8 quick tips for data-model integration in ecology

TL;DR

This paper offers practical tips and guiding principles for theoretical ecologists to effectively integrate diverse ecological data into models, emphasizing transparency and understanding uncertainty.

Contribution

It provides a set of structured rules and best practices specifically tailored for early-career ecologists to improve data-model integration in ecology.

Findings

Emphasizes iterative data-model integration process

Highlights leveraging multiple data sources

Stresses transparent communication of modeling choices

Abstract

Theoretical ecologists have long leveraged empirical data in various forms to advance ecology. Recently increased volumes and access to ecological data present an expanding set of opportunities for theoreticians to inform model development, framing, and interpretation. Whereas statisticians have collective guidance on best practices for data use, theoreticians might lack formal education on how to integrate diverse types of data into a single ecological model. As a group of predominantly early-career theoretical ecologists, we have developed guiding principles and practical tips to support theoretical ecologists in synthesizing multiple types of data at different phases of the modeling process. Our rules fall into three overarching themes: iteration in the data-model integration process, leveraging multiple sources of data), and understanding uncertainty. Across these rules, we emphasize that the data-model integration requires transparent, justifiable, and defensible communication of modeling choices to support readers in appropriately contextualizing the model and its implications.