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129 results for “Island biodiversity”
Figures 1−8 in Revision of the family Metarbelidae (Lepidoptera) of the Oriental Region. V. Genus Marcopoloia Yakovlev & Zolotuhin gen. nov. from the Taiwan Island and Indo-Burma biodiversity hotspot
Figures 1−8. Adult specimens of Marcopoloia: 1. M. discipuncta, male, Taiwan, Prov. Ping-Tung, 10 km SE Mutan, 470 m, 07−10.iii. 1996, leg. Gy. Fabian & L. Nemeth (MWM); 2. M. discipuncta, male, Taiwan, Prov. Ping-Tung, Huang-Lion Recreation Area, 210 m, 06.iii.1996, leg. Gy. Fabian & L. Nemeth (MWM); 3. M. discipuncta, female, holotype of Arbela discipuncta Wileman, Kanshirei, Formosa, 1000 ft., 27.iv.1908, A.E. Wileman (NHMUK); 4. M. discipuncta, female, holotype of Arbela baibarana Matsumura, Horisha, 22.iv.1926, R. Saito & K. Kikuchi (ZMSU); 5. M. leloi, holotype (MWM); 6. M. nangmai, holotype (MWM); 7. M. siniaevi, holotype (MWM); 8. M. thaica, holotype (MWM).
Data from: Cryptic introductions and the interpretation of island biodiversity
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Data from: Approximate Bayesian computation reveals the crucial role of oceanic islands for the assembly of continental biodiversity
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Data from: Studying biodiversity-ecosystem function relationships in experimental microcosms among islands
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Data from: Predicting biodiversity loss in island and countryside ecosystems through the lens of taxonomic and functional biogeography
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Data from: Marine biodiversity at the end of the world: Cape Horn and Diego Ramírez islands
The vast and complex coast of the Magellan Region of extreme southern Chile possesses a diversity of habitats including fjords, deep channels, and extensive kelp forests, with a unique mix of temperate and sub-Antarctic species. The Cape Horn and Diego Ramírez archipelagos are the most southerly locations in the Americas, with the southernmost kelp forests, and some of the least explored places on earth. The giant kelp Macrocystis pyrifera plays a key role in structuring the ecological communities of the entire region, with the large brown seaweed Lessonia spp. forming dense understories. Kelp densities were highest around Cape Horn, followed by Diego Ramírez, and lowest within the fjord region of Francisco Coloane Marine Park (mean canopy densities of 2.51 kg m-2, 2.29 kg m-2, and 2.14 kg m-2, respectively). There were clear differences in marine communities among these sub-regions, with the lowest diversity in the fjords. We observed 18 species of nearshore fishes, with average species richness nearly 50% higher at Diego Ramírez compared with Cape Horn and Francisco Coloane. The number of individual fishes was nearly 10 times higher at Diego Ramírez and 4 times higher at Cape Horn compared with the fjords. Dropcam surveys of mesophotic depths (53-105 m) identified 30 taxa from 25 families, 15 classes, and 7 phyla. While much of these deeper habitats consisted of soft sediment and cobble, in rocky habitats, echinoderms, mollusks, bryozoans, and sponges were common. The southern hagfish (Myxine australis) was the most frequently encountered of the deep-sea fishes (50% of deployments), and while the Fueguian sprat (Sprattus fuegensis) was the most abundant fish species, its distribution was patchy. The Cape Horn and Diego Ramírez archipelagos represent some of the last intact sub-Antarctic ecosystems remaining and a recently declared large protected area will help ensure the health of this unique region.
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>
Data from: Marine biodiversity at the end of the world: Cape Horn and Diego Ramírez islands
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Fig. 5 in Integrative biodiversity inventory of ants from a Sicilian archipelago reveals high diversity on young volcanic islands (Hymenoptera: Formicidae)
Fig. 5 Median (circles) and corresponding 95% confidence intervals (lines) of all intraspecific genetic distances between specimens from individual Aeolian islands and the two putative source populations Sicily and mainland Italy. The areas of the circles are proportional to the sample sizes available for each island.
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Allen Brain Atlas
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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.