Iterative near-term ecological forecasting: Needs, opportunities, and challenges

By M. Dietze, A. Fox, Lindsay M. Beck-Johnson, J. Betancourt, Mevin B. Hooten, Catherine S. Jarnevich, Timothy H. Keitt, M. Kenney, C. Laney, L. Larsen and 9 more. Proceedings of the National Academy of Sciences of the United States of America, 2018

533 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems (2020, background): “The emergence of the concept of ecological forecasting (Dietze et al., 2018) however, aims to probe and illustrate the degree to which the concepts of data assimilation can help constrain predictions dependent on accurate representation of ecosystem processes (Fer et al., 2020; Niu et al., 2014).”
Process‐Guided Deep Learning Predictions of Lake Water Temperature (2019, methodology): “…temperature is an ecosystem “ master variable ” (Magnuson et al., 1979), these improvements could translate directly into improved modeling of biota (e.g., Hansen et al., 2017; Mainali et al., 2015; Paerl & Huisman, 2008) and forecasting of ecosystem conditions and services (Dietze et al., 2018).”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, methodology): “This difference is exacerbated when making predictions under novel or no-analog conditions (Williams and Jackson 2007, Dietze et al. 2018) where machine learning methods favor model complexity motivated by model fit over transferability and ecological theory (Sinclair et al. 2010, Bell and…”