Bridging implementation gaps to connect large ecological datasets and complex models.

Autor: Raiho AM; Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA., Nicklen EF; Denali National Park and Preserve National Park Service Fairbanks Alaska USA., Foster AC; School of Informatics, Computing, and Cyber Systems Northern Arizona University Flagstaff Arizona USA., Roland CA; Denali National Park and Preserve National Park Service Fairbanks Alaska USA., Hooten MB; Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA.; Department of Statistics Colorado State University Fort Collins Colorado USA.; Colorado Cooperative Fish and Wildlife Research Unit U.S. Geological Survey Fort Collins Colorado USA.
Jazyk: angličtina
Zdroj: Ecology and evolution [Ecol Evol] 2021 Dec 14; Vol. 11 (24), pp. 18271-18287. Date of Electronic Publication: 2021 Dec 14 (Print Publication: 2021).
DOI: 10.1002/ece3.8420
Abstrakt: Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree-ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species ( Picea mariana and Picea glauca ) simulated by a forest gap model, University of Virginia Forest Model Enhanced-UVAFME. Through this process, we provide preliminary ecological inference about the long-term competitive dynamics between slow-growing P. mariana and relatively faster-growing P. glauca . Incorporating tree-ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values for P. mariana minimum growth under stress and P. glauca maximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster-growing P. glauca may outcompete P. mariana under climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting.
(© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)
Databáze: MEDLINE
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