Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology

Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framewo...

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Bibliographic Details
Published in:Restoration ecology Vol. 27; no. 5; pp. 1053 - 1063
Main Authors: Barnard, David M., Germino, Matthew J., Pilliod, David S., Arkle, Robert S., Applestein, Cara, Davidson, Bill E., Fisk, Matthew R.
Format: Journal Article
Language:English
Published: Malden, USA Wiley Periodicals, Inc 01.09.2019
Blackwell Publishing Ltd
Subjects:
Accuracy
Adaptive management
Artemisia
artificial intelligence
basins
Data
data collection
decision support systems
Decision trees
ecological prediction
ecological restoration
Ecology
ensemble modeling
Geographic information systems
Geographical information systems
Learning algorithms
Machine learning
model comparison
Modelling
postfire restoration
prediction
Prediction models
predictive framework
Resource management
Restoration
United States
United States
ISSN:1061-2971, 1526-100X
Online Access:Get full text
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Summary:Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framework for restoration ecology using 11 modeling frameworks (including machine learning, inferential, and ensemble approaches) and three data groups (field data, geographic data [GIS], and a combination thereof). We test this approach with a dataset from a large postfire sagebrush reestablishment project in the Great Basin, U.S.A. Predictive power varied among models and data groups, ranging from 58% to 79% accuracy. Finer‐scale field data generally had the greatest predictive power, although GIS data were present in the best models overall. An ensemble prediction computed from the 10 models parameterized to field data was well above average for accuracy but was outperformed by others that prioritized model parsimony by selecting predictor variables based on rankings of their importance among all candidate models. The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes.
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ISSN:1061-2971
1526-100X
DOI:10.1111/rec.12938