There are a myriad of studies of animal habitat use that employ the notion of “home range”. Aggregated information on animal locations provide insight into a geographically discrete units that represents the use of space by an animal. Among various methods to delineate home range is the commonly used Kernel Density Estimation (KDE). The KDE method delineates home ranges based on an animal’s Utilization Distribution (UD). Specifically, a UD estimates a three-dimensional surface representing the probability or intensity of habitat use by an animal based on known locations. The choice of bandwidth (i.e., kernel radius) in KDE determines the level of smoothing and thus, ultimately circumscribes the size and shape of an animal’s home range. The bounds of interest in a home range can then be delineated using different volume contours of the UD (e.g., 95% or 50%). Habitat variables can then be assessed within the chosen UD contour(s) to ascertain selection for certain habitat characteristics. Home range analyses that utilize the KDE method, and indeed all methods of home range delineation, are subject to the Modifiable Areal Unit Problem (MAUP) whereby the changes in the scale at which data (e.g., habitat variables) are analysed can alter the outcome of statistical analyses and resulting ecological inferences. There are two components to MAUP, the scale and zoning effects. The scale effect refers to changes to the data and, consequently the outcome of analyses as a result of aggregating data to coarser spatial units of analysis. The aggregation of data can result in a loss of fine-scale detail as well as change the observed spatial patterns. The zone effect refers to how, when holding scale constant, the delineation of areal units in space can alter data values and ultimately the results of analyses. For example, habitat features captured within 1km2 gridded sampling units may change if instead 1km2 hexagon units are used. This thesis holds there are three “modifiable” factors in home range analyses that render it subject to the MAUP. The first two relate specifically to the use of the KDE method namely, the choice of bandwidth and UD contour. The third is the grain (e.g., resolution) by which habitat variables are aggregated, which applies to KDE but also more broadly to other quantitative methods of home range delineation In the following chapters we examine the changes in values of elevation and slope that result from changes to KDE bandwidth (Chapter 2) UD contour (Chapter 3) and DEM resolution (Chapter 4). In each chapter we also examine how the observed effects of altering each individual parameter of scale (e.g., bandwidth) changes when different scales of the other two parameters are considered (e.g., contour and resolution). We expected that the scale of each parameter examined would change the observed effect of other parameters. For example, that the homogenization of data at coarser resolutions would reduce the degree of difference in variable values between UD contours of each home range. To explore the potential effects of MAUP on home range analyses we used as model population 13 northern woodland caribou (Rangifer tarandus). We created seasonal home ranges (winter, calving, summer, rut and fall) for each caribou using three different KDE bandwidths. Within each home range we delineated four contours based on differing levels of an animal’s UD. We then calculated values of elevation and slope (mean, standard deviation and coefficient of variation) using a Digital Elevation Model (DEM) aggregated to four different resolutions within the contours of each seasonal home range. We found that each parameter of scale significantly changed the values of elevation and slope within the home ranges of the model caribou population. The magnitude as well as direction of change in slope and elevation often varied depending the specific contour or season. There was a greater decrease in the variability of elevation within the fall and winter seasons at smaller KDE bandwidths. The topographic variables were significantly different between all contours of caribou home ranges and the difference between contours were in general, significantly higher in fall and winter (elevation) or calving and summer (slope). The mean and SD of slope decreased at coarser resolutions in all caribou home ranges, whereas there was no change in elevation. We also found interactive effects of all three parameters of scale, although these were not always as direct as initially anticipated. Each parameter examined (bandwidth, contour and resolution) may potentially alter the outcome of northern woodland caribou habitat analyses. We conclude that home range analyses that utilize the KDE method may be subject to MAUP by virtue the ability to modify the spatial dimensions of the units of analysis. As such, in habitat analyses using the KDE careful consideration should be given to the choice of bandwidth, UD contour and habitat variable resolution. / Graduate / 0366 / 0329 / spicym@uvic.ca