Density estimation

  1. Exclude colonies represented by less than a median number of drones in all density calculations in order to overcome the limitation that distant colonies will contribute fewer drones than colonies located in the vicinity of a DCA.
  2. Quantify the number of colonies represented by an equal or higher than median number of drones.
  3. Divide this number by the mean mating area of drones (for the drone samples, 2.5 km2, Jaffé et al., 2009a) or queens (for the worker samples, 4.5 km2, Jaffé et al., 2009a) to obtain an estimate of the local density of colonies at the sampling location (see Fig. 28).

Pros: less tedious than finding all nests in an area. Method independent of nest spatial distribution (Arundel et al., 2012).

Cons: Fails to detect colonies that do not produce drones. Season dependence when based on drone trapping, and thus a relevant density figure can only be obtained during mating season when most colonies produce drones. Assumes a similar drone investment by all colonies. Inaccuracy due to variable/non predictable size of mating areas of drones and queens, which can be different between regions and honey bee populations. High costs involved in genetic analyses, and a suitable lab space and equipment is needed.

Fig. 28. Schematic representation of the approach to estimate honey bee colony densities based on the frequency distribution of drones among the reconstructed colonies. For a given sample of drones from a specific location, the median number of drones per colony is first calculated. In order to estimate the local density of colonies, those colonies represented by less than a median number of drones (red columns) need to be discarded. The number of remaining colonies (blue columns), are then divided by the mean mating area of drones or queens. This approach aims to avoid the overestimation of colony densities due to the inclusion of low-represented colonies, likely to be located beyond mean flight distances of drones or queens.