Abstract

1. The size structure of communities is shaped by biotic and abiotic interactions. Therefore, comparative analyses of size spectra may reveal the major drivers governing patterns and processes in size-structured communities. 2. We tested the suitability of non-taxonomic, size-related variables as tools for elucidating systematic shifts in lake fish assemblages along the gradients of environmental factors and lake-use intensity. Catch data of multimesh gillnets from 78 lowland lakes in northern Germany were analysed. 3. We first identified the correlations, and hence inherent redundancy, among 18 size-related variables. The correspondence between eight weakly correlated size variables and descriptors of lake morphometry, lake productivity, lake-use intensity and taxonomic and functional fish-assemblage composition was tested using ordination by non-metric multidimensional scaling (NMDS). The three axes of the NMDS analysis were strongly correlated with five size variables, which in turn corresponded to lake area, mean and maximum depth, total phosphorus and chlorophyll a concentration, predator abundance and predator/prey length ratios (PPLR). 4. The number of size classes increased with increasing lake area. The slopes of normalised length spectra were flatter (less negative) and size diversity was higher in deep, less nutrient-rich lakes and in lakes with a higher numerical predator abundance, indicating a higher relative abundance of large fish. The exponent of the Pareto type II mass spectra was larger and maximum fish length was smaller in shallow, nutrient-rich lakes and in lakes with lower predator biomass and smaller PPLR, indicating a higher relative proportion of medium-sized fish. 5. Analyses of size spectra at regional scales can contribute important information to the evaluation of the ecological quality of lakes. We suggest further studies at a broader range of environmental and geographical scales to understand the subtle response of size-related variables to biotic interactions, abiotic stressors and geographical patterns.

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