BEINTEMA AJ (1992) Mayfield, a must: excercises in calculation of nesting success. LIMOSA 65 (4): 155-162.

This paper demonstrates why the classical standard method of calculating nesting success (percentage of nests found hatching) is wrong, and why the Mayfield (1961, 1975) method (calculating the daily survival rate p using p = aj(a+b), where a = total exposure in nest days and b = number of nests lost) is not only right, but also the only way to make different studies comparable, when more than one cause of nest loss is operating. This was demonstrated by means of computer simulation of nest losses in a population, and simulating activities of investigators collecting data in these populations. Figure I gives one simulated data set, table I shows the possible conclusions of an investigator comparing four sets with different properties. This paper also discusses the consequences of survival rates not being constant, partial losses within nests, the methods of estimating the number of nest days, and the accuracy of the method in relation to sample size (fig. 2). In order to achieve maximum accuracy, the investigator is strongly advised to maximize his number of nest days, by minimizing his number of visits to nests found, and use his time to find new nests instead. Even over very long intervals between two visits, the error introduced by estimating the number of nest days (how to decide when nests have disappeared during the interval) is negligible as compared to the error caused by sample size. A priori knowledge is important to estimate nest days when using very long intervals between checks, and can be used to estimate the expected accuracy for a certain sample size. For expected values of p and a one can calculate b from Mayfield's formula, and then use Johnson's (1979) formula sd =y'?a-b) x bl a3) for the standard deviation of p. Thus, one can estimate which differences can be detected when a certain sample size is used, or what sample size will be needed to detect a certain diffe rence.

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