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504 results for “ecological diversity”
Fig. 5 in Exploring the diversity and ecology of testate amoebae in West Siberian peatlands
Fig. 5. Ordination biplot of RDA of testate amoeba species data showing vector for sample moisture content and centroids for sites (red) and vegetation types (green). Site codes: Gorno-Slinkino1: GS1, Gorno-Slinkino2: GS2, Surgut1: S1, Tobolsk1: T1, Tobolsk2: T2, Tobolsk3: T3, Tobolsk4: T4, Urengoi1: U1, Urengoi2: U2, Urengoi3: U3, Vinokurova1: V1, Vinokurova2: V2, Zapolyarnyi1: Z1.
Fig. 4 in Exploring the diversity and ecology of testate amoebae in West Siberian peatlands
Fig. 4. NMDS ordination of testate amoeba community data based on Bray-Curtis distance. Showing assemblages by site with symbol size proportional to moisture content of sample. Plot A shows data based on relative abundance (percentage) while Plot B shows data based on absolute abundance (concentration).
Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.
<p>Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.:</p> <p><br> - Trait-based distances calculated for each pair of taxa;</p> <p>- CWM, CWM(LN) values, and their difference (CWMDIFF)</p> <p>Refer to the published articles for further details.</p>
Fig. 3 in Ecological Analysis Of Butterflies And Day-Flying Moths Diversity Of The Gouraya National Park (Algeria)
Fig. 3. Projection of the butterfly and day-flying moth species of the three stations studied on the first two axes of the correspondence factor analysis.
Fig. 2 in Ecological Analysis Of Butterflies And Day-Flying Moths Diversity Of The Gouraya National Park (Algeria)
Fig. 2. Evolution of the species richness of butterflies and day-flying moths at the three stations of Gouraya National Park, mean temperature in Bejaia (DAAE, 2012).
Vegetation and vantage point influence visibility across diverse ecosystems: implications for animal ecology
<p class="MsoNormal"><span>Visual information can influence animal behavior and habitat use in diverse ways. Visibility is the property that relates 3D habitat structure to accessibility of visual information. Despite the importance of visibility in animal ecology, this property remains largely unstudied. Our objective was to assess how habitat structure from diverse environments and animal position within that structure can influence visibility. We gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest, shrub-steppe, prairie, and desert) to characterize viewsheds (i.e., estimates of visibility based on spatially explicit sightlines) from multiple vantage points. Both ecosystem-specific structure and animal position influenced potential viewsheds. Generally, as height of the vantage point above the ground increased, viewshed extent also increased, but the relationships were not linear.<span> </span>In low-structure ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds decreased as vantage points increased to heights above the vegetation canopy. In the forest, however, variation in viewsheds was highest at intermediate heights, and markedly lower at the lowest and highest vantage points. These patterns are likely linked to the amount, heterogeneity, and distribution of vegetation structure occluding sightlines. Our work is the first to apply a new method that can be used to estimate viewshed properties relevant to animals (i.e., viewshed extent and variability). We demonstrate that these properties differ across terrestrial landscapes in complex ways that likely influence many facets of animal ecology and behavior.<span> </span></span></p>
Frontiers in Ecology and Evolution 01 frontiersin.org Why grazing and soil matter for dry grassland diversity: New insights from multigroup structural equation modeling of micro-patterns
<p>Grazing is recognized as a major process driving the composition of plant<br> communities in grasslands, mostly due to the heterogeneous removal of<br> plant species and soil compaction that results in a mosaic of small patches<br> called micro-patterns. To date, no study has investigated the differences in<br> composition and functioning among these micro-patterns in grasslands in<br> relation to grazing and soil environmental variables at the micro-local scale.<br> In this study, we ask (1) To what extent are micro-patterns different from each<br> other in terms of species composition, species richness, vegetation volume,<br> evenness, and functioning? and (2) based on multigroup structural equation<br> modeling, are those differences directly or indirectly driven by grazing and soil<br> characteristics? We focused on three micro-patterns of the Mediterranean dry<br> grassland of the Crau area, a protected area traditionally grazed in the South-<br> East of France. From 70 plant community relevés carried out in three micro-<br> patterns located in four sites with different soil and grazing characteristics,<br> we performed univariate, multivariate analyses and applied structural equation<br> modeling for the first time to this type of data. Our results show evidence<br> of clear differences among micro-pattern patches in terms of species<br> composition, vegetation volume, species richness, evenness, and functioning<br> at the micro-local scale. These differences are maintained not only by direct<br> and indirect effects of grazing but also by several soil variables such as fine<br> granulometry. Biological crusts appeared mostly driven by these soil variables,<br> whereas reference and edge communities are mostly the result of different<br> levels of grazing pressure revealing three distinct functioning specific to each<br> micro-pattern, all of them coexisting at the micro-local scale in the studied<br> Mediterranean dry grassland. This first overview of the multiple effects of<br> grazing and soil characteristics on communities in micro-patterns is discussed<br> within the scope of the conservation of dry grasslands plant diversity.</p>
Diagramms and Figures for a Bachelor Thesis: "Morphological Diversity of Bryophytes: Methodological Approaches and Ecological Implications"
<p>This depository holds all diagramms and figures that were created with the collected data used in the Bachelor's Thesis using R Version 4.4.0 or Python Version 3.12.4. <br><br></p>
Fig. 2 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 2. The Rarity and Ecological Diversity (RED)-index of the different aquatic habitats (aquatic habitats with the same letter are not significantly different at p = 0.05 by non-parametric Tukey-test)
Fig. 1 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 1. The map of Hungary with the position of the sampling sites (filled squares show light traps)
Fig. 3 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 3. The diversity (A) and RAR-index (B) of the different aquatic habitats (aquatic habitats with the same letter are not significantly different at p = 0.05 by non-parametric Tukey-test)
FIGURE 4 in What are the best modern analogs for ancient South American mammal communities? Evidence from ecological diversity analysis (EDA)
FIGURE 4. Linear regression of MAP on correspondence axis 1 (CA1) score; estimated MAP for each of fossil locality based on CA1 score is indicated. Abbreviations: LV, La Venta; QH, Quebrada Honda; RU, Rümikon; SC, Santa Cruz; TG, Tinguiririca.
FIGURE 6. Classification Tree results and predictions for the five fossil localities. A in What are the best modern analogs for ancient South American mammal communities? Evidence from ecological diversity analysis (EDA)
FIGURE 6. Classification Tree results and predictions for the five fossil localities. A) Results and predictions for CT1, vegetative cover. B) Results and predictions for CT2, biogeographic realm. Abbreviations: LV, La Venta; QH, Quebrada Honda; RU, Rümikon; SC, Santa Cruz; TG, Tinguiririca.
FIGURE 3 in What are the best modern analogs for ancient South American mammal communities? Evidence from ecological diversity analysis (EDA)
FIGURE 3. Axes three and four of the correspondence analysis. A) Positions of the fossil localities and 179 modern ecoregions; B) Positions of the 22 variables. Note that the scale is not the same in the two graphs.
FIGURE 2 in What are the best modern analogs for ancient South American mammal communities? Evidence from ecological diversity analysis (EDA)
FIGURE 2. Axes one and two of the correspondence analysis. A) Positions of the fossil localities and 179 modern ecoregions; B) Positions of the 22 variables. Note that the scale is not the same in the two graphs.
FIGURE 1 in What are the best modern analogs for ancient South American mammal communities? Evidence from ecological diversity analysis (EDA)
FIGURE 1. Locations of the 179 modern ecoregions (colored areas) and the five fossil localities (stars) used in this study. Ecoregions are overlain on a grayscale global mean annual precipitation (MAP) map derived from Fick and Hijmans (2017), with lighter regions indicating areas of higher MAP and darker regions indicating areas of lower MAP. Abbreviations: LV, La Venta, Colombia; QH, Quebrada Honda, Bolivia; RU, Rümikon, Switzerland; SC, Santa Cruz, Argentina; TG, Tinguiririca, Chile.
FIGURE 7 in What are the best modern analogs for ancient South American mammal communities? Evidence from ecological diversity analysis (EDA)
FIGURE 7. Classification Tree results for CT3, biome. Abbreviations: LV, La Venta; QH, Quebrada Honda; RU, Rümikon; SC, Santa Cruz; TG, Tinguiririca.
FIGURE 5 in What are the best modern analogs for ancient South American mammal communities? Evidence from ecological diversity analysis (EDA)
FIGURE 5. Composite hierarchical cluster analysis with the positions of the five fossil localities (dagger symbols) indicated. Solid black lines indicate the results when all five fossil localities are included in the analysis; dashed black lines indicate the results when each fossil locality is analyzed individually. The position of Rümikon did not vary in the two analysis. Abbreviations: LV, La Venta; QH, Quebrada Honda; RU, Rümikon; SC, Santa Cruz; TG, Tinguiririca.
Figure 1 in New data on pond snails (Mollusca: Gastropoda: Lymnaeidae) inhabiting the Ukrainian Transcarpathian: diversity, distribution and ecology
Figure 1. Map showing the localities of samples studied. Details for each sampling point are given in Table 1.
Рис. 2. ЭкоΛого-географическая характеристика зоопΛанктона техногенных воΑоемов Fig. 2. Ecological and geographical characteristics of zooplankton in technogenic reservoirs in Zooplankton species diversity in technogenic reservoirs of the Southeastern Transbaikalia
Рис. 2. ЭкоΛого-географическая характеристика зоопΛанктона техногенных воΑоемов Fig. 2. Ecological and geographical characteristics of zooplankton in technogenic reservoirs
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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.