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727 results for “phylogenetic diversity”
FIGURE 9 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 9. Non-stationary associations between ecological diversity (ED) and standard deviation in altitude (ALTstd). The maps show the spatial variation in local beta coefficients (b) for ALTstd as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equalarea projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 5 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 5. Non-stationary associations between ecological diversity (ED) and net primary productivity (NPP). The maps show the spatial variation in local beta coefficients (b) for NPP as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 4 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 4. Non-stationary associations between ecological diversity (ED) and mean annual temperature (TEMP). The maps show the spatial variation in local beta coefficients (b) for TEMP as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of tropics in the Northern and Southern Hemispheres.
FIGURE 8 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 8. Non-stationary associations between ecological diversity (ED) and coefficient of variation in annual precipitation (PRECcv). The maps show the spatial variation in local beta coefficients (b) for PRECcv as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 7 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 7. Non-stationary associations between ecological diversity (ED) and annual range in temperature (TEMPr). The maps show the spatial variation in local beta coefficients (b) for TEMPr as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equalarea projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 1 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 1. Spatial patterns of variation in the ecological diversity (ED) of different mammal groups over the Americas. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 14 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 14. Non-stationary associations between phylogenetic diversity (AvPD) and coefficient of variation in annual precipitation (PRECcv). The maps show the spatial variation in local beta coefficients (b) for PRECcv as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 6 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 6. Non-stationary associations between ecological diversity (ED) and annual precipitation (PREC). The maps show the spatial variation in local beta coefficients (b) for PREC as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of tropics in the Northern and Southern Hemispheres.
FIGURE 13 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 13. Non-stationary associations between phylogenetic diversity (AvPD) and annual range in temperature (TEMPr). The maps show the spatial variation in local beta coefficients (b) for TEMPr as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 12 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 12. Non-stationary associations between phylogenetic diversity (AvPD) and annual precipitation (PREC). The maps show the spatial variation in local beta coefficients (b) for PREC as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 10 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 10. Non-stationary associations between phylogenetic diversity (AvPD) and mean annual temperature (TEMP). The maps show the spatial variation in local beta coefficients (b) for TEMP as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 2 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 2. Spatial patterns of variation in the phylogenetic diversity (AvPD) of different mammal groups over the Americas. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 3 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 3. Non-stationary associations between ecological diversity (ED) and phylogenetic diversity (AvPD). The maps show the local beta coefficients (b) for AvPD as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Following (Matthews & Yang 2012) non-significant values (p> 0.05) are excluded from the maps to optimize the visualization of patterns. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
Data from: Individual and interactive effects of chronic anthropogenic disturbance and rainfall on taxonomic, functional and phylogenetic composition and diversity of extrafloral nectary-bearing plants in Brazilian Caatinga
<p>Chronic anthropogenic disturbance (CAD) and climate change represent two of the major threats to biodiversity globally, but their combined effects are not well understood. Here we investigate the individual and interactive effects of increasing CAD and decreasing rainfall on the composition and taxonomic (TD), functional (FD) and phylogenetic diversity (PD) of plants possessing extrafloral nectaries (EFNs) in semi-arid Brazilian Caatinga. EFNs attract ants that protect plants against insect herbivore attack and are extremely prevalent in the Caatinga flora. EFN-bearing plants were censused along gradients of disturbance and rainfall in Catimbau National Park in north-eastern Brazil. We recorded a total of 2,243 individuals belonging to 21 species. Taxonomic and functional composition varied along the rainfall gradient, but not along the disturbance gradient. There was a significant interaction between increasing disturbance and decreasing rainfall, with CAD leading to decreased TD, FD and PD in the most arid areas, and to increased TD, FD and PD in the wettest areas. We found a strong phylogenetic signal in the EFN traits we analysed, which explains the strong matching between patterns of FD and PD along the environmental gradients. The interactive effects of disturbance and rainfall revealed by our study indicate that the decreased rainfall forecast for Caatinga under climate change will increase the sensitivity of EFN-bearing plants to anthropogenic disturbance. This has important implications for the availability of a key food resource, which would likely have cascading effects on higher trophic levels.</p>
Assessing taxonomic, functional, and phylogenetic diversity of giant clams across the Indo-Pacific for conservation prioritisation
<p>Datasets and R scripts</p>
Data from: Crop health is predicted by soil microbial diversity across phylogenetic scales
<p>Soils contain diverse living communities that provide key ecosystem functions in agroecosystems. In many systems, ecosystems functions are positively related to the taxonomic, phylogenetic, and functional diversity of the community. Despite calls to incorporate microbial diversity in measures of soil health, whether increased microbial diversity <em>per se</em> can predict increased crop health and productivity has rarely been documented. Here we used microbial communities from commercial potato fields varying in diversity and composition, and experimentally assessed their ability to promote crop yield under low or high nutrient conditions and to suppress a soil-borne pathogen. Across two independent sets of communities, we found that yields under low nutrient conditions were predicted by high initial microbial diversity measured at broad phylogenetic levels, consistent with greater niche complementarity among unrelated taxa leading to greater total resource use. However, disease suppression was inconsistently linked to diversity and explained as well or better by microbial composition rather than diversity <em>per se</em>. Ecosystem multifunctionality was predicted by high diversity at broad to intermediate phylogenetic scales. These results indicate that the diversity of microbial taxa may influence multiple soil functions; however, the mechanisms underlying the diversity-function relationships may vary.</p>
Figure 2. Calibrated phylogenetic tree obtained with BEAST v.1.10.4 in Cryptic lineages, cryptic barriers: historical seascapes and oceanic fronts drive genetic diversity in supralittoral rockpool beetles (Coleoptera: Hydraenidae)
Figure 2. Calibrated phylogenetic tree obtained with BEAST v.1.10.4 of Ochthebius with focus on subgenus Cobalius (purple shade) and quadricollis species group (green shade) (former subgenus 'Calobius'). Numbers at nodes represent posterior probabilities, and 95% highest posterior density are given in blue horizontal rectangles. Calibrations points used in analysis are specified by grey dots.
Increases in Species Richness Lead to Decreases in Phylogenetic Diversity in Mediterranean Species Assemblages
<p><span>The relationship between taxonomic and phylogenetic diversity remains underexplored. Our goal was to determine whether a causal link exists between species richness and phylogenetic diversity. We wanted to evaluate whether species richness determines the phylogenetic diversity in realized assemblages (<em>taxonomic determinant hypothesis</em>) or phylogenetic diversity determines the species richness (<em>phylogenetic determinant hypothesis</em>). We also hypothesize that this causal framework could shift in different bioclimatic regions. We sampled over 1700 plant assemblages in grasslands and shrublands across three bioclimatic regions in Navarra, Spain. Using non-recursive structural equation modelling, we found that species richness influences phylogenetic diversity, and </span><span>that this causal relationship remains consistently negative and is unaffected by climate differences among regions</span><span>. Specifically, greater plant richness leads to increased phylogenetic convergence, resulting in reduced phylogenetic diversity. This means that the incorporation of new species into assemblages involves adding closely related species in phylogenetic terms, regardless of the bioclimatic region.</span></p>
Fig. 4 in Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta)
Fig. 4 Phylogenetic tree mapping the larval development site (LDS) (0 = unconcealed, 1 = concealed) for each taxon analyzed in this study. The circle with a picture of a species developing in a concealed site (the apoid wasp Stizus continuus (Crabronidae), emerging from its nest) marks the ancestral state for Apocrita, while the circle with a question mark indicate the unclear ancestral state for Hymenoptera. The histogram in the lower part of the figure show the distribution of cases for a given rank of Zn, for species with either LDS type
Fig. 2 in Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta)
Fig. 2 Phylogenetic tree mapping the ranked Zn % for each taxon analyzed in this study. Zn was ranked as 0 = <0.1 wt%; 1 = 0.1– 1.0 wt%; 2 = 1.0–5.0 wt%; 3 = 5.0–10.0 wt%; and 4 => 10 wt%. Node 1 (in violet) identifies Zn enrichment as ancestral state for Apocrita (proportional likelihoods for Zn %: 0 = 0.0015, 1 = 0.0015, 2 = 0.121, 3 = 0.9785, 4 = 0.0062). Nodes 2 (Proctotrupidae + Pelecinidae) and 3 (Agaonidae) (in pink) identify losses of Zn enrichment in non-Aculeata (proportional likelihoods for Zn %: node 2: 0 = 0.9390, 1 = 0.0036, 2 = 0.0483, 3 = 0.0052, 4 = 0.0037; node 3: 0 = 0.9454, 1 = 0.0038, 2 = 0.0389, 3 = 0.0050, 4 = 0.0068). Nodes 4 (Chrysidoidea), 5 (Formicidae), 6 (Mutillidae + Sapygidae), and 7 (Ampulicidae) (in violet) identify re-acquisitions of Zn enrichment within Aculeata (proportional likelihoods for Zn %: node 4: 0 = 0.0597, 1 = 0.0112, 2 = 0.3890, 3 = 0.5156, 4 = 0.0244; node 5: 0 = 0.2138, 1 = 0.0173, 2 = 0.0174, 3 = 0.2796, 4 = 0.4717; node 6: 0 = 0.3439, 1 = 0.0233, 2 = 0.1286, 3 = 0.1291, 4 = 0.3748; node 7: 4 = 1)
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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
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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.