EcoEvoApps: Interactive apps for theoretical models in ecology and evolutionary biology

Abstract

The integration of theory and data drives progress in science, but a persistent barrier to such integration in ecology and evolutionary biology is that theory is often developed and expressed in the form of mathematical models that can feel daunting and inaccessible for students and empiricists with variable quantitative training and attitudes towards math. A promising way to make mathematical models more approachable is to embed them into interactive tools with which one can visually evaluate model structures and directly explore model outcomes through simulation. To promote such interactive learning of quantitative models, we developed EcoEvoApps, a collection of free, open‐source, and multilingual R/Shiny apps that include model overviews, interactive model simulations, and code to implement these models directly in R. The package currently focuses on canonical models of population dynamics, species interactions, and landscape ecology. These apps help illustrate fundamental results from theoretical ecology and can serve as valuable teaching tools in classroom settings. We present data from student surveys which show that students rate these apps as useful learning tools, and that using interactive apps leads to substantial gains in students' interest and confidence in working with mathematical models. This points to the potential for interactive activities to make theoretical models more accessible to a wider audience, and thus facilitate the feedback between theory and data across ecology and evolutionary biology.

ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research

“…The use of open-source ShinyApps has seen significant growth in stimulating teaching and research activities in the fields of ecology and evolution in the past 5 years (Kass et al, 2023;McGuire et al, 2022;Segrestin et al, 2021;Weigelt et al, 2022). In conclusion, we hope that the ZooTraits app will become a valuable and dynamic resource for ecology and evolution.…”

“…The use of open‐source ShinyApps has seen significant growth in stimulating teaching and research activities in the fields of ecology and evolution in the past 5 years (Kass et al., 2023 ; McGuire et al., 2022 ; Segrestin et al., 2021 ; Weigelt et al., 2022 ). However, when it comes to trait‐based ecology, a key challenge lies in the knowledge shortfalls that hinder our ability to select appropriate traits or access information for certain taxonomic groups or geographical regions (Gonçalves‐Souza et al., 2023 ; Maitner et al., 2023 ).…”

Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

“…These could be theoretical models, or simulation models that can reproduce the dynamics described in theoretical models. Numerous R software packages exist that allow simulations of commonly used ecological models such as logistic growth and multi-species interaction models (gauseR; [15]), consumer functional response models (FRAIR; [16]), infectious disease models (EpiDynamics; [17]), and island biogeography and metapopulation models (EcoEvoApps; [18]), and researchers can use open-source code to extend the model to consider evolution. The EcoEvo Mathematica package (https://github.com/cklausme/EcoEvo) allows simulations and equilibrium condition analyses of species-and trait-based eco-evolutionary models, using differential equations with intraand inter-specific interactions and either quantitative genetic or adaptive dynamics trait models.…”

Harnessing open science practices to teach ecology and evolutionary biology using interactive tutorials

“…Using scaffolded interactive tutorials that cater to a variety of student proficiency levels (e.g., programming skills or statistical background) can reduce cognitive load and improve student engagement and comprehension when learning programming and discipline‐specific content. This approach allows students to engage with material most comfortable to them, whether that means directly writing associated code or adjusting data inputs in an interactive application (Custer et al., 2021; Farrell & Carey, 2018; McGuire et al., 2022; Rissanen & Costello, 2023). A self‐paced, scaffolded, multi‐proficiency level approach to learning empowers students to challenge themselves in a productive way by tackling complex problems while gaining computational literacy and scientific skills (Dill‐McFarland et al., 2021; Emery et al., 2021; Farrell & Carey, 2018).…”