Stochastic Population Models

2021 ◽  
pp. 85-102
Author(s):  
Timothy E. Essington
Keyword(s):  
Stochastic Model ◽  
Extinction Risk ◽  
Ecological Systems ◽  
Population Models ◽  
Random Component ◽  
Deterministic Simulation ◽  
Density Dependent ◽  
State Variable ◽  
Stochastic Population Models ◽  
Core Concepts

The chapter “Stochastic Population Models” introduces the concept of stochasticity, why it is sometimes incorporated into models, the consequences of stochasticity for population models, and how these types of models are used to evaluate extinction risk. Ecological systems are (seemingly) governed by randomness, or “stochasticity.” A stochastic model is one that explicitly includes randomness in the prediction of state variable dynamics. Because these models have a random component, each model run will be unique and will rarely look like a deterministic simulation. In this chapter, simple unstructured and density-dependent models are presented to show core concepts, and extensions to structured and density-dependent models are given.

Recent investigations involving stochastic population models

1984 ◽  
Vol 16 (1) ◽  
pp. 4-5
Author(s):  
M. S. Bartlett
Keyword(s):  
Stochastic Model ◽  
Stochastic Models ◽  
Lattice Models ◽  
Population Models ◽  
Nearest Neighbour ◽  
Doubly Stochastic ◽  
Stochastic Population Models

After some introductory general remarks on recent investigations involving population models, two broad classes of stochastic model are discussed, further, viz., spatial nearest-neighbour lattice models, and doubly stochastic models.

scholarly journals Harvesting can stabilize population fluctuations and buffer the impacts of climate change

2021 ◽  
Author(s):  
Bart Peeters ◽  
Vidar GrØtan ◽  
Marlène Gamelon ◽  
Vebjørn Veiberg ◽  
Aline Magdalena Lee ◽  
...  
Keyword(s):  
Life Histories ◽  
Resource Competition ◽  
Extinction Risk ◽  
Population Models ◽  
Environmental Stochasticity ◽  
Environmental Drivers ◽  
Population Fluctuations ◽  
Vital Rates ◽  
Density Dependent ◽  
Environmental Perturbations

Harvesting can magnify the destabilizing effects of environmental perturbations on population dynamics and, thereby, increase extinction risk. However, population-dynamic theory predicts that impacts of harvesting depend on the type and strength of density-dependent regulation. Here, we used population models for a range of life histories and an empirical reindeer case study to show that harvesting can actually buffer populations against environmental perturbations. This occurs because of density-dependent environmental stochasticity, where negative environmental impacts on vital rates are amplified at high population density due to intra-specific resource competition. Simulations from our population models show that even low levels of proportional harvesting may prevent overabundance, thereby dampening population fluctuations and reducing the risk of population collapse and quasi-extinction induced by environmental perturbations. Thus, depending on the species’ life history and the strength of density-dependent environmental drivers, harvesting can improve population resistance to increased climate variability and extreme weather expected under global warming.

Recent investigations involving stochastic population models

1984 ◽  
Vol 16 (01) ◽  
pp. 4-5
Author(s):  
M. S. Bartlett
Keyword(s):  
Stochastic Model ◽  
Stochastic Models ◽  
Lattice Models ◽  
Population Models ◽  
Nearest Neighbour ◽  
Doubly Stochastic ◽  
Stochastic Population Models

After some introductory general remarks on recent investigations involving population models, two broad classes of stochastic model are discussed, further, viz., spatial nearest-neighbour lattice models, and doubly stochastic models.

Lack of relationship between simulated fish population responses and their life history traits: inadequate models, incorrect analysis, or site-specific factors?

2005 ◽  
Vol 62 (4) ◽  
pp. 886-902 ◽  
Author(s):  
Kenneth A Rose
Keyword(s):  
Life History ◽  
Life History Traits ◽  
Lake Trout ◽  
Fish Population ◽  
Population Models ◽  
Site Specific ◽  
Density Dependent ◽  
Population Responses ◽  
Adult Growth ◽  
Specific Factors

Relationships between fish population responses to changes in their vital rates and commonly available life history traits would be a powerful screening tool to guide management about species vulnerability, to focus future data collection on species and life stages of concern, and to aid in designing effective habitat enhancements. As an extension of previous analyses by others, I analyzed the responses to changes in fecundity and yearling survival of age-structured matrix and individual-based population models of 17 populations comprising 10 species. Simulations of the matrix models showed that the magnitude of population responses, but not the relative order of species sensitivity, depended on the state (sustainable or undergoing excessive removals) of the population. Matrix and individual-based models predicted population responses that appeared to be unrelated to their species-level life history traits when responses were plotted on a three-end-point life history surface. Density-dependent adult growth was added to the lake trout (Salvelinus namaycush) matrix model, and simulations demonstrated the potential importance to predicted responses of density-dependent processes outside the usual spawner–recruit relationship. Four reasons for the lack of relationship between population responses and life history traits related to inadequate population models, incorrect analysis, inappropriate life history model, and important site-specific factors are discussed.

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