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193 results for “diversity dynamics”
Data for: Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed
<p>These are the data used in the analyses described in the paper titled "Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed", accepted at Estuaries and Coasts. We acknowledge the tangata whenua for the rohe in which these data were collected, Ngāi Tārewa and Ngāti Īrakehu. We thank the Akaroa Taiāpure for their support of this research.</p> <p>The data included are:</p> <p>Raw count data of taxa for each tow, associated with additional metadata including the date of collection, tow coordinates, and estimated seagrass cover (MonthlyRawSampling_Duvauchelle_2020.csv). This data was put through cleaning steps outlined in the file docs/dataCleaning.Rmd prior to being used in any analyses.</p> <p>The cleaned community composition data (cleanedCommunity.csv), output from <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a> and used in the downstream community and population analyses.</p> <p>The GPS coordinates for the tows (gpsdat.csv). These were extracted from the raw data in the data cleaning process.</p> <p>NZsyngnathids_measurements.csv contains the measurements of the pipefish from images. These data also underwent a cleaning process documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a>.</p> <p>The cleaned pipefish trait data (pipefishTraits.csv), output from docs/dataCleaning.Rmd and used in the downstream population analysis documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/populationAnalyses.Rmd">docs/populationAnalyses.Rmd</a>.<br> </p>
Supplementary material 1: Elevation data of FSN Dynamics Plot from: Tree Diversity and Dynamics of the Forest of Seu Nico, Viçosa, Minas Gerais, Brazil - Biodiversity Data Journal 3: e5425 (31 July 2015) https://doi.org/10.3897/BDJ.3.e5425
X, Y and elevation values of all vertices from 100 subplots from the one hectar FSN Dynamics Plot relative to starting point forming the northwestern vertex
Equilibrium Dynamics Shape Diversity Patterns Across Terrestrial Tetrapod Clades
<p>This repository contains all scripts, data, and documentation supporting the analyses in our study. The materials are organized into folders corresponding to specific steps of the workflow. This README provides a detailed guide to the structure, contents, and usage of each folder.</p> <h2>Folder Structure and Contents</h2> <h3>1. Environmental Variables (<code>Grid_level_environment</code>)</h3> <ul> <li> <p>Contains grid-cell level environmental variables in <code>environmental_data.rds</code>.</p> <ul> <li> <p>Includes <strong>temperature</strong> (°C), <strong>precipitation</strong> (mm), and <strong>net primary productivity (NPP)</strong>.</p> </li> <li> <p>Includes <strong>grid cell IDs</strong> and <strong>latitude/longitude coordinates</strong>.</p> </li> </ul> </li> </ul> <h3>2. Evolutionary Rates Across Species and Grid Cells (<code>Grid_level_speciation</code>)</h3> <ul> <li> <p>Contains present-day <strong>speciation rate estimates</strong> for each species.</p> <ul> <li> <p>Includes <strong>DR</strong>, <strong>BAMM</strong>, and <strong>ClaDS</strong> estimates.</p> </li> <li> <p>Maps each species’ speciation rate to its corresponding grid cells.</p> </li> </ul> </li> </ul> <h3>3. Evolutionary Time Across Grid Cells (<code>Grid_level_assemblage_age</code>)</h3> <ul> <li> <p>Contains files used for <strong>BioGeoBEARS DEC model integration</strong> at the grid-cell level for each tetrapod clade (amphibians, reptiles, birds, mammals).</p> </li> <li> <p>Each clade is organized in a separate folder with the following files:</p> <ul> <li> <p><strong>Assemblage age:</strong> <code>arrival_time_clade*.csv</code></p> </li> <li> <p><strong>Most likely biogeographic areas per grid cell:</strong> <code>clade*_biogeo_area.csv</code></p> </li> <li> <p><strong>Presence/absence matrix:</strong> <code>clade*_PAM.csv</code></p> </li> <li> <p><strong>Clade phylogenetic tree:</strong> <code>clade*_tree.tre</code></p> </li> <li> <p><strong>DEC area file:</strong> <code>geo_area_clade*.data</code></p> </li> <li> <p><strong>DEC outputs:</strong> <code>results_DEC_clade*.Rdata</code></p> </li> <li> <p><strong>Estimated geographic area plots across the phylogeny:</strong> <code>DEC_plot_clade*.pdf</code></p> </li> </ul> </li> </ul> <h3>4. Path Analysis Example (<code>Path_model</code>)</h3> <ul> <li> <p>Contains an example R script: <code>path_analysis_clades.R</code>.</p> <ul> <li> <p>Illustrates <strong>path models</strong> applied to tetrapod clades.</p> </li> <li> <p>Example uses <strong>mammalian clades</strong>; replace the dataset to run on other clades.</p> </li> </ul> </li> </ul> <h3>5. Path Analysis Outputs (<code>Path_outputs_&_clade_traits</code>)</h3> <ul> <li> <p>Contains outputs from <strong>path analyses</strong> for each tetrapod clade.</p> <ul> <li> <p><code>Path_all_effects.csv</code> consolidates all path outputs and includes <strong>clade-level traits</strong> (see Methods in the main paper).</p> </li> <li> <p>Other files include:</p> <ul> <li> <p><code>Path_direct_effects.csv</code> – direct effects across clades</p> </li> <li> <p><code>Path_indirect_via_productivity.csv</code> – indirect effects via productivity</p> </li> <li> <p><code>Path_indirect_via_speciation.csv</code> – indirect effects via speciation</p> </li> <li> <p><code>Path_indirect_via_time.csv</code> – indirect effects via evolutionary time</p> </li> </ul> </li> </ul> </li> </ul> <h3>6. Path Output Figures (<code>Path_outputs_figures</code>)</h3> <ul> <li> <p>Contains R scripts to <strong>visualize path model outputs</strong> across tetrapod clades:</p> <ul> <li> <p>Direct effects: <code>Path_direct_effects.R</code></p> </li> <li> <p>Indirect effects: <code>Path_indirect_via_productivity.R</code>, <code>Path_indirect_via_speciation.R</code>, <code>Path_indirect_via_time.R</code></p> </li> <li> <p>Total effects: <code>Path_total_effects.R</code></p> </li> </ul> </li> </ul> <h3>7. Clade-Level Trait Effects on Richness (<code>Clade_level_effects.R</code>)</h3> <ul> <li> <p>Contains the R script <code>Clade_level_effects.R</code>.</p> </li> <li> <p>Explores whether <strong>the effects of the tested predictors on species richness depend on clade-level traits</strong>, including:</p> <ul> <li> <p><strong>Physiological traits:</strong> endothermy vs ectothermy</p> </li> <li> <p><strong>Spatial–historical traits:</strong> climate origin, range size, centroid displacement, displacement rate</p> </li> <li> <p><strong>Temporal/size-related traits:</strong> clade age, species richness</p> </li> </ul> </li> </ul>
Fig. 1 in The Composition, Diversity And Community Dynamics Of Limnetic Zooplankton In A Tropical Caldera Lake (Lake Taal, Philippines)
Fig. 1. Map of Lake Taal with the six sampling sites (NB – North Basin, SB – South Basin). The insert shows the location of Lake Taal and the other lakes mentioned in the text (P – Lake Paoay, Lb – Lake Laguna de Bay, N – Lake Naujan and Ln – Lake Lanao).
Fig. 4 in The Composition, Diversity And Community Dynamics Of Limnetic Zooplankton In A Tropical Caldera Lake (Lake Taal, Philippines)
Fig. 4. Monthly variations in Shannon-Wiener Diversity (H') Index values of rotifers and cladocerans in the north and south basins of Lake Taal.
Fig. 3 in The Composition, Diversity And Community Dynamics Of Limnetic Zooplankton In A Tropical Caldera Lake (Lake Taal, Philippines)
Fig. 3. Mean monthly biomass (μg / l) of common zooplankton species from the north and south basins of Lake Taal for the year 2008.
Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna
<p>The dataset associated with the manuscript "Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna" (Teixeira et al.) includes 6 different datasets, for which we provided one metadata.<br> </p> <p><strong>Version 2</strong> includes an update of the biomass data set, including the correct transformation to g/m2 on fine roots biomass data.<br><br><strong>Version 3 </strong>includes an update of the belowground traits data set based on correcting formatting errors in the belowground traits data. <br><br><strong>Version 4 </strong>Sorry for the inconvenience. This version includes the correct updated belowground traits data file based on the correct formatting errors in the belowground trait data. <br><br>fluxes: it includes data related to net ecosystem C and water exchange. NEE and ET from each plot were measured using the LiCOR 7500 infrared gas analyzer (Li-Cor Inc.). See the method section in the manuscript for full details.</p> <p>soil_carbon: it includes carbon soil data.<br><br>biomass_v2: it includes data related to aboveground and belowground biomass. Aboveground data were collected in 0.5m2 subplot and belowground at 0.25m2 at 20cm depth both within 1m2 sampling plot. See the method section in the manuscript for full details.</p> <p>aboveground_traits: all aboveground functional traits from plant species. See the method section in the manuscript for full details.</p> <p>belowground_traitsv3: all roots functional traits from plant species. See the method section in the manuscript for full details.</p> <p>species_composition: plant community composition. See the method section in the manuscript for full details.</p> <p><br><strong>Abstract</strong><br>Fire is an evolutionary environmental filter in tropical savanna ecosystems altering functional diversity and associated C pools in the biosphere and fluxes between the atmosphere and biosphere. Therefore, alterations in fire regimes (e.g. fire exclusion) will strongly influence ecosystem processes and associated dynamics. In those ecosystems, C dynamics and functions are underestimated by the fire-induced offset between C output and input. To determine how fire shapes ecosystem C pools and fluxes in an open savanna across recently burned and fire excluded areas, we measured the following metrics: (I) plant diversity including taxonomic (i.e. richness, evenness) and plant functional diversity (i.e. functional diversity, functional richness, functional dispersion and community weighted means); (II) structure (i.e. above- and below-ground biomass, litter accumulation); and (III) functions related to C balance (i.e. net ecosystem carbon dioxide (CO<sub>2</sub>)<sub> </sub>exchange (NEE), ecosystem transpiration (ET), soil respiration (soil CO<sub>2</sub> efflux), ecosystem water use efficiency (eWUE) and total soil organic C (SOC). We found that fire promoted aboveground live and belowground biomass, including belowground organs, and coarse and fine root biomass, and contributed to higher biomass allocation belowground. Fire also increased both functional diversity and dispersion. NEE and total SOC were higher in burned plots compared to fire-excluded plots whereas soil respiration recorded lower values in burned areas. Both ET and eWUE were not affected by fire. Fire strongly favored functional diversity, fine root, and belowground organ biomass in piecewise SEM models but the role of both functional diversity and ecosystem structure to mediate the effect of fire on ecosystem functions remain unclear. Fire regime will impact C balance, and fire exclusion may lead to lower C input in open savanna ecosystems.</p>
Data from: Diversity dynamics of microfossils from the Cretaceous to the Neogene show mixed responses to events
<p>Microfossils have a ubiquitous and well-studied fossil record with temporally and spatially fluctuating diversity, but how this arises and how major events affect speciation and extinction is uncertain. We present the first application of PyRate to a micropaleontological global occurrence data set, reconstructing diversification rates within a Bayesian framework from the Mesozoic to the Recent in four microfossil groups: planktic foraminiferans, calcareous nannofossils, radiolarians and diatoms. Calcareous and siliceous groups demonstrate opposed, but inconsistent, responses in diversification. Siliceous groups increased origination from ~104 Ma, maintaining high rates into the Cenozoic. Calcareous microfossils diversification rates significantly decline across the Cretaceous–Paleogene boundary, while rates in siliceous microfossil groups remain stable until the Paleocene–Eocene transition. Diversification rates in the Cenozoic are largely stable in calcareous groups, whereas the Paleogene is a turbulent time for diatoms. Diversification fluctuations are driven by climate change and fluctuations in sea surface temperatures, promoting selectivity in both microfossil composition and foraminiferal size. Extinctions appear induced by changes in anoxia, acidification, and stratification, while speciation tends to be associated with upwelling, productivity, and ocean circulation. These results show promise for further quantitative analyses in micropaleontological diversity studies and effects of major transitions in the fossil record. Despite extensive occurrence data, regional diversification events were not recovered, neither were some global events. These unexpected results show the need to consider multiple spatiotemporal levels of diversity and diversification analyses, and implies occurrence data sets of different clades may be more appropriate to testing some hypotheses than others.</p>
The evolution, complexity and diversity of models of long-term forest dynamics
<p><span>1. To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies.</span></p> <p><span>2. We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis.</span></p> <p><span>3. Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes.</span></p> <p><span>4. Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the 'prevailing spirit of the epoch'. The lack of firm theories, laws and 'first principles' in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation.</span></p> <p><span>5. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity – with the exception of global models – can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.</span></p>
Data from: Functional diversity buffers the effects of a pulse perturbation on the dynamics of tritrophic food webs
<p>Biodiversity decline causes a loss of functional diversity, which threatens ecosystems through a dangerous feedback loop: this loss may hamper ecosystems' ability to buffer environmental changes, leading to further biodiversity losses. In this context, the increasing frequency of human-induced excessive loading of nutrients causes major problems in aquatic systems. Previous studies investigating how functional diversity influences the response of food webs to disturbances have mainly considered systems with at most two functionally diverse trophic levels. We investigated the effects of functional diversity on the robustness, i.e. resistance, resilience and elasticity, using a tritrophic ---and thus more realistic---plankton food web model. We compared a non-adaptive food chain with no diversity within the individual trophic levels to a more diverse food web with three adaptive trophic levels. The species fitness differences were balanced through trade-offs between defense/growth rate for prey and selectivity/half-saturation constant for predators. We showed that the resistance, resilience and elasticity of tritrophic food webs decreased with larger perturbation sizes and depended on the state of the system when the perturbation occurred. Importantly, we found that a more diverse food web was generally more resistant and resilient but its elasticity was context-dependent. Particularly, functional diversity reduced the probability of a regime shift towards a non-desirable alternative state. The basal-intermediate interaction consistently determined the robustness against a nutrient pulse despite the complex influence of the shape and type of the dynamical attractors. This relationship was strongly influenced by the diversity present and the third trophic level. Overall, using a food web model of realistic complexity, this study confirms the destructive potential of the positive feedback loop between biodiversity loss and robustness, by uncovering mechanisms leading to a decrease in resistance, resilience and potentially elasticity as functional diversity declines.</p>
Figure 2 in Surveillance of population dynamics and breeding habitat diversity of Anopheles subpictus in different areas of Odisha, East Central India
Figure 2. House Index of Anopheles subpictus of different localities under study. (Buguda, Ballipadar, Aska, Bhetanai, Bhanjanagar and Baunsalundi).
Figure 3 in Surveillance of population dynamics and breeding habitat diversity of Anopheles subpictus in different areas of Odisha, East Central India
Figure 3. Container Index of Anopheles subpictus of different localities under study. (Buguda, Ballipadar, Aska, Bhetanai, Bhanjanagar and Baunsalundi)
Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor
<p>Molecular dynamics simulations of AF1c region of human glucocorticoid receptor and its phosphovariants as described in the below paper: </p> <p>Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor</p> <p>Vasily Akulov, Alba Jiménez Panizo, Eva Estébanez-Perpiñá, John van Noort, Alireza Mashaghi</p> <p> </p> <p>The data related to this project has been deposited in two repositories. This repository contains the second part of the data; the first part can be found at DOI: 10.5281/zenodo.13820169</p>
Replication Data For: Spiking patterns in the globus pallidus highlight convergent neural dynamics across diverse genetic dystonia syndromes
<div><strong>Human Globus Pallidum Single-Unit Activity Dataset in Genetic Dystonia Patients</strong></div> <div> </div> <div>This dataset consists of tabular data encompassing diverse neural features extracted from spiking trains of stable single-unit activity. These units were isolated from raw microelectrode recordings obtained from the globus pallidum of genetic dystonia patients who underwent globus pallidus internal (GPi) deep brain stimulation (DBS) surgery. The dataset includes anonymized patient IDs, details about the patient's genetic dystonia mutation, as well as information on the hemisphere and depth of microelectrode recordings (MER). Additionally, it features neural properties such as firing rate, spiking regularity, neural bursts, oscillations, and pause characteristics of isolated single-unit activities (SUAs).</div> <div> </div> <div>To process the raw MER, we applied a semi-parametric offline spike sorting algorithm to isolate SUAs. The SUAs were analyzed both in the temporal and frequency domains to derive a comprehensive set of features related to spiking patterns.</div> <div> </div> <div>For those interested in replicating or understanding the feature extraction process, the MATLAB source code is available in the <a href="github.com/ahmetofficial/Spike-Feature-Generator">Github repository</a>.</div>
Fig. 6 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 6. Variation in Equitability (J) and Berger-Parker dominance (DBP) of Diptera (A) and Auchenorrhyncha (B) during 12 months of sampling over six 500 m elevation zones at Doi Inthanon in 2014. Values of J (bars) and DBP (lines) were computed in PAST and 95% confidence intervals obtained by bootstrapping using 9999 random samples. In Kruskal-Wallis H-tests there was a significant difference between the medians for Berger-Parker dominance in Diptera (H = 26.7, p <0.01) and Auchenorrhyncha (H = 14.9, p <0.01). Equitability was significantly different for Diptera (H = 36.5, p <0.01) but not for Auchenorrhyncha (H = 10.7, p = 0.0582).
Fig. 10 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 10. Variation in Mean Monthly Turnover (βwM) of Diptera (A) and Auchenorrhyncha (B) during 12 months of sampling over six 500 m elevation zones at Doi Inthanon in 2014. The mean value of βwM in each elevation zone ± standard error is indicated. Note that the vertical axis does not extend to zero. In Kruskal-Wallis H-tests there was a significant difference between the medians for Diptera (H = 29.0, p <0.01) and Auchenorrhyncha (H = 22.1, p <0.01).
Fig. 2 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 2. Observed species richness (Sobs) of Diptera and Auchenorrhyncha trapped in six elevation zones over 12 months sampling at Doi Inthanon in 2014. Diptera, open circles; Auchenorrhyncha, closed circles.). In Kruskal-Wallis H-tests there was a significant difference between the medians for Diptera (H = 22.1, p <0.01) and Auchenorrhyncha (H = 14.3, p <0.05).
Fig. 1. Relative abundance, A in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 1. Relative abundance, A* (number of individuals caught. trap-1. month-1) of Diptera and Auchenorrhyncha trapped in six elevation zones over 12 months sampling at Doi Inthanon in 2014. Standard errors indicated. Note log10 scale. Data were fitted to a linear regression model in PAST; Diptera, open circles (r2 = 0.8567, p = 0.0081); Auchenorrhyncha, closed circles (r2 = 0.3182, p = 0.2434). In Kruskal-Wallis H-tests of untransformed data there was a significant difference between the medians for Diptera (H = 29.3, p <0.01) but not for Auchenorrhyncha (H = 3.3, p = 0.657).
Fig. 8 in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 8. Variation in species turnover measured as βw of Diptera (a) and Auchenorrhyncha (b) during 12 months of sampling over six 500 m elevation zones at Doi Inthanon in 2014. Pairwise calculations of βw between each quadrat of a grid of elevation and month with the quadrat with maximum species richness (April/1,500–2,000 m quadrat for Diptera and June/500–1,000 m quadrat for Auchenorrhyncha) were mapped using the multiquadric gridding algorithm in the gridding module of PAST. Values of βw (indicated by colour scale bar) vary between 0 (complete identity) and 1.0 (complete non-identity). Data are not available for January and February at <500 m and 500–1,000 m.
Fig. 3. Relative abundance, A in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 3. Relative abundance, A* (number of individuals caught. trap-1. month-1) of Diptera and Auchenorrhyncha over 12 months sampling at Doi Inthanon in 2014. Standard errors indicated. Note log10 scale. In Kruskal-Wallis H-tests of untransformed data there was a significant difference between the medians for Diptera (H = 24.5, p <0.05) and Auchenorrhyncha (H = 34.3, p <0.01).
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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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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.
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