Abstract
Estimates of survival probabilities in natural populations can be obtained through capture–mark–recapture (CMR) models. However, when capture sessions are unevenly spaced, age–dependent models can lead to erroneous estimates of survival when individuals change age class during the time interval between two capture occasions. We propose a solution to correct for the mismatch between time intervals and age class duration in two age class models. The solution can be implemented in different ways. The first consists of adding dummy occasions to the encounter histories and fixing the corresponding recapture probabilities at zero. The second makes use of the log–link function available in some CMR software (e.g. program MARK). We used simulated and real data to show that the proposed solution delivers unbiased estimates of age–dependent survival probabilities.
Highlights
Capture–mark–recapture methods (CMR) are widely used for diagnosis of natural populations because they can be applied to obtain robust estimates of demographic parameters accounting for imperfect detection of individuals (Lebreton et al, 1992; Williams et al, 2002; Sanz–Aguilar et al, 2016)
To demonstrate the problem generated by unequal time interval in combination with age–dependent models, we considered a simple scenario with a model assuming two age classes and constant survival and recapture parameters
We demonstrate that when not handled properly, the combination of unequal time intervals and age dependence in capture–recapture models can lead to erroneous estimates of survival and model selection
Summary
Capture–mark–recapture methods (CMR) are widely used for diagnosis of natural populations because they can be applied to obtain robust estimates of demographic parameters accounting for imperfect detection of individuals (Lebreton et al, 1992; Williams et al, 2002; Sanz–Aguilar et al, 2016). When animals are marked as young this assumption does not hold because newly marked individuals typically have a lower survival probability than already marked individuals (adults) This difference can be accommodated by including age–dependent parameters into the CMR model (Pollock, 1981; Lebreton et al, 1992). While age–classes are spaced, intervals between capture–recapture occasions may not be spaced on the same scale, leading to erroneous estimates (see the problem in, for example, Covas et al, 2002; Zabala et al, 2011; Zuberogoitia et al, 2016) This is because individuals would change their age class within the interval between two sampling occasions rather than at the end as assumed by CMR models. We briefly introduce the problem and illustrate how it can be solved by taking advantage of the flexibility of CMR models
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