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A hybrid empirical and parametric approach for managing ecosystem complexity: Water quality in Lake Geneva under nonstationary futures.

ABSTRACT: Severe deterioration of water quality in lakes, characterized by overabundance of algae and declining dissolved oxygen in the deep lake (DO_B), was one of the ecological crises of the 20th century. Even with large reductions in phosphorus loading, termed "reoligotrophication," DO_B and chlorophyll (CHL) have often not returned to their expected pre-20th-century levels. Concurrently, management of lake health has been confounded by possible consequences of climate change, particularly since the effects of climate are not neatly separable from the effects of eutrophication. Here, using Lake Geneva as an iconic example, we demonstrate a complementary alternative to parametric models for understanding and managing lake systems. This involves establishing an empirically-driven baseline that uses supervised machine learning to capture the changing interdependencies among biogeochemical variables and then combining the empirical model with a more conventional equation-based model of lake physics to predict DO_B over decadal time-scales. The hybrid model not only leads to substantially better forecasts, but also to a more actionable description of the emergent rates and processes (biogeochemical, ecological, etc.) that drive water quality. Notably, the hybrid model suggests that the impact of a moderate 3°C air temperature increase on water quality would be on the same order as the eutrophication of the previous century. The study provides a template and a practical path forward to cope with shifts in ecology to manage environmental systems for non-analogue futures.

SUBMITTER: Deyle ER

PROVIDER: S-EPMC9245694 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGlobal regression models under an implicit assumption of spatial stationarity were commonly applied to estimate the environmental effects on aquatic species distribution. However, the relationships between species distribution and environmental variables may change among spatial locations, especially at large spatial scales with complicated habitat. Local regression models are appropriate supplementary tools to explore species-environment relationships at finer scales.MethodWe applied geographically weighted regression (GWR) models on Yellow Perch in Lake Erie to estimate spatially-varying environmental effects on the presence probabilities of this species. Outputs from GWR were compared with those from generalized additive models (GAMs) in exploring the Yellow Perch distribution. Local regression coefficients from the GWR were mapped to visualize spatially-varying species-environment relationships. K-means cluster analyses based on the t-values of GWR local regression coefficients were used to characterize the distinct zones of ecological relationships.ResultsGeographically weighted regression resulted in a significant improvement over the GAM in goodness-of-fit and accuracy of model prediction. Results from the GWR revealed the magnitude and direction of environmental effects on Yellow Perch distribution changed among spatial locations. Consistent species-environment relationships were found in the west and east basins for adults. The different kinds of species-environment relationships found in the central management unit (MU) implied the variation of relationships at a scale finer than the MU.ConclusionsThis study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships. The GWR results can provide support for identification of unique stocks and potential refinement of the current jurisdictional MU structure toward more ecologically relevant MUs for the sustainable management of Yellow Perch in Lake Erie.

Project description:Cryptosporidium, a protozoan parasite that can cause severe diarrhea in a wide range of vertebrates including humans, is increasingly recognized as a parasite of a diverse range of wildlife species. However, little data are available regarding the identification of Cryptosporidium species and genotypes in wild aquatic environments, and more particularly in edible freshwater fish. To evaluate the prevalence of Cryptosporidiumspp. in fish from Lake Geneva (Lac Léman) in France, 41 entire fish and 100 fillets (cuts of fish flesh) were collected from fishery suppliers around the lake. Nested PCR using degenerate primers followed by sequence analysis was used. Five fish species were identified as potential hosts of Cryptosporidium: Salvelinus alpinus, Esox lucius, Coregonus lavaretus, Perca fluviatilis, and Rutilus rutilus. The presence of Cryptosporidium spp. was found in 15 out of 41 fish (37%), distributed as follows: 13 (87%) C. parvum, 1 (7%) C. molnari, and 1 (7%) mixed infection (C. parvum and C. molnari). C. molnari was identified in the stomach, while C. parvum was found in the stomach and intestine. C. molnari was also detected in 1 out of 100 analyzed fillets. In order to identify Cryptosporidium subtypes, sequencing of the highly polymorphic 60-kDa glycoprotein (gp60) was performed. Among the C. parvum positive samples, three gp60 subtypes were identified: IIaA15G2R1, IIaA16G2R1, and IIaA17G2R1. Histological examination confirmed the presence of potential developmental stages of C. parvum within digestive epithelial cells. These observations suggest that C. parvum is infecting fish, rather than being passively carried. Since C. parvum is a zoonotic species, fish potentially contaminated by the same subtypes found in terrestrial mammals would be an additional source of infection for humans and animals, and may also contribute to the contamination of the environment with this parasite. Moreover, the risk of human transmission is strengthened by the observation of edible fillet contamination.

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A hybrid empirical and parametric approach for managing ecosystem complexity: Water quality in Lake Geneva under nonstationary futures.

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