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114 results for “Local richness”
Data from: Increases in local richness (α-diversity) following invasion are offset by biotic homogenization in a biodiversity hotspot
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Data from: Global fern and lycophyte richness explained: how regional and local factors shape plot richness
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Data from: Species richness-productivity relationships of tropical terrestrial ferns at regional and local scales
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The relative influence of history, climate, topography and vegetation structure on local animal richness varies among taxa and spatial grains
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FIGURE 1 in The genus Manota Williston (Diptera: Mycetophilidae) in Peruvian Amazonia, with description of sixteen new species and notes on local species richness
FIGURE 1. Location of the study area in Allpahuayo-Mishana National Reserve, Peruvian Amazonia.
Local plant richness predicts bee abundance and diversity in a study of urban residential yards
<p>Understanding the drivers of biodiversity in cities is a central goal of urban ecology. There is currently intense scientific and public interest in the factors that influence pollinator diversity in cities and their surroundings. Existing studies point to a variety of landscape and local factors as potentially important, including urbanization (often defined as impervious surface cover in the surrounding lands), tree canopy cover and the diversity and abundance of locally flowering plants. However, few studies have sought to weigh the relative importance of these predictors of bee community metrics. Using a set of 27 residential yards chosen to represent a gradient of both urbanization and tree canopy cover at a landscape scale, we used pan trapping and netting to assess the abundance and diversity of local bee communities across the City of Ottawa, Ontario, Canada. Surprisingly, the landscape factors (urbanization and tree cover) described only a tiny fraction (< 1%) of the total variance in bee abundance and diversity across sites. This was true regardless of the scale of analysis at which the landscape factors were measured. Instead, a yard's floral richness, and, to a somewhat lesser extent, its floral abundance, emerged as the most important predictors of a yard's bee community abundance and diversity. Our study offers an important counterpoint to a growing body of work emphasizing the impacts of landscape factors on bee communities. Instead, our research suggests that improving bee floral resources by increasing the plant species richness and abundance locally is a powerful tool to support bee conservation, regardless of the level of urbanization or tree cover in the surrounding landscape. Our work highlights that the practice of promoting 'bee-friendly' plantings in private yards, currently being undertaken by a number of non-profits around the world, can play an important role in restoring and maintaining urban pollinator communities.</p>
Pollinator richness, pollination networks and diet adjustment along local and landscape gradients of resource diversity
<p>Loss of habitats and native species, introduction of invasive species, and changing climate regimes lead to the homogenization of landscapes and communities, affecting the availability of habitats and resources for economically important guilds, such as pollinators. Understanding how pollinators and their interactions vary along resource diversity gradients at different scales may help to determine their adaptability to current diversity loss related to global change. We used data on 20 plant-pollinator communities along gradients of flower richness (local diversity) and landscape heterogeneity (landscape diversity) to understand how the diversity of resources at local and landscape scales affected: (1) wild pollinator abundance and richness (accounting also for honeybee abundance); (2) the structure of plant-pollinator networks; (3) the proportion of actively selected interactions (those not occurring by neutral processes) and (4) pollinator diet breadth and species' specialization in networks. Wild pollinator abundance was higher overall in flower-rich and heterogeneous habitats, while wild pollinator richness increased with flower richness (more strongly for beetles and wild bees) and decreased with honeybee abundance. Network specialization (H2'), modularity, and functional complementarity were all positively related to floral richness and landscape heterogeneity, indicating niche segregation as the diversity of resources increases at both scales. Flower richness also increased the proportion of actively selected interactions (especially for wild bees and flies), whereas landscape heterogeneity had a weak negative effect on this variable. Overall, network-level metrics responded to larger landscape scales than pollinator-level metrics did. Higher floral richness resulted in a wider taxonomic and functional diet for all the study guilds, while functional diet increased mainly for beetles. Despite this, specialization in networks (d') increased with flower richness for all the study guilds, because pollinator species fed on a narrower subset of plants as communities became richer in species. Our study indicates that pollinators are able to adapt their diet to resource changes at local and landscape scales. However, resource homogenization might lead to poor and generalist pollinator communities, where functionally specialized interactions are lost. This study highlights the importance of including different scales to understand the effects of global change on pollination service through changes in resource diversity.</p>
Text-fig. 8. Mhengere Hill fossiliferous localities (1–3). Silicified tree trunks and fragments of wood are abundant in the poorly indurated basal deposits (marly sand and conglomerate) as well as in the silicified lime-rich sandstones that form the hill, which is interpreted to be the remains of a palaeopan. Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 8. Mhengere Hill fossiliferous localities (1–3). Silicified tree trunks and fragments of wood are abundant in the poorly indurated basal deposits (marly sand and conglomerate) as well as in the silicified lime-rich sandstones that form the hill, which is interpreted to be the remains of a palaeopan. Image modified from Google Earth.
Figure 1 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figure 1 Location of the sampled area within the Alagoas coast. Jaraguá fishing harbor and the fishery sites were indicated.
Figure 2 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figure 2 Rarefaction curves for the samples caught during dry and rain seasons using the bootstrap richness estimator.
Figures 6-9 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figures 6-9 Mean ± sd of the number of fishes, fish total length, species richness, and CPUE by climatic seasons (6); CPUE (mean ± sd) by month (7); precipitation by month (8) and wind strength by month (9).
Figures 3-4 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figures 3-4 Whittaker plot of fishes from Alagoas coast sampled during the rainy season (3) and the dry season (4).
Figure 5 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figure 5 Diversity profile of the dry and rain seasons on the coast of Alagoas. Alpha = 1 Emphasizes the separation of richness and diversity profile.
Figures 3-4 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figures 3-4 - Whittaker plot of fishes from Alagoas coast sampled during the rainy season (3) and the dry season (4).
Figure 2 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figure 2 - Rarefaction curves for the samples caught during dry and rain seasons using the bootstrap richness estimator.
Figure 1 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figure 1 - Location of the sampled area within the Alagoas coast. Jaraguá fishing harbor and the fishery sites were indicated.
Figure 5 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figure 5 - Diversity profile of the dry and rain seasons on the coast of Alagoas. Alpha = 1 Emphasizes the separation of richness and diversity profile.
Figures 6-9 from: Souza CD, Batista VS, Fabré NN (2018) What are the main local drivers determining richness and fishery yields in tropical coastal fish assemblages? Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e12898
Figures 6-9 - Mean ± sd of the number of fishes, fish total length, species richness, and CPUE by climatic seasons (6); CPUE (mean ± sd) by month (7); precipitation by month (8) and wind strength by month (9).
Figure. Map of Georgia with main administrative divisions delimited. Shade intensity indicates the richness of mayfly species for respective administrative unit. Dots indicate localities sampled for mayflies prior to this study. in The first annotated checklist of mayflies (Ephemeroptera: Insecta) of Georgia with new distribution data and a new record for the country
Figure. Map of Georgia with main administrative divisions delimited. Shade intensity indicates the richness of mayfly species for respective administrative unit. Dots indicate localities sampled for mayflies prior to this study.
Figure 6 from: Agnarsson I, Jencik B, Veve G, Hanitriniaina S, Agostini D, Goh S, Pruitt J, Kuntner M (2015) Systematics of the Madagascar Anelosimus spiders: remarkable local richness and endemism, and dual colonization from the Americas. ZooKeys 509: 13-52. https://doi.org/10.3897/zookeys.509.8897
Figure 6 - Anelosimus sallee: A–B palp, ventral, mesal C epigynum, ventral. Anelosimus andasibe: D–E epigynum, ventral, dorsal.
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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.