The role of predation for seasonal variability patterns among phytoplankton and ciliates

Abstract

Investigating the mechanisms which underlie the biomass fluctuations of populations and communities is important to better understand the processes which buffer community biomass in a variable environment. Based on long‐term data of plankton biomass in Lake Constance (Bodensee), this study aims at explaining the different degree of synchrony among populations observed within two freshwater plankton groups, phytoplankton and ciliates. Established measures of temporal variability such as the variance ratio and cross‐correlation coefficients were combined with first‐order autoregressive models that allow estimating species interactions from time‐series data. We found that predation was an important driver of the observed seasonal variability patterns in phytoplankton and ciliates, and that competitive interactions only played a subordinate role. In Lake Constance copepods and cladocerans, two major invertebrate predator groups, focus their grazing pressure at different times of the season. Model results suggested that compensatory dynamics detected in phytoplankton originate from the differential vulnerability of species to either one of these two predator groups. For ciliates model results advocated that synchrony among species occurs because ciliates tend to be vulnerable to both predator groups. Our findings underline the necessity of extending studies of community variability to multiple trophic levels because accounting for predator‐prey interactions may often be more important than accounting for competitive interactions at one trophic level.

Phytoplankton response to climate warming modified by trophic state

“…This process necessarily leads to the exclusion of mechanisms that are known to be important in other systems. For instance, we are aware of the fact that grazing by copepods and ciliates can be an important loss factor for phytoplankton early in the year (Huber and Gaedke 2006;Tirok and Gaedke 2006;Peeters et al 2007). But, a model that included the effect of winter grazers (based on observed densities and clearance rates from the literature) as additional forcing factors negligibly improved the fit of the model (not shown).…”

Effects of climate change on zooplankton community interactions in an Alaskan lake

“…MAR models expand on the univariate Gompertz model, and can be described as several interrelated multiple regressions (one for each species) carried out with time-lagged data and then solved simultaneously to find the most parsimonious model describing the changes in species abundance as a function of intra-and interspecific interactions within a community and key exogenous drivers [10]. MAR models have been successfully used to investigate the structural features of plankton communities both within and between lakes [10][11][12][13]. MAR models have a broad range of applications and have been used to estimate community stability [10], estimate the direct effects of planktivory on zooplankton communities [11] and nutrients on phytoplankton [14], to assess the effects of temporal scale of observation on community dynamics [12,15,16], to understand the response of plankton communities to environmental change on long [17] and short [18] time scales, and to investigate the role of fishing pressure and fish declines on food web dynamics [19,20].…”

Seasonal Variations Alter the Impact of Functional Traits on Plankton Dynamics

“…During winter when grazing and competition for nutrients are reduced, both groups present coherent dynamics whereas during summer and fall, when both grazing pressure and nutrient limitation are present, edible and less-edible phytoplankton exhibit compensatory dynamics [11]. Moreover, a clear pattern of positive and negative covariances was found within and among functional groups of phytoplankton, mainly driven by different types of predators occurring at different parts of the growing season [12]. This implies that certain functional traits are only important at certain times, suggesting that species which are dynamically similar at certain times are not at others, and challenging the view that functional traits may provide an objective classification for species aggregation in complex food webs.…”