Best fitting models for each response variable calculated using mixed model, repeated measures ANOVA.

<p>Models investigated direct trophic interactions using every possible covariate combination as well as the effect of fish biomass (treatment covariate). Each row represents an individual model with response variables grouped by color (red = nitrogen, blue = phosphorus, green = phytoplankton, yellow = zooplankton, orange = benthic organic matter, pink = GPP, purple = NPP). For a given response variable, models are ranked by goodness of fit according to Δ<i>_i</i> AIC value. <i>P</i>-values for the main effects (SR = species richness) and covariate(s) (fb = total fish biomass, phyto = phytoplankton, zoo = zooplankton) from ANOVA models are given within cells and shaded cells represent significant results (α = 0.05; full ANOVA results given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084568#pone.0084568.s005" target="_blank">Table S3</a>). Only models with Δ<i>_i</i> AIC <3 are shown, except that models where fish biomass exceeded the <3 Δ<i>_i</i> AIC threshold are still presented and denoted in italics and * next to Δ<i>_i</i> AIC value. As GPP and NPP are composite measures of whole ecosystem processes, direct trophic interaction covariate models were not explored.</p

Mean and standard error of response variables (raw values) by treatment and over time.

<p>Mean and standard error of response variables (raw values) by treatment and over time.</p

Mean and standard error of unstandardized residuals of response variables by treatment and over time.

<p>Residuals are from simple linear regression of the response variable and fish biomass, and therefore are measures of the response variable among treatments that control for the potentially confounding effect of fish biomass.</p

Expected food web for 18 fish species based on known trophic relationships (see Table S1).

<p>The width of black, white, and gray bars for each fish species (nodes) represent square root abundance, log₁₀ mean biomass of individuals (g), and the square root mean body size (cm) of individuals from community sampling data, respectively. The number for each species is its ordered sum rank abundance (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084568#pone.0084568.s007" target="_blank">Appendix S1</a>).</p

Loss of Rare Fish Species from Tropical Floodplain Food Webs Affects Community Structure and Ecosystem Multifunctionality in a Mesocosm Experiment

<div><p>Experiments with realistic scenarios of species loss from multitrophic ecosystems may improve insight into how biodiversity affects ecosystem functioning. Using 1000 L mesocoms, we examined effects of nonrandom species loss on community structure and ecosystem functioning of experimental food webs based on multitrophic tropical floodplain lagoon ecosystems. Realistic biodiversity scenarios were developed based on long-term field surveys, and experimental assemblages replicated sequential loss of rare species which occurred across all trophic levels of these complex food webs. Response variables represented multiple components of ecosystem functioning, including nutrient cycling, primary and secondary production, organic matter accumulation and whole ecosystem metabolism. Species richness significantly affected ecosystem function, even after statistically controlling for potentially confounding factors such as total biomass and direct trophic interactions. Overall, loss of rare species was generally associated with lower nutrient concentrations, phytoplankton and zooplankton densities, and whole ecosystem metabolism when compared with more diverse assemblages. This pattern was also observed for overall ecosystem multifunctionality, a combined metric representing the ability of an ecosystem to simultaneously maintain multiple functions. One key exception was attributed to time-dependent effects of intraguild predation, which initially increased values for most ecosystem response variables, but resulted in decreases over time likely due to reduced nutrient remineralization by surviving predators. At the same time, loss of species did not result in strong trophic cascades, possibly a result of compensation and complexity of these multitrophic ecosystems along with a dominance of bottom-up effects. Our results indicate that although rare species may comprise minor components of communities, their loss can have profound ecosystem consequences across multiple trophic levels due to a combination of direct and indirect effects in diverse multitrophic ecosystems.</p></div

Slice effects for best fitting models for each response variable.

<p>Each row represents an individual model when a significant species richness (SR) by time interaction was observed with response variables grouped by color (red = nitrogen, blue = phosphorus, pink = GPP, purple = NPP). <i>P</i>-values for the main effects and time steps from ANOVA models are given within cells and shaded cells represent significant results (α = 0.05; full ANOVA results are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084568#pone.0084568.s005" target="_blank">Table S3</a>).</p

Rank abundance curve and example of stocking abundances.

<p>A) Rank abundance curve of 59 fish species derived from community sampling data from isolated lagoons (n = 11) in the Upper Paraná River floodplain during austral spring from 2000–2007. Species to the left of the dashed line represent the pool of species (n = 18) used in the experiment. B) Proportional rank abundance curve of the 18 species from (A) that provides the basis for experiment stocking abundances. C) Example of stocking abundances for the 18 species richness treatment (total 65 individuals per mesocosm).</p