Applying empirical dynamic modeling to distinguish abiotic and biotic drivers of population fluctuations in sympatric fishes

Abstract

Fluctuations in the population abundances of interacting species are widespread. Such fluctuations could be a response to abiotic factors, biotic interactions, or a combination of the two. Correctly identifying the drivers is critical for effective population management. However, such effects are not always static in nature. Nonlinear relationships between abiotic factors and biotic interactions make it difficult to parse true effects. We used a type of nonlinear forecasting, empirical dynamic modeling, to investigate the context‐dependent species interaction between a common fish (three‐spine stickleback) and an endangered one (northern tidewater goby) in a fluctuating environment: a central California bar‐built estuary. We found little evidence for competition, instead both species largely responded independently to abiotic conditions. Stickleback were negatively affected by sandbar breaching. The strongest predictor of tidewater goby abundance was stickleback abundance; however, this effect was not a uniform negative effect of stickleback on goby as would be hypothesized under interspecific competition. The effect of stickleback on gobies was positive, though it was temporally restricted. Tidewater goby abundance in the summer was strongly positively correlated to stickleback abundance in the spring, which represents an offset in the reproductive and recruitment peaks in the two species that may help minimize competition and promote coexistence. Our study demonstrates how empirical dynamic modeling can be applied to understand drivers of population abundance in putative competitors and inform management for endangered species.

Evaluating top‐down, bottom‐up, and environmental drivers of pelagic food web dynamics along an estuarine gradient

“…Past studies on zooplankton and forage fishes in estuaries (including the SF Estuary) have found stronger influences of abiotic compared to biotic drivers (Rollwagen‐Bollens et al, 2020; Thomson et al, 2010; Wasserman et al, 2022). However, we found net biotic and abiotic effects to be of comparable magnitude.…”

Detecting the Non‐Separable Causality in Soil Moisture‐Precipitation Coupling With Convergent Cross‐Mapping

“…Takens Theorem ensures that such a shadow attractor is a diffeomorphism of the original. As diffeomorphism, shadow attractors have two important features: they “replicated” the dynamic information from their original attractor (Rosenstein et al., 1994; Uzal et al., 2011; Wasserman et al., 2022), and they share similar fractal dimension with their original attractor. Likewise, a shadow attractor My can also be reconstructed from Y ( t ), or Mz from Z ( t ): they are all diffeomorphic to Ms .…”

A model free method of predicting transient dynamics in anaerobic digestion

“…Since the majority of the underlying theory involved in EDM is mathematical, most of the methods can be modified for various applications. For example, Brias & Munch [ 14 ] have used EDM to propose management strategies in multi-species systems, Tsonis et al [ 15 ] have used the S-map to test if a system is highly nonlinear or stochastic and Wasserman et al [ 16 ] have used the S-map to determine the temporally changing interaction strengths among competing fish species. Additionally, the EDM literature has led to many interesting theoretical advances in ecological forecasting [ 17 ], dealing with missing data or non-uniform sampling times [ 18 ] and stability of potential fixed points [ 11 , 19 ].…”