Σύγκριση μεθόδων

	Ανάλυση Circuitscape	Μοντελοποίηση Θώλου (Niche Modeling)	Ανάλυση Βιωσιμότητας Πληθυσμού
Πεδίο	Οικολογία	Οικολογία	Οικολογία
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	2008	1999	1981
Δημιουργός	Brad McRae	Steven Phillips and David Stockwell	Mark Shaffer
Τύπος	movement and connectivity modeling	species distribution prediction	extinction risk assessment
Θεμελιώδης πηγή	Bradford, D. F., McCreary, D. D., & Groves, C. R. (2014). Optimizing sampling for large-area habitat assessment. Ecological Monographs, 84(3), 351-375.	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.
Εναλλακτικές ονομασίες	circuit theory, resistance distance, connectivity analysis, landscape conductance	species distribution modeling, habitat suitability modeling, ecological niche model, MaxEnt	PVA, extinction risk, minimum viable population, MVP
Συναφείς	4	4	4
Σύνοψη	Circuitscape, developed by Brad McRae (2008), applies circuit theory from electrical engineering to predict organism movement and genetic connectivity across landscapes. The method treats landscapes as electrical networks where habitat quality is resistance and organism movement is electrical current. By analogy, organisms diffusing through a landscape follow paths determined by landscape resistance: corridors of low resistance (good habitat) are preferentially used. Circuitscape predicts movement probabilities, identifies critical corridors, and quantifies connectivity between habitat patches.	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.