#Hypothesis team doing the survey has an influence on detectabilty

#Hypohesis landcover and the team has an influence density

Results of the models;

nPars AIC delta AICwt cumltvWt

hntm_LC 4 324.78 0.00 1.0e+00 1.00

hn_tm 3 358.48 33.70 4.8e-08 1.00

Delta AIC of the hntm_LC cover is zero indicating a good fit. The landcover and the team has an influence on detectability.

Detectabilty of the covariate team categories i.e team A and team B was done.

Results;

DetectteamB

Backtransformed linear combination(s) of Detection estimate(s)

Estimate SE LinComb (Intercept) TeamB

90.2 10.1 4.5 1 0

Transformation: exp

DetectteamA

Backtransformed linear combination(s) of Detection estimate(s)

Estimate SE LinComb (Intercept) TeamB

110 11.7 4.7 1 1

Transformation: exp

The half-normal standard deviation in team B is 90m

In contrast, the half-normal standard deviation in team A is 110m

Detectability is lower for team B.

Confidence intervals

Team B CI Team A CI

0.025 0.975 0.025 0.975

88.93429 135.1803 72.48405 112.3094

ESWH for team B is 22m

ESWH for team A is 27m

There are overlaps, would it mean that detectability between the two teams is not mutually exclusive? I do understand that both teams did survey in both landcover types.

How do I therefore have the teams and landcover classes separated?

How to calculate detectability of team B in a grassland and Team B in a wetland? I tried this code

```
DetectteamBG <- backTransform(linearComb(hntm_LC,
coefficients = c(1, 0),
type = "det"))
DetectteamBG
```

Error in h(simpleError(msg, call)) :

error in evaluating the argument ‘obj’ in selecting a method for function ‘backTransform’: error in evaluating the argument ‘coefficients’ in selecting a method for function ‘linearComb’: object ‘TeamB’ not found