[MARMAM] New Paper: Performance evaluation of cetacean species distribution models

elizabeth.becker at noaa.gov elizabeth.becker at noaa.gov
Thu May 14 07:16:53 PDT 2020


Dear Colleagues,

We are pleased to announce the publication of the following paper in Ecology
and Evolution:

Becker EA, Carretta JV, Forney KA, Barlow J, Brodie S, Hoopes R, Jacox MG,
Maxwell SM, Redfern JV, Sisson NB, Welch H, Hazen EL.  Performance
evaluation of cetacean species distribution models developed using
generalized additive models and boosted regression trees.  Ecol Evol.
2020;00:1-28. 

 

Abstract - Species distribution models (SDMs) are important management tools
for highly mobile marine species because they provide spatially and
temporally explicit information on animal distribution. Two prevalent
modeling frameworks used to develop SDMs for marine species are Generalized
Additive Models (GAMs) and Boosted Regression Trees (BRTs), but comparative
studies have rarely been conducted; most rely on presence-only data; and few
have explored how features such as species distribution characteristics
affect model performance. Since the majority of marine species BRTs have
been used to predict habitat suitability, we first compared BRTs to GAMs
that used presence/absence as the response variable. We then compared
results from these habitat suitability models to GAMs that predict species
density (animals km-2) because density models built with a subset of the
data used here have previously received extensive validation. We compared
both the explanatory power (i.e., model goodness-of-fit) and predictive
power (i.e., performance on a novel dataset) of the GAMs and BRTs for a
taxonomically diverse suite of cetacean species using a robust set of
systematic survey data (1991-2014) within the California Current Ecosystem.
Both BRTs and GAMs were successful at describing overall distribution
patterns throughout the study area for the majority of species considered,
but when predicting on novel data, the density GAMs exhibited substantially
greater predictive power than both the presence/absence GAMs and BRTs,
likely due to both the different response variables and fitting algorithms.
Our results provide an improved understanding of some of the strengths and
limitations of models developed using these two methods. These results can
be used by modelers developing SDMs and resource managers tasked with the
spatial management of marine species to determine the best modeling
technique for their question of interest.

The paper can be freely downloaded from:

 <https://doi.org/10.1002/ece3.6316> https://doi.org/10.1002/ece3.6316

Best regards,

Elizabeth

 

Elizabeth A. Becker

Contractor, Ocean Associates Inc.

Marine Mammal & Turtle Division

Southwest Fisheries Science Center, NMFS, NOAA

8901 La Jolla Shores Drive 

La Jolla, CA, 92037, USA

 <mailto:Elizabeth.Becker at noaa.gov> Elizabeth.Becker at noaa.gov

 

 

 

 

 

 

 

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