Camera Trapping for Wildlife Research (Data in the Wild) by Francesco Rovero and Fridolin Zimmermann

Author:Francesco Rovero and Fridolin Zimmermann
Language: eng
Format: azw3
Publisher: Pelagic Publishing
Published: 2016-06-16T16:00:00+00:00

The realised N is simply the number of individuals actually detected plus a model-based estimate of the number of individuals in the region of interest that remain undetected (Johnson et al. 2010). The probability of remaining undetected is a function of location (high at the edges, low near detectors). The expected N is the integral (sum) over the mask of the product of (estimated) local density and the local probability of remaining undetected. The estimates of expected and realised N are generally very similar, or identical, but realised N usually has lower estimated variance, especially if the n detected animals comprise a large fraction (Efford 2015).

There are differences in movement patterns between male and female lynx: resident adult males have larger home ranges than females (Breitenmoser-Würsten et al. 2001), and adult males regularly patrol their territory borders, to deposit scent marks, to defend their territory against potential intruders, and during the mating season to find potential mates (Breitenmoser and Breitenmoser-Würsten 2008). For these reasons, we expect differences between the sexes in the baseline encounter probability (g0) and the parameter σ. We therefore investigate the effect of sex on the detection function parameters g0 and σ using the null and the best model (Mbk) of the previous analysis in order to check which model best explains our data. Because gender was missing for some individuals, the argument hcov in secr.fit is used to fit a hybrid mixture model (Efford 2015). ‘Hybrid’ refers to a flexible combination of latent classes (as for finite mixtures) and known classes (as for classic categorical covariates). A hybrid mixture model automatically estimates the parameter ‘pmix’, which corresponds to the mixing proportion of the different classes and additionally allows more complex model structures in which the detection parameters can be modelled as class specific (~h2). This is particularly useful for investigating sex ratios and sex differences in detection. The argument hcov="SEX" identifies in the corresponding column the individual covariate that should be a factor with two levels, or contain character values that will be coerced to a factor (e.g. ‘female’, ‘male’). Missing values (NA) are used for individuals of unknown class. It is assumed that males and females are equally likely to be sexed from photographs. Models can be compared by means of AIC provided they all have the same hcov covariate in the call to secr.fit, whether or not the h2 notation appears in the model formulation:

M_g0bk_sigma<-secr.fit(lynx_capthist, mask=mask_prox,

model = list(D~1, g0~bk, sigma~1), hcov="SEX")

M_g0sex_sigmasex<-secr.fit(lynx_capthist, mask=mask_prox,

model = list(D~1, g0~h2, sigma~h2), hcov="SEX")

M_g0_sigmasex<-secr.fit(lynx_capthist, mask=mask_prox,

model = list(D~1, g0~1, sigma~h2), hcov="SEX")

M_g0bk_sigmasex<-secr.fit(lynx_capthist, mask=mask_prox,

model = list(D~1, g0~bk, sigma~h2), hcov="SEX")

M_g0bksex_sigma<-secr.fit(lynx_capthist, mask=mask_prox,

model = list(D~1, g0~(bk+h2), sigma~1), hcov="SEX")

M_g0bksex_sigmasex<-secr.fit(lynx_capthist, mask=mask_prox,

model = list(D~1, g0~(bk+h2), sigma~h2), hcov="SEX")

AIC(M_g0_sigmasex,M_g0sex_sigmasex,M_g0bk_sigma,

M_g0bk_sigmasex,M_g0bksex_sigma,M_g0bksex_sigmasex, criterion="AIC")

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.