The importance of temporal scale in distribution modeling of migratory Caspian Kutum, Rutilus frisii

Abstract

The choice of temporal resolution has high importance in ecological modeling, which can greatly affect the identification of the main drivers of an organism's distribution, considering the spatiotemporal dynamism of environmental predictors as well as organisms’ abundance. The present study aimed to identify the spatiotemporal distribution patterns of Caspian Kutum, Rutilus frisii, along the southern coast of the Caspian Sea, north of Iran, evaluating multiple temporal resolutions of data. The boosted regression trees (BRT) method was used to model fish catch distribution using a set of environmental predictors. Three temporal scales of data, including seasonal, sub‐seasonal, and monthly time frames over the catch season (October–April), were considered in our modeling analyses. The monthly models, utilizing more detailed data scales, exhibited the highest potential in identifying the overall distribution patterns of the fish, compared to temporally‐coarse BRT models. The best models were the BRTs fitted using data from March and April, which represented the final months of the catch season with the highest catch levels. In the monthly models, the main determinants of the Kutum's aggregation points were found to be dynamic variables including sea surface temperature, particulate organic and inorganic carbon, as opposed to static topographic parameters such as distance to river inlets. Seasonal and sub‐seasonal models identified particulate inorganic matter and distance to river inlets as the predictors with the highest influence on fish distribution. The geographical distributions of fish biomass hotspots revealed the presence of a stable number of fish aggregation hotspot points along the eastern coast, while some cold‐spot points were identified along the central and western coasts of the Caspian Sea. Our findings indicate that utilizing fine time scales in modeling analyses can result in a more reliable explanation and prediction of fish distribution dynamics. The investigated approach allows for the identification of intra‐seasonal fluctuations in environmental conditions, particularly dynamic parameters, and their relationship with fish aggregation.