scholarly journals From individual vital rates to population dynamics: An integral projection model for European native oysters in a marine protected area

2020 ◽  
Vol 30 (11) ◽  
pp. 2191-2206
Author(s):  
Alice E. Lown ◽  
Leanne J. Hepburn ◽  
Rob Dyer ◽  
Tom C. Cameron
Keyword(s):  
Population Dynamics ◽  
Protected Area ◽  
Marine Protected Area ◽  
Vital Rates ◽  
Integral Projection Model ◽  
Projection Model ◽  
Integral Projection

Demographic processes in socially structured populations

2021 ◽  
pp. 341-350
Author(s):  
Maria Paniw ◽  
Gabriele Cozzi ◽  
Stefan Sommer ◽  
Arpat Ozgul
Keyword(s):  
Population Dynamics ◽  
Social Groups ◽  
Population Models ◽  
Structured Populations ◽  
Model Complexity ◽  
Vital Rates ◽  
Integral Projection Model ◽  
Projection Model ◽  
Structured Population Models ◽  
Structured Population

In socially structured animal populations, vital rates such as survival and reproduction, are affected by complex interactions among individuals of different social ranks and among social groups. Due to this complexity, mechanistic approaches to model vital rates may be preferred over commonly used structured population models. However, mechanistic approaches come at a cost of increased modelling complexity, computational requirements, and reliance on simulated metrics, while structured population models are analytically tractable. This chapter compares different approaches to modelling population dynamics of socially structured populations. It first simulates individual-based data based on the life cycle of a hypothetical cooperative breeder and then projects population dynamics using a matrix population model (MPM), an integral projection model (IPM), and an individual-based model (IBM). The authors demonstrate that, when projecting population size or structure, the relatively simpler MPM can outperform both the IPM and IBM. However, mechanistic details parametrised in the more complex IBM are required to accurately project interactions within social groups. The R scripts in this chapter provide a roadmap to both simulate data that best describe a socially structured system and assess the level of model complexity needed to capture the dynamics of the system.

Population dynamics of the cactus Mammillaria gaumeri: an integral projection model approach

2012 ◽  
Vol 54 (2) ◽  
pp. 321-334 ◽  
Author(s):  
Merari Esther Ferrer-Cervantes ◽  
Martha Elena Méndez-González ◽  
Pedro-Francisco Quintana-Ascencio ◽  
Alfredo Dorantes ◽  
Gabriel Dzib ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Integral Projection Model ◽  
Projection Model ◽  
Integral Projection ◽  
Model Approach

Integral projection models

2021 ◽  
pp. 181-196
Author(s):  
Edgar J. González ◽  
Dylan Z. Childs ◽  
Pedro F. Quintana-Ascencio ◽  
Roberto Salguero-Gómez
Keyword(s):  
Population Dynamics ◽  
Environmental Variables ◽  
Linear Models ◽  
Vital Rates ◽  
Matrix Population Models ◽  
Generalised Linear Models ◽  
Integral Projection Models ◽  
Integral Projection ◽  
Over Time

Integral projection models (IPMs) allow projecting the behaviour of a population over time using information on the vital processes of individuals, their state, and that of the environment they inhabit. As with matrix population models (MPMs), time is treated as a discrete variable, but in IPMs, state and environmental variables are continuous and are related to the vital rates via generalised linear models. Vital rates in turn integrate into the population dynamics in a mechanistic way. This chapter provides a brief description of the logic behind IPMs and their construction, and, because they share many of the analyses developed for MPMs, it only emphasises how perturbation analyses can be performed with respect to different model elements. The chapter exemplifies the construction of a simple and a more complex IPM structure with an animal and a plant case study, respectively. Finally, inverse modelling in IPMs is presented, a method that allows population projection when some vital rates are not observed.

A dynamic energy budget–integral projection model (DEB-IPM) to predict population-level dynamics based on individual data: a case study using the small and rapidly reproducing species Engraulis japonicus

2020 ◽  
Vol 71 (4) ◽  
pp. 461
Author(s):  
Baochao Liao ◽  
Xiujuan Shan ◽  
Can Zhou ◽  
Yanan Han ◽  
Yunlong Chen ◽  
...  
Keyword(s):  
Energy Budget ◽  
Environmental Changes ◽  
Population Level ◽  
Model Parameters ◽  
Integral Projection Model ◽  
Dynamic Energy Budget ◽  
Projection Model ◽  
Engraulis Japonicus ◽  
Integral Projection ◽  
Primary Model

The coupling of a dynamic energy budget (DEB) model with an integral projection model (IPM; i.e. generating a DEB-IPM) is a promising new method to predict the population-level dynamics of species based on individuals. In a single framework, the DEB component provides links to the individual-level physiological processes, and the IPM component provides an alternative way to investigate ecological changes in quantitative life history characteristics and population dynamics. In this paper we present a DEB-IPM to analyse a Japanese anchovy (Engraulis japonicus) population in Chinese seas. The coupled model describes the dynamics of a population of individuals, where each individual follows an energy budget. Primary model parameters (e.g. energy conductance, ὺ; allocation coefficient, κ; and volume-specific somatic maintenance, [ṗM]) were estimated. The mean population growth rate (rp) was calculated to be 3.4year–1. The predicted demographic rates (e.g. growth, survival and reproduction) were well within observed ranges, and fit within average recorded values, and captured known seasonal trends. DEB-IPMs could be a useful tool to capture the dynamics of biodiversity amidst global environmental changes.

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