We use a spatially explicit biogeochemical end-to-end ecosystem model, Atlantis, to simulate impacts from the Deepwater Horizon oil spill and subsequent recovery of fish guilds. Dose-response relationships with expected oil concentrations were utilized to estimate the impact on fish growth and mortality rates. We also examine the effects of fisheries closures and impacts on recruitment. We validate predictions of the model by comparing population trends and age structure before and after the oil spill with fisheries independent data. The model suggests that recruitment effects and fishery closures had little influence on biomass dynamics. However, at the assumed level of oil concentrations and toxicity, impacts on fish mortality and growth rates were large and commensurate with observations. Sensitivity analysis suggests the biomass of large reef fish decreased by 25% to 50% in areas most affected by the spill, and biomass of large demersal fish decreased even more, by 40% to 70%. Impacts on reef and demersal forage caused starvation mortality in predators and increased reliance on pelagic forage. Impacts on the food web translated effects of the spill far away from the oiled area. Effects on age structure suggest possible delayed impacts on fishery yields. Recovery of high-turnover populations generally is predicted to occur within 10 years, but some slower-growing populations may take 30+ years to fully recover.

Management Strategy Evaluation (MSE) is a framework to explore the tradeoffs amongst fishing strategies and assess the consequences for achieving management goals provided sources of uncertainty by means of simulation models (referred to as operating models). Single-species stock assessment often implements simulations for MSE, but the operating models often omit the dynamics of key biological interactions. This could be a disadvantage for the evaluation of tradeoffs as species interactions could have an impact on the performance of harvesting strategies. Tools for conducting ecosystem-based fisheries management (EBFM), such as integrated ecosystem assessments, include executing MSEs with ecosystem models, many of which explicitly include biological interactions. Although the support for EBFM has grown over the years, the amount of information provided by MSEs based on ecosystem models appears to be limited. A clear summary of such efforts would provide beneficial information for future efforts for EBFM. Herein, an inventory of applications simulating MSEs with ecosystem models that explicitly include biological interactions was developed based on findings from a literature review. First, the methodologies and foci across all identified applications are analyzed. Next, summaries of each application are provided. Lastly, general observations are provided along with recommendations for future applications.

In spite of considerable effort to predict wildland fire behaviour, the effects of firebrand lift-off, the ignition of resulting spot fires and their effects on fire spread, remain poorly understood. We developed a cellular automata model integrating key mathematical models governing current fire spread models with a recently developed model that estimates firebrand landing patterns. Using our model we simulated a wildfire in an idealised Pinus ponderosa ecosystem. Varying values of wind speed, surface fuel loading, surface fuel moisture content and canopy base height, we investigated two scenarios: (i) the probability of a spot fire igniting beyond fuelbreaks of various widths and (ii) how spot fires directly affect the overall surface fire’s rate of spread. Results were averages across 2500 stochastic simulations. In both scenarios, canopy base height and surface fuel loading had a greater influence than wind speed and surface fuel moisture content. The expected rate of spread with spot fires occurring approached a constant value over time, which ranged between 6 and 931% higher than the predicted surface fire rate of spread. Incorporation of the role of spot fires in wildland fire spread should be an important thrust of future decision-support technologies.

Mass mortality events (MMEs) are a key concern for the management of marine ecosystems. Specific stages and species are at risk and the causes may be single or cumulative pressure from a range of sources including pollutants, anthropogenic climate change or natural variability. Identifying risk and quantifying effects of plausible scenarios including MMEs are key to stakeholders and a quest for scientists. MMEs affect the whole ecosystem, but are traditionally only studied in relation to specific species, disregarding ecological feedbacks. Here we use an end-to-end ecosystem model adapted to the Nordic and Barents seas to evaluate the species-specific and ecological impacts for 50 years following an MME. MMEs were modeled as 10, 50, or 90% reduced recruitment for cod, herring and haddock, individually or in combination. The MME scenarios were compared to a base case model run that includes the current fishing mortality. All species showed declines in population biomass following an MME, increasing in duration and severity with increasing mortality. Cod biomass rebounded to the base case level within 3–13 years post the MME independent of scenario, while neither haddock nor herring fully rebounded to base case levels within the considered time horizon. Haddock responded much more variably to the mortality scenarios than cod or herring, with some scenarios yielding much higher levels of biomass than the base case. Herring responded negatively to all scenarios, leading to lower herring biomass and a steeper decline of the species than seen in the base case due to persistent harvest pressure. Corresponding responses showed that the demersal guild biomass increased substantially, while the pelagic guild biomass declined. Few effects were observed on the other guilds, including the top predators. Ecosystem effects as measured by ecological indicators were greatest after 5 years, but persisted through the entire model run. Fishery indicators showed the same features, but the responses were stronger than for the ecosystem indicators. Taken together this indicates long-term, ecological response to MMEs that can be described as regime shifts, highlighting the importance of using ecosystem models when evaluating effects of MMEs.