Linganisha mbinu

	Uundaji wa Niche	Sampuli ya Umbali
Nyanja	Ikolojia	Ikolojia
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1999	1993
Mwanzilishi	Steven Phillips and David Stockwell	Stephen Buckland
Aina	species distribution prediction	population abundance estimation
Chanzo asilia	Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.	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.
Majina mbadala	species distribution modeling, habitat suitability modeling, ecological niche model, MaxEnt	line transect, point transect, distance estimation, detection probability
Zinazohusiana	4	4
Muhtasari	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.	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.