genetic variability of luxembourg badgers
Previous studies, using different types of genetic markers, have reported low to moderate values for the genetic variability of Eurasian badgers both in the UK (Carpenter et al. 2003; Domingo-Roura et al. 2003) and on the European mainland (Bijlsma et al. 2000; Domingo-Roura et al. 2003). Compared with these studies, the average HE of 0·64 in Luxembourg was relatively high. However, high variability (in addition to small product size) was one of the criteria for choosing our specific loci out of the panel of 39 markers reported by Carpenter et al. (2003). PID-Sib values showed that the seven loci exhibited sufficient variability to produce individual-specific profiles with more than 99% certainty in the Luxembourg population, but not in the local study population. However, given the fact that PID-Sib is the upper limit of the possible ranges of PID in a population (Waits, Luikart & Taberlet 2001) and that the two most informative loci were enough to distinguish the profiles of the animals captured in the core study area, we judged the seven loci to be sufficient to produce individual-specific profiles.
hair-sampling as a practical method of estimating population size
If genotyping of non-invasively collected hair samples is to be an effective way of estimating the size of badger populations, it needs to be reliable, accurate and cost-effective. Here, we assess our results with respect to these three attributes.
As regards reliability, it is necessary that a high proportion of hair samples yields reliable genotypes without the need for repeated amplifications. Of 113 hair samples, five did not produce any amplifiable DNA and three originated from at least two different animals. By comparing with reliable reference genotypes of captured badgers, by comparing samples amongst themselves and by triple amplification of unique profiles, we showed that all extracts that contained amplifiable DNA, including those (about a third of the samples) that were obtained from single guard hairs, produced 100% accurate profiles in a single round of amplifications. Thus, the DNA extracted from remotely collected badger hairs allowed error-free genotyping.
A previous study has been equally successful in obtaining reliable genotypes from single hairs (Sloane et al. 2000) whereas others have needed to pool up to 10 hair follicles (Goossens, Waits & Taberlet 1998; Woods et al. 1999). A comparison of these studies suggests that a possible explanation for these discrepancies might be a delay between hair plucking and DNA extraction. According to Roon, Waits & Kendall (2003), the DNA quality of hair samples started to degrade after a storage period of 6 months and we suggest that it is important for DNA to be extracted as soon as possible after collection of the hair, as was done in our study. It is less likely that discrepancies between the studies arose from differences in the laboratory procedures as the studies mentioned above used exactly the same extraction procedure, namely a simple 5% Chelex-100 protocol. The advantage of this method is that it is simple enough to be carried out in the field on the day of sample collection to reduce the delay between collection and extraction.
It is possible that, in a high-density population, the proportion of samples that are a mix of two individuals will be greater than observed here. However, even though reliable genotypes can be obtained from single-hair extracts, on a practical level it is desirable to pool hairs in order to increase DNA quantity, the amount of PCR product and, ultimately, the ease with which samples can be genotyped. In future applications of this methodology, we recommend that two extractions should be performed with multiple-hair samples, one containing a single guard hair with a clearly visible follicle and the other containing all the remaining hairs. While mainly working with the pooled extracts, the corresponding single-hair extracts could always be used at a later stage to confirm or reject the profile generated using the first extract.
As regards accuracy, we tried to validate the counts obtained from genetic profiles by direct enumeration of badgers observed at the same setts. Overall, 13 animals were observed at the five setts whereas genotyping of hair samples yielded 15 profiles. However, because two setts, Grott and Knäipenhecken, were difficult to observe, it is unlikely that all the badgers from these setts were counted. Also, it is generally suspected that direct observation leads to underestimation of population sizes (Macdonald, Mace & Rushton 1998; Tuyttens et al. 2001), and the fact that more profiles were generated than badgers were counted by direct observation is consistent with this view. Nevertheless, it is encouraging that at Ermsdorf 1, Ermsdorf 2 and Grott setts, where badgers were relatively easy to observe and where most of the hair samples were collected, the number of badgers observed corresponded exactly with the number of genetic profiles compiled from hair samples.
The genetic profiles of two juvenile badgers that were caught in 2002 near the five target setts were not identified from the hair samples collected non-invasively in February/March 2003. Badger EMb2, a female that was captured and radio-collared in November 2002, was found dead on the 29 March 2003 on a road 8 km linear distance away from its natal sett. It therefore seems likely that the animal had already dispersed at the start of the hair-capture exercise. As no such claim can be made about female badger KH2, captured as a cub in 2002 at Knäipenhecken sett, it is possible that a total of 16 badgers was present in the study area. Both males and females have been shown to disperse from their native group (Cheeseman et al. 1988; Christian 1994; Revilla & Palomares 2002).
We estimated the true size of the population by applying a rarefaction analysis using the equations of Kohn et al. 1999), Eggert, Eggert & Woodruff (2003) and Valière (2002). The asymptotic population size obtained using Chessel's equation was lower than the number of different profiles identified, while Kohn's equation suggested that about four animals remained undetected using hair capture. With Eggert's equation, the estimated population size of 14·23 animals corresponded well not only with the number of genetic profiles obtained over a long study period (i.e. 15) but also with the higher estimate of 16 badgers. Thus, applying a rarefaction analysis based on Eggert's equation to a data set collected over a 3-week period generated a result similar to the best alternative baseline estimate.
Two studies have reported simulations on the accuracy of the projected results of all three equations for the rarefaction curve and the results reported here, especially the superiority of Eggert's equation, correspond well with the theoretical predictions. The gimlet manual (Valière 2002) reported results from limited simulations on the accuracy of Kohn's and Chessel's methods. It was predicted that the estimates generated using Chessel's method would be lower than the ones produced by Kohn's. Furthermore, in the presence of heterogeneity of capture probability amongst individuals, Chessel's method would underestimate population size while Kohn's method would generate an overestimate if a large proportion of the population had been sampled. Eggert, Eggert & Woodruff (2003) compared the accuracy of the estimates generated using Kohn's and Eggert's equations for the rarefaction curves. The results suggested that, while Kohn's method significantly overestimated population size, Eggert's approach would produce consistently unbiased results.
Finally, we turn to the issue of cost-effectiveness. During the main study, a relatively large number of hair samples (71) was collected at the five hair traps during a relatively short collection period (3 weeks). This was sufficient to provide a good estimate of the number of badgers in each social group, without the need for expensive repeated amplifications of DNA. However, setts needed to be prebaited for up to 4 months in order to attract badgers to the bait, and at one sett (Grott) bait was never taken. Lengthy prebaiting would obviously lower the cost-effectiveness of the technique.
In Luxembourg, badgers are unused to the presence of humans and this may be why they are relatively bait-shy (Schley 2000). In the UK, badgers are readily attracted to peanut bait (Delahay et al. 2000) so that the use of baited barbed wire enclosures should be feasible. An alternative approach, however, would be to suspend barbed wire over well-used badger runs, as was done at the Grott sett. The fact that this technique yielded three different profiles from four usable hair samples collected at a single run suggests that it deserves further testing. It might also be possible to suspend barbed wire or double-sided adhesive tape over sett entrances (Sloane et al. 2000).
To illustrate the efficacy of genotyping badger hair DNA compared with badger faecal DNA, it is worth considering the requirements for genotyping 100 samples of both types of DNA extract. First, virtually all the hair samples collected yielded fully amplifiable DNA, while this was only the case with 74% of faecal samples (Frantz et al. 2003). Thus fewer hair samples need to be collected and extracted to obtain the desired quantity of DNA samples. Secondly, while it would in principle require 700 PCR reactions to obtain genetic profiles consisting of seven loci from hair DNA, approximately 2240 reactions would be required to obtain reliable profiles from faecal DNA (with an average of 3·2 PCR per locus per genotype; Frantz et al. 2003). Thirdly, failed reactions occurred more frequently with faecal than hair DNA. Generally, sufficient PCR product was obtained from badger hair DNA to allow the visualization of all seven microsatellite loci in one lane of a polyacrylamide gel. This was not the case with faecal DNA, where often a microsatellite locus could only be visualized if it was run alone in a single gel lane. In other words, considerably more polyacrylamide gels are required when working with faecal compared with hair DNA samples. As a rough guide, genotyping 100 faecal samples would cost about £1000 (1500 euros) more, in consumables alone, than genotyping the same number of hair samples.
Our results show that genotyping of remotely plucked badger hair does not suffer from the drawbacks of faecal DNA typing. Reliable microsatellite profiles were obtained in a single round of amplifications, even from single-hair extracts. Baited barbed wire enclosures or suspension of barbed wire over sett entrances or clearly visible badger runs should allow easy collection of hair samples from most members of a social group, independent of population density. As we demonstrate that population size estimated from remotely collected hair is similar to a conservative baseline estimate, this method has the potential to form the basis of a feasible and practicable technique of estimating badger abundance, applicable independently of habitat characteristics and over a range of population densities. If methods of hair collection can be improved or prebaiting time reduced, the methodology will also be cost-effective.
Based on the present study and on our work with faecal DNA (Frantz et al. 2003; Wilson et al. 2003), we suggest that wildlife researchers working with badgers consider remotely plucked hair rather than faeces as a source of non-invasive DNA. However, it should be emphasized that the reliability of faecal DNA is species-dependent (Piggott & Taylor 2003a) and that it can be more practical and less disruptive to the target species to genotype faeces rather than plucked hair DNA ( Vigilant et al. 2001; Eggert, Eggert & Woodruff 2003). Nevertheless, hair trapping is a feasible approach in a variety of species, even in populations that are small and sparsely distributed (Foran, Minta & Heinemeyer 1997; Woods et al. 1999).