Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Campionament per distàncies× | Anàlisi de Viabilitat de Poblacions× | |
|---|---|---|
| Camp | Ecologia | Ecologia |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 1993 | 1981 |
| Autor original≠ | Stephen Buckland | Mark Shaffer |
| Tipus≠ | population abundance estimation | extinction risk assessment |
| Font seminal≠ | 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 ↗ | Shaffer, M. L. (1981). Minimum population sizes for species conservation. BioScience, 31(2), 131-134. DOI ↗ |
| Àlies | line transect, point transect, distance estimation, detection probability | PVA, extinction risk, minimum viable population, MVP |
| Relacionats | 4 | 4 |
| Resum≠ | 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. | 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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