Exploiting habitat and gear patterns for efficient detection of rare and non-native benthos and fish in Great Lakes coastal ecosystems.
Despite the continued arrival and impacts of non-native aquatic species in the Great Lakes, there is as yet no comprehensive early-detection monitoring program for them. As a step towards implementing such a program, we evaluated strategies for efficient non-native species monitoring based on the ability to detect a diverse set of benthos and fish species currently present in a heavily invaded, spatially complex Great Lakes subsystem. Taxa accumulation analyses confirmed that reliable detection of rare species requires substantial sampling effort but also that there is potential for exploiting patchiness in distributions to increase efficiency. While non-native species monitoring warrants generally comprehensive spatial coverage, it may be possible to identify areas where such taxa are broadly most prevalent (e.g., the lower reaches of our study system) as a way to focus effort. On a finer scale, richness of non-native taxa may vary substantially among stations in close proximity - which in this system was driven by habitat variability rather than distance from potential introduction points. Microhabitats that differ in physical attributes are also likely to differ in species composition and richness. Randomization analyses indicated that some monitoring effort should be directed towards all distinct habitats but that detection rates are maximized by biasing effort towards those habitats or gear yielding the most total, non-native, or rare taxa. For benthic invertebrates, shallow structurally complex (vegetated) habitats yielded the most taxa but shallow open and deep habitats also contributed unique taxa. For fish, fyke-net stations (shallowest habitats) yielded the most taxa, but electrofishing (intermediate-depth) and trawling (deepest) also contributed unique taxa. Our approach to identifying relevant sampling strata and exploiting difference among them to increase the efficiency of early-detection monitoring is applicable to a broad variety of systems.