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312 results for “ecological model”
Figure 1 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 1. Study area and simulation unit
Modeling data and R code for Chrysodeixis chalcites ecological niche
<p>The golden twin-spot moth, <em>Chrysodeixis chalcites</em> Esper (Lepidoptera: Noctuidae), is a polyphagous, polyvoltine crop pest occurring natively from northern Europe to Mediterranean Africa and the Canary Islands. Larvae feed on a wide variety of naturally occurring plants as well as soybean and other legume crops, short staple cotton, tomato, potato, peppers, tobacco, and banana. <em>Chrysodeixis chalcites</em> has been recorded in agricultural lands in the Ontario peninsula in eastern Canada and in northern counties of Indiana, USA. Given the strong potential for <em>C. chalcites</em> to invade USA crop lands, it is important to identify environments most likely to sustain growing populations of this pest. Though <em>C.</em> chalcites is native to Europe and North Africa, it has invaded sub-Saharan Africa. Using occurrence data form the native and invaded ranges, and environmental predictors including bioclimatic conditions and human disturbance, we trained three ecological niche models to estimate an ensemble prediction of environmental suitability in the contiguous US. Because human impact is potentially a confounding predictor, models were trained both with and without it. High environmental suitability was projected for the Atlantic coast from New England to Florida, the Gulf coast, the lower Midwest, and the Pacific coast and Central Valley of California.</p>
Scripts developed to modelling the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery.
<p>The zipfile contains the scripts developed in the paper on the modelling of the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery, as published in Remote Sensing Applications: Society and Environment journal (RSASE - Elsevier). </p> <ul> <li>Scripts developed for the extraction of light sources from night orthophotography ; [SAFE Software Inc. (2017). FME Desktop Esri Edition.Version 2017.1.]</li> <li>ModelBuilder developed for the visibility modelling of light sources ; [ESRI (2017). ArcGIS Desktop Pro.Version 2.1.]</li> </ul>
Datasets associated with: Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography
<p>Data associated with the paper 'Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography' by Lembrechts JJ et al., published in Global Ecology and Biogeography.</p> <p>Contains a dataset containing all extracted and measured temperature variables for all 106 measurement plots (climatedata), as well as the climate and species data used in the Species Distribution Models (SDMs). </p> <p>For details on the content of the table, see the readme-file, for details on methodology, see the original paper. </p>
Emergent Ecological Patterns and Modelling of Gut Microbiomes in Health and in Disease
<p><strong><em>Data associated with the paper "Emergent Ecological Patterns and Modelling of Gut Microbiomes in Health and in Disease".</em></strong></p> <p><strong>Content:</strong></p> <ul> <li><strong>Metagenomic curated data considering healthy and diseased state of the human individuals. Aligned against RefSeq with Kaiju.</strong></li> <li><strong>Curated metadata with anonymised physiological and medical information</strong></li> </ul> <p><strong>Paper authors</strong>: Jacopo Pasqualini, Sonia Facchin, Andrea Rinaldo, Amos Maritan, Edoardo Vincenzo Savarino, Samir Suweis</p> <p><strong>Paper preprint</strong>: https://www.biorxiv.org/content/10.1101/2023.10.19.563037v2</p> <p><strong>Data Curator</strong>: Jacopo Pasqualini.</p> <p><strong>Pipeline used to generate the data</strong>: https://github.com/jacopopasqualini/MetaGym</p> <p><strong>Complete description of data generation</strong>: https://www.biorxiv.org/content/10.1101/2023.10.19.563037v2</p>
Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
The evolutionary dynamics of complex ecological traits – including multistate representations of diet, habitat, and behavior – remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.
Making virtual species less virtual by reverse engineering of spatiotemporal ecological models v3
<p>The most up-to-date version of the archive that contains all the necessary data to perform analyses supporting the paper <em>Making virtual species less virtual by reverse engineering of spatiotemporal ecological models </em>(<a href="https://doi.org/10.1111/2041-210X.14176">https://doi.org/10.1111/2041-210X.14176</a>) .</p> <p>The research was supported by the National Science Centre, Poland (grant no. 2018/29/B/NZ8/00066) and Poznań Supercomputing and Networking Centre (grant no. 403). </p> <p> </p>
Unpacking the "black box": improving ecological interpretation of regression based models
<p><strong>Aim</strong><br>Many tree species distribution models use black-box machine learning techniques that often neglect interpretative aspects and instead focus mainly on maximising predictive accuracy. In this study, we outline an interpretative modelling framework to gain better ecological insights while mapping abundance patterns of six North American species.</p> <p><strong>Location</strong><br>Continental United States and Canada</p> <p><strong>Methods</strong><br>We develop an innovative procedure using regression trees by stabilising variance and mapping dominant rules which we term 'optimized regression tree bagging for interpretation and mapping' (ORTBIM). We apply this technique to understand ecological features influencing the abundance patterns of three eastern (<em>Pinus</em> <em>strobus</em>, <em>Acer</em> <em>saccharum</em>, and <em>Quercus</em> <em>montana</em>), and three western (<em>Picea</em> <em>engelmannii</em>, <em>Pinus</em> <em>ponderosa</em>, and <em>Pseudotsuga</em> <em>menziesii</em>) tree species in North America. For these species, we assess and map the dominant climate-terrain interactions that partly determine abundance patterns in the eastern and western regions. In the process, we examine the role of varying responses and scales and explore finer-scale species climate-terrain niches and non-linear relationships.</p> <p><strong>Results</strong><br>Our study emphasizes the prominent role of elevation and heat-moisture variables in the west and the greater importance of seasonal precipitation and seasonal temperature in the east. The abundance patterns under future climate (SSP5–8.5) show climate-terrain habitats shifting northward and westward into Canada and Alaska for the eastern species, and predominantly north-westward for the western species.</p> <p><strong>Conclusion</strong><br>Our interpretative modelling framework can be used to gain a more comprehensive understanding of the abundance patterns across the full species range, to formulate better predictive models, and to facilitate improved management practices under climate change.</p>
Data from: Integrating ecological niche and hydrological connectivity models to assess the impacts of hydropower plants on an endemic and imperiled freshwater turtle
<p>We built this dataset to assess the impacts of hydropower plants on the distribution of an endemic and imperiled freshwater turtle with very unique ecological requirements, the Williams' side-necked turtle (<em>Phrynops</em> <em>williamsi</em>). To prevent and mitigate impacts, we prioritized sites for species conservation by classifying planned HPP locations according to their predicted adverse effects on species distribution. The dataset has two files: i) species occurrence records and ii) hydropower plant data. The first dataset was fully built by the authors and the second was modified from the Brazilian Electricity Regulatory Agency (ANEEL) georeferenced data system.</p>
Low- and high-intensity fire in the riparian savanna: demographic impacts in an avian model species and implications for ecological fire management
<ol> <li><span>Climate change is driving changes in fire frequency and intensity, making it more urgent for conservation managers to understand how species and ecosystems respond. In tropical monsoonal savannas – Earth's most fire-prone landscapes – ecological fire management aims to prevent intense wildfires late in the dry season through prescribed low-intensity burns early in the dry season. Riparian habitats embedded within tropical savannas represent critical refuges for biodiversity, yet are particularly fire-sensitive. Better understanding of the impact of fire – including prescribed burns – on riparian habitats is therefore key but requires long-term detailed post-fire monitoring of species' demographic rates, as effects may persist and/or be delayed.</span></li> <li><span>We analyse impacts of (prescribed) low-intensity and (prescribed but escaped) high-intensity fire in northern Australian riparian and adjacent savanna habitat. We quantify multi-year impacts on density, survival, reproduction and dispersal of an Endangered riparian bird, the western purple-crowned fairy-wren (<em>Malurus coronatus coronatus</em>), in a well-studied individually-marked population.</span></li> <li><span>Following low-intensity fire, bird density was reduced by >20% in burnt compared to adjacent unburnt riparian habitat for ≥2.5 years. This was a result of reduced breeding success and recruitment for two years immediately following fire, rather than mortality or dispersal of adults.</span></li> <li><span>In contrast, high-intensity fire (in a dry year) resulted in a sharp decline in population density by 50% 2–8 months after fire, with no signs of recovery after 2.5 years. The decline in density was due to post-fire adult mortality, rather than dispersal. Breeding success of the (few) remaining individuals was low but not detectably lower than in unburnt areas, likely because breeding success was poor overall due to prevailing dry conditions.</span></li> <li> <span><em>Synthesis and applications</em>. </span><span>Even if there is no or very low mortality during fire, and no movement of birds away from burnt areas, both low- and high-intensity fire in the riparian zone reduce population density. However, the mechanism by which this occurs, and recovery time, differs with fire intensity. To minimise impacts of fire on riparian zones in tropical savannas, we suggest employing low-intensity prescribed burns under optimal conditions shortly after the breeding season.</span> </li> </ol>
Influence of model complexity, training collinearity, collinearity shift, predictor novelty, and their interactions on ecological forecasting
<p dir="auto">This is a repository for revised manuscript submitted to <em>Global Ecology and Biogeography</em>.</p> <p dir="auto">Update: The manuscript was accepted by Global Ecology and Biography and published online on 29 November 2023.</p> <p dir="auto">Preprint: The 1st version submitted to <em>Proceedings of the National Academy of Sciences (PNAS)</em> can be found at: <a href="https://doi.org/10.32942/X2MS38" rel="nofollow">https://doi.org/10.32942/X2MS38</a></p> <p dir="auto">DOI: <a href="https://doi.org/10.1111/geb.13793" rel="nofollow">https://doi.org/10.1111/geb.13793</a></p> <p dir="auto">Citation: Chen, X., Liang, Y., & Feng, X. (2023). Influence of model complexity, training collinearity, collinearity shift, predictor novelty and their interactions on ecological forecasting. <em>Global Ecology and Biogeography</em>, 33, 371-384.</p> <p dir="auto">GitHub repository: https://github.com/xinxxxin/Influence-of-collinearity-and-novelty-on-ecological-forecasting/tree/main</p>
Graph models underlying templates for annotating a study in ecology and evolution
<p><em>Schematic representation of the graph models, resources and links to ontologies underlying a set of templates designed to annotate studies in invasion biology, and more generally ecology or evolution. The main template is the “General scoping” template, while each other box would allow a more detailed description of Dataset, Study system, Study design, and research question and hypotheses in Invasion biology. This figure was presented during the <a href="https://hiknowledgeworkshops.com/workshop-2/">June 2023 workshop of the Hi Knowledge initiative</a>. </em></p>
Data from: Too much of a good thing? Supplementing current species observations with fossil data to assess climate change vulnerability via ecological niche models
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Data from: Integrating ecological niche and hydrological connectivity models to assess the impacts of hydropower plants on an endemic and imperiled freshwater turtle
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Scale-dependence of ecological assembly rules: insights from empirical datasets and joint species distribution modelling
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Low- and high-intensity fire in the riparian savanna: demographic impacts in an avian model species and implications for ecological fire management
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Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models
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In the right place, at the right time: the integration of bacteria into the Plankton Ecology Group model
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Data from: A new null model approach to quantify performance and significance for ecological niche models of species distributions
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Unpacking the "black box": improving ecological interpretation of regression based models
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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.