Processes and Predictions in Ecological Models: Logic and Causality

Christian Damgaard

Journal of Forecasting, 2025, vol. 44, issue 5, 1658-1665

Abstract: To make credible ecological predictions for terrestrial ecosystems in a changing environment and increase our understanding of ecological processes, we need plant ecological models that can be fitted to spatial and temporal ecological data. Such models need to be based on a sufficient understanding of ecological processes to make credible predictions and account for the different sources of uncertainty. Here, I argue (1) for the use of structural equation models in a hierarchical framework with latent variables and (2) to specify whether our current knowledge of relationships among state variables may be categorized primarily as logical (empirical) or causal. Such models will help us to make continuous progress in our understanding of and ability to predict the dynamics of terrestrial ecosystems and provide us with local predictions with a known degree of uncertainty that are useful for generating adaptive management plans. The hierarchical structural equation models I recommend are analogous to current general epistemological models of how knowledge is obtained.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/for.3267

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:44:y:2025:i:5:p:1658-1665

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().