Ecological Archives M085-006-A1
Jacob E. Allgeier, Craig A. Layman, Peter J. Mumby, and Amy D. Rosemond. 2015. Biogeochemical implications of biodiversity and community structure across multiple coastal ecosystems. Ecological Monographs 85:117–132. http://dx.doi.org/10.1890/14-0331.1
Appendix A. Methodological details for bioenergetics models.
Bioenergetics models
Bioenergetics modeling allows for nutrient excretion rates of an organism to be estimated using a mass balance approach given a priori knowledge of the natural history (e.g., diet, feeding activity), physiology (e.g., stoichiometry of predator and prey, assimilation efficiency of nutrients, consumption rates, energy density of prey) and environmental conditions (temperature) (Schreck and Moyle 1990, Hanson et al. 1997). Stoichiometry data for each family/genera was determined by averaging the percent nutrient content for a suite of species within the given level of classification. Use of parameters for closely related species may increase error in model estimates (Hansen et al. 1993, Ney 1993) ; however, empirical work suggests that variation in excretion rates vary little within families but widely among families (Vanni et al. 2002). Energy densities of prey items were obtained from Cummins and Wuycheck (1981). Assimilation efficiencies, which have been shown to have only marginal influence on model estimates (Hood et al., 2005) were assumed to be 80% for N and 70% for P based on literature recommendations (Hanson et al.1997, Schreck and Moyle 1990). The growth rate of an animal has been shown to be a particularly influential parameter in bioenergetics (Hood et al. 2005), as such published growth rate values were found for each taxon of interest. Other parameter estimates were obtained from literature values specific to the given taxonomic level. Dietary parameters were determined from diet data collected by the authors (Layman and Silliman 2002, Layman et al. 2007, Hammerschlag-Peyer and Layman 2010, J. Allgeier unpublished) and from published data (Munro 1983).
We used Fish Bioenergetics 3.0 software (Hanson et al. 1997), to determine consumption rates for the dominant feeding guilds present in our data sets: predators – consuming a mixed diet of vertebrate and invertebrate prey (e.g., Lutjanidae), mesopredators – consuming almost exclusively invertebrate prey items (e.g., Labridae), and herbivores – consuming >90% primary producer material (e.g., Scaridae). To do this we chose the taxon per feeding guild for which we had the best parameter estimates (e.g., Lutjanidae >100 individuals, thousands of diet data, etc.) and used the software to calculate consumption rates based on energetic demands of the taxon. These consumption rates were then used for all families within that particular guild, holding this parameter constant and allowing other important estimates (e.g., body stoichiometry, prey stoichiometry and growth rate) to have influence over the model. Bioenergetics models were run using R software (R Core Development Team 2012). See Allgeier et al. (2013) and Burkepile et al. (2013) for further details on bioenergetics models.
To account for inherent error that occurs when parameterizing such models, we propagated uncertainty associated with diet content and consumption rates, two parameters that are highly influential in bioenergetics models (Hanson et al. 1997, Schindler 1997, Schreck and Moyle 1990), through the models using Monte Carlo simulations, as also performed by Allgeier et al. (2014). Specifically, a normal distribution of values was created for each parameter with a standard deviation of 5% of the maximum potential value of that parameter (in both cases the parameters represent a proportion, so the standard deviation was 0.05). For each model run, random draws were taken from within these distributions 500–10,000 times, depending on the size range of the fish within that family. Note the number of draws within this range did not change the outcome of the model (Allgeier et al. 2014).
Literature cited
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Hammerschlag-Peyer, C. M., and C. A. Layman. 2010. Intrapopulation variation in habitat use by two abundant coastal fish species. Marine Ecology-Progress Series 415:211–220.
Hansen, M. J., D. Boisclair, S. B. Brandt, S. W. Hewett, J. F. Kitchell, M. C. Lucas, and J. J. Ney. 1993. Applications of bioenergetics models to fish ecology and management - where do we go from here? Transactions of the American Fisheries Society 122:1019–1030.
Hanson, P. C., T. B. Johnson, D. E. Schindle, and J. F. Kitchell. 1997. Fish Bioenergetics 3.0. University of Wisconsin System Sea Grant Institute, Madison.
Layman, C. A., J. P. Quattrochi, C. M. Peyer, and J. E. Allgeier. 2007. Niche width collapse in a resilient top predator following ecosystem fragmentation. Ecology Letters 10:937–944.
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Ney, J. J. 1993. Bioenergetics modeling today - growing pains on the cutting edge. Transactions of the American Fisheries Society 122:736–748.
R Core Development Team. 2012. R: A language and environment for statistical computing. http://www.r-project.org.
Schreck, C. B., and P. B. Moyle. 1990. Methods for fish biology. American Fisheries Society, Bethesda, Maryland, USA.
Vanni, M. J., A. S. Flecker, J. M. Hood, and J. L. Headworth. 2002. Stoichiometry of nutrient recycling by vertebrates in a tropical stream: linking species identity and ecosystem processes. Ecology Letters 5.