Classifying reservoirs to streamline fisheries management

In many cases, fisheries managers are no longer data limited.  Rather, successful management relies on logical and appropriate data leveraging with the proper analytical tools.  Here, we use watershed (watershed area [WA], proportion agricultural and forested land uses, reservoir northing, and the ratio of watershed area to surface area), reservoir morphometric (surface area [SA], development of shoreline [D_L], maximum depth, the ratio of mean to maximum depths, and relative depth [z_r]), and productivity (Secchi transparency, total suspended solids, and total phosphorus, total nitrogen, and chlorophyll a concentrations) data sets for 84 Ohio reservoirs to develop six groups of similar reservoirs.  Reservoir groups differed significantly (assessed with multi-response permutation procedures, p < 1 x 10^-6) due to broad differences in WA, SA, D_L, and z_r (assessed through analysis of variance, all F_5,78 > 9.4 and p < 0.001).  Further, we found evidence for differences in largemouth bass (LMB) relative abundance (catch per effort, CPE, determined through standardized electrofishing surveys) in these groups, with greater LMB CPE in systems with smaller WA, deeper z_r, and lower productivity.  Consequently, managers can use derived reservoir groups, once they are properly defined and assessed, to better inform management of important sportfish such as largemouth bass.