Development of a reliable, non-invasive method for wild boar (Sus scrofa) population size estimation

Wild boars belong to the most wide spread ungulates in the world. They are characterized by a well performed adaption to their environment mainly due to their omnivorous dietary. The wild boar population in Germany increased during the past three decades. Nowadays the high wild boar density leads to problems in agricultural areas due to damage of crops and plays a significant role as disease vector. For an effective population management population size information is of crucial importance.
There exist different traditional methods to estimate population sizes of wild boar populations such as direct sightings, faecal drop counts or hunting harvest, which provide only relative estimates and population trends. Absolute population sizes could be yielded by a Capture-Mark-Recapture (CMR) approach. However, capturing of wild boars is difficult to realize and costly in terms of personnel and field effort. Furthermore, the capture probabilities are heterogeneous due to the variable behaviour of individuals influenced by age, sex, and experience of the animals. Non-invasive genetic methods are a promising complement to the traditional methods for population size estimation particularly for wild boar. These methods reduce stress and capture bias and increase the number of re-captures. Faeces proved to be a suitable DNA source for wild boar genotyping, due to almost equal capture probability. However, working with faeces implicates difficulties such as low DNA quality and quantity, genotyping errors as dropout and false alleles.
The main aim of the present project was to develop a reliable, cost-efficient, reproducible and practicable labor method for wild boar genotyping. This method should provide a reliable dataset of genotypes obtained from the collected faeces samples. Individual identification formed the basis for an improved mark-recapture approach. As there is no sound method for absolute population counts in free living wild boar, reference values for the validation of this new approach are missing. Therefore, different routines to reduce and assess genotyping errors were compared. For maximum amplification rate, the storage, the extraction methods and the PCR-procedure were optimised. A step by step procedure was evaluated in order to determine the minimum required microsatellite number for reliable individual identification including a test with family groups (female and embryo tissue) to distinguish between even close relatives. A multiple-tubes approach, post-amplification checking and different correction procedures were applied to reduce genotyping errors. In order to quantify real genotyping error rates (GER) of datasets derived from sampling in the Palatinate Forest in western Germany, different methods for GER determination were compared with each other, obtaining GERs between 0% and 57.5%. As a consequence, more strict criteria for the multi-tube approach and increased repetition number of homozygous samples were used. An additional method validation was the implementation of a blind test to achieve the reliability of the genotyping and error checking procedure. Finally, a strict and practicable proposal for the lab procedure was developed, by beginning with faecal sample collection and ending with a reliable dataset with genotypes of each sample.


Kathrin Theissinger