TY - JOUR
T1 - Environmental drivers of golden tilefish (Lopholatilus chamaeleonticeps) commercial landings and catch-per-unit-effort
AU - Nesslage, Geneviève
AU - Lyubchich, Vyacheslav
AU - Nitschke, Paul
AU - Williams, Erik
AU - Grimes, Churchill
AU - Wiedenmann, John
N1 - Funding Information:
This project was funded by the National Oceanic and Atmospheric Administration's Fisheries and The Environment program (NA14OAR4320158). We thank the two anonymous reviewers of our manuscript for their time and constructive comments. We also thank all those who provided data and advice for the project, including SCDNR’s Marine Resources Monitoring Assessment and Prediction (MARMAP) program, Tracey Smart, Kevin McCarthy, and Harvey Walsh. Analyses and conclusions resulting from the use of MARMAP data are not necessarily those of the originating program. The Florida Current cable and section data are made freely available on the Atlantic Oceanographic and Meteorological Laboratory web page ( www.aoml.noaa.gov/phod/floridacurrent/ ) and are funded by the DOC‐NOAA Climate Program Office – Ocean Observing and Monitoring Division. The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the authors and do not necessarily reflect those of NOAA or the Department of Commerce. This is contribution number 5994 of the University of Maryland Center for Environmental Science.
Funding Information:
This project was funded by the National Oceanic and Atmospheric Administration's Fisheries and The Environment program (NA14OAR4320158). We thank the two anonymous reviewers of our manuscript for their time and constructive comments. We also thank all those who provided data and advice for the project, including SCDNR?s Marine Resources Monitoring Assessment and Prediction (MARMAP) program, Tracey Smart, Kevin McCarthy, and Harvey Walsh. Analyses and conclusions resulting from the use of MARMAP data are not necessarily those of the originating program. The Florida Current cable and section data are made freely available on the Atlantic Oceanographic and Meteorological Laboratory web page (www.aoml.noaa.gov/phod/floridacurrent/) and are funded by the DOC-NOAA Climate Program Office ? Ocean Observing and Monitoring Division. The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the authors and do not necessarily reflect those of NOAA or the Department of Commerce. This is contribution number 5994 of the University of Maryland Center for Environmental Science.
Publisher Copyright:
© 2021 John Wiley & Sons Ltd
PY - 2021/9
Y1 - 2021/9
N2 - We explored a range of potential low and high-frequency environmental drivers of fishery production (landings) and catch-per-unit-effort (CPUE) for northern and southern stocks of golden tilefish (Lopholatilus chamaeleonticeps), a stenothermic species that prefers a narrow band of habitat along the continental shelf and upper slope of the eastern US. Random forest regression, a machine learning technique, was used to examine the impact of numerous and sometimes correlated environmental covariates. We used important random forest covariates to inform construction of a more parsimonious generalized additive mixed model for each data type and stock. We identified several potential environmental drivers of golden tilefish fishery and stock dynamics, including low-frequency climate indices, oceanographic currents, and high-frequency oceanographic conditions. Both Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation indices were associated with historical golden tilefish landings for the northern stock spanning 1915–2000 at lags of 7 and 3–4 years, respectively. CPUE for both stocks (north: 1995–2017, south: 1994–2018) was associated with the AMO and oceanographic currents. In addition, northern stock CPUE was negatively related to Labrador Current flow and positively related to northerly position of the Gulf Stream. Southern stock CPUE was associated with seasonal Florida Current transport, monthly sea surface temperatures, and latitude. Oceanographic currents and water temperature primarily influenced within-year CPUE, indicating a potential effect on adult fish or fisher behavior. In contrast, low-frequency climate indices were associated with CPUE and landings at lags of 3–7 years, indicating their primary impact was on recruitment strength.
AB - We explored a range of potential low and high-frequency environmental drivers of fishery production (landings) and catch-per-unit-effort (CPUE) for northern and southern stocks of golden tilefish (Lopholatilus chamaeleonticeps), a stenothermic species that prefers a narrow band of habitat along the continental shelf and upper slope of the eastern US. Random forest regression, a machine learning technique, was used to examine the impact of numerous and sometimes correlated environmental covariates. We used important random forest covariates to inform construction of a more parsimonious generalized additive mixed model for each data type and stock. We identified several potential environmental drivers of golden tilefish fishery and stock dynamics, including low-frequency climate indices, oceanographic currents, and high-frequency oceanographic conditions. Both Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation indices were associated with historical golden tilefish landings for the northern stock spanning 1915–2000 at lags of 7 and 3–4 years, respectively. CPUE for both stocks (north: 1995–2017, south: 1994–2018) was associated with the AMO and oceanographic currents. In addition, northern stock CPUE was negatively related to Labrador Current flow and positively related to northerly position of the Gulf Stream. Southern stock CPUE was associated with seasonal Florida Current transport, monthly sea surface temperatures, and latitude. Oceanographic currents and water temperature primarily influenced within-year CPUE, indicating a potential effect on adult fish or fisher behavior. In contrast, low-frequency climate indices were associated with CPUE and landings at lags of 3–7 years, indicating their primary impact was on recruitment strength.
KW - Golden tilefish
KW - Lopholatilus chamaeleonticeps
KW - environmental driver
KW - generalized additive model
KW - machine learning
KW - mixed model
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85111938545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111938545&partnerID=8YFLogxK
U2 - 10.1111/fog.12540
DO - 10.1111/fog.12540
M3 - Article
AN - SCOPUS:85111938545
SN - 1054-6006
VL - 30
SP - 608
EP - 622
JO - Fisheries Oceanography
JF - Fisheries Oceanography
IS - 5
ER -