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83 results for “Biodiversity loss”
No evidence of increased forest loss from a mining rush in a biodiversity hotspot
<p>This repository contains the data and code used in the study entitled 'No evidence of impacts from a mining rush in a protected forest'. 2024. Authors: Katie Devenish, Simon Willcock, Kathryn M. Goodenough, Rio Heriniaina, O. Sarobidy Rakotonarivo, Julia P.G. Jones.</p> <p>The main focus of this study was an evaluation of the impacts of the 2016-17 sapphire rush at Bemainty in Eastern Madagascar on deforestation and forest degradation (temporary tree cover loss). We used the Tropical Moist Forests (TMF) data (Vancutsem et al., 2021) to measure forest changes and the synthetic control method to estimate counterfactual outcomes. Supplementary data from informal interviews and lemur surveys conducted in the field October - November 2019 by Rio Heriniaina were drawn upon to explore the wider impacts and trade-offs of the mining rush, and assess the health of lemur populations 2 years after the rush. </p> <p>This repository contains the raw interview and lemur survey data collected by Rio Heriniaina at Bemainty October - November 2019. It also contains the code (entitled Script_updated_for_publication), spatial data, and input data required to reproduce the results from the main forest loss analysis. </p> <p>Data on deforestation and degradation was obtained from the Tropical Moist Forests dataset (Vancutsem et al, 2021). Annual deforestation was measured using the Deforestation year data. Annual deforestation per drainage basin was calculated in ArcGIS and is already contained within the attributes of the basins layer (Mada_basins_Lev9_edited_final).</p> <p>However, annual degradation data was obtained by adapting the TMF rawAnnual Disruptions data in Google Earth Engine. The output of this process (Annual_Disruptions_YEAR_masked) was loaded into R and annual degradation calculated per drainage basin. Annual deforestation and degradation rates were calculated as a percentage of forest cover in each drainage basin at the start of each year. To obtain annual forest cover estimates for each sub-basin we reclassified the TMF Annual Change datasets and extracted forest area per sub-basin. Both of these processes were extremely computationally expensive. The code includes these steps, but also contains an option (Option 1) to skip these steps by uploading csv files of annual forest cover (For_Area_ext2_21_04) and annual degradation per basin (Deg_Area_ext2_21_04) included in this repository. This data can be joined back to the basins layer. This allows users to skip this computationally expensive stage and progress straight to the analysis. The For90 layer (Vieilledent et al., 2018) and the Annual Disruptions data (Annual_Disruptions_YEAR_masked) are only needed if following Option 2 in the code.</p> <p> </p>
Data from: Island biodiversity in peril: anticipating a loss of mammals' functional diversity with future species extinctions
<p>Islands are biodiversity hotspots that host unique assemblages. However, a substantial proportion of island species are threatened and their long-term survival is uncertain. Identifying and preserving vulnerable species has become a priority, but it is also essential to combine this information with other facets of biodiversity like functional diversity, to understand how future extinctions might affect ecosystem stability and functioning. Focusing on mammals, we (i) assessed how much functional space would be lost if threatened species go extinct, (ii) determined the minimum number of extinctions which would cause a significant functional loss, (iii) identified the characteristics (e.g., biotic, climatic, geographic, or orographic) of the islands most vulnerable to future changes in the functional space, and (iv) quantified how much of that potential functional loss would be offset by introduced species. Using trait information for 1,474 mammal species occurring in 318 islands worldwide, we built trait probability density functions to quantify changes in functional richness and functional redundancy in each island if the mammals categorized by IUCN as threatened disappeared. We found that the extinction of threatened mammals would reduce the functional space in 63% of the assessed islands, although these extinctions in general would cause a reduction of less than 15% of their overall functional space. Also, on most islands, the extinction of just a few species would be sufficient to cause a significant loss of functional diversity. The potential functional loss would be higher on small, isolated and/or species rich islands and, in general, the functional space lost would not be offset by introduced species. Our results show that the preservation of native species and their ecological roles remains crucial for maintaining the current functioning of island ecosystems. Therefore, conservation measures considering functional diversity are imperative to safeguard the unique functional roles of threatened mammal species on islands.</p> <p><strong>Datasets and R scripts provided: </strong></p> <p><strong>1. Mammals_occurrence_islands.xlxs</strong> - Matrix of presence/absence of mammals species (columns) per island (rows) and bibliographic sources from which the information has been extracted. Islands are grouped according to the zoogeographical regions proposed by Holt el al. (2013).</p> <p><strong>2. Traits_matrix.csv</strong> – Matrix of species functional traits included in this study and the bibliographic sources of this information. Note that trait values are scaled and centered and that some of them have been imputed (see Methods section of the original manuscript).</p> <p><strong>3.</strong> <strong>Functional_space_analysis.R</strong> – script to:</p> <p>· Build the functional space of islands</p> <p>· Calculate observed functional richness and functional redundancy</p> <p>· Calculate functional richness and functional redundancy after simulating the extinction of threatened species</p> <p>· Calculate the functional space offset by introduced species </p> <p><strong>4. mixed_models_islands.R</strong> - R script to perform the linear mixed models to explore whether islands with higher values of predicted functional diversity loss due to threatened species extinction share some characteristics.</p> <p><strong>5. data_models_islands.csv </strong>- Dataset used to perform the models. For each island the following information is provided:</p> <ul> <ul> <li>Island ID (ID)</li> <li>Number of species (SppRich)</li> <li>Number of threatened species (Thre_sp)</li> <li>Functional richness (Island_FRic)</li> <li>Functional redundancy (Island_Red)</li> <li>Functional richness standard effect size (SES_FRic)</li> <li>Functional redundancy standard effect size (SES_FRed)</li> <li>Island group (Archipielago)</li> <li>Island past connectivity (Type)</li> <li>Percentage of protected area coverage (protected_percentageI_VI)</li> <li>Distance to the nearest continent (dContinent_km)</li> <li>Mean annual temperature (Anntemp_promedio)</li> <li>Mean annual precipitation (Annprec_promedio)</li> <li>Island area (Area_km2)</li> <li>Maximum elevation (Elev_max)</li> <li>Species richness (SR)</li> <li>Mean human footprint (Human_foot)</li> <li>Distance to the nearest larger landmass (distance_biggerLandmass)</li> </ul> </ul> <p>Island area, distance to the nearest continent and distance to the nearest larger landmass were calculated with ArcMap (ESRI, 2019) and the ‘terra 1.7-71’ R package (Hijmans, 2023), using the shapefile of the world’s islands available in Martin et al. (2022). Distance to the nearest mainland and to the nearest larger landmass were calculated as the shortest distance between coastlines (Weigelt & Kreft, 2013). Maximum elevation of each island was extracted from the Global Bathymetry and Elevation Database (Becker et al., 2009). We also used this database to access the bathymetry around the continents and islands and determine whether an island was connected to the mainland during the Last Glacial Maximum (about 20,000 years ago), assuming a sea level of 122 m below the present level (glacial maximum mainland connection; Weigelt, Jetz and Kreft, 2013). Averaged values of annual temperature and annual precipitation for each island were calculated using the climatic variables available in the CHELSA 2.1 database (Karger et al., 2018) at a resolution of 30 arc seconds. To calculate the percentage of protected area on each island, we gathered the protected surface’s shapefile from The World Database on Protected Areas (UNEP-WCMC & IUCN, 2022). Finally, we used the mean human footprint index from Human Footprint maps (see Venter et al., 2018).</p> <p><strong>References</strong></p> <p>Becker, J. J., Sandwell, D. T., Smith, W. H. F., Braud, J., Binder, B., Depner, J., Fabre, D., Factor, J., Ingalls, S., Kim, S.-H., Ladner, R., Marks, K., Nelson, S., Pharaoh, A., Trimmer, R., Von Rosenberg, J., Wallace, G., & Weatherall, P. (2009). Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. <em>Marine Geodesy</em>, 32(4), 355–371. <a href="https://doi.org/10.1080/01490410903297766">https://doi.org/10.1080/01490410903297766</a></p> <p>ESRI (2019). <em>ArcGis for Desktop</em>. Retrieved from https://desktop.arcgis.com/en/</p> <p>Hijmans, R. (2023). terra: Spatial Data Analysis_. R package version 1.7-3, <https://CRAN.R-project.org/package=terra>.</p> <p>Holt, B. G., Lessard, J.-P., Borregaard, M. K., Fritz, S. A., Araújo, M. B., Dimitrov, D., Fabre, P.-H., Graham, C. H., Graves, G. R., Jønsson, K. A., Nogués-Bravo, D., Wang, Z., Whittaker, R. J., Fjeldså, J., & Rahbek, C. (2013). An update of Wallace’s zoogeographic regions of the world. <em>Science</em>, 339(6115), 74–78. https://doi.org/10.1126/science.1228282</p> <p>Karger D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2018). Data from: Climatologies at high resolution for the earth's land surface areas [Dataset]. <em>Dryad</em>. https://doi.org/10.5061/dryad.kd1d4</p> <p>Martin, M., Sayre, R., VanGraafeiland, K., McDermott Long, O., Weatherdon, L., Will, D., Spatz, D. R., & Holmes, N. D. (2020). Global Islands (M. I. Goldstein & D. A. B. T.-E. of the W. B. DellaSala (eds.); pp. 47–50). Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.12475-3</p> <p>UNEP-WCMC & IUCN (2022). <em>Protected Planet: The World Database on Protected Areas (WDPA).</em> Cambridge, UK: UNEP-WCMC and IUCN. Retrieved from <a href="http://www.protectedplanet.net">www.protectedplanet.net</a>. [Accessed 12/2022]</p> <p>Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J., Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete, B. M., Levy, M. A., & Watson, J. E. (2018). <em>Last of the Wild Project, Version 3 (LWP-3): 2009 Human Footprint, 2018 Release</em>. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). <a href="https://doi.org/10.7927/H46T0JQ4">https://doi.org/10.7927/H46T0JQ4</a></p> <p>Weigelt, P., & Kreft, H. (2013). Quantifying island isolation – insights from global patterns of insular plant species richness. <em>Ecography,</em> 36(4), 417-429. https://doi.org/10.1111/j.1600-0587.2012.07669.</p> <p>Weigelt, P., Jetz, W., & Kreft, H. (2013). Bioclimatic and physical characterization of the world’s islands. <em>Proceedings of the National Academy of Sciences</em>, 110(38), 15307–15312. https://doi.org/10.1073/pnas.1306309110</p> <p> </p>
Habitat loss alters effects of intransitive higher-order competition on biodiversity: a new metapopulation framework
Recent studies have suggested that intransitive competition, as opposed to hierarchical competition, allow more species to coexist. Furthermore, it is recognized that the prevalent paradigm, which assumes that species interactions are exclusively pairwise, may be insufficient. More importantly, whether and how habitat loss, a key driver of biodiversity loss, can alter these complex competition structures and therefore species coexistence remain unclear. We thus present a new simple yet comprehensive metapopulation framework which can account for any competition pattern and more complex higher-order interactions (HOIs) among species. We find that competitive intransitivity increases community diversity and that HOIs generally enhance this effect. Essentially, intransitivity promotes species richness by mitigating competitive exclusion, while HOIs facilitate species coexistence through stabilizing community dynamics. However, variation in species vital rates and habitat loss can weaken or even reverse such higher-order effects, as their interaction can lead to a more rapid decline in competitive intransitivity under HOIs. Thus, it is essential to correctly identify the most appropriate interaction model for a given system before models are used to inform conservation efforts. Overall, our simple model framework provides a more parsimonious explanation for biodiversity maintenance than existing theory.
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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.