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Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time
- Source :
- Ecological Applications. 27:2313-2329
- Publication Year :
- 2017
- Publisher :
- Wiley, 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 (
- Subjects :
- 0106 biological sciences
010504 meteorology & atmospheric sciences
Species distribution
Fisheries
Ecological forecasting
Models, Biological
01 natural sciences
California
Ecoinformatics
Animals
0105 earth and related environmental sciences
Billfish
Pacific Ocean
Ecology
biology
010604 marine biology & hydrobiology
Swordfish
Fishes
Computational Biology
Pelagic zone
Sea-surface height
biology.organism_classification
Remote Sensing Technology
Environmental science
Fisheries management
Subjects
Details
- ISSN :
- 10510761
- Volume :
- 27
- Database :
- OpenAIRE
- Journal :
- Ecological Applications
- Accession number :
- edsair.doi.dedup.....94df45b8dc21eaba80b42d32b4d3167e