Estimating benthic invertebrate production in lakes: a comparison of methods and scaling from individual taxa to the whole-lake level |
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| Authors: | Katrina J Butkas Yvonne Vadeboncoeur M Jake Vander Zanden |
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| Institution: | (1) Center for Limnology, University of Wisconsin-Madison, 680 N. Park St., Madison, WI 53706, USA;(2) Department of Biological Sciences 235A BH, Wright State University, Dayton, OH 45435, USA;(3) Present address: 4477 County Rd 32, Bloomfield, NY 14469, USA; |
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| Abstract: | Studies of aquatic invertebrate production have been primarily conducted at the level of individual taxa or populations. Advancing
our understanding of the functioning and energy flow in aquatic ecosystems necessitates scaling-up to community and whole-lake
levels, as well as integrating across benthic and pelagic habitats and across multiple trophic levels. In this paper, we compare
a suite of non-cohort based methods for estimating benthic invertebrate production at subpopulation, habitat, and whole-lake
levels for Sparkling Lake, WI, USA. Estimates of the overall mean benthic invertebrate production (i.e. whole-lake level)
ranged from 1.9 to 5.0 g DM m−2 y−1, depending on the method. Production estimates varied widely among depths and habitats, and there was general qualitative
agreement among methods with regards to differences in production among habitats. However, there were also consistent and
systematic differences among methods. The size-frequency method gave the highest, while the regression model of Banse and
Mosher (Ecol Monogr 50:355–379, 1980) gave the lowest production estimates. The regression model of Plante and Downing (Can J Fish Aquat Sci 46:1489–1498, 1989) had the lowest average coefficients of variation at habitat (CV = 0.17) and whole-lake (CV = 0.08) levels. At the habitat
level, variance in production estimates decreased with sampling effort, with little improvement after 10–15 samples. Our study
shows how different production estimates can be generated from the same field data, though aggregating estimates up to the
whole-lake level does produce an averaging effect that tends to reduce variance. |
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