Long-term survey data reveal large predator and temperature effects on population growth of multiple zooplankton species

• Published in: Limnology and oceanography 2020-04, Vol.65 (4), p.694-706
• Main Authors: Marino, John A., Vanderploeg, Henry A., Pothoven, Steven A., Elgin, Ashley K., Peacor, Scott D.
• Format: Article
• Language: English
• ISSN: 0024-3590; 1939-5590
• DOI: 10.1002/lno.11340

Predators can strongly affect prey communities, but their influence may be difficult to distinguish from bottom-up and other environmental effects. The problem of assessing predator impact is especially difficult in large systems that do not allow for comparisons across multiple units (e.g., small lakes) that have varying predator density. For instance, the invasion of the predatory zooplankter, Bythotrephes longimanus, into the Laurentian Great Lakes contributed to the nearly complete disappearance of several zooplankton species, but current effects on extant zooplankton are not well understood. We used generalized additive models (GAMs) applied to long-term data time series (1994–2012) to examine B. longimanus effects on zooplankton species in Lake Michigan. Because B. longimanus abundance varied over time, our approach allowed assessment of predator effects from field data while accounting for other factors, including food resources, temperature, and seasonality. Results suggest that B. longimanus substantially reduces some zooplankton population growth rates, with the largest effects on species that B. longimanus affected more strongly in experiments. For example, at maximum B. longimanus abundance, Daphnia mendotae, Bosmina longirostris, and Diacyclops thomasi population growth rates were estimated to be reduced by 17%, 30%, and 21%, respectively, compared to no effect on calanoid copepods. Results further indicated positive temperature effects on population growth that differed by species. Our study thus provides field-based evidence for ongoing impacts of invasive species and temperature on zooplankton production and composition, with potential consequences for planktivorous fish, and exemplifies how GAMs can be used to determine predator effects from time series data.