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7 results for “community-level modeling”
Code and data for Bayesian joint species distribution model selection for community-level prediction
<p>Code and data for reproducing the analysis in the manuscript "Bayesian joint species distribution model selection for community-level prediction." Provided data include percent cover observations for 39 modeled vascular plant species within boreal forest understory communities and environmental model covariates. R code is provided to generate model inputs, apply alternative models, generate out-of-sample predictions, and calculate associated community and species log scores and alternative model evaluation metrics. Further, R source code is provided to implement the multinomial joint species distribution model defined in the manuscript. Details on the data, its processing, and the alternative model definitions and structure can be found in the main text of the manuscript. Provided data are currently being used in ongoing analyses and coordination with authors may be warranted to avoid duplicate publication. Potential users are encouraged to consider collaboration with authors when useful and appropriate. Misinterpretation of data may occur if used outside the context of the original analysis. All data are made available in their current state. While significant efforts have been made to ensure data accuracy, complete accuracy cannot be guaranteed. Data may be updated periodically. It is the responsibility of the data user to check for updated versions of the data.</p>
Code and data for Bayesian joint species distribution model selection for community-level prediction
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Data from: Controlled comparison of species- and community-level models across novel climates and communities
Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.
Data from: Ecological genomics meets community-level modeling of biodiversity: mapping the genomic landscape of current and future environmental adaptation
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Data from: Controlled comparison of species- and community-level models across novel climates and communities
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Data from: Multiresponse algorithms for community-level modeling: review of theory, applications, and comparison to species distribution models
1.Community-level models (CLMs) consider multiple, co-occurring species in model fitting and are lesser known alternatives to species distribution models (SDMs) for analyzing and predicting biodiversity patterns. CLMs simultaneously model multiple species, including rare species, while reducing overfitting and implicitly considering drivers of co-occurrence. Many CLMs are direct extensions of well-known SDMs and therefore should be familiar to ecologists. However, CLMs remain underutilized, and there have been few tests of their potential benefits and no systematic reviews of their assumptions and implementations. Here we review this emerging field and provide examples in R to fit common CLMs. Our goal is to introduce CLMs to a broader audience, and discuss their attributes, benefits, and limitations relative to SDMs. 2.We review i) statistical implementations and applications of CLMs, ii) their advantages and limitations, and iii) comparative analyses of CLMs and SDMs. We also suggest directions for future research. 3.We identify seven CLM algorithms with similar data structures and predictive outputs as SDMs that should be most accessible to ecologists familiar with species-level modeling, including five methods that predict assemblage composition and individual species distributions and two methods that model compositional turnover along environmental gradients. CLMs have been applied to numerous taxa, regions, and spatial scales, and a variety of topics (e.g., studying drivers of community structure or assessing relationships between community composition and functional traits). Studies suggest that the relative benefits of CLMs and SDMs may be case specific, especially in terms of predicting species distributions and community composition. However, CLMs may offer advantages in terms of computational efficiency, modeling rare species, and projecting to no-analog climates. A major shortcoming of CLMs is their reliance on presence-absence community composition data. 4.Studies are needed to assess the relative merits of SDMs and CLMs, and different CLM algorithms, with a focus on three key areas: i) under which circumstances CLMs improve predictions for rare species, ii) how CLMs perform under different community compositions (e.g. relative abundance of rare vs. common species), including the extent to which co-occurrence patterns are structured by biotic interactions, and iii) ability to project across time/space.
Data from: Multiresponse algorithms for community-level modeling: review of theory, applications, and comparison to species distribution 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.