A vector GIS dataset of candidate areas for terrestrial ecological restoration based on landscape context. The dataset was created using NLCD 2011 (www.mrlc.gov) and morphological spatial pattern analysis (MSPA) (http://forest.jrc.ec.europa.eu/download/software/guidos/mspa/). There are 13 attributes for the polygons in the dataset, including presence and length of roads, candidate area size, size of surround contiguous natural areas, soil productivity, presence and length of road, areas suitable for wetland restoration, and others. This dataset is associated with the following publication: Wickham, J., K. Riiters, P. Vogt, J. Costanza, and A. Neale. An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context. RESTORATION ECOLOGY. Blackwell Publishing, Malden, MA, USA, 25(6): 894-902, (2017).

Spatial management tools, such as marine spatial planning and marine protected areas, are playing an increasingly important role in attempts to improve marine management and accommodate conflicting needs. Robust data are needed to inform decisions among different planning options, and early inclusion of stakeholder involvement is widely regarded as vital for success. One of the biggest stakeholder groups, and the most likely to be adversely impacted by spatial restrictions, is the fishing community. In order to take their priorities into account, planners need to understand spatial variation in their perceived value of the sea. Here a readily accessible, novel method for quantitatively mapping fishers’ spatial access priorities is presented. Spatial access priority mapping, or SAPM, uses only basic functions of standard spreadsheet and GIS software. Unlike the use of remote-sensing data, SAPM actively engages fishers in participatory mapping, documenting rather than inferring their priorities. By so doing, SAPM also facilitates the gathering of other useful data, such as local ecological knowledge. The method was tested and validated in Northern Ireland, where over 100 fishers participated in a semi-structured questionnaire and mapping exercise. The response rate was excellent, 97%, demonstrating fishers’ willingness to be involved. The resultant maps are easily accessible and instantly informative, providing a very clear visual indication of which areas are most important for the fishers. The maps also provide quantitative data, which can be used to analyse the relative impact of different management options on the fishing industry and can be incorporated into planning software, such as MARXAN, to ensure that conservation goals can be met at minimum negative impact to the industry. This research shows how spatial access priority mapping can facilitate the early engagement of fishers and the ready incorporation of their priorities into the decision-making process in a transparent, quantitative way.

These data show the locations of research conducted at the below ground plots near Konza Headquarters. Record type 1 (GIS350) describes the 64 belowground plots receiving a variety of nutrient, burn, and mowing treatments. Data for BMS01, BMS02, and BNS01 are collected on these plots. Record type 6 (GIS355) describes the locations of the Micro-Rhizotrons. Two spatial datasets lie on the belowground plots, but are classified separately. These are the Lysimeters on belowground plots (GIS455) and Aboveground biomass on belowground plots (GIS505) datasets. GIS505 may be used alongside the BGPVC dataset, because it shares sample locations with PBB01.

This data set includes polygons for ecological sections within Subregions within the conterminous United States. This data set contains regional geographic delineations for analysis of ecological relationships across ecological units. MetadataThis 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 CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This dataset defines the internal boundaries of the Konza Prairie Biological Station (KPBS). Data type one (GIS010) is a record of all fenced areas on KPBS with GIS011 providing locations for all gates and type of gate (exterior, bison, and cattle). Data type three (GIS012) represents various large-scale research areas on Konza including bison grazed, cattle grazed, fire reversal, etc. These data are available as zipped (.zip) shapefiles (.shp).

This article uses a multiscale approach for assessing landscape changes in one of the world’s biodiversity hotspots in Brazil, the Rio Doce State Park (PERD). In this article, we assess land use changes over a 30 year period. Our results show that, while inside the park landscape changes were minimal, in the park buffer zone human induced changes are steadily rising due to an increase in eucalyptus plantations and urban sprawl that grew by 4% and 1.9%, respectively. Agricultural land has been reduced by 6.35%, but there are trends that a form of welcome forest transition has been occurring. We report an increase in native forests from 40,588 ha in 1985 to 45,690 ha in 2015. The analysis of human impacts in the study area delivers very different results when varying the pixel size from 25 ha to 900 m2. The former shows a very high level of human influence while the latter reveals small but vital patches of native forest offering hopeful opportunities for sustainable natural resource management in this critical biome. Our work stresses the importance of better targeted policy making and sympathetic land use management of buffer zones of protected areas. Currently, such zones suffer from many development pressures and often experience contradictory policy frameworks which encourage a clash between biodiversity conservation and intensive agro husbandry production. Highlights: • We characterize land use transitions in a hotspot of biodiversity in Brazil. • Analysis at finer resolution show that there is still hope for forest recovery. • For instilling sustainable forest transitions there is the need for fresh governance.

This data set includes polygons for ecological subsections within Subregions within the conterminous United States. This data set contains regional geographic delineations for analysis of ecological relationships across ecological units. Metadata

Layers describing study area, Cranes Moor New Forest, including survey areas

This data support the paper "A systematic review on the integration of remote sensing and GIS to forest and grassland ecosystem health attributes, indicators, and measures " by Irini Soubry, Thuy Doan, Thuan Chu and Xulin Guo 2021 in the journal of "Remote Sensing" by MDPI. It includes the "Search_Effort.csv" list with the keywords and number of studies selected for further examination, the "Potential_Studies.csv" with the post-filtering of suitability and notes related to each study, the "Metadata.csv" with the information collected for each metadata variable per study, and the "ExtractedData.csv" with the information collected for each extracted dta variable per study. More information about the data collection and procedures can be found in the respective manuscript.

This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Grey-headed Robin (Heteromyias albispecularis).

The Washington State Department of Ecology has defined a facility/site as an operation at a fixed location that is of interest to the agency because it has an active or potential impact upon the environment. Ecology recognizes that this definition is broad and generic; but the agency has found that such a definition is required in order to encompass all the facilities and sites in Washington that are within the purview of its programs. These programs cover a wide variety of environmental aspects and conditions including air quality, water quality, shorelands, water resources, toxics cleanup, hazardous waste, toxics reduction, and nuclear waste. The definitions of a facility and/or a site vary significantly across these programs, both in practice and law. Examples of facilities/sites include: operation that pollutes the air or water, spill cleanup site, hazardous waste management facility, hazardous waste generator, licensed laboratory, SUPERFUND site, farm which draws water from a well, solid waste recycling center, etc.

This feature layer depicts Canada's ecozones and ecoregions. An ecozone is a large sub-continental geographical division with distinct representative biotic and abiotic features. There are 15 ecozones in Canada.

An ecoregion further divides an ecozone. These geographical units exhibit regional ecological characteristics distinct from neighbouring ecoregions, though there are typically gradual gradations between them. There are 194 ecoregions.

Peridomestic exposure to Borrelia burgdorferi-infected Ixodes scapularis nymphs is considered the dominant means of infection with black-legged tick-borne pathogens in the eastern United States. Population level studies have detected a positive association between the density of infected nymphs and Lyme disease incidence. At a finer spatial scale within endemic communities, studies have focused on individual level risk behaviors, without accounting for differences in peridomestic nymphal density. This study simultaneously assessed the influence of peridomestic tick exposure risk and human behavior risk factors for Lyme disease infection on Block Island, Rhode Island. Tick exposure risk on Block Island properties was estimated using remotely sensed landscape metrics that strongly correlated with tick density at the individual property level. Behavioral risk factors and Lyme disease serology were assessed using a longitudinal serosurvey study. Significant factors associated with Lyme disease positive serology included one or more self-reported previous Lyme disease episodes, wearing protective clothing during outdoor activities, the average number of hours spent daily in tick habitat, the subject’s age and the density of shrub edges on the subject’s property. The best fit multivariate model included previous Lyme diagnoses and age. The strength of this association with previous Lyme disease suggests that the same sector of the population tends to be repeatedly infected. The second best multivariate model included a combination of environmental and behavioral factors, namely hours spent in vegetation, subject’s age, shrub edge density (increase risk) and wearing protective clothing (decrease risk). Our findings highlight the importance of concurrent evaluation of both environmental and behavioral factors to design interventions to reduce the risk of tick-borne infections.

This file provides the raw data of an online survey intended at gathering information regarding remote sensing (RS) and Geographical Information Systems (GIS) for conservation in academic education. The aim was to unfold best practices as well as gaps in teaching methods of remote sensing/GIS, and to help inform how these may be adapted and improved. A total of 73 people answered the survey, which was distributed through closed mailing lists of universities and conservation groups.

This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Fairy Martin (Petrochelidon (Petrochelidon) ariel).

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

A README file is included into the dataset.Abstract of the corresponding article:Biodiversity conservation measures designed to ensure ecological connectivity depend on the reliable modeling of species movements. Least cost path modeling makes it possible to identify the most likely dispersal paths within a landscape and provide two items of ecological relevance: (i) the spatial location of these least cost paths (LCPs) and (ii) the accumulated cost along them ('cost distance', CD). This spatial analysis requires that cost values be assigned to every type of land cover. The sensitivity of both LCPs and CDs to the cost scenarios has not been comprehensively assessed across realistic landscapes and diverging cost scenarios. We therefore assessed it in diverse landscapes sampled over metropolitan France and with widely diverging cost scenarios. The spatial overlap of the LCPs was more sensitive to the cost scenario than the CD values were. Besides, highly correlated CD matrices could derive from very diff?erent cost scenarios. Although the range of the cost values and the properties of each cost scenario signi?ficantly in influenced the outputs of LCP modeling, landscape composition and con?guration variables also explained their variations. Accordingly we provide guidelines for the use of LCP modeling in ecological studies and conservation planning.

A high-resolution map of Singapore’s terrestrial ecosystems. To the reviewers from Data Journal, this is the supplementary material for Figure 1.

This hosted feature layer has been published in RI State Plane Feet NAD 83. Conservation ecologists have coined the term Ecological Land Units (ELU) to describe and map the physical properties of landscapes. Typically, ELUs are defined by the geology, soils, elevation, and landform (hilltop, hillside, valley). A specific ELU has a unique combination of soils, geology, landform, and elevation. ELUs are derived from soil and elevation data using a GIS. It was important that we used readily available data and we kept the derivation of ELUs as simple as possible. After consulting the published literature and conferring with expert soil scientists and plant ecologists, we focused on two aspects of soils, soil drainage class and soil texture. Soil drainage class is very good at distinguishing wet versus dry habitats. Soil texture (sandy, silty, loamy, etc.) is an important habitat component for plants. Using USDA SSURGO (State Soil Survey Geographic Database) data that is readily available from RIGIS, we created a raster dataset (50 feet cell size) of the different soil drainage classes and another raster dataset of the soil texture classes. There are many properties of soils that are available to use for analyses such as this, for example stoniness, depth to bedrock, etc. The two factors we chose are extremely important soil properties in supporting different plant communities. Landform represents where a location is with respect to elevation, slope, and aspect (direction a hillside is facing). Landform distinguishes hilltops, hill sides, valley bottoms, etc. We used the RIGIS digital terrain model as our source of elevation data to measure landform. Landform classes were identified using GIS modeling of slope, aspect, and elevation. The final ELU map is made by adding together the raster datasets for landform, drainage class, and soil texture. Because we were careful with our encoding system, the sum of the three rasters provides us a composite of the individual datasets. For example, a location that is a well-drained (code value 2000) and consists of gravelly sand (code value 100) a sits on a hilltop (code value 21) and would combine to be ELU 2121 (2000+100+21). This process yielded 204 unique ELUs for the state of Rhode Island. Examination of a cumulative distribution function (CDF) of the ELUs showed that most of the ELUs were small and did not occur very often. Conversely, 20 ELUs were quite common and encompassed almost 85% of the land area of RI.Find out more about Mapping ELUs

This data set includes polygons for ecological provinces within the conterminous United States. This data set contains regional geographic delineations for analysis of ecological relationships across ecological units. MetadataThis 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 CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.