Do Salamanders Limit the Abundance of Groundwater Invertebrates in Subterranean Habitats?

Several species of surface salamanders exploit underground environments; in Europe, one of the most common is the fire salamander (Salamandra salamandra). In this study, we investigated if fire salamander larvae occurring in groundwater habitats can affect the abundance of some cave-adapted species. We analyzed the data of abundance of three target taxa (genera Niphargus (Amphipoda; Niphargidae), Monolistra (Isopoda; Sphaeromatidae) and Dendrocoelum (Tricladida; Dedrocoelidae)) collected in 386 surveys performed on 117 sites (pools and distinct subterranean stream sectors), within 17 natural and 24 artificial subterranean habitats, between 2012 and 2019. Generalized linear mixed models were used to assess the relationship between target taxa abundance, fire salamander larvae occurrence, and environmental features. The presence of salamander larvae negatively affected the abundance of all the target taxa. Monolistra abundance was positively related with the distance from the cave entrance of the sites and by their surface. Our study revealed that surface salamanders may have a negative effect on the abundance of cave-adapted animals, and highlited the importance of further investigations on the diet and on the top-down effects of salamanders on the subterranean communities.

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l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

Statistical Methods in Medical Research ◽

10.1177/09622802211017583 ◽

2021 ◽

pp. 096228022110175

Author(s):

Jan P Burgard ◽

Joscha Krause ◽

Ralf Münnich ◽

Domingo Morales

Keyword(s):

Parameter Estimation ◽

Medical Treatment ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Linear Mixed Models ◽

Obesity Prevalence ◽

Model Parameter ◽

Model Parameter Estimation ◽

Public Health Reporting ◽

Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

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