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Modeling hypolimnetic dissolved oxygen depletion using monitoring data.

ABSTRACT: Eutrophication increases hypoxia in lakes and reservoirs, causing deleterious effects on biological communities. Quantitative models would help managers develop effective strategies to address hypoxia issues, but most existing models are limited in their applicability to lakes with temporally resolved dissolved oxygen data. We describe a hierarchical Bayesian model that predicts dissolved oxygen in lakes based on a mechanistic understanding of the factors that influence the development of hypoxia during summer stratification. These factors include the days elapsed since stratification, dissolved organic carbon concentration, lake depth, and chlorophyll concentration. We demonstrate that the model can be fit to two datasets: one in which temporally resolved dissolved oxygen profiles were collected from 20 lakes in a single state and one in which single profiles were collected from 381 lakes across the United States. Analyses of these two datasets yielded similar relationships between volumetric oxygen demand and chlorophyll concentration, suggesting that the model structure appropriately represented the effects of eutrophication on oxygen depletion. Combining both datasets in a single model further improved the precision of predictions.

SUBMITTER: Yuan LL

PROVIDER: S-EPMC7252502 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Modeling hypolimnetic dissolved oxygen depletion using monitoring data.

Yuan Lester L LL Jones John R JR

Canadian journal of fisheries and aquatic sciences. Journal canadien des sciences halieutiques et aquatiques 20200501 5

Eutrophication increases hypoxia in lakes and reservoirs, causing deleterious effects on biological communities. Quantitative models would help managers develop effective strategies to address hypoxia issues, but most existing models are limited in their applicability to lakes with temporally resolved dissolved oxygen data. We describe a hierarchical Bayesian model that predicts dissolved oxygen in lakes based on a mechanistic understanding of the factors that influence the development of hypoxi ...[more]

PMID: 32461710

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Project description:Few studies have evaluated the optimal sampling design for tracking small mammal population trends, especially for rare or difficult to detect species. Spatially explicit capture-recapture (SECR) models present an advancement over non-spatial models by accounting for individual movement when estimating density. The salt marsh harvest mouse (SMHM; Reithrodontomys raviventris) is a federal and California state listed endangered species endemic to the San Francisco Bay-Delta estuary, California, USA; where a population in a subembayment has been continually monitored over an 18-year period using mark-recapture methods. We analyzed capture data within a SECR modeling framework that allowed us to account for differences in detection and movement between sexes. We compared the full dataset to subsampling scenarios to evaluate how the grid size (area) of the trap design, trap density (spacing), and number of consecutive trapping occasions (duration) influenced density estimates. To validate the subsampling methods, we ran Monte Carlo simulations based on the true parameter estimates for each specific year. We found that reducing the area of the trapping design by more than 36% resulted in the inability of the SECR model to replicate density estimates within the SE of the original density estimates. However, when trapping occasions were reduced from 4 to 3-nights the density estimates were indistinguishable from the full dataset. Furthermore, reducing trap density by 50% also resulted in density estimates comparable to the full dataset and was a substantially better model than reducing the trap area by 50%. Overall, our results indicated that moderate reductions in the number of trapping occasions or trap density could yield similar density estimates when using a SECR approach. This approach allows the optimization of field trapping efforts and designs by reducing field efforts while maintaining the same population estimate compared to the full dataset. Using a SECR approach may help other wildlife programs identify sampling efficiencies without sacrificing data integrity for long term monitoring of population densities.

Dataset Information

Modeling hypolimnetic dissolved oxygen depletion using monitoring data.

Publications

Modeling hypolimnetic dissolved oxygen depletion using monitoring data.

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