Modelling the impacts of environmental variation on the habitat suitability of swordfish, Xiphias gladius, in the equatorial Atlantic Ocean

Abstract

Chang, Y.-J., Sun, C.-L., Chen, Y., Yeh, S.-Z., DiNardo, G., and Su, N.-J. 2013. Modelling the impacts of environmental variation on the habitat suitability of swordfish, Xiphias gladius, in the equatorial Atlantic Ocean. – ICES Journal of Marine Science, 70: 1000–1012. We developed a habitat suitability index (HSI) model to identify the optimal habitats of swordfish (Xiphias gladius) in the equatorial Atlantic Ocean. Environmental variables, including sea surface temperature (SST), mixed layer depth (MLD), chlorophyll-a concentrations, and sea surface height anomaly, as well as catch and effort data from Taiwanese longline fisheries, were used. The geometric mean model including all the above environmental variables was identified as the most parsimonious model for yielding HSI predictions coinciding with productive fishing grounds with high fishing effort. Swordfish mainly aggregated in the northwest region during March–May and spread southeast thereafter in response to seasonal shifts in oceanographic conditions. There was annual variation in the distribution of habitat patches, and the habitat quality was reduced in the northwest region of the equatorial Atlantic Ocean during 2005. The apparent spatial shifts in optimal habitats might be linked to reduced MLD and elevation in sea surface height, which might be related to climate variability (e.g. Niño-Southern Oscillation and/or Northern Atlantic Oscillation). Because environmental data regarding climate change scenarios are becoming readily available, we can utilize the proposed HSI models to evaluate possible changes in habitat suitability resulting from climate change and provide scientific advice for the development of management regulations.

Temporal habitat suitability modeling of Caspian shad (Alosa spp.) in the southern Caspian Sea

“…The SI curves could be defined according to expert knowledge (Brown et al, 2000) or a piecewise linear function (Vincenzi et al, 2007), quantile regression (Feng et al, 2007), spline smooth regression and nonlinear regression Tian et al, 2009), cubic spline smoothing function (Chang et al, 2012), or fuzzy chart analysis (Zohmann et al, 2013). Nonlinear spline smooth regression method has been the most widely used method of quantifying SI curves worldwide Tian et al, 2009;Chang et al, 2013;Li et al, 2014), including for Caspian Sea studies (Haghi Vayghan et al, 2013).…”

“…The HSI models were developed to reflect the hypotheses of species-habitat relationships and facilitate environmental impact studies (Schamberger et al, 1982). When combined with adequate data about key habitat variables, the models have successfully predicted changes in fish species' spatial patterns worldwide Tian et al, 2009;Chen et al, 2010;Chang et al, 2013), including in the Caspian Sea (Haghi Vayghan et al, 2013).…”

Modelling the impacts of environmental variation on habitat suitability for Pacific saury in the Northwestern Pacific Ocean

“…A spline smooth regression method (Chang et al., ) was used to fit the SI models. Standard diagnostics plots were used to evaluate error distributions and model fits.…”

Interannual and seasonal variability of winter-spring cohort of neon flying squid abundance in the Northwest Pacific Ocean during 1995–2011

“…The MLD had been proved to influence the fish population dynamics (Chang et al, 2013). However, the research conducted to examine the relationship between O. bartramii and MLD was extremely limited.…”