Journal article
Fit to predict? Ecoinformatics for predicting the catchability of a pelagic fish in near real-time
Ecological Applications, Vol.27(8), pp.2313-2329
2017
Abstract
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 EngineeringElliott L Hazen (Author) - University of California Santa Cruz, United StatesSara M Maxwell (Author) - Old Dominion University, United StatesHeidi Dewar (Author) - NOAA Southwest Fisheries Science Center, United StatesSuzanne Kohin (Author) - NOAA Southwest Fisheries Science Center, United StatesMichael G Jacox (Author) - University of California Santa Cruz, United StatesChristopher A Edwards (Author) - University of California Santa Cruz, United StatesDana K Briscoe (Author) - Hopkins Marine Station of Stanford University, United StatesLarry B Crowder (Author) - Hopkins Marine Station of Stanford University, United StatesRebecca L Lewison (Author) - San Diego State University, United StatesSteven 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; 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
- Research Statement
- false
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