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174 results for “biodiversity assessment”
Results from retrospective Baltic Sea biodiversity indicator status assessment using BEAT 3.0 tool
<p>Here we present all our results from retrospective Baltic Sea biodiversity indicator data analysis using BEAT 3.0. The BEAT tool (Nygård et al. 2018) is an R coded software (Murray & Nygård 2018, available online: <a href="https://zenodo.org/record/1288315#.XRxNp2cXYg4">https://zenodo.org/record/1288315#.XRxNp2cXYg4</a>) developed for analyzing marine biodivesity status. It follows the strucuture of EU's Marine Strategy Framework Directive. For more detailed metadata about the tool, see Nygård et al. 2018.</p> <p>We used data from various biodiversity indicators in two areas of the Baltic Sea: Bothnian Sea and Gulf of Finland. BEAT integrates indicators to ecosystem components and aggregates them spatially (more details can be found in Nygård et al. 2018). We produced retrospective time series of integrated and aggregated indicators. The yearly assessments are done using the moving average of the indicator status of the past 5 years in order to gain a more robust assessment result. The assessment follows the protocol of biodiversity assessment in the HELCOM Holistic assessment <a href="http://www.helcom.fi/baltic-sea-trends/holistic-assessments">http://www.helcom.fi/baltic-sea-trends/holistic-assessments</a></p> <p>In the results table, all different spatial levels as well as ecosystem components are shown. All indicator results have a value between 0 and 1. If the indicator has a value over 0.6, it is considered to be in a good environmental status. Below are short description of the different columns:</p> <ul> <li>SAUID: ID of the spatial assessment unit (SAU) used. The largest SAU is Baltic Sea with an ID 1. It is divided to smaller SAUs and all individual SAUs have their own ID.</li> <li>SAUlevel: The highest possible level of SAU is the Baltic Sea and it is the level 1. The sea basins (for example Bothnian Sea) are the level 2 and so on.</li> <li>ECID: Ecosystem component ID. All possible indicators have their own ID. See the list of ecosystem components in the input files of the tool (Murray & Nygård 2018).</li> <li>EClevel: Ecosystem component level. The level 1 is biodiversity, in the level 2 it is divided to pelagic habitat, birds, fish, benthic habitat and mammals and so on.</li> <li>EcosystemComponent: this column tells the ecosystem component. It can be higher level e.g. biodversity or an individual indicator for certain taxa.</li> <li>EQR: ecological quality ratio. The status of the certain ecosystem component in a certain SAU. The value varies between 0 and 1. If it is over 0.6, the ecosystem component is considered to be in a good status.</li> <li>Columns H-T: These refer to certain descriptors of Marine Strategy Framework Directive. If the ecosystem component is considered to have a link to a certain descriptor, an EQR value is given.</li> <li>year: year of the assessment. Note: all the yearly values are a moving average of past 5 years.</li> </ul> <p> </p>
Using Biodiversity Data to Assess Species-Habitat Relationships in Glacier National Park, Montana
Biodiversity surveys are becoming increasingly popular. However, standard analysis techniques for these data have not yet been developed. This paper explores the use of multivariate ordination techniques for assessing species-habitat relationships using biodiversity data. The research was conducted in Glacier National Park, Montana, and birds and butterflies were chosen as the taxonomic groups of study. Biodiversity assessment sites were established through a range of habitats and monitored from 1987 through 1989. Presence/absence sampling over the total number of sampling sites was used to classify species commonness and rarity. Approximately 86% of the historically recorded butterflies and 70% of the historically recorded bird species have been observed in the 3 yr of sampling. During the 3 yr of this study there was a striking continuity of species richness per site. There was also a striking overlap between the sites that support high species diversity and sites that support rare species. Principal components analysis and cluster analysis worked well in discerning species-habitat relationships. Elevation, structural diversity of the site, and moisture were the major factors explaining species distributions. A chi-square analysis also provided some insights into species-habitat relationships, showing birds were more habitat specific than butterflies. Habitat diversity analyses demonstrated a positive but non-significant correlation between remotely sense spectral-class diversity of a site and species richness for both birds and butterflies. Aspect, slope and elevation diversity had a negative or negligible relationship with species richness.
Biodiversity impact assessment considering land use intensities and fragmentation
<p>The data provide supplementary information for the paper entitled "Biodiversity impact assessment considering land use intensities and fragmentation".</p><p> </p><p><strong>Coverage of the characterization factors</strong></p><ul><li>5 species groups: plants, amphibians, birds, mammals, and reptiles</li><li>5 broad land use types: cropland, pasture, plantations, managed forests, and urban areas</li><li>3 land use intensities: minimal, light, intense (sometimes, intensity levels had to be merged because the data did not allow to differentiate between them)</li><li>825 terrestrial ecoregions of the world (according to WWF / Olson et al. 2001)</li></ul><p> </p><p><strong>Files</strong></p><p>Main data</p><ul><li>CF.csv: characterization factors (CFs) for ecoregions and 5 species groups</li></ul><p>Taxonomically aggregated data</p><ul><li>CF_kingdom.csv: CFs aggregated from 5 species groups to plant and animal kingdoms</li><li>CF_domain.csv: CFs aggregated from plant and animal kingdoms to the domain of Eukaryota</li></ul><p>Spatially aggregated data</p><ul><li>CF_country.csv: CFs aggregated from ecoregions to countries</li><li>CF_global.csv: CFs aggregated from ecoregions to the globe</li></ul><p>Taxonomically and spatially aggregated data</p><ul><li>CF_kingdom_country.csv: CFs aggregated to countries and plant and animal kingdoms</li><li>CF_domain_country.csv: CFs aggregated to countries and the domain of Eukaryota</li><li>CF_kingdom_global.csv: CFs aggregated to the globe and plant and animal kingdoms</li><li>CF_domain_global.csv: CFs aggregated to the globe and the domain of Eukaryota</li></ul><p> </p><p><strong>Units</strong></p><p>CFs for land occupation: PDF/m2</p><p>CFs for land transformation: PDF⋅yr/m2</p><p> </p><p><strong>Columns</strong></p><ul><li>realm: 2-letter code to identify one of 8 biogeographical realms</li><li>biome: ID to identify one of 14 biomes</li><li>eco_id: ID to identify the ecoregion, combining numbers for the realm, biome, and ecoregion within each biome nested within each realm</li><li>eco_name: ecoregion name</li><li>species_group: species group</li><li>kingdom: kingdom as a taxonomic rank</li><li>habitat_id: ID to link to the land use type and intensity as used in land_use.tif. An ID with .5 represents a merged land use class considering the two habitats with the IDs when rounding the value both up and down.</li><li>habitat: land use type and intensity</li><li>CF_*: characterization factor</li><li>*_occ*: land occupation</li><li>*_tra*: land transformation</li><li>*_avg*: average approach</li><li>*_mar*: marginal approach</li><li>*_reg: regional relative species loss</li><li>*_glo: global relative species loss</li><li>*_rsd: relative standard deviation as a measure of spatial uncertainty due to aggregation (only concerns country and globally aggregated CFs)</li><li>quality_*: data quality, distinguishing between original estimates and the use of proxies</li><li>objectid: object id of the country</li><li>iso3cd: iso3 code of the country</li><li>romnam: romanized name of the country</li><li>m49code: M49 code of the country, a standard code used by the United Nations</li><li>weighting: aspect based on which the CFs were weighted (only concerns globally aggregated CFs)</li></ul><p> </p><p><strong>Data quality</strong></p><ul><li>original: original estimate (for globally aggregated CFs: mostly original estimates, proxies only considered in areas with current land use)</li><li>proxy_intensity: intensity level was missing; CF was derived from another CF of the same ecoregion and land use type but different intensity level and scaled to the right intensity level</li><li>proxy_type: land use type was missing; CF was derived from the average regional CFs for light use in the same biome and the ecoregion-specific GEP and scaled to the right intensity level if needed</li><li>proxy_gep: global extinction probability (GEP) was missing (only concerns CFs for global relative species loss); GEP estimated based on average GEP per area unit in the same biome and the ecoregion area</li><li>proxy_partial: some species groups were missing but not all (only concerns taxonomically aggregated CFs); aggregation done based on partly original estimates and partly proxies</li><li>proxy_neighbours: country was missing (only concerns country-aggregated CFs); values were estimated based on the average of the three nearest neighbouring countries</li><li>proxy: proxies were considered even in areas without current land use (only concerns globally aggregated CFs)</li></ul><p>Note: proxies in country-aggregated CFs apply to at least one of the ecoregions overlapping with the country and not necessarily all ecoregions</p><p> </p><p><strong>Land use type and intensity data</strong></p><p>Raster file: land_use.tif</p><p>Spatial resolution: 0.08333333, 0.08333333 (x, y)</p><p>Spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p><p>Coordinate reference system: WGS 84 (EPSG:4326)</p><p> </p><p>Codes</p><ol><li>Primary_vegetation_Minimal (incl. sparse/no vegetation)</li><li>Cropland_Intense</li><li>Cropland_Light</li><li>Cropland_Minimal</li><li>Managed_forest_Intense</li><li>Managed_forest_Light</li><li>Managed_forest_Minimal</li><li>Pasture_Intense</li><li>Pasture_Light</li><li>Pasture_Minimal</li><li>Plantation_Intense</li><li>Plantation_Light</li><li>Plantation_Minimal</li><li>Urban_Intense</li><li>Urban_Light</li><li>Urban_Minimal</li></ol>
Data underlying the assessment of biodiversity and diverse values for nature in University of Helsinki campuses / Aineisto luonnon monimuotoisuutta ja moninaisia arvoja Helsingin yliopiston kampuksilla käsittelevän selvitystyön taustalla
<p>These records contain the spatial and nonspatial datasets underlying the assessment of biodiversity and diverse values for nature in the four campuses of University of Helsinki, namely the Kumpula, Meilahti, Viikki, and City center campuses in Helsinki. The assessment was a part of the University of Helsinki Sustainability and responsibility plan 2022-2024.</p> <p>For further information on the types and contents of the data, see the pdf "README.pdf" and the assessment report.</p> <p> </p>
A practice-led assessment of landscape restoration potential in a biodiversity hotspot
<p>Effective restoration planning tools are needed to mitigate global carbon and biodiversity crises. Published spatial assessments of restoration potential are often at large scales or coarse resolutions inappropriate for local action. Using a Tanzanian case study, we introduce a systematic approach to inform landscape restoration planning, estimating spatial variation in cost-effectiveness, based on restoration method, logistics, biomass modelling and uncertainty mapping. We found potential for biomass recovery across 77.7% of a 53,000 km<sup>2</sup> region, but with some natural spatial discontinuity in moist forest biomass, that was previously assigned to human causes. Most areas with biomass deficit (80.5%) were restorable through passive or assisted natural regeneration. However, cumulative biomass gains from planting outweighed initially high implementation costs meaning that, where applicable, this method yielded greater long-term returns on investment. Accounting for ecological, funding and other uncertainty, the top 25% consistently cost-effective sites were within protected areas and/or moderately degraded moist forest and savanna. Agro-ecological mosaics had high biomass deficit but little cost-effective restoration potential. Socio-economic research will be needed to inform action towards environmental and human development goals in these areas. Our results highlight value in long-term landscape restoration investments and separate treatment of savannas and forests. Furthermore, they contradict previously asserted low restoration potential in East Africa, emphasising the importance of our regional approach for identifying restoration opportunities across the tropics.</p>
Fig. 14 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 14. Ptychadenid, ranid, and rhacophorid frogs from Mount Nimba Integrated Nature Reserve: Ptychadena retropunctata (A); P. stenocephala female (B); P. submascareniensis female (C); P. submascareniensis male (D); P. tournieri female (E); Amnirana sp. 'albolabris west' female (F); Amnirana sp. 'albolabris west' male (G); Chiromantis rufescens male (H).
Fig. 13 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 13. Pipid and ptychadenid frogs from Mount Nimba Integrated Nature Reserve: Xenopus tropicalis female (A); Ptychadena arnei female (B); P. arnei male (C); P. bibroni male (D); P. longirostris male (E); P. oxyrhynchus male (F); P. pujoli male (G); P. pumilio female (H).
Fig. 11 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 11. Hyperoliid, odontobatrachid, and phrynobatrachid frogs from Mount Nimba Integrated Nature Reserve: Kassina cochranae male (A); Odontobatrachus arndti male (B); Phrynobatrachus alleni male (C); P. annulatus male (D); P. francisci female (E); P. fraterculus female (F); P. guineensis male (G); P. gutturosus female (H).
Fig. 10 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 10. Hyperoliid frogs from Mount Nimba Integrated Nature Reserve: Hyperolius picturatus female (A); H. picturatus male (B); H. soror female (C); H. soror male (D); H. cf. sylvaticus male (E); H. cf. sylvaticus male in ventral view (F); H. sp. male (NGK-Nimba 0068) (G); H. sp. male (NGK-Nimba 0068) in ventral view (H).
Fig. 9 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 9. Hyperoliid frogs from Mount Nimba Integrated Nature Reserve: Hyperolius concolor female (A); H. concolor male (B); H. fusciventris fusciventris male (C); H. guttulatus female (D); H. lamottei female (E); H. lamottei male (F); H. nimbae female (G); H. nimbae male (H).
Fig. 7 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 7. Bufonids from Mount Nimba Integrated Nature Reserve: Nimbaphrynoides occidentalis juvenile (A); N. occidentalis female (B); Sclerophrys maculata male (C), the yellow color is only exhibited by some males during breeding; S. maculata female (D); S. regularis male (E); S. regularis female (F); S. togoensis male (G); S. togoensis female (H).
Fig. 8 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 8. Conrauid, dicroglossid, hemisotid, and hyperoliids from Mount Nimba Integrated Nature Reserve: Conraua alleni (A–B); Hoplobatrachus occipitalis (C); Hemisus marmoratus (D); Afrixalus dorsalis (E); A. fulvovittatus (F); Hyperolius chlorosteus (G–H).
Fig. 5 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 5. The Yéalé village at the foothills of Mounts Nimba showing altered former forest habitat (habitat D). The mountains are visible in the background.
Fig. 6 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 6. Arthroleptid frogs from Mount Nimba Integrated Nature Reserve: Arthroleptis crusculum male (A); Arthroleptis poecilonotus- complex female (B); Astylosternus occidentalis female (C); Cardioglossa occidentalis male (D); Leptopelis macrotis male (E); Leptopelis occidentalis male (F); Leptopelis spiritusnoctis female (G); Leptopelis viridis female (H).
Fig. 12 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 12. Phrynobatrachid frogs from Mount Nimba Integrated Nature Reserve: Phrynobatrachus latifrons female (A); P. latifrons male (B); P. liberiensis male (C); P. natalensis male (D); P. natalensis female (E); P. phyllophilus male (F); P. tokba female (G); P. tokba male (H).
Fig. 1 in Amphibian diversity of a West African biodiversity hotspot: an assessment and commented checklist of the batrachofauna of the Ivorian part of the Nimba Mountains
Fig. 1. Geographical location of the Mount Nimba Integrated Nature Reserve, within the westernmost extension of Ivory Coast at the border crossing point with Guinea and Liberia. Four distinct habitats with different amphibian assemblages based on altitudinal and vegetation types are indicated: Montane grasslands at the highest elevations (A: red star; Fig. 2); Mid-elevation savannah grasslands (B: purple star; Fig. 3); Dense, broadleaf and evergreen forests from lower to mid-elevations (C: orange star; Fig. 4); and Altered forests (D: green star; Fig. 5). The inset figure indicates the location of Ivory Coast (green patch) on the African continent.
Mediterranean risk assessment data based on the concurrency between climate change, fisheries, stocks, and biodiversity
<p>Data associated to the paper "Detecting Ecosystem Risk Hotspots: A Mediterranean Case Study" by G. Coro, L. Pavirani, A. Ellenbroek.</p>
Figure 3. A & B in A rapid biodiversity assessment of Lesotho's first proposed Biosphere Reserve: a case study of Bokong Nature Reserve and Tšehlanyane National Park
Figure 3. A & B, the spectacular scenery of mountaineous landscapes forming part of the proposed BR; C, the endemic spiral aloe; D, the near-endemic Lesotho lily; E, the Lesotho red-hot poker; F, locally uncommon fern – bracken; G, endemic Maloti minnow; H, common eland; and I, its presence on rock paintings; J, some of the wetlands in the BNR; K, some of them damaged by diggings of Sloggett's ice rat (Source: K. Kobisi).
Figure 1. A & B in A rapid biodiversity assessment of Lesotho's first proposed Biosphere Reserve: a case study of Bokong Nature Reserve and Tšehlanyane National Park
Figure 1. A & B, Lesotho's first proposed Biosphere Reserve, showing the core (Tšehlanyane National Park and Bokong Nature Reserve), buffer and transition zones (Source: T. Leballo).
Figure 2 in A rapid biodiversity assessment of Lesotho's first proposed Biosphere Reserve: a case study of Bokong Nature Reserve and Tšehlanyane National Park
Figure 2. Transect walks covered during the different phases of the biodiversity survey (data collection) in the proposed Biosphere Reserve (Source: T. Leballo).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.