Monitoring global biodiversity is essential for understanding and countering its current loss. However, monitoring of many species is hindered by their difficult detection due to crepuscular activity, hidden phases of the life cycle, short activity period and low population density. Few statistical power analyses of declining trends have been published for terrestrial invertebrates. Consequently, no knowledge exists of the success rate of monitoring elusive invertebrates. Here data from monitoring transects of the European stag beetle, Lucanus cervus, is used to investigate whether the population trend of this elusive species can be adequately monitored. Data from studies in UK, Switzerland and Germany were compiled to parameterize a simulation model explaining the stag beetle abundance as a function of temperature and seasonality. A Monte-Carlo simulation was used to evaluate the effort needed to detect a population abundance decline of 1%/year over a period of 12 years. To reveal such a decline, at least 240 1-hour transect walks on 40 to 100 transects need to be implemented in weekly intervals during warm evenings. It is concluded that monitoring of stag beetles is feasible and the effort is not greater than that which has been found for other invertebrates. Based on this example, it is assumed that many other elusive species with similar life history traits can be monitored with moderate efforts. As saproxylic invertebrates account for a large share of the forest biodiversity, although many are elusive, it is proposed that at least some flagship species are included in monitoring programmes.

Lucanus cervus, Natura 2000 monitoring, elusive saproxylic beetles, Monte-Carlo simulation, population decline

Monitoring global biodiversity is essential for nature conservation in order to understand and counter its current loss due to anthropogenic disturbances (Jones et al. 2015; Lindenmayer and Likens 2010; Reynolds et al. 2011). However, it has often been argued that species selected for monitoring or conservation are biased towards more familiar species (Clark and May 2002; Franklin et al. 2011; Regan et al. 2008), while invertebrates are often under-represented (Cardoso et al. 2011; D’Amen et al. 2013; Leather 2013). Among other reasons, technical issues, i.e. the difficulty to monitor these species, have been argued. Bosso et al. (2013), for example, highlight the difficulty of monitoring the elusive Rosalia alpina due to difficult detection in forests and its short life span of adults while Roets et al. (2013) mention the nocturnal activity, hidden phases of the life cycle and short activity period for the Colophon stag beetle. A monitoring plan should be designed effectively and cost-efficiently (e.g. Lindenmayer and Likens 2010; Reynolds et al. 2011). Statistical power analysis is a widely acknowledged tool for that goal (Di Stefano 2003; La Morgia et al. 2015; Reynolds et al. 2011) in which a simulation is used to calculate the probability for correctly rejecting the null hypothesis (H0) when the alternative hypothesis (H1) is true with a given monitoring scenario. In other words, what is the chance of detecting a simulated decline? Monitoring of elusive species, in general, yields a low power (e.g. Jones et al. 2015; Steenweg et al. 2016; Williams and Thomas 2009). Unfortunately, only a few power analysis studies on the populations trends of terrestrial invertebrates have been published in the peer reviewed literature (Bried and Pellet 2012; Lang et al. 2016; Schmucki et al. 2016, all on butterflies). Consequently, the possibility of adequately monitoring the population trends of invertebrates in general and elusive invertebrates in particular has hardly been evaluated.

The European stag beetle, Lucanus cervus (further called the stag beetle), is a good model species to investigate whether the population trend of a strongly elusive terrestrial invertebrate can be adequately monitored. This saproxylic species is often considered as an umbrella species, representing the large saproxylic diversity inhabiting forests and half open landscapes (Luce 1996; Thomaes et al. 2008). The stag beetle is included in the second annex of the European Habitats Directive and consequently, species specific protection and monitoring is mandatory for every member state. As this species can only be observed during a very narrow time window (Campanaro et al. 2016; Harvey et al. 2011a), it can be argued that monitoring this species would yield insufficient data to evaluate its population trend. The stag beetle life cycle takes 3 to 5 years (Fremlin, Hendriks and Thomaes unpublished data, Rink and Sinsch 2008) which are spent mainly in underground dead wood. After eclosion in late summer, the adults overwinter in a quiescent stage and become active above ground for mating and dispersal next summer. After emergence, males live for about 8 weeks while females can live up to 12 weeks (Harvey et al. 2011a). Even then, the species remains mostly hidden underground, being active during a short period around sunset (Campanaro et al. 2016; Rink and Sinsch 2007). Furthermore, the activity of adults strongly depends on temperature (Harvey et al. 2011a; Rink and Sinsch 2011; Sprecher-Uebersax 2001; Thomaes et al. 2008). Finally, relative humidity, rain, wind speed and other weather variables have been found to influence the stag beetles’ activity (e.g. Fremlin and Fremlin 2010; Rink and Sinsch 2007). However, as these variables are likely to be related to temperature and none of them has been tested in combination with temperature, conclusions should be interpreted carefully. Finally, the stag beetles’ activity is suggested to depend on the moon cycle (Mader 2009) but this has not been confirmed by Sprecher-Uebersax (2001) and Campanaro et al. (2016).

Different monitoring protocols have been evaluated for the stag beetle: acoustic larval detection (Harvey et al. 2011b), baited or unbaited traps (e.g. Chiari et al. 2014) and direct observations of living and/or dead beetles along transects (e.g. Campanaro et al. 2011; Campanaro et al. 2016; Fremlin and Fremlin 2010; Mader 2009; Sprecher-Uebersax 2001). Vrezec et al. (2012b) found detection efficiency of evening transects (>90%) to be higher than that of trunk surveys and pit fall traps in the ground or attached to a tree (about 30–50%). Other techniques of trapping or indirect monitoring have also yielded poor results (Chiari et al. 2014; Harvey et al. 2011b). Consequently, a walked transect in the evening currently seems to be the best available sampling technique. As it is a generally well known species, a citizen science approach with many simultaneous transects is a feasible monitoring strategy (www.stagbeetlemonitoring.org). Few transects have been followed up nearly daily. In most cases, a weekly follow up has been used with a fixed day (e.g. Campanaro et al. 2011) or with a variable day depending on the weather (Campanaro et al. 2016). Finally, it can be argued whether monitoring days should be concentrated around the short activity peak or over a longer period (Campanaro et al. 2016; Fremlin and Fremlin 2010; Vrezec et al. 2012a). Due to the short activity period within a day, only one evening transect can be walked per observer and, due to the short season, only a limited number of days per year are suitable for monitoring, especially under colder climatic conditions. Consequently, cost efficiency is low and the power of such monitoring can be questioned.

Here, data have been used from three transects in north-western Europe which have been monitored nearly daily for seven up to ten years to parameterize a simulation model that estimates the stag beetles’ relative abundance. This model is then adjusted to include a population decline of 1%/year and used for a Monte-Carlo simulation. This decline was derived from European guidelines (European Topic Centre on Biological Diversity 2011) which state that a population decline of more than 1%/year within 12 years (short term) or 24 years (long term) should result in a negative report for this species. Finally, different monitoring scenarios are evaluated using the simulated data in order to determine the effort needed to successfully detect this population decline (power analysis). We hypothesize that despite a very narrow window of activity and a high variability in abundance, the stag beetle can still be successfully monitored with a moderate monitoring cost when the monitoring scenario is adapted to the phenology of this species.

With the statistical power analysis presented, it was shown that it is at least feasible to monitor population density changes of the stag beetle with an effort of 240 days/y. This effort can be applied successfully with different combinations of scenarios, number of transects (between 30 and 100) and frequency. Before concluding which monitoring strategy and effort is most advisable to employ, the impact and alternatives for the missing density function, the limitations of the data used and the consequences of methods used for the results of the power analysis will be discussed first.

One of the main methodological problems for population trend analysis is the use of a relative abundance measure (here number of stag beetles found along a transect) to estimate the absolute population size (e.g. Bried and Pellet 2012; LaCommare et al. 2012; Williams and Thomas 2009). In many cases, the relationship between them is unknown and consequently a linear relationship must be assumed (LaCommare et al. 2012). Although a linear relationship might often be reasonable, several exceptions have been mentioned including differential population declines across sexes or life-history stages (Shea et al. 2006; Reynolds et al. 2011). For example, it is known that stag beetle transect walks are biased towards observations of male adults (e.g. Vrezec et al. 2012b). Consequently, threats affecting males in a selective way might result in an overestimate of the real population decline. Solutions that have been suggested to cope with this are distance sampling, mark-recapture procedures and presence-absence methods. Distance sampling is not realistic to apply to flying insects and mark-recapture procedures have yielded little recaptures for stag beetles (e.g. Chiari et al. 2014). Joseph et al. (2006) determined that for low density and hard-to-detect species, presence-absence methods equated or outperformed abundance methods at tracking changes in population size. For the stag beetle, this does not seem to be the case as the effort suggested by Campanaro et al. (2016) to assess the presence is 3 transect walks per year and transect which is comparable to some of our conclusions. However, presence-absence methods in general need many more transects than abundance methods so the overall effort will be higher.

The between-site variation on the number of the stag beetles observed is difficult to assess as only three sites have been monitored. Bart et al. (2004) mention that the variability in habitat and environment between sites is important when balancing the number of sites and monitoring frequency per site. As our three sites represent quite distant locations and habitats in north-western Europe, the results can be interpreted as based on maximal between-site variation and, consequently, as estimates of the outer limits of effort needed for monitoring the species in north-western Europe or countries within this region. Outside this region, the species response to temperature and season might differ and possibly also the effort needed to monitor it. LaCommare et al. (2012), Pais et al. (2014) and Jones et al. (2015) also concluded that the optimal monitoring strategy might differ across locations. Consequently, care must be taken when applying the results in other parts of the range of the species. For example, it might be expected that the species is less temperature restricted in warmer climates and consequently lower effort is needed. Furthermore, if the monitoring covers a large area, more variability between sites is likely to be expected and therefore more transects should be selected (Meyer et al. 2010; Pollock et al. 2002).

The most efficient way of monitoring the stag beetle seems to involve a scenario with weekly transects walks during the warmest evening. The scenario with transect walks concentrated after a first evening with 18°C or higher seems to have missed the period with abundance peak resulting in a very low power. Possibly, the stag beetle emergence in this region is triggered by lower temperatures and this causes the mismatch. However, if this peak period can be predicted, then the power of such a monitoring scenario might be much higher. When T-season is used in the simulation model instead of C-season, the Peak temperature scenario has the lowest effort needed to reach a power of 90% (results not shown). This is due to the fact that the simulation model and data sampling are then ideally tuned as both are based on the same hypothesis i.e. the period with abundance peak starts on the first evening with a temperature of 18°C. In reality, the start of this peak might be more complex and therefore more difficult to predict. Especially in different regions, stag beetle emergence might be expected to respond differently and thus different monitoring protocols might be needed for each region if this were to be applied. Consequently, it might be difficult to organise a large network of transects and instruct volunteers if the monitoring differs at each transect depending on the local temperature or climate zone. In that case, it might be easier to have transects that need to be walked weekly on the warmest day or even on a fixed day.

The Warmest of 7d scenario is simulated with the simplifications of a perfect weather forecast (i.e. the warmest evening is known at the beginning of the week) and so, in reality, the power might decrease slightly due to an imperfect weather forecast. However, as the power of the Weekly scenario is quite similar, this effect is expected to be limited. For more southern locations, this effect might be even smaller as days with unsuitable weather become rare.

An advantage of the Warmest of 7d above the Peak temperature scenario is that the effect of season remains evaluated. By this, changes in seasonality can also be detected. For example, climate change is expected to negatively affect the activity period (Rink and Sinsch 2011) which might not be detected with a Peak temperature scenario. Furthermore, data sampled in other periods (due to different monitoring strategies) or additional transect walks can still be included in the analysis as season and temperature remain in the validation model. This is not possible for the Peak temperature scenario where season and temperature are left out of the validation model and consequently balanced data is needed, thus making this scenario less robust (cfr. Schmucki et al. 2016).

Daily sampling clearly results in oversampling of a site in terms of population trend detection and is therefore not advised when trying to optimise the monitoring effort. However, this sampling technique might be very useful when only a limited number of transects is available or to study other population parameters, e.g. gaining insight into the period with peak abundance.

When comparing different options with the same effort, it seems that, in the presented simulation, the number of transects and frequency has little additional impact on the power. Thus, different combinations can be used to bring this monitoring into practice. Due to some simplifications that were included in the simulation, e.g. constant seasonal effects at all locations and equal decline at all sites, it is not advisable to use the lowest sufficient effort calculated but rather select a more robust estimate of the effort needed. Therefore, it is concluded that any combination with the Warmest of 7d scenario and an effort of minimal 240 days per year and between 40 and 100 transects can be used to realise the monitoring of this species to detect the given trend. A higher number of transects only slightly improves the power (cfr. Meyer et al. 2010). However, it is also important to take into account the costs for selecting and installing additional transects and finding and training volunteers (cfr. Jones et al. 2015; Lang et al. 2016; Williams and Thomas 2009). Therefore, it might be more realistic to realise only 40 transects with 6 days of monitoring per year and transect than 80 transects with 3 days per year and transect.

When comparing our results with other studies, it is concluded that the effort needed to monitor this elusive stag beetle (240 surveys/y) is not higher than for other invertebrates. Bried and Pellet (2012) concluded that the minimum allowable effort for occupancy monitoring of the Karner blue butterfly was 360 (40 sites x 9 surveys) for the spring generation and 200 (20 sites x 10 surveys) for the summer generation. Keizer-Vlek et al. (2012) found that more than 1000 sites must be sampled to detect a 40% change in the frequency for monitoring rare river inhabiting macroinvertebrates (50 sites for common species). Lang et al. (2016) found the need for about 600 to 2200 transect and four survey events to detect a population decline of frequent and rare butterflies respectively. However, as the scales, methods and detection thresholds differ, a one to one comparison is not possible.

Based on the current study, it is assumed that many other elusive species with similar life history traits can likely be monitored with a similar magnitude of effort. Many other stag beetles species share the short activity period, crepuscular activity and temperature dependence (e.g. Roets et al. 2013) and thus it is likely that comparable efforts are needed to study them. Other saproxylic beetles, like Rosalia alpina, Morimus asper or Cerambyx cerdo (all European Habitats Directive species), also share these life history traits despite being mainly monitored by trapping (respectively Bosso et al. 2013; Buse et al. 2008; Vrezec et al. 2012b) and consequently might need efforts of comparable magnitude.

It is concluded that it is possible to monitor a rather small population density decline of 1% per year for the elusive stag beetle with a moderate monitoring cost of 240 transect walks per year. Based on this example, it is assumed that many other elusive species with similar life history traits can be monitored with moderate efforts. This finding is especially important as saproxylic insects represent a large share of the total forest biodiversity (e.g. Müller et al. 2008, Horak et al. 2012) although many are elusive. Based on the current finding, we propose that at least some flagship species of this group are included in species monitoring programmes as their monitoring seems feasible.

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors. Thanks to two anonymous reviewers for improving an earlier version of this paper.

Special issue published with the contribution of the LIFE financial instrument of the European Union.