One aim of the MIPP Project (http://www.lifemipp.eu) was to develop non-invasive monitoring methods for selected saproxylic beetles. In this paper, a method is proposed for monitoring the larvae of Osmoderma eremita in their natural habitat (i.e. hollow trees), using a conservation detection dog (CDD). Wood mould sampling (WMS), the standard method to detect hermit beetles and other saproxylic insects inside tree hollows, is time-consuming and exposes the target species and the whole saproxylic communities to some risks. In contrast, CDDs pose no risk to the species living inside trees while, at the same time, offer a powerful tool for surveying the insects. In this paper, the methods applied to train the dog are presented, together with the results for accuracy (the overall proportion of correct indications), sensitivity (the proportion of correct positive indications) and specificity (the proportion of correct negative indications) obtained once the CDD had been fully trained. Results are presented for nitrocellulose filters with the odour of the target species, for larvae living inside hollow trees, for frass and for the remains of adults. A comparison of the efficiency between CDD and WMS showed that employing the dog was much less time-consuming than WMS.

The literature on training CDDs for nature conservation tasks, with particular reference to cases involving Coleoptera, was also reviewed.

Conservation detection dog, Osmoderma eremita, saproxylic beetles, Habitats Directive, monitoring methods, conservation biology

In the last few decades, conservation detection dogs (CDDs) (Beebe et al. 2016) have been increasingly used for the protection of wildlife due to their keen sense of smell for locating biological targets. These skills offered by dogs for the collection of wildlife data are well recognised and documented in recent reviews (Helton 2009, Dahlgren et al. 2012, Johnen et al. 2013, Beebe et al. 2016). The first case of dogs employed in nature conservation dates back to the 1890s in New Zealand when they were used to find endangered bird species: the kiwi (Apteryx sp.) and the kakapo (Strigops habroptila Gray, 1845) (Hurt and Smith 2009, Bebee et al. 2016). At present, the use of CDDs is widespread throughout the world for assisting in an array of activities, including detection of wildlife (plants and animals), carcasses of birds and bats, scats, pathogens and other biological materials (Bebee et al. 2016). In the majority of projects, dogs were used for the detection of scats, while direct searching for wildlife is the second most important use and, only in a minority of cases, carcasses were detected (Bebee et al. 2016). The two main reasons why species are being searched for are: i) they are rare and endangered and ii) they are alien species. In the management of endangered and threatened species, dogs are mostly used to search for mammals (Wasser et al. 2004, Browne et al. 2006, Hurt and Smith 2009, Long et al. 2007, Coppolillo et al. 2015), birds (Browne et al. 2006, Hurt and Smith 2009) and reptiles (Browne 2005, Cablkand Heaton 2006, Cablk et al. 2008, Nussear et al. 2008, Hurt and Smith 2009). Dogs have been used to search for accidentally imported alien species particularly focusing on reptiles (Savidge et al. 2011), rodents (Gsell et al. 2010) and nematodes (Richards et al. 2008). Conservation dogs are also employed to detect the occurrence of birds and bats killed by turbines in order to evaluate the impact of wind farms on wildlife (Arnett 2006, Paula et. al 2011).

Concerning invertebrates, pest, alien and invasive insect species are the main targets and indeed, conservation dogs were used for the first time to detect the gypsy moth Lymantria dispar (Linnaeus, 1758) (Wallner and Ellis 1976). Since then, many species belonging to different insect orders have been searched by dogs: Coleoptera (Nakash et al. 2011, Errico 2012, Hoyer-Tomiczek and Sauseng 2013, Kelley 2013, Hoffman 2014, Suma et al. 2014, Coppolillo et al. 2015, Hoyer-Tomiczek et al. 2016), Diptera (Welch 1990), Hemiptera (Pfiester et al. 2008, Rolón et al. 2011), Hymenoptera (Lin et al. 2011, Brocos and González 2015), Isoptera (Brooks et al. 2003, Zahid et al. 2012) and Lepidoptera (Wallner and Ellis 1976). So far, dogs have been used in a limited number of projects to locate protected or endangered insects, such as bumblebees (Waters et al. 2011, O’Connor et al. 2012).

In the LIFE Project MIPP (Monitoring of Insects with Public Participation, www.lifemipp.eu, Action A.4: “Acquisition and training of Osmodog”), a CDD was trained to find the larval stages of the hermit beetle Osmoderma eremita (Scopoli, 1763) in its natural habitat (see Mason et al. 2015 and Carpaneto et al. 2017). O. eremita is an endangered saproxylic beetle protected in the European Union by the Bern Convention and listed in annexes II and IV of the Habitats Directive 92/43/EEC and also reported as “Near Threatened” in IUCN Red Lists of Threatened Species (Nieto et al. 2009, Nieto and Alexander 2010). This species is associated with old hollow broad-leaved trees in mature forest ecosystems, as well as outside closed forests (e.g. pasture woodlands) (Ranius et al. 2005, Giangregorio et al. 2015, Maurizi et al. 2017).

The CDD of the MIPP project is the first case of a dog searching for an endangered beetle species, O. eremita. Indeed, a special feature of conservation dogs is that they can detect cryptic and/or elusive species (Hurt and Smith 2009) and this makes them excellent tools to locate insects. In general, the use of dogs in nature conservation offers many advantages. For example, it can facilitate the location of target species in unreachable habitats (Chambers et al. 2015, Hoyer-Tomiczek et al. 2016), thus decreasing the risk of disturbance and performing non-destructive sampling (Chambers et al. 2015), with particular reference to saproxylic species with larvae living inside hollow trees (Hoyer-Tomiczek et al. 2016). Moreover, the use of dogs minimises the sampling effort, in terms of number of personnel involved in fieldwork and time spent in the field (Browne et al. 2006, Harrison 2006, Duggan et al. 2011, Paula et al. 2011) and reduces the bias in sampling (Wasser et al. 2004, Browne et al. 2006). All of these characteristics fit well with the O. eremita study case. Indeed, without the aid of a dog, most of the sampling methods commonly used to detect (and monitor) the hermit beetles involve wood mould sampling (Ranius et al. 2005, Chiari et al. 2014) and passive or active traps (Ranius 2001, Svensson and Larsson 2008, Chiari et al. 2013, Maurizi et al. 2017), which are characterised by the disadvantages listed above. Locating larvae inside trunks using wood mould sampling is the main method which allows the identification of breeding sites of hermit beetles and other saproxylic insects. It consists of digging inside tree cavities to find specimens or their remains. However, it is not often possible with wood mould sampling to investigate all cavities of a tree; in particular, cavities located high up or with narrow openings are very difficult to explore. A further drawback of this method is the physical risk for the target species and for the whole saproxylic community living inside the trunk (Tikkamäki and Komonen 2011, Chiari et al. 2014). Last, but not least, a dog-handler team only requires a few minutes to analyse a single tree, while wood mould sampling is a time-consuming technique (Chiari et al. 2014).

Searching for larvae has some additional advantages as adults have a short period of activity (Maurizi et al. 2017) and population size of adults fluctuates during the activity period and in correlation with environmental factors. In contrast, larvae are always present inside hollow trees during their development which lasts about 3 years. In this paper, a summary of the training techniques for dogs employed in nature conservation, with particular reference to the cases involving Coleoptera is presented. The information obtained by this literature review was used to plan and perfect the training strategy for the dog.

The choice of the right dog is critical to the success in the training for finding insects. First of all, a dog must have a suitable drive for the specific task required and these motivational characteristics differ between breeds. As indicated by Cablk and Heaton (2006), choosing a breed with a mix of hunt drive and play drive would be preferable for a CDD. In general, pure-bred dogs are preferred to mixed breeds, as the traits of mixed dogs could be unpredictable (Dahlgren et al. 2012). Recently, it has been shown that breeds that had been originally and specifically selected for scent work (e.g. Beagle, German Pointer, Bracco Italiano), do, in fact, demonstrate a higher olfactory acuity than breeds that had not been selected for such purpose (e.g. English greyhound, Siberian husky) (Polgár et al. 2016). Furthermore, it would be preferable to choose a dog from a specific line that has already shown traits and skills suitable for the specific work. Finally, individual traits are very important in a given breed: dogs can differ in physical and psychological traits (e.g. agility, physical fitness, various aspects of intelligence, predisposition to collaboration and curiosity), all of which can strongly influence the quality of work (Dahlgren et al. 2012). In previous studies, the breed most used in searches for Coleoptera were Golden Retriever (Nakash et al. 2000, Suma et al. 2014), Labrador Retriever (Errico 2012, Coppolillo et al. 2015) and Border Collie (Hoffman 2014, Hoyer-Tomiczek et al. 2016).

It is preferable to use adult dogs for the fieldwork as juvenile dogs may have lower levels of attention and concentration than adults (Hurt and Smith 2009). In addition, adult dogs can perform research routines better than juvenile dogs (Suma et al. 2014). Hurt and Smith (2009) suggest that dogs can be ready to work when they are between 12 and 24 months old. However, the training can start earlier (Dahlgren et al. 2012). In particular, before starting the actual training, the dog needs to undertake base obedience training (Welch 1990, Richards et al. 2008) and needs to be introduced to the search routines through easy search games by encouraging the dog to find toys or other fun targets (Dahlgren et al. 2012).

Living targets, in this case insect larvae, have an odour which is characteristic for each species and specific for the environment in which they live. This is a very important parameter to consider when choosing the scent target to use for the training. Indeed, the saproxylophagous and xylophagous species, living inside wood mould or creating galleries in trunks, have a broad scent bouquet due to different sources of odours present in their habitat (e.g. fungi, sawdust and other organic materials) (Hoyer-Tomiczek et al. 2016). For this reason, as the target for the dog, the training routines include living specimens as well as material from their natural habitat and the training must proceed by presenting odours of increasing complexity (Errico 2012, Suma et al. 2014, Hoyer-Tomiczek et al. 2016). After the first target, a dog can learn to recognise more odours (see below) and, in some cases, more than 20 odours can be recognised (Long et al. 2007, Coppolillo et al. 2015).

The method to train conservation dogs in finding live animals is similar to those used to find unanimated and non-biological targets and it is based on the positive reinforcement of the dog’s behaviour (Hurt and Smith 2009, Braun 2013, Johnen et al. 2013). The method is built on rewarding the dog with food or play as the primary reinforcement, immediately after correct signalling. It was used in all studies in which dogs detected Coleoptera (Nakash et al. 2011, Errico 2012, Kelley 2013, Hoffman 2014, Suma et al. 2014, Hoyer-Tomiczek et al. 2016). Clickers, devices that emit a double-click sound (Smith and Davis 2008), can be used as positive secondary reinforcement when the trainer is distant from the dog and cannot give the primary reinforcement immediately after correct signalling (Braun 2013). Although the clicker is mainly a predictor for food (Johnen et al. 2013), it has other complex implications for the dog (Smith and Davis 2008). Training with positive reinforcement should be a progressive process. During the first phase of the training, the dog must be “imprinted” on the target odour (Hoyer-Tomiczek et al. 2016); in practice, the dog must learn that the recognition of a specific odour is linked to a reward. In this context, imprinting has the meaning of “teaching odour discrimination skills to a dog” as reported by Fjellanger (2003). The imprinting starts when the dog makes, for the first time, an olfactory contact with the target odour and immediately receives the reward. This scent detection routine should be repeated until the dog has learned clearly and unambiguously how to find and indicate its target (Hoyer-Tomiczek et al. 2016). The target can be presented to the dog naked or inside a small perforated box.

During the second phase of training, the scent discrimination phase, the dog learns to discriminate between the target odour and other scents by consolidating, at the same time, the search behaviour and the signalling display; this process is also called “generalisation” of an odour (Hurt and Smith 2009). The generalisation can be stimulated by offering to the dog simultaneously the target odour and other scenting materials and rewarding only after correct signalling. Several kinds of settings can be arranged for the training: the scenting target and non-target material can be presented simultaneously to the dog or one or more targets can be hidden in natural settings or in fenced training areas (Braun 2013). Although the training area must have similar characteristics to those where the real research work will be carried out, the area must be free of the target to avoid confusing the dog (Errico 2012). During the discrimination phase, it is very important to correctly build the communication between the dog and the handler, to develop the dog’s search behaviour and to reinforce the focus on the target odour (Hurt and Smith 2009). All search routines and behaviour must be shaped and reinforced by the trainer, by encouraging the dog until it can perform long search sessions without loss of attention (Johnen et al. 2013, Hoyer-Tomiczek et al. 2016). To activate the dog’s search behaviour, a specific word from the handler (e.g. search or find it) must be associated to the specific work that the dog has to perform (Wallner and Ellis 1976, Welch 1990, Rolón et al. 2011, Hoyer-Tomiczek et al. 2016).

Johnen et al. (2013) suggest that the duration of training for sniffer dogs can vary between 7 days and 16 months, but for CDD this period may be longer, as searching for biological targets in nature is likely to be complicated by the presence of numerous olfactory stimuli or by the smell of species related to the target species (Wallner and Ellis 1976, Hurt and Smith 2009). The basic training period (imprint phase) for inexperienced dogs searching for insects is between 1 to 3 months and the subsequent discrimination phase lasts for a further 6 to 7 months (Wallner and Ellis 1976, Suma et al. 2014, Hoyer-Tomiczek et al. 2016). Dogs that have already been trained to search for biological targets and in fieldwork need less time to be ready to work (Brooks et al. 2003, Lin et al. 2011). The total length of training is related to numerous factors, such as the trainer’s experience, the trainer’s skills, the characteristics of the breed and the traits of individual dogs (Hurt and Smith 2009, Johnen et al. 2013). The weekly rate of training and the daily length of the working sessions can vary in function from the level of training, the familiarity with fieldwork and the skills of the trainer. Different authors indicate a rate from 3 to 5 training sessions per week; each session can last from 2 to 4 hours with breaks related to the level of fatigue in the dog and to temperature (Harrison 2006, Hurt and Smith 2009, Lin et al. 2011, Suma et al. 2014).

A dog can be trained to offer an active or passive response to the target odour (Braun 2013). A passive response consists of pointing with its nose towards the target and/or stopping and sitting close to the scent source (Long et al. 2007). This response is preferable in cases in which the dog might potentially cause disturbance or frighten the target species and also in cases in which the dog might be at risk (by aggressive or dangerous species) (Braun 2013). Active signalling includes various reinforcing behaviour in addition to pointing, such as scratching, barking or sitting and looking at the scent source and handler (Hoyer-Tomiczek et al. 2016). If needed, all these behavioural activities can be enhanced and shaped by delaying the reward after correct signalling (Johnen et al. 2013, Hoyer-Tomiczek et al. 2016). In general, an active response is suitable for conservation dogs searching for beetles.

Creating a cohesive and efficient dog-handler team is critical to the success of the work; the dog must be able to search, locate and signal the target, while the handler has to make this possible by managing the dog in the field and, at the same time, correctly interpreting the behaviour of the dog. It is important to recognise that the searching ability varies between different dog-handler teams (Johnen et al. 2013, Hoyer-Tomiczek et al. 2016). In particular, the handler must: 1) motivate and handle the dog during the work; 2) create a relationship of trust with the dog and maintain a relaxed atmosphere during each search session; 3) understand the dog’s searching behaviour and its reactions in the presence of a possible target, as the dog’s reactions may depend on several factors such as its psychophysical conditions or weather conditions (Long et al. 2007, Hurt and Smith 2009, Dahlgren et al. 2012); 4) reward the dog adequately after correct signalling, as each activity in the field is simultaneously a training session for it and 5) take care of the dog during the field search, taking into account its needs and other factors that may cause fatigue and loss of concentration, such as thirst, long work sessions on steep terrain or adverse weather conditions that could compromise the quality of work (Dahlgren et al. 2012).

Several authors suggest that the best parameters to describe the ability of conservation dogs in correctly locating their target are the overall percentage of correct indications and the percentage of correctly detected targets for the total number of targets (Wallner and Ellis 1976,Welch 1990, Engeman et al. 2002, Brooks et al. 2003, Cablk and Heaton 2006, Long et al. 2007, Richards et al. 2008, Gsell et al. 2010, Lin et al. 2011, Waters et al. 2011, Suma et al. 2014, Hoyer-Tomiczek et al. 2016). Although these parameters are often characterised by different names, for this work, the definition used by Allouche et al. (2006) was adopted in relation to the assessment of presence-absence predictive models i.e. accuracy (the overall proportion of correct indications), sensitivity (the proportion of correct positive indications) and specificity (the proportion of correct negative indications).

In the majority of the previously listed cases, the average accuracy for conservation dogs was around 90%, although it was variable. Low accuracy may be caused by several factors, such as: age of the dog, insufficient training, problems in the dog-handler team communication, inexperience of the trainer and/or handler (Savidge et al. 2011, Johnen et al. 2013). Exercise and training can increase the accuracy of the dog: dog-handler teams with more experience have a higher accuracy (Savidge et al. 2011). Moreover, dogs employed in new environments have initially low accuracy although this can be increased with training (Wallner and Ellis 1976).

While working in the field, several sources of disturbance can further decrease the accuracy of the search: temperature, wind, fatigue and presence of wild animals, their traces or humans (Hurt and Smith 2009, Dahlgren et al. 2012, Hoyer-Tomiczek et al. 2016). It is important to consider that the accuracy can be artificially increased by the handler; in fact, a handler who knows the hiding places of the targets can unconsciously give this information to the dog (Clever Hans effect) or the dog can follow the odour trail made by the trainer while hiding the target (Lin et al. 2011, Johnen et al. 2013). For this reason, accuracy must to be assessed with a “double blind test”, in which the location of the targets is unknown to both the dog and the handler and special precautions must be taken to avoid helping the dog to find its target (Brooks et al. 2003, Cablk and Heaton 2006, Johnen et al. 2013, Hoyer-Tomiczek et al. 2016).

The protocol developed to train the conservation detection dog in the MIPP project was successful in teaching the dog the specific task required, i.e. to find larvae of the saproxylic beetle O. eremita living inside hollow trees. Therefore the results showed that this rare and elusive beetle can be monitored with the aid of a trained dog. These results are in line with research on other animal species living in wood or in burrows (Brooks et al. 2003, Hoyer-Tomiczek et al. 2016, Nielsen et al. 2016) that were successfully detected with the aid of dogs. In the case of Teseo, the results obtained for accuracy, sensitivity and specificity in natural conditions are high and close to the ones obtained with nitrocellulose filters with the smell of the larvae of O. eremita in controlled conditions (Table 8). This supports the fact that the training plan, which involved the use of filters, was well suited for the project aim. The filters were important because they minimised the number of larvae employed for the training. For the trainer, the use of filters was much easier than using live larvae during the training sessions. A further important point is that the dog showed a high sensitivity to the target odour (i.e. the smell of larvae of O. eremita) and distinguished reliably between trees harbouring the larvae and trees without them (Table 8).

The highest values were obtained if only larvae were considered as the target (i.e. excluding frass and remains) (Table 8) and this is a further indication that Teseo only signalled the presence of larvae. Similar levels of accuracy were reported by Hoyer-Tomiczek et al. (2016) for field tests on an alien longhorn beetle. It is important to point out that not all the trees in which frass and remains have been found were also colonised by larvae; only the larvae of Osmoderma reliably indicate the presence of the species in a tree.

Confirmation that the dog has been successfully imprinted is also provided by the results of the preliminary discrimination test and by the low sensitivity in signalling larvae of other flower chafers species which often share the same cavity with O. eremita. In particular, Teseo showed a much lower sensitivity to larvae of flower chafers (29%) when compared to the larvae of O. eremita (69%). This result is similar to Brooks et al. (2003), who found that trained dogs falsely signalled termites in 25% of tests when termite-damaged wood without termites was presented. In contrast, Lin et al. (2011) reported false positives in only 4% of tests when working with ants and Pfiester et al. (2008) found false positive rate of 3% on bed bug faeces.

These results on accuracy and sensitivity of Teseo are consistent with those obtained in other studies involving dogs in the search for saproxylic beetles. For example, a sensitivity of 78% was obtained for dogs trained to find the red palm weevil, Rhynchophorus ferrugineus (Suma et al. 2014) and Hoyer-Tomiczek et al. (2016) obtained accuracy values between 81% and 94% for larvae of a xylophagous longhorn beetle. They also showed that dogs had a lower accuracy when searching for wood shaving and frass without larvae of the target species, as was also observed for Teseo.

The difference between the values of accuracy measured with nitrocellulose filters and larvae may depend on several factors. In general, working in a natural setting with live targets is certainly more complicated than searching for filters placed by operators. Other studies have also shown that sensitivity was lower under realistic conditions (Hoyer-Tomiczek et al. 2016). In fact, natural populations of insects can have an non-homogeneous distribution and different trees can contain a variable number of larvae and consequently a different concentration of odour. During the fieldwork, it was obvious that, in presence of strong odour sources, the dog detected the target from several metres distance. This effect can be increased by the wind that can carry the target odour away from the source. Chambers et al. (2015) and Hoyer-Tomicek et al (2016) also reported that the scent of species living in trees was transported by wind. These two variables (high concentration of target odour and wind) can confound the results and lead to signalling of trees which do not contain the target, but which are close to trees with larvae of O. eremita. Similarly Chambers et al. (2015) and Hoyer-Tomicek et al. (2016) concluded that wind can lead to signalling of the incorrect tree.

A further important point to consider is which factors increase the level of fatigue in the dog and consequently decrease its reliability (e.g. DeShon et al. 2016). For example, trees can be distant from each other, the vegetation can be impervious and scheduled searches might need to be conducted under sub-optimum weather conditions (e.g. high temperatures or rain).

Precautions should be taken to minimise the effects of these factors, such as: i) carry out some preliminary surveys to allow the dog to become familiar with the working area; in fact, it is well known that accuracy measurements in new areas can initially be low and can increase in later surveys (Wallner and Ellis 1976); ii) choose the most suitable time of the day for the activity of the dog, (e.g. avoid the hours with the highest temperatures, rainy days and strong wind) and iii) pay attention to the level of fatigue in the dog, decreasing the duration of the working sessions and increasing the length of the breaks if necessary or even interrupting the daily work if the dog does not show an appropriate level of concentration.

The lower accuracy measured on trees colonised by larvae of O. eremita may also depend on factors related to the detection probability (i.e. the probability to detect the larvae in a tree, if present) of the wood mould sampling method. In Forcella Buana and San Vito, results obtained by wood mould sampling were used to validate the accuracy, sensitivity and specificity of the signalling of Teseo. However, Chiari et al. (2014) showed that the detection probability for wood mould sampling can vary between 34% and 50%. This means that a high percentage of trees harbouring O. eremita were not correctly identified by wood mould sampling. In these cases, the complete signalling by the dog was incorrectly counted as errors (false signalling).

Another very important factor to take into account is the relationship between the dog and the handler (cf. Beebe et al. 2016). A handler who correctly understands the dog’s behaviour during fieldwork can increase the overall accuracy by more than 10%. Although the improvement of communication between the dog and handler can be obtained through specific double blind training sessions, it is clear that differences exist in the searching ability of different dog and handler teams (Hoyer-Tomicek et al. 2016).

A conservation detection dog is a powerful tool for locating O. eremita and these results can be useful for the other related European species of Osmoderma (Audisio et al. 2007, 2009, Zauli et al. 2016) and in general for other saproxylic insects. In fact, the use of a trained dog is a fast, accurate and non-invasive method that allows the detection of a target species in an area and to identify the colonised trees; this means that a CDD can locate new populations, can confirm the presence of the target species and can assist in the mapping of the distribution of colonised trees in an area, accurately and efficiently. Furthermore, monitoring by means of dogs can be carried out in the same area repeatedly and this allows the detection probability for this method to be obtained. These values can then be compared with detection probabilities for other methods employed for the same species (e.g. wood mould sampling). Repeating the search with a CDD in the same area but in subsequent years would allow changes in the number and distribution of trees occupied by the beetle to be monitored.

We would like to thank the following people for help during fieldwork: Bianchi E., Capogna E., Cini A., Cuccurullo A., Frangioni F., Gallitelli L., Garzuglia F., Grant F., Khroa S., Mancini K., Mantoni C., Nigro G., Petruccelli L., Piermaria L., Redolfi De Zan L., Santoro R. We are grateful to Altea T., Romano M., Eusepi L., Desprini F., Filippone I. (Comando Unità Tutela Forestale Ambientale e Agroalimentare Carabinieri - CUTFAA, formerly CFS, Italian State Forestry Corps, local biodiversity office of Castel di Sangro), Quilghini G., Zoccola A., Marsella S., Rossi B., Bertinelli S. (CUTFAA, local biodiversity office of Pratovecchio), Febbo D., Sulli C., Tollis P. (Abruzzo, Lazio and Molise National Park, in particular), Fedrigoli L. and Mazzocchi F. (CUTFAA, local biodiversity office of Verona) for fieldwork and logistics. The MIPP staff is grateful to Di Marzio V., Tommasini Degna M. (Centro Veterinario Gregorio VII www.gregoriovii.com), Paoletti S., De Cato C., Gasparri S. (A.N.U.C.S.S. www.anucss.org) and to Hoyer-Tomiczek U. (Department of Forest Protection, BFW – Austrian Research Centre for Forests) and Sauseng G. We would like to thank Emilia Capogna for allowing us to use her photographs. Fabio and Teseo particularly like to thank Silvia, Gabriele and Valerio for their invaluable help for the physical and professional growth of Teseo.

The present work was developed within the EU project LIFE11 NAT/IT/000252, with the contribution of the LIFE financial instrument of the European Union.

With the contribution of the LIFE financial instrument of the European Union.