IMPACT OF WEATHER ON A LAKE ECOSYSTEM, ASSESSED BY CYCLO-STATIONARY MCCA OF LONG-TERM OBSERVATIONS

Abstract

Temperate lake ecosystems are generally characterized by a strong annual cycle, and the relationships between observations of such ecosystems and external forcing variables can exhibit a complex structure. Furthermore, the observational data record is often short. This makes it difficult to assess the relationships between external forcing factors and their impact on the biological succession. Cyclo-stationary maximum cross-covariance analysis (MCCA) allows the effects of seasonality to be modeled in a flexible way, and we describe this statistical technique in detail. MCCA offers an objective method to approximate the high-dimensional total cross-covariance structure by defining “weighting” patterns. With a predictor set of reduced dimension, a suitable regression between forcing variables and ecological response variables can be set up. Cyclo-stationary MCCA is used here to analyze the influence of meteorological variables (air temperature, wind speed, global radiation, humidity, and precipitation) on 13 biological and biogeochemical indicator variables of Plußsee, a small lake in northern Germany. The main weather influence on the indicator variables was found to be connected to winter temperature. From the covariance structure the following major signals were detected to be related to higher winter temperature: a more intense spring algal maximum, a higher zooplankton biomass during the algal maximum, a less intense loss of nutrients to the hypolimnion, a higher summer bloom together with changes in the nutrient concentrations, and stronger oxygen consumption in autumn.