Using remote-sensing data to detect habitat suitability for yellowfin tuna in the Western and Central Pacific Ocean

Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean

“…The SST varies seasonally and varies up to 7 °C throughout the year away from the equator, and the interannual variability is mainly associated with the climatic variability [23,29]. Yellowfin tuna primarily occur in waters where surface temperatures are 20 °C-30 °C, although their low numbers have been reported to occur in waters having a temperature of 15 °C [1,[12][13][14][15]. Temperature appears to influence the distribution of yellowfin tuna in all regions, except for the eastern Pacific Ocean where both temperature and oxygen concentrations contribute to a cold hypoxic environment, restricting the distribution of most tuna and billfish [30].…”

“…Remote satellite observations of the sea surfaces provide considerable information for assessing and improving the potential yield of fishing grounds. The sea surface temperature (SST) and chlorophyll (Chl)-a concentration have been suggested to play a role in generating distribution patterns and variability in tuna abundance [12][13][14][15]. Studies have indicated that yellowfin tuna prefer warm waters and are found in regions with a high Chl-a concentration and net primary productivity [1,[12][13][14][15].…”

“…The sea surface temperature (SST) and chlorophyll (Chl)-a concentration have been suggested to play a role in generating distribution patterns and variability in tuna abundance [12][13][14][15]. Studies have indicated that yellowfin tuna prefer warm waters and are found in regions with a high Chl-a concentration and net primary productivity [1,[12][13][14][15]. The sea surface height can be used to infer oceanic features, such as current dynamics and fronts [16], and the mesoscale oceanic dynamical processes including eddies, convergences and upwelling can be reflected in sea surface height anomaly (SSHA).…”

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

“…As an example, WorldView-2 data, and particularly a thresholding classifier using the Coastal band (400-450 nm), detected whales with up to 84.6% PA and 76.3% UA (Fretwell et al 2014). Instead, the most common way to estimate distribution of animal species, including mammals, birds, fishes, or invertebrates, is to model it based on proxies, such as spectral or structural properties (Suarez-Seoane et al 2002;Buchanan et al 2005;Vogeler et al 2014;Bejarano et al 2010;Mairota et al 2015), habitat suitability (Duro et al 2014;Yen et al 2012;Melin et al 2013), or detection of colonies (Fretwell and Trathan 2009;Fretwell et al 2012). Suarez-Seoane et al (2002) combined AVHRR with topographic and Geographic Information System (GIS) data to model the occurrence of three agricultural steppe birds in Spain, using PCA and Generalized Additive Models (GAM).…”

“…Airborne LiDAR data have also been used to model the presence of invertebrates spider (Vierling et al 2011) and beetle (Müller and Brandl 2009), using Constrained Redundancy Analysis (CRA), and Canonical Correlation Analysis (CCA) and Multiple Linear Regression Models (MLRM), respectively. Other passive data, such as MODIS (Kumar et al 2009;Yen et al 2012), VEGETATION (Pittiglio et al 2012), Landsat (Arias-González et al 2011;Koy et al 2005), or Tropical Rainfall Measuring Mission's Microwave Imager (TRMM/TMI) and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) (Zainuddin et al 2006) have been used in different animal abundance modelling studies with satisfactory accuracies, employing mainly a plethora of regression techniques.…”