How to make ecological models useful for environmental management
Abstract
Understanding and predicting the ecological consequences of different management alternatives is becoming increasingly important to support environmental management decisions. Ecological models could contribute to such predictions, but in the past this was often not the case. Ecological models are often developed within research projects but are rarely used for practical applications. In this synthesis paper, we discuss how to strengthen the role of ecological modeling in supporting environmental management decisions with a focus on methodological aspects. We address mainly ecological modellers but also potential users of modeling results. Various modeling approaches can be used to predict the response of ecosystems to anthropogenic interventions, including mechanistic models, statistical models, and machine learning approaches. Regardless of the chosen approach, we outline how to better align the modeling to the decision making process, and identify six requirements that we believe are important to increase the usefulness of ecological models for management support, especially if management decisions need to be justified to the public. These cover: (i) a mechanistic understanding regarding causality, (ii) alignment of model input and output with the management decision, (iii) appropriate spatial and temporal resolutions, (iv) uncertainty quantification, (v) sufficient predictive performance, and (vi) transparent communication. We discuss challenges and synthesize suggestions for addressing these points.
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BibTeX
@article{schuwirth2026make,
title = {{How to make ecological models useful for environmental management}},
author = {Nele Schuwirth and Florian Borgwardt and Sami Domisch and Martin Friedrichs and Mira Kattwinkel and David Kneis and Mathias Kuemmerlen and Simone D Langhans and Javier Martínez-López and Peter Vermeiren},
journal = {Ecological Modelling},
year = {2026},
month = sep,
doi = {10.1016/j.ecolmodel.2019.108784},
url = {https://hal.inrae.fr/hal-05738376v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - How to make ecological models useful for environmental management
AU - Nele Schuwirth
AU - Florian Borgwardt
AU - Sami Domisch
AU - Martin Friedrichs
AU - Mira Kattwinkel
AU - David Kneis
AU - Mathias Kuemmerlen
AU - Simone D Langhans
AU - Javier Martínez-López
AU - Peter Vermeiren
PY - 2026
DA - 2026/09/04
JO - Ecological Modelling
DO - 10.1016/j.ecolmodel.2019.108784
UR - https://hal.inrae.fr/hal-05738376v1
AB - Understanding and predicting the ecological consequences of different management alternatives is becoming increasingly important to support environmental management decisions. Ecological models could contribute to such predictions, but in the past this was often not the case. Ecological models are often developed within research projects but are rarely used for practical applications. In this synthesis paper, we discuss how to strengthen the role of ecological modeling in supporting environmental management decisions with a focus on methodological aspects. We address mainly ecological modellers but also potential users of modeling results. Various modeling approaches can be used to predict the response of ecosystems to anthropogenic interventions, including mechanistic models, statistical models, and machine learning approaches. Regardless of the chosen approach, we outline how to better align the modeling to the decision making process, and identify six requirements that we believe are important to increase the usefulness of ecological models for management support, especially if management decisions need to be justified to the public. These cover: (i) a mechanistic understanding regarding causality, (ii) alignment of model input and output with the management decision, (iii) appropriate spatial and temporal resolutions, (iv) uncertainty quantification, (v) sufficient predictive performance, and (vi) transparent communication. We discuss challenges and synthesize suggestions for addressing these points.
SN - 0304-3800
SN - 1872-7026
ER -