Find research datasets worth reusing

opencc-by-4.0Jan 2022View details →

Database for Perceived Inclusivity and Trust in Protected Area Management Decisions among Stakeholders in Alaska

This database is part of a state-wide survey in Alaska, USA. An online Qualtrics interface was used to administer the survey to a panel of Alaskan residents from June to August 2020.

opencc-by-4.0Mar 2022View details →

Data from: Elevated fires during COVID-19 lockdown and the vulnerability of protected areas

Related article: Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x. In this dataset: This dataset contains information about monthly fire incidence and precipitation for the protected areas of Madagascar from January 2012 to December 2020. The fire data is sourced from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product and the precipitation data from the Global Precipitation Measurement (GPM) mission (for years 2016-2020) and its predecessor The Tropical Rainfall Measuring Mission (TRMM) (for years 2011-2015) at spatial resolution 10 km. The fire and precipitation data was overlayed with the protected area polygons of the June 2020 release of the World Database of Protected Areas. For sources and more details on how the data was compiled see the related article. The data can be used to inspect temporal dynamics of wildfires inside protected areas and for informing adaptive protected area management and planning. Please cite this dataset as: Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x. Column names NAME: Name of protected area Fires_sum: Number of observed fires (VIIRS) Month: Month Year: Year Precipitation: Precipitation (mm) Plag_1:Plag_12: Precipitation during previous month; 2 months ago; 3 months ago…12 months ago YEAR_CREAT: Year of establishment of protected area Biome: Biome REP_AREA: Area of protected area (km2) Fires_per_km2: Fires per km2 Prec_acc_12m: Accumulated precipitation during the last 12 months fBiome: Biome as factor fNAME: Name as factor sPrecipitation: Precipitation (scaled; see Methods section of article) sPlag_1: Precipitation in previous month (scaled; see Methods section of article) sPrec_acc_12m: Accumulated precipitation during the last 12 months (scaled; see Methods section of article) Pred_Zinb_1a: Predicted fires (see Methods section of article) Diff_Zinb_1a: Difference: Observed fires - predicted fires Year_pred: Year for prediction License Creative Commons Attribution 4.0 International.

opencc-by-4.0Mar 2022View details →

First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS

This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI). Abstract: Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.

opencc-by-4.0Apr 2022View details →

OpenStreetMap+ Protected nature areas in continental Europe (IUCN status + Natura 2000)

Twelve maps of continental Europe indicating the protected nature area status in 2019 according to <a href="https://ec.europa.eu/environment/nature/natura2000/index_en.htm">Natura 2000</a> and the <a href="https://www.iucn.org/">International Union for Conservation of Nature</a> (IUCN). The IUCN status was extracted from crowdsourced data obtained from OpenStreetMap through geofabrik.de. This dataset contains: <ul> <li>3 raster maps representing Natura 2000 protection status (A, B and C), named Natura2000_[status].tif</li> <li>8 raster maps representing OSM-derived IUCN protection status (1a, 1b, 2, 3, 4, 5, 6, and 'other'), named OSM_IUCN_[status].tif</li> <li>1 aggregated map (adm_protected.area_natura2000.osm_p_30m_0..0cm_2019..2021_eumap_epsg3035_v0.1) where each of the 11 protection statuses, as well as pixels where multiple statuses apply, are assigned a unique value. This map can also be accessed interactively at <a href="https://maps.opendatascience.eu/?base=OpenStreetMap%20(grayscale)&layer=Natura2000-OSM%20Protected%20areas&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45">maps.opendatascience.eu</a>.</li> </ul> All files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files for the aggregated raster are provided in both SLD and QML format.

opencc-by-4.0Jun 2024View details →

Derived Data from "Expanding European protected areas through rewilding"

We present the major derived data obtained through the study "Expanding European protected areas through rewilding" published in Current Biology. Data refer to three shapefiles and it is structured as:  1) "Rewilding Patches" folder - presenting European rewilding patches (human footprint <=5), classified by area 2) "Marxan Solutions" folder - presenting optimized solutions to expand current European protected areas through rewilding such to achieve ,in each country, 30% area with protected areas ("PA_all" sub-folder) and 10% area with strict protected areas ("PA_strict" sub-folder) For detail on data, users are adviced to read the "Readme" files in each folder.

opencc-by-4.0Jul 2021View details →

Short-term Monitoring of Coral Reef Marine Protected Areas (MPAs) in the Municipality of Liloan, Central Visayas, Philippines

This is a sampling-event dataset of the short-term monitoring of Poblacion and Kadurong Reefs, two of the marine protected areas Municipality of Liloan, Cebu, Philippines. Water quality and ecological assessments were carried out to monitor the status and trends of biological and physical parameters associated with coral reefs using the standard protocols for surveying tropical marine resources. Specifically, the following measurements were conducted: (1) physico-chemical parameters, (2) phytoplankton and zooplankton occurrence and abundance, (3) fish occurrence and density, and (4) percent cover of benthic components of coral reef. The data can serve as the basis for the formulation and implementation of relevant measures for conservation and protection management of the Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines. In this version, occurrence.csv was revised as described below: <ul> <li>taxonID for Abudefduf vaigiensis (Quoy & Gaimard, 1825) and  Hemiaulus P.A.C. Heiberg, 1863  were corrected.</li> <li>Author names with corrupted characters/symbols were corrected. </li> </ul>

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Level of Protection for Areas Designated Protected and Open Space - Ipswich Watershed - Idrisi Raster File.

This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This datalayer shows the level of protection for areas that are defined as Protected and Recreational Open Space by MassGIS (www.state.ma.us/mgis). This layer is derived from the Protected and Recreational Open Space layer provided by for each town. The values are from the Level of Protection (lev_prot) field in the data table. To show the what level of protection open space areas in the Ipswich watered are under.

openCC (other)Jan 2020View details →

opencc-zeroDec 2019View details →

Phenological metrics for Protected Area "GranParadiso", MODIS terra tile h18v04

Phenological metrics derived from satellite data by use of R-package "phenex" (Lange (2017)). NDVI is filtered by modified BISE algorithm (see Viovy (1992)). NDVI curve is modelled with method DLogistic (see Doktor (2017), Lange (2017)). Phenological metrics include: (1) Start of season / green-up (GU); (2) End of season / senescence (SEN); (3) Length of vegetation period (VP); (4) GPP proxy (integral over vegetation period, GSIVI); (5) minimum NDVI (MinNDVI); (6) maximum NDVI (MaxNDVI); (7) day of minimum NDVI as julian date (MinDOY); (8) day of maximum NDVI as julian date (MaxDOY); (9) r-square of modelled NDVI curve; (1)-(4) are derived by using local threshold (LT) and global threshold (GT) method (see Doktor (2017), Lange (2017)). (1)-(6) include mean and standard deviation; References: [Viovy 1992]: Viovy N, Arino O, Belward A (1992) The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing 13(8):1585–1590; [Doktor 2017]: Doktor D, Lange M (2017) Disparate applicability and broad spatio-temporal satellite resolution affects extracted trends of European spring phenology for 1989-2007. In preparation for Global Ecology and Biogeographie; [Lange 2017]: Lange M, Doktor D (2017) phenex: Auxiliary Functions for Phenological Data Analysis. R package version 1.4-5, https://CRAN.R-project.org/package=phenex, Last accessed on 2017-05-29;

opencc-zeroDec 2019View details →

Phenological metrics for Protected Area "HighTatra", MODIS terra tile h19v04

Plate 1 in Taxonomic Accounts With Notes On Spatial Diversity And Relative Abundance Pattern Of Horseflies (Diptera: Tabanidae) From Sonamukhi Protected Forest Area Of West Bengal, India

Plate 1. Habitus of six species of Tabanidae, i.e. A: Chrysops dispar (Fabricius, 1798); B: Atylotus virgo (Wiedemann, 1824); C: Tabanus dorsiger Wiedemann, 1821 (new record from Sonamukhi protected forest area under arid zone of West Bengal); D: Tabanus (Tabanus) rubidus Wiedemann, 1821; E: Tabanus (Tabanus) striatus Fabricius, 1787 and F: Tabanus (Tabanus) tenens Walker, 1850.

Map 1 in Taxonomic Accounts With Notes On Spatial Diversity And Relative Abundance Pattern Of Horseflies (Diptera: Tabanidae) From Sonamukhi Protected Forest Area Of West Bengal, India

Map 1. GIS map showing distribution and richness of family Tabanidae in Sonamukhi protected forest, on the basis of eco-regions in Indo-malayan biome of arid region of west Bengal below and satellite map of all the stations showing study sites of Tabanidae above.

Figure 2. A-C in Taxonomic Accounts With Notes On Spatial Diversity And Relative Abundance Pattern Of Horseflies (Diptera: Tabanidae) From Sonamukhi Protected Forest Area Of West Bengal, India

Figure 2. A-C. Graphs showing log series model of rank abundance of tabanids depicting maximum abundance during pre-monsoon and monsoon of species T. straitus (17, 15) and of species T. striatus (3) and H. javana (3) respectively during post monsoon.

Figure 3 in Taxonomic Accounts With Notes On Spatial Diversity And Relative Abundance Pattern Of Horseflies (Diptera: Tabanidae) From Sonamukhi Protected Forest Area Of West Bengal, India

Figure 3. Species accumulation curves showing sample rarefaction (Mau Tau's) of the tabanids sampled throughout the season.

opencc-zeroJun 2020View details →

Species occurrence and occupancy in protected areas of the Natura2000 network in Belgium

Context Invasive alien species have been pointed out as an important driver of biodiversity loss. Many policy responses are being developed to address this threat. Protected areas often represent and preserve hotspots of biological diversity and ensure the maintenance of ecosystem services crucial to human livelihoods. The impact of biological invasions can be particularly severe in protected areas and their occurrence and impact in such areas is an important element of the risk they pose. To address this, there is a need for data on the occurrence and extent of alien species invasions in protected areas. Description This dataset contains species occurrence and occupancy in protected areas of the Natura2000 network in Belgium (Special Conservation Areas sensu Habitat Directive and Special Protection Areas sensu Bird Directive). The dataset was generated using the <a href="https://doi.org/10.5281/zenodo.3637911">Belgian occurrence cube at species level</a> and the <a href="https://doi.org/10.5281/zenodo.3635510">Belgian occurrence cube for non-native taxa</a> (both containing GBIF data aggregated using Oldoni et al. 2020), the 1x1km <a href="https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2">EEA reference grid</a> and the <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-spatial-data/natura-2000-shapefile-1">Natura2000 protected areas shapefiles</a> from the European Environment Agency. Data are grouped by protected area (<code>SITECODE</code>), year (<code>year</code>) and (infra)species (<code>taxonKey</code>, <code>speciesKey</code>). For each group, it provides the number of occurrences found in GBIF (<code>n</code>), the area of occupancy (<code>aoo</code>: number of 1 km2 squares), the coverage (<code>coverage</code>: % of 1 km2 squares), the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (<code>min_coord_uncertainty</code>), and the alien status (<code>is_alien</code>) based on the <a href="https://doi.org/10.15468/xoidmd">Global Register of Introduced and Invasive Species - Belgium</a>. For infraspecific taxa in the latter, the <a href="https://github.com/trias-project/indicators/blob/00e1ae72df3fb98b2a215c3af8769e53fbcd0182/reference/species_of_infraspecific_alien_taxa.tsv">alien status of the species</a> is looked up and included. The dataset is built on open science principles and intended to be completely reproducible: <ul> <li>The input data are publicly available on Zenodo, with the download DOIs listed in the related identifiers of this dataset package.</li> <li>The <a href="https://trias-project.github.io/indicators/10_species_observations_occupancy_in_protected_areas.html">code</a> to process the data is publicly available and documented on GitHub.</li> </ul> Files <ul> <li>protected_areas_species_occurrence.csv: number of occurrences (<code>n</code>), area of occupancy (<code>aoo</code>) and <code>coverage</code> of taxa (<code>taxonKey</code>) in Natura2000 areas of Belgium (<code>SITECODE</code>). Other columns included: <code>speciesKey</code> (for species is <code>speciesKey</code> = <code>taxonKey</code>), <code>SITETYPE</code> containing the site type of the Natura2000 area (one of <code>A</code>, <code>B</code> or <code>C</code>), <code>min_coord_uncertainty</code> with the lowest coordinate uncertainty in meters, <code>is_alien</code> containing the alien status (<code>TRUE</code> or <code>FALSE</code>) and <code>remarks</code> containing, if present, the infraspecific alien taxa whose occurrences contribute to the calculated <code>aoo</code> (only for species).</li> <li>protected_areas_species_info.csv: taxonomic information of taxa in <code>protected_areas_species_occurrence.csv</code> as retrieved from <a href="https://www.gbif.org/dataset/d7dddbf4-2cf0-4f39-9b2a-bb099caae36c">GBIF Backbone Taxonomy</a>. Columns: <code>taxonKey</code>, <code>speciesKey</code>, <code>scientificName</code>, <code>kingdom</code>, <code>phylum</code>, <code>order</code>, <code>class</code>, <code>genus</code>, <code>family</code>, <code>species</code>, <code>rank</code> and <code>includes</code>. The latter contains the infraspecific taxa and synonyms whose occurrences contribute to the number of occurrences at species level.</li> <li>protected_areas_metadata.csv: protected area information for areas included in <code>protected_areas_species_occurrence.csv</code>. Columns: <code>SITECODE</code> as in <code>protected_areas_species_occurrence.csv</code> (<code>BE*******</code>), <code>SITENAME</code> containing the name of the protected area, <code>SITETYPE</code> as in <code>protected_areas_species_occurrence.csv</code>, <code>flanders</code>, <code>wallonia</code> and <code>brussels</code> containing whether the area is situated respectively in Flanders, Wallonia or Brussels-Capital Region (<code>TRUE</code> or <code>FALSE</code>). Field codes are in line with <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-tabular-data-12-tables">EEA element definitions</a> for Natura 2000 sites.</li> </ul> Potential use of the dataset Currently, there is no comprehensive reporting system for invasive alien species in Natura 2000 sites. This dataset provides a baseline as to which species occur in which protected area. We envisage this dataset can be an interesting starting point for various types of analyses on alien species in protected areas in Belgium, but that it can also be used in complement to other data on alien species in protected areas to study more general patterns. Some examples of research questions: <ul> <li>Which protected areas are most invaded by alien species</li> <li>Which alien species are most distributed in protected areas and which traits do they have</li> <li>How does the proportion of alien species in protected areas change in time</li> <li>How does the occurrence/occupancy of alien species in protected areas match lists of regulated species (e.g. Union List, EPPO lists)</li> <li>To what extent can the network of protected areas contribute to providing safe refuge to native species from the impacts of invasive alien species</li> <li>How widespread are the impacts of certain alien species on protected areas</li> </ul> <h2>Acknowledgements</h2> This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS), the European Union's LIFE program (LIFE19 NAT/BE/000953 - LIFE RIPARIAS).

opencc-by-4.0Jun 2020View details →

List and date of establishment of Marine Protected Areas and Key Biodiversity Areas of the Alboran Sea

List of Marine Protected Areas and Key Biodiversity Areas for the Alboran Sea (Abbreviation in Spanish, French and English with lenguage among brackets- Fr: French; S: Spanish), indicating its figure of conservation, year of establishment for each figure of protection and national or regional management body (in brackets). IBA: Importante Bird Area; IMMA: Important Marine Mammals Area; MR: Marine Reserve; MR/FR: Marine  and Fishing Reserve; NA: Natural Area; NM: Natural monument; NP: Natural Park; SPAMI: Specially Protected Areas of Mediterranean Importance; RAMSAR: Wetlands of International Importance (Ramsar Sites); SCI: Site of Community Importance of Natura 2000; SAC: Special Area of Conservation of Natura 2000; SPA: Special Protection Area of Natura 2000; ZEPA: Zona de Especial Protección para las Aves; ZEPIM: Zonas Especialmente Protegidas de Importancia para el Mediterráneo; LIC: Lugar de Importancia Comunitaria de Natura 2000; ZEC: Zona de Especial Conservación de Natura 2000; ASPIM: Aire Spécialement Protégée d'Importance Méditerranéenne; SIC: Site d'Importance Communautaire; ZPS: Zones de Protection Spéciale.

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Data from: Shark movement strategies influence poaching risk and can guide enforcement decisions in a large, remote Marine Protected Area

<ol> <li>Large, remote marine protected areas (MPAs) containing both reef and pelagic habitats, have been shown to offer considerable refuge to populations of reef-associated sharks. Many large MPAs are, however, impacted by illegal fishing activity conducted by unlicensed vessels. While enforcement of these reserves is often expensive, it would likely benefit from the integration of ecological data on the mobile animals they are designed to protect. Consequently, shark populations in some protected areas continue to decline, as they remain a prime target for illegal fishers.</li> <li>To understand shark movements and their vulnerability to illegal fishing, three years of acoustic tracking data, from 101 reef-associated sharks, were analysed as movement networks to explore the predictability of movement patterns and identify key movement corridors within the British Indian Ocean Territory (BIOT) MPA. We examined how space use and connectivity overlap with spatially-explicit risk of illegal fishing, through data obtained from the management consultancy enforcing the MPA.</li> <li>Using individual-based models, the movement networks of two sympatric shark species were efficiently predicted with distance-decay functions (>95% movements accurately predicted). Model outliers were used to highlight the locations with unexpectedly high movement rates where MPA enforcement patrols might most efficiently mitigate predator removal.</li> <li>Activity space estimates and network metrics illustrate that silvertip sharks were more dynamic, less resident and link larger components of the MPA than grey reef sharks. However, we show that this behaviour potentially enhances their exposure to illegal fishing activity.</li> <li> Synthesis and applications. Marine protected area (MPA) enforcement strategies are often limited by resources. The British Indian Ocean Territory MPA, one of the world's largest 'no take' MPAs, has a single patrol vessel to enforce 640,000 km2 of open ocean, atoll and reef ecosystems. We argue that to optimise the patrol vessel search strategy and thus enhance their protective capacity, ecological data on the space use and movements of desirable species, such as large-bodied reef predators, must be incorporated into management plans. Here, we use electronic tracking data to evaluate how shark movement dynamics influence species mortality trajectories in exploited reef ecosystems. In doing so we discuss how network analyses of such data might be applied for protected area enforcement.</li> </ol>

opencc-zeroMay 2020View details →

Fig. 8 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 8. Plot of the non-metric multidimensional scaling (nMDS) based on the by Bray–Curtis similarity index for logarithmic values of meiobenthos taxa density in the recognized habitats of the Snake Island MPA (Black Sea).

Fig. 7 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 7. Cluster analysis dendrogram based on meiobenthos density on the different habitats in MPA of the Snake Island (Black Sea).

Fig. 4 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 4. The average density (N, means ± SE ind.·m–2) and biomass (B, means ± SE mg·m–2) of the total meiobenthos with contribution permanent and temporary taxa in the different habitats of the Snake Island MPA (Black Sea).

Fig. 3 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 3. Meiobenthic community structure of different substrate types in the MB143 habitat of the Snake Island MPA (Black Sea).