Potential range of impact of an ecological trap network: the case of timber stacks and the Rosalia longicorn

  • Paweł Adamski
  • Jakub Michalcewicz
  • Adam Bohdan
  • Michał Ciach
Open Access
ORIGINAL PAPER

Abstract

Although the negative impact of timber stacks on populations of saproxylic beetles is a well-known phenomenon, there is relatively little data concerning the scale of this impact and its spatial aspect. Beech timber stored in the vicinity of the forest can act as an ecological trap for the Rosalia longicorn (Rosalia alpina), so in this study we have attempted to determine the spatial range of the impact of a network of timber stacks. Timber stacks in the species’ range in the study area were listed and monitored during the adult emergence period in 2014–2016. Based on published data relating to the species’ dispersal capabilities, buffers of four radii (500, 1000, 1600, 3000 m) were delineated around the stacks and the calculated ranges of potential impact. The results show that the percentage of currently known localities of the Rosalia longicorn impacted by stacks varies from 19.7 to 81.6%, depending on the assumed impact radius. The percentage of forest influenced by timber stacks was 77% for the largest-radius buffer. The overall impact of the ecological trap network is accelerated by fragmentation of the impact-free area. It was also found that forests situated close to the timber stacks where the Rosalia longicorn was recorded were older and more homogeneous in age and species composition than those around stacks where the species was absent. Such results suggest that timber stacks act as an ecological trap in the source area of the local population.

Keywords

Spatio-temporal population dynamics Timber stack Ecological trap Forest management Species conservation Dispersal behaviour 

Introduction

The spatial approach is widely used in conservation biology, both in theoretical studies and for supporting decision-making in conservation actions. The spatio-temporal approach, the core of the metapopulation theory (Gilpin and Hanski 1991; Hanski et al. 1996; Schnell et al. 2013; Porter and Ellis 2011), primarily addresses the relation between habitat structure and population dynamics (Gering et al. 2003; Hughes et al. 2000; Bulman et al. 2007). It enables a more reliable description of a population’s decline, and the subsequent design of effective conservation measures (Harrison 1991; Adamski and Witkowski 2007; Cormont et al. 2011; Hanski 2011). In conservation practice, however, application of the metapopulation model is often limited by gaps in the knowledge of both population dynamics parameters and the habitat requirements of species (Cushman 2006; Rudnick et al. 2012). Despite its obvious limitations, expert knowledge can also be quite effective in nature conservation when urgent decisions have to be made (Kuhnert 2011; Martin et al. 2012).

One of the critical aspects of conservation biology is the spatial pattern of sources of threat; knowledge of this pattern is indispensable for any assessment of their overall impact (Coffin 2007; Nelson et al. 2009; Groot et al. 2010). A key, spatially-related threat at the population level is the ecological trap (Dwernychuk and Boag 1972; Oaks et al. 2004). This is the situation when an organism—usually an animal—attempts to reproduce in sites where reproduction is in fact not possible, even though such sites are plentiful (Kokko and Sutherland 2001; Gilroy and Sutherland 2007; Robertson et al. 2010). It is usually the effect of a broken link between the real quality of a site and the aspect that persuades an organism to select it (Oaks et al. 2004), or of perceptual errors as in the mechanism of sensory exploitation (Horváth et al. 2007; Norris et al. 2013). Ecological traps are usually fairly easily distinguishable, often anthropogenic, objects in space (Robertson and Hutto 2006; Horváth et al. 2007; Harabiš and Dolný 2012), so analysing the extent and range of their impact is perfectly feasible. A well-known example of this approach is analysing the spatial impact of light pollution: there are convincing descriptions of how artificial light sources act as ecological traps (Longcore and Rich 2004; Robertson et al. 2013). There are also numerous studies highlighting the importance of the spatial pattern of ecological traps (Weldon and Haddad 2005; Kempenaers et al. 2010; Bates et al. 2014). The presence of ecological traps in an area inhabited by a population causes the quality of the local habitat to deteriorate, even though other parameters, such as the availability of critical resources, are maintained. As habitat quality is considered to be the key factor responsible for the success of conservation efforts related to insect populations (Adamski and Witkowski 2007; Sutherland et al. 2013), it is important to recognise whether it is impoverished by ecological traps.

Studies of light pollution have demonstrated the significance of the spatial pattern of threat factors, which is crucial to assessing their overall effect (Longcore and Rich 2004; Gaston et al. 2012). In our study, we refer to the spatial context of a well-known type of ecological trap, i.e. timber stacks, which endangers xylophagous insects (Jonsell et al. 2007; Hedin et al. 2008; Jonsell 2008; Lassauce et al. 2012; Adamski et al. 2013, 2016). Although attention has been drawn to the negative impact of timber stacks on saproxylophagous beetle populations for quite a long time (Gutowski 2004b; Jonsell et al. 2007; Hedin et al. 2008; Jonsell 2008; Michalcewicz et al. 2011), research has focused mainly on determining the species composition of saprophages colonising potential traps (Jonsell 2008) or on the parameters of the stored timber preferred by these organisms (Jonsell et al. 2007; Hedin et al. 2008; Lassauce et al. 2012), without reference to the spatial range of this impact.

One endangered species threatened by this forest management practice is the Rosalia longicorn Rosalia alpina (L.) (Cerambycidae) (IUCN 2014). Timber stacks are known to be a serious danger to the Rosalia longicorn: this has been highlighted in numerous publications and species management plans (e.g. Starzyk 2004; Witkowski 2007; Horák et al. 2010; Nieto and Alexander 2010; Michalcewicz and Ciach 2012), and explicitly demonstrated in studies undertaken in the Carpathians (Adamski et al. 2013, 2016). However, no attempts have been made to standardise the methodology for assessing the overall impact of a network of timber stacks on populations of xylophages, including the Rosalia longicorn. The present work aimed to assess the spatial extent of the potential impact of a timber stack network on a Rosalia longicorn population. We applied a GIS-based method, which takes into consideration a species’ movement capabilities, in order to assess the potential spatial impact of ecological traps.

Materials and methods

Species

The Rosalia longicorn has a Euro-Caucasian distribution, and its geographical range covers central and southern Europe (Sama 2002; Shapovalov 2012; IUCN 2014). However, its current central European distribution of this species is highly fragmented, and it inhabits only a few isolated areas in its range (Binner and Bussler 2006). In this part of Europe, the Rosalia longicorn occurs mainly in mature, > 100-year-old open canopy, mountain forest stands of European beech Fagus sylvatica (Burakowski et al. 1990). Classified by some authors as a primeval forest relict (Gutowski 2004a; Pawłowski 2008; Eckelt et al. 2017), the species is commonly treated as an icon of saproxylic biodiversity throughout its range.

At the microhabitat scale, this beetle inhabits old, strongly insolated trees that have been damaged by frost, wind or fire, yet are still alive. It also inhabits dead trees, both standing and fallen, and freshly logged or stockpiled timber. It occurs in both softened wood, rotten and strongly decomposed by wood-decay fungi, and in the bark-less yet still hard and undecayed wood of live trees (Capecki 1969; Gutowski 2004b; Starzyk 2004). Regardless of the extent and the stage of decay, strong insolation is a feature common to the species’ breeding substrate (Russo et al. 2011). The Rosalia longicorn is highly mobile and its movement ability has been assessed at between 3000 m (Drag et al. 2011) to more than 10 km (Lachat et al. 2013).

Study area

The study was conducted in the Polish part of the western Bieszczady Mountains (Carpathians, SE Poland) (Kondracki 2011). The Bieszczady are part of the eastern Beskids, which stretch from the Łupków Pass and the valley of the River Osława in the west to the Uzh Pass and the sources of the River San on the Ukrainian border in the east (Fig. 1). A characteristic feature of the western Bieszczady is the lack of upper alpine Norway spruce Picea abies forests, as a result of which deciduous forests are dominant in the area. The Bieszczady National Park was founded in 1973 and currently covers an area of 270.6 km2. Subsequently, two landscape parks—the Cisna-Wetlina and San Valley LP—were established in 1992. All three parks were included in the international biosphere reserve delineated on the borders of Poland, Slovakia and Ukraine. There are numerous nature reserves in this region (Kondracki 2011). The PLC180001 Bieszczady Natura 2000 area covers much of the western Bieszczady Mts.

Fig. 1

The study area: (a) Polish state border, (b) border of the Western Carpathians region [according to Kondracki (2011)], (c) Bieszczady National Park, (d) study area, (e) forest with dominant beech, (f) forest with dominance of another tree species

The territory in question is therefore covered by several, partially overlapping, protected areas with varying conservation regimes. The National Park and a few small natural reserves are therefore strictly protected, whereas in the Landscape Park and Natura 2000 sites, the activities of the forest industry are restricted only by the provisions of the management plans of protected areas, which are usually focused on particular species, habitats or other natural values (Parliament of the Republic of Poland 2004). The average annual amount of beech timber logged in 2012–2015 was estimated at 237.2 m3/km2, and the harvested timber was stacked in fixed locations, designated by the forest administration (Adamski et al. 2016).

Data collection and analysis

This survey of timber stacks was carried out in the part of Bieszczady Mts. covered by the currently known distribution range of the Rosalia longicorn (Michalcewicz and Ciach 2015); the Bieszczady National Park, where timber is neither harvested nor stored, was excluded from the analysis. In 2014–2016 we searched the study area for timber stacks and then monitored them during the adult emergence period (July–August). The locations of the beech timber stacks were recorded using a GPS data logger. Each stack was monitored and the presence of the species noted.

Buffer zones for assessing the potential impact of timber stacks on the Rosalia longicorn population were delimited around each one. Buffer zone radii, corresponding to the dispersal potential categories of imagines, were determined on the basis of data relating to species movement (Fig. 2): (A) 500 m—the dispersal range of approx. 8% of individuals; (B) 1000 m—the dispersal range of approx. 4% of individuals; (C) 1600 m—the maximum dispersal range found during a mark-capture-recapture trial; and (D) 3000 m—potential dispersal range based on modelling (Drag et al. 2011).

Fig. 2

Areas free from the impact of the network of ecological traps, depending on the adopted radius of impact (based on the movement capabilities of the Rosalia longicorn Rosalia alpina [Drag et al. (2011)]. A 500 m buffers, B 1000 m buffers, C 1600 m buffers, D 3000 m buffers; (a) Polish state border, (b) border of the study area, (c) Bieszczady National Park, (d) beech timber stacks, (e) forest with dominant beech, (f) forest with dominance of another tree species

If the buffers surrounding two or more stacks partially overlapped, they were combined into a common buffer. In order to avoid methodological artefacts, the parts of buffers extending beyond the predetermined study area were excluded from the analysis (Sadahiro 2005). The area of forest covered by buffers of different radii was calculated separately for (1) the whole forest-covered area and (2) forest with dominant beech only. Moreover, the numbers of currently known localities of the Rosalia longicorn (Michalcewicz and Ciach 2015) covered by the buffers were counted. These numbers were recalculated to the proportions with the confidence calculated according to Newcombe (1998).

Based on the percentage of the total forest area covered by buffers, the expected number of known Rosalia longicorn localities was calculated using the formula:
$${\text{N}}_{{\text{e}}} = {\text{N}}_{{\text{t}}} \frac{{{\text{A}}_{{\text{b}}} }}{{{\text{A}}_{{\text{t}}} }}$$
(1)
where Ne is expected number of localities, Nt is total number of localities, Ab is forest area covered by buffers, At is total forest area at the study area.

Calculations were done for each buffer radius, separately for the buffers around the stacks where the Rosalia longicorn had been observed and for those where the species had not been observed, as well as the whole area covered by the buffers. Subsequently, the expected numbers were compared with the observed ones using the chi-squared (χ2) test.

The forests beyond the buffers were treated as impact-free stands. The distance to the nearest neighbour was designated for each buffer radius in such areas. Then, the relation between this distance and the buffer radius was analysed. As the distribution of the nearest distance between intact impact-free forest areas deviated significantly from normality (Shapiro–Wilk test; W726 = 0.120, p < 0.0001, Table 1), the non-parametric Kruskal–Wallis test and the Steel–Dwass post-hoc test were applied.

Table 1

Average size of forest units within the study region, Bieszczady Mts. (Carpathians, SE Poland)

 

Total

Dominant species: European beech Fagus sylvatica

Dominant species: other

Number

8470

2838

5632

Mean area [ha]

9.67

15.17

7.34

Area SD

12.30

14.60

10.34

Area median

4.47

10.03

3.26

Lower quartile

1.56

3.69

1.20

Upper quartile

12.99

23.48

8.87

As the distribution of forest unit areas differs significantly from the normal (W = 0.3268; p < 0.0001), the mean and SD are less useful parameters than medians and quartiles

Data on forest type were provided by the Forest Data Bank (FDB 2016). This contains maps and descriptive data relating to forest areas managed by the State Forests National Forest Holding as of 1 January 2016, including information on the dominant tree species as well as the stand size and age of each forest unit (forest management practice in Poland divides all forests into spatial units, where forests are relatively uniform with respect to species composition, age, height and spatial structure).

To determine the parameters of forest stands related to the presence of the Rosalia longicorn, the buffers around the stacks with and without records of the species (Fig. 2) were compared for each buffer radius separately, using a logistic regression model for the binominal distribution of the effect and the logit linked function. The dependent variable was the absence or presence of the Rosalia longicorn on timber stacks, whereas predictors were forest stand parameters related to the known habitat requirements of the Rosalia longicorn: (i) forest stand age and (ii) dominance of beech were used as indicators of habitat quality, since the Rosalia longicorn is associated with mature beech forests; (iii) forest unit size, which is considered an indicator of habitat heterogeneity. Large units implied contiguous patches of homogeneous forest stand, whereas small ones were treated as indicators of habitat heterogeneity, since they contain small patches of stands of different ages and dominant species. Stand age and forest unit area were expressed as continuous variables, whereas beech dominance was a categorical one.

ArcGIS 10.1 (ESRI 2010) and the mlogit package (Croissant 2015) developed in R software (R Core Development Team 2015) were used in the analysis.

Results

The majority (77.6%) of the study area is covered with forest stands (total area = 998.29 km2), and European beech is the dominant tree species in 35.4% of the forest units (N = 8015). There was, however, a significant difference (Wilcoxon test; Z = 28.90, p < 0.0001) between the average sizes of the forest units with dominant beech and of those where other tree species were dominant (Table 2). As a result, forest units with dominant beech constituted 46.46% (total area = 775.06 km2) of all forest stands.

Table 2

Areas of buffer zones and areas of tree stands within them—these are the actual areas and percentages of the same type of forest within the study region

Buffer zone radius (m)

All forests

Forest with dominant beech

Forest with dominance of another species

Area [km2]

Ratio [%]

Area [km2]

Ratio [%]

Area [km2]

Ratio [%]

500

50.55

6.5

21.79

6.0

28.75

6.9

1000

163.63

21.1

77.44

21.4

86.20

20.8

1600

328.17

42.3

160.88

44.5

167.29

40.4

3000

599.67

77.4

301.77

84.5

297.90

72.0

A total of 88 sites (0.88/10 km2 of the total area and 1.14/10 km2 of the forest area) where beech timber was stored during the Rosalia longicorn’s emergence period were located in the study area. The proportion of forest area covered by the buffers delineated around the timber stacks varied from 6.5% in the buffers of 500 m radius to 77.4% in the 3000 m buffers (Fig. 2; Table 2). Owing to the high density of timber stacks and buffer overlap, forest areas situated beyond the range of the potential impact of ecological traps form scattered and isolated patches of habitats (Figs. 2, 3).

Fig. 3

Ratios (± CL) of Rosalia longicorn Rosalia alpina localities covered by buffers with different radii

The buffers covered a significant proportion of the localities where the Rosalia longicorn is currently recorded (Fig. 3).

The distance between compact, impact-free forest patches depended on the buffer radius (Kruskal–Wallis test; H = 15.631, p = 0.004) and was significantly higher for the 3000 m buffers than for the 500 and 1000 m buffers (Fig. 4).

Fig. 4

Distances to the nearest impact-free forest area; 1—median, 2—lower/upper quartile, 3—range, 4—differences obtained from post hoc comparison; *p < 0.05, **p < 0.001

Adult Rosalia longicorns were recorded at 15% (N = 88) of the monitored timber stacks. Comparison of forest traits around the timber stacks where Rosalia longicorns were or were not present indicated that within the 500 m radius, the forest stands surrounding a stack with Rosalia longicorns were older than those around stacks without records of this beetle (Table 3). For larger buffer radii, stand age lost its significance in favour of forest unit area (Table 3): the forest units surrounding timber stacks with Rosalia longicorns were larger than those where the species was not recorded.

Table 3

Logistic regression models comparing parameters of forest stands (age, area and European beech Fagus sylvatica dominance) surrounding the stacks where the Rosalia longicorn Rosalia alpina was present or absent; species presence/absence was used as a dichotomous dependent variable (1/0) and four separate models for various radii of buffer zones (500, 1000, 1600 and 3000 m) are presented

Parameter

Estimate

SE

t

p

Buffer 500 m (F3, 1082 = 6.341; p = 0.0001)

 Intercept

0.726

0.300

2.419

0.016

 Age

0.011

0.003

3.728

0.000

 Area

0.017

0.016

1.047

0.295

 Beech dominance

0.151

0.214

0.705

0.481

Buffer 1000 m (F3, 2312 = 4.364; p = 0.0052)

 Intercept

1.015

0.183

5.547

0.001

 Age

0.003

0.002

1.811

0.070

 Area

0.018

0.007

2.643

0.008

 Beech dominance

0.121

0.127

0.954

0.340

Buffer 1600 m (F3, 4083 = 5.531; p = 0.0000)

 Intercept

1.018

0.128

7.932

0.000

 Age

0.002

0.001

1.665

0.096

 Area

0.013

0.004

3.176

0.002

 Beech dominance

0.087

0.088

0.985

0.324

Buffer 3000 m (F3, 6256 = 5.298; p = 0.0014)

 Intercept

1.020

0.083

12.318

0.000

 Age

− 0.002

0.001

− 0.250

0.803

 Area

0.009

0.002

3.887

0.000

 Beech dominance

0.037

0.057

0.511

0.511

The numbers of known localities of the Rosalia longicorn covered by the buffers surrounding the stacks significantly exceeded the expected values (calculated using formula 1). Moreover, the number of previously recorded localities was significantly higher in the buffers around stacks where the species was observed during the study period than in the buffers surrounding the stacks where the Rosalia longicorn was not present (Table 4).

Table 4

Numbers of the known localities of Rosalia alpina in the study area covered by buffer with different radii

 

In buffers around the stacks

Outside the buffers

C

Chi squared for comparison

RA was recorded

A

RA was not recorded

B

(A + B) vs. C

A vs. B

500 m

 Observed

1

4

71

χ2 = 13.754

p = 0.001

χ2 = 10.467

p = 0.005

 Expected

7

12

57

1000 m

 Observed

3

13

60

χ2 = 21.111

p < 0.001

χ2 = 12.648

p = 0.002

 Expected

15

21

40

1600 m

 Observed

7

25

44

χ2 = 9.207

p = 0.010

χ2 = 9.375

p = 0.009

 Expected

21

24

31

3000 m

 Observed

17

42

17

χ2 = 0.788

p = 0.674

χ2 = 28.106

p < 0.001

 Expected

39

23

14

The expected number was calculated using formula 1, based on the proportion of the study area (Table 2)

Discussion

Timber stacks in the Bieszczady Mts., known to be a threat to the Rosalia longicorn (Gutowski 2004b), fulfil the criteria of an ecological trap for this species (Adamski et al. 2013, 2016). However, no study investigating the importance of their spatial pattern has been carried out to date.

The general mechanism of the impact of timber stacks on saproxylic insects is similar to that relating to artificial light sources, i.e. imagines are attracted to sites of incorrectly assessed habitat quality, where reproduction is impossible or ineffective. Unfortunately, however, the mechanisms by which dead wood attracts saproxylic beetles are more complex, mostly because the propagation of the chemical signal depends on numerous local conditions, such as weather as well as micro- and mesoclimatic parameters (Hedgren 2007; Svensson and Larsson 2008; Chiari et al. 2013). Moreover, recent studies have demonstrated the presence of an aggregating pheromone produced by the Rosalia longicorn (Žunic-Kosi et al. 2017). This complicates analyses of the extent of timber stack impact on saproxylic insects, because the attractive power of the timber stack seems to depend not only on the presence of large amounts of the preferred host plant (potential breeding substrate), but also on the presence of Rosalia longicorn individuals themselves, which may reinforce the luring effect of timber stacks. Consequently, the present interpretation of the impact of an ecological trap should be treated as a minimum estimate. There are several reasons for this. The first relates to the radii of this impact. As mentioned earlier, these were taken from the study by Drag et al. (2011). One may argue that less than 4% of individuals disperse over distances greater than 1000 m. However, this proportion applies only to recaptured individuals. In the discussion of their results, those authors admitted that, because of the rather low level of detectability, the ratios and dispersal distances they calculated were underestimated (see Drag et al. 2011). They suggested that the maximum migration distance of Rosalia longicorns could be as far as 10 km (Drag et al. 2011), a statement corroborated in another study (Lachat et al. 2013). Another simplification made during the present work was to treat timber stacks as points, whereas in fact they may cover areas of several hundred square metres or extend for some considerable distance, e.g. along forest roads. For this reason, the actual area of an ecological trap’s impact range is larger than that derived from point locations. Moreover, our analyses did not take into account the small firewood storage sites situated near properties situated in the study area, which could also be a part of the ecological trap network affecting the Rosalia longicorn population.

One can make the point that such limited and imprecise information about the species’ dispersal range precludes reliable analyses of the spatial scale of ecological trap impact. However, as mentioned earlier, conservation practice shows that for practical purposes, expert knowledge—even if quite imprecise—is often effective and therefore useful (Kuhnert 2011; Martin et al. 2012). Therefore, despite these limitations, the proposed method seems to provide a convincing approximation of the potential range of impact of timber stacks on the Rosalia longicorn. The results indicate that just ca. 23% of the forests in the study area are beyond the reach of the impact of timber stacks (Table 2; Fig. 2). One could argue that the estimated area of impact for smaller buffer radii is much lower. However, a number of threat factors can lead to habitat perforation—a particular instance of fragmentation, when spots of hostile habitat appear within patches of suitable habitat (Miller et al. 2001); this can influence large-scale habitat quality (Forsman et al. 2010). It is also worth stressing that such impact-free forest does not cover a compact area but is fragmented and distributed across the whole study area, and the distances between them increase with the buffer radii (Figs. 2, 4). Comparison of the spatial distributions of the timber stacks and previously recorded sites of the Rosalia longicorn shows that they are related. Moreover, the known localities more often occur in the vicinity of the stacks where the Rosalia longicorn was observed (Table 4; Fig. 5). This shows that timber stacks, effectively as ecological traps, are in operation in the core of the local population. Unfortunately, there are insufficient data for estimating the strength of this impact, although the above-mentioned co-occurrence itself is an important sign of the threat. It should, however, be emphasised that the core area of the distribution of a population is usually also its source (Pulliam 1988). Therefore, the whole population is extremely sensitive to factors reducing reproductive effectiveness in such an area (Holt 1996; Lawton 1993; Diaz 1996). As a result, the decrease in reproductive success in the population’s source area, such as a reduction of density, may shift to a population sink (Holt 1985; Dias 1996), so there is a risk of the threat being underestimated. In the case of the study area, one may speculate that the difference between the spatial distributions of the Rosalia longicorn localities in the study area in 2000–2013 (Michalcewicz and Ciach 2015) and 2014–2016 (Fig. 5) may be explained by the above mechanism.

Fig. 5

Distribution of timber stacks as well as known localities of the Rosalia longicorn in the study area, (a) localities where the Rosalia longicorn was recorded between 2000 and 2013, (b) timber stacks surveyed in 2014–2016 where Rosalia alpina was present, (c) timber stacks surveyed in 2014–2016 where Rosalia alpina was absent

The source area of the population can also be determined by comparing habitat parameters—here, by comparing the surroundings of stacks occupied by the Rosalia longicorn and of those where this species was not present. The comparison showed that the forest near the timber stacks occupied by the Rosalia longicorn demonstrated the importance of forest stand homogeneity. The main factor governing the presence of the Rosalia longicorn is the surface area of a forest unit, whereas the dominance of European beech is insignificant and the age of a tree stand is important only in the immediate vicinity of the stack (500 m buffer). As beech is the Rosalia longicorn’s main host plant, the lack of a significant relationship between its dominance in the vicinity of a timber stack and the species’ presence there may seem rather surprising. However, it should be stressed that European beech occurs as an admixture in the majority of forest stands in which it is not the dominant species (Kozak et al. 2002; Jaworski and Kołodziej 2004). According to Forest Management Plans of the study area, 85% of the forest units in Bieszczady Mts. contain at least 10% European beech (RDLP Krosno 2015a). This prevalence of European beech suggests that most of the forest stands in the study area constitute potential habitat for the Rosalia longicorn. In consequence, this beetle may find trees suitable for larval development in almost all types of forest stands. The significant part played by forest unit area can also be explained by the relation between forest heterogeneity and the degree of habitat fragmentation: larger forest units that are uniform habitat patches may favour the species’ movements. Moreover, as a result of forest management practices, small forest units may represent more intensively managed forest, which in turn could be unfavourable to the species’ movements. All these aspects may be responsible for the fact that the Rosalia longicorn was more often found to occupy timber stacks situated within large, relatively intact, forest habitat patches.

Owing to the high density of timber stacks in the study area, the forest area free from their influence cannot act as an effective species refuge. Presumably, the spatial network of ecological traps leads to a functional fragmentation of the forest, free from their impact (Bruggeman et al. 2009; Lu et al. 2012), but this problem has not yet been studied or documented in depth. It is worth mentioning that the consequences of habitat fragmentation are not always limited to total habitat loss. In the least-cost patch approach, the increase in migratory cost is treated as an increase in isolation between subpopulations or habitat patches (Cushman et al. 2009). We recommend, therefore, that when the spatial structure of Rosalia longicorn habitat is studied, the distance from the nearest ecological trap should be included in analyses based on the resistant kernel approach (Compton et al. 2007). However, the insufficient amount of field data on Rosalia longicorn dispersal behaviour, mentioned earlier, is the real problem in reliable modelling of this process. Theoretically, most appropriate diffusion models of dispersal demand precise information on dispersal distances as well as the isolation power of different habitat types (Compton et al. 2007; Ovaskainen 2004, 2008). Such data with sufficient accuracy are available only for a few flying beetle species studied with radio-tracking techniques (Dubois and Vignon 2008; Tini et al. 2018). Unfortunately, none of those results can be used for estimating modelling parameters for the Rosalia longicorn in our study area.

The problem of the impact-free, functional fragmentation of a forest recalls the significance of large protected areas as effective species refugia (Hanski and Simberloff 1997; Ellis et al. 2011). In relation to the beech timber stacks in the Bieszczady Mts., only the Bieszczady National Park can be regarded as stack-free: it is thus a fairly large and intact habitat patch. This highlights the importance of the conservation method according to which a large protected area limits certain threats to a population.

The similarity between the mechanisms of the impact of timber stacks on xylophagous insects and that of light pollution should lead to similar conservation recommendations. Unfortunately, there are no data based on which the intensity of attraction of Rosalia longicorns to stacked beech timber can be compared with suitable natural oviposition habitats. However, it is worth recalling that timber stacks are usually set up near forest roads on open, strongly insolated sites. Such placements increase the attractiveness of the stacks to the thermophilous Rosalia longicorn (Drag et al. 2011; Russo et al. 2011). The strong attraction of timber stacks for this species in the Bieszczady Mts. has been borne out by numerous field observations (Gutowski 2004b; Michalcewicz and Ciach 2012; Adamski et al. 2013, 2016). It should be stressed that these results only outline the potential area impacted by timber stacks in an ecological trap network; they do not provide any estimate of their influence on population condition.

The results of this study draw attention to one, hitherto not considered conservation topic: the possibility of reducing the area impacted by timber stacks by aggregating these: one large stack would affect a much smaller area that several smaller ones. However, the lack of sufficient data on the luring mechanisms of timber stacks highlights the need for further investigation of this problem. This may also be useful from the point of view of conservation practice. As the dispersal potential of the Rosalia longicorn is still insufficiently understood, beech timber stacks in the species’ range have to be monitored anyway. This activity would consume far less effort for a few large timber stacks than for numerous smaller ones. The above idea is consistent with the current conservation recommendation stressing the need to avoid storage of beech timber at sites and times accessible to the Rosalia longicorn (Gutowski 2004b; Michalcewicz and Ciach 2012; Adamski et al. 2016). However, recommendations to date have focused mainly on the time limitation, whereas the spatial approach has not been considered.

Conclusion

The results indicate that the Rosalia longicorn is exposed to strong pressure from timber stacks in managed forest areas during its mating and oviposition periods. The spatial distribution of timber stacks means that the majority of the population is endangered by ecological traps. The age and homogeneity of the forest stands surrounding timber stacks may contribute to the effectiveness of ecological traps. The same mechanisms may also be in operation in relation to other saproxylic species that are regularly observed wherever timber is stored (Müller et al. 2007). For conservation practice, it is crucial to avoid stacking beech timber during the Rosalia longicorn’s oviposition season; moreover, the concentration of numerous small stacks into a very limited number of bigger ones could reduce the spatial range of their impact.

Notes

Acknowledgements

The authors would like to thank Marcos Méndez and the anonymous reviewer for their very inspiring and important suggestions during the preparation of this paper. The collection of the field data was partly supported by the EEA Fund (Grant No. PL05-0096). Financial support for this study was partly provided by the Polish Ministry of Science and Higher Education by statutory grant (DS 3421) as well as the statutory funds of the Institute of Nature Conservation, Polish Academy of Sciences.

Compliance with ethical standards

Conflict of interest

There are no conflicts of interest between the authors, the institution and fund donors.

Ethical approval

The paper has been prepared and submitted in accordance with the ethical standards of the Committee on Publication Ethics (COPE). Moreover, the methodology used in this study involved only a low degree of invasiveness, so no ethical permit was required.

References

  1. Adamski P, Witkowski ZJ (2007) Effectiveness of population recovery projects based on captive breeding. Biol Conserv 140:1–7CrossRefGoogle Scholar
  2. Adamski P, Holly M, Michalcewicz J, Witkowski Z (2013) Decline of Rosalia longicorn Rosalia alpina (L.) (Coleoptera: Cerambycidae) in Poland—selected mechanisms of the process. In: Ząbecki W (ed) The role and contribution of insects in the functioning of forest ecosystems. Wydawnictwo UR, Kraków, pp 185–200 Google Scholar
  3. Adamski P, Bohdan A, Michalcewicz J, Ciach M, Witkowski Z (2016) Timber stacks: potential ecological traps for an endangered saproxylic beetle, the Rosalia longicorn Rosalia alpina. J Insect Conserv 20(6):1099–1105CrossRefGoogle Scholar
  4. Bates AJ, Sadler JP, Grundy D, Lowe N, Davis G, Baker D, Bridge M, Freestone R, Gardner D, Gibson C, Hemming R, Howarth S, Orridge S, Shaw M, Tams T, Young H (2014) Garden and landscape-scale correlates of moths of differing conservation status: significant effects of urbanization and habitat diversity. PLoS ONE 9:e86925CrossRefPubMedPubMedCentralGoogle Scholar
  5. Binner V, Bussler H (2006) Erfassung und Bewertung von Alpenbock-Vorkommen. Naturschutz Landschaftsplanung 38:378–382Google Scholar
  6. Bruggeman DJ, Jones ML, Scribner K, Lupi F (2009) Relating tradable credits for biodiversity to sustainability criteria in a dynamic landscape. Landsc Ecol 24:775–790CrossRefGoogle Scholar
  7. Bulman CR, Wilson RJ, Holt AR, Bravo LG, Early RI, Warren MS, Thomas CD (2007) Minimum viable metapopulation size, extinction debt, and the conservation of a declining species. Ecol Appl 17:1460–1473CrossRefPubMedGoogle Scholar
  8. Burakowski B, Mroczkowski S, Stefańska J (1990) Chrząszcze—Coleoptera. Cerambycidae and Bruchidae. Katalog Fauny Polski XXIII, 15. PWN, WarszawaGoogle Scholar
  9. Capecki Z (1969) Owady uszkadzające drewno buka zwyczajnego (Fagus silvatica L.) na obszarze jego naturalnego zasięgu w Polsce. Prace Instytutu Badań Leśnych 367:1–166Google Scholar
  10. Chiari S, Carpaneto GM, Zauli A, Zirpoli GM, Audisio P, Ranius T (2013) Dispersal patterns of a saproxylic beetle, Osmoderma eremita, in Mediterranean woodlands. Insect Conserv Divers 6:309–318CrossRefGoogle Scholar
  11. Coffin AW (2007) From roadkill to road ecology, a review of the ecological effects of roads. J Transp Geogr 15:396–406CrossRefGoogle Scholar
  12. Compton BW, McGarigal K, Cushman SA, Gamble LR (2007) A Resistant Kernel Model of Connectivity for Amphibians that breed in Vernal Pools. Conserv Biol 21:788–799CrossRefPubMedGoogle Scholar
  13. Cormont A, Malinowska AH, Kostenko O, Radchuk V, Hemerik L, Wallis-DeVries MF, Verboom J (2011) Effect of local weather on butterfly flight behaviour, movement, and colonization, significance for dispersal under climate change. Biodivers Conserv 20:483–503CrossRefGoogle Scholar
  14. Croissant Y (2015) Estimation of multinomial logit models in R: The mlogit Packages https://cran.r-project.org/web/packages/mlogit/vignettes/mlogit.pdf, 2 Sept 2016.
  15. Cushman SA (2006) Effect of habitat loss and fragmentation on amphibians: a review and prospectus. Biol Cons 128:231–240CrossRefGoogle Scholar
  16. Cushman SA, McKelvey KS, Shwarts MK (2009) Use of empirically derived source-destination models to map the regional conservation corridors. Conserv Biol 23:368–376CrossRefPubMedGoogle Scholar
  17. De Groot RS, Alkemade R, Braat L, Hein L, Willemen L (2010) Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol Complex 7:260–272CrossRefGoogle Scholar
  18. Dias PC (1996) Sources and sinks in population biology. Trends Ecol Evol 11(8):326–330CrossRefPubMedGoogle Scholar
  19. Drag L, Hauck D, Pokluda P, Zimmermann K, Čížek K (2011) Demography and dispersal ability of a threatened saproxylic beetle, a mark-recapture study of the Rosalia Longicorn (Rosalia alpina). PLoS ONE 6:e21345CrossRefPubMedPubMedCentralGoogle Scholar
  20. Dubois G, Vignon V (2008) First results of radio-tracking of Osmoderma eremita (Coleoptera: Cetoniidae) in French chestnut orchards. In: Revue d’écologie, 2008, SUP10″ 4ème Colloque sur la Conservation des Coléoptères Saproxyliques”, Vivoin, Sarthe, FRA, 2006-06-27. Société nationale de protection de la nature et d’acclimatation de France, ParisGoogle Scholar
  21. Dwernychuk LW, Boag DA (1972) Ducks nesting in association with gulls-An ecological trap? Can J Zool 50:559–563CrossRefGoogle Scholar
  22. Eckelt A, Müller J, Bense U, Brustel H, Bußler H, Chittaro Y. Cizek L, Adrienne A, Holzer E, Kadej M, Kahlen M, Köhler F, Möller G, Mühle H, Sanchez A, Schaffrath U, Schmidl J, Smolis A, Szallies A, Németh T, Wurst C, Thorn S, Haubo R, Christensen B, Seibold S (2017). Primeval forest relict beetles of Central Europe: a set of 168 umbrella species for the protection of primeval forest remnants. J Insect Conserv.  https://doi.org/10.1007/s10841-017-0028-6 Google Scholar
  23. Ellis S, Wainwright D, Berney F, Bulman C, Bourn N (2011) Landscape-scale conservation in practice, lessons from northern England, UK. J Insect Conserv 15:69–81CrossRefGoogle Scholar
  24. Forsman JT, Reunanen P, Jokimäki J, Mönkkönen M (2010) The effects of small-scale disturbance on forest birds, a meta-analysis. Can J For Res 40:1833–1842CrossRefGoogle Scholar
  25. Gaston KJ, Davies TW, Bennie J, Hopkins J (2012) Review, Reducing the ecological consequences of night-time light pollution, options and developments. J Appl Ecol 49:1256–1266CrossRefPubMedPubMedCentralGoogle Scholar
  26. Gering JC, Crist TO, Veech JA (2003) Additive partitioning of species diversity across multiple spatial scales: implications for regional conservation of biodiversity. Conserv Biol 17:488–499CrossRefGoogle Scholar
  27. Gilpin ME, Hanski I (1991) Metapopulation dynamics, empirical and theoretical investigations. Academy Press, LondonGoogle Scholar
  28. Gilroy JJ, Sutherland WJ (2007) Beyond ecological traps, perceptual errors and undervalued resources. Trends Ecol Evol 22:351–356CrossRefPubMedGoogle Scholar
  29. Gutowski JM (2004a) Longhorn beetles (Cerambycidae). In: Bogdanowicz W, Chudzicka E, Pilipiuk I, Skibińska E (eds) Fauna of Poland—characteristics and checklist of species, vol 1. Muzeum i Instytut Zoologii PAN, Warszawa, pp 49–53Google Scholar
  30. Gutowski JM (2004b) Rosalia alpina (Linnaeus, 1758), Nadobnica alpejska. Gatunki zwierząt (z wyjątkiem ptaków). In: Adamski P, Bartel P, Bereszyński R, Kepel A, Witkowski Z (eds) Poradniki ochrony siedlisk i gatunków Natura 2000 — podręcznik metodyczny. Ministerstwo Środowiska, Warszawa, pp 130–134Google Scholar
  31. Hanski I (2011) Habitat loss, the dynamics of biodiversity, and a perspective on conservation. AMBIO 40:248–255CrossRefPubMedPubMedCentralGoogle Scholar
  32. Hanski IA, Simberloff D (1997) The metapopulation approach, its history, conceptual domain and application to conservation. In: Hanski IA, Gilpin ME (eds) Metapopulation biology, Ecology, genetics and evolution. Academic Press, London, pp 5–26CrossRefGoogle Scholar
  33. Hanski I, Moilanen A, Pakkala T, Kuussaari M (1996) The quantitative incidence function model and persistence of an endangered butterfly metapopulation. Conserv Biol 10:587–758CrossRefGoogle Scholar
  34. Harabiš F, Dolný A (2012) Human altered ecosystems, suitable habitats as well as ecological traps for dragonflies (Odonata), the matter of scale. J Insect Conserv 16:121–130CrossRefGoogle Scholar
  35. Harrison S (1991) Local extinction in a metapopulation context, an empirical evaluation. Biol J Linnaean Soc 42:73–88CrossRefGoogle Scholar
  36. Hedgren PO (2007) Early arriving saproxylic beetles (Coleoptera) and parasitoids (Hymenoptera) in low and high stumps of Norway spruce. For Ecol Manag 241:155–161CrossRefGoogle Scholar
  37. Hedin J, Isacsson G, Jonsell M, Komonen A (2008) Forest fuel piles as ecological traps for saproxylic beetles in oak. Scandinavian J For Res 23:348–357CrossRefGoogle Scholar
  38. Holt RD (1985) Population dynamics in two-patch environments: some anomalous consequences of an optimal habitat distribution. Theor Popul Biol 28(2):181–208CrossRefGoogle Scholar
  39. Holt RD (1996) Adaptive evolution in source-sink environments: direct and indirect effects of density-dependence on Niche evolution. Oikos 75(2):182CrossRefGoogle Scholar
  40. Horák J, Tezcan S, Mico E, Schmidl J, Petrakis P (2010) Rosalia alpina. The IUCN Red List of Threatened Species 2010. http://www.iucnredlist.org
  41. Horváth G, Malik P, Kriska G, Wildermuth H (2007) Ecological traps for dragonflies in a cemetery, the attraction of Sympetrum species (Odonata, Libellulidae) by horizontally polarizing black gravestones. Freshw Biol 52:1700–1709CrossRefGoogle Scholar
  42. Hughes JB, Daily GC, Ehrlich PR (2000) Conservation of insect diversity: a habitat approach. Conserv Biol 14:1788–1797CrossRefGoogle Scholar
  43. IUCN (2014) World Conservation Monitoring Centre 1996. Rosalia alpina. The IUCN Red List of Threatened Species. Version 2014.3. http://www.iucnredlist.org, 28 Nov 2016.
  44. Jaworski A, Kołodziej Z (2004) Beech (Fagus sylvatica L.) forests of a selection structure in the Bieszczady Mountains (southeastern Poland). J For Sci 50:301–312CrossRefGoogle Scholar
  45. Jonsell M (2008) Saproxylic beetle species in logging residues, which are they and which residues do they use? Nor J Entomol 55:109–122Google Scholar
  46. Jonsell M, Hansson J, Wedmo L (2007) Diversity of saproxylic beetle species in logging residues in Sweden—comparisons between tree species and diameters. Biol Conserv 138:89–99CrossRefGoogle Scholar
  47. Kempenaers B, Borgström P, Loës P (2010) Artificial night lighting affects dawn song, extra-pair siring success, and lay date in songbirds. Curr Biol 20:1735–1739CrossRefPubMedGoogle Scholar
  48. Kokko H, Sutherland WJ (2001) Ecological traps in changing environments, Ecological and evolutionary consequences of a behaviourally mediated Allee effect. Evol Ecol Res 3:537–551Google Scholar
  49. Kondracki J (2011) Geografia regionalna Polski. PWN, WarszawaGoogle Scholar
  50. Kozak I, Menshutkin V, Jóźwina M, Potaczała G (2002) Computer simulation of fir forest dynamics in the Bieszczady Mountains in response to climate change. J For Sci 48:425–431Google Scholar
  51. Kuhnert PM (2011) Four case studies in using expert opinion to inform priors. Environmetrics 22:662–674CrossRefGoogle Scholar
  52. Lachat T, Ecker K, Duelli P, Wermelinger B (2013) Population trends of Rosalia alpina (L.) in Switzerland, a lasting turnaround? J Insect Conserv 17(4):653–662CrossRefGoogle Scholar
  53. Lassauce A, Lieutier F, Bouget C (2012) Woodfuel harvesting and biodiversity conservation in temperate forests, Effects of logging residue characteristics on saproxylic beetle assemblages. Biol Cons 147:204–212CrossRefGoogle Scholar
  54. Lawton JH (1993) Range, population abundance and conservation. Trends Ecol Evol 8:406–413CrossRefGoogle Scholar
  55. Longcore T, Rich C (2004) Ecological light pollution. Front Ecol Environ 2:191–198CrossRefGoogle Scholar
  56. Lu N, Jia CX, Lloyd H, Sun YH (2012) Species-specific habitat fragmentation assessment, considering the ecological niche requirements and dispersal capability. Biol Conserv 152:102–109CrossRefGoogle Scholar
  57. Martin TG, Burgman MA, Fidler F, Kuhnert PM, Low-Choy S, McBridge M, Mengersen K (2012) Eliciting expert knowledge in conservation science. Conserv Biol 26:29–38CrossRefPubMedGoogle Scholar
  58. Michalcewicz J, Ciach M (2012) Ochrona nadobnicy alpejskiej Rosalia alpina (L.) (Coleoptera, Cerambycidae) w Polsce—aktualne problemy i sposoby ich rozwiązania. Chrońmy Przyrodę Ojczystą 68:347–357Google Scholar
  59. Michalcewicz J, Ciach M (2015) Current distribution of the Rosalia longicorn Rosalia alpina (LINNAEUS, 1758) (Coleoptera, Cerambycidae) in Poland. Pol J Entomol 84:9–20Google Scholar
  60. Michalcewicz J, Ilek A, Szafarska J, Wach A (2011) Nadobnica alpejska Rosalia alpina (L.) (Coleoptera, Cerambycidae) w Nadleśnictwie Łosie (SE Polska)—rozsiedlenie, wybrane aspekty ekologii, zagrożenia i ochrona gatunku. Acta Agraria et Silvestria series Silvestria 49:25–34Google Scholar
  61. Miller JR, Fraterrigo JM, Hobbs NT, Theobald DM, Wiens JA (2001) Urbanization, avian communities, and landscape ecology. In: Marzluff JM, Bowman R, Donnely R (eds) Avian ecology and conservation in an urbanizing world. Springer, Boston, pp 117–137CrossRefGoogle Scholar
  62. Müller J, Hothorn T, Pretzsch H (2007) Long-term effects of logging intensity on structures, birds, saproxylic beetles and wood-inhabiting fungi in stands of European beech Fagus sylvatica L. For Ecol Manag 242:297–305CrossRefGoogle Scholar
  63. Nelson E, Mendoza G, Regetz J, Polasky S, Tallis H, Cameron D, Chan KMA, Daily GC, Goldstein J, Kareiva PM, Lonsdorf E, Naiduoo E, Ricketts TH, Shaw MR (2009) Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Front Ecol Environ 7:4–11CrossRefGoogle Scholar
  64. Newcombe RG (1998) Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med 17:857–872CrossRefPubMedGoogle Scholar
  65. Nieto A, Alexander KNA (2010) European red list of Saproxylic Beetles. Publications Office of the European Union, LuxemburgGoogle Scholar
  66. Norris DR, Flockhart DT, Strickland D (2013) Contrasting patterns of survival and dispersal in multiple habitats reveal an ecological trap in a food-caching bird. Oecologia 173(3):827–835CrossRefPubMedGoogle Scholar
  67. Oaks JL, Gilbert M, Virani MZ, Watson RT, Meteyer BA, Rideout BA, Shivaprasad HL, Ahmed S, Chaudhry MJI, Arshad M, Mahmood S, Ali A, Khan AA (2004) Diclofenac residues as the cause of vulture population decline in Pakistan. Nature 427:630–633.  https://doi.org/10.1038/nature02317 CrossRefPubMedGoogle Scholar
  68. Ovaskainen O (2004) Habitat-specific movement parameters estimated using mark-recapture data and diffusion model. Ecology 85:242–257CrossRefGoogle Scholar
  69. Ovaskainen O (2008) Analytical and numerical tools for diffusion—based movement models. Theor Popul Biol 73:198–211CrossRefPubMedGoogle Scholar
  70. Parliament of the Republic of Poland (2004). Ustawa o ochronie przyrody [Nature conservation law]. Dziennik Ustaw 90:poz 880Google Scholar
  71. Pawłowski J (2008) Reliktowe chrząszcze Coleoptera “Puszczy Karpackiej”. Roczniki Bieszczadzkie 16:317–324Google Scholar
  72. Porter K, Ellis S (2011) Securing viable metapopulations of the marsh fritillary butterfly, Euphydryas aurinia,(Lepidoptera: Nymphalidae) in Northern England. J Insect Conserv 15(1–2):111–119CrossRefGoogle Scholar
  73. Pulliam HR (1988) Sources, sinks, and population regulation. Am Nat 132:652–661CrossRefGoogle Scholar
  74. R Core Development Team (2015). R, A language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing. http://www.R-project.org, 13 May 2016.
  75. RDLP (2015a) Prognoza oddziaływania na środowisko planu urządzania lasu nadleśnictwa Lutowiska. Regional Directorate of State Forests, KrosnoGoogle Scholar
  76. RDLP (2015b) Prognoza oddziaływania na środowisko planu urządzania lasu nadleśnictwa Stuposiany. Regional Directorate of State Forests, KrosnoGoogle Scholar
  77. Robertson BA, Hutto RL (2006) A framework for understanding ecological traps and an evaluation of existing evidence. Ecology 87:1075–1085CrossRefPubMedGoogle Scholar
  78. Robertson B, Kriska G, Horváth V, Horváth G (2010) Glass Buildings As Bird Feeders, Urban Birds Exploit Insects Trapped By Polarized Light Pollution. Acta Zoologica Academiae Scientiarum Hungaricae 56:283–293Google Scholar
  79. Robertson BA, Rehage JS, Sih A (2013) Ecological novelty and the emergence of evolutionary traps. Trends Ecol Evol 28:552–560CrossRefPubMedGoogle Scholar
  80. Rudnick D, Ryan SJ, Beier P, Cushman SA, Dieffenbach F, Epps CW, Gerber LR, Hartter J, Jenness JS, Kints J, Merenlender AM, Perkl RM, Preziosi DV, Trombulak SC (2012) The role of landscape connectivity in planning and implementing conservation and restoration priorities. Issues in ecology. Report No. 16. Ecological Society of America, Washington, DCGoogle Scholar
  81. Russo D, Cistrone L, Garonna A (2011) Habitat selection by the highly endangered long-horned beetle Rosalia alpina in Southern Europe, a multiple spatial scale assessment. J Insect Conserv 15:685–693CrossRefGoogle Scholar
  82. Sadahiro Y (2005) Buffer operation on spatial data with limited accuracy. Trans GIS 9(3):323–344CrossRefGoogle Scholar
  83. Sama G (2002) Atlas of the Cerambycidae of Europe and the Mediterranean Area. Volume 1, Northern, Western, Central and Eastern Europe. British Isles and Continental Europe from France (excl. Corsica) to Scandinavia and Urals. Nakladatelství Kabourek, ZlínGoogle Scholar
  84. Schnell JK, Harris GM, Pimm SL, Russell GJ (2013) Estimating extinction risk with metapopulation models of large scale fragmentation. Conserv Biol 27:520–530CrossRefPubMedGoogle Scholar
  85. Shapovalov AM (2012) Longicorn-beetles (Coleoptera, Cerambycidae) of Orenburg Region, fauna, distribution, bionomy. Archives of Orenburg Branch of Russian Entomological Society, 3. Orenburg Branch of Russian Entomological Society, OrenburgGoogle Scholar
  86. Starzyk JR (2004) Rosalia alpina (Linnaeus, 1758). In: Głowaciński Z, Nowacki P (eds) Polish red data book of animals invertebrates. IOP PAN, AR Poznań, Kraków, pp 148–149Google Scholar
  87. Sutherland WJ, Freckleton RP, Godfray HCJ, Beissinger SR, Benton T, Cameron DD, Carmel Y, Coomes DA, Coulson T, Emmerson MC, Hails RS, Hays GC, Hodgson DJ, Hutchings MJ, Johnson D, Jones JPG, Keeling MJ, Kokko H, Kunin WE, Lambin X, Lewis OT, Malhi Y, Mieszkowska M, Milner-Gulland EJ, Norris K, Phillimore AB, Purves DW, Reid JM, Reuman DC, Thompson K, Travis JMJ, Turnbull LA, David A, Wardle DA, Wiegand T (2013) Identification of 100 fundamental ecological questions. J Ecol 101:58–67CrossRefGoogle Scholar
  88. Svensson GP, Larsson MC (2008) Enantiomeric specificity in a pheromone–kairomone system of two threatened saproxylic beetles, Osmoderma eremita and Elater ferrugineus. J Chem Ecol 34:189–197CrossRefPubMedGoogle Scholar
  89. Tini M, Bardiani M, Chiari S, Campanaro A, Maurizi E, Toni I, Mason F, Audisio PA, Carpaneto GM (2018) Use of space and dispersal ability of a flagship saproxylic insect: a telemetric study of the stag beetle (Lucanus cervus) in a relict lowland forest. Insect Conserv Divers 11(1):116–129CrossRefGoogle Scholar
  90. Weldon AJ, Haddad NM (2005) The effects of patch shape on Indigo Buntings, evidence for an ecological trap. Ecology 86:1422–1431CrossRefGoogle Scholar
  91. Witkowski Z (2007) Krajowy plan zarządzania gatunkiem nadobnica alpejska (Rosalia alpina L.). Ministerstwo Środowiska, WarszawaGoogle Scholar
  92. Žunic-Kosi A, Zou Y, Hoskovec M, Vrezec A, Stritih N, Millar JG (2017) Novel, male-produced aggregation pheromone of the cerambycid beetle Rosalia alpina, a priority species of European conservation concern. PLoS ONE 12(8):e0183279CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Institute of Nature ConservationPolish Academy of SciencesKrakówPoland
  2. 2.Institute of Forest Ecosystem ProtectionUniversity of AgricultureKrakówPoland
  3. 3.Fundacja “Dzika Polska”BiałowieżaPoland
  4. 4.Department of Forest Biodiversity, Institute of Forest Ecology and Silviculture, Faculty of ForestryUniversity of AgricultureKrakówPoland

Personalised recommendations