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11 results for “gap ecology”
Moss functional trait ecology: Trends, gaps, and biases in the current literature
<p>Functional traits are critical tools in plant ecology for capturing organism-environment interactions based on trade-offs as well as making links between organismal and ecosystem processes. While broad frameworks for functional traits have been developed for vascular plants, we lack the same for bryophytes, despite an escalation in the number of bryophyte functional trait studies conducted in the last 45 years and an increased recognition of the ecological roles bryophytes play across ecosystems. In this review, we compiled data from 282 published articles (10005 records) focusing on functional traits measured in mosses, and sought to (a) examine trends in types of traits measured, (b) capture taxonomic and geographic breadth of trait coverage, (c) reveal biases in coverage in the current literature, and (d) develop a bryophyte-function index (BFI) to describe completeness of current trait coverage and identify global gaps to focus research efforts. The most commonly measured response traits (those related to growth/reproduction in individual organisms) and effect traits (those that directly affect community/ecosystem scale processes) fell into the categories of morphology (e.g. leaf area, shoot height) and nutrient storage/cycling, and our BFI revealed that these data were most commonly collected from temperate and boreal regions of Europe, North America and east Asia. However, fewer than 10% of known moss species have available functional trait information. Our synthesis revealed that there is a need for research on traits related to ontogeny, sex, and intraspecific plasticity, and on co-measurement of traits related to water-relations and bryophyte-mediated soil processes. </p>
Figure 5 in Knowledge gaps regarding frugivorous ecological networks between birds and plants in Brazil
Figure 5. Complex ecological network constructed from information about interactions between frugivorous birds (triangles) and non-native plants (circles) in the Brazilian Atlantic Forest (A) and Cerrado (B). Each color represents an agglomerate of species that are more connected among themselves than with species of other agglomerates. Names in triangles and circles represent are abbreviations of species names. Abbreviations are defined in the supplementary material (S1).
Figure 3 in Knowledge gaps regarding frugivorous ecological networks between birds and plants in Brazil
Figure 3. Complex ecological network constructed with information about interactions between frugivorous birds (triangles) and plants (circles) in the Brazilian Atlantic Forest. Each color represents an agglomerate of species that are more connected among themselves than with species of other agglomerates. Species with the highest values of connectivity and centrality are circled. Names in triangles and circles are abbreviations of species names. Abbreviations are defined in the supplementary material (S1).
Figure 4 in Knowledge gaps regarding frugivorous ecological networks between birds and plants in Brazil
Figure 4. Complex ecological network constructed with information about interactions between frugivorous birds (triangles) and plants (circles) in the Brazilian Cerrado. Each color represents an agglomerate of species that are more connected among themselves than with species of other agglomerates. Species with the highest values of connectivity and centrality are circled. Names in triangles and circles are abbreviations of species names. Abbreviations are defined in the supplementary material (S1).
Data from: The roles of non-production vegetation in agroecosystems: a research framework for filling process knowledge gaps in a social-ecological context
<p>1. An ever-expanding human population, climatic changes, and the spread of intensive farming practices is putting increasing pressure on agroecosystems and their inherent biodiversity. Non-production vegetation elements, such as woody patches, riparian margins, and restoration plantings, are vital for conserving agroecosystem biodiversity. Further, such elements are key building blocks that are manipulated via land management, thereby influencing the biotic and abiotic processes that underpin functioning agroecosystems.</p> <p>2. Despite this critical role, there has been a lack of synthesis on which types of vegetation elements drive and/or support ecological processes, and the mechanisms by which this occurs. Using a systematic, quantitative literature review of 342 articles, we asked: what are the effects of non-production vegetation on agroecosystem processes and how are these processes measured within global agroecosystems?</p> <p>3. Woody patches, hedgerows and borders, riparian margins, and shelterbelts were the most studied types of non-production vegetation. The majority (61%) of studies showed positive effects of non-production vegetation on ecological processes, where the presence, level or rate of the studied process was increased or enhanced.</p> <p>4. However, four key research gaps were revealed: (1) most studies (83%) used proxies for, instead of direct measurements of, ecosystem processes related to non-production vegetation; (2) study designs used to investigate non-production vegetation effects on ecosystem processes directly were largely limited to observational comparisons of non-production vegetation types, farm-scale vegetation configurations, and different proximities to vegetation in terms of the effect on ecological processes; relatively few studies used manipulative experiments (3) the relatively few studies directly measuring ecosystem processes were dominated by four process categories: invertebrate biocontrol, predator and natural enemy spillover, animal movement, and ecosystem cycling, and (4) the methods used to directly measure non-production vegetation effects comprised a surprisingly limited set of approaches.</p> <p>5. To fill key research gaps that will inform the use of non-production vegetation to enhance agroecosystem processes, we present a framework for future research that emphasises the need to combine an understanding of human decision making with carefully-designed and targeted investigations into the roles of taxa, ecosystem processes, and landscape heterogeneity related to non-production vegetation, at multiple spatial scales within agroecosystems.</p>
Data from: The roles of non-production vegetation in agroecosystems: a research framework for filling process knowledge gaps in a social-ecological context
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Moss functional trait ecology: Trends, gaps, and biases in the current literature
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Socio-ecological gap analysis to forecast species range contractions for conservation
Conservation requires both a needs assessment and prioritization scheme for planning and implementation. Range maps are critical for understanding and conserving biodiversity, but current range maps often omit content, negating important metrics of variation in populations and places. Here, we integrate a myriad of conditions that are spatially explicit across distributions of carnivores to identify gaps in capacity necessary for their conservation. Expanding on traditional gap analyses that focus almost exclusively on quantifying discordance in protected area coverage across a species' range, our work aggregates threat layers (e.g., drought, human pressures) with resources layers (e.g., protected areas, cultural diversity) to identify gaps in available conservation capacity (ACC) across ranges for 91 African carnivores. Our model indicated that all species have some portion of their range at risk of contraction, with an average of 15 percentage range loss. We found that the ACC differed based on body size and taxonomy. Results deviated from current perceptions of extinction risks for species with an International Union for Conservation of Nature (IUCN) threat status of Least Concern and yielded insights for species categorized as Data Deficient. Our socio-ecological gap analysis presents a geospatial approach to inform decision-making and resource allocation in conservation. Ultimately, our work advances forecasting dynamics of species' ranges that are increasingly vital in an era of great socio-ecological change to mitigate human–wildlife conflict and promote inclusive carnivore conservation across geographies.
Pervasive gaps in Amazonian ecological research
<p>Raw data and code for the analyses on research probability across the Brazilian Amazonia. Briefly, the code reproduces the Random Forest models and the intersection of research probability with susceptibility to current and future anthropogenic disturbances, performs a site-level analysis of the resulting outputs; and then illustrates the building of figures used in the main manuscript.</p> <p>The modelling framework was built using the directory structure informed in the README.pdf file. The R-code provided has steps designed to replicate the directory structure as reported, but understanding it is a good starting point to navigate the output produced. There are five zip files whose content we described below:</p> <p><strong>AvgOutputs.zip:</strong> a folder containing four files with the results layer from the research probability model. The files represent the final result for each habitat type and the average across habitat types.</p> <p><strong>Datasets.zip</strong>: represents the "Datasets" folder directory, as illustrated in the README.pdf. This zip file contains the files ‘SamplingSites.csv’, which informs metadata for the entire list of occurrence data of community data (organism group, coordinates (long and lat) and habitat); and the ‘VarImportance.csv’, which was produced in the Script2_RCode_RandomForestModels.R, and used to build Figure 3 in the manuscript.</p> <p><strong>GlobalChangeLayers.zip</strong>: folder with input and intermediary layers used for generating Figure 4. Degradation files are from Lapola et al. (2023, DOI: <a href="https://doi.org/10.1126/science.abp8622">10.1126/science.abp8622</a>), while climate layers are from IPCC interactive atlas (Gutiérrez et al. 2021, DOI: <a href="https://dx.doi.org/10.1017/9781009157896.021">10.1017/9781009157896.021</a>.).</p> <p><strong>Predictors.zip</strong>: includes the raster files produced for the "Predictors" folder. There are five rasters representing the layers: DryMonths, LandTenure, NearbtDND, ResearchEduc, and TravelTime. All rasters were generated at the spatial resolution of 1 km spatial resolution using the files in Script1_GEECode_PredictorLayerPreparation.zip. </p> <p><strong>Projections.zip:</strong> the projections of research probability for each habitat type and organism are contained in this folder. There are 11 files, named as aquatic_benthos.tif, aquatic_fishes.tif, aquatic_heteropterans.tif, aquatic_macrophytes.tif, aquatic_odonates.ti, upland_ants.tif, upland_beetles.tif, upland_birds.tif, upland_trees.tif, wetland_birds.tif, wetland_trees.tif.</p> <p><strong>RasterMasks.zip</strong>: contains the masks created for each habitat to remove areas outside each habitat and areas without forest. The files are aquatic.tif, upland.tif and wetland.tif. They were created in the Google Earth Engine platform</p> <p><strong>RData.zip:</strong> represents the "RData" folder directory, which is designed to store RData files produced while running the R-scripts. It currently includes three files to facilitate the plotting of main figures.</p> <p>Scripts: a set of seven files, described in extra details in the following.</p> <ul> <li> <p><strong>Script1_GEECode_PredictorLayerPreparation.zip</strong>: a set of three GEE-scripts developed to prepare raster masks and predictor layers in the Google Earth Engine platform.</p> </li> <li> <p><strong>Script2_RCode_RandomForestModels.R</strong>: R-script to perform data partition, Random Forest model training, model validation, and extraction of variable importance and partial effects per predictor.</p> </li> <li> <p><strong>Script3_RCode_DeltaClimate.R</strong>: R-script to compute the metric of absolute climate change.</p> </li> <li> <p><strong>Script4_RCode_Figure1.R</strong>: R-script to build graphical pieces composing the Figure 1.</p> </li> <li> <p><strong>Script5_RCode_Figure2.R:</strong> R-script to build the Figure 2.</p> </li> <li> <p><strong>Script6_RCode_Figure3.R:</strong> R-script to build graphical pieces composing the Figure 3.</p> </li> <li> <p><strong>Script7_RCode_Figure 4.R</strong>: R-script to build graphical pieces composing the Figure 4.</p> </li> </ul> <p><strong>Shapefiles.zip:</strong> represents the "Shapefiles" folder directory, as illustrated in the README.pdf. There is one shapefile in this zip, which corresponds to the study area limits according to Bullock et al. (2020, DOI: 10.1111/gcb.15029).</p> <p> </p>
Socio-ecological gap analysis to forecast species range contractions for conservation
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Field courses narrow demographic achievement gaps in ecology and evolutionary biology
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Allen Brain Atlas
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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.