Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression

Hidden variable models reveal the effects of infection from changes in host survival

The impacts of disease on host vital rates can be demonstrated using longitudinal studies, but these studies can be expensive and logistically challenging. We examined the utility of hidden variable models to infer the individual effects of infectious disease from population-level measurements of survival when longitudinal studies are not possible. Our approach seeks to explain temporal deviations in population-level survival after introducing a disease causative agent when disease prevalence cannot be directly measured by coupling survival and epidemiological models. We tested this approach using an experimental host system (Drosophila melanogaster) with multiple distinct pathogens to validate the ability of the hidden variable model to infer per-capita disease rates. We then applied the approach to a disease outbreak in harbor seals (Phoca vituline) that had data on observed strandings but no epidemiological data. We found that our hidden variable modeling approach could successfully detect the per-capita effects of disease from monitored survival rates in both the experimental and wild populations. Our approach may prove useful for detecting epidemics from public health data in regions where standard surveillance techniques are not available and in the study of epidemics in wildlife populations, where longitudinal studies can be especially difficult to implement.

Exploring watershed effects on nutrient concentrations in shallow lakes through stable isotope analysis

Biogeochemistry patterns in shallow lakes are influenced by both in-lake factors such as ecosystem state as well as watershed-level factors such as land use, but the relative importance of in-lake versus watershed factors is poorly known. This knowledge gap makes it difficult for lake mangers to prioritize efforts on watershed versus in-lake strategies for stabilizing the clear-water state. We studied 48 shallow lakes in Minnesota, USA to assess the relative influence of lake size, land use in watersheds, and ecosystem state (turbid versus clear) on water column total nitrogen (TN) and total phosphorus (TP), as well as δ¹⁵N and δ¹³C in three species of fish. Our land use categories included natural areas, row crop agriculture, and all agriculture (row crops plus alfalfa). A model selection approach revealed different control mechanisms on the behavior of stable isotopes and nutrients. δ¹³C ratios in fish were most strongly influenced by lake size, while δ¹⁵N ratios were influenced by all agriculture in watersheds. In contrast, water column TN and TP concentrations were influenced by the in-lake factor of ecosystem state, with both nutrients lower in the clear state. We detected no effects of land use on TN or TP concentrations, likely due to strong effects of ecosystem state masking watershed effects. However, the strong relationship between agriculture and δ¹⁵N in fish indicated that watersheds did influence nutrient processing in shallow lakes, and that effects are not a legacy from past watershed events. Collectively, these observations indicate that lake managers should minimize agricultural intensity in shallow lake watersheds to facilitate the clear-water state, which will, in turn reduce water-column TN and TP relative to the turbid state.