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29 results for “Spatially-explicit”
Simulated spatially-explicit above ground biomass of forests (larch) in the vicinity of the Ilirney lake system region, Chukotka, Russia
<p>The model LAVESI (Kruse et al. 2016) was updated (Kruse 2023) and forced with historical and future climate forcing for 3 simulation repeats. The data set contains simulated larch above ground biomass (AGB, in kg m<sup>-2</sup>) for the three climate forcings RCP 2.6, 4.5 and 8.5 and each complemented with a hypothetical cooling scenario from year 2300 CE onwards. The data provided is from years 2020, 2050, 2100 and proceeding in 100-year steps until 3000 CE.</p> <p>Format: Geotiff; projection UTM58N and 30x30 m tiles; extent: 640008.2, 649998.2, 7475006, 7494716 m (xmin, xmax, ymin, ymax)</p>
Data from: Complementary strengths of spatially-explicit and multi-species distribution models
<p><span><span><span><span><span><span><span><span><span><span><span> Species distribution models (SDMs) project the outcome of community assembly processes - dispersal, the abiotic environment, and biotic interactions - onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock (<i>Tsuga canadensis</i>L.) and five additional co-occurring overstory tree species in 35,569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span> Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species-environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.</span></span></span></span></span></span></span></span></span></span></p>
spectre: An R package to estimate spatially-explicit community composition using sparse data
<p>An understanding of how biodiversity is distributed across space is key to much of ecology and conservation. Many predictive modelling approaches have been developed to estimate the distribution of biodiversity over various spatial scales. Community modelling techniques may offer many benefits over single-species modelling. However, techniques capable of estimating precise species makeups of communities are highly data intensive and thus often limited in their applicability. Here we present an R package, spectre, which can predict regional community composition at a fine spatial resolution using only sparsely sampled biological data. The package can predict the presence and absence of all species in an area, both known and unknown, at the sample site scale. Underlying the spectre package is a min-conflicts optimisation algorithm that predicts species' presences and absences throughout an area using estimates of α-, β-, and γ-diversity. We demonstrate the utility of the spectre package using a spatially-explicit simulated ecosystem to assess the accuracy of the package's results. spectre offers a simple-to-use tool with which to accurately predict community compositions across varying scales, facilitating further research and knowledge acquisition into this fundamental aspect of ecology.</p>
spectre: An R package to estimate spatially-explicit community composition using sparse data
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Data from: Complementary strengths of spatially-explicit and multi-species distribution models
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Data from: Spatially-explicit depiction of a floral epiphytic bacterial community reveals role for environmental filtering within petals
<p>The microbiome of flowers (anthosphere) is an understudied compartment of the plant microbiome. Within the flower, petals represent a heterogeneous environment for microbes in terms of resources and environmental stress. Yet little is known of drivers of structure and function of the epiphytic microbial community at the within-petal scale. We characterized the petal microbiome in two co-flowering plants that differ in pattern of ultraviolet (UV) absorption along their petals. Bacterial communities were similar between plant hosts, with only rare phylogenetically distant species contributing to differences. The epiphyte community was highly culturable (75% of families) lending confidence to the spatially-explicit isolation and characterization of bacteria. In one host, petals were heterogeneous in UV absorption along their length and in these there was a negative relationship between growth rate and position on the petal, as well as lower UV tolerance in strains isolated from the UV absorbing base than from UV reflecting tip. A similar pattern was not seen in microbes isolated from a second host whose petals had uniform patterning along their length. Across strains, variation in carbon utilization and chemical tolerance followed common phylogenetic patterns. This work highlights the value of petals for spatially-explicit explorations of bacteria of the anthosphere.</p>
Data from: Mate choice strategies in a spatially-explicit model environment
Decisions about the choice of a mate can greatly impact both individual fitness and selection processes. We developed a novel agent-based model to investigate two common mate choice rules that may be used by female gray treefrogs (Hyla versicolor). In this model environment, female agents using the minimum-threshold strategy found higher quality mates and traveled shorter distances on average, compared with female agents using the best-of-n strategy. Females using the minimum-threshold strategy, however, incur significant lost opportunity costs, depending on the male population quality average. The best-of-n strategy leads to significant female:female competition that limits their ability to find high quality mates. Thus, when the sex ratio is 0.8, best-of-5 and best-of-2 strategies yield mates of nearly identical quality. Although the distance traveled by females in the mating task varied depending on male spatial distribution in the environment, this did not interact with female choice for the best-of-n or minimum-threshold strategies. By incorporating empirical data from the frogs in this temporally- and spatially-explicit model, we thus show the emergence of novel interactions of common decision-making rules with realistic environmental variables.
MetaSqueeze: A spatially-explicit metapopulation model for Banksia hookeriana in south-west Australia
<p>Climate change, with warming and drying weather conditions, is reducing the growth, seed production, and survival of fire-adapted plants in fire-prone regions such as Mediterranean-type ecosystems. These effects of climate change on local plant demographics have recently been shown to reduce the persistence time of local populations of the fire-killed shrub <em>Banksia hookeriana</em> dramatically. In principle, extinctions of local populations may be partly compensated by recolonization events through long-distance dispersal mechanisms of seeds, such as post-fire wind and bird-mediated dispersal, facilitating persistence in spatially structured metapopulations. However, to what degree and under which assumptions metapopulation dynamics might compensate for the drastically increased local extinction risk remains to be explored. Given the long timespans involved and the complexity of interwoven local and regional processes, mechanistic, process-based models are one of the most suitable approaches to systematically explore the potential role of metapopulation dynamics and its underlying ecological assumptions for fire-prone ecosystems. Here we extend a recent mechanistic, process-based, spatially implicit population model for the well-studied fire-killed and serotinous shrub species <em>B</em><em>.</em><em> hookeriana</em> to a spatially explicit metapopulation model. We systematically tested the effects of different ecological processes and assumptions on metapopulation dynamics under past (1988–2002) and current (2003–2017) climatic conditions, including (i) effects of different spatiotemporal fires, (ii) effects of (likely) reduced intraspecific plant competition under current conditions, and (iii) effects of variation in plant performance among and within patches. In general, metapopulation dynamics had the potential to increase the overall regional persistence of <em>B</em><em>.</em><em> hookeriana</em>. However, increased population persistence only occurred under specific optimistic assumptions. In both climate scenarios, the highest persistence occurred with larger fires and intermediate to long inter-fire intervals. The assumption of lower intraspecific plant competition caused by lower densities under current conditions alone was not sufficient to increase persistence significantly. To achieve long-term persistence (defined as > 400 years) it was necessary to additionally consider empirically observed variation in plant performance among and within patches, i.e., improved habitat quality in some large habitat patches (≥ seven) that could function as source patches and a higher survival rate and seed production for a subset of plants, specifically the top 25% of flower producers based on current climate conditions monitoring data. Our model results demonstrate that the impacts of ongoing climate change on plant demographics are so severe that even under optimistic assumptions, the existing metapopulation dynamics shift to an unstable source-sink dynamic state. Based on our findings, we recommend increased research efforts to understand the consequences of intraspecific trait variation on plant demographics, emphasizing the variation of individual traits both among and within populations. From a conservation perspective, we encourage fire and land managers to revise their prescribed fire plans, which are typically short interval, small fires, as they conflict with the ecologically appropriate spatio-temporal fire regime for <em>B. hookeriana</em>, and likely as well for many other fire-killed species.</p>
Dataset from: Transport and water age dynamics in soils: a comparative study of spatially-integrated and spatially-explicit models
<p>This dataset contains high-resolution vegetated lysimeter experimental dataset carried out in EPFL, Lausanne, Switzerland in March-August 2016. The lysimeter is 100 cm long with a diameter of 120 cm. During this experiment, a simultaneous spike injection of five different solutes (2,5-DFBA, 2-TFMBA, 3,4-DFBA, 2,6-DFBA, 3-TFMBA) took place on the 3rd of March 2016 at 14:00 in an hour. The solutes' mass recovery at the bottom of lysimeter was observed for these solutes.</p> <p>This dataset contains three files which are described in the following:</p> <ul> <li>"hydrologic_data.dat" contains the hourly fluxes (precipitation, irrigation, evapotranspiration measured from load cells, and the water draining at the bottom of lysimeter ) in mm/hr between the 19th of Feb-the 1st of Sep 2016.</li> <li>"tracer_data.dat" contains the tracer concentration observed at the bottom of lysimeter in mg/L. These samples are collected at variable frequencies with an approximate average of 1.5 samples per day.</li> <li>"additional_data.dat" informs you on dry mass and soil volume in the lysimeter, vegetation type, and volume of injection per solute.</li> </ul> <p> </p>
Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America : Dataset
<p>This dataset is linked to the paper “Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-16-1661-2023). It contains the installer of the model, its user guide, as well as all the input files (inventory, thinning, meteorology and soil horizons files for each stand used in the evaluation and calibration steps), the R scripts and associated data used to analyse the model outputs.</p>
Data from: Mate choice strategies in a spatially-explicit model environment
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Data from: Spatially-explicit depiction of a floral epiphytic bacterial community reveals role for environmental filtering within petals
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Data from: Modeling multilocus selection in an individual-based, spatially-explicit landscape genetics framework
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Data and code from: Spatially-explicit foraging by an apex predator linked to nearshore prey and their accessibility in lakes
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MetaSqueeze: A spatially-explicit metapopulation model for Banksia hookeriana in south-west Australia
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Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement in an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses. Incorporation of prior information about stock abundance and movement resulted in more precise and plausible estimates of mixture compositions in time and space. Inclusion of a population dynamics model also allowed robust interpolation of expected catch composition at areas and times with no genetic observations. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species.
Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction
<p>This dataset contains the spatially-explicit gridded database based on social media data for over 150 different tourism-related classes that depicts tourism density (supply and demand) and perceived satisfaction in Europe, and the related exposure to selected climate extreme events. Information on tourism density (supply and demand) and perceived satisfaction are categorised for Attractions, Culinary, and Hospitality, while the exposure analysis of those clases are provided in separate, specific files. The provided dataset is made accessible to support large-scale and regional tourism research and extends its relevance to other fields that are part of tourism as a complex system, such as risk assessment and vulnerability studies. For citing this work, please refer to the research article "Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction", DOI 10.1088/1748-9326/ad3e91. Suggested citation: "Camatti, N., Hrast Essenfelder, A., & Giove, S. (2024). Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction. Environmental Research Letters."</p>
Data from: Unraveling conflicting density- and distance-dependent effects on plant reproduction using a spatially-explicit approach
1. Density- and distance-dependent (DDD) mechanisms are important determinants of plant reproductive success (PRS). Different components of sequential PRS can operate either in the same or in different directions and thus reinforce or neutralize each other, and they may also operate at different spatial scales. Thus, spatially-explicit approaches are needed to detect such complex DDD effects across multiple PRS components and spatial scales. 2. To reveal DDD effects of different components of early PRS of the Iberian pear (Pyrus bourgaeana) sampled over three consecutive years, we used marked point pattern analysis. Our special interest is to identify conflicting processes that regulate populations at different spatial scales, e.g. whether DDD on fruit initiation and on fruit development acted in opposite directions. To evaluate the significance of observed mark correlation functions based on empirical data (e.g. fruiting success) we compared them to expectations given by spatially-explicit null models. 3. Diverse DDD processes affected several aspects of PRS in a variable extent over the three seasons. First, early fruit set was higher for individuals with more neighbors at small distances (i.e. up to 40m). However, late P. bourgaeana fruit set decreased with increasing number of nearby neighbors, but these effects canceled for overall fruit set that did not show DDD effects. Second, the absolute number of fruits produced (crop sizes) by trees showed positive density dependence in 2011 and 2012 but not in 2013. Finally, the total number of seeds produced did not show DDD effects, indicating that conflicting demographic processes can disrupt the initial spatial pattern of tree investment in reproduction. 4. Synthesis: Understanding complex spatial effects of density- and distance-dependent (DDD) processes requires dissection of component processes to attain the complete picture since contrasting DDD processes may be hidden behind a single cumulative measure of reproductive success. The combination of novel and classic mark correlation functions used here constitute a powerful spatially-explicit tool that can be broadly applied to unravel conflicting mechanisms of DDD regulating the persistence of sessile organisms at a range of spatial scales. Our findings help to explain why some authors failed to find expected DDD of PRS and highlight the importance of detailed multi-year field studies on plant reproductive success.
Data from: How large spatially-explicit optimal reserve design models can we solve now? an exploration of current models’ computational efficiency
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Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
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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.