Ecological Informatics Submission Guide: Ecology, Data Science, and Validation

Ecological Informatics spans computational ecology, data science, biogeography, ecosystem analysis, and related information problems. The strongest manuscript explains why computation is necessary for a specific ecological inference.

Write the contribution in this order:

1. ecological process, pattern, monitoring need, or management problem;
2. information barrier that prevents a reliable answer;
3. informatics advance that crosses the barrier;
4. evidence showing the answer is more reliable or usable; and
5. boundary beyond which the result is not established.

Avoid opening with an algorithm catalog. An editor should understand the ecological consequence before comparing architectures or hyperparameters.

Ecological observations are rarely independent in the way a random train/test split assumes. Nearby sites, repeated seasons, related taxa, shared sensors, or duplicated observation events can make held-out performance optimistic.

Describe sampling design, label provenance, missingness, preprocessing, leakage controls, baselines, tuning, uncertainty, and failed cases. Report why the split matches the real use rather than saying only that data were divided into training and testing sets.

Prediction is not mechanism. Feature importance, attention, or partial-dependence plots can reveal model behavior but do not automatically establish ecological causation. Separate:

• predictive evidence from causal evidence;
• patterns in the sampled system from transfer to other systems;
• management relevance from demonstrated management effect; and
• model confidence from ecological uncertainty.

When mechanism is part of the claim, connect the model result to ecological theory, independent evidence, intervention, natural experiment, or another design capable of supporting that inference.

An ecological informatics result is difficult to evaluate without provenance. Record dataset origin, collection design, licenses, transformations, coordinate or temporal handling, taxonomic resolution, exclusions, label construction, and version.

For code, preserve the release that generated the paper, environment or dependencies, configuration, seeds where meaningful, and a clear path from raw inputs to headline artifacts. If access is restricted for ethical, conservation, Indigenous data-governance, or privacy reasons, state the boundary and a legitimate access process rather than implying open availability.

Lead with the ecological problem and the information barrier. Introduce the computational approach at the level needed to understand why it can solve that barrier. Present baselines and validation before celebrating performance. Use results to answer ecological questions, then discuss transfer, uncertainty, and practical consequences.

A useful first-artifact sequence is:

1. study system and sampling diagram;
2. pipeline or data-integration map;
3. realistic validation design;
4. performance and uncertainty; and
5. ecological interpretation or decision artifact.

This order prevents the architecture diagram from displacing the ecological story.

Use the live Guide for Authors for current article types, file formats, title-page information, section requirements, declarations, data fields, and submission checklist. Before upload:

• provide editable source files for text, tables, and figures as required;
• reconcile author names, affiliations, order, and corresponding-author details;
• complete funding, conflicts, authorship, ethics, data, and AI disclosures;
• remove tracked changes and private comments;
• cite every table, figure, supplement, dataset, and code record;
• verify permissions and accessibility; and
• inspect the compiled review PDF page by page.

These failure patterns connect a visible weakness to a repair an author can make before upload; they do not predict the editorial outcome for a particular manuscript. In our pre-submission review work, the most useful test is whether the validation split preserves the ecological dependence structure that the final inference assumes.

Random splitting inflates performance. Rebuild validation around site, time, taxon, or study boundary that matches use.

The ecology disappears after the introduction. Organize results around ecological questions, not only model metrics, and interpret failure cases ecologically.

Feature importance is called mechanism. Rename the result as predictive association or add evidence capable of testing mechanism.

The pipeline cannot be reconstructed. Publish a versioned workflow or document the restricted components, transformations, and access path precisely.

Worked example: species distribution beyond a benchmark

A model may outperform a baseline for species occurrence. The stronger Ecological Informatics paper shows that the test geography is genuinely held out, examines calibration under prevalence shift, identifies where environmental extrapolation occurs, and explains how the output changes survey prioritization. The ecological decision and transfer boundary turn model performance into information gain.

Browse the best ecology journals, compare the Ecological Indicators submission guide and Ecology submission guide, or review how to choose a journal for a paper.

Once Ecological Informatics is the chosen route, a free manuscript readiness scan can help test whether the ecological claim, validation, and reusable evidence support the same conclusion.