influence du climat et de la prédation sur l'utilisation de l'habitat et la ...

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Statistical analyses
Habitat selecti on
We built models of habitat selection by comparing used and random locati ons to obtain
Resource Selection Functions (RSF, Manly et al. 2002). RSFs are equati ons that predi ct the
relati ve probabil ity of use, taking the form:
(1)
where w(x) is the RSF value, e is the base of the naturallogarithm, ~ i are the estimated
coeffi cients, and Xi are habitat variables. We used matched case-control or conditional
logisti c regression (Hosmer & Lemeshow 1989) to estimate coeffi cients. Each pair of usedrandom
points was defined as a stratum in analyses so that conditional logisti c regression
compared use with availability in a given pair, whi ch is parti cularly adapted to studies of
microhabi tat and/or microclimate selecti on (Compton et al. 2002). Because we had repeated
measurements of habitat use on the same individuals, measurements were not independent.
Such correlati on does not influence coefficient estimates (~ values), but biases their standard
errors (Nielson et al. 2002). We used a robust sandwich estimate of the covariance matrix
(Lin & Wei 1989, Wei et al. 1989) to obtain robust standard en-ors of coefficients. For that
we defined ail observati ons coming fro m a given individual as a cluster, pairs of used-random
points as a stratum, and analysed our data using SAS software, proc PHREG, vers ion 9.1
(SAS 2002) fo llowing Fortin et al. (2005). Each estimated coeffi cient is interpreted as usual
fo r logisti c regression: a one unit increase in an explanatory variable results in a e Pi increase
in the odds ratio. For low-probability events (such as the presence of a juvenile porcupine),
the odds rati o approximates the relative ri sk, i.e. the ratio of the probability of event x (e.g. a
porcupine being present) given A to the probability of x given B (Hosmer & Lemeshow
1989, Compton et al. 2002). Because we used a Cox proporti onal hazards model fo r
regression analyses (also used fo r survival analyses, see below), we show hazard rati os
instead of typical odds ratios. However, interpretati on of hazard ratios is similar to
in te rpretation of odds ratio.
Effects of meteorological conditions on the use of coyer
Our data set included repeated measurements made on the same individuals at di fferent
dates so we fi tted mixed effect models with porcupine identity as a repeated factor. We used
SAS software, version 9. 1 (SAS 2002) to analyse the effects of meteorological covari ates on