The above mentioned species have been grouped according to the broad similarities in methods used to estimate density, determine abundance indices or simple presence/ absence indicators. They occupy a wide variety of habitats, display different population dynamics and are reservoir hosts for multiple pathogens. Although this group incorporates species from two different families (Cricetidae and Muridae; Wilson and Reeder, 2005), the methods to determine population density are nevertheless comparable. Species specific suggestions and pitfalls of abundance estimation can be found under points 3.2 and 3.3. Various pathogens have been identified previously using pathogen-specific methods (for reviews see Meerburg et al., 2009; Ulrich et al., 2009), but recently also using broad-spectrum nucleic acid amplification methods (see Drexler et al., 2012) and next-generation sequencing (Phan et al., 2011). Some rodent species carry also non-zoonotic pathogens, such as herpesviruses (Ehlers et al., 2007), adenoviruses (Klempa et al., 2009) and papillomaviruses (Schulz et al., 2012). Here for each species only the pathogens with zoonotic potential are summarized.
Brief ecological descriptions and epidemiological relevance are as follows:

Microtus arvalis (Family: Cricetidae)

The common vole M. arvalis (Pallas, 1778) is the most abundant mammal in Europe. It inhabits an almost continuous range throughout the continental European area from the Atlantic coast of France to central Russia with isolated populations on the Iberian Peninsula where it can occur from sea level up to 3,000 m (Braun and Dieterlen, 2005; http://www.iucnredlist.org/). It is absent from the Mediterranean, most of Fennoscandia, northern Russia and the British Isles, except Orkney Islands, where it had been introduced prehistorically approximately 4,800±120 BP (Haynes et al., 2003). The eastern distribution is limited to Ukraine and Russia, from the Dniester River towards the north-east (Mitchell-Jones et al., 1999). Their main habitat is open grassland but also cultivated agricultural land and short meadows (for review see Jacob et al., 2014), where high densities of >3,000 individuals per ha have been reported (Bryja et al., 2001). Common voles show a polyphasic, short-termed activity rhythm of ca. 2 h. This rhythm seems to be dusk/dawn-locked (Daan and Slopsema, 1978).

The population density of M. arvalis can fluctuate on various temporal scales. Annually, densities generally increase from spring onwards reaching a peak in autumn, while on a multiannual level cyclic superabundant densities (outbreaks) can be observed roughly every 3-5 years, often followed by a rapid collapse of the population (Jacob & Tkadlec, 2010). The reasons for explosive dynamics seem to be two remarkable features: first, females can already mate at 2 weeks of age (Tkadlec and Zejda; 1995) and second, due to a highly flexible social behaviour, they form large groups or colonies of related individuals (Frank 1957). More recently, a Europe-wide dampening of these cyclic events has been described for several vole species suggesting large scale climate change as a prominent driver for this phenomenon (Cornulier et al., 2013).

M. arvalis is a well-known reservoir host for Tula hantavirus (TULV), tick-borne Encephalitis virus (TBEV), cowpox virus, Borrelia spp. Cryptosporidium spp., Leptospira spp., Listeria monocytogenes, Francisella tularensis, Brucella microti, Babesia microti, Echinococcus multilocularis and Coxiella burnetii (Ulrich et al., 2009; Meerburg et al., 2009; Schmidt-Chanasit et al., 2010; Achazi et al., 2011; Kinnunen et al., 2011; Kuiken et al., 1991; Scholz et al., 2008; Bajer, 2008; Hansen et al., 2004; Schmidt et al., 2014).

Myodes glareolus (Family: Cricetidae)

The bank vole M. glareolus (Schreber, 1780), formerly Clethrionomys glareolus, is one of the most common rodents in Europe and is considered a pest in forests due to the occurrence of abundance peaks in association with damage to young forest trees (Hansson and Zejda, 1977). Bank voles are distributed from the British Isles (introduced to Ireland in 1964 (Smal, 1987)) to Lake Baikal and northern Asia (http://www.iucnredlist.org/). In the north of Europe they occur up to the northern limit of Norway spruce at the latitudes 68-69 N and in the south to the Balkans, Italian mountains and northern Spain. They are absent from the Mediterranean islands, but prevalent on most Baltic and Atlantic islands. Bank voles can be found from sea level to an altitude of 2,376 m (Spitzenberger, 2001). In the temperate zone, their multiannual fluctuations are closely associated with a tree seed production (masting) (Secher-Jensen, 1981, Jedrzejewski et al, 1991, Clement et al., 2010; Tersago et al., 2009) while in boreal northern Europe vole fluctuations are predator driven (Henttonen et al. 1987, Hanski et al. 1991, Hanski et al. 2001, Korpela et al 2014). This species prefers moist deciduous, mixed, conifer and montane forests, but is also observed in parks, gardens, hedgerows, clear-cuttings but rarely on plain grassland (Mitchell-Jones et al., 1999). Bank voles exhibit multi-phase activity pattern with peak activity reached at dusk and dawn (Braun and Dieterlen, 2005), though this pattern can vary seasonally (Wojcik & Wolk, 1985). Peak densities in bank voles are lower compared to open habitat vole species. Early work in southern Sweden reported peak densities of up to 200 individuals per hectare (Bergested,  1965). Similar values have been reported for the forest of Bialowieza in Poland (Stenseth et al.,2002).

M. glareolus is a well-known reservoir host for Puumala hantavirus, TBEV, cowpox virus, Ljungan virus, hepacivirus, Borrelia spp., Leptospira spp., Francisella tularensis, Bartonella spp., Cryptosporidium spp., Capillaria hepatica and Babesia microti (Ulrich et al., 2009; Meerburg et al., 2009; Schmidt et al., 1998; Telfer et al., 2011; Schmidt et al., 2014; Achazi et al., 2011; Kinnunen et al., 2011; Drexler et al., 2013; Hubalék, 2007).

Arvicola amphibius; Arvicola sapidus (Family: Cricetidae)

There is some debate regarding the taxonomy of Arvicola at least for certain European regions (Gippoliti, S. 2012, Carleton & Musser, 2005) resulting in confusing use of species names. There are two distinct ecotypes within Arvicola amphibius (Linnaeus, 1758)that have only recently been treated as separate species with still debated nomenclature. The aquatic form inhabits wetland and riverine habitats with a preference for aquatic habitats and is referred to as A. amphibius. The terrestrial form inhabits grassland, orchards or horticulture and exhibits rather classic fossorial traits and is named A. scherman. This is reflected in the polymorphism within the species as size and fur colour vary substantially. A. scherman also exhibits far greater abundance amplitudes during outbreak scenarios with up to 600 ind/ha (Giraudoux et al., 1997) causing severe damage in horti- and agriculture, while A. amphibius rarely exceed 100 ind/ha (Jacob & Tkadlec, 2010). A. amphibius is a widespread Palaearctic species whose distribution ranges from Great Britain, where it is considered endangered, to Siberia in the East and from the Arctic circle in the north to northern Iran and the Near East in the south (http://www.iucnredlist.org/). A. amphibius is herbivorous, feeding mainly on the above-ground parts of plants as well as tree roots and bulbs. Arvicola sapidus (Miller, 1908) is restricted to Western Europe and the Iberian Peninsula (http://www.iucnredlist.org/), where it is strictly water-dependent (Mitchell-Jones et al., 1999) inhabiting sedge or reed vegetation (Pita et al., 2011) similar to the aquatic form of A. amphibius. A. sapidus depends on bank-side grass and other green vegetation as food source and probably does not exceed 5 individuals /100m river bank length (Mitchell-Jones et al., 1999).

French populations of the water vole were reported to host hantavirus, lymphocytic choriomeningitis virus as well as cowpox virus (Charbonnel et al., 2008). A more recent study confirmed molecularly Tula virus infections in A. amphibius from different regions of Germany and Switzerland, most likely representing spillover infections (Schlegel et al., 2012). For British populations pathogens include Leptospira spp., Bartonella spp., Giardia spp. and Campylobacter spp. (Gelling et al., 2011). Infections of Arvicola amphibius with pathogens such as Listeria monocytogenes, Francisella tularensis, Echinococcus multilocularis were also reported (Meerburg et al., 2009; Mörner and Addison, 2001).

Apodemus flavicollis; Apodemus sylvaticus (Family: Muridae)

In contrast to the other species in this review both the yellow-necked mouse A. flavicollis (Melchior 1834) and wood mouse A. sylvaticus (Linnaeus 1758) belong to the Muridae family and are sympatric over a large part of their distribution (http://www.iucnredlist.org/). They can be predominately found in deciduous woodland, , although A. sylvaticus is known to use open habitat as well (Tew et al., 2006). Apodemus spec. are mainly granivorous feeding on seeds but also eat berries, fungi or insects (Hansson, 1985). Population densities fluctuate but the peak densities are not comparable to the densities reached during vole outbreaks A. flavicollis can reach up to 58 ind/ha (Niethammer & Krapp, 1978) while for A. sylvaticus a value of 92 ind/ha was shown (Halle, 1993). High population densities have been associated with the mast of forest trees in the previous year. In combination with beneficial winter climate this can lead to rapid population growth in the following spring.

Apodemus sp. is a reservoir host for Cryptosporidium parvum (Bajer et al., 1997) and Toxoplasma gondii (Hejlicek & Literak, 1998). In many areas of southeastern Europe the Dobrava-Belgrade hantavirus (DOBV), genotype Dobrava, has been associated with Apodemus flavicollis (Klempa et al., 2013). In addition, Apodemus spp.was found to be infected by TBEV, cowpox virus, Borrelia spp., Leptospira spp., Babesia microti, Bartonella spp., Escherichia coli (STEC/VTEC), Capillaria hepatica and Francisella tularensis (Ulrich et al., 2009; Meerburg et al., 2009; Schmidt et al., 1998; Achazi et al., 2011; Kinnunen et al., 2011; Tadin et al., 2012; Schmidt et al., 2014).

The gold standard to estimate population densities species that are regularly active above ground is trapping with live traps (e.g., Ugglan or Sherman traps) and applying a capture-mark-recapture method (Seber, 1965). It was first used by C.G.J. Petersen in 1896 (Southwood and Henderson, 2000) and is successfully applied to many different study aims since (see Chapter 3.2.1). Population size can be estimated from four to five visits to the trapping site, but more visits can be made, especially if further information on survival or movement is desired. Animals are released and remain unharmed. Besides the possibility to monitor and identify a broad range of small mammal species accurately or to take additional sampling, e.g. blood and tissues, live trapping is a time consuming, expensive and work-intensive process (Sibbald et al., 2006). Handling of live animals (blood sampling, marking) does require country-specific permits (animal ethics), which have to be considered well in advance. For subterranean species, burrow counts are recommended.

General reviews

A variety of methods have been used to estimate the abundance of small mammals (Schwarz & Seber 1999; Sibbald et al., 2006).

Capture-mark-recapture

A sound estimation of population density using capture-mark-recapture methods (CMR) is well established in population abundance estimation. The statistical models (see review in Seber, 1986) have been and are still undergoing constant evaluation to adjust for departures from the underlying assumptions (see review by Schwarz, 1999; Efford, 2004; Efford et al., 2009). Heterogeneity in model parameters, especially in secretive small mammals has been shown to occur from a variety of intrinsic or extrinsic sources. Observed variability in capture probability, due to different activity and home ranges of different functional groups in the populations, violating the underlying conventional estimation assumptions, are often being identified as a crucial pitfall in population density determination. Factors like species, age or gender can influence individual home ranges. In relation to the layout of the trapping-grid (i.e. edge effects) this is often leading to high degrees of capture heterogeneity among individuals (Pledger and Efford, 1998). A much overlooked determinant of precision in CMR-studies, especially for small mammals, is the trap setup. A web-grid with varying trap-spacing improves the estimation of movement pattern within the trapping area and allows for accurate estimation of the effective trapping area reducing edge effects (Parmenter, 2003). Additionally, estimates of home ranges of target species should be incorporated into calculating the trap layout and spacing. More recently, spatially explicit capture-recapture statistics have been proposed to reduce edge effects altogether (Efford & Fewster, 2012).

Snap-trapping

This method is well established in estimating rodent abundance (Lidicker, 1973; Village and Myhill, 1990), and has advantages especially in studies designed for pathogen monitoring. When country specific permits are obtained it allows for the analysis of various organs that might be of interest for studying a particular pathogen. Although several parameters of population dynamics cannot be estimated by snap trapping (survival, movement) it is sufficient for most pathogen studies compared to the more labor intensive live-trapping method and traps designed to be inserted in tunnels can catch subterranean species. Results on estimated abundance seem to be well correlated to live-trapping estimates (Hanski et al., 1994). However, it has been discussed that removal trapping might introduce a bias towards trapping more transient, dispersing individuals when used frequently (>3 times per year) at the same site over a long period of time (Stenseth et al., 2002). This has to be taken into account when defining the method of choice for a particular study goal.

Active burrow index

A very important index for estimations of vole abundance in middle and Eastern Europe are counts of active burrow entrances in a defined area (Liro, 1974). Since 1970 the active burrow count replaced live trapping methods in agricultural monitoring, because in comparison to them the index of active burrow entrances is inexpensive and easier to measure (Lisicka et al., 2007). However, at low population densities the relative sampling error can reach 400%. In contrast at high population densities the error will be less than 10%. Lisicka and co-workers also proved that at high densities the population change will be overestimated due to a non-linear relation between the index and the population size (Lisicka et al., 2007). Based on field observations Heise and Wieland (1991) reported that each individual of German common vole population in Thuringia is capable of opening on average 2.5 burrow entrances per day. This can allow the conversion of an index to actual population density, though the impact of sex ratio and the social structure of a population on burrowing capacity in relation to abundance are still unknown.

Owl pellet analysis

The relative abundance can be estimated by analysing the diet of avian predators. As these birds cannot digest bones, claws, teeth and fur, they have to disgorge these components regularly. Therefore, large sample sizes of the prey can be easily identified to species level by examining jawbones, teeth or skulls from spit pellets (Love et al., 2000). In Europe the barn owl is mostly used for pellet analysis in small mammals as around 90% of its diet consists of rodents and shrews. The favoured roosting sites are in man-made constructions and pellets are therefore easier to find and decompose less rapidly compared to pellets from other owls (Glue, 1974). Further advantages of pellet analysis are low cost, the variety of prey species obtained including mainly subterranean species, detecting rare species and the recognition of annual and seasonal changes of pellet composition. Since barn owls are nocturnal and the habitat of small mammals may differ from owl territories, certain prey species may be under-represented (Sibbald et al., 2006). Arvicola spec. is a large prey for the barn owl to tackle and in the presence of alternative food sources might give misleading results in abundance estimation.

Field sign index

Various indices of population size for burrowing mammals exist (Hubbs et al., 2000) and are commonly used to assess population abundance in ecological studies, although all indices lack total confidence in accurate species identification. Wilkinson et al., (2004) reported that for lowland habitats the presence of surface runways is a good sign index for field vole (Microtus agrestis) activity. However, identifying specific vole species using this method could prove to be problematic. In contrast Lambin et al. (2000) found that for upland habitats fresh grass clippings were better indicators of vole activity than runways or fresh/old droppings. Although some species leave characteristic feeding signs, it can be difficult to distinguish between grass clippings of different vole species (Sibbald et al., 2006). Moreover, fresh and old droppings might not be suitable for vole identification to species level (Sargent and Morris, 2003).

An additional, simple and economical method for estimating relative abundance is footprint tracking by tracking-tunnels or track-plates (Carey et al., 1991). A cardboard with non-drying and sealed ink is deployed. Problems associated with tracking counts are overlaid footprints that disturb a proper identification (Brown et al., 1996) or in general to distinguish between prints of different rodent genera. Thus only trained experts may verify the genus or species (Hasler et al., 2004). Tracking-plates are rarely used in monitoring abundance of the relevant species because it is difficult to distinguish tracks of closely related sympatric species. In addition, stability of boards and tracks are higly dependent on weather conditions, which is a reason for applying tracking plates mainly indoors. Overall, the sign index could give misguided estimates of relative abundance if more than one species is responsible for the occurrence of field signs (Village and Myhill, 2009).

Exemptions to the nonspecific sign indices are hair traps. Baited tunnels with sticky tape allow for obtaining hair samples from multiple individuals. Where DNA extraction and PCR protocol are established (Ruibal et al., 2010) individuals can be identified and used for density estimation.

Abundance estimation by snap trapping according a standard protocol given.

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Characteristic

Observations

Distribution

All species have wide distributional ranges (see descriptions under point 1). Special care has to be taken when considering populations of A. sylvaticus and A. flavicollis. Both have overlapping distribution and share many morphological characteristics (Michaux et al., 2005). For example, a gradient in the visibility of the characteristic yellow collar of A. flavicollis is known from north to south (Niethammer & Krapp, 1978). In regions with close resemblance, species determination in the field solely based on fur colour could be misleading. Arvicola sapidus and Arvicola amphibius only have regionally overlapping ranges and can be distinguished morphologically.

Population trends

Care should be taken when populations are locally endangered. In such cases live trapping or sign indices need to have priority. Although most species in this group are not threatened, local habitat modification can lead to increased fragmentation of isolated subpopulations, threatening populations in the long term. This holds especially true for A. sapidus and the aquatic ecotype of A. amphibius in Great Britain. The decline of wetland habitat in its distributional range has led to a marked reduction in total abundance and A. sapidus populations are consequently classified as vulnerable under IUCN (Rigaux et al., 2008).

Density range

Populations of all species in this review fluctuate considerably temporally as well as spatially. During high abundances, species can spread from refuges to a variety of adjacent areas where food and habitat can be found (A. amphibius in crops, orchards; M. arvalis in annual crops). M. arvalis typically invades annual crop fields from nearby refuges during outbreaks. Generally, during low abundance sign indices are an easy way to identify potential refuges where then more sophisticated methods can be applied. All suggested methods in section 3 are highly dependent on target species density (Parmenter, 2003; Lisicka et al., 2007). Especially, density estimates from live trapping are often precluded or produces misleading results when few individuals are captured and recaptured. Species of generally low abundance (A. sapidus or the amphibious ecotype of A. amphibius) are consequently susceptible to density estimation errors.

Main habitat

Predominantly woodland species (M. glareolus, A. sylvaticus, A., flavicollis) often preclude the use of sign indices (activity, grass clipping, droppings etc.) due the irregular composition of the forest floor often depending on soil type and dominant tree species.

Introduction-Releases

No releases affecting abundance estimation are known.

Activity rhythms

Most species in this group show polyphasic, short-termed activity patterns of several hours. These rhythms seem to be dusk/dawn-locked (Daan and Slopsema, 1978).

Detectability

Detectability is dependent on multiple extrinsic and intrinsic factors. For all species a rough estimate of general home range sizes has to form the basis for calculating the optimal trapping area and trap spacing. These can vary substantially, ranging from <100m² for M. arvalis (Jacob & Hempel 2003) to up to 22,000m² for A. flavicollis (Radda et al., 1969). For grassland species (e.g. M. arvalis) vegetation height is critical as short vegetation has been shown to reduce activity (Jacob, 2008). Arvicola generally requires larger traps compared to smaller species as small traps would preclude entrance. In live-trapping studies Arvicola spec. might be reluctant to enter above-ground traps due to the strict fossorial lifestyle. To increase trapping success live traps can be inserted into excavated burrow systems (see review by T Prolux (1997). This is however labour intensive, only suitable for small scale studies and needs to be considered carefully.

Method

Gold
standard^1,2,3,4

Snap-trapping^1,2,3,4

Active burrow counts^1,4

Owl pellet analysis^1,2,3

Field signs^1,4

Abundance / Density

A/D

A/D

A/D

A

A

Temporal / Spatial trends

T/S

T/S

T/S

T

T/S

Info on population structure (Y/N)

4

4

3

?

?

Precision

4

3

2

2

2

Seasonal independence

4

3

2

2

2

Visibility independence

5

5

5

5

5

Effort effectiveness

2

3

4

3

4

Budget effectiveness

Ease of learning

2

4

5

3

5

Applicable at large scales

2

3

5

4

Useful at very low density

3

4

3

?

?

Useful at  very high density

4

4

4

?

?