Optimal Sampling Regimes for Estimating Population Dynamics

Rebecca E Atanga,Ryad A Ghanam,Edward L Boone,Ben Stewart-Koster

doi:10.3390/stats4020020

Rebecca E Atanga, Ryad A Ghanam + Show 2 more

Open Access

https://doi.org/10.3390/stats4020020

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Abstract

Ecologists are interested in modeling the population growth of species in various ecosystems. Specifically, logistic growth arises as a common model for population growth. Studying such growth can assist environmental managers in making better decisions when collecting data. Traditionally, ecological data is recorded on a regular time frequency and is very well-documented. However, sampling can be an expensive process due to available resources, money and time. Limiting sampling makes it challenging to properly track the growth of a population. Thus, this design study proposes an approach to sampling based on the dynamics associated with logistic growth. The proposed method is demonstrated via a simulation study across various theoretical scenarios to evaluate its performance in identifying optimal designs that best estimate the curves. Markov Chain Monte Carlo sampling techniques are implemented to predict the probability of the model parameters using Bayesian inference. The intention of this study is to demonstrate a method that can minimize the amount of time ecologists spend in the field, while maximizing the information provided by the data.

Highlights

IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
This simulation study demonstrates methods that can be used to design sampling schedules for ecologists applied to three theoretical scenarios of logistic growth provided by Table 1
Simulated annealing is implemented first to demonstrate the global optimum for each scenario as a reference when comparing sequential optimality

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Population growth models are commonly used in ecology when studying plants, animals, and organisms as they grow over time and interact with the environment. Hsu et al [1] evaluate seedling growth under laboratory and field conditions. According to Huang et al [2], early life-cycle events play critical roles in determining the dynamics of plant populations and their interactions with the surrounding environment

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