Methods for closed populations

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These methods assume that the population does not change over the period of study, which is termed a closed population. It is therefore essential that this period is short compared to life expectancy. Further, individuals must not be able to enter or leave the study area. Methods in which the probability of capture is not assumed constant require a series of occasions (at least two) on which animals previously marked are recaptured, marked again, and released. The methods available within Simply Tagging are generalised for 2 to 500 sampling occasions. Some methods require full individual data, while others only require summary data that can be generated by batch tagging methods.


The simplest experiment has only two sampling occasions, on the first of which animals are captured, marked and released, and on the second, captured and the number of marked and unmarked animals recorded. From such data the Petersen-Lincoln method is used to calculate population size.


Within Simply Tagging these calculations will be obtained on the Closed methods menu, selecting Temporal change in capture probability (Schnabel ML, Mt), as when only two samples are available this method will calculate a simple Petersen-Lincoln estimate.


select shnabelcensus



Simply Tagging uses the nomenclature and approach developed by Gary White, David Anderson, Kenneth Burnham and David Otis in which a number of models, each of which makes different assumptions about the probability of capture both through time and between individuals, are considered. Each model can be selected in turn from the Closed methods drop-down menu, and their results compared.


Closed methods available:

Model M0:        Constant probability of capture

Model Mt:        Maximum likelihood Schnabel census (Petersen-Lincoln index if samples = 2)

Model Mt:        Chao method

Model Mh:        Individual variation in probability of capture

Model Mb:        Behavioural changes following capture

Model Mth:        Temporal changes in catchability and individual heterogeneity