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Μήτρα Leslie×Δειγματοληψία Απόστασης×
ΠεδίοΟικολογίαΟικολογία
ΟικογένειαProcess / pipelineProcess / pipeline
Έτος προέλευσης19451993
ΔημιουργόςPatrick LeslieStephen Buckland
Τύποςstructured population dynamicspopulation abundance estimation
Θεμελιώδης πηγήLeslie, P. H. (1945). On the use of matrices in certain population mathematics. Biometrika, 33(3), 183-212. DOI ↗Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (1993). Distance Sampling: Estimating Abundance of Biological Populations. Chapman and Hall, London. link ↗
Εναλλακτικές ονομασίεςLeslie model, age-structured population model, matrix population model, population dynamicsline transect, point transect, distance estimation, detection probability
Συναφείς44
ΣύνοψηThe Leslie matrix is a deterministic model of age-structured population dynamics, introduced by Patrick Leslie (1945). It projects population size and structure forward in time using age-specific fertility and survival rates. A Leslie matrix encodes these vital rates in a square matrix; multiplying the matrix by a population vector yields the population's composition at the next time step. This approach enables calculation of the population's asymptotic growth rate (λ), identification of stable age structure, and sensitivity analysis—understanding which vital rates most strongly influence population growth.Distance sampling is a statistical method for estimating population abundance from data on distances between observers and detected individuals. Developed by Buckland and colleagues (1993) and formalized in the software Distance, this approach accounts for imperfect detection: animals far from an observer are less likely to be detected. By modeling the detection function (probability of detecting an animal at various distances), distance sampling produces unbiased estimates of abundance and density even when detection is incomplete.
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ScholarGateΣύγκριση μεθόδων: Leslie Matrix · Distance Sampling. Ανακτήθηκε στις 2026-06-17 από https://scholargate.app/el/compare