Evaluating red tide effects on the West Florida Shelf using a spatiotemporal ecosystem modeling framework

Abstract

The West Florida Shelf (WFS), located in the eastern Gulf of Mexico, fosters high species richness and supports highly valuable fisheries. However, red tide events occur regularly that can impact fisheries resources as well as ecosystem state, functioning, and derived services. Therefore, it is important to evaluate and quantify the spatiotemporal impacts of red tides to improve population assessments, mitigate potential negative effects through management, and better understand disturbances to support an ecosystem-based management framework. To model red tide effects on the marine community, we used Ecospace, the spatiotemporal module of the ecosystem modeling framework Ecopath with Ecosim. The inclusion of both lethal and sublethal response functions to red tide and a comprehensive calibration procedure allowed to systematically evaluate red tide effects and increased the robustness of the model and management applicability. Our results suggest severe red tide impacts have occurred on the WFS at the ecosystem, community, and population levels in terms of biomass, catch, and productivity. Sublethal and indirect food-web effects of red tide triggered compensatory responses such as avoidance behavior and release from predation and/or competition.. This study represents a step forward to operationalize spatiotemporal ecosystem models for management purposes that may increase the ability of fisheries managers to respond more effectively and be more proactive to episodic mortality events, such as those caused by red tides.

Making Ecosystem Modeling Operational–A Novel Distributed Execution Framework to Systematically Explore Ecological Responses to Divergent Climate Trajectories

“…In terms of systematic uncertainty assessments, the multi‐run framework provides a foundation for systematically combining parametric uncertainty assessments (e.g., Steenbeek et al., 2018; Vilas et al., 2023) with other forms of uncertainty related to model structure (e.g., Coll et al., 2020; Heneghan et al., 2021), Initialization and Internal variability uncertainty (this case study) and scenario uncertainty (e.g., de Mutsert et al., 2021; Lotze et al., 2019; Schewe et al., 2019; Tittensor et al., 2021).…”

“…Combining uncertainty assessments with validation strategies that also consider state variables, modellers can systematically disentangle a model's strengths and weaknesses in search of better model calibrations. Here lies the next big challenge for the global modeling community: to work toward (semi‐)automated calibration of spatial‐temporal MEMs (e.g., Vilas et al., 2023). By now allowing any modeling group to mass‐execute their models systematically across available hardware, the framework can serve as a scaffolding for orchestrating the great number of runs required, which will involve some form of looped and MEM‐specific sensitivity testing, parameter estimation and validation scheme.…”

Making ecosystem modelling operational - a novel distributed execution framework to systematically explore ecological responses to divergent climate trajectories

“…In terms of systematic uncertainty assessments, the multi-run framework provides a foundation for systematically combining parametric uncertainty assessments (e.g., Steenbeek et al, 2018;Vilas et al, 2023) with other forms of uncertainty related to model structure (e.g., Coll et al, 2020;Heneghan et al, 2021), Initialization and Internal variability uncertainty (this case study) and scenario uncertainty (e.g., de Mutsert et al, 2021;Lotze et al, 2019;Schewe et al, 2019;Tittensor et al, 2021).…”

“…Combining uncertainty assessments with validation strategies that also consider state variables, modellers can systematically disentangle a model's strengths and weaknesses in search of better model calibrations. Here lies the next big challenge for the global modelling community: to work towards (semi-)automated calibration of spatial-temporal MEMs (e.g., Vilas et al, 2023). By now allowing any modelling group to mass-execute their models systematically across available hardware, the framework can serve as a scaffolding for orchestrating the great number of runs required, which will involve some form of looped and MEM-specific sensitivity testing, parameter estimation and validation scheme.…”