Abrupt shifts in the Gironde fish community: an indicator of ecological changes in an estuarine ecosystem

For decades, global climate change has directly and indirectly affected the structure and function of ecosystems. Abrupt changes in biodiversity have been observed in response to linear or sudden modifications to the environment. These abrupt shifts can cause long-term reorganizations within ecosystems, with communities exhibiting new functional responses to environmental factors. Over the last 3 decades, the Gironde estuary in southwest France has experienced 2 abrupt shifts in both the physical and chemical environments and the pelagic community. Rather than describing these shifts and their origins, we focused on the 3 inter-shift periods, describing the structure of the fish community and its relationship with the environment during these periods. We described fish biodiversity using a limited set of descriptors, taking into account both species composition and relative species abundances. Inter-shift ecosystem states were defined based on the relationship between this description and the hydro-physico-chemical variables and climatic indices defining the main features of the environment. This relationship was described using generalized linear mixed models on the entire time series and for each inter-shift period. Our results indicate that (1) the fish community structure has been significantly modified, (2) environmental drivers influencing fish diversity have changed during these 3 periods, and (3) the fish-environment relationships have been modified over time. From this, we conclude a regime shift has occurred in the Gironde estuary. We also highlight that anthropogenic influences have increased, which re-emphasizes the importance of local management in maintaining fish diversity and associated goods and services within the context of climate change.

Explaining dinophysis cf. acuminata abundance in Antifer (Normandy, France) using dynamic linear regression

Classical regression analysis can be used to model time series. However, the assumption that model parameters are constant over time is not necessarily adapted to the data. In phytoplankton ecology, the relevance of time-varying parameter values has been shown using a dynamic linear regression model (DLRM). DLRMs, belonging to the class of Bayesian dynamic models, assume the existence of a non-observable time series of model parameters, which are estimated on-line, i.e. after each observation. The aim of this paper was to show how DLRM results could be used to explain variation of a time series of phytoplankton abundance. We applied DLRM to daily concentrations of Dinophysis cf. acuminata, determined in Antifer harbour (French coast of the English Channel), along with physical and chemical covariates (e.g. wind velocity, nutrient concentrations). A single model was built using 1989 and 1990 data, and then applied separately to each year. Equivalent static regression models were investigated for the purpose of comparison. Results showed that most of the Dinophysis cf. acuminata concentration variability was explained by the configuration of the sampling site, the wind regime and tide residual flow. Moreover, the relationships of these factors with the concentration of the microalga varied with time, a fact that could not be detected with static regression. Application of dynamic models to phytoplankton time series, especially in a monitoring context, is discussed.