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Ed,
I've held off on replying to this post because I'm not familiar with RCapture, but I thought I'd jump in as no one else. The answer is that, yes, your sample size is too small. What is happening is that the maximum likelihood estimates for the capture probabilities lie on the boundary of the parameter space -- they are exactly equal to one. Unfortunately, the approximate normality of maximum likelihood estimates breaks down at this point, so the standard errors don't make sense. Computing standard errors from the usual approximation (inverse information matrix) results in standard errors of 0 for the capture probabilities -- suggesting that there is no uncertainty. This in turn means that there is no uncertainty in abundance. If the capture probabilities were truly 1 with no uncertainty then you would definitely have captured every individual in the population and you would know the abundance exactly. Clearly that's not true. The reason for this is that your sample is too small. Note that most of the individuals were never recaptured and that there was never a gap between captures -- individuals were recaptured on subsequent occasions until they were never seen again. This is perfectly consistent with the inference that capture is perfect and individuals are seen on every occasion until they leave the population, which is what the results are telling you. My guess is that this may be close to the truth and, by chance with the small sample, you have hit a data set that leads to boundary estimates. Is it reasonable to believe that this species has a fairly short life-span (relative to the time between captures) and that the capture probability is high? One solution is to use profile likelihood intervals to compute estimates of uncertainty for the parameters on the boundary (the p's). Again, I don't know about RCapture, but this is possible in Program MARK. Alternatively, you could work with a Bayesian analysis using a prior selected to keep the parameters away from the boundary. I hope this helps. Cheers, Simon On 2016-09-15 2:00 PM, McGinley, Ed wrote: -- Simon Bonner Assistant Professor of Environmetrics/Director MMASc Department of Statistical and Actuarial Sciences/Department of Biology University of Western Ontario Office: Western Science Centre rm 276 Email: [hidden email] | Telephone: 519-661-2111 x88205 | Fax: 519-661-3813 Twitter: @bonnerstatslab | Website: http://simon.bonners.ca/bonner-lab/wpblog/ _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
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