Fit to predict? Ecoinformatics for predicting the catchability of a pelagic fish in near real-time

Kylie L Scales; Elliott L Hazen; Sara M Maxwell; Heidi Dewar; Suzanne Kohin; Michael G Jacox; Christopher A Edwards; Dana K Briscoe; Larry B Crowder; Rebecca L Lewison; Steven J Bograd

doi:10.1002/eap.1610

Back

Fit to predict? Ecoinformatics for predicting the catchability of a pelagic fish in near real-time

Journal article

Open access

Peer reviewed

Fit to predict? Ecoinformatics for predicting the catchability of a pelagic fish in near real-time

Kylie L Scales, Elliott L Hazen, Sara M Maxwell, Heidi Dewar, Suzanne Kohin, Michael G Jacox, Christopher A Edwards, Dana K Briscoe, Larry B Crowder, Rebecca L Lewison, …

Ecological Applications, Vol.27(8), pp.2313-2329

2017

DOI: https://doi.org/10.1002/eap.1610

Files and links (2)

pdf

PDF - Author's Accepted Version1.44 MBDownload View

Accepted VersionPDF - Author Accepted Version Open Access

url

https://doi.org/10.1002/eap.1610View

Published Version

Related media

Expert Quote 15-Feb-2025

Avoiding a sea of regret

UniSC News (Clare McKay)

Abstract

dynamic ocean management

fisheries

remote sensing

satellite

ROMS

ocean model

species distribution model

ecological forecasting

The ocean is a dynamic environment inhabited by a diverse array of highly migratory species, many of which are under direct exploitation in targeted fisheries. The timescales of variability in the marine realm coupled with the extreme mobility of ocean-wandering species such as tuna and billfish complicates fisheries management. Developing ecoinformatics solutions that allow for near real-time prediction of the distributions of highly mobile marine species is an important step towards the maturation of dynamic ocean management and ecological forecasting. Using 25 years (1990-2014) of NOAA fisheries' observer data from the California drift gillnet fishery, we model relative probability of occurrence (presence-absence) and catchability (total catch) of broadbill swordfish Xiphias gladius in the California Current System (CCS). Using freely-available environmental datasets and open source software, we explore the physical drivers of regional swordfish distribution. Comparing models built upon remotely-sensed datasets with those built upon a data-assimilative configuration of the Regional Ocean Modelling System (ROMS), we explore trade-offs in model construction and address how physical data can affect predictive performance and operational capacity. Swordfish catchability was found to be highest in deeper waters (>1500m) with surface temperatures in the 14-20°C range, isothermal layer depth (ILD) of 20-40m, positive sea surface height anomalies and during the new moon ( <20% lunar illumination). We observed a greater influence of mesoscale variability (sea surface height, wind speed, isothermal layer depth, Eddy Kinetic Energy) in driving swordfish catchability (total catch) than was evident in predicting the relative probability of presence (presence-absence), confirming the utility of generating spatio-temporally dynamic predictions. Data-assimilative ROMS circumvent the limitations of satellite remote sensing in providing physical data fields for species distribution models (e.g. cloud cover, variable resolution, sub-surface data), and facilitate broad-scale prediction of dynamic species distributions in near real-time.

Details

Title: Fit to predict? Ecoinformatics for predicting the catchability of a pelagic fish in near real-time
Authors: Kylie L Scales (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
Elliott L Hazen (Author) - University of California Santa Cruz, United States
Sara M Maxwell (Author) - Old Dominion University, United States
Heidi Dewar (Author) - NOAA Southwest Fisheries Science Center, United States
Suzanne Kohin (Author) - NOAA Southwest Fisheries Science Center, United States
Michael G Jacox (Author) - University of California Santa Cruz, United States
Christopher A Edwards (Author) - University of California Santa Cruz, United States
Dana K Briscoe (Author) - Hopkins Marine Station of Stanford University, United States
Larry B Crowder (Author) - Hopkins Marine Station of Stanford University, United States
Rebecca L Lewison (Author) - San Diego State University, United States
Steven J Bograd (Author) - NOAA Southwest Fisheries Science Center, United States
Publication details: Ecological Applications, Vol.27(8), pp.2313-2329
Publisher: John Wiley & Sons Inc.
Date published: 2017
DOI: 10.1002/eap.1610
ISSN: 1051-0761
Copyright note: Copyright ©. This is the accepted version of the following article: Scales, K. L., Hazen, E. L., Maxwell, S. M., Dewar, H. , Kohin, S. , Jacox, M. G., Edwards, C. A., Briscoe, D. K., Crowder, L. B., Lewison, R. L. and Bograd, S. J. (2017), Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time. Ecol Appl, 27: 2313-2329. doi:10.1002/eap.1610, which has been published in final form at http://dx.doi.org/10.1002/eap.1610
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
Language: English
Record Identifier: 99451164902621
Output Type: Journal article

Metrics

25 File views/ downloads

620 Record Views

58 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Ecology; Environmental Sciences

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

Source: InCites