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40 results for “Biodiversity indicator”
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>
Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Deliverable 2.2: Biodiversity indicator database for focus regions
<p>To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa.</p> <p>The files provided in this repository (1 km<sup>2 </sup>resolution) include:</p> <p>1- Species richness (number of species).</p> <p>2- Endemism (species rarity). For this metric, we used the corrected weight endemism<sup> </sup>index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa).</p> <p>3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data.</p> <p>These results provide a valuable first step in describing human-induced changes in vegetation and biodiversity patterns across both South America and Africa.</p>
Deliverable 2.3: Biodiversity impact estimates and documentation for indicators on multi-dimensional biodiversity aspects ready for use in WP3
<p>To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa.</p> <p>The files provided in this repository (1 km2 resolution) include:</p> <p>1- Species richness (number of species).</p> <p>2- Endemism (species rarity). For this metric, we used the corrected weight endemism index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa).</p> <p>3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data.</p> <p>These results provide a improve in previous deliverable 2.2.</p>
Maps and R code from: Marginality indices for biodiversity conservation in forest trees
<p>This dataset provides the raster map of marginality indices for eight European tree species as described in the article "Marginality indices for biodiversity conservation in forest tree". It also provided the R code to compute the marginality indices.</p> <p>The eight species are the following:</p> <ul> <li><em>Abies alba</em> Mill. (silver fir)</li> <li><em>Fagus sylvatica</em> L. (European beech)</li> <li><em>Picea abies</em> (L.) H.Karst. (Norway spruce) </li> <li><em>Pinus halepensis</em> Mill. (Aleppo pine)</li> <li><em>Pinus nigra</em> J.F.Arnold (black pine)</li> <li><em>Pinus pinaster</em> Aiton (maritime pine) </li> <li><em>Pinus pinea</em> L. (stone pine)</li> <li><em>Pinus sylvestris</em> L. (Scots pine)</li> </ul> <p>For each species, a 7z archive of a multi-layered image of the raster maps of the marginality indices is given. Each image has 12 layers:</p> <ul> <li>Layer 1: distribution map of the species</li> <li>Layer 2: map of the probability of being marginal according to the Maxent model using eight marginality indices and countries as predictors</li> <li>Layer 3: map of the environmental marginality index, defined as the z-transform of the suitability of each location as predicted by a species distribution model using climatic variables as predictors</li> <li>Layer 4: map of the segmented distribution according to morphological spatial pattern analysis</li> <li>Layer 5: map of the area index</li> <li>Layer 6: map of the gravity index</li> <li>Layer 7: map of the centroid index</li> <li>Layer 8: map of the edge index</li> <li>Layer 9: map of the isolation index</li> <li>Layer 10: map of the second nearest core index</li> <li>Layer 11: map of the north/south index</li> <li>Layer 12: map of the east/west index</li> </ul>
Multinational evaluation of genetic diversity indicators for the Kunming-Montreal Global Biodiversity Framework
<p>Under the recently adopted Kunming-Montreal Global Biodiversity Framework, 196 Parties committed to report the status of genetic diversity for all species. To facilitate reporting, three genetic diversity indicators were developed, two of which focus on processes contributing to genetic diversity conservation: maintaining genetically distinct populations and ensuring populations are large enough to maintain genetic diversity. The major advantage of these indicators is that they can be estimated with or without DNA-based data. However, demonstrating their feasibility requires addressing the methodological challenges of using data gathered from diverse sources, across diverse taxonomic groups, and for countries of varying socioeconomic status and biodiversity levels. Here, we assess the genetic indicators for 919 taxa, representing 5,271 populations across nine countries, including megadiverse countries and developing economies. Eighty-three percent of taxa assessed had data available to calculate at least one indicator. Our results show that although the majority of species maintain most populations, 58% of species have populations too small to maintain genetic diversity. Moreover, genetic indicator values suggest that IUCN Red List status and other initiatives fail to assess genetic status, highlighting the critical importance of genetic indicators.</p>
Reference map illustrating the major Philippine faunal regions as defined by the Pleistocene Aggregate Island Complexes (PAICs). Selected island groups, such as the Babuyans, Batanes, the Romblon Island Group (RIG), and the Sulu Archipelago, are also indicated. in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
Reference map illustrating the major Philippine faunal regions as defined by the Pleistocene Aggregate Island Complexes (PAICs). Selected island groups, such as the Babuyans, Batanes, the Romblon Island Group (RIG), and the Sulu Archipelago, are also indicated.
The Philippine Archipelago, with major landmasses and other geographical features indicated. Prepared by Jeffrey L. Weinell. in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
The Philippine Archipelago, with major landmasses and other geographical features indicated. Prepared by Jeffrey L. Weinell.
Deliverable 2.2: Biodiversity indicator database for focus regions (phylogenetic files)
<p>The files provided in this repository complements the previous version of the Deliverable 2.2: Biodiversity indicator database for focus regions (same DOI) by including:</p> <ul> <li>Phylodiversity maps</li> <li>Organized database in Excel</li> <li>Code for the results .txt</li> </ul>
Landscape and biodiversity indicators for Samaria
<p>Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions.</p> <p>Regarding Samaria, Landscape and biodiversity indicators were generated for 1985, 1995, 2000, 2005, 2010, 2015. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object.</p> <p>The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.</p>
Landscape and biodiversity indicators for Lake Prespa
<p>Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions.</p> <p>Regarding Lake Prespa, Landscape and biodiversity indicators were generated for 2012. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object.</p> <p>The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.</p>
Landscape and biodiversity indicators for Curonian Lagoon
<p>Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions.</p> <p>Regarding Curonian Lagoon, Landscape and biodiversity indicators were generated for 2013-2014. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object.</p> <p>The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.</p>
Landscape and biodiversity indicators for Sierra Nevada
<p>Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions.</p> <p>Regarding Sierra Nevada, Landscape and biodiversity indicators were generated for three different land cover vegetation maps. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object.</p> <p>The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.</p> <p> </p>
Landscape and biodiversity indicators for Montado
<p>Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions.</p> <p>Regarding Montado, Landscape and biodiversity indicators were generated for 2007, 2012. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object.</p> <p>The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.</p>
Landscape and biodiversity indicators for La Palma - Canary Island
<p>Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions.</p> <p>Regarding La Palma – Canary Island, Landscape and biodiversity indicators were generated for 2007. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object.</p> <p>The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.</p>
Figure 2 in Spiders (Arachnida: Araneae) of Saba Island, Lesser Antilles: Unusually high species richness indicates the Caribbean Biodiversity Hotspot is woefully undersampled
Figure 2. Spider species richness by island area showing dramatic undersampling of most islands and lack of expected positive species-area relationship. Islands are ordered by size from smaller to larger as follows: Saba, Nevis, St. Kitts, Antigua, Grenada, Turks and Caicos, Barbados. See text for data sources.
Figure 1 in Spiders (Arachnida: Araneae) of Saba Island, Lesser Antilles: Unusually high species richness indicates the Caribbean Biodiversity Hotspot is woefully undersampled
Figure 1. Saba Island (17o38'N, 63o14'W), Lesser Antilles. Thirty-three collection sites mapped using Google Earth. Numbers correspond to sites listed in Table 1.
Dynamics-based characterisation and classification of biodiversity indicators
<p>Various biodiversity indicators, such as species richness, total abundance, and species diversity indices, have been developed to capture the state of ecological communities over space and time. As biodiversity is a multifaceted concept, it is important to understand the dimension of biodiversity reflected by each indicator for successful conservation and management. Here we utilised the responsiveness of biodiversity indicators' dynamics to environmental changes (i.e. environmental responsiveness) as a signature of the dimension of biodiversity. We present a method for characterising and classifying biodiversity indicators according to environmental responsiveness and apply the methodology to monitoring data for a marine fish community under intermittent anthropogenic warm water discharge. Our analysis showed that ten biodiversity indicators can be classified into three super-groups based on the dimension of biodiversity that is reflected. Group I (species richness and community mean of centre of distribution latitude (cCOD)) showed the greatest robustness to temperature changes; Group II (species diversity and total abundance) showed an abrupt change in the middle of the monitoring period, presumably due to a change in temperature; Group III (species evenness) exhibited the highest sensitivity to environmental changes, including temperature. These results had several ecological implications. First, the responsiveness of species diversity and species evenness to temperature changes might be related to changes in the species abundance distribution. Second, the similar environmental responsiveness of species richness and cCOD implies that fish migration from lower latitudes is a major driver of species compositional changes. The study methodology may be useful in selecting appropriate indicators for efficient biodiversity monitoring.</p>
Dynamics-based characterisation and classification of biodiversity indicators
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Multinational evaluation of genetic diversity indicators for the Kunming-Montreal Global Biodiversity Framework
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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.