Bat species richness and activity in forest habitats close to lakes versus far from lakes, in Sweden

Full text

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 Degree project work  

Bat species richness and activity in

forest habitats close to lakes versus far from lakes,

in Sweden

Silvia Zuniga

Supervisors: Johnny de Jong/

Roland Engkvist

Examiner: Andreas Svensson Semester: VT13, 2013:Bi12 Subject: Biology

Level: First cycle Course code: 2BI01E

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Abstract

The long-term effects of large-scale changes in forestry, agriculture and other land use on habitats and the large-scale expansion of wind farming affects bats foraging environments. In order to predict consequences of exploitations on local bat species and populations, good surveys are important. To get good background information for an Environmental Impact Assessment (EIA) it is crucial to rapidly assess which areas are most important for bats. The aim of this work was to measure the importance of the two types of forest environment for bats foraging : forest areas located close to or far from the lakes. Bat activity and species diversity was measured with automatic ultrasound recorders in 211   nights of fieldwork at 155 locations in 23 areas in different parts of Sweden during June, July and the first two weeks of August 2011 and 2012. A total of 11 species were recorded in forest far from lakes and 8 species in forest close to lakes. Eptesicus nilssonii , Myotis sp. and Pipistrellus pygmaeus were the most common taxa in both habitat types. Activity levels were higher in the vicinity of lakes compared to forests far away from lakes. Species diversity calculated on base on Chao 2 was similar for both types of habitats . The results suggest that the forests close to lakes are the most important habitats to surveys for bats in Sweden and that inventory efforts should be primarily invested in them.

Abstrakt

All markanvändning, som t.ex. skogs- och jordbruk, samt annan exploatering som t.ex. vindkraft, påverkar fladdermössens födosöksmiljöer. För att kunna förutse konsekvenserna av olika åtgärder i landskapet, t.ex. vid framtagandet av en miljökonsekvensbeskrivning, krävs inventeringar av god kvalitet, dock är ofta resurserna och tiden begränsade. Syftet med det här arbetet var att undersöka betydelsen av två olika skogliga miljöer för fladdermöss: skogsområden som ligger långt från sjöar, samt skogsområden som ligger nära sjöar. Aktiviteten av fladdermöss samt art sammansättningen mättes med automatisk ultraljudsregistrering under 211 nätter inom 23 undersökningsområden i olika delar av Sverige under juni, juli och de två första veckorna i augusti 2011 och 2012. Inom varje undersökningsområde mättes fladdermusförekomsten på flera lokaler, och totala antalet lokaler uppgick till 155. Totalt 11 arter registrerades på lokaler nära sjöar, och 8 arter på lokaler långt ifrån sjöar. Nordisk fladdermus (Eptesicus nilssonii), Myotis spp. och dvärgfladdermus (Pipistrellus

pygmaeus) var de tre vanligaste taxor i båda miljöerna. Aktivitetsnivån var signifikant högre i miljöer

nära sjöar, medan det inte var någon skillnad i artantal. Resultatet visar att det är viktigast att koncentrera inventeringar till skogsområden nära sjöar eftersom det då är störst chans att hitta alla förekommande arter inom inventeringsområdet.

Key words:

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Table of contents

1 Introduction ……… 4

2 Method ………... 7

Fig. 1 Forest far from lakes locations……… 9

Fig. 2 Forest close from lakes locations. ………... 10

3 Result………..…… 11

Table 1 Bat species recorded by ultrasound detectors ………. 11

Fig. 3 Bat activity index……… 12

Fig. 4 Species composition ……….. 13

4 Discussion and Conclusions………... 13

5 Acknowledgements……… 15

6 Reference……… 16

7. Appendix 1………... 22

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1. Introduction

Bats are the major predator of night-flying insects; for example a single big brown bat (Eptesicus fuscus) can eat between 3,000 and 7,000 insects in a night, with large populations of bats consuming thousands of tons of potentially harmful forest and agricultural pests annually (Jones, E.J., Megalos, M.A. & Turner, J.C. 2013). Since bats have slow reproductive rates with typically only one offspring cycle are vulnerable to perils introduced by man.

The long-term effects of large-scale changes in forestry, agriculture and other land use on habitats are the main potential threat for bats (Ahlén et al. 2007). In order to predict consequences of exploitations on local bat species and populations, good surveys are important. Often time is limited and it is necessary to select some habitats or areas for the survey, which are regarded as the most important, Thus to identify optimal habitats for surveys is a key factor for high quality Environment Impact Assessment (EIA).

In Sweden, the need for EIA has recently increased considerably because the establishment of wind farms. Wind farming is rapidly expanding in Sweden as well in many other countries, as part of the move towards green energy in an effort to reduced CO2 emissions (Rydell et al. 2012). During the last few year from various countries shown a significant number of bats killed by wind turbines (Dürr & Bach 2004; Arnett 2005; Johnson 2005; Behr & Helversen 2005; Brinkmann et al., 2006; Dürr 2007).

Bat Conservation International has reported in 2007 a mortality rate between of 0.9 - 0.6 individuals per turbine by night at two Wind turbines parks in West Virginia and Pennsylvanian in U.S.A. A total of 457 wind turbines, were monitored, counting 14 538 bats killed per year (based on a 6-week period), it was suggested that earlier investigations have underestimated the number of bat casualties at wind farms (Ahlén et al. 2007). Bats are killed because of direct impact with the wings of the wind turbine as well as from barotrauma when flying in close proximity to turbine blades (Baerwald et al., 2008)

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5   Bats selection of foraging habitat varies among species depending on their body size, wing morphology, foraging mode, and echolocation call structure (Aldridge and Rautenbach 1987; Crome and Richards 1988; Fenton 1990). In general, larger species with bigger wing structure and wing loading are expected to forage in open habitats, while smaller species with reduced aspect ratios and wing loading are expected to use more cluttered environments (Loeb & O’Keefe 2006).

Bats tend to be specialized in their choice of roosting habitat most species roosting either in the cavities of trees, snags, caves, buildings, behind hanging tiles, boarding or in roofs spaces (Ethier and Fahring 2011; Kuns and Fenton 2003). In Sweden, however all species roost in trees (except Northern at, E. nilssonii) or in houses (except the Common noctule, Nyctalus

noctula) (de Jong, pers. Comm. 2013). Bats forage in forest areas, forest gaps, open areas, and

along forest edges, depending on variation in prey availability and spatial clutter (Furlonger et

al. 1987; Wunder and Carey 1996; Patriquin and Barclay 2003; Morris et al. 2010). In

Europe, for instance: while grater mouse-eared bat (Myotis myotis) hunts predominantly in forest, its nursery roosts mostly are located in buildings in human settlement areas. However, other species, such as Bechstein’s bat (Myotis bechesteinii), spend their entire life in the forest and are thus dependent on suitable tree holes for their day roost. Other species, such as the grey long-eared bat (Plecotus austriacus) and the lesser mouse-eared bat (Myotis oxygnathus) in Central Europe use buildings as roost and hunt particularly in the open countryside (Dietz

et al. 2011).

In the forest the spatial structure of this habitat type is an important factor for the resource partitioning of different bat species. Dense vegetation is avoided by many species. Often tree lines, forest edges or hedges are used as guidance structures for the fight from roost to hunting grounds – but also for hunting, since such structures accumulate many flying insects in the evening (Dietz et al. 2011). Some species, such as the lesser horseshoe bat (Rhinolophus

hipposideros), whiskered and Brandt’s bat (Myotis mystacinus and M. brandtii) as well as the

long eared bats (genus Plecotus), have adapted their flight style to fly through the smallest gaps in the vegetation, while others, such as mouse-­‐eared   bat   (Myotis   oxygnathus)   are   more  adapted  to  open  or  semi  open  habitats (Dietz et al., 2011).

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Water surfaces, from ponds and lakes to smaller streams or pool in the forest, provide a particularly rich supply of insects for bats (Fuki et al. 2006; Dietz et al. 2011) given that supplemental water increases plant diversity and production in arid environments, resulting in higher insect densities (Wenninger and Ionuye 2008). Many flying insects develop in water and when the adults hatch, it offers a rich food supply for bats and birds. When many flies or caddisflies stayed over water, not only the common Daubenton’s bats (Myotis daubentonii) come to take advantage of this resource, but at the waterside there are additionally long-eared bats, all small Myotis species, common pipistrelle (Pipistrelle pipistrellus) and Nathusiu’s pipistrelle (P. nathusii) and, high above, the noctule bat (Nyctalus spp.) (Dietz et al. 2011).

A growing number of publications are gradually revealing the habitat preference of bat species around the word (e.g. Meyer, Schwarz & Fahr 2004; Ford et al. 2005; Sattler et al. 2007), and for many species ponds, lakes and rivers habitats are emerging as the most important foraging habitats (Vauhan et al. 1997;Grindal, Morisette & Brigham 1999; Russo & Jones 2003; Kusch et al. 2004; Zukal & Rehäk 2006; Monadjem & Reside 2009; Greif & Siemers 2010; Bellamy et at. 2012). Bats benefit from roosting as close as possible to foraging grounds (Barclay 1989). Consequently, the highest populations of bats should be present in areas where both roost and foraging habitats are present (Dunning et al.1992).

Researches in Sweden confirm the importance of water (de Jong & Ahlén 1991). However, recent observations (de Jong, pers. Com. 2013) indicate that the abundance of bats near water mainly is explained by some very dominating species, while other species seem to be absent or very rare. If so, not only forest habitats close to water are important but also some other habitats far from water, an this will be important information for the development of EIA. In this paper I test the hypothesis that forest close to water are the most important habitats to survey for bats in Sweden by investigate  the  species  richness  and  activity  of  bats  in  forest   close  to  lakes  and  far  from  lakes.  

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2. Method

Trained bat inventory personal at Ecocom AB collected the data used in this study in June 2011 and August 2012, in order to evaluate sites suggested for wind park establishment in Sweden. Sampling was restricted to the end of June, July and first two weeks of August, as this is the main activity period for bats in Sweden and the time when bats are using the area close to the colonies (de Jong, pers. Com. 2013).

 

Bats were recorded by using automatic ultrasound recorders, Pettersson D500X   (Petterson Elektronik AB, Uppsala, Sweden), which was set to record ultrasound emissions during the whole night. Bats were identified by the program Omnibat   (www.omnibat.se) which automatically distinguishes calls from other ultrasounds, and it also provides a preliminary species separation. Bats species richness and activity in each habitat were measured by counting the number of recording of bats. Thus, the ultrasound detectors only measure the activity, not the number of individuals. (Kunz et al. 2007).

Species of the genus Myotis are difficult to separate by their sound. This is especially true for the three most common species, Myotis daubentonii, M. mystacinus and M. brandtii. Therefore in this study these species were clumped together as Myotis spp.

Automatic ultrasound recorders had the following recording specifications: sensitivity = very high, sample frequency = 500 kHz, pretrig (off), rec-length = 3, HP filter = y, autorec = y, input gain = 60, trigger lvl = 30, and interval = 5. These settings generated a lot of "junk noise" but are very sensitive to maximize prospects capturing bat sounds from 'quiet' species or species that pass by at great distance (A. Eriksson, pers. Comm.).

In total sampling were carried out at 23 study areas ( in north, central and south Sweden), divided into 155 sites (one site is a place where one ultrasound detector was placed). I divided the 155 sites in two categories: forest close to lakes (deciduous, pines, productive or old forest, close to lakes) and forest far from lakes (deciduous, pines, productive or old forest, far from lakes). The distance between lakes and forest was not measured and the position of each site was determinate by maps and Ecocom reports.

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Chao 2, a non-parametric estimator was used to compare the species richness based on the occurrence (presence/absence) of the bat species between forest close to lakes and forest far from lakes. Chao 2 is a rarefaction method which make it possible to compare the number of species and number if individuals in different habitats, in which the sampling effort is different (Chao 1984; Gotelli and Colwell 2010; Chao and Lin 2012).

Since I would expect that greater sampling effort would yield a large sample and more species, I can´t only compare the number of species found in each habitat. Chao 2 uses the data from a large sample to answers the question “How many species would have been found in a smaller sample?” If I found n organisms in the less sampled habitat, Chao 2 takes hypothetical subsamples of n organisms from the more sampled habitat, and calculates the average number of species in such subsamples. This average can be compared to the number of species actually found in the less-sample habitat. The method computes a variance and standard deviation to help me judge how significant any difference is (Chao 1984; Gotelli and Colwell 2010; Chao and Lin 2012).

The t test was conducted for each Chao 2 mean values to calculate the difference between the richness in forest close to lakes and forest far from lakes (Gotelli & Cowell 2011).

The chi-squares test was used to compare the activity of the two common species (Pipistrellus

pygmaeus , Eptesicus nilssonii ), the genus Myotis and the group of less common species in

the two different habitats. For all statistical analyses I used a α level of 0.05 (Sokal 2012).

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                        Fig.  1    

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3. Result

14 092 bat sounds were recorded over a total of 211 nights fieldwork. Taxonomic identification was made for 13 867 individuals. In total 11 bat species were identified at forest far from lakes and 8 bat species at forest close to lakes (Table 1).

 

Table 1 Species composition at sites close to versus far from lakes. T (n) = number of nights of fieldwork and SA (n) = number of study areas.

 

  Species  

Forest  close  to   lakes   T  (n)  =  36   SA  (n)  =  12  

Forest  far        from   lakes   T  (n)  =  175   SA  (n)  =  19  

Abundant species    

Myotis  sp. +   +  

Pipistrellus  pygmaeus  (Leach,  1825)   +   +  

Eptesicus  nilssonii  (Keyserling  &  Blasius,  1839)   +   +  

Less common species    

Pipistrellus  nathusii  (Keyserling  &  Blasius,  1839)   +   +  

Pipistrellus  pipistrellus  (Schreber,  1774)     +  

Nyctalus  noctula  (Schreber  1774)   +   +  

Nyctalus  leisleri  (Kuhl,  1817)     +  

Eptesicus  serotinus  (Schreber,  1774)     +  

Vespertilio  murinus  (Linnaeus,  1758)   +   +  

Barbastella  barbastellus  (Schreber,  1774)   +   +  

Plecotus  auritus  (Linnaeus,  1758)   +   +  

Bats activity was greater in forest close to lakes than forest far from lakes (X2 = 9,75, df = 3, p ≤ 0.05) (Fig. 3). The taxa with highest activity were Eptesicus nilssonii, Myotis sp. and

Pipistrellus pygmaeus, while other taxa were less common (Fig. 3 and 4).

The species richness was calculated base on the Chao 2 estimator exhibited that there are no statistics different between both habitats (Chao 2 mean for forest close to lakes = 1.52 ± 0.70 Chao 2 mean for forest far from lakes = 1.38 ± 0.19; t = 0.41, gl = 17, p ≥ 0.05).

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Fig. 3 Bat activity index (number of bat observations divided by the number of monitoring

nights) of 13 867 bats sounds recorded from Eptesicus nilssonii, the genus Myotis,

Pipistrellus pygmaeus and the group of less common species in forest close to lakes and forest

far from lakes (X2 = 9.75, df = 3, p ≤ 0.05, T (n) = 211).

0   10   20   30   40   50   60   70   80  

Eptesicus  nilssonii   Myotis  sp.     Pipistrellus  

pygmaeus   Less  common  species  

Acti vi ty  i ndex   Species  

Forest  close  to  lakes   Forest  far  from  lakes  

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Fig. 4 Species composition at forest close to lakes (a) and forest far from lakes (b) based on the number of passes (relative activity) of bats in 211  nights of fieldwork.

4. Discussion and conclusion

This study demonstrates that, even though the species richness is the same in forests far from lakes and in forests close to lakes, the activity of bats is much higher close to lakes. Thus, the hypothesis is accepted concerning richness, but not concerning the activity. The results emphasize that forests close to lakes are important to include in surveys.

Why is there a higher activity of bats in forests close to lakes than forests far from lakes in Sweden? I suggest it is due to the higher concentrations and may be higher diversity of insects in this habitat. Different bat species are well known to have separate ecological foraging niches. This enables them to concentrate in forest close to lakes. Although I did not measure prey abundance over forests close to lakes, habitats with high abundance of nocturnal insects close to lakes has been demonstrated in many other studies (Russo & Jones, 2003; Almenar

et al., 2006; Rainho, 2007; Rebelo & Rainho, 2009; Lisón & Calvo, 2011). In Sweden, de

Jong and Ahlén (1991) has shown that bats abundance and species diversity in the landscape

Myostis  sp.   40.5  %   Eptesicus   nilssonii   46.7%   Pipistrellus   pygmaeus   10.75%   Other  sp.   2.03%  

 (a)  

Myotis  sp.   16%   Pipistrellus   pygmaeus   18%   Eptesicus   nilssonni   55%   Other  sp.   11%  

(b)  

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is related to insects abundance. Flying insects that emerge from the surface of water bodies is plentiful and a predictable supply of prey for bats (Grindal, et al., 1999). Bats forage to a great extent “opportunistically”, taking prey that can be detected (Dietz et al. 2011).

I   found   that   Eptesicus   nilssonii,   Myotis   sp.   and   Pipistrellus   pygmaeus   were   the   most   abundant   taxa   in   both   habitats.   These   tree   taxa   are   aerial   insectivores   bats   occupying   tree  different  guilds  (food,  feeding  method  and  habitat  selection;  Petterson  et  al.,  2005).   The   occupation   of   different   guilds   may   explain   why   they   are   so   abundant,   this   phenomenon  is  know  as  density  compensation  (Crowell,  1962;  Hawkins  and  MacMahon,   1989).  

It  is  likely  that  the  majority  of  individuals  identified  as  Myotis  sp.  belongs  to  the  Myotis  

daubentonni,  M.  brandtii  or  M.  mystacinus.  Close  to  water  the  most  common  Myotis  is  M.   daubentonii,   while   the   other   two   species   are   more   common   far   from   water.   However,  

any  of  them  could  occur  both  close  and  far  from  water  (de Jong, pers. Com. 2013).  There   is  also  a  small  risk  that  other  species  of  Myotis,  such  as  M.  nattereri  and  M.  dasycneme,   are  hiding  within  Myotis  sp.  There  are  some  uncertainties  in  the  identifications  when  we   comparing  the  number  of  species  and  the  different  habitats  in  the  genus  Myotis.    

To conclude my data strongly support the hypothesis that forests close to water are the most important habitats to survey for bats in Sweden. Survey and monitoring these habitats will provide information about temporal availability of resources, the rates of change of these resources and the influence of habitat change on bat species found in Sweden, which is important information for the development of EIA.

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5. Acknowledgements

My thesis would never have been finished without the huge supervisor and support of: Johnny De Jong

Research Group Leader, CBM, Uppsala. Ecocom AB i Kalmar

Alexander Eriksson Amie Ringberg Sofia Nygårds

I adress special thanks to the following companies for allowing me to use their inventary information.

Arise AB Bjäre Kraft AB DGE Mark & Miljö E. ON Vind Sverige AB Energi och miljöstrategi Fennicus Natur

Gamesa Energy Sweden AB Green Extreme AB

Kraftö Vind AB (WSP) O2 Vindkompaniet AB Samkraft AB

Statkraft Södra Vindkraft AB Tranås Energi AB

VindIn AB

Vindstrategi Energi och Miljösstrategi AB Vindvision Norr AB

wpd Scandinavia AB

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Appendix  1.  

Summary  of  inventory  sites  in  2011  in  forest  close  to  lakes  and  forest  far  from  lakes  in   Sweden.  ID  N  =  ultrasound  detector  number  T  (n)  =  number  of  nights of fieldwork.

Forest close to lakes

Study areas DATUM

T(n) Jönköping 2011-07-29 2011-07-30 2011-07-31 3 Jönköping 2011-07-29 2011-07-30 2011-07-31 3 Gävle 2011-07-07 2011-07-08 2 Tydlige 2011-07-04 1 Ydre 2011-07-03 1 Ydre 2011-07-03 1 Ydre 2011-07-03 2011-07-04 2

Forest far from lakes

Jönköping 2011-07-29 2011-07-30 2011-07-31 3 Jönköping 2011-07-29 2011-07-30 2011-07-31 3 Gävle 2011-07-07 1 Gävle 2011-07-07 1 Gävle 2011-07-07 1 Gävle 2011-07-07 1 Gävle 2011-07-07 2011-07-08 2 Gävle 2011-07-07 2011-07-08 2 Mönsterås 2011-06-26 2011-06-27 2 Ydre 2011-07-03 2011-07-04 2 Σ T (n) 31

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23  

Appendix  2.  

Summary  of  inventory  sites  in  2012  in  forest  close  to  lakes  and  forest  far  from  lakes  in   Sweden.  ID  N  =  ultrasound  detector  number  T  (n)  =  number  of  nights of fieldwork.  

Study areas DATUM T(n)

Forest close to lakes

Boxholm 2012-07-19 1 Boxholm 2012-07-19 1 Gällivare/Kiruna 2012-07-30 1 Gällivare/Kiruna 2012-07-30 1 Gällivare/Kiruna 2012-07-31 1 Avesta 2012-07-27 1 Avesta 2012-07-28 1 Avesta 2012-07-28 1 Avesta 2012-07-28 1 Hofors 2012-07-29 1 Hofors 2012-07-29 1 Hofors 2012-07-29 1 Hofors 2012-07-30 1 Hofors 2012-07-31 1 Boxholm 2012-07-12 1 Boxholm 2012-07-11 1 Ulricehamn 2012-07-16 1 Ulricehamn 2012-07-17 1 Ulricehamn 2012-07-17 1 Svenljunga 2012-06-30 1 Svenljunga 2012-09-18 1 Svenljunga 2012-09-18 1

Forest close from lakes1

Söderhamn 2012-07-23 1 Söderhamn 2012-07-23 1 Nordmaling 2012-07-18 1 Nordmaling 2012-07-18 1 Nordmaling 2012-07-19 1 Nordmaling 2012-07-20 1 Nordmaling 2012-07-20 1 Nordmaling 2012-07-20 1 Gällivare/Kiruna 2012-07-30 1 Gällivare/Kiruna 2012-07-31 1 Gällivare/Kiruna 2012-07-31 1 Söderhamn 2012-07-23 1 Söderhamn 2012-07-24 1 Söderhamn 2012-07-24 1 Nordmaling 2012-07-18 1

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      Nordmaling 2012-07-20 1 Gällivare/Kiruna 2012-07-30 1 Gällivare/Kiruna 2012-07-31 1 Gällivare/Kiruna 2012-07-31 1 Avesta 2012-07-27 1 Avesta 2012-07-28 1 Avesta 2012-07-28 1 Avesta 2012-07-28 1 Hofors 2012-07-29 1 Avesta 2012-07-27 1 Avesta 2012-07-27 1 Avesta 2012-07-28 1 Avesta 2012-07-27 1 Avesta 2012-07-27 1 Avesta 2012-07-27 1 Avesta 2012-07-27 1 Avesta 2012-07-27 1 Avesta 2012-07-27 1 Avesta 2012-07-27 1 Avesta 2012-07-28 1 Avesta 2012-07-28 1 Avesta 2012-07-28 1 Avesta 2012-07-28 1 Avesta 2012-07-30 1 Hofors 2012-07-30 1 Hofors 2012-07-31 1 Hofors 2012-07-31 1 Hofors 2012-07-31 1 Hofors 2012-07-31 1 Nässjö 2012-07-16 1 Nässjö 2012-07-17 1 Nässjö 2012-07-16 1 Nässjö 2012-07-17 1 Nässjö 2012-07-16 1 Nässjö 2012-07-17 1 Boxholm 2012-07-11 1 Boxholm 2012-07-12 1 Boxholm 2012-07-11 1 Boxholm 2012-07-12 1 Boxholm 2012-07-11 1 Boxholm 2012-07-12 1 Boxholm 2012-07-11 1 Torsås 2012-07-03 1 Torsås 2012-07-03 1 Torsås 2012-07-03 1

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      25   Torsås 2012-07-03 1 Torsås 2012-07-03 1 Torsås 2012-07-03 1 Torsås 2012-07-03 1 Torsås 2012-07-03 1 Torsås 2012-07-04 1 Torsås 2012-07-04 1 Torsås 2012-07-04 1 Mörbylånga 2012-07-07 1 Mörbylånga 2012-07-12 1 Mörbylånga 2012-07-12 1 Mörbylånga 2012-07-12 1 Ulricehamn 2012-07-16 1 Ulricehamn 2012-07-17 1 Ulricehamn 2012-07-16 1 Ulricehamn 2012-07-17 1 Ulricehamn 2012-07-17 1 Ulricehamn 2012-07-18 1 Ulricehamn 2012-0717 1 Kalmar/ Nybro 2012-07-18 1 Kalmar/ Nybro 2012-07-18 1 Kalmar/ Nybro 2012-07-16 1 Kalmar/ Nybro 2012-07-16 1 Kalmar/ Nybro 2012-07-16 1 Kalmar/ Nybro 2012-07-16 1 Kalmar/ Nybro 2012-07-16 1 Kalmar/ Nybro 2012-07-16 1 Kalmar/ Nybro 2012-07-18 1 Kalmar/ Nybro 2012-07-18 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1

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Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Kalmar/ Nybro 2012-08-07 1 Kalmar/ Nybro 2012-08-08 1 Svenljunga 2012-06-28 1 Svenljunga 2012-06-29 1 Svenljunga 2012-06-28 1 Svenljunga 2012-06-28 1 Svenljunga 2012-06-29 1 Svenljunga 2012-06-28 1 Svenljunga 2012-06-28 1 Svenljunga 2012-06-29 1 Svenljunga 2012-06-30 1 Svenljunga 2012-06-30 1 Svenljunga 2012-06-30 1 Svenljunga 2012-09-17 1 Svenljunga 2012-09-17 1 Svenljunga 2012-09-17 1 Svenljunga 2012-09-18 1 Svenljunga 2012-09-17 1 Svenljunga 2012-09-19 1 Svenljunga 2012-09-17 1 Svenljunga 2012-09-18 1 Svenljunga 2012-09-18 1 Svenljunga 2012-09-19 1 Valdemarsvik 2012-07-25 1 Valdemarsvik 2012-07-26 1 Valdemarsvik 2012-07-26 1 Valdemarsvik 2012-07-26 1 Mönsterås 2012-06-26 1 Mönsterås 2012-06-26 1 Mönsterås 2012-06-26 1 Mönsterås 2012-06-26 1 Mönsterås 2012-06-27 1 Mönsterås 2012-06-28 1 Mönsterås 2012-06-28 1 Mönsterås 2012-07-17 1 Mönsterås 2012-07-17 1 Mönsterås 2012-07-17 1 Mönsterås 2012-06-20 1 Mönsterås 2012-06-21 1 Mönsterås 2012-06-20 1 Mönsterås 2012-06-21 1

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27   Mönsterås 2012-07-01 1 Mönsterås 2012-07-01 1 Torsås 2012-07-04 1 Torsås 2012-07-04 1 Torsås 2012-07-04 1 Torsås 2012-07-04 1 Torsås 2012-07-04 1 Σ T (n) 180

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