Improving confidence in complex ecosystem models: The sensitivity analysis of an Atlantis ecosystem model

Insights into planktonic food-web dynamics through the lens of size and season

Knowledge of the trophic structure and variability of planktonic communities is a key factor in understanding food-web dynamics and energy transfer from zooplankton to higher trophic levels. In this study, we investigated how stable isotopes of mesozooplankton species varied seasonally (winter, spring, autumn) in relation to environmental factors and plankton size classes in a temperate coastal ecosystem. Our results showed that spring is characterized by the strongest vertical and size-structured plankton food-web, mainly fueled by the phytoplankton bloom. As a result, spring displayed the largest isotopic niche space and trophic divergence among species. On the contrary, both pelagic and benthic-derived carbon influenced low productive seasons (winter and autumn), resulting in more generalist strategies (trophic redundancy). Stable isotope mixing models were used to explore how different seasonal structures influenced the overall food web up to predatory plankton (i.e., mysids, chaetognaths, and fish larvae). Different feeding strategies were found in spring, with predators having either a clear preference for larger prey items (> 1 mm, for herring and dab larvae) or a more generalist diet (sprat and dragonets larvae). During low productive seasons, predators seemed to be more opportunistic, feeding on a wide range of size classes but focusing on smaller prey. Overall, the food-web architecture of plankton displayed different seasonal patterns linked to components at the base of the food web that shaped the main energy fluxes, either from phytoplankton or recycled material. Additionally, these patterns extended to carnivorous plankton, such as fish larvae, emphasizing the importance of bottom-up processes.

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.

Cumulative human health risk analysis of trihalomethanes exposure in drinking water systems

Chlorinated compounds on reaction with natural organic substances present in water leads to the formation of trihalomethanes (THMs), a major type of disinfection by-products (DBPs). Trihalomethanes (THMs) are the most widely investigated DBPs in drinking water systems because of their carcinogenic potential and subsequent adverse effects on human health. This study investigated the effect of gastro-intestinal absorption factor on human health risk assessment. Monitoring and analysis of water quality parameters and THMs levels in drinking water treatment plants revealed that the average values (306.5 μg/L) exceeded the recommended US EPA guidelines of 80 μg/L. Spearman rank (rho) correlation coefficient indicated that dissolved organic carbon is the major parameter influencing THMs formation. Monte Carlo simulations base risk assessment study was conducted for three different exposure pathways. The observed human health risk exposure effects due to THMs were below the recommended USEPA level (1.0 × 10^-6) for both the drinking water treatment plants. Seasonal disparity on risk estimation analysis revealed higher risk in summer season followed by autumn which is principally due to high concentration of THMs in summers.