern United States (Illinois to Virginia, and New York to Alabama) were assigned to 20 x 20 km grid cells. Habitat suitability for these faunal groups was modeled using logistic regression with twenty predictor variables within each grid cell, such as percent karst, soil features, temperature, precipitation, and elevation. The models successfully predicted the presence of a group greater than 65 percent of the time (mean=88 percent) for the presence of single grid cell endemics, and for all faunal groups except pseudoscorpions. The most common predictor variables were latitude, percent karst, and the standard deviation of the Topographic Position Index (TPI), a measure of landscape rugosity within each grid cell. The overall success of these models points to a number of important connections between the surface and cave environments, and some of these, especially soil features and topographic variability, suggest new research directions. These models should prove to be useful tools in predicting the presence of species in understudied areas.

The attribute data schema & domains of this dataset follow that of the MassGIS open space data file. Not all properties within this data file are protected in perpetuity. Every effort is made to make this data as complete and accurate as possible, however data omissions or inaccuracies may occur. Please notify the Martha's Vineyard Commission if you believe there are data errors.This collection of properties represents those lands that are understood to be conserved &/or recreational land, length of protection & mechanism providing protection may vary. Please review the respective attributes to understand the degree of protection.Some properties within this dataset are privately owned. Do not assume that all properties represented here are open for public access. Boundary modifications may have been performed as needed to conform to the Town's GIS digital parcel boundaries.

In this research, the best management practices include vegetative/structural conservation practices (SCP) across crop fields, such as grassed waterways and terraces. This reference dataset includes 500,000 pair patches (false-color image (B1: NIR, B2: Red, B3: Green) and binary label (SCP: yes[1] or no[0]). These training samples were randomly extracted from Iowa BMP project (https://www.gis.iastate.edu/gisf/projects/conservation-practices) and present 90% of patches with SCP areas and 10% of patches non-SCP area. The patch dimension is 256 x 256 pixels at 2-m resolution. Due to the file size, the images were upload in different *.rar files (imagem_0_200k.rar, imagem_200_400k.rar, imagem_400_500k.rar), and the user should download all and merge them in the same folder. The corresponding labels are all in "class_bin.rar" file.

Application: These pair images are useful for conservation practitioners interested in the classification of vegetative/structural SCPs using deep-learning semantic segmentation methods.

Further information will be available in future.