Integrated population models ( To examine the behaviour of model parameter estimates, we simulated stable populations closed to immigration and emigration. We simulated two scenarios that might induce error into survival estimates: marker induced bias in the capture–mark–recapture data and heterogeneity in the mortality process. We subsequently fit capture–mark–recapture, state‐space and fecundity models, as well as Simulation results suggested that when model assumptions are violated, estimation of additional, previously unidentifiable, parameters using Our results have important implications for biological inference when using
Metapopulation models include spatial population dynamics such as dispersion and migration between subpopulations. Integral projection models (IPMs) can include demographic rates as a function of size. Traditionally, metapopulation models do not included detailed populaiton models such as IPMs. In some situations, both local population dynamics (e.g. size‐based survival) and spatial dynamics are important. We present a Python package, We demonstrate how Moving beyond our example system, we describe how
- PAR ID:
- 10457848
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 14
- Issue:
- 9
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 2243-2249
- Size(s):
- p. 2243-2249
- Sponsoring Org:
- National Science Foundation
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Abstract IPM s) have become increasingly popular for the modelling of populations, as investigators seek to combine survey and demographic data to understand processes governing population dynamics. These models are particularly useful for identifying and exploring knowledge gaps within life histories, because they allow investigators to estimate biologically meaningful parameters, such as immigration or reproduction, that were previously unidentifiable without additional data. AsIPM s have been developed relatively recently, there is much to learn about model behaviour. Behaviour of parameters, such as estimates near boundaries, and the consequences of varying degrees of dependency among datasets, has been explored. However, the reliability of parameter estimates remains underexamined, particularly when models include parameters that are not identifiable from one data source, but are indirectly identifiable from multiple datasets and a presumed model structure, such as the estimation of immigration using capture‐recapture, fecundity and count data, combined with a life‐history model.IPM s that estimated additional parameters.IPM s may be extremely sensitive to these violations of model assumption. For example, when annual marker loss was simulated, estimates of survival rates were low and estimates of immigration rate from anIPM were high. When heterogeneity in the mortality process was induced, there were substantial relative differences between the medians of posterior distributions and truth for juvenile survival and fecundity.IPM s, as well as future model development and implementation. Specifically, using multiple datasets to identify additional parameters resulted in the posterior distributions of additional parameters directly reflecting the effects of the violations of model assumptions in integrated modelling frameworks. We suggest that investigators interpret posterior distributions of these parameters as a combination of biological process and systematic error. -
Abstract Metapopulation and source–sink dynamics are increasingly considered within spatially explicit management of wildlife populations, yet the application of these concepts has generally been limited to comparisons of the performance (e.g., demographic rates or dispersal) inside vs. outside protected areas, and at spatial scales that do not encompass an entire metapopulation. In the present study, a spatially explicit, size‐structured matrix model was applied to simulate the dynamics of an Eastern oyster (
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Higher fat stores contribute to persistence of little brown bat populations with white‐nose syndrome
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Abstract Periodically harvested closures are a widespread, centuries‐old form of fisheries management that protects fish between pulse harvests and can generate high harvest efficiency by reducing fish wariness of fishing gear. However, the ability for periodic closures to also support high fisheries yields and healthy marine ecosystems is uncertain, despite increased promotion of periodic closures for managing fisheries and conserving ecosystems in the Indo‐Pacific.
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