MD Per Hjerpe
email: per.hjerpe@vgregion.se

PhD Henrik Ohlsson

MD, PhD Ulf Lindblad

MD, PhD Kristina Bengtsson Boström

MD, PhD Juan Merlo

Understanding adherence to therapeutic guidelines: a multilevel analysis on statin prescription in the Skaraborg Primary care database

Keywords: Primary Health Care, Medical Records Systems, Computerized, Drug Prescriptions, Multilevel Analysis, Quality Assurance, Health Care

Purpose: In Skaraborg, Sweden the economical responsibility of tax-financed drug cost was transferred from the regional administration to the local HCC in the year 2003. The aim of this study was to investigate the impact of the decentralization of the economic responsibility on adherence to guidelines on prescription of lipid lowering drugs. Methods: From the Skaraborg Primary Care Database (SPCD) with data from all 24 public health care centres (HCC) in Skaraborg, prescriptions of lipid lowering drugs during 2003 and 2005 were extracted. Multilevel regressions analysis (MLRA) was used to disentangle the variances at different levels of the data (patient, physician, HCC). The outcome variable on the patient level was prescription of recommended statin (y/n). Sex and age of the patients and sex, age and occupational status of the physician was included as fixed effects. The variance was expressed as Median Odds Ratio (MOR). Results: The prevalence of adherence with guidelines for prescription of statins increased from 77 % in 2003 to 84 % in 2005. The MLRA showed that in 2003 the variance was equally distributed between HCC and physician levels (MOR HCC2003 = 1.89 vs. MOR PHYSICIAN2003 = 1.88) in 2003. The variance between physicians and between HCCs decreased considerably between 2003 and 2005. The inclusion of individual and physician characteristics did not explain any of the remaining variance.

Conclusion:

The decentralized budget seemed to increase adherence to guidelines and reduce inefficient variation in prescribing.

Introduction

The use of prescription guidelines aiming to implement general therapeutic standards is today a common feature in many health care systems [START_REF] Grimshaw | Effectiveness and efficiency of guideline dissemination and implementation strategies[END_REF]. The need for guidelines arises from a general understanding that promoting evidence based and efficient medical care may reduce unnecessary medical practice variation and improve quality [START_REF]Appropriat care -aboute management for quality and patient security in Health and Medical care)[END_REF]. Moreover guidelines may also be an aid for prescribers that are hardly able to assimilate the increasing volume of new scientific information [START_REF] Grimshaw | Effectiveness and efficiency of guideline dissemination and implementation strategies[END_REF][START_REF] Brindis | Physician adherence to clinical practice guidelines: does it really matter?[END_REF][START_REF] Koutsavlis | Disseminating practice guidelines to physicians[END_REF][START_REF] Thorsen | Changing professional practice -Theory and practice of clinical guidelines implementation[END_REF]. Even though the investigation of adherence to guidelines is attracting increasing interest, it is still not sufficiently understood what and how factors at different levels of the health care organization condition adherence to guidelines [6][START_REF] Kasje | Physician, organisational and patient characteristics explaining the use of angiotensin converting enzyme inhibitors in heart failure treatment: a multilevel study[END_REF][START_REF] Ohlsson | Understanding adherence to official guidelines on statin prescribing in primary health care-a multi-level methodological approach[END_REF][START_REF] Ohlsson | Understanding the effects of a decentralized budget on physicians compliance with guidelines for statin prescription; a multilevel methodological approach[END_REF].

In Sweden, every county council has a therapeutic committee responsible of the issuing of evidence-based guidelines [START_REF] Sjöqvist | Drug therapeutics committees: a Swedish experience[END_REF]11]. Despite these recommendations several studies have demonstrated substantial and unexplained differences in the adherence with guidelines among physicians and among Health Care Centres (HCC) [START_REF] Ohlsson | Understanding adherence to official guidelines on statin prescribing in primary health care-a multi-level methodological approach[END_REF][START_REF] Ohlsson | Understanding the effects of a decentralized budget on physicians compliance with guidelines for statin prescription; a multilevel methodological approach[END_REF][START_REF] Ohlsson | Therapeutic traditions, patient socio-economic characteristics and physicians' early new drug prescribing -a multilevel analysis of rosuvastatin prescription in South Sweden[END_REF][START_REF] Ohlsson | Is physician adherence to prescription guidelines a general trait of health care practices or dependent on drug type?--a multilevel logistic regression analysis in South Sweden[END_REF]].These differences might express themselves as a clustering of similar prescription behavior among physicians at the same HCC and suggest the existence of local therapeutic traditions. Quantifying and understanding this variation is relevant for the planning of interventions aimed to improve the quality of drug prescription.

In the Region of Skåne, Sweden, the implementation of a decentralised drug budget increased adherence to guidelines and promotes efficient drug therapy [START_REF] Ohlsson | Understanding the effects of a decentralized budget on physicians compliance with guidelines for statin prescription; a multilevel methodological approach[END_REF]. Similarly, in Skaraborg, Sweden the economical responsibility of tax-financed drug cost was transferred from the regional administration to the local HCC in the year 2003. However, the effects of this intervention are still unknown. In the present study, using multilevel regression analyses, and data from the Skaraborg Primary Care Database (SPCD) [START_REF] Hjerpe | Validity of registration of ICD codes and prescriptions in a research database in Swedish primary care A cross-sectional study in Skaraborg primary care database (SPCD)[END_REF] we evaluate the effect of this decentralised drug budget. We focus on adherence to statin (HMG-CoA reductase inhibitors) prescription since this group of cholesterol lowering drugs has very homogeneous indications and similar efficacy which nearly eliminates the possibility of confounding by indication and patient mix when comparing different practices and physicians. We also aimed to disentangle the relevance of different levels (i.e. patients, physicians, HCCs) for understanding adherence with guidelines. Based on previous studies [START_REF] Ohlsson | Understanding the effects of a decentralized budget on physicians compliance with guidelines for statin prescription; a multilevel methodological approach[END_REF] our hypothesis was that the decentralized budget would result in an increased prevalence and a decreased variance between physicians and between HCCs, concerning prescription of recommended statins.

Materials and Methods

The Skaraborg Primary Care Database (SPCD) Skaraborg, one of four administrative areas of the region of Västra Götaland in the southwest of Sweden is mostly rural and it is inhabited by approximately 250 000 individuals within 15 municipalities. Inpatient care is offered by three public hospitals and primary care by 24 public and one private HCC as well as by a few private solo practitioners. About 250 000 office visits are registered at the public HCCs every year. Approximately 75% of all drug prescriptions are issued outside the hospitals and 85% of them at public HCCs.

The SPCD started in 2000 and is based on a common computerized medical journal used by all the 24 publicly administrated HCCs in the area. Among other information all drug prescriptions, laboratory tests, and current diagnosis at every consultation are recorded. The SPCD allows the identification of the HCC, the physician and the patient by a unique anonymized identification number.

Study population

From the SPCD, we identified all patients with at least one prescription of statin defined according to the Anatomical Therapeutical Chemical (ATC) classification system code C10AA [START_REF] Who | About the ATC/DDD system[END_REF]. Thereafter we selected two datasets, (i) patients with at least one statin

Individual level variables

The outcome variable was prescription of recommended statin (yes v. no). In the 2003 dataset, these drugs were Simvastatin (Zocord® or generic simvastatin) and Pravastatin (Pravachol®) and in the 2005 dataset only Simvastatin (Zocord® or generic simvastatin). We categorized the age of the patients into four age groups: -54 years, 55-64, 65-74, and 75-, and used the youngest group as reference in the comparisons. We included the sex of the patients as a dummy variable using women as reference in the analysis. Based on the actual evidence neither age nor sex or any other patient characteristic should motivate the preferential prescription of a specific statin before any other. Therefore, the inclusion of individual-level variables in the analysis was not motivated by the need of adjustment for confounding but rather, because we wanted to gain an understanding of the importance of these factors in the prescribing process.

Physician level variables

Previous studies have shown that prescription behaviour is influenced by prescriber characteristics. We, therefore, included physician's occupational status categorized as Intern (IN), Resident (RS), General practitioner (GP) or Locum (LOC). Each category was split into two groups according to the median age of the specific group (Table 1). Of the eight different groups older GPs were used as reference in the analysis. We included the sex of the physician as a dummy variable using women as reference in the analyses. However, information on physician's characteristics was only available for the 2005 dataset.

Multi-level regression models

We used multilevel logistic regression analysis [START_REF] Goldstein | Multilevel Statistical Models[END_REF]17] to estimate the probability of prescribing a recommended statin, while accounting for the hierarchical structure of the data (i.e., patients nested within physicians nested within HCCs). We developed three consecutive In the random-effects part of the multilevel analysis, we obtained the variance and 95% credible intervals at the physician and the HCC levels and used this information to calculate the median odds ratio (MOR) and 95% credible intervals [START_REF] Goldstein | Multilevel Statistical Models[END_REF][17][START_REF] Larsen | Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression[END_REF][START_REF] Larsen | Interpreting parameters in the logistic regression model with random effects[END_REF]. The MOR translates the variance into the odds ratio (OR) scale, and is thereby comparable with OR of individual or area variables. The MOR can be interpreted as the amount by which a randomly selected patients odds of receiving a recommended statin would increase (in median) if this patient moved to a physician/HCC with higher adherence to guidelines. If the MOR was equal to 1, there would be no differences between physicians or HCCs in the odds of prescribing a recommended statin. If there were important physician-level or HCC differences, the MOR would be large on this level. The study was approved by The Regional Ethical Review Board in Gothenburg.

Results

Table 1 indicates that overall the prevalence of adherence with guidelines for prescription of statins increased from 77 % in 2003 to 84 % in 2005 (Relative Ratio: 1.09 (95% CoI, 1.01 -1.19)). In 2003 older patients had higher adherence to guidelines, but these age differences disappeared in 2005. Men were prescribed statins more often than women, but there were no gender related differences in the prescription of recommended drugs. In 2005, 68% of all the statins were prescribed by male doctors who also showed a slightly lower percentage of prescription of recommended statins than female colleagues. Young intern physicians showed the highest (90%) and older locums the lowest (77%) adherence with guidelines.

Multi-level regression analysis

Table 2 shows the association between on the one hand patient and physician characteristics and on the other prescription of recommended statins according to the multilevel regression analysis. In 2003 adherence to guidelines increased with age of the patients, but in 2005 there were no differences between age groups. Older locum physicians presented a lower probability of prescribing a recommended statin than older GPs Model A in table 3, only includes two levels (i.e., patients and HCCs) and shows that in median a randomly selected patient's odds of receiving a recommended statin would increase

Discussion

Our investigation demonstrates that transferring the economical responsibility from the central health care authorities at the County Council to the local HCCs seems to have improved adherence to statin prescription guidelines. The use of recommended statins increased from 77 % in 2003 to 84 % in 2005 and the variance among HCCs and among physicians decreased. Our results fully agree with a previous analysis performed in the county of Scania [START_REF] Ohlsson | Understanding the effects of a decentralized budget on physicians compliance with guidelines for statin prescription; a multilevel methodological approach[END_REF]. However, the current analysis also adds information regarding the physician level. We show that approximately 50 % of the higher level variance could be attributed to the physician level, which is in line with other studies [START_REF] Brookhart | Explained variation in a model of therapeutic decision making is partitioned across patient, physician, and clinic factors[END_REF]. In 2005, after the decentralized budget, the differences between HCCs and between physicians became almost negligible and therefore the possibility to distribute the variance to different levels of analysis was limited. This is also illustrated in figure 2 by the fact that none of the HCC and only a few physicians conclusively differed from the overall mean adherence in 2005.

While our analytical approach of focusing on both changes in variance and changes in prevalence has been implemented in other research fields [START_REF] Braumoeller | Explaining Variance; Or, Stuck in a Moment We Can't Get Out Of[END_REF][START_REF] Merlo | Multilevel analytical approaches in social epidemiology: measures of health variation compared with traditional measures of association[END_REF][START_REF] Rasbash | Modelling the variance as a function of explanatory variables[END_REF] it has rarely been used for investigating practice variation in pharmacoepidemiology. Observing an increase in the prevalence of prescription of recommended statins does not necessarily imply an improvement since the variation around the prevalence could be very high. The desired outcome is obviously not only to increase adherence with guidelines but also to eliminate unnecessary practice variation.

An advantage of the multilevel regression analysis is that it allows disentangling the variance in the outcome among the different levels of analysis and using this information for identifying which level could be most relevant for a possible intervention. In 2003 the variance at the higher levels was rather large and equally distributed among HCCs and physicians, indicating that any intervention aimed to improve adherence with guidelines should be focused on both levels simultaneously. The decentralized budget, that was implemented, was a general intervention towards all HCCs and all physicians that was disseminated through the HCCs. Still it appeared to effectively decrease the variance at both levels. However, an intervention directed towards specific HCCs and physicians would theoretically also be effective in reducing variance. In 2005 the higher level variance was very small which suggests that any further intervention directed towards specific HCCs or physicians would render less effective.

In 2005 we observed that older locum physician had a lower adherence to prescription guidelines than older GPs, which may reflect intrinsic characteristics of this personal category. Locum physicians share the common work environment and the same constraints as other physicians at the HCC but only for a limited period of time and therefore might be less affected by the therapeutic traditions acting at the HCC. However, an intervention focusing on locums will possible not be very efficient [START_REF] Merlo | A brief conceptual tutorial of multilevel analysis in social epidemiology -Interpreting neighbourhood differences and the effect of neighbourhood characteristics on individual health[END_REF] since the variance at the physician level (and the corresponding MOR) was negligible. Observational epidemiological studies are often the only option for investigating questions that for practical, economical, or ethical reasons cannot be analysed by randomized trials [START_REF] Black | Why we need observational studies to evaluate the effectiveness of health care[END_REF][START_REF] Merlo | Neighbourhood effects and the real world beyond randomized community trials: a reply to Michael J Oakes[END_REF][START_REF] Schneeweiss | Developments in post-marketing comparative effectiveness research[END_REF][START_REF] Sorensen | Implications of the results of community intervention trials[END_REF]. Confounding and selection bias may threaten the validity of the studies. However, because of similar indications and efficacy, statins are an ideal medication group for investigating prescribing behaviour [START_REF] Larosa | Effect of statins on risk of coronary disease: a metaanalysis of randomized controlled trials[END_REF], and there is no rationale for considering patient characteristics as confounding factors when investigating practice variation. The value of including individual and physician covariates resides in the understanding of the prescription process. In the present investigation we only considered basic individual variables such as age and gender of the patient, and our results showed that for dataset 2003 younger patients had a lower propensity to receive recommended statins. This circumstance cannot be justified by any medical argument, but may rather reflect the influence of constructed social roles and expectations. From the perspective of equity in health care, it is relevant to question the physicians´ choice of more expensive, but not more efficient, brands for some groups of patients, given that a large part of this medication expenditure is funded by the public reimbursement system. We can not exclude that unmeasured factors besides the decentralized budget might have influenced the observed results (i.e., increase in prevalence and reduction in variance). In addition, the expiration of the Zocord patent in 2003, with the following decline in price for generic simvastatin and increase in cost difference with other statins, might have contributed in choosing the recommended statin. Information campaigns issued by the local therapeutic committee and the growing awareness of rising costs for medication are other possible contributing factors.

Most patients receive statins as a long time therapy. However, according the Swedish law, a prescription can not be issued for a period longer than one year. Therefore, in routine care repeat prescriptions are sometimes issued by phone and eventually by a different physician.

We have tried to identify homogeneous physician-patient relations by only including patients with all cardiovascular prescriptions by the same physician. However, we can not exclude a delayed effect of the decentralized budget conditioned by the fact that new users are prescribe recommended drugs, but for continuous users change to recommended drugs are slow, especially if the repeated prescription is written by another physician.

Our study investigates statin prescription in primary health care. Therefore, our results are not directly applicable to those drug prescribed for patients at hospital care. Older patients treated in municipal homecare are also excluded since their prescriptions are not registered in the database. On the other hand, a study of statin treatment in a local community in Skaraborg showed that very few old (85 or more years) used statins [START_REF] Mehner | Cholesterol in women at high cardiovascular risk is less successfully treated than in corresponding men. The Skaraborg Hypertension and Diabetes Project[END_REF].

The period of analyses was relatively short so it can not be excluded that an immediate positive response is followed by a later gradual return to the pre intervention situation. Therefore, in order to obtain some information on the effect of the intervention beyond 2005 we also analysed the prevalence of recommended statins in the time period from July 2008 to December 2009. Because at the moment of the analyses this data was not complete (i.e., it included only 23 out of 24 HCCs and there was not information on prescribers) we did not include it in the main multilevel analysis. This analysis indicated that the overall prevalence of recommended statin prescriptions in 2008-2009 was 89%, which suggest that the effects of the intervention were stable (analyses available on request). Practice variation is a common phenomenon that is not necessarily inappropriate but rather may reflect different therapeutic approaches confronting a similar health problem [START_REF] Crump | The Good Indicators Guide: Understanding how to use and choose indicators[END_REF].

However, when the same pharmacological therapy is available as different brands at different prices and the prescriber selects the more costly, there are reasons to question the suitability of the observed practice variation.

In conclusion, our study suggests that decentralization of the drug budget to the HCCs, i.e., transferring the economical responsibility and the power in management and decision-making to the HCCs, seemed to be an appropriate intervention for reducing inefficient therapeutic traditions. As a natural consequence, adherence to the drug committee's recommendations increased and differences between physicians and between HCCs decreased.

Figure legends

  prescription during May 2002 to October 2003 (i.e., 2003 dataset, n=7460), and (ii) during July 2004 to December 2005 (i.e., 2005 dataset, n=9643). If a patient received more than one statin prescription during each time period, the last one was selected. Prescriptions for other cardiovascular drugs, Long-acting nitrates (ATC codes: C01DA08, C01DA14), Loop diuretics (C03C), Potassium-sparing diuretics (C03D), Diuretic combinations (C03E),Thiazides (C03A, C03B),Beta blockers (C07), Calcium channel blockers (C08), ACE-inhibitors (C09A, C09B), Angiotensin receptor blockers (C09C, C09D), Fibrates (C10AB), and Resins (C10AC) were also extracted. In order to identify homogeneous physician-patient relations, only patients with all his/her cardiovascular drugs issued by the same physician were included, resulting in 6205 patients, treated by 425 physicians at 24 HCCs in the 2003 dataset and 7979 patients treated by 402 physicians at 24 HCCs in the 2005 dataset.

  models (A, B, C) for data set 2003 and 4 models (A, B, C and D) for data set 2005. Model A was an empty two level model including only patients and HCCs as random effects. Model B was a three-level model in which patients were nested within physicians that were in turn nested within HCCs. Model C and Model D added the patient characteristics respectively patient and physician characteristics. In this way we aimed to investigate if the individual and contextual characteristics could explain the residual variation between physicians and between HCCs. We estimated odds ratios and 95 % credible intervals (95 % CI) from the models. Model fit was evaluated by comparing change in the deviance information criteria (DIC).

  We calculated the percentage of change in magnitude of variance (PCV) that was explained between the initial (references) model (Var initial ) when including more variables in an extended model (Var more ): Percentage of change (PCV) = ((Var initial -Var more )/( Var initial ))x100 We used this percentage to estimate the relevance of the individual and contextual characteristics for understanding a possible clustering of prescriptions of recommended statins. Parameters were estimated by Markov chain Monte Carlo (MCMC) methods with the MLwiN 2.00 software [20]. We also calculated the absolute change in variance (CV) between the two time periods (2003 and 2005) and a t-test was performed to calculate their 95% confidence intervals (CoI). Change in variance (CV) = Var 2005 -Var 2003

Figure 1

 1 Figure1illustrates that although there were some HCCs with rather low adherence to

2 .

 2 14 times in 2003 (MOR HCC2003 = 2.14 ) and 1.37 times in 2005 (MOR HCC2005 = 1.37) if he/she moved to an HCC with higher adherence to guidelines. Model B in table 3 includes three levels and shows that the HCC and physician levels accounted each for approximately 50 % each of the variation at the higher levels in 2003 (MOR HCC2003 = 1.89 vs. MOR PHYSICIAN2003 = 1.88). In 2005 the variance among HCCs and physicians was lower (MOR HCC2005 = 1.30 vs. MOR PHYSICIAN2003 = 1.52). From 2003 to 2005 the variance between physicians and between HCCs decreased by 55 % and 82% respectively. The inclusion of patient and physician characteristics (Models C and D) did not have an influence on the variance at the different levels. Figure 2 illustrates that in 2003 about 30% of the HCCs and approximately 4% of the physicians differed from the overall mean adherence while in 2005 none of the HCCs and only a few physicians differed conclusively from the overall mean.

Fig. 1

 1 Fig. 1 Prescription of statins, for the 24 health care centres in Skaraborg included in the

Table 1 .

 1 Characteristics of the patients cared by only one physician in relation to the prescription of recommended statins in the Skaraborg Primary Health Care Database (SPHD).

	Tables
					Values are number of
	patients (percentage) if not other indicated
		2003			2005
		Yes	No		Yes	No
	Patient variables
	Individuals	4772 (77) 1433 (23)		6719 (84) 1260 (16)
	Age: min-max, (mean years) 30-89 (66) 31-89 (64)		18-93 (66) 25-104 (66)
	-54	637 (73)	233 (27)		855 (84)	166 (16)
	55-64	1465 (75) 497 (25)		2009 (83) 404 (17)
	65-74	1710 (78) 471 (22)		2354 (85) 424 (15)
	75-	960 (81)	232 (19)		1501 (85) 266 (15)
	Men	2498 (76) 779 (24)		3603 (84) 660 (16)
	Women	2274 (78) 654 (22)		3116 (84) 600 (16)
				Number
				of
	Physician variables (Age)			physicians
	Young intern (-29)	-	-	21	113 (90)	12 (10)
	Old intern (30-)	-	-	22	91 (82)	20 (18)
	Young resident (-34)	-	-	32	420 (85)	72 (15)
	Old resident (35-)	-	-	29	306 (83)	64 (17)
	Young general practitioner (-49)	-	-	65	2419 (84) 459 (16)
	Old general practitioner (50-)	-	-	64	2910 (85) 513 (15)
	Young locum (-46)	-	-	89	243 (82)	54 (18)
	Old locum (47-)	-	-	80	217 (77)	66 (23)
	Men	-	-	271	4568 (84) 895 (16)
	Women	-	-	131	2151 (86) 365 (14)

Table 2 .

 2 Association between on the one hand patient and physician characteristics and on the other prescription of recommended statins in 2003 and 2005 according the multilevel regression analysis on the Skaraborg Primary Health Care Database (SPHD). Measures are Odds ratios (OR) and 95% credible intervals (95%CI) Model C is a three level (patients, physicians and HCCs) analysis including HCC and physician as random intercepts and patient characteristics as fixed effects. Model D develops model C by also including physician characteristics

		Model C		Model D
		2003	2005	2005
		OR (95%CI)	OR (95%CI)	OR (95%CI)
	Patient characteristics
	Age group
	-54	1.00 (Reference)	1.00 (Reference)	1.00 (Reference)
	55-64	1.05 (0.84-1.29)	0.96 (0.78-1.17)	0.95 (0.77-1.16)
	65-74	1.32 (1.08-1.62)	1.07 (0.88-1.30)	1.06 (0.86-1.29)
	75-	1.51 (1.20-1.89)	1.09 (0.87-1.35)	1.07 (0.86-1.33)
	Sex (men vs. women)	1.01 (0.88-1.14)	1.07 (0.95-1.22)	1.08 (0.95-1.23)
	Physician characteristics
	Young interns	-	-	1.67 (0.78-3.43)
	Old	-	-	0.85 (0.48-1.58)
	Young residents	-	-	0.91 (0.62-1.32)
	Old	-	-	0.80 (0.55-1.18)
	Young GPs	-	-	0.87 (0.69-1.10)
	Old GPs	-	-	1.00 (Reference)
	Young locum physicians	-	-	0.75 (0.51-1.11)
	Old locums physicians	-	-	0.56 (0.38-0.82)
	Sex (men vs. women)	-	-	0.87 (0.71-1.07)

Acknowledgements

This research was funded by the Skaraborg research and development council, Skaraborg primary care research and development council, and the Skaraborg Institute, Skövde, Sweden. The Swedish Research Council (Juan Merlo, Dnr 2004-6155 and The Centre for Economic Demography), the Swedish council for working life and social research (Juan Merlo, Dnr: 2007-1772), by an ALF Government Grant Research program (Juan Merlo, Dnr M: B 39 977)

Conflict of Interest

The authors declare that they have no conflict of interest.


Jose Luis

Yves Ferrand

Beatriz Arroyo

J L Guzmán

Origin and migration of woodcock Scolopax rusticola wintering in Spain

Keywords: Management, Migration, Circum-Baltic Region, Ringing, Spain

Woodcock Scolopax rusticola is an important game species in Northern Spain, where it is mainly a wintering species. Knowledge about the migration and origin of the woodcock wintering in Spain is relatively sparse, existing to date only qualitative analyses dating more than a decade. From the analyses of ringing recoveries of woodcock wintering in Spain, we evaluate the relative importance of various countries or regions as sources of the woodcock wintering in Spain, an estimate of their migratory route. Our analyses show the Circum-Baltic Region to be the most important breeding area of the woodcock wintering in Spain. Within that area, both Sweden and Western Russia appear to be particularly important. Analyses of the ringing locations of woodcock ringed during migration and recovered in Spain in winter suggest that woodcock wintering in Spain migrate primarily through the South of the Baltic Sea, Germany and France. The proportion of woodcock ringed in different French regions during the postnuptial migration months (October and November) that was subsequently recovered in Spain (mainly through hunting) declined with the proportion of those birds that was recovered in France (also mainly through hunting). We discuss the management implications of these results.

Introduction

Migrant game species are usually subject to hunting pressure throughout its migratory route, which may make their management a challenge. Game management is usually regulated at a local or at most national level, but in the case of migratory species, efficient management is only obtained if there is coordination among the countries or regions through which the species passes along the annual cycle. It is thus important to know the connectivity between breeding and wintering areas, as well as migratory routes, to determine which countries should have stronger coherence in the management of these species.

Woodcock Scolopax rusticola is a forest wader with a wide Palaearctic distribution, present throughout temperate areas in Asia and Europe, from the British Isles to Kamtchatka, and from the Arctic Polar Circle to the North of Africa and the Middle East [START_REF] Cramp | Handbook of the birds of Europe, the Middle East and North Africa, III Waders to Gulls[END_REF]. The European breeding population spreads from Fennoscandia to the Mediterranean Region (in mountains) and from Western Europe to Eastern Russia [START_REF] Hagemeijer | The EBBC atlas of European breeding birds. Their distribution and abundance[END_REF]. It is a pre-Saharan migrant with a leapfrog migration [START_REF] Alerstam | The development of bird migration theory[END_REF]: Populations from Russia and North Europe are mainly migrant, spending the winter from Atlantic to Mediterranean regions and North Africa, while the Southern and Western populations are mainly sedentary, with short-distance migrations [START_REF] Henderson | The hunting of snipe and woodcock in Europe: a ringing recovery analysis[END_REF]. Through ring recoveries, it has been shown that populations wintering in the UK breed mainly in the Circum-Baltic Region, Norway and Denmark [START_REF] Hoodless | Survival rates and movements of British and continental woodcock Scolopax rusticola in the British Isles[END_REF], whereas those wintering France breed mainly in Central Russia [START_REF] Bauthian | Quantifying the origin of woodcock wintering in France[END_REF], Gossmann et al. 1998).

Knowledge about the migration and origin of the woodcock wintering in Spain is relatively sparse, existing to date only qualitative analyses dating more than a decade, which pointed to the Scandinavian countries as their main breeding quarter [START_REF] Onrubia | La Caza de la Aves Migrantes en el Territorio Histórico de Bizkaia[END_REF]. This study presents a comprehensive analysis of ringing recoveries made in Spain in winter, in order to obtain a quantitative estimate of the importance of various countries or regions as sources of the woodcock wintering in Spain, as well as an estimate of their migratory route, thus completing the pattern of migration of woodcock for Western Europe. We discuss our results in the context of management of this species.

Methods

Data sources

This study is based on the analyses of woodcock ringed in Europe and subsequently recovered in Spain between October and March. These data were obtained from the Central Bureau of Ringing in Europe (Euring), the Centro de Migración de Aves (CMA, Spain), and the Sociedad de Ciencias Aranzadi (Spain). Most of the recoveries (91.1%) occurred through hunting. Ringing effort (number of woodcock ringed) of France and Russia was obtained through the Office National de la Chasse et de la Faune Sauvage (ONCFS). For other countries, it was obtained through the respective ringing centers. Ringing data correspond to the period 1911-2007 and recoveries to the period 1911-2008. Both ringing and recovery data were grouped into three periods (breeding, migration, or wintering) according to date. We defined breeding period as April to September and migration period as February-March and October-November [START_REF] Lucio | La Becada en España[END_REF][START_REF] Saari | Spread of the woodcock Scolopax rusticola to finnish Lapland[END_REF]).

We analyzed a total of 244 ringing recoveries, of which 57 corresponded to birds ringed in the breeding period (Table 1) and 187 to birds ringed during migration (Fig. 1).

Origin of the wintering population

To determine the main breeding regions or countries of woodcock wintering in Spain, we considered recoveries of birds ringed during the breeding period (n=57). Initially, we analyzed data at the country level (Table 1). Subsequently, for statistical analyses (and given the low sample sizes), we grouped countries according to geographic location as follows: Circum-Baltic Region, with Sweden, Finland, Latvia, Estonia, and Western Russia (regions of Pskov and St. Petersburg, Gossmann et al. 1998);Central Russia (regions of Arkangel'sk, Vólogda, Tver, Vladimir, Moscow, Smolensk;Gossmann et al. 1998);Central Europe, with Hungary, Czech Republic, Switzerland, and Germany;Atlantic Coast (Netherlands, Belgium, France) and British Isles (UK and Republic of Ireland).

We calculated the percentage of woodcock recovered from each country/region out of total woodcock ringed there. For all countries except France, we did not have information about the number of birds ringed in each period, so we used total number of birds ringed as an estimate of the effort of ringing during the breeding period. This is probably correct for most of the northern countries, where ringing does not occur in winter. In the case of France (where most ringing occurs during migration and winter), we only considered birds ringed during the breeding period. We tested whether the proportion of woodcock recovered in Spain varied among regions with a Chi-square test. We used the standardized residuals (SR) to look for the significance of each cell. SR higher or lower than 1.95 indicates a significant deviation of the observed from the expected values [START_REF] Zar | Biostatistical analysis[END_REF]). Subsequently, we calculated an index of the importance of each region of country as a source of woodcock wintering in Spain, multiplying the percentage of recoveries by the breeding population estimate in each area and calculating the percent value of the resulting figure .  To analyze the variability in the winter distribution within Spain of woodcock coming from different breeding areas, according to the leapfrog migration described for the species [START_REF] Alerstam | The development of bird migration theory[END_REF], we classed each ringing location during the breeding period in two groups: "short-distance" for locations within the countries closer to Spain (less than 2,000 km from Central Spain: Central Europe countries, France and British Isles) and "longdistance" (more than 2,000 km from Central Spain: Circum-Baltic countries and Russia). We tested with a general linear model whether latitude or longitude of the winter location varied according to the region of origin as described above.

Migratory route

For the analysis of the migratory route, we considered ringing locations of birds recovered in Spain in winter but ringed during the migration period (n=187). Using the geographical coordinates of all of those locations, we evaluated (through a regression analyses) the equation that best defined the dispersion of those locations using the program Tablecurve 2D v5.01. Of all possible curves describing the dispersion, we chose the one with highest percentage of explained deviance. A more detailed analysis of spatial and temporal migration variation through France may be of interest, as it has implications for management. For example, ringing data may give indications about timing of spring migration through France, which are relevant for regulating closure of hunting seasons there, and determining whether certain French regions are particularly important as a migratory flyway for woodcock wintering in Spain is also useful as hunting pressure varies strongly among regions there. To analyze the spatial and temporal variation of movements within France, we analyzed data of birds ringed in France during migration or wintering months, and subsequently recovered in Spain, by month and region of ringing. Regions were defined as in [START_REF] Bauthian | Quantifying the origin of woodcock wintering in France[END_REF] (Fig. 2). We used Chi-square tests, and SR to look for the significance of each cell.

Results

Origin of the wintering population

Overall, the proportion of woodcock ringed during the breeding period and recovered in Spain during winter varied significantly with ringing region (X 4 2 =53.78, P<0.001): woodcock coming from the Circum-Baltic Region and the Atlantic Coast were recovered in Spain more frequently than expected, whereas the opposite was observed for woodcock coming from the British Isles (Table 2). Taking into account breeding population size, it would appear that more than 80% of the woodcock wintering in Spain would breed in the Circum-Baltic Region (including Western Russia) (Table 1).

Figure 3 suggests a different distribution of birds of different origin within Spain, with the most western and southern recoveries corresponding to long-distance migrants and short distance migrants being restricted to northern and eastern areas. However, neither longitude nor latitude of the winter location varied significantly between both groups (F 1,55 =0.10, P=0.7 for latitude; F 1,55 =0.47, P=0.5 for longitude).

Migratory route

General migratory route

Figure 4a shows the modeled curve from the distribution of locations of woodcock ringed during migration and subsequently recovered in Spain in winter (n=187). This curve explained 58% of the variance and suggests that woodcock wintering in Spain probably pass primarily close to the South of the Baltic Sea and subsequently through Germany and France. Temporal and spatial variation through France Not surprisingly, the probability of recovering in Spain woodcock ringed in France varied significantly according to ringing month (X 5 2 =33.03, P<0.001), being significantly higher than expected for October and November and significantly lower than expected for January and February (Table 3).

The probability of recovering in Spain woodcock ringed in France during the postnuptial migration months (October and November) also varied significantly according to ringing region (X 5 2 =19.4, P<0.005): Recoveries in Spain are higher than expected for birds ringed in the NE and CN regions and lower than expected for the Western Region (Table 4). There was a negative correlation between the percentage of woodcock ringed in each French region during the postnuptial migration months that is subsequently recovered in Spain and the percentage of those woodcock subsequently recovered in France (Fig. 5, r=-0.906, P<0.0001).

Discussion

Origin and distribution of woodcock wintering in Spain

Our analyses indicate that the Circum-Baltic Region is the most important breeding area of woodcock wintering in Spain. Within that area, both Sweden and Western Russia appear to be particularly important (although it is important to remark that maybe the difference between Central and Western Russia is not as clear as we present here: as the ringing effort in this area takes place in September, when postnuptial migration may have started, some of the birds ringed in Western Russia may have originated further east, e.g., in Central Russia). Woodcock breeding along the Atlantic Coast would also have a relatively high probability of wintering in Spain, but given the lower importance of the breeding populations in those countries, the total contribution to the wintering population in Spain would be lower. Finally, birds breeding in the British Isles and Central Europe would only occasionally winter in Spain.

These data fit among those described for the British Isles [START_REF] Hoodless | Survival rates and movements of British and continental woodcock Scolopax rusticola in the British Isles[END_REF], which indicate a strong preponderance of the Circum-Baltic and Scandinavian regions as origin for the woodcock wintering in that area, and those described for France (Gossmann et al. 1998;[START_REF] Bauthian | Quantifying the origin of woodcock wintering in France[END_REF], where ringing data showed a stronger importance of the centre of Russia as origin of the French wintering woodcock. Hence, our analyses complete the general migratory pattern of woodcock in Western Europe, suggesting a strong longitudinal gradient among breeding and wintering areas, with birds breeding further east wintering also further east.

On the other hand, we did not find any firm evidence that woodcock coming from different breeding areas had different wintering distribution within Spain, although lack of statistical differences may be due to the small sample sizes, so this issue merits further attention.

Migratory routes and timing

Analyses of the ringing locations of woodcock ringed during migration and recovered in Spain in winter suggest that woodcock wintering in Spain migrate through a route closer to the Baltic Sea than to Central Europe, in accordance with the above-mentioned results. These results also coincide with those obtained from various woodcock tagged with satellite transmitters since 2007 (see Fig. 4b), arising from a study by the Spanish Woodcock Hunting Club (CCB) (http://rtvs.ccbp.org/).

A previous study determined the existence of two migratory routes for woodcock wintering in France, one going through Fennoscandia and another one through Eastern Europe [START_REF] Bauthian | Quantifying the origin of woodcock wintering in France[END_REF]). This study was based on birds ringed in winter in France and subsequently recovered (mainly through hunting) along the migratory route. Our study, in contrast, in based on the ringing location of birds recovered in Spain. This implies that the importance of certain countries in the migratory route may be hidden if they do not have woodcock ringing programs, as is the case in many Eastern Europe countries. It is therefore plausible that woodcock arrive to Spain also through two migratory routes, including another eastern one, although this will need to be confirmed in further studies.

France is a compulsory corridor for the woodcock wintering in Spain, and more than 40% of all ringing recoveries in Spain belonged to birds ringed in France in migratory months. According to our results, the probability of recovering woodcock ringed during migration in western regions is lower than elsewhere, suggesting that there may be a lower migratory flux through those regions (as also suggested from Fig. 4). Recoveries from north-central and north-eastern regions were more common than expected by chance, which is also in accordance with the general migratory flux. In contrast, it was surprising that southwestern or central-southern regions were not significant. Alternatively, this pattern may be related to the spatial variation in hunting effort within France. The negative correlation found between the probability of recovery in France and Spain of woodcock ringed during the migrating months in different French regions supports this hypothesis. It is thus possible that hunting pressure suffered by migrating woodcock in France affects the populations wintering in Spain. However, it is important to interpret these results with caution, because they are just correlations, not causal relationships. It would be important to do more detailed studies to evaluate the relative importance of the heterogeneous passage through France and the influence of hunting pressure on wintering populations in Spain.

The analysis of temporal variation in probability of recovery indicates that the peak of post-breeding migration through France occurs in October and November and that pre-breeding migration through France does not occur before March. These results agree with the average departure dates (October 10) and arrival dates (9 May) observed in Southeastern Finland [START_REF] Saari | Spread of the woodcock Scolopax rusticola to finnish Lapland[END_REF], as well as with migration data suggested for Spain based on ICAs [START_REF] Lucio | La Becada en España[END_REF]. Similarly, these data are coherent with observed departure dates (to throughout March) from Spain toward breeding grounds obtained from satellite transmitters and for radio-tracked woodcock in Northern Spain (Guzmán and Arroyo, unpublished data).

Management implications

As the main source of woodcock wintering in Spain appears to be the Circum-Baltic Region (particularly Sweden and Western Russia), it would be particularly important to establish connections between those countries, in order to include productivity in those regions in the hunting management in Spain. For example, hunting bags could be cupped up in relation to the productivity in those regions. In a recent study, spring temperature in Norway appeared to be a good predictor of woodcock hunted in that country in early autumn [START_REF] Selas | Patterns in grouse and Woodcock Scolopax rusticola hunting yields from central Norway 1901-24 do not support the alternative prey hypothesis for grouse cycles[END_REF], which would point to spring temperature as a potential predictor of woodcock productivity. If verified, this could be an interesting management tool, allowing the determination of various scenarios ("good" or "poor" reproduction years) and thus various degrees of hunting bag limits.

Looking at the countries that form part of the migratory route of woodcock wintering in Spain (according to results in this study) and the duration of the hunting season in those countries [START_REF] Lutz | European Union management plan for woodcock Scolopax rusticola. 2007-2009[END_REF], woodcock wintering in Spain are apparently hunted throughout the annual cycle, except in June, July, and March. For example, in countries like Russia, Lithuania, and Latvia, hunting is practiced in April and May [START_REF] Ferrand | Elements for a woodcock (Scolopax rusticola) management plan[END_REF][START_REF] Blokhin | Different methods for the estimation of the woodcock hunting bags in Russia[END_REF], which would include part of the pre-breeding migration and the breeding period, when hunting is prohibited in European Union according to Annex II of the Birds Directive (European Commission 2004).

According to old estimations, France could be responsible about 25% of all woodcock hunted in the European Union [START_REF] Ferrand | Elements for a woodcock (Scolopax rusticola) management plan[END_REF]. However, appropriate bag estimates for Spain are lacking, so the impact of hunting pressure in Spain on woodcock populations is unknown. In any case, France is a compulsory corridor for woodcock wintering in Spain, and Spain is a refuge for French wintering woodcock when they move south in cold spells [START_REF] Gossmann | Impact of the 1996-97 cold spell on Woodcock in France based on ring recoveries[END_REF]. Thus, coordination between both countries seems critical for the long-term and sustainable use of the species. Additionally, it would be important to determine the effect of different management guidelines on populations. One example is the dates of hunting closure implemented in Spain (early or late January, depending on regions) and France (where hunting is allowed until February 20) [START_REF] Lutz | European Union management plan for woodcock Scolopax rusticola. 2007-2009[END_REF]. Hunting in February in France has been a source of conflict in Spain, as it could include migratory birds. Our results suggest that pre-breeding migration of woodcock wintering in Spain occurs mostly in March, so hunting in February in France should not have a strong impact on Spanish wintering woodcock. In contrast, the data above suggest that hunting in the autumn in western regions may be having an impact on the Spanish wintering population. Future studies should address the effect of different management guidelines on populations.

c

  Fig. 1 Ringing location of woodcock recovered in Spain during winter, according to ringing time (breeding period: filled circles; migration: open circles)

Fig. 2

 2 Fig.2Regions in France used for analyses (modified from[START_REF] Bauthian | Quantifying the origin of woodcock wintering in France[END_REF]

Fig. 3

 3 Fig. 3 Recovery location of woodcock coming from short-distance countries (filled circles) or long-distance countries (white circles)

Fig. 4 a

 4 Fig.4a Modeled migratory route of the woodcock wintering in Spain. Filled circles, best fit curve; open circles, 95% confidence limit; open triangles, 95% prediction limits. b Best-quality locations of two woodcock tagged with satellite transmitters by the Spanish Woodcock Hunting Club (CCB). Data obtained from March to May 2009 and kindly made available to us by CCB

Fig. 5

 5 Fig.5Relationship between the percentage of woodcock ringed in each French region during the postnuptial migration months (October-November) that are recovered subsequently (mainly through hunting) in either Spain or France

Table 1

 1 Countries where there has been woodcock ringed during the breeding season and subsequent recoveries in Spain during winter Index represents the estimated proportion of woodcock wintering in Spain coming from each country or region

	Country	Number ringed	Number recovered	Percent recoveries	Breeding population a	Breeding estimate b	Index
	Denmark	1,968	0	0.0	2,000-3,000	2,500	0.0
	Norway	?	2	?	30,000-50,000	40,000	0.4 c
	Finland	3,236	9	0.27	100,000-150,000	125,000	3.3
	Sweden	1,783	17	0.95	250,000-1,000,000	625,000	58.9
	Latvia	388	1	0.25	20,000-30,000	25,000	0.6
	Estonia	360	1	0.27	20,000-50,000	35,000	0.9
	W Russia	1,986	9	0.45	1,200,000-5,000,000 d	465,000 e	20.8
	C Russia	1,112	3	0.26	1,200,000-5,000,000 d	465,000 e	12.0
	Switzerland	282	0	0.0	1,130-1,630	1,380	0.0
	Czech Republic	?	2	?	2,000-4,000	3,000	0.1 c
	Hungary	61	1	1.63	10-20	15	0.0
	Germany	?	0	0.0	12,000-24,000	18,000	0.0
	Netherlands	?	1	?	2,000-3,000	2,500	0.1 c
	Belgium	811	4	0.49	1,600-2,800	2,200	0.1
	France	153	2	1.31	10,000-30,000	20,000	2.6
	British Isles	9,862	5	0.05	7,900-23,700	15,800	0.1

a BirdLife

International (2004)

b Median of the estimate in the previous column

Table 2

 2 Proportion of woodcock ringed in various regions and subsequently recovered in Spain during winter

	Region	Number	Number	Percent	SR
		ringed	recovered	recoveries
	Circum-Baltic 5,767	37	0.64	4.87
	Central Russia 1,112	3	0.26	-0.15
	Central	343	1	0.29	-0.01
	Europe a
	Atlantic Coast b 964	6	0.62	3.17
	British Isles	9,862	5	0.05	-4.46
	SR standardized residuals of the Chi-square test
	a Data from Germany or Czech Republic not included since no ringing
	effort was available
	b Data from Netherlands not included since no ringing effort was available

Table 3

 3 Proportion of woodcock ringed in France and subsequently recovered in Spain according to ringing month

	Ringing month Number ringed Number	Percent	SR
			recovered	recoveries
	October	1,715	9	0.52	2.72
	November	16,144	51	0.32	2.67
	December	14,796	30	0.20	-0.39
	January	10,191	6	0.06	-3.44
	February	6,763	7	0.10	-2.02
	March	5,003	16	0.32	1.54
	SR standardized residuals of the Chi-square test

Acknowledgments This study has been funded by the Comité Interautonómico de Caza y Pesca Continental in Spain and has been carried out within the Framework of the Interregional Study on Woodcock Scolopax rusticola in Spain (project number 200430E471). We thank the staff in ringing offices that provided woodcock ringing data: Chris du Feu (EURING Data Bank), Óscar Frías (Oficina de Especies Migratorias, Dirección General para la Biodiversidad, Ministerio de Medio Ambiente), Ainara Azkona (Oficina de Anillamiento de Aranzadi), Kaarel Kaisel (Estonia), Jacquie Clark (British Trust for Ornithology), Juris Kazubiernis (Letonia), Jari Valkama (Finlandia), Dr Thord Fransson (Bird Ringing Centre, Swedish Museum of Natural History), and Didier Vangeluwe (Institut Royal des Sciences Naturelles de Belgique, Centre Belge de Baguage). Additionally, we are grateful to all the Spanish ringers that contributed to the project: David Lizaso, Miguel San Martín, Alberto Artácoz, Felipe Oiarzabal, Iñaki Balda, Peio Oiarzabal, Juan Jesús Picabea, Nestor Domeño, Iñaki Ripa, Angel María Sarratea, Eduardo Sarratea, Javier Oiaregui, Ion Aldunzin, Fernando Belzunce, Rafael López de Aberasturi, Jesús Gutierrez, Melchor Alzugara, Agustín Mendiburu, Mikel Olano, Esteban Iriarte, Alejandro Onrubia, Ibón Telletxea, Javier Monge, Pedro Zuleta, Marco Escudero, José Luis Rivas, Javier Lucientes, Carlos Pérez, Javier Ferreres, Rubén Ibáñez, Marco Escudero, Marc Palles, Celestino Suarez, Miguel González-Vélez, Justo José Robles, and Pablo González. Enrique Castién provided useful comments on the manuscript.


Christiane Frosch

Peter Haase

Carsten Nowak
email: cnowak@senckenberg.de

First set of microsatellite markers for genetic characterization of the Eurasian beaver (Castor fiber) based on tissue and hair samples

Keywords: Microsatellites, Noninvasive sampling, Wildlife genetics, Eurasian beaver, Castor fiber

HAL is

Introduction

The Eurasian beaver, Castor fiber Linnaeus 1758, was reduced to only a few relict populations during the nineteenth century as a consequence of massive persecution [START_REF] Durka | Mitochondrial phylogeography of the Eurasian beaver Castor fiber L[END_REF]. Because of its popularity, and the fact that beavers are key species in freshwater ecosystems [START_REF] Rosell | Castor fiber and Castor canadensis and their ability to modify ecosystems[END_REF], considerable reintroduction efforts were started in several regions in the twentieth century [START_REF] Nolet | Come back of the beaver Castor fiber: an overview of old and new conservation problems[END_REF]. In Central Europe, beavers were repeatedly reintroduced using source populations from different subspecies. Because of the high morphological similarity and potential interbreeding among subspecies in the field, genetic tools are needed in order to reconstruct range dynamics and long-term colonization success of reintroduced beaver populations. Mitochondrial DNA (mtDNA) markers have been shown to provide a possibility to differentiate among beavers from different subspecies [START_REF] Durka | Mitochondrial phylogeography of the Eurasian beaver Castor fiber L[END_REF][START_REF] Ducroz | Genetic variation and population structure of the Eurasian beaver Castor fiber in Eastern Europe and Asia[END_REF]. However, mitochondrial sequence markers provide no information concerning crossbreeding among different evolutionary lineages. In addition, published mtDNA markers do not provide sufficient resolution for safe subspecies discrimination in Germany (own data). Microsatellite markers, in contrast, enable high-resolution genetic analysis of population substructuring. While two panels of microsatellite markers exist for the Canadian beaver, Castor canadensis [START_REF] Crawford | Isolation and characterization of microsatellite loci in the beaver (Castor canadensis)[END_REF][START_REF] Pelz-Serrano | Development of nine new microsatellite loci for the American beaver, Castor canadensis (Rodentia: Castoridae), and cross-species amplification in the European beaver, Castor fiber[END_REF], no microsatellites have been developed for C. fiber so far.

The aim of this study was to provide a genetic tool for the investigation of genetic population structure and subspecies differentiation in the Eurasian beaver. Therefore, we developed and tested a panel of variable microsatellite markers for this species. In order to evaluate the possibility of noninvasive sampling strategies for genetic sample collection [START_REF] Taberlet | Noninvasive genetic sampling: look before you leap[END_REF], we compared marker performance based on both tissue and hair samples.

Materials and methods

For the construction of a genomic DNA library, we extracted DNA from a C. fiber albicus tissue sample from Hesse (Germany). DNA extraction of tissue samples was performed with the Qiagen Blood and Tissue Kit, following the instructions of the manufacturer. After shearing of the genomic DNA by nebulization, fragments of two size classes, namely 1.5-2.5 kb and >2.5 kb, were separated electrophoretically, electroeluted, and purified. Both groups of fragments were ligated into pUC18 vector plasmids cut with Sma I and then transformed into DA10B Escherichia coli host cells via electroporation. To identify fragments containing typical microsatellite DNA sequence motifs, 36,000 insert-containing clones were picked on gridded nitrocellulose filters (Schleicher & Schuell, BA85) for colony filter hybridization. Clones were screened for microsatellites, using seven different radioactive labeled synthetic oligonucleotide probes [(GA) 15 , (CA) 15 , (AAT) 10 , (GGC) 10 , (AAG) 10 , (ATG) 10 , (GATA) 6 ]. One hundred ninety-two hybridizing clones were picked at random, and their inserts were amplified by polymerase chain reaction (PCR), using standard forward and reverse vector primers.

Fifty-eight primer pairs were designed manually, or using Primer 3 [START_REF] Rozen | Primer3 on the WWW for general users and for biologist programmers[END_REF] and tested on eight tissue samples of mixed origin for consistent amplification success. The 10-μl PCRs included 3.8 μl of DNA extract, 1.5 mM MgCl 2 , 0.2 mM dNTPs in equal ratio, 0.3 μM of each primer, and 0.5 U/μl HotStarTaq-Polymerase (Qiagen/Hilden). Fragments were amplified for 15 min at 95°C, followed by 45 cycles of 30 s at 94°C, 90 s annealing at 50°C, 60 s at 72°C, and a final elongation step of 30 min at 72°C. For all PCRs, positive and negative controls were included.

Twenty-five primer pairs yielded reproducible amplicons of the correct size and were, thus, considered for fragment length analysis. Fragment length at all 25 loci were determined on a 3730 DNA Analyzer (Applied Biosystems) for 33 C. fiber individuals (17 ethanol 96% stored tissue samples from shot beavers, 12 ethanol stored samples of carcasses found in the field, 1 treated fur, 3 hair samples stored in paper envelopes at room temperature) from Hesse (n=2), Rhineland-Palatinate (n=4), and Bavaria (n=27). We used 3-5 guard hairs or 5-15 wool hairs for hair extraction with the Investigator Kit (Qiagen), following the protocol of the manufacturer. We included negative controls in the hair-extraction process to check for cross contamination during this step. Hair samples were prepared in a separate laboratory, considering standard routines for noninvasive sample treatment to avoid cross contamination [START_REF] Taberlet | Noninvasive genetic sampling: look before you leap[END_REF].

Genemarker 1.6 software (SoftGenetics) was used to score fragment length. Mean number of alleles per locus (A), percentage of polymorphic loci (P), expected (H E ,) and observed (H O ) heterozygosity were calculated with GenAlex [START_REF] Peakall | Genalex 6: genetic analysis in Excel. Population genetic software for teaching and research[END_REF]. Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium estimations were conducted using Genepop [START_REF] Raymond | Genepop (version-1.2) -Population Genetics Software for exact tests and ecumenicism[END_REF]. The suitability of the marker set for individualization of samples (Probability of Identity=P (ID) ) was calculated for an outcrossing (P (ID) ) or inbred (P (ID)sib ) [START_REF] Paetkau | Microsatellite analysis of genetic variation in black bear populations[END_REF]) population, using Gimlet 1.3.3 [START_REF] Valiere | gimlet: a computer program for analysing genetic individual identification data[END_REF].

In order to evaluate the feasibility of noninvasive sampling methods for the beaver, we compared amplification success of hair and tissue samples, and checked for genotyping errors among hair samples. Hair and tissue samples of the same individual were analyzed from 11 of the 33 specimens considered in this study. Tissue and plucked hairs were taken from six freshly shot beavers, four cadavers, and one treated fur. Samples were genotyped at the final set of 15 microsatellite loci. Genotyping errors (allelic dropouts, false alleles) were checked manually by comparing genetic profiles based on hair samples to the respective reference genotypes obtained from tissue genotyping. For this reason, we did not conduct replicates in the hair amplification step.

Results

Of the 25 markers tested, 20 were found to be polymorphic in the test sample. Fifteen markers allowed for safe and consistent genotyping, and were thus considered for the final marker set (Table 1). The observed number of alleles per locus of the final marker set ranged from two to six, with observed heterozygosity values ranging from 0.24 to 0.77. Significant linkage disequilibrium (Fisher's exact test, p<0.05) was revealed for nine combinations of loci. However, none were significant following Bonferroni correction. A significant heterozygote deficit was observed for three loci (CF06, CF18, CF49; Exact HW test; [START_REF] Guo | Performing the exact test of Hardy-Weinberg proportions for multiple alleles[END_REF]. Calculated values for the probability of identity of the marker set were P (ID) =4.23×10 -9 , and P (ID) sib =1.13×10 -4 for an inbred population.

Amplification success of hair samples was significantly lower compared to tissue samples (one-tailed paired t-test, p<0.001). Comparison of amplification success in 11 samples resulted in 9-11 successfully amplified fragments per locus for tissue and 5-10 successful PCR reactions based on hair extractions (Table 2). Genotyping errors in hair samples were detected at two loci (CF33 and CF48).

Discussion

Here, we provide 15 novel variable markers, which can be easily multiplexed due to a harmonization of reaction conditions. Along with moderate H E levels, only few different alleles were found at most loci. We are not able to decide here, however, if these rather low levels of genetic variability are due to genetic homogeneity of the reintroduced beaver populations or a characteristic of the marker system itself. Despite of the moderate diversity levels per locus, P (ID) and PID (ID)sib values show that even for an inbred population, the marker system is well suitable for individual identification with a high statistical confidence. Due to the fact that our samples were collected from across Southern Germany, revealed deviations from HWE at three loci are likely caused by population substructuring.

In a cross-species amplification test, Pelz-Serrano et al. ( 2009) identified nine microsatellite markers developed for the Canadian beaver, which showed variation within C. fiber. Of these markers, however, only four (Cca4, Cca13, Cca18, Cca92) were variable among our 33 samples (data not shown). Along with these loci, the presented markers will be used to differentiate among C. fiber subspecies and to reconstruct the contribution of different founder populations to the currently observed recolonization success in Southern Germany. As amplification failure and genotyping error for hair samples was rather low, we recommend the use of beaver hair trapping in order to allow for increased sample sizes and a standardized sampling design.

Table 1

 1 Primer sequences and characterization of 15 variable microsatellite loci for the Eurasian beaver, Castor fiber (n=33) N A Number of alleles, H O Observed Heterozygosity, H E Expected Heterozygosity. Significant departures from Hardy-Weinberg equilibrium (p<0.05) are indicated before ( * ) and after ( ** ) Bonferroni correction

	Locus Accession no. Sequence (5′-3′)

Table 2

 2 Amplification success (defined as presence of scorable fragments within the expected size range in the fragment length analysis; +PCR) of 15 microsatellite loci based on hair and tissue samples from 11 Castor fiber individuals and the number of correct genotypes obtained from hair samples (+genotype)

	Locus	+PCR (tissue)	+PCR (hairs)	+Genotype (hairs)
	CF05	9/11	9/11	9/9
	CF06	11/11	9/11	9/11
	CF07	7/11	7/11	7/7
	CF17	11/11	9/11	9/11
	CF18	10/11	7/11	7/10
	CF19	11/11	8/11	8/11
	CF21	10/11	10/11	10/10
	CF30	11/11	10/11	10/11
	CF31	9/11	7/11	7/9
	CF32	11/11	7/11	7/11
	CF33	11/11	9/11	5/11 ab
	CF41	11/11	9/11	9/11
	CF44	10/11	5/11	5/10
	CF48	11/11	8/11	7/11 b
	CF49	11/11	9/11	9/11

a false alleles, b allelic drop out

Eur J Wildl Res (2011) 57:679-682

Acknowledgments We are grateful to all the beaver managers in Germany for kindly providing us samples. Genomic microsatellite identification was performed by GENterprise Genomics, Mainz, Germany. The study was funded by the Regierungspräsidium Darmstadt, federal state of Hesse. Additional funding was provided by the Hessian initiative for scientific and economic excellence (LOEWE -Landes-Offensive zur Entwicklung wissenschaftlichökonomischer Exzellenz) of the Hessian Ministry of Higher Education, Research, and the Arts.

Declaration The authors of this manuscript declare that all experiments performed in this study comply with the current laws in Germany. Furthermore, we declare that we have no conflict of interest concerning the published data.