Deer populations have increased dramatically in size and distribution over recent decades both in Europe and North America (Andersen et al. 1998, Fuller & Gill 2001). As a result, in many areas, management goals are changing from protection to population control (Milner et al. 2006). Controlling deer populations is required to prevent damage to farming and forestry, deer-vehicle collisions or the spread of diseases (Rooney & Waller 2003, Côté et al. 2004, Gordon et al. 2004, Seiler 2004). In areas where large predators have been eliminated, deer populations are usually controlled through hunting (Langvatn & Loison 1999, Festa-Bianchet 2003, Milner et al. 2006). To define the hunting quotas, managers typically use estimates of population size obtained from counting methods (e.g. Williams et al. 2002).

Counting methods have repeatedly been shown to be neither accurate nor precise (CV > 30%; Andersen 1953, Gaillard et al. 2003) except when a high proportion of individuals are marked (Strandgaard 1967, Vincent et al. 1991). Andersen (1953) showed, for example, that counts in a population of roe deer Capreolus capreolus can lead to a threefold underestimation of the population size. Moreover, the absence of repeated counts within a year prevents the assessment of the precision of population estimates. Lastly, even the best estimate of population size does not provide the required information to set hunting plans because it does not provide information to managers about the state of the plant-herbivore system (Morellet et al. 2007).

In this context, researchers have developed new tools for managing ungulate populations, which are based on the monitoring of a set of indicators of ecological change (IEC; Cederlund et al. 1998, Morellet et al. 2007). An IEC is an easily measured parameter, sensitive to changes in the relationship between the population and its habitat (Cederlund et al. 1998). IEC are based on the concept of density-dependence (Bonenfant et al. 2009), from which wildlife managers should be able to monitor deer populations without population size estimates. Morellet et al. (2007) proposed to simultaneously monitor three categories of IEC describing 1) animal performance (Bonenfant et al. 2002, Zannèse et al. 2006, Garel et al. 2011), 2) habitat quality (Morellet et al. 2001, Chevrier et al. 2012) and 3) animal abundance (Vincent et al. 1991, Loison et al. 2006, Garel et al. 2010). It is important to note that only the complementarity of different IEC monitored over several years allows for the measurement of population-habitat relationships.

Among indices of red deer Cervus elaphus abundance, spotlight counts are commonly used by managers (e.g. Gunson 1979, McCullough 1982, Fafarman & DeYoung 1986). Recently, Garel et al. (2010) showed that, after controlling for observation conditions, spotlight counts can provide a reliable IEC of relative abundance for the monitoring and management of red deer populations living in forested areas. Spotlight counts are inexpensive and allow for rapid cover of large areas (McCullough 1982). However, this method is restricted to areas with good network of roads and tracks. An alternative option could be to carry out roaring counts from fixed spots (Albaret et al. 1989).

Counting red deer during the period of reproduction (rut) in autumn is often used in addition to spotlight counts, or when spotlight counts are difficult to apply (Bobek et al. 1986, Albaret et al. 1989), to assess trends in population abundance. Mature red deer males, like many other ungulates, commonly spend most of the year in areas separated from those occupied by the females except in the breeding season (Clutton-Brock & Albon 1979, Clutton-Brock et al. 1982). At this particular time of the year, stags use vocalisations (roars) to monopolise a number of hinds. With their highly conspicuous behaviour, red deer are easily detected during the rut based on acoustic and visual clues. Hence, roaring counts have two components: first, counting the number of roaring stags, and second, counting hinds and stags seen in rutting areas, both being used for the calculation of the abundance index. However, Ciucci et al. (2009) have suggested that roaring counts are poor indicators of red deer abundance because the timing of the survey is not consistently synchronous with the roaring peak. Overall, the roaring counts method has never been formally validated.

In our study conducted at La Petite Pierre, France, we examined the usefulness of roaring counts for the management of red deer populations. Managers of our study site have monitored red deer using both spotlight and roaring counts for 31 years. Providing that roaring counts allow for reliable assessment of the abundance of red deer resident populations, we expected a positive correlation between roaring and spotlight counts because spotlight counts provide a suitable measure of annual variation in abundance of the resident population (Garel et al. 2010). Second, as variation in hunting pressure accounted for most annual changes in population size in the studied population (Bonenfant et al. 2002, Richard et al. 2010; see also section Material and methods), the number of counted red deer during rut should decrease with the number of deer harvested during the previous hunting season (Garel et al. 2005b).

Material and methods

Results

Roaring counts (inter-annual CV = 22%) led to an average of 69.0 ± 1.9 animals during the study period. Visual and acoustic detections accounted for 70% and 30% of the total number of observed red deer, respectively. Temperature ranged from 7.3 to 20.6°C (averaging 13.3°C across study years) and was negatively correlated with AI-R (β = −0.024 ± 0.010, P = 0.016). AI-R tended to decrease with the occurrence of rainfall (β = −0.083 ± 0.049, P = 0.095). Once the conditions of observation were accounted for, AI-R was highly variable from year to year (χ² = 121.8, df = 30, P < 0.001) ranging from 46.0 ± 5.6 animals in 1991 to 108.4 ± 12.1 animals in 1997 (Fig. 2). Since the value of deviance divided by the number of degrees of freedom was close to 1 (Φ = 1.41), the model fitted the data satisfactorily.

In contradiction with our first prediction, the log-transformed AI-R was not correlated to log-transformed AI-I (r = −0.01, CI = −0.36 - 0.34, N = 31 years; Fig. 3). We also assessed the relationship between the two components of AI-R (AI-Ra and AI-Rv) and AI-I, but our results remained unchanged with or without distinguishing between visual and acoustic detection of deer (see Appendix I).

On average, 43 red deer were shot each year between 1978 and 2007 in the PPNR, but the hunting effort varied considerably among years, with 78 deer shot during the 1979 hunting season and 24 during the 1996 hunting season. Contrary to our second prediction, log-transformed AI-R was not influenced by the log number of male and female red deer killed during the previous hunting season (r = −0.27, CI = −0.58 - 0.09, N = 30 years).

Discussion

Roaring counts are commonly used in addition to spotlight counts by managers in European countries to monitor red deer populations (Ciucci et al. 2009). For instance, the method has been systematically used since the 1970s to monitor the endangered Corsican red deer C. e. corsicanus in Sardinia (Lovari et al. 2007). Indeed, at the time of the mating season, red deer gather and are easily detected, particularly roaring stags, due to movements and sounds. Although roaring counts might be a useful alternative to counting methods such as spotlight counts in mountainous areas (Bobek et al. 1986, Albaret et al. 1989), a formal evaluation of its reliability has been lacking. In our study, we found no relationship between the total number of red deer observed during roaring counts (AI-R) and spotlight counts, suggesting that roaring counts do not capture the variation of abundance of the resident deer population.

Bias can occur in analysis of counts when data contain sources of variation other than the changes in the size of the population. Potential sources of variation include weather, habitat or differences between observers in their ability to detect animals (Garel et al. 2005a). These effects should be taken into account to get reliable measures of population change (for an example in red deer, see Garel et al. 2010). Here, we reported a negative relationship between temperature and AI-R, which can be explained by a reduction of animal activity when temperature increases. Garel et al. (2005b) found similar results on ground and aerial counts during summer in mouflon Ovis gmelini musimon. Roars can usually be heard from a long distance in good weather conditions, and therefore, sampling points in the field are generally separated by several hundred metres. Heavy rainfall decreases the vocal activity of stags (Pépin et al. 2001). However, the negative effect of rainfall on AI-R we reported here reflected the difficulty for observers to see animals under rainy conditions rather than a difficulty to hear roars (see Appendix I).

In addition to weather conditions, the proportion of roaring stags in a population depends on several factors such as condition of the stags, age structure, population density, quality and spatial distribution of the resources (Clutton-Brock & Albon 1979, Carranza 1995, Pépin et al. 2001, Yoccoz et al. 2002). Moreover, differential visibility of the various age and sex classes introduces additional sources of uncertainty in the population abundance estimates (Ciucci et al. 2009). Deer activity during the rut is fundamentally different between sexes, with stags roaring and moving a lot, whereas females remain rather elusive during this period. The detection probability is thus higher and less variable in males than in females which explains that 62% of the red deer recorded during the rut in the PPNR were males.

In mountainous areas, the altitudinal movements of red deer from a low-elevation winter range to a high-elevation summer range is a common pattern of migration (Mysterud et al. 2001). In the PPNR, due to a very low range in elevation (see section Material and methods), such an altitudinal migration to benefit from high quality food did not occur. However, a marked sex-specific seasonal migration occurred in relation to mating opportunities. While females tend to occupy the same particular area during the whole year, in autumn, males move to rutting areas that are up to 30 km from the area occupied during the rest of the year (Bonenfant 2004:226). This form of partial migration (sensu Dingle & Drake 2007) has also been reported in a red deer population in Sweden where an average distance of 14 km and a maximum distance of 47 km were recorded between rut and summer/winter observations (Jarnemo 2008). Consequently, a high proportion of the stags that are observed during roaring counts are not resident of PPNR. On the contrary, red deer counted and monitored during spotlight counts that occurred from February to April were resident deer of PPNR. A combination of seasonal migration and unreliability of roaring counts are likely to explain the lack of a positive relationship between AI-R and spotlight counts found in our study. Although we can not prove that roaring counts were unreliable because the population size in autumn was unknown, the observed effects of hunting provided further support to our interpretation. Indeed, we found an absence of a relationship between AI-R and the number of red deer harvested in the reserve during the previous hunting season, whereas hunting quotas led to a decrease in the number of red deer counted during spotlight counts.

Successful management of large herbivores requires the monitoring of IEC describing animal performance, herbivore impact on habitat and animal abundance (Morellet et al. 2007). Monitoring temporal changes in these indicators provides a basis for setting hunting quotas to achieve specific management objectives. In this sense, spotlight counts provide a reliable index of the abundance of resident populations in a given area for red deer (Garel et al. 2010). On the other hand, roaring counts cannot be interpreted as a reliable IEC because they do not track abundance of the resident population of red deer despite a much larger sampling effort compared to spotlight counts (average annual number of 184 observers for roaring counts vs 72 for spotlight counts). Based on a least-cost approach for achieving management objectives, roaring counts should not be used for managing resident red deer populations.