**T**his morning, I read a paper by Roland Langrock and Ruth King in a 2013 issue of Annals of Applied Statistics that had gone too far under my desk to be noticed… This problem of using continuous variates in capture-recapture models is a frustrating one as it is not clear what one should do at times the subject and therefore its covariates are not observed. This is why I was quite excited by the [trinomial] paper of Catchpole, Morgan, and Tavecchia when they submitted it to JRSS Series B and I was the editor handling it. In the current paper Langrock and King build a hidden Markov model on the capture history (as in Jérôme Dupui’s main thesis paper, 1995), as well as a discretised Markov chain model on the covariates and a logit connection between those covariates and the probability of capture. (At first, I thought the Markov model was a sheer unconstrained Markov chain on the discretised space and found curious that increasing the number of states had a positive impact on the estimation but, blame my Métro environment!, I had not read the paper carefully.)

“The accuracy of the likelihood approximation increases with increasing m.” (p.1719)

While I acknowledge that something has to be done about the missing covariates, and that this approach may be the best one can expect in such circumstances, I nonetheless disagree with the above notion that increasing the discretisation step m will improve the likelihood approximation, simply because the model on the covariates that was chosen *ex nihilo* has no reason to fit the real phenomenon, especially since the value of the covariates impact the probability of capture: the individuals are not (likely to get) missing at random, i.e., independently from the covariates. For instance, in a lizard study on which Jérôme Dupuis worked in the early 1990’s, weight and survival were unsurprisingly connected, with a higher mortality during the cold months where food was sparse. Using autoregressive-like models on the covariates is missing the possibility of sudden changes in the covariates that could impact the capture patterns. I do not know whether or not this has been attempted in this area, but connecting the covariates between individuals at a specific time, so that missing covariates can be inferred from observed covariates, possibly with spatial patterns, would also make sense.

*In fine*, I fear there is a strong and almost damning limitation to the notion of incorporating covariates into capture-recapture models, namely, if a covariate is determinantal in deciding of a capture or non-capture, the non-capture range of the covariate will never be observed and hence cannot be derived from the observed values.