Theoretical Population Biology | VOL. 138
Read

Fitting stochastic predator-prey models using both population density and kill rate data.

Publication Date Jan 1, 2021

Abstract

Most mechanistic predator-prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator-prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data - including in small amounts - can make the estim...

Concepts
Powered ByUnsilo

Stochastic Predator-prey Model
Interaction
Time Series Of Densities
Identifiability
Inverse Modelling
Population Count Data
Population Counts
Stochastic
Predator-prey Model
Biotic Interactions

Introducing Weekly Round-ups!Beta

Powered by R DiscoveryR Discovery

Round-ups are the summaries of handpicked papers around trending topics published every week. These would enable you to scan through a collection of papers and decide if the paper is relevant to you before actually investing time into reading it.

Coronavirus Research Articles published between Aug 01, 2022 to Aug 07, 2022

R DiscoveryAug 08, 2022
R DiscoveryArticles Included:  5

Introduction: The corona virus disease (COVID)-19 is a severe acute respiratory syndrome (SARS-CoV-2) which is posing a great threat to mankind and ha...

Read More

Climate change Research Articles published between Aug 01, 2022 to Aug 07, 2022

R DiscoveryAug 08, 2022
R DiscoveryArticles Included:  5

We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Cooki...

Read More

Good health Research Articles published between Aug 01, 2022 to Aug 07, 2022

R DiscoveryAug 08, 2022
R DiscoveryArticles Included:  2

Don’t have an account? Create a Free Account If you don't remember your password, you can reset it by entering your email address and clicking the Res...

Read More

Quality Of Education Research Articles published between Aug 01, 2022 to Aug 07, 2022

R DiscoveryAug 08, 2022
R DiscoveryArticles Included:  2

Introduction: In 2014 the Accreditation Council for Graduate Medical Education modified adult training requirements for child neurology certification ...

Read More

Gender Equality Research Articles published between Aug 01, 2022 to Aug 07, 2022

R DiscoveryAug 08, 2022
R DiscoveryArticles Included:  3

Introduction: Opioid use disorder (OUD) is characterized as a problematic cycle of substance intoxication and binging, followed by associated withdraw...

Read More

Coronavirus Pandemic

You can also read COVID related content on R COVID-19

R ProductsCOVID-19

ONE PROBLEM . ONE PURPOSE . ONE PLACE

Creating the world’s largest AI-driven & human-curated collection of research, news, expert recommendations and educational resources on COVID-19

COVID-19 Dashboard

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on “as is” basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The Copyright Law.