Chapter 8: Analysis of GIS, Landscape Matrices and Health Variables Table 3: Health variables considered in the analysis. Italics variables were suggested after the analysis was conducted and will be considered in future analyses. No Health component 1 Growth/Condition
2
Reproduction
3
Immunity
4
Stress
Variables BCI alk_phos_UpL straight-line length progesterone_ngml estrogen_pgml prolactin_ngml luteinizing_ngml testosterone_ngml wbc_e9pL neu_percent lym_percent lym_neu_ratio mono_percent eos_percent baso_percent total protein globulin AG_ratio total_cortisol_UW ggt_UpL glucose_mmolpL hsp_60_uw hsp_70_uw
Covary with age/sex age/sex age/sex age/sex age/sex age/sex age/sex
capture capture capture capture capture capture capture capture capture hsp are sample size limited
Most of the health variables were reduced in sample size when only the first capture of a bear was used for the analysis (Table 4). This was a key limiting factor in analyses given that the overall number of observations used in any type of analysis is based upon the variable with the lowest sample size. Sample sizes were higher when multiple capture events were used. Analysis strategies Previous analyses (Boulanger 2006) focused on eliminating redundant variables from each of the respective health and environmental variables using principal component analysis (McGarigal et al 2000). Mixed models were then used to test for potential relationships between single health variables and suites of environmental variables. For the next phase of this analysis I focused on constrained ordination methods that simultaneously tested the relationship between suites of health variables and environmental variables. I used program CANOCO for constrained ordination analyses (Jongman et al 1995, Ter Braak and Smilauer 2002, Leps and Smilauer 2003). In particular, redundancy analysis was used to assess the relationship between suites of health response variables and environmental predictors. The significance of environmental predictors was assessed using Monte Carlo tests for parameter significance (Ter Braak and Smilauer 2002). When applicable, covariates were used to condition out nuisance variables in the analysis such as the differential effect of snare or helidarting on some of the stress variables. Program CANOCO 169