Linganisha mbinu

	Sampuli ya Umbali	Uundaji wa Niche	Uchanganuzi wa Uwezekano wa Kuishi kwa Idadi ya Watu
Nyanja	Ikolojia	Ikolojia	Ikolojia
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	1993	1999	1981
Mwanzilishi	Stephen Buckland	Steven Phillips and David Stockwell	Mark Shaffer
Aina	population abundance estimation	species distribution prediction	extinction risk assessment
Chanzo asilia	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.	Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.	Shaffer, M. L. (1981). Minimum population sizes for species conservation. BioScience, 31(2), 131-134.
Majina mbadala	line transect, point transect, distance estimation, detection probability	species distribution modeling, habitat suitability modeling, ecological niche model, MaxEnt	PVA, extinction risk, minimum viable population, MVP
Zinazohusiana	4	4	4
Muhtasari	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.	Niche modeling, also called species distribution modeling (SDM), predicts the geographic range and habitat suitability of species using presence-only or presence-background occurrence data and environmental variables. MaxEnt (Maximum Entropy, Phillips et al. 2006) and GARP (Genetic Algorithm for Rule-set Prediction, Stockwell & Peters 1999) are two prominent algorithms. These methods identify the environmental conditions under which species are likely to occur, enabling prediction of distribution beyond sampled areas and assessment of habitat suitability across landscapes.	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.