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33 results for “ecological levels”
Data from: The price of looking sexy: visual ecology of a three level predator-prey system
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Data from: Ecological causes of multi-level covariance between size and first-year survival in a wild bird population
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Genetic and species-level biodiversity patterns are linked by demography and ecological opportunity
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The effects of foraging ecology and allometry on avian skull shape vary across levels of phylogeny
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Data from: Should we sync? Seascape-level genetic and ecological factors determine seagrass flowering patterns
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Data from: How phylogeny and foraging ecology drive the level of chemosensory exploration in lizards and snakes
The chemical senses are crucial for squamates (lizards and snakes). The extent to which squamates utilize their chemosensory system, however, varies greatly among taxa and species' foraging strategies, and played an influential role in squamate evolution. In lizards, Scleroglossa evolved a state where species use chemical cues to search for food (active-foragers), while Iguania retained the use of vision to hunt prey (ambush-foragers). However, such strict dichotomy is flawed since shifts in foraging modes have occurred in all clades. Here, we attempted to disentangle effects of foraging ecology from phylogenetic trait conservatism as leading cause of the disparity in chemosensory investment among squamates. To do so, we used species' tongue-flick rate (TFR) in absence of ecological relevant chemical stimuli as a proxy for its fundamental level of chemosensory investigation, i.e. baseline TFR. Based on literature data of nearly 100 species and using phylogenetic comparative methods, we tested whether and how foraging mode and diet affect baseline TFR. Our results show that baseline TFR is higher in active than ambush foragers. Although baseline TFRs appear phylogenetically stable in some lizard taxa, that is a consequence of concordant stability of foraging mode: when foraging mode shifts within taxa, so does baseline TFR. Also, baseline TFR is a good predictor of prey chemical discriminatory ability, as we established a strong positive relationship between baseline TFR and TFR in response to prey. Baseline TFR is unrelated to diet. Essentially, foraging mode, not phylogenetic relatedness, drives convergent evolution of similar levels of squamate chemosensory investigation.
Data from: Inferring species interactions in ecological communities: a comparison of methods at different levels of complexity
1. Natural communities commonly contain many different species and functional groups, and multiple types of species interactions act simultaneously, such as competition, predation, commensalism or mutualism. However, experimental and theoretical investigations have generally been limited by focusing on one type of interaction at a time or by a lack of a common methodological and conceptual approach to measure species interactions. 2. We compared four methods to measure and express species interactions. These approaches are, with increasing degree of model complexity, an extinction-based model, a relative yield model and two generalized Lotka-Volterra (LV) models. All four approaches have been individually applied in different fields of community ecology, but rarely integrated. We provide an overview of the definitions, assumptions and data needed for the specific methods and apply them to empirical data by experimentally deriving the interaction matrices among 11 protist and rotifer species, belonging to three functional groups. Furthermore, we compare their advantages and limitations to predict multispecies community dynamics and ecosystem functioning. 3. The relative yield method is, in terms of final biomass production, the best method in predicting the 11-species community dynamics from the pairwise competition experiments. The LV model, which is considering equilibrium among the species, suffers from experimental constraints given the strict equilibrium assumption, and this may be rarely satisfied in ecological communities. 4. We show how simulations of a LV stochastic community model, derived from an empirical interaction matrix, can be used to predict multispecies community dynamics across multiple functional groups. 5. Our work unites available tools to measure species interactions under one framework. This improves our ability to make management-oriented predictions of species coexistence/extinction and to compare ecosystem processes across study systems.
Fig. 2 in The nutritional ecology of Dectes texanus (Coleoptera: Cerambycidae): Does host choice affect the macronutrient levels in overwintering larvae?
Fig. 2. Mean (± SE) levels of A) protein, B) carbohydrates, and C) lipids per unit mass in pith of sunflower (Sun) and soybean (Soy). An asterisk indicates that the levels are significantly different.
Input data and Supplementary Results for "Community-level signatures of ecological succession in natural bacterial communities"
<p> </p> <p><strong>README<br> ======</strong></p> <p><br> This file describes the content of the different files included in this repository to<br> reproduce results from [1] and some of its supplementary results.</p> <p> </p> <p><strong>## Input files ##</strong></p> <p><strong>* 20151016_Functions_remainder.csv</strong></p> <p> Functions measured in [2]. The relevant quantities used in [1] are labelled with "7", and include:<br> <br> * Community: Id of the sample<br> * Replicate<br> * Plate<br> * mgCO2.7: CO2 measured along 7 days of experiment<br> * CPM7: Cell counts at the end of the experiment<br> * pgRPC.7: CO2 per cell<br> * ATP7: ATP measured (nM)<br> * mG7: beta glucosidase (mM)<br> * mN7: beta chitinase (mM)<br> * mX7: xylosidase (mM)<br> * mP7: phosphatase (mM)<br> <br> <strong>* samples_metadata_time0.tsv</strong></p> <p> * Samples: Id of the sample <br> * Part.dates: Date of sampling<br> * Part.GPS.PAM: Optimal sampling sites <br> * Part.SparCC.PAM.t0: Optimal partition using SparCC<br> * Part.SJD.PAM.t0: Optimal partition using Jensen-Shannon Divergence<br> * Part.Dir.t0: Optimal partition using Dirichlet mixtures<br> * Part.month: Month in which the community was sampled<br> <strong>* Dist_GPS-Haversine.dat</strong></p> <p> Haversine (spatial) distances between samples</p> <p><strong>* corMat-SparCC_20151016_OTU_remainder.clean.samples.txt</strong></p> <p> Matrix of correlations between samples computed with SparCC<br> <br> <strong>* distMat_ShannonJensen_Samples_Time0.clean.dat</strong></p> <p> Distance matrix computed with Jensen-Shannon divergence.</p> <p> </p> <p><br> <strong>## Supplementary results ##</strong></p> <p><strong>* SEMmodels.zip</strong></p> <p> Results for the Structural Equation Models analysed. The structure of the folders follows the one<br> available at the repository of the [project ](https://github.com/apascualgarcia/TreeHoles_descriptive).<br> <br> <strong>* TaxaSummaries.zip</strong></p> <p> The file contains one folder for each community-class, with matrices in different formats (biom and txt) computing the relative abundances of the OTUs at different taxonomic levels (labelled L2 being the proxy for Phylum to L6, the proxy of species). These matrices can be visualized interactively opening with a web browser the files area_charts.html.</p> <p><strong>#### References ####</strong></p> <blockquote> <p> [1] Pascual-García, A., & Bell, T. (2019). Community-level signatures of ecological succession in natural bacterial communities. Nature Communications (In press)</p> </blockquote> <blockquote> <p> [2] Rivett, Damian W., and Thomas Bell. Abundance determines the functional role of bacterial phylotypes in complex communities." Nature microbiology 3.7 (2018): 767.</p> </blockquote> <p> </p>
Data from: Inferring species interactions in ecological communities: a comparison of methods at different levels of complexity
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Data from: How phylogeny and foraging ecology drive the level of chemosensory exploration in lizards and snakes
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Determination of Ecological Awareness and Attitudes Levels of Undergraduate Physiotherapy Students in Istanbul
ClinicalTrials.gov study NCT06310447. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
JustConservation - global data of site-level biodiversity conservation and its social-ecological outcomes (1970-2019)
<p>This dataset contains evidence collected from 723 studies published in English-language peer-reviewed journals that describe and explore conservation efforts at a single site in 104 countries (1970-2019). The pool of studies was obtained through a keyword search on Web of Science, which returned 69,246 publications, followed by a screening of titles and abstracts using the open access machine-learning assisted software Colandr (11,100 publications screened; cut-off inclusion rate: 3%) and a data extraction phase (1,054 publications), based on the following criteria for inclusion: (1) the study is about biodiversity conservation; (2) the study describes a deliberate intervention; (3) the study provides empirical evidence; (4) the conservation intervention takes place in a specific site through a specific actor or actors; (5) the study identifies a discernible conservation aim, and; (6) the paper identifies a discernible conservation approach.</p> <p>Curated and compiled dataset with the following columns:</p> <ul> <li><strong>study_id</strong>: unique identifier of the study</li> <li><strong>title</strong>: title of the study</li> <li><strong>year</strong>: publication year of the study</li> <li><strong>authors</strong>: authors of the study</li> <li><strong>name_journal</strong>: name of the journal the study was published in</li> <li><strong>author_affiliation_type</strong>: type of institution the lead author of the study works for (University/Academic; NGO; public/state/UN; think thank/research institute; private company; independent; other). Only lists first affiliation.</li> <li><strong>author_affiliation_country: </strong>country of affiliation of the lead author. Only lists first affiliation.</li> <li><strong>author_continent</strong>: country of affiliation of the lead author. Only lists first affiliation.</li> <li><strong>author_north_south</strong>: indicates whether the lead author is affiliated with an institution in the Global North (N) or Global South (S). Only lists first affiliation.</li> <li><strong>major_funder_ngo</strong>: are any authors affiliated with a major conservation funder or organisation with an interest in the conservation intervention being researched (yes; no)</li> <li><strong>funding_source</strong>: funding source of the paper (not of the conservation intervention)</li> <li><strong>conflict_of_interest: </strong>overlap between the affiliations of authors, the funding source and the conservation intervention being researched (0 = no; 1 = yes)</li> <li><strong>study_country</strong>: country where the intervention was studied</li> <li><strong>study_locality</strong>: locality or region where the intervention was studied</li> <li><strong>study_continent</strong>: continent where the intervention was studied</li> <li><strong>study_north_south: </strong>indicates whether the conservation intervention was conducted in the Global North (N) or Global South (S)</li> <li><strong>primary_ecosystem_type</strong>: type of ecosystem of the intervention (Forest/Woodland; Savanna/Dry forest; Dryland/Semi-arid; Grassland/Shrubland; Rivers/Wetlands; Coastal/Marine; Mountain; Cultivated; Other)</li> <li><strong>name_primary_intervention</strong>: name of the conservation intervention or program</li> <li><strong>year_primary_intervention</strong>: year the conservation intervention or program started (single year; many years)</li> <li><strong>target_type_ecosystem</strong>: the conservation intervention targets ecosystems or habitats (yes; no)</li> <li><strong>target_type_species</strong>: the conservation intervention targets specific species (yes; no)</li> <li><strong>target_type_ess</strong>: the conservation intervention targets ecosystem services or processes (yes; no)</li> <li><strong>target_type_humans: </strong>the conservation intervention targets humans and livelihoods (yes; no)</li> <li><strong>description_primary_target</strong>: short description of the primary conservation target</li> <li><strong>description_secondary_target: </strong>short description of any additional conservation target (if applicable)</li> <li><strong>intervention_type_paca</strong>: the conservation intervention takes places through protected areas or conserved areas (yes; no)</li> <li><strong>intervention_type_restoration</strong>: the conservation intervention takes places through restoration (yes; no)</li> <li><strong>intervention_type_species_use</strong>: the conservation intervention takes places through species protection or sustainable use measures (yes; no)</li> <li><strong>intervention_type_livelihoods_tourism</strong>: the conservation intervention takes places through livelihoods support or tourism (yes; no)</li> <li><strong>intervention_type_incentives</strong>: the conservation intervention takes places through incentives, compensation schemes, or market instruments (yes; no)</li> <li><strong>intervention_type_education</strong>: the conservation intervention takes places through education or capacity building (yes; no)</li> <li><strong>intervention_type_stewardship</strong>: the conservation intervention takes places through local/indigenous stewardship (yes; no)</li> <li><strong>description_primary_type</strong>: short description of the primary conservation type</li> <li><strong>description_secondary_type: </strong>short description of any additional conservation types (if applicable)</li> <li><strong>study_covers_governance: </strong>the paper describes governance processes (yes; no)</li> <li><strong>iplc_involved: </strong>indigenous people and/or local communities are involved in the conservation intervention (no; yes, partially; yes, locally-led)</li> <li><strong>description_governance</strong>: short description of the governance process and main actors involved</li> <li><strong>additional_notes: </strong>notes and/or remarks about conservation intervention/governance</li> <li><strong>ecological_outcomes_incl:</strong> study describes ecological outcomes or conservation effectiveness (yes; no)</li> <li><strong>ecological_outcomes: </strong>the reported ecological outcomes are positive, negative, or mixed (positive; negative; mixed; N/A)</li> <li><strong>social_outcomes_incl</strong>: study describes social impacts or outcomes associated with conservation (yes; no)</li> <li><strong>social_outcomes:</strong> the reported social outcomes are positive, negative, or mixed (positive; negative; mixed; N/A)</li> </ul>
ScienceDex guides
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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.