Details:

Journal Title:
PLoS ONE
Personal Author:
Fox, Eric W. ; Ver Hoef, Jay M. ; Olsen, Anthony R.
Fox, Eric W. ; Ver Hoef, Jay M. ; Olsen, Anthony R. Less -
NOAA Program & Office:
NMFS (National Marine Fisheries Service) ; AFSC (Alaska Fisheries Science Center)
NMFS (National Marine Fisheries Service) ; AFSC (Alaska Fisheries Science Center) Less -
Description:
Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records that are spatially autocorrelated. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. A primary application is mapping MMI predictions and prediction errors at 1.1 million perennial stream reaches across the conterminous United States. For the spatial regression model, we develop a novel transformation procedure that estimates Box-Cox transformations to linearize covariate relationships and handles possibly zero-inflated covariates. We find that the spatial regression model with transformations, and a subsequent selection of significant covariates, has cross-validation performance comparable to random forests. We also find that prediction interval coverage is close to nominal for each method, but that spatial regression prediction intervals tend to be narrower and have less variability than quantile regression forest prediction intervals. A simulation study is used to generalize results and clarify advantages of each modeling approach.
Keywords:
[+]
Big Data Data Curation Ecology Forecasting
Source:
PLoSONE 15(3) 1-22
DOI:
https://doi.org/10.1371/journal.pone.0229509
Pubmed Central ID:
PMC7089425
Document Type:
Journal Article
Rights Information:
CC0 1.0
Compliance:
PMC
Main Document Checksum:
[+]
urn:sha256:60bb74049f5d6b3727910d84903028fc507985edfe1b5957d2d55ee107b9bf34
Download URL:
https://repository.library.noaa.gov/view/noaa/31803/noaa_31803_DS1.pdf
File Type:

Supporting Files

• Fox_PLOSOne_2020_XML.xml

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