Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology

TL;DR

This paper introduces a Bayesian hierarchical model that accounts for temporal preferential sampling in population abundance data, improving estimates and enabling inference of growth rates and phenometrics, demonstrated on simulated and mosquito data.

Contribution

The paper presents a novel Bayesian hierarchical model that explicitly accounts for temporal preferential sampling in population studies, enhancing abundance estimation and inference.

Findings

Improved abundance estimates during infrequent sampling periods.

Effective inference of population growth rates.

Successful application to mosquito population data.

Abstract

To study population dynamics, ecologists and wildlife biologists use relative abundance data, which are often subject to temporal preferential sampling. Temporal preferential sampling occurs when sampling effort varies across time. To account for preferential sampling, we specify a Bayesian hierarchical abundance model that considers the dependence between observation times and the ecological process of interest. The proposed model improves abundance estimates during periods of infrequent observation and accounts for temporal preferential sampling in discrete time. Additionally, our model facilitates posterior inference for population growth rates and mechanistic phenometrics. We apply our model to analyze both simulated data and mosquito count data collected by the National Ecological Observatory Network. In the second case study, we characterize the population growth rate and abundance of several mosquito species in the Aedes genus.