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212 results for “Landscape change”
Data from: Applying landscape genomic tools to forest management and restoration of Hawaiian koa (Acacia koa) in a changing environment
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Data from: State-and-transition simulation models: a framework for forecasting landscape change
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Evolutionary changes in the chromatin landscape contribute to reorganization of a developmental gene network during rapid life history evolution in sea urchins
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Extinction-immigration dynamics lag behind environmental filtering in shaping the composition of tropical dry forests within a changing landscape
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Data from: Landscape context explains changes in the functional diversity of regenerating forests better than climate or species richness
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Elevation and landscape change drive the distribution of a montane, endemic grassland bird
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Data from: Ecosystem services and disservices by birds, bats and monkeys change with macadamia landscape heterogeneity
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Data from: Integrating water quality monitoring and diatom community trends to determine landscape-level change in protected lakes.
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Data from: Resource selection and landscape change reveal mechanisms suppressing population recovery for the world's most endangered antelope
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Data from: Quantifying past and present connectivity illuminates a rapidly changing landscape for the African elephant
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Data from: Changing landscapes of Southeast Asia and rodent-borne diseases: decreased diversity but increased transmission risks
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Data from: The influence of landscape context on short- and long-term forest change following a severe ice storm
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Changes in arthropod communities mediate the effects of landscape composition and farm management on pest control ecosystem services in organically managed strawberry crops
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Impact of landscape fragmentation and climate change on body size variation of bumblebees during the last century
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Future fire-driven landscape changes along a southwestern US elevation gradient
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Data from: Climate change and landscape-use patterns influence recent past distribution of giant pandas
<p>Climate change is one of the most pervasive threats to biodiversity globally, yet the influence of climate relative to other drivers of species depletion and range contraction remain difficult to disentangle. Here, we examine climatic and non-climatic correlates of giant panda (<i>Ailuropoda melanoleuca</i>) distribution using a large-scale 30-year dataset to evaluate whether a changing climate has already influenced panda distribution. We document several climatic patterns, including increasing temperatures, and alterations to seasonal temperature and precipitation. We found that while climatic factors were the most influential predictors of panda distribution, their importance diminished over time, while landscape variables have become relatively more influential. We conclude that the panda's distribution has been influenced by changing climate, but conservation intervention to manage habitat is working to increasingly offset these negative consequences.</p>
Data from: Fisher's geometrical model of fitness landscape and variance in fitness within a changing environment
The fitness of an individual can be simply defined as the number of its offspring in the next generation. However, it is not well understood how selection on the phenotype determines fitness. In accordance with Fisher's fundamental theorem, fitness should have no or very little genetic variance, whereas empirical data suggest that is not the case. To bridge these knowledge gaps, we follow Fisher's geometrical model and assume that fitness is determined by multivariate stabilizing selection towards an optimum that may vary among generations. We assume random mating, free recombination, additive genes, and uncorrelated stabilizing selection and mutational effects on traits. In a constant environment, we find that genetic variance in fitness under mutation-selection balance is a U-shaped function of the number of traits (i.e. of the so-called "organismal complexity"). Because the variance can be high if the organism is of either low or high complexity, this suggests that complexity has little direct costs. Under a temporally varying optimum, genetic variance increases relative to a constant optimum and increasingly so when the mutation rate is small. Therefore mutation and changing environment together can maintain high genetic variance. These results therefore lend support to Fisher's geometric model of a fitness landscape.
Рис. 3. Изменение виΑового богатства и таксономической структуры зонаΛьных фаун прямокрыΛых северных обΛастей УраΛа. Fig. 3. Changes in species richness and taxonomic structure of Orthoptera fauna of the Northern Ural. in Fauna and landscape-zonal distribution of Orthoptera in the Komi Republic (Russia)
Рис. 3. Изменение виΑового богатства и таксономической структуры зонаΛьных фаун прямокрыΛых северных обΛастей УраΛа. Fig. 3. Changes in species richness and taxonomic structure of Orthoptera fauna of the Northern Ural.
Supporting Data for Martin et al. 2024 in ESPL: "Quantifying landscape change following catastrophic dam failures in Edenville and Sanford, Michigan, USA"
<p><strong>Source:</strong></p> <p>This dataset supports the publication: Martin, H.K., Edmonds, D.A., Yanites, B.J. & Niemi, N.A. (2024) Quantifying landscape change following catastrophic dam failures in Edenville and Sanford, Michigan, USA. Earth Surf. Process. Landforms, Available from: <a href="https://doi.org/10.1002/esp.5855" target="_blank" rel="noopener">https://doi.org/10.1002/esp.5855</a>. All of the details about how these data were collected and processed are provided in that paper, particularly in the Supporting Information.</p> <p><strong>Brief Summary:</strong></p> <p>On May 19, 2020, the Edenville and Sanford dams near Midland, Michigan, USA failed as the result of significant rainfall over the preceding two days. We analyzed the geomorphic impacts of these failures using a pre-failure airborne lidar dataset and three UAV-based lidar surveys collected two weeks ("2020-06"), three months ("2020-08)", and eleven months ("2021-04") post-failure. The pre-failure airborne lidar dataset was merged from two datasets hosted on OpenTopography as part of the USGS 3DEP project. These are linked to in the Supporting Information document for the above manuscript. This upload contains data from the three UAV-based surveys we collected.</p> <p><strong>Structure/Contents:</strong></p> <p>The .zip file is structured first by dates, with each folder corresponding to one of our three field campaigns. <br>Within each date folder, there are two folders: one for data collected at Edenville and another for data collected at Sanford. <br>Within each location folder, there are two items. <br> 1. One is a .las file that contains the ground-classified point cloud data. For Edenville, these point clouds are merged from data collected from multiple launch locations. The point cloud file has had corrections applied to it. It corresponds to the output that results from the Supporting Information through the end of section 2.3: Lidar processing. These files can be opened and worked with using most-any software that handles point clouds; users preferring free and open source software could consider using CloudCompare [https://cloudcompare.org/].<br> 2. The second is a folder containing DEMs, which are raster images derived from the point cloud data in the corresponding location folder. Each pixel contains the elevation of that area in meters above sea level. Two copies are provided: one with the pixel size set to 50 cm by 50 cm, and the other set to 1 m by 1 m. These were created by constructing a triangular lattice between ground-classified points in the point cloud and then sampling the mesh. These maps are provided for convenience for users who prefer to work with raster data over point clouds or would otherwise prefer not to have to rasterize point cloud data themselves. These files can be opened and worked with using any GIS software; users preferring free and open source software could consider using QGIS [https://www.qgis.org/en/site/]. The CRS is NAD83 UTM Zone 16N.</p> <p><strong>Citation/License:</strong> </p> <p>Please use our data! We ask that, if you do so and any published work results, you please cite the following manuscript as well as this zenodo dataset:</p> <p>Martin, H.K., Edmonds, D.A., Yanites, B.J. & Niemi, N.A. (2024) Quantifying landscape change following catastrophic dam failures in Edenville and Sanford, Michigan, USA. Earth Surf. Process. Landforms, Available from: <a href="https://doi.org/10.1002/esp.5855" target="_blank" rel="noopener">https://doi.org/10.1002/esp.5855</a>.</p> <p>These data are released under the CCBY-NC-SA 4.0 license [https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en]. </p> <p>In plain language, it means that you should feel free to download these data and use or modify them for whatever purpose you wish, as long as you i) attribute us as original authors of the dataset, ii) do not use them for commercial purposes, and iii) use the same license for any derivative works you create using these data. </p> <p> </p> <p>For any questions or concerns, please don't hesitate to reach out to Harrison Martin at hkm@caltech.edu, or via https://harrison.studies.rocks.</p>
Sex chromosome differentiation via changes in the Y chromosome repeat landscape in African annual killifishes Nothobranchius furzeri and N. kadleci
<p><span>Repetitive DNA represents an important driver of sex chromosome differentiation. Yet repetitive sequences tend to be misrepresented or overlooked in genomic studies. We analysed repetitive landscape of sex chromosomes in several populations of a turquoise killifish <em>Nothobranchius</em> <em>furzeri</em> and its sister species <em>N</em>. <em>kadleci</em> (Teleostei: Nothobranchiidae), representatives of African annual killifishes with high rate of karyotype and sex chromosome evolution. We combined bioinformatic analyses of repeatome with molecular cytogenetic techniques such as comparative genomic hybridization, fluorescence in situ hybridization with satellite sequences, genes for ribosomal RNAs (rDNA) and bacterial artificial chromosomes (BACs) and immunostaining of </span><span>SYCP3 and MLH1 proteins, which marked lateral elements of synaptonemal complexes and recombination sites, respectively</span><span>. We revealed that <em>N</em>. <em>furzeri</em> and <em>N</em>. <em>kadleci</em> share the XY sex chromosome system, which is thus much older than previously assumed. Sex chromosomes are mostly heteromorphic as evidenced by distinct distribution of satellite DNAs and major rDNA. Yet, the heteromorphic X and Y sex chromosomes pair almost exclusively regularly in meiosis, which implies synaptic adjustment. Physical mapping of BACs identified inversions on Y chromosomes of the <em>N</em>. <em>kadleci</em> populations, similar to the pattern previously reported in <em>N</em>. <em>furzeri</em>. Yet, the repetitive DNA landscape of X and Y sex chromosomes either diverged in parallel in populations of both species, or it evolved in their common ancestor and thus predates the inversions. The observed differentiation via repeat repatterning thus cannot be explained by the classical sexual antagonistic model. Rather, we hypothesized that relaxed meiotic drive and recombination reduced by neutral processes could drive changes in repeatome and secondary inversions could be maintained </span><span>by sexually antagonistic regulatory effects resulting from evolution of dosage compensation. </span><span><br></span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.