Hi all,
I was recently spurred to try out secr in R after finding a response to my initial query in this thread: viewtopic.php?f=1&t=1903
Just to reiterate on my sampling and study, I have carried out a mark recapture study of solitary wasps using pheromone baits to attract individuals. In 18 trapping days (occasions), the first 9 occasions were mark and recapture at 9 fixed baiting points (detectors). The following 9 occasions were random spot samples throughout the site to recover already marked individuals.
The reason for the dual sampling strategy is connected to my two main objectives for secr analysis:
- To derive population density estimates
- To look for evidence of learned avoidance to traps post capture
(Home range size would be a bonus and looks like secr can help me here too)
I am interested to garner some thoughts on how I might make a more nuanced model of learning than the
g0 ~ b model which I have tried.
I understand the g0 ~ b models a step change in detection probability of individuals after first detection. However a more realistic model of wasp learning would be one where the step change in detection probability only reduces recapture for the specific trap location of first detection, with subsequent detection probabilities remaining constant for all other traps.
Is there a way that sessions or covariates could be used to achieve this type of model?
Thank you in advance for any help!
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In case you're interested I've run with and without learning on the full dataset as well as just the first 9 occasions. There is no difference in models for the first 9 occasions. This leads me to believe that the better fit of the learning model for the entire study is confounded by switching sampling strategy.
FIRST 9 OCCASIONS ONLY
model detectfn npar logLik AIC AICc dAICc AICwt
Mark0 D~1 g0~1 sigma~1 halfnormal 3 -607.3535 1220.707 1220.757 0.000 0.701
Markb D~1 g0~b sigma~1 halfnormal 4 -607.1889 1222.378 1222.461 1.704 0.299
ENTIRE 18 OCCASIONS
secrb D~1 g0~b sigma~1 halfnormal 4 -1191.533 2391.066 2391.147 0.000 1
secr0 D~1 g0~1 sigma~1 halfnormal 3 -1325.065 2656.130 2656.178 265.031 0