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USE OF AGE- AND STAGE-STRUCTURED MATRIX MODELS TO PREDICT LIFE HISTORY SCHEDULES FOR SEMELPAROUS POPULATIONS
- Source :
- Natural Resource Modeling. 29:538-558
- Publication Year :
- 2016
- Publisher :
- Wiley, 2016.
-
Abstract
- Many studies of semelparous salmon populations use Leslie matrices that classify individuals on the basis of age alone and do not explicitly impose death upon reproduction. Although these models may suffice for studying long-term population dynamics (like asymptotic growth rate), they do not accurately represent the diversity of individual life history outcomes in semelparous populations. Cohorts breeding at different ages have different life history traits (e.g., age at first reproduction and remaining life expectancy) that are obscured in Leslie models and this distorts our understanding of life history diversity and its importance for semelparous population dynamics. We present a simple transformation that uses age-specific breeding probabilities to reconfigure Leslie matrices as explicitly semelparous models. Explicitly semelparous models conserve asymptotic measures like population growth rate, vital rate elasticities, life expectancy at birth, and generation time but also better predict life history schedules and reproductive values. Strictly age-classified Leslie models underestimate ages at first reproduction and mean ages at death for older breeders but overestimate mean ages at death for early breeders. Leslie models also slightly overestimate variance in lifetime reproductive success, and underestimate entropy exhibited by life history outcomes.
- Subjects :
- 0106 biological sciences
education.field_of_study
Generation time
Reproductive success
010604 marine biology & hydrobiology
Population
Leslie matrix
Environmental Science (miscellaneous)
Biology
010603 evolutionary biology
01 natural sciences
Life history theory
Modeling and Simulation
Life expectancy
Population growth
education
Semelparity and iteroparity
Demography
Subjects
Details
- ISSN :
- 08908575
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- Natural Resource Modeling
- Accession number :
- edsair.doi...........82cd73448e06018bf53927b5a4e60f29
- Full Text :
- https://doi.org/10.1111/nrm.12109