Optimal Sampling Regimes for Estimating Population Dynamics

Rebecca E. Atanga, Edward L. Boone, Ryad A. Ghanam and Ben Stewart-Koster
Additional contact information
Rebecca E. Atanga: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
Edward L. Boone: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
Ryad A. Ghanam: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
Ben Stewart-Koster: Australian Rivers Institute, Griffith University, Southport 4222, Australia

Stats, 2021, vol. 4, issue 2, 1-17

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.

Keywords: environmental flow; logistic growth; bayesian hierarchical models; population dynamics; optimality criteria (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-905X/4/2/20/pdf (application/pdf)
https://www.mdpi.com/2571-905X/4/2/20/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:4:y:2021:i:2:p:20-307:d:531532

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().