Suitability indices and habitat suitability index model of Caspian kutum (Rutilus frisii kutum) in the southern Caspian Sea

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

“…The role of SLA has been demonstrated in fish habitat modeling as describes ocean processes (Larnicol et al, 2002;Pujol and Larnicol, 2005), being useful in measuring productivity, and often influencing the distribution of species throughout their life stages (Giannoulaki et al, 2008). Furthermore, SLA was found to be a predictor in fish habitat studies in the southern Caspian Sea (Haghi Vayghan et al, 2013) and Mediterranean Sea (Crechriou et al, 2008;Tserpes et al, 2008).…”

“…The SIs revealed that the fish selected deeper areas in offshore waters with lower TOM, benthos biomass, and Chla levels. As a wintering location of numerous fish species, deep water naturally has a low concentration of nutrients and organic matter (Damalas et al, 2010) and have been influenced suitable habitat of some fish such as Caspian kutum in the Caspian Sea (Haghi Vayghan et al, 2013, 2015. Caspian shad prefer wintering in deeper waters in the southern Caspian Sea (Coad, 1995;Coad, 1997;Afraei Bandpei et al, 2006), where temperature at deep water was at least two degree more than other season in the southern of Caspian Sea at winter time .…”

“…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).…”

“…In the present study, Alosa was recorded with average weights of 55.9±4.54 (means ±SE) and 155.7±14.49 (means ±SE) in spring and winter seasons, respectively. To determine stock abundance index, representing the habitat preference (Maunder and Punt, 2004;Tian et al, 2009;Haghi Vayghan et al, 2013), the catch per unit area (CPUA) and number per unit area (NPUA) of each site were calculated using an equation designed by Sparre and Venema (1998).…”

“…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).…”