Abstract
Skipjack tuna (SJT) pelagic hotspots in the western North Pacific (WNP) were modelled using fishery and satellite remotely sensed data with Ecological Niche Factor Analysis (ENFA) models. Our objectives were to model and predict habitat hotspots for SJT and assess the monthly changes in sub-surface temperatures and mixed layer depths at fishing locations. SJT presence-only monthly resolved data, sea surface temperature, chlorophyll-a, diffuse attenuation coefficient, sea surface heights and surface wind speed were used to construct ENFA models and generate habitat suitability indices using a short-term dataset from March-November 2004. The suitability indices were then predicted for July-October (2007 and 2008). Monthly aggregated polygons of areas fished by skipjack tuna pole and line vessels were also overlaid on the predicted habitat suitability maps. Distributions of sub-surface temperatures and mixed layer depths (MLD) at fishing locations were also examined. Our results showed good fit for ENFA models, as indicated by the absolute validation index, the contrast validation index and the continuous Boyce index. The predicted hotspots showed varying concurrences when compared with 25-degree polygons derived from fished areas. Northward shifts in SJT hotspots corresponded with declining MLDs from March to September. The MLDs were shallower in summer and deeper in autumn and winter months. The habitat hotspots modeled using ENFA were consistent with the known ecology and seasonal migration pattern of SJT. The findings of this work, derived from a short-term dataset, enable identification of SJT hotspots in the WNP, thus contributing valuable information for future research on SJT habitat prediction models.
Highlights
Pelagic biological hotspots in the ocean are areas of elevated productivity, created by physical processes or features such as upwelling, fronts, eddies, bathymetry, or river discharge among other factors [1,2,3,4,5,6]
Global marginality factors obtained from Ecological Niche Factor Analysis (ENFA) models were above 0.5, while specialization factors were all above 1 (Table 3), pointing to utilization of habitat that was different from the average conditions in the western North Pacific
The global marginality factor is the lowest in July and the highest in September and November while the global specialization is lowest in October and highest in August
Summary
Pelagic biological hotspots in the ocean are areas of elevated productivity, created by physical processes or features such as upwelling, fronts, eddies, bathymetry, or river discharge among other factors [1,2,3,4,5,6]. They are characterized by high concentrations of organisms, that attract large numbers of top predators, becoming fishery targets [7, 8]. One of the pressing issues entails developing robust tools to identify biological hotspots and predict their spatial and temporal dynamics
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