Beginning in 2026, Methods in Ecology and Evolution will accept submissions of a new paper type – Workflows – for consideration for review and eventual publication in the journal. Up until now, we have generally considered manuscripts that describe a way to organise existing methods into a useful sequence to analyse an interesting set of data, make one’s computational life easier, or creating a package of such existing methods to be out of scope of what the journal publishes (Ellison, 2023). Although simple workflows that apply only to a single taxon, ecosystem, or narrow problem unlikely to be of interest to the general readership of the journal will still be considered out of scope and not considered for review, we do recognize that there can be workflows that are applicable to a broad class of problems, types of datasets, or experimental designs. Manuscripts describing the design testing, and analysis of the performance of such more complex workflows are what we will be looking for in Workflows manuscripts.

We are especially interested in two types of Workflows manuscripts:

1. Descriptions, testing, and analysis of novel and broadly useful assemblages of (usually) existing software or informatic tools into useful pipelines for analysis of complex, Big datasets of demonstrable importance to ecology and evolutionary biology; and 
2. Descriptions, testing, and analysis of empirical or experimental procedures that are used to generate Big datasets, including (but not limited to) eDNA and ‘omic, metabarcodes, and varKodes (e.g., de Madeiros et al., 2025), and products of continental- or larger-scale remotely-sensed observations (e.g., Fischer et al., 2024). 

A reviewable Workflows manuscript must:

• Represent a substantial and quantifiable improvement over existing methodologies;
• Include detailed and well-commented open-source code for computational workflows or detailed instructions for empirical/experimental workflows; 
• Document the thorough analysis of error propagation illustrating how the sequential application of multiple functions or procedures in the workflow contributes to overall uncertainty (see Chapter 7 in Dorman & Ellison, 2025 for useful examples); and
• For computational workflows only, extensive sensitivity analysis (including ablation analysis or the like for workflows that use machine- or deep-learning routines) with simulated or benchmark data and testing of the workflow on either an existing benchmarked dataset or a new Big dataset that is likely to become a benchmark dataset. 

Workflows that apply only to a single taxon, ecosystem, or narrow problem, or that lack (as necessary) code, instructions sets, uncertainty analysis, testing with simulated data, application to a benchmark dataset, or comparisons with similar methods and workflows will not be reviewed. We also will not consider frameworks that suggest workflow but do not actually adequately describe, test, or analyze it.

Like Research Articles in the journal, Workflows should have a maximum of 7000–8000 words (including tables/figure captions, statements and references list) and be formatted according to the Manuscript Specifications available on the journal homepage. Submissions of Workflows should be accompanied by a Workflow Checklist, also available on the journal home page, that should be uploaded as a file for editors only. Archiving a computational workflow or posting it to CRAN, PyPI, JuliaHub, etc., or publishing a new Big dataset on the UC Irvine Machine Learning Repository or Kaggle prior to submitting the manuscript will not be considered as a prior publication and will not preclude your submission being considered by the journal.

We encourage anyone interested in submitting a Workflows manuscript to send a pre-submission inquiry to the editorial office. At a minimum, please include the working title of the manuscript, the abstract, and the completed Workflows manuscript checklist with your presubmission inquiry. 

References

de Medeiros, B.A.S., Cai, L., Flynn, P.J. Yan, Y., Duan, X., Marinho, L.C., Anderson, C., & Davis, C.C. (2025) A composite universal DNA signature for the tree of life. Nature Ecology & Evolution 9, 1426–1440. DOI: 10.1038/s41559-025-02752-1

Dormann, C.F., & Ellison, A.M. (2025) Statistics by Simulation: A Synthetic Data Approach. Princeton University Press, Princeton, New Jersey, USA.

Ellison, A.M. (2023), Plus ça change, plus c’est la même chose: On our quattuordecennial, a good Methods paper still is not about my friend the dolphin. Methods in Ecology and Evolution, 14, 2904–2906. DOI: 10.1111/2041-210X.14232

Fischer, F.J., Jackson, T., Vincent, G., & Jucker, T. (2024). Robust characterisation of forest structure from airborne laser scanning—A systematic assessment and sample workflow for ecologists. Methods in Ecology and Evolution, 15, 1873–1888. DOI: 10.1111/2041-210X.14416