Understanding the trophic drivers of zooplankton population variability is critical for predicting ecosystem responses to climate change. In the Gulf of Maine, the copepod Calanus finmarchicus is a foundational species linking primary producers to higher trophic levels, yet the biotic drivers shaping its seasonal and interannual abundances remain incompletely understood. Here, we assess how predators impact C. finmarchicus abundances using over four decades of survey data. We find strong evidence for seasonally-structured trophic control, with spring C. finmarchicus abundances driving mid-year predator increases, which subsequently imposes significant top-down pressure on fall C. finmarchicus populations. This interplay is especially pronounced in the deep, retentive inner basins of the Gulf of Maine, where predator-prey dynamics tend to dominate over advective exchange. Our results reveal shifting interactions between bottom-up and top-down controls, highlighting the need to incorporate seasonal trophic mechanisms into ecosystem models to improve projections under further environmental change.
Disentangling predator-prey relationships in ecosystems has been a major challenge in ecology for more than a century. A complex interplay of both intrinsic and extrinsic nonlinear dynamics and environmental factors obscure direct causal links between species, making detecting trophic controls particularly difficult. A foundational framework for modeling predator-prey interactions was established more than 100 years ago by Alfred J. Lotka and Vito Volterra, who independently developed a set of nonlinear differential equations to describe population cycles driven by density-dependent feedbacks in which prey populations grow exponentially without predators, and predator abundances decrease in the absence of sufficient prey. Such dynamics have been exemplified across terrestrial ecosystems, including the classic case of the snowshoe hares and lynxes in Canada, where historical fur-trapping records revealed periodic oscillations in their populations. In marine ecosystems, similar patterns have been observed in the Adriatic Sea during World War I, as Volterra's application of the model could effectively explain the proliferation of sharks as result of reduced fishing pressure. Hence, the original Lotka-Volterra models have been useful for capturing idealized population cycles and have since been extended to incorporate more complex biological behaviors. However, many population models rely on simplifying assumptions, such as spatial homogeneity, constant interaction strengths, no temporal lags, and static long-term dynamics, which are not always valid generalizations in complex ecological systems.
To address these limitations in traditional models, ecologists and mathematicians have developed a wide array of mechanistic models that incorporate more realistic features into population dynamics. Yet, two major challenges permeate throughout mathematical ecology and population dynamics modeling. First, since most state variables in a system are unobserved, we need approaches that can reconstruct useful information using only the few variables that we have data for. Second, the precise form of the mechanistic governing equations is usually uncertain, so flexible, nonparametric methods are necessary to capture nonlinear population dynamics. Empirical dynamic modeling (EDM) has emerged as a particularly useful approach to overcome these challenges by combining nonparametric function approximation with state-space reconstruction to understand and forecast complex, nonlinear systems. In particular, convergent cross mapping (CCM) is a technique within this framework that enables the detection of dynamic relationships in partially observed dynamical systems without assuming fixed interaction structures. CCM has gained traction in recent years due to its success in uncovering potentially causal relationships in a wide range of ecosystems, including terrestrial environments, freshwater lakes and rivers, and marine food webs.
The Gulf of Maine is an ecologically and economically important marine ecosystem, supporting a diverse community of zooplankton, forage fish, and higher trophic level predators. Wilkinson and Jordan Basins form the deep regions of the inner Gulf of Maine (Fig. 1), and the Gulf's unique circulation structure leads to long residence times in these basins. This makes the inner Gulf of Maine a valuable refuge due to the retention of nutrients, plankton, and overwintering populations of key species. Within this complex ecosystem, the copepod Calanus finmarchicus plays a foundational role, as it acts as a bridge between primary producers and higher trophic level consumers. However, despite its critical position in the food web, the drivers of C. finmarchicus abundance remain incompletely understood, as it is extremely difficult to differentiate the multiple mechanisms affecting population growth and mortality. Additionally, the complex interplay between internal production versus external exchange of C. finmarchicus populations in Wilkinson and Jordan Basins has been a major focus of study in recent years, particularly as climate change threatens to disrupt regional circulation patterns and alter the phenology of biological processes that sustain these populations. Since the Gulf of Maine is an intensively sampled marine ecosystem, there is a unique opportunity to integrate this high-resolution, multi-decadal plankton survey data with modern statistical techniques to disentangle the relative roles of local production, predation, and advective exchange driving C. finmarchicus population dynamics.
Here, we apply CCM along with other statistical approaches to investigate the predator-prey relationships influencing populations of C. finmarchicus in the inner basins of the Gulf of Maine. Past studies indicate that temperature and advection likely are not the primary drivers of population variability, so we hypothesize that internal production driven by seasonal density-dependent predator-prey interactions play a key role in regulating the population dynamics of C. finmarchicus in these basins. Our analysis extends beyond previous CCM studies focused on marine ecosystems (e.g.), as we resolve season-specific trophic links to quantify the distinct contributions of multiple predators' impact on C. finmarchicus dynamics across the spring, summer, and autumn. We apply these rigorous statistical techniques in a novel, seasonally-partitioned framework, offering a transferable template for other ecosystems to test classical predator-prey theory under real-world complexity. Our results indicate that siphonophores, euphausiids, and chaetognaths exert strong, seasonally-resolved top-down control on C. finmarchicus. These findings provide new insight and statistical evidence for the seasonal biotic mechanisms driving this foundational marine copepod in the Gulf of Maine.