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| Model integrálnej projekcie× | Analýza životaschopnosti populácie× | |
|---|---|---|
| Odbor | Ekológia | Ekológia |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 2000 | 1981 |
| Tvorca≠ | Stephen Ellner and Mark Rees | Mark Shaffer |
| Typ≠ | size-structured population projection | extinction risk assessment |
| Pôvodný zdroj≠ | Easterling, M. R., Ellner, S. P., & Dixon, P. M. (2000). Size-specific sensitivity: applying a new structured population model. Ecology, 81(3), 694-708. DOI ↗ | Shaffer, M. L. (1981). Minimum population sizes for species conservation. BioScience, 31(2), 131-134. DOI ↗ |
| Ďalšie názvy | IPM, continuous size structure, kernel model, size-structured population | PVA, extinction risk, minimum viable population, MVP |
| Príbuzné | 4 | 4 |
| Zhrnutie≠ | Integral projection models (IPMs) are a class of structured population models that use continuous traits (size, age, height) to describe population dynamics. Introduced by Easterling and colleagues (2000) and developed extensively by Ellner, Rees, and collaborators, IPMs overcome limitations of age- or stage-structured models by treating individual traits as continuous. They use integration to project populations forward in time, making them particularly suitable for organisms with continuous size distributions or flexible developmental pathways. IPMs enable estimation of population growth rate (λ), sensitivity analysis, and projection under changing environmental conditions. | Population Viability Analysis (PVA), introduced by Shaffer (1981), estimates the probability that a population will persist over a given time period under specified conditions. PVA combines demographic models (Leslie matrices, IPMs) with stochastic simulation to project population trajectories, quantifying extinction risk. This allows conservation planners to assess whether a population will likely persist, evaluate management scenarios, and estimate the minimum viable population (MVP) size for long-term persistence. PVA is a decision-support tool, not a precise predictor. |
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