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318 results for “Ecoregions”
Niche suitability and spatial distribution patterns of anurans in a unique Ecoregion mosaic of Northern Pakistan
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Relative cover and leaf economic traits for native and non-native plants across five U.S. ecoregions
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Plant community data for European ecoregions
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MODIS annual maximum NDVI for Tanana-Yukon Uplands Ecoregion from 2000-2012
This dataset contains maximum NDVI from the MODIS sensor onboard Aqua and Terra satellites. The NDVI has been filtered to contain only NDVI values exceeding 0.4 which typically represent vegetated pixels in the boreal region of Alaska. Scaling factor of 10,000 was applied to convert 4-byte floating point to 2-byte integer NDVI values (for example 0.4 to 40000,0.9 to 9000)
Ecoregional patterns of protist communities in mineral and organic soils: assembly processes, functional traits and diversity of testate amoebae in Northern Eurasia
<p>TA_Traits.csv file includes trait data for testate amoebae. TA_Abundance.csv file includes environmental variables (soil type and region) and relative abundance data of testate amoebae.</p>
Figure 4 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 4. Response graphs of habitat suitability (ordinate axis) according to the explanatory variables that intervened in the adjustment of the model for brown brocket deer (A, B, C). The temperature is expressed in degrees Celsius.Source of bioclimatic variables (bio), site https://www.worldclim.org/data/bioclim.html.
Figure 8 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 8. Species richness maps obtained using three algorithms, (A) "fuzzy union″, (B) "species richness″ and (C) "total beta″.
Figure 7 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 7. Response graphs of habitat suitability (ordinate axis) according to the explanatory variables that intervened in theadjustment of themodel for collared peccary (A,B, C).Temperature is expressed in degrees Celsius and altitude in meters.Source of bioclimatic variables (bio), site https://www.worldclim.org/data/bioclim.html.
Data from: mapping endemic freshwater fish richness to identify high priority areas for conservation: an ecoregion approach
<p>Freshwater ecosystems are experiencing accelerating global biodiversity loss. Thus, knowing where these unique ecosystems' species richness reaches a peak can facilitate their conservation planning. By hosting more than 290 freshwater fishes, Iran is a major freshwater fish hotspot in the Middle East. Considering the accelerating rate of biodiversity loss, there is an urgent need to identify species rich areas and understanding of the mechanisms driving biodiversity distribution. In this study, we gathered distribution records of all endemic freshwater fishes of Iran (85 species) to develop their richness map and determine the most critical drivers of their richness patterns from an ecoregion approach. We performed a generalized linear model (GLM) with quasi-Poisson distribution to identify contemporary and historical determinants of endemic freshwater fish richness. We also quantified endemic fish similarity among the 15 freshwater ecoregions of Iran. Results showed that endemic freshwater fish richness is highest in the Zagros Mountains while moderate level of richness was observed between Zagros and Alborz Mountains. High, moderate and low richness of endemic freshwater fish match with Upper Tigris & Euphrates, Namak, and Kavir & Lut Deserts ecoregions respectively. Kura - South Caspian Drainages and Caspian Highlands were the most similar ecoregions and Orumiyeh was the most unique ecoregion according to endemic fish presence. Precipitation and precipitation change velocity since the Last Glacial Maximum were the most important predictors of endemic freshwater fish richness. Areas identified to have the highest species richness have high priority for the conservation of freshwater fish in Iran, therefore, should be considered in future protected areas development.</p>
An ecoregion-based approach to restoring the world's intact large mammal assemblages
<p>Assemblages of large mammal species play a disproportionate role in the structure and composition of natural habitats. Loss of these assemblages destabilizes natural systems, while their recovery can restore ecological integrity. Here we take an ecoregion-based approach to identify landscapes that retain their historically present large mammal assemblages, and map ecoregions where reintroduction of 1–3 species could restore intact assemblages. Intact mammal assemblages occur across more than one-third of the 730 terrestrial ecoregions where large mammals were historically present, and 22% of these ecoregions retain complete assemblages across >20% of the ecoregion area. Twenty species, if reintroduced or allowed to recolonize through improved connectivity, can trigger restoration of complete assemblages over 54% of the terrestrial realm (11,116,000 km2). Each of these species have at least two large, intact habitat areas (>10,000 km2) in a given ecoregion. Timely integration of recovery efforts for large mammals strengthens area-based targets being considered under the Convention on Biological Diversity.</p>
Marine ecoregions and subecoregions within Indo-West Australian waters: A statistical approach based on species distributions
<p>Aim: The Marine Ecoregions of the World (MEOW) system delineates the oceans into 232 ecoregions. Here, we aimed to evaluate the suitability of this system to represent species distributions within Indo-West Australian waters, explore alternative ecoregions and new subecoregions, and investigate environmental variables that are correlated with species distributions within those waters.</p> <p>Location: Indo-West Australia</p> <p>Taxa: Vertebrates, invertebrates, marine plants</p> <p>Methods: We downloaded occurrence data for 14,513 marine species from the Ocean Biogeographic Information System. We analysed differences in species composition among nine ecoregions within Indo-West Australian waters using pairwise permutational multivariate analysis of variance to evaluate how well the MEOW system represents species distributions within those waters. We delineated subecoregions within each distinct ecoregion using hierarchical cluster analysis with the unweighted pair-group method using arithmetic averages. We analysed relationships between environmental variables and species composition using distance-based linear models.</p> <p>Results: Species composition was significantly different among ecoregions, except for three adjacent regions, which were combined into a single large ecoregion. Hence, seven distinct ecoregions were further analysed. Our study identified 13 subecoregions within these ecoregions that each separate into 'inshore' and 'offshore' zones. Depth explained the most variation in species composition of the combined taxa and sea surface temperature was the most important parameter in explaining the variability in most taxa.</p> <p>Main conclusion: The MEOW system did not represent well the distribution of marine species within Indo-West Australian waters. Alternatively, we show that those waters encompass seven distinct ecoregions with 13 subecoregions. The main environmental drivers of species distributions could be depth and sea surface temperature. The proposed ecoregions and subecoregions allow us to improve the biogeographic hypotheses for understanding the evolution of marine species and identify representative marine habitats and species composition for the setting of Marine Protected Area networks within Indo-West Australian waters.</p>
Code&Data_'Choosing fit-for-purpose biodiversity impact indicators for agriculture in the Brazilian Cerrado ecoregion'
<p>File "R_Scripts_biodiversity_indicators.zip" includes codes for calculating the biodiversity impact on the terrestrial vertebrates of the Cerrado biome using the countryside Species Area Relationship (cSAR), the Species Threat Abatement and Restoration (STAR) metric and the Species Habitat Index (SHI).</p> <p>File "results_biodiversity_indicators.zip" includes the result tables of the calculations done with the above mentioned codes. </p>
Cantabrian Mixed Forests ecoregion
<p>Here are included two files related to the vegetation survey: </p> <p>González- García, V., Fernández- Pascual, E., Font, X. & Jiménez- Alfaro, B. (2024) Forest habitat diversity in the Cantabrian Mixed Forests ecoregion (NW Iberian Peninsula), a climatic refugium in western Europe. Applied Vegetation Science, 27, e12793. Available from: https://doi.org/10.1111/avsc.12793</p> <p> </p> <p><strong>forests_header.csv</strong> includes all the relevés classified during this research: original SIVIM code, coordinates in UTM grid cells of 10x10, 1x1km and decimal degrees, original elevation of the relevés, plants cover (%), aspect, slope, the area (m2), syntaxa originally associate to each relevé, year of publication and accuracy of the decimal degrees coordinates.</p> <p><strong>iberoatlantic.rar</strong> includes the shapefile of the ecoregion, as redesign for this research, using as base the "ibero-atlantic territories" proposed by Fernández Prieto et al. 2023.</p>
Human-related ignitions increase the number of large wildfires across U.S. ecoregions
<p>This data was used in the analysis in the article "Human-related ignitions increase the number of large wildfires across U.S. ecoregions" by R. Chelsea Nagy, Emily Fusco, Bethany Bradley, John T. Abatzoglou, and Jennifer Balch. This article was accepted for publication in the journal Fire on 22 January 2018.</p>
Climate-projected distributional shifts for North American ecoregions
<p>Climate-projected distributional shifts for North American ecoregions</p> <p>Citation for dataset<br> --------------------<br> Stralberg, D. Climate-projected distributional shifts for North American ecoregions. http://doi.org/10.5281/zenodo.1407176</p> <p>Data layers <br> -----------------<br> Data layers represent ecoregion projections at 1-km resolution for North America: <br> _predcurrent.tif<br> _pred_XXXXX_YYYY.tif<br> where:<br> XXXXX = Representative Concentration Pathway (rcp45 or rcp85)<br> YYYYY = Time period (2050s or 2080s)</p> <p>See "ecoregion_lookup.csv" for ecoregion definitions and projected change summaries.</p> <p>Projection information<br> -------------------<br> "+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs"<br> -------------------<br> Projection LAMBERT<br> Spheroid GRS80<br> Units METERS<br> Zunits NO<br> Xshift 0.0<br> Yshift 0.0<br> Parameters <br> 49 0 0.0 /* 1st standard parallel<br> 77 0 0.0 /* 2nd standard parallel<br> -95 0 0.0 /* central meridian<br> 0 0 0.0 /* latitude of projection's origin<br> 0.0 /* false easting (meters)<br> 0.0 /* false northing (meters)</p>
Velocity-based macrorefugia for North American ecoregions
<p>Climate-change refugia, or areas of species persistence under climate change, may vary in proximity to a species' current distribution, with major implications for their conservation value. Thus, the concept of climate velocity (Loarie et al. 2009)---the speed at which an organisms must migrate to keep pace with climate change---is useful to compare and evaluate refugia. Using analog climate methods, both forward and backward velocity can be calculated, providing complementary information about spatio-temporal responses to climate change (Hamann et al. 2014, Carroll et al. 2015). In particular, backward velocity calculations can be used to identify areas of high potential refugium value for a given time period and species or ecoregion (Stralberg et al. 2018a). Refugia for a given ecoregion represent areas where the climates of that ecoregion may persist into the future. </p> <p>I used random forest model projections of future ecoregions (Stralberg et al. 2018b) to generate an index of climate-change refugia potential for individual ecoregions, using the methods outlined in Stralberg et al. (2018a). The index ranges from 0 to 1, with values close to 1 indicating overlap or very close proximity to the current mapped ecoregion, across multiple climate models. Because the random forest algorithm is a classifier that assigns an ecoregion class to every future pixel, it does not account for novel climates that are not currently found in any North American ecoregion. Of course novelty is relative and can be measured in many different ways. I calculated a multivariate environmental similarity surface (MESS) following Elith et al. (2010) to generate an index of novelty for each future ecoregion (negative values indicate dissimilarity).</p> <p>For mapping purposes, novel climates for each ecoregion, RCP, and time period were identified as those with values lower than the 1st percentile of dissimiarity values for the baseline periods:</p> <p><a href="https://drive.google.com/file/d/1mxJupbS2hQ7MNPNEycWRMYO98sBbsBsh/view?usp=sharing">1. RCP 8.5, 2080s</a></p> <p><a href="https://drive.google.com/file/d/10v2MGRyCVTrOoSMBOoBJ79XtVUDXnVoP/view?usp=sharing">2. RCP 8.5, 2050s</a></p> <p><a href="https://drive.google.com/file/d/14gfhuYI5M_NdaTcYg_rAaHm6L6GiEkVJ/view?usp=sharing">3. RCP 4.5, 2080s</a></p> <p><a href="https://drive.google.com/file/d/1xhMfck9COX0hBozB9sis1GfJwJrxGq_y/view?usp=sharin">4. RCP 4.5, 2050s</a></p> <p> </p> <p>References</p> <p>Carroll, C., J. J. Lawler, D. R. Roberts, and A. Hamann. 2015. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10:e0140486.</p> <p>Elith, J., M. Kearney, and S. Phillips. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution 1:330-342.</p> <p>Hamann, A., D. Roberts, Q. Barber, C. Carroll, and S. Nielsen. 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21:997-1004.</p> <p>Stralberg, D., C. Carroll, J. H. Pedlar, C. B. Wilsey, D. W. McKenney, and S. E. Nielsen. 2018a. Macrorefugia for North American trees and songbirds: Climatic limiting factors and multi-scale topographic influences. Global Ecology and Biogeography 27:690-703. https://doi.org/10.1111/geb.12731 </p> <p>Stralberg, Diana. 2018b. Climate-projected distributional shifts for North American ecoregions [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1407176<br> </p>
Figure 1 in The Chilopoda fauna of the Hyrcanian ecoregion
Figure 1. Location of the Hyrcanian forests. Abbreviations: Ar – Armenia, Az – Azerbaijan.
Diet overlap among non-native trout species and native Cutthroat Trout (Oncorhynchus clarkii) in two U.S. ecoregions
<p>The invasion of freshwater ecosystems by non-native species can constitute a significant threat to native species and ecosystem health. Non-native trouts have long been stocked in areas where native trouts occur and have negatively impacted native trouts through predation, competition, and hybridization. This study encompassed two seasons of sampling efforts across two ecoregions of the western United States: The Great Basin in summer 2016 and the Yellowstone River Basin in summer 2017. We found significant dietary overlaps among native and non-native trouts within the Great Basin and Yellowstone River Basin ecoregions. Three orders of invertebrates (Ephemeroptera, Trichoptera and Diptera) composed the majority of stomach contents and were responsible for driving the observed patterns. Great Basin trout had higher body conditions (k) and non-native Great Basin trout had higher gut fullness values than Yellowstone River Basin trout, indicating a possible limitation of food in the Yellowstone River Basin. Native fishes were the least abundant and had the lowest body condition in each ecoregion. These findings may indicate a negative impact on native trouts by non-native trouts. We recommend additional monitoring of native and non-native trout diets, regular invertebrate surveys to identify the availability of diet items, and reconsidering stocking efforts that can result in overlap of non-native fishes with native cutthroat trout.</p>
FIG. 6 in Populations of a new morphotype of corrugate Lessonia Bory in the Beagle Channel, sub-Antarctic Magellanic ecoregion: a possible case of on-going speciation
FIG. 6. — MJ network of ITS1 haplotypes of Magellanic Lessonia Bory, spp. individuals.
Data and code from: Environmental drivers of wild bee reproductive performance across a South American dryland ecoregion
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.