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259 results for “landscape scale”
Landscape-Scale Forest Dynamics in the Luquillo Experimental Forest in Puerto Rico 1936-1989
This study examined landscape-scale forest dynamics in the Luquillo Experimental Forest (Puerto Rico). The analysis was based on vegetation maps created from aerial photographs taken in 1936 and 1989. For details on methods and results, please see the published paper (Foster, D. R., M. Fluet and E. R. Boose. 1999. Human or natural disturbance: landscape-scale dynamics of the tropical forests of Puerto Rico. Ecological Applications 9: 555-572). The Abstract from the paper is reproduced below. "Increasingly ecologists are recognizing that human disturbance has played an important role in tropical forest history and that many assumptions concerning the relative importance of natural processes warrant re-examination. To assess the historical role of broad-scale human versus natural disturbance on an intensively studied tropical forest we undertook a landscape-level analysis of forest dynamics in the Luquillo Experimental Forest (LEF; 10,871 ha) in eastern Puerto Rico. Using aerial photographs (1936 and 1989), GIS, a model of topographic exposure to hurricane winds, and historical data, we sought to: (1) document historical changes in extent, cover and type of forest vegetation, (2) evaluate the distribution of land-use and hurricane impacts, (3) assess the contributions of these processes in controlling current vegetation patterns, and (4) relate these results to ongoing ecological, conservation and natural resource discussions. "With over 1000 m of relief in the LEF, the broad vegetation zones of Tabonuco (below 600 m a.s.l.), Colorado (600-900 m), Dwarf (above 900 m), and Palm forest are determined by environmental gradients. However, over the past 60-100 years forest extent, cover, and type have been transformed: in 1936, 40% of the LEF was unforested or secondary forest and less than 50% had continuous canopy (more than 80% cover); in 1989, less than 97% was continuous forest. Secondary forest and agricultural lands in 1936 were replaced largely by Tabonuco and Colo
MCR LTER: Coral Reef: Landscape-scale patterns of nutrient enrichment in a coral reef ecosystem: implications for coral to algae phase shifts, Adam et al., Ecol. Appl.
These data and analyses code were generated in support of the manuscript: Adam TC, Burkepile DE, Holbrook SJ, Carpenter RC, Claudet J, Loiseau C, Thiault L, Brooks, AJ, Washburn L, and RJ Schmitt, Ecological Applications We investigated the potential role of anthropogenic nutrient loading in driving recent coral-to-macroalgae phase shifts on reefs in the lagoons surrounding Moorea, French Polynesia. We used nitrogen (N) tissue content and stable isotopes (δ15N) in an abundant macroalga (Turbinaria ornata) together with empirical models of nutrient discharge to describe spatial and temporal patterns of nutrient enrichment in the lagoons. Turbinaria ornata were collected at 190 sites around Moorea in January, May, and August 2016. These sampling periods corresponded with distinct seasonal shifts in rainfall and wave forcing. Our results revealed that patterns of N enrichment were linked to rainfall, wave-driven circulation, and distance from anthropogenic nutrient sources, especially human sewage. In addition to describing high resolution patterns of N enrichment from 2016, we also analyzed core MCR time series on N tissue content in Turbinaria ornata from three habitats (fringing reef, back reef, and reef crest) at the six core MCR LTER sites between 2007 and 2013. These data showed that fringing reefs have been consistently enriched in N relative to back reefs, which are enriched relative to the reef crest. Further, these patterns mirror long-term patterns of nitrate and nitrite concentrations in the water column. We also analyzed core MCR time series on benthic communities and fishes and found that back reef sites that were consistently enriched in N between 2007 and 2013 experienced large increases in macroalgae while macroalgae remained much less abundant at back reef sites with lower N. These phase shifts to macroalgae occurred despite island-wide increases in the density and biomass of herbivorous fishes over the time period. Together, these results indicate th
The Femern-project: a large-scale excavation of a Stone Age landscape - supplementary data
<p>This dataset contains all radiocarbon dates from the Femern project.</p> <p>Please cite the dataset as: </p> <p>Måge, B.T., Groß, D., Kanstrup, M. 2023. The Femern-project: a large-scale excavation of a Stone Age landscape. In: Groß, D. and Rothstein, M.: Changing Identity in a Changing World. Archaeological Studies on Human Interaction in Northern Europe around 4000 cal BC. Leiden: Sidestone, supplementary material.</p> <p>19.02.2024: Dataset updated: Wrong species ID in original dataset for AAR-27426</p>
Scaling landscape fire history in sagebrush: Wildfires not historically frequent in the main population of threatened Gunnison Sage-grouse
<p>The main population of ∼5,000 Threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90-143 years in GUSG ranges, which is not frequent fire (≤25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, Thiessen polygons around sites, and sagebrush. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90-135 years, a land-survey estimate 82-131 years, and three spatial scar-based estimates 93-107 years, showing agreement. However, comparing land-survey and fire-scar results showed that using fire scars spatially only 43% matched land surveys. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. An adequate historical fire reconstruction could require ∼45-60 fire-scar sites, making only ∼30,000 ha of sagebrush feasible. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82-135 years. </p>
Dataset (81 forest parcels) supplementing the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)"
<p>The dataset about 81 small-scale private forest parcels contains the answer variables and predictors used in the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)".</p>
Virgin Islands National Park: Coral Reef: Population Dynamics: Landscape-scale Variation in Scleractinian Corals
This study provides a landscape-scale context to a decadal-scale analysis of community structure on shallow reefs along 4 km of the south shore of St. John, US Virgin Islands. By focusing on 12-14 sites along ~100 km of the shores of St. John and St. Thomas, surveys conducted in 2011 were used to contrast: (1) a local-scale with a landscape-scale analysis on two islands, (2) reefs around St. John and St. Thomas, and (3) reefs on north and south shores. Reefs were censused using photoquadrats that were analyzed for percentage cover first by functional groups (coral, macraolagae and CTB), and then by coral genus. In general, among-site variation for the coarse-resolution analysis eclipsed shore and island effects, but the fine-resolution analysis revealed strong site-specific differences for multiple coral genera that could be the product of priority effects in community succession. Over the next decade these differences probably will create unique community trajectories at each site.
Hubbard Brook Experimental Forest: Landscape scale (valley-wide) soil carbon and nitrogen cycling data
The valley-wide plots are a grid of 431 sites along fifteen N–S transects established at 500-m intervals spanning the entire Hubbard Brook Valley. This dataset includes total soil carbon, nitrogen and organic matter content, potential net nitrogen mineralization and nitrification rates, microbial respiration rates, soil water content and holding capacity, soil ammonium and nitrate concentrations, soil pH, and tree composition in a subset of 100 randomly selected plots in 2000. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. An analysis of these data can be found in: Venterea, R. T., Lovett, G. M., Groffman, P. M., & Schwarz, P. A. (2003). Landscape patterns of net nitrification in a northern hardwood-conifer forest. Soil Science Soc. Amer. J., 67, 527–539. https://doi.org/10.2136/sssaj2003.5270
Data from: Mean landscape-scale incidence of species in discrete habitats is patch size dependent
<p>Contains data and code for the manuscript 'Mean landscape-scale incidence of species in discrete habitats is patch size dependent'.</p> <p>Raw data consist of 202 published datasets collated from primary and secondary (e.g., government technical reports) sources. These sources summarise metacommunity structure for different taxonomic groups (birds, invertebrates, non-avian vertebrates or plants) in different types of discrete metacommunities including 'true' islands (i.e., inland, continental or oceanic archipelagos), habitat islands (e.g., ponds, wetlands, sky islands) and fragments (e.g., forest/woodland or grass/shrubland habitat remnants). </p> <p>The aim of the study was to test whether the size of a habitat patch influences the mean incidences of species within it, relative to the incidence of all species across the landscape. In other words, whether high-incidence (widespread) or low-incidence (narrow-range) species are found more often than expected in smaller or larger patches. To achieve this, a new standardized effect size metric was developed that quantifies the mean observed incidence of all species present in every patch (the geometric mean of the number of patches in which all species were observed) and compares this with an expectation based on re-sampling the incidences of all species in all patches. Meta-regression of the 202 datasets was used to test the relationship between this metric, the 'mean species landscape-scale incidences per patch' (MSLIP), and the size of habitat patches, and for differences in response among metacommunity types and taxonomic groups. </p>
Data from: Seasonal bee communities vary in their responses to resources at local and landscape scales: Implication for land managers
<p><strong>Context</strong>:<em> </em>There is great interest in land management practices for pollinators; however, a quantitative comparison of landscape and local effects on bee communities is necessary to determine if adding small habitat patches can increase bee abundance or species richness. The value of increasing floral abundance at a site is undoubtedly influenced by the phenology and magnitude of floral resources in the landscape, but due to the complexity of measuring landscape-scale resources, these factors have been understudied.</p> <p><strong>Objectives</strong>: To address this knowledge gap, we quantified the relative importance of local versus landscape scale resources for bee communities, identified the most important metrics of local and landscape quality, and evaluated how these relationships vary with season.</p> <p><strong>Methods</strong>: We studied season-specific relationships between local and landscape quality and wild-bee communities at 33 sites in the Finger Lakes region of New York, USA. We paired site surveys of wild bees, plants, and soil characteristics with a multi-dimensional assessment of landscape composition, configuration, insecticide toxic load, and a spatio-temporal evaluation of floral resources at local and landscape scales.</p> <p><strong>Results</strong>:<em> </em>We found that the most relevant spatial scale and landscape factor varied by season. Early-season bee communities responded primarily to landscape resources, including the presence of flowering trees and wetland habitats. In contrast, mid to late-season bee communities were more influenced by local conditions, though bee diversity was negatively impacted when sites were embedded in highly agricultural landscapes. Soil composition had complex impacts on bee communities, and likely reflects effects on plant community flowering. </p> <p><strong>Conclusions</strong>:<em> </em>Early-season bees can be supported by adding flowering trees and wetlands, while mid to late-season bees can be supported by local addition of summer and fall flowering plants. Sites embedded in landscapes with a greater proportion of natural areas will host a greater bee species diversity.</p>
Simulated treatment effects on bird communities inform landscape‐scale dry conifer forest management
<p>Human land use and climate change have increased forest density and wildfire risk in dry conifer forests of western North America, threatening various ecosystem services, including habitat for wildlife. Government policy supports active management to restore historical structure and ecological function. Information on potential contributions of restoration to wildlife habitat can allow assessment of tradeoffs with other ecological benefits when prioritizing treatments. We predicted avian responses to simulated treatments representing alternative scenarios to inform landscape‐scale forest management planning along the Colorado Front Range. We used data from the Integrated Monitoring in Bird Conservation Regions program to inform a hierarchical multispecies occupancy model relating species occupancy and richness with canopy cover at two spatial scales. We then simulated changes in canopy cover (remotely sensed in 2018) under three alternative scenarios, (1) a "fuels reduction" scenario representing landscape‐wide 30% reduction in canopy cover, (2) a "restoration" scenario representing more nuanced, spatially variable treatments targeting historical conditions, and (3) a reference, no‐change scenario. Model predictions showed areas of potential gains and losses for species richness, richness of ponderosa pine forest habitat specialists, and the ratio of specialists to generalists at two (1 km<sup>2</sup> and 250 m<sup>2</sup>) spatial scales. Under both fuels reduction and restoration scenarios, we projected greater gains than losses for species richness. Surprisingly, despite restoration more explicitly targeting ecologically relevant historical conditions, fuels reduction benefited bird species richness over a greater spatial extent than restoration, particularly in the lower montane life zone. These benefits reflected generally positive species associations with moderate canopy cover promoted more consistently under the fuels reduction scenario. In practice, contemporary forest management is likely to lie somewhere between the fuels reduction and restoration scenarios represented here. Therefore, our results inform where and how active forest management can best support avian diversity. Although our study raises questions regarding the value of including landscape‐scale heterogeneity as a management objective, we do not question the value of targeting finer-scale heterogeneity (i.e., stand and treatment level). Rather, our results combined with those from previous work clarify the scale at which targeting structural heterogeneity and historical reference conditions can promote particular ecosystem services.</p>
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
Data from: Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity
<p> </p> <p><strong>Short description</strong></p> <p>This repository contains the relevant data and code used for the analyses of the scientific publication: "<em>Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity</em>", published in the Journal of Applied Ecology.</p> <p>For further details please see the original article and its supplementary materials.</p> <p> </p> <p><strong>Organization of the data</strong></p> <p>The repository contains two main folders:</p> <p> 1. Target indicators & spatial analysis</p> <p><em>‘target indicators.csv’</em>: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay).</p> <p><em>‘bio-suitability segments.csv’</em>: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm.</p> <p><em>‘spatial analysis.xlsx’</em>: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site.</p> <p><em>‘Summary model development Zwalm.pptx’</em>: Additional information on the models that have been used in the CoMOLA optimization framework.</p> <p> 2. CoMOLA input & parameterisation</p> <p>The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA</p> <p><em>‘config.ini’</em>: Basic configuration file of CoMOLA (needs to be adjusted to local settings)</p> <p><em>‘input’ folder</em>: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file.</p> <p><em>‘models’ folder</em>: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.</p> <p> </p>
Supplement to Pandemic-Scale Phylogenomics Reveals A Landscape of SARS-CoV2 Recombination
<p>Extended Data S1-S2 and Tables S6-S9 corresponding to the publication entitled Pandemic-Scale Phylogenomics Reveals A Landscape of SARS-CoV2 Recombination. Data S1 contains the phylogeny analyzed in the aforementioned study, and Data S2 contains a table of all samples considered to be descendant of a recombinant node in the study, as well as the relevant recombinant node ID. Tables S6-S9 are acknowledgments tables corresponding to GISAID (Table S6), China National Center for Bioinformation (Table S7), COVID-19 Genomics UK (COG-UK) (Table S8), and National Center for Biotechnology Information database (Table S9) on which the study was based.</p>
Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type
<p>Contains all raw data measured in the Schwingbach Earth Observatory (SEO) from Spring, Summer and Autumn 2020. Data was measured with an on-site LGR laser from the GHG emissions, and with 100cm³ soil cores for the soil characteristics. Details can be found in the corresponding manuscript "Number of chamber measurement locations for accurate quantification of landscape-scale greenhouse gas fluxes: Importance of land use, seasonality, and greenhouse gas type"</p>
Disentangling direct and indirect drivers of farmland biodiversity at landscape scale
<p><span>To stop the ongoing decline of farmland biodiversity there are increasing claims for a paradigm shift in agriculture, namely from conserving and restoring farmland biodiversity at field scale (α-diversity) to doing it at landscape scale (γ-diversity). However, knowledge on factors driving farmland γ-diversity is currently limited. Here, we quantified farmland γ-diversity in 123 landscapes and analysed direct and indirect effects of abiotic and land-use factors shaping it using structural equation models. The direction and strength of effects of factors shaping γ-diversity were only partially consistent with what is known about factors shaping α-diversity, and indirect effects were often stronger than direct effects or even opposite. Thus, relationships between factors shaping α-diversity cannot simply be up-scaled to γ-diversity, and also indirect effects should no longer be neglected. Finally, we show that local mitigation measures benefit farmland γ-diversity at landscape scale and are therefore a useful tool for designing biodiversity-friendly landscapes. </span></p>
Data and R code from: Spatiotemporal risk factors predict landscape-scale survivorship for a northern ungulate
<p>These data and computer code (written in R, https://www.r-project.org) were created to statistically evaluate a suite of spatiotemporal covariates that could potentially explain pronghorn (Antilocapra americana) mortality risk in the Northern Sagebrush Steppe (NSS) ecosystem (50.0757<sup>o</sup> N, −108.7526<sup>o</sup> W). Known-fate data were collected from 170 adult female pronghorn monitored with GPS collars from 2003-2011, which were used to construct a time-to-event (TTE) dataset with a daily timescale and an annual recurrent origin of 11 November. Seasonal risk periods (winter, spring, summer, autumn) were defined by median migration dates of collared pronghorn. We linked this TTE dataset with spatiotemporal covariates that were extracted and collated from pronghorn seasonal activity areas (estimated using 95% minimum convex polygons) to form a final dataset. Specifically, average fence and road densities (km/km2), average snow water equivalent (SWE; kg/m2), and maximum decadal normalized difference vegetation index (NDVI) were considered as predictors. We tested for these main effects of spatiotemporal risk covariates as well as the hypotheses that pronghorn mortality risk from roads or fences could be intensified during severe winter weather (i.e., interactions: SWE*road density and SWE*fence density). We also compare an analogous frequentist implementation to estimate model-averaged risk coefficients. Ultimately, the study aimed to develop the first broad-scale, spatially explicit map of predicted annual pronghorn survivorship based on anthropogenic features and environmental gradients to identify areas for conservation and habitat restoration efforts.</p> <p> </p>
Scale insects support natural enemies in both landscape trees and shrubs below them
<p>Scale insects are frequently abundant on urban trees. Although scales can worsen tree condition, some tree species tolerate moderate scale densities. Scales are prey for many natural enemies. Therefore, scale-infested trees may conserve natural enemies in their canopies and in nearby plants. We examined if scale-infested oaks—<em>Quercus</em> <em>phellos</em> L.—hosted more natural enemies than scale-uninfested oaks—<em>Q</em>. <em>acutissima</em> Carruth. and <em>Q</em>. <em>lyrata Walter</em> in Raleigh, NC. USA. We also tested if natural enemies were more abundant in holly shrubs (<em>Ilex</em> spp.) planted below scale-infested compared to scale-uninfested oaks. We collected natural enemies from the canopies of both tree types and from holly shrubs planted below these trees. To determine if tree type affected the abundance of natural enemies that passively dispersed to shrubs, we created hanging cup traps to collect arthropods as they fell from trees. To determine if enemies became more abundant on shrubs below scale-infested compared to scale-uninfested trees over short time scales, we collected natural enemies from holly shrubs below each tree type at three to six-day intervals. Scale-infested trees hosted more natural enemies than scale-uninfested trees and shrubs below scale-infested trees hosted more natural enemies than shrubs under scale-uninfested trees. Natural enemy abundance in hanging cup traps did not differ by tree type; however, shrubs underneath scale-infested trees accumulated more natural enemies than shrubs under scale-uninfested trees in six to nine days. Tolerating moderate pest densities in urban trees may support natural enemy communities, and thus biological control services, in shrubs below them.</p>
The genomic landscapes of desert birds form over multiple time scales
<p><span>Spatial models show that genetic differentiation between populations can be explained by factors ranging from geographic distance to environmental resistance across the landscape. However, genomes exhibit a landscape of differentiation, which could indicate that multiple spatial models better explain divergence in different portions of the genome. We test whether alternative geographic predictors of intraspecific differentiation vary across the genome in ten bird species that co-occur in Sonoran and Chihuahuan Deserts of North America. Using population-level genomic data, we characterized the genomic landscapes across species and modeled five predictors that represented historical and contemporary mechanisms. The characteristics of genomic landscapes differed across the ten species, influenced by varying levels of population structuring and admixture between deserts. General dissimilarity matrix modeling indicated that the best-fit models differed from the whole genome and partitions along the genome. Almost all of the historical and contemporary mechanisms were important in explaining genetic distance, particularly historical and contemporary environment, while contemporary abundance, position of the barrier to gene flow, and distance explained relatively less. Individual species have significantly different patterns of genomic variation. These results illustrate that the genomic landscape of differentiation was influenced by alternative geographic factors operating on different portions of the genome.</span></p>
Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns
<p>Data for paper "Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns" in Scientific Reports.</p> <p>Code for analysis and plots <a href="https://github.com/vojtechabraham/SpatialScalingPollenDiversity">https://github.com/vojtechabraham/SpatialScalingPollenDiversity</a>. Download original pollen and resample them to the same pollen sum by function spectra_to_target_sum in <a href="https://github.com/vojtechabraham/pollen">https://github.com/vojtechabraham/pollen</a> or work with resampled datasets below.</p> <p>Original pollen data stored in <a href="https://www.neotomadb.org/">https://www.neotomadb.org/</a>:</p> <table> <tbody> <tr> <td><strong>species-poor region Bohemian-Moravian Highland (Vrchovina)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>Račín</td> <td>V06</td> <td><a href="https://data.neotomadb.org/54872">54872</a></td> <td>Plíčky</td> <td>V03</td> <td><a href="https://data.neotomadb.org/54870">54870</a></td> </tr> <tr> <td>Vepřová-Žlábek</td> <td>V07</td> <td><a href="https://data.neotomadb.org/54873">54873</a></td> <td>Louky u Černého lesa</td> <td>V04</td> <td><a href="https://data.neotomadb.org/54871">54871</a></td> </tr> <tr> <td>Stropnická cesta</td> <td>V18</td> <td><a href="https://data.neotomadb.org/54882">54882</a></td> <td>Suché Kopce</td> <td>V10</td> <td><a href="https://data.neotomadb.org/54874">54874</a></td> </tr> <tr> <td>Žižkov</td> <td>V19</td> <td><a href="https://data.neotomadb.org/54883">54883</a></td> <td>Pihoviny</td> <td>V11</td> <td><a href="https://data.neotomadb.org/54875">54875</a></td> </tr> <tr> <td>Chlum</td> <td>V21</td> <td><a href="https://data.neotomadb.org/54885">54885</a></td> <td>Kocanda</td> <td>V12</td> <td><a href="https://data.neotomadb.org/54876">54876</a></td> </tr> <tr> <td>Míšek</td> <td>V22</td> <td><a href="https://data.neotomadb.org/54886">54886</a></td> <td>Porostliny</td> <td>V13</td> <td><a href="https://data.neotomadb.org/54877">54877</a></td> </tr> <tr> <td>Knížecí studánka</td> <td>V23</td> <td><a href="http://data.neotomadb.org/54887">54887</a></td> <td>Bahna</td> <td>V14</td> <td><a href="https://data.neotomadb.org/54878">54878</a></td> </tr> <tr> <td>Pod Šindelným vrchem</td> <td>V24</td> <td><a href="https://data.neotomadb.org/54888">54888</a></td> <td>Ratajské rybníky</td> <td>V15</td> <td><a href="https://data.neotomadb.org/54879">54879</a></td> </tr> <tr> <td>Rampoltův mlýn</td> <td>V25</td> <td><a href="https://data.neotomadb.org/54889">54889</a></td> <td>Zubří</td> <td>V16</td> <td><a href="https://data.neotomadb.org/54880">54880</a></td> </tr> <tr> <td>Brožova skála</td> <td>V26</td> <td><a href="https://data.neotomadb.org/54890">54890</a></td> <td>Nový Rybník</td> <td>V17</td> <td><a href="https://data.neotomadb.org/54881">54881</a></td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>Samotín</td> <td>V20</td> <td><a href="https://data.neotomadb.org/54884">54884</a></td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>species-rich region White-Carpathians Mountains (Bílé Karpaty)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>BK1</td> <td><a href="https://data.neotomadb.org/54770">54770</a></td> <td>BK2</td> <td><a href="https://data.neotomadb.org/54771">54771</a></td> </tr> <tr> <td>BK3</td> <td><a href="https://data.neotomadb.org/54772">54772</a></td> <td>BK4</td> <td><a href="https://data.neotomadb.org/54773">54773</a></td> </tr> <tr> <td>BK5</td> <td><a href="https://data.neotomadb.org/54774">54774</a></td> <td>BK6</td> <td><a href="https://data.neotomadb.org/54775">54775</a></td> </tr> <tr> <td>BK9</td> <td><a href="https://data.neotomadb.org/54777">54777</a></td> <td>BK8</td> <td><a href="https://data.neotomadb.org/54776">54776</a></td> </tr> <tr> <td>BK11</td> <td><a href="https://data.neotomadb.org/54779">54779</a></td> <td>BK10</td> <td><a href="https://data.neotomadb.org/54778">54778</a></td> </tr> <tr> <td>BK13</td> <td><a href="https://data.neotomadb.org/54781">54781</a></td> <td>BK12</td> <td><a href="https://data.neotomadb.org/54780">54780</a></td> </tr> <tr> <td>BK15</td> <td><a href="https://data.neotomadb.org/54783">54783</a></td> <td>BK14</td> <td><a href="https://data.neotomadb.org/54782">54782</a></td> </tr> <tr> <td>BK16</td> <td><a href="https://data.neotomadb.org/54784">54784</a></td> <td>BK20</td> <td><a href="https://data.neotomadb.org/54788">54788</a></td> </tr> <tr> <td>BK17</td> <td><a href="https://data.neotomadb.org/54785">54785</a></td> <td>BK23</td> <td><a href="https://data.neotomadb.org/54791">54791</a></td> </tr> <tr> <td>BK18</td> <td><a href="https://data.neotomadb.org/54786">54786</a></td> <td>BK25</td> <td><a href="https://data.neotomadb.org/54793">54793</a></td> </tr> <tr> <td>BK19</td> <td><a href="https://data.neotomadb.org/54787">54787</a></td> <td>BK27</td> <td><a href="https://data.neotomadb.org/54795">54795</a></td> </tr> <tr> <td>BK21</td> <td><a href="https://data.neotomadb.org/54789">54789</a></td> <td>BK29</td> <td><a href="https://data.neotomadb.org/54797">54797</a></td> </tr> <tr> <td>BK22</td> <td><a href="https://data.neotomadb.org/54790">54790</a></td> <td>BK31</td> <td><a href="https://data.neotomadb.org/54799">54799</a></td> </tr> <tr> <td>BK24</td> <td><a href="https://data.neotomadb.org/54792">54792</a></td> <td>BK33</td> <td><a href="https://data.neotomadb.org/54801">54801</a></td> </tr> <tr> <td>BK26</td> <td><a href="https://data.neotomadb.org/54794">54794</a></td> <td>BK35</td> <td><a href="https://data.neotomadb.org/54803">54803</a></td> </tr> <tr> <td>BK28</td> <td><a href="https://data.neotomadb.org/54796">54796</a></td> <td>BK36</td> <td><a href="https://data.neotomadb.org/54804">54804</a></td> </tr> <tr> <td>BK30</td> <td><a href="https://data.neotomadb.org/54798">54798</a></td> <td>BK38</td> <td><a href="https://data.neotomadb.org/54805">54805</a></td> </tr> <tr> <td>BK32</td> <td><a href="https://data.neotomadb.org/54800">54800</a></td> <td>BK39</td> <td><a href="https://data.neotomadb.org/54806">54806</a></td> </tr> <tr> <td>BK34</td> <td><a href="https://data.neotomadb.org/54802">54802</a></td> <td>BK40</td> <td><a href="https://data.neotomadb.org/54807">54807</a></td> </tr> <tr> <td> </td> <td> </td> <td>BK41</td> <td><a href="https://data.neotomadb.org/54808">54808</a></td> </tr> </tbody> </table> <p> </p>
Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
<p>Unraveling the intricate mechanisms that govern community coexistence remains a daunting challenge, particularly amidst ongoing environmental change. To understand the response of individual animals to environmental change, physiology and individual metabolism are often studied. However, this perspective is currently largely lacking in community ecology. We argue that the integration of individual metabolism into community theory can offer new insights into coexistence. We present the first individual-based metabolic community model for a terrestrial mammal community to simulate energy dynamics and home range behavior in different environments. Using this model, we investigate how ecologically similar species coexist and maintain their energy balance under food competition. Only if individuals of different species are able to balance their incoming and outgoing energy over the long-term will they be able to coexist. After thoroughly testing and validating the model against real-world patterns such as of home range dynamics and field metabolic rates, we applied it as a case study to scenarios of habitat fragmentation - a widely discussed topic in biodiversity research. First, comparing single-species simulations with community simulations, we find that the effect of habitat fragmentation on populations is strongly context-dependent. While populations of species living alone in the landscape were mostly positively affected by fragmentation, the diversity of a community of species was highest under medium fragmentation scenarios. Under medium fragmentation, energy balance and reproductive investment were also most similar among species. We therefore suggest that similarity in energy balance among species promotes coexistence. We argue that energetics should be part of community ecology theory, as the relative energetic status and reproductive investment can reveal why and under what environmental conditions coexistence is likely to occur. As a result, landscapes can potentially be protected and designed to maximize coexistence. The metabolic community model presented here can be a promising tool to investigate other scenarios of environmental change or other species communities to further disentangle global change effects and preserve biodiversity.</p>
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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)
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.