I am using secr to assess density of an herbivore using trail cameras. I expect the detection of animals to vary among trap locations based on a "frac_m" covariate (which has a different value for each trap location). I defined this trap-covariate in read.capthist() and verified it's definition using covariates(trap()), where it properly showed the frac_m value for each trap location. I ran secr.fit() with frac_m influencing g0 and sigma, and expected an output of beta coefficients for each location based on their values of frac_m. Am I misunderstanding secr's use of detection covariates? Is there a proper way to obtain beta coefficients for this effect?

DeerCH <- read.capthist('Indiv_All.txt', 'SECR_Input.txt', detector='count',

covnames=c("GpSz", "Sex"), trapcovnames="frac_m")

covariates(traps(DeerCH))

secrDeerg0sigfm <- secr.fit(DeerCH, model=c(g0~frac_m, sigma~frac_m), trace=FALSE)

Beta parameters (coefficients)

beta SE.beta lcl ucl

D 0.1402256 0.1148725 -0.08492031 0.3653716

g0 0.6781673 0.2984780 0.09316110 1.2631734

g0.frac_m -7.3820955 0.7526339 -8.85723096 -5.9069601

sigma 5.3144099 0.3335357 4.66069190 5.9681280

sigma.frac_m 3.3401357 0.8620058 1.65063531 5.0296360

I greatly appreciate your input!