Sabrina Jaffe | Search USGS

USGS2026

A Bayesian hierarchical modeling approach for species diversity in ecology

Species diversity is the foundation of many ecological disciplines. This metric is often approximated using species richness and evenness, even though actual richness likely exceeds observations due to imperfect sampling methods. Estimating the “true” species richness, which includes identifying the number of missing species, has intrigued ecologists for decades. We adopted a parametric model that appeared in Fisher et al. (1943), which models the numbers of individuals from different species as random samples from a negative binomial distribution, and developed a Bayesian computational approach to directly estimate the distribution model parameters. The model parameters represent species abundance and evenness, and can be used to derive species richness. We evaluated our parametric approach using (1) a simulation study and (2) three historical data sets. Furthermore, we illustrated the hierarchical modeling approach to combine data from multiple parallel studies using a biannual fishery survey data set. Our parametric model formulation is computationally efficient, and the hierarchical structure facilitates embedding diversity estimation into broader application, such as assessing spatial and temporal trends in species diversity associated with environmental stressors. Additionally, because the two parameters of the negative binomial distribution model represent species abundance and evenness of a community, this parametric approach facilitates a deeper understanding of the ecological systems under study. The negative binomial distribution model works with a wide range of species frequency distribution types. As a result, our emphasis on a parametric model can help us characterize the structure of an ecosystem and provide a greater depth of ecologically meaningful information.

Ecological Informatics↗

USGS2024

Assessing the probability of grass carp (Ctenopharyngodon idella) spawning in the Sandusky River using discharge and water temperature

Grass carp ( Ctenopharyngodon idella , Val.) is an invasive species in the Laurentian Great Lakes region with the potential for damaging the lake ecosystem and harming the region's economy. Grass carp spawning was documented in the Sandusky River, Ohio, in 2015 through targeted egg sampling. Continued egg sampling in the Sandusky River suggested that grass carp spawning is related to discharge and water temperature. We used egg sampling data from 2014 to 2021 to develop a Bayesian model to understand the likely conditions related to grass carp spawning in the Lake Erie watershed. The resulting model estimates the likelihood of spawning as a function of discharge and water temperature. The results suggest that spawning is most likely to occur when discharge is above 10 m 3 /s and water temperature is below 25 ℃. The model provides a tool for setting research and management priorities to develop management strategies to reduce the grass carp population in Lake Erie. Furthermore, the Bayesian nature of the model makes the model updatable when new data are available, whether from the same river or from another river, to incorporate river-specific features to identify likely spawning rivers.

Ohio↗