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Methods for ecological and evolutionary data analysis
Submission status
Open
Submission deadline
The volumes of genetic, biodiversity, and environmental data that can be obtained from individual studies are growing at an impressive rate, as are the public repositories sharing these data. Such large and complex datasets necessitate innovations in the computational and statistical methods used to investigate questions ranging from the origins of life to the conservation of biological diversity in the face of global change. The editors of Nature Methods, Nature Communications, Communications Biology, Communications Earth & Environment and Scientific Reports invite submissions of papers introducing new methods or significant developments to existing methods for the quantitative analysis of ecological and evolutionary data.
To be considered for the Collection, the method may have applications in any branch of ecology or evolutionary biology, but should be of broad interest for practitioners in that field. Submissions introducing new or updated methods should include method validation and, if relevant, benchmarking against related approaches. However, as the focus is on the resource value of the methods themselves, we will not necessarily expect a full application to a research question, though this would of course be welcome. We will also consider submissions that critique or compare the performance of existing methods, as long as these evaluations produce significant methodological insight for the field.
Mapping ecological variables using machine-learning algorithms based on remote-sensing data has become a widespread practice in ecology. Here, the authors use forest biomass mapping as a study case to show that the most common model validation approach, which ignores data spatial structure, leads to overoptimistic assessment of model predictive power.
Multiple sequence alignments are widely used to predict protein structure, function, and phylogeny, but are uncertain with more diverged sequences. Muscle5 generates ensembles of alternative high-accurate alignments, enabling novel confidence estimates in alignments, trees, and other inferences.
CherryML is a method to scale up maximum likelihood estimation for general phylogenetic models of molecular evolution, providing several orders of magnitude speedup over traditional methods.
The direct relationship between societal development and local ecosystem services breaks down at relatively minor levels of human modification of large river delta landscapes, according to a statistical analysis of 235 deltas.