Among methods for estimating population sizes, mark-recapture is a popular choice. It assumes population closure (void of deaths, births, immigration and emigration) and equal probability of capture. Since populations often cannot be encompassed entirely, some individuals cross in and out of the sampling area in violation of aforementioned assumptions, which is termed edge effect. The time-tested Huggins model does not in itself use spatial information to estimate parameters; however, it does enable use of an individual covariate. In this thesis, we use simulations to test whether including spatial information through an individual covariate helps alleviate edge effect. Our findings suggest that including spatial information does improve the model. For practical purposes, the difference in estimates of probability of capture ($\hat{p}$) between models is negligible. Bias of $\hat{p}$ is smallest in cases where home range size is small relative to sampling area size and large for cases where home range is comparatively large. This is also evident in density estimates, which are highly biased in cases where home range is relatively large compared to sampling area. We increased the sampling area radius based on distributions used to calculate the individual covariate and managed to somewhat alleviate the bias, provided that the calculated home range shape and size are representative.
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