Although GPS collar technology is expensive compared to Very High Frequency (VHF) radio collars, it allows for the collection of many locations of radio-collared animals without disturbing the study animals and possibly biasing their resource use. The collars used in my study also have a VHF signal that allows the animal to be found with a hand-held receiver by researchers on the ground or while flying in an airplane. When using GPS collars that do not
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transmit positional data, but instead store it on-board within the collar, there are inherent risks such as mechanical collar failures or loss of the animal and/or the collar (Dettki et al. 2004).
Capture and collaring moose with chemical immobilization results in mortality rates between 6% and 19% (Roffe et al. 2001). One study found significant mortality with 9 of 11 captive moose dying when given a combination of carfentanil and xylazine (Kreeger 2000). The cause of the mortalities was related to xylazine, which is a sedative drug that increases the risk of bloat, regurgitation and aspiration of rumen contents (Arnemo et al. 2003). However, Roffe et al. (2001) immobilized 41 free-ranging female moose with a combination of xylazine and
carfentanil without mortality. The authors attributed their success in capture and the recovery of moose to using low-stress techniques including quietly processing the blind-folded moose. Roffe et al. (2001) found that capture-related mortality occurred when the body condition of female moose was below a threshold. Chemical immobilization was not used in my study due to the increased mortality risk to moose. Additionally, I wanted to prevent recovering moose that had been chemically immobilized from inadvertently crossing highways and roads, which could result in MVCs.
Carpenter and Innes (1995) observed a mortality rate of less than 1%, when using net guns fired from helicopters to capture moose, with cervical fractures as the most common cause of capture mortality. Ideal conditions for net gun capture of moose occur in winter to prevent heat stress and with adequate snow depth to slow the running animal (Carpenter and Innes 1995). Unlike other ungulates such as elk or deer, moose are usually solitary animals and more tolerant of humans (Roffe et al. 2005). The solidary nature of moose generally results in successful capture when using net guns fired from helicopters. Arnemo et al. (2003) suggests that chemical immobilization from a helicopter in winter is the best capture method for moose, but that a mortality rate greater than 2% during chemical immobilization is unacceptable. Consequently, the capture of moose in my study was completed during periods with adequate snowfall using net-guns fired from a helicopter.
2.8.2. Spatial Autocorrelation
Spatial autocorrelation is the degree of interdependence between variables and its presence violates the assumption that samples or sites are spatially independent. Animal
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to be available from its current location, which results in autocorrelated locations for long time durations (Cushman 2010). In the spatial analysis of animal observations, temporal
autocorrelation of locations leads to underestimation of home range size (Swihart and Slade 1985, White and Garrott 1990). Subsampling to achieve statistical independence also results in substantial underestimation of range size and rates of movement (Rooney et al. 1998).
A priori, there is no way to determine what the time duration between successive
locations to achieve independence will be (Harris et al. 1990). However, filtering or subsampling data to achieve statistical independence results in information loss. Further, even long sampling intervals do not guarantee independence (Cushman et al. 2005). Legendre (1993) and Legendre and Fortin (1989) argue that spatial autocorrelation is an informative ecological parameter that reveals scales and patterns of ecological processes. Autocorrelation in a data set should be interpreted with an ecological perspective (Hundertmark 2007). Reducing the frequency of location points through subsampling allows the data to appear statistically independent and not show significant autocorrelation (Fieberg et al. 2010). However, sparse sampling of an
autocorrelated data stream does not remove the effect of autocorrelation from the data, rather it only reduces the ability to detect it (Fortin and Dale 2005). Bahn and McGill (2013) state that spatial autocorrelation is less of a concern when analysing resource selection within a region rather than using the model to predict into new geographic areas. Based on the current knowledge of spatial autocorrelation in the literature, I did not remove location data or
subsample data for the purpose of analysing female moose resource selection within my study area.
2.8.3. Determining Pregnancy
Progesterone levels in blood serum have been successfully used to detect pregnancy in moose (Haigh et al. 1982, Testa and Adams 1998, Poole et al. 2007, Milner et al. 2013). This method has also been used to detect pregnancy in caribou (Demars et al. 2013). Pregnancy can be confirmed by observing the female with young (Demars et al. 2013). In Saskatchewan, rectal palpation has also been used to diagnosis pregnancy in moose in March (Haigh et al. 1982). In March, moose progesterone levels range from 5.4 to 11.5 ng/ml in pregnant moose, 0.42 ng/ml in a non-pregnant female and 0.15 ng/ml in a male (Haigh et al. 1982).
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2.8.4. Hypothesis-testing Framework
The information-theoretic approach used in my study is suitable for complex ecological studies examining multiple working hypotheses (Chamberlin 1965). Multiple working
hypotheses means that it is possible for more than one hypothesis to be simultaneously true (Chamberlin 1965). This approach is commonly used in the disciplines of psychology, statistics, and wildlife biology (Elliott and Brook 2007). Unlike techniques of null-hypothesis testing, this approach is well suited to ecological research where a true control and treatment design is not possible, particularly when analyzing habitat selection of wildlife outside of captivity (Johnson and Omland 2004, Dochtermann and Jenkins 2011). Researcher bias is reduced by allowing numerous potential explanations to be explored by simultaneously evaluating a set of competing hypotheses, as this approach circumvents the natural tendency of investigator attachment to a single hypothesis (Elliott and Brook 2007).
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