Hydrogeomorphological factors influencing the distribution of cichlid nesting sites in the Bladen River, Belize
Cichlids are among the most diversified families of fish in Central America and thus represent an important component of aquatic biodiversity. Cichlid nesting site selection is important for assemblages that face an uncertain future due to potential invasion by alien species and environmental change. We determined what hydrogeomorphological factors correlate to the preferred nesting sites of four native cichlid species in the Bladen River, Belize. The study was conducted in the portion of the river draining the Bladen Nature Reserve, an area relatively untouched by recent anthropogenic influences. We developed innovative two-dimensional species distribution models to assess the relative influences of physical variables on the distribution of occupied nesting sites. We recorded the locations of nesting sites of four native cichlid species and collected hydrological and geomorphic data through the study reaches, including flow velocity, depth, sediment type, and fish cover. Nest locations and physical habitat data were used to construct spatially explicit habitat models using the maximum entropy approach (Maxent). Models were tested against external data collected in a validation reach and by cross validation in the training reach. The models provided some evidence that physical habitat variables influence the distribution of the nesting sites. We found that two species were most heavily influenced by water depth (A. spilurus and V. maculicauda), one was influenced by sediment type (C. salvini), and one was most responsive to water velocity (T. meeki). The distribution of nests for the smaller-bodied species tended to be more closely related to the habitat parameters we measured. This study revealed that nesting sites within this cichlid community are more predictable than random and are at least partially governed by physical habitat controls. Understanding the physical controls governing cichlid nesting locations can aid in detecting suitable areas for conservation and predicting impacts by climate change and the encroaching logging, mining, and agricultural industries.