Role of Sub Surface Temperature, Salinity and Chlorophyll to Albacore Tuna Abundance in Indian Ocean

Abstract

The swimming layer is one of the important factors to get maximum catches, especially on tuna longline effort. The vertical abundance of the albacore tuna was investigated based on catch data and 3-DINDESO Ocean Model data, such as sub-surface conditions of sea water potential temperature (Temp), salinity (Sal) and mass concentration of diatoms and flagellates expressed as chlorophyll (Chl) in the Eastern Indian Ocean period 2014-2015. Combining he statistical method of generalized additive model (GAM) was performed to analysis in this study. There were seven GAM models that generated with the number of ALB vertical abundance as a response variable, and Temp, Sal, and Chl as predictor variables. Sal has highly significant (P < 0.001) while Chl and Temp significant (P < 0.01) to ALB vertical abundance. Deduced from GAMs, indicated that a negative effect of Sal on the number of ALB was observed at salinity >34.52 psu. There was a positive effect of salinity on the number of ALB, which was from 34.30 to 34.47 psu and Chl showed a positive effect of this variable on the number of ALB caught occurred between 0.01 mg/m3 and 0.12 mg/m3 in the region of high confidence level where negative effect on > 0.13 mg/m3. While ALB catches abundance varied in the temperature range with the highest frequency at 24.0-24.9 °C. Sal was the most important environmental variable to the number of ALB vertically caught, followed by Chl and Temp.

Detection of Potential Fishing Zones of Bigeye Tuna (Thunnus Obesus) at Profundity of 155 m in the Eastern Indian Ocean

“…Our analysis showed that the majority of Bigeye tuna angling sets are 34.5 -34.8 psu in Sub_SS. e nding was con rmed by Novianto & Susilo (2016) who stated that Bigeye tuna generally disseminated at a salinity value of 34 psu. In addition, high salinity were believed defer the relocation of Bigeye tuna.…”

Drivers for spatial modelling of a critically endangered seabird on a dynamic ocean area: Balearic shearwaters are non‐vegetarian

“…There is a fairly large body of evidence that shows that areas of high CHL‐a concentrate a huge number of marine top predators, including predatory fish (Novianto & Susilo, 2016), cetaceans (Panigada et al, 2008; Gill et al, 2011; Breen et al, 2016) and seabirds (Weimerskirch et al, 2005; Louzao et al, 2012). Moreover, previous studies have succeeded in using CHL‐a as the main explanatory variable in niche models for the Balearic shearwater (Louzao et al, 2006a; Louzao et al, 2012; Araújo et al, 2017).…”

Evaluation of the Impacts of Climate Change on Albacore Distribution in the South Pacific Ocean by Using Ensemble Forecast

“…The environmental variables previously investigated for possible effects on the distribution of south Pacific albacore (Briand et al, 2011;Novianto and Susilo, 2016;Erauskin-Extramiana et al, 2019) were included in this study: sea surface temperature (SST, • C), sea surface salinity (SSS, PSU), mixed layer depth (MLD, m), dissolved oxygen concentration under 100 m depth (DO100, mmol L −1 ), and chlorophyll-a concentration (CHL, kg L −1 ). Monthly averaged satellite data [SST, SSS, MLD, DO100, and CHL] from 1997 to 2016 were used to characterize the environmental preferences of south Pacific albacore by using the GAM analysis (see section "Species Distribution Model").…”

“…In this context, it was appropriate to fit the delta approach which models the probability of encounter of a fish population and the non-zero CPUE when fish are encountered (Pennington, 1983(Pennington, , 1996Lo et al, 1992). Due to the low percentage of zero catches (<10%) of the quarterly 5 • × 5 • aggregated dataset that implying zero inflation was not an issue (Ichinokawa and Brodziak, 2010), we only fit the catch rate model using the log-transformed CPUEs of albacore, with a small constant (10% of the grand mean) added to avoid log-transformation problems (Campbell et al, 1996;Howell and Kobayashi, 2006;Mugo et al, 2010). GAMs were built using the gam function of the "mgcv" package in R-language TABLE 1 | List of IPCC atmosphere-ocean general circulation models where the future environmental variables, including the sea surface temperature (SST, • C), sea surface salinity (SSS, PSU), mixed layer depth (MLD, m), chlorophyll-a concentration (CHL, kg L −1 ), and dissolved oxygen concentration under 100 m depth (DO100, mmol L −1 ), were obtained to generate potential habitat maps of south Pacific albacore under ocean warming scenarios (RCP 4.5 and RCP 8.5).…”

“…Brill (1994) suggested that the dissolved oxygen (DO) is a good index of albacore habitat suitability that the reduced ambient oxygen levels would prolong the time required for albacore to recover from strenuous exercise. Salinity and chlorophyll-a (CHL) concentration were also suggested to be related to the albacore abundance and distribution through affecting the availability of their prey (Xu et al, 2013;Novianto and Susilo, 2016).…”