Assessing Energetic Pathways and Time Lags in Estuarine Food Webs

Detecting strong species interactions in food webs is often challenging due to difficulties related to adequate experimentation and the prevalence of generalist diets throughout nature. A promising new mathematical technique, empirical dynamic modeling (EDM), has demonstrated the potential to identify trophic interactions between populations by assessing time lags between associated time series. We attempted to analyze trophic linkages both within a subtropical estuary, as well as a simulated, theoretical ecosystem, to determine how energy moves through these systems. Additionally, we intended to evaluate the technique's ability to detect biological relationships in ecosystems of different complexity. In both datasets, we were able to clearly identify strong consumer-resource interactions, which were generally related to bottom-up drivers. Overall, trophic connections at lower trophic levels were more easily detected than linkages higher in the food web. The ability of EDM to detect food web interactions appeared to be strongly influenced by the degree of observation error exhibited in the data. In the empirical dataset, several examples of bottom-up processes were clearly evident including effects of discharge, nutrients, and/or chlorophyll-a concentrations on anchovies (Anchoa spp.), Gulf flounder (Paralichthys albiguttata), and red drum (Sciaenops ocellatus). We also observed instances where lengths of time lags decreased as trophic level distances between consumers and resources decreased (for example, Anchovies, Gulf flounder, young-of-the-year seatrout). This analysis demonstrates the promising application of EDM to detect energetic pathways in systems of varying complexity.

Automated summary

The new mathematical technique of Empirical Dynamic Modeling (EDM) has shown promise in identifying trophic interactions in food webs by examining time lags between associated time series. This study analyzed trophic linkages in a subtropical estuary and a simulated ecosystem to understand how energy flows through these systems and evaluated the technique's ability to detect biological relationships in different ecosystems. The results demonstrated that strong consumer-resource interactions can be clearly identified, particularly those driven by bottom-up processes. The detection of trophic connections is influenced by observation error in the data and lower trophic levels are more easily detected than higher levels. The study highlights the potential of EDM in detecting energetic pathways in ecosystems of varying complexity.