Evaluation of a popular spatial cumulative impact assessment method for marine systems: A seagrass case study

Incorporating stressor interactions into spatially explicit cumulative impact assessments

Human-induced stressors are impacting the oceans and reducing the biodiversity of marine ecosystems. The many stressors affecting marine environments do not act in isolation. However, their cumulative impact is difficult to predict. Most of the available methods for quantifying cumulative impacts on marine ecosystems sum the impact of individual stressors to estimate cumulative impact. We demonstrate how experimental evidence from interacting stressors can be accounted for in cumulative impact assessments. We adapted a widely used additive model to incorporate nonadditive stressor interactions into a marine spatially explicit cumulative impact assessment for seagrasses. We combined experimental data on the impact of multiple stressors with spatial data on stressor intensity to test whether stressor interactions impact seagrasses in a case study region in South Australia. We also assessed how uncertainty about cumulative impacts changes when uncertainty in stressor interactions is included in the impact mapping. The results from an additive spatial cumulative impact assessment model were compared with results from the model incorporating interactions. Cumulative effects from the interaction model were more variable than those produced by the additive model. Five of the 15 stressor interactions that we tested produced impacts that significantly deviated from those predicted by an additive model. Areas of our study region that showed the largest discrepancies between the additive and interactive outputs were also associated with higher uncertainty. Our study demonstrates that the inclusion of stressor interactions changes the pattern and intensity of modeled spatial cumulative impact. Additive models have the potential to misrepresent cumulative impact intensity and do not provide the opportunity for targeted mitigation measures when managing the interactive effects of stressors. Appropriate inclusion of interacting stressor data may have implications for the identification of key stressors and the subsequent spatial planning and management of marine ecosystems and biodiversity.

A data-driven approach to multiple-stressor impact assessment for a marine protected area

The coastal environment is not managed in a way that considers the impact of cumulative threats, despite being subject to threats from all realms (marine, land, and atmosphere). Relationships between threats and species are often nonlinear; thus, current (linear) approaches to estimating the impact of threats may be misleading. We developed a data-driven approach to assessing cumulative impacts on ecosystems and applied it to explore nonlinear relationships between threats and a temperate reef fish community. We used data on water quality, commercial fishing, climate change, and indicators of recreational fishing and urbanization to build a cumulative threat map of the northern region in New South Wales, Australia. We used statistical models of fish abundance to quantify associations among threats and biophysical covariates and predicted where cumulative impacts are likely to have the greatest impact on fish. We also assessed the performance of no-take zones (NTZs), to protect fish from cumulative threats across 2 marine protected area networks (marine parks). Fishing had a greater impact on fish than water quality threats (i.e., percent increase above the mean for invertivores was 337% when fishing was removed and was 11% above the mean when water quality was removed inside NTZs), and fishing outside NTZs affected fish abundances inside NTZs. Quantifying the spatial influence of multiple threats enables managers to understand the multitude of management actions required to address threats.

Cumulative effects of multiple stressors impact an endangered seagrass population and fish communities

Coastal ecosystems are becoming increasingly threatened by human activities and there is growing appreciation that management must consider the impacts of multiple stressors. Cumulative effects assessments (CEAs) have become a popular tool for identifying the distribution and intensity of multiple human stressors in coastal ecosystems. Few studies, however, have demonstrated strong correlations between CEAs and change in ecosystem condition, questioning its management use. Here, we apply a CEA to the endangered seagrass Posidonia australis in Pittwater, NSW, Australia, using spatial data on known stressors to seagrass related to foreshore development, water quality, vessel traffic and fishing. We tested how well cumulative effects scores explained changes in P. australis extent measured between 2005 and 2019 using high-resolution aerial imagery. A negative correlation between P. australis and estimated cumulative effects scores was observed (R2 = 22 %), and we identified a threshold of cumulative effects above which losses of P. australis became more likely. Using baited remote underwater video, we surveyed fishes over P. australis and non-vegetated sediments to infer and quantify how impacts of cumulative effects to P. australis extent would flow on to fish assemblages. P. australis contained a distinct assemblage of fish, and on non-vegetated sediments the abundance of sparids, which are of importance to fisheries, increased with closer proximity to P. australis. Our results demonstrate the negative impact of multiple stressors on P. australis and the consequences for fish biodiversity and fisheries production across much of the estuary. Management actions aimed at reducing or limiting cumulative effects to low and moderate levels will help conserve P. australis and its associated fish biodiversity and productivity.

Exploring multiple stressor effects with Ecopath, Ecosim, and Ecospace: Research designs, modeling techniques, and future directions

Understanding the cumulative effects of multiple stressors is a research priority in environmental science. Ecological models are a key component of tackling this challenge because they can simulate interactions between the components of an ecosystem. Here, we ask, how has the popular modeling platform Ecopath with Ecosim (EwE) been used to model human impacts related to climate change, land and sea use, pollution, and invasive species? We conducted a literature review encompassing 166 studies covering stressors other than fishing mostly in aquatic ecosystems. The most modeled stressors were physical climate change (60 studies), species introductions (22), habitat loss (21), and eutrophication (20), using a range of modeling techniques. Despite this comprehensive coverage, we identified four gaps that must be filled to harness the potential of EwE for studying multiple stressor effects. First, only 12% of studies investigated three or more stressors, with most studies focusing on single stressors. Furthermore, many studies modeled only one of many pathways through which each stressor is known to affect ecosystems. Second, various methods have been applied to define environmental response functions representing the effects of single stressors on species groups. These functions can have a large effect on the simulated ecological changes, but best practices for deriving them are yet to emerge. Third, human dimensions of environmental change - except for fisheries - were rarely considered. Fourth, only 3% of studies used statistical research designs that allow attribution of simulated ecosystem changes to stressors' direct effects and interactions, such as factorial (computational) experiments. None made full use of the statistical possibilities that arise when simulations can be repeated many times with controlled changes to the inputs. We argue that all four gaps are feasibly filled by integrating ecological modeling with advances in other subfields of environmental science and in computational statistics.

Incorporating anthropogenic thresholds to improve understanding of cumulative effects on seagrass beds

Cumulative human impact analysis is a promising management tool to estimate the impacts of stressors on ecosystems caused by multiple human activities. However, connecting cumulative impact scores to actual ecosystem change at appropriate spatial scales remains challenging. Here, we calculated cumulative effects (CE) scores for 187 seagrass beds in Atlantic Canada that accounts for both bay-scale and local-scale anthropogenic activities. We then developed a CE threshold to evaluate where degradation of seagrass beds from multiple human activities is more likely. Overall, the CE score was the best predictor of human impacts for seagrass beds. Locations with high watershed land alteration and nitrogen loading had the highest CE scores; however, we also identified seagrass beds with high CE scores in regions characterized by generally low levels of human activities. Forty-nine seagrass beds exceeded the CE threshold and, of these, 86% had CE scores that were influenced by three or more stressors that cumulatively amounted to a large score. This CE threshold approach can provide a simplified metric to identify areas where management of cumulative effects should be prioritized and further highlights the need to consider multiple human activities when assessing anthropogenic impacts to coastal habitats.