Life-Cycle Trade-Offs in Matrix Population-Models
Matrix projections allow identification of those phases in the life cycle with a high potential impact on the population growth rate. This impact is assessed by the sensitivity or elasticity of the matrix elements that are computed from life cycle data such as age-dependent survival or fecundity. Co...
| Published in: | Ecology |
|---|---|
| Main Author: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
1995
|
| Subjects: | |
| Online Access: | https://pure.knaw.nl/portal/en/publications/1141e007-cc5c-4629-978a-d1b6274e1451 https://doi.org/10.2307/2265822 https://hdl.handle.net/20.500.11755/1141e007-cc5c-4629-978a-d1b6274e1451 |
| Summary: | Matrix projections allow identification of those phases in the life cycle with a high potential impact on the population growth rate. This impact is assessed by the sensitivity or elasticity of the matrix elements that are computed from life cycle data such as age-dependent survival or fecundity. Covariation among life cycle components, e.g., due to trade- offs, is a main subject of life history theory. Sensitivities or elasticities of matrix elements do not take this covariation into account. Integrated sensitivities and elasticities measure the net effect of a matrix element, combining its direct effect and indirect effects through correlation with other matrix elements. These measures are related to the selection differentials and expected selection responses from quantitative genetics. The use of these measures is illustrated with data on disease resistance in a perennial weed Plantago lanceolata, and the demography of killer whale pods. [KEYWORDS: Demography; killer whales; life history; matrix projections; population growth rate; selection differential; selection response Finding confidence-limits; growth rates; demography; selection; acquisition; parameters; allocation; elasticity; characters;resources] Matrix projections allow identification of those phases in the life cycle with a high potential impact on the population growth rate. This impact is assessed by the sensitivity or elasticity of the matrix elements that are computed from life cycle data such as age-dependent survival or fecundity. Covariation among life cycle components, e.g., due to trade- offs, is a main subject of life history theory. Sensitivities or elasticities of matrix elements do not take this covariation into account. Integrated sensitivities and elasticities measure the net effect of a matrix element, combining its direct effect and indirect effects through correlation with other matrix elements. These measures are related to the selection differentials and expected selection responses from quantitative genetics. The use of ... |
|---|