matrix population model

Knowledge graph centered on matrix population model with 44 nodes and 144 connections. Top connected: Rocky Mountain Biological Laboratory, Gothic, Marmota, Yellow-bellied Marmot, Colorado East River Valley.

Method synopsis

Construction and parameterization of periodic matrix population models to project population dynamics under different scenarios of environmental periodicity strength.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Demographic consequences of changes in environmental periodicity (2023), Ecology. Implementations in other papers (listed below) may differ.

1. Vital rate modeling
   Used generalized linear models (GLMMs) and mixed models to model stage-specific vital rates as functions of seasonal or post-fire habitat states. For marmots and meerkats, estimated survival, reproduction, and transition probabilities between seasons. For dewy pines, modeled vital rates across different time-since-fire states. Incorporated random effects for year and individual where appropriate.
   Quantities: Separate models for each life stage and vital rateDuration: Applied to full datasets for each speciesConditions: Statistical modeling using observational data
   Equipment: R software, MuMIn R package, glm and glmer functions, lme4 R package
2. Matrix population model construction
   Built periodic matrix population models (MPMs) for each season or post-fire state using vital rate predictions. For seasonal species, created separate matrices for winter and summer transitions. For dewy pines, created matrices for each time-since-fire state (TSF₀ through TSF>₃). Used matrix product to compute annual population projection matrices.
   Quantities: Species-specific number of matrices per annual cycleDuration: Applied across full time series for projectionsConditions: Computational matrix modeling
   Equipment: R software
3. Population dynamic simulations
   Projected population dynamics for 100 years using 500 simulations. For control scenarios, randomly sampled years corresponding to low seasonality (LS, bottom 50th percentile) and high seasonality (HS, top 50th percentile) years. For perturbed scenarios, altered the threshold to change strength of periodicity. Calculated stochastic growth rate (log λs), variance of annual growth rates (var(log λ)), and quasi-extinction probability (pqext) with 15% threshold.

Typical Equipment

• R software
• MuMIn R package
• lme4 R package

Output Measurements

• stochastic growth rate
• variance of growth rates
• quasi-extinction probability

Papers Using This Protocol (1)

Species Studied With This Protocol

Places Where Used