Identifying critical habitat for highly mobile species such as sharks is difficult, but essential for effective management and conservation. In regions where baseline data are lacking, non-traditional data sources have the potential to increase observational capacity for species distribution and habitat studies. In this study, a research and education organization conducted a five year (2013-2018) survey of shark populations in the coastal waters of west-central Florida, an area where a diverse shark assemblage has been observed but no formal population analyses have been conducted. The objectives of this study were to use Boosted Regression Tree (BRT) modeling to quantify environmental factors impacting the distribution of the shark assemblage, create species distribution maps from the model outputs, and identify spatially explicit hot spots of high shark abundance. A total of 1,036 sharks were captured, encompassing eleven species. Abundance hot spots for four species and for immature sharks (collectively) were most often located in areas designated as “No Internal Combustion Engine” zones and seagrass bottom cover, suggesting these environments may be fostering more diverse and abundant populations. The BRT models were fitted for immature sharks and five species where n>100: the nurse shark (Ginglymostoma cirratum), blacktip shark (Carcharhinus limbatus), blacknose shark (C. acronotus), Atlantic sharpnose shark (Rhizoprionodon terraenovae), and bonnethead (Sphyrna tiburo). Capture data were paired with environmental variables: depth (m), sea surface temperature (ºC), surface, middle, and bottom salinity (psu), dissolved oxygen (mg/l), and bottom type (seagrass, artificial reef, or sand). Depth, temperature, and bottom type were most frequently identified as predictors with the greatest marginal effect on shark distribution, underscoring the importance of nearshore seagrass and barrier island habitats to the shark assemblage in this region. This approach demonstrates the potential contribution of unconventional science to effective management and conservation of coastal sharks.

Blank or missing values are denoted as "null." "ReadMe.csv" document contains methods of collection and data header descriptions. Coordinates have been rounded to the nearest tenth to protect locations of vulnerable or endangered species.