A Ricard

C Blouin

Genetic analysis of the longevity of French sport horses in jumping competition 1

Keywords: heritability, horse, jumping, longevity, survival analysis

The longevity of sport horses is an economic and ethical issue. The aim of this study was to analyze the duration of the competitive life of jumping horses in France to assess the potential for genetic evaluation and to propose rules of management for sport horses. Data included lifetimes spent in jumping competitions for the 209,296 horses born from 1968 onward with performances between 1972 and 2008; the data set contained 22% right-censored records. Longevity was measured in years. Discrete survival analysis included fixed effects of region of birth, month of birth, year of recording, age at first competition, interaction between sex and level of jumping performance as measured by the logarithm of earnings adjusted for sex, age, year, and random sire and maternal grand-sire effects. There were 16,668 sires and maternal grand-sires. All fixed effects were highly significant (P < 0.001). Management of the sports career had an important effect on longev-ity: against common belief, the younger the horse started competing, the longer it stayed in competition. For horses that started competing at an age of 6 yr, the risk of culling was 1.33-fold that of horses having started at 4 yr of age. The less success in competition, the greater was the chance for leaving competition, especially for horses without earnings. For a gelding without earnings, the risk of culling was 1.40-fold that of an averageperformance gelding and 2.57-fold that of a top-rated gelding (performance at least 2 SD above the mean). Mares always had greater relative risk than geldings or stallions because they may be culled from competition to be used for breeding. The risk of culling for females was 1.45-fold that of a gelding with the same performance. The heritability of the length of competitive life was 0.10. Breeding values were predicted for sires, and 3,303 sires showed an accuracy greater than 0.60. Among these sires, 262 were used for breeding in 2008.

INTRODUCTION

Successful performance in riding sports, especially jumping, is the main breeding objective of Warmblood sport horse stud books all over the world [START_REF] Koenen | An overview of breeding objectives for warmblood sport horses[END_REF]. However, breeders also aspire to limit the early retirement of horses due to disease-related loss of performance or invalidity, and increase the duration of competitive life. A mature sport horse is not only a financial investment to be preserved, but its welfare is also a moral imperative. Longevity in competition has therefore been claimed to be part of an industry-wide breeding objective; however, no breeding values have been formally included in the breeding objective. To date the only selection for longevity has been based on indirect selection using conformation traits already relevant for selection [START_REF] Wallin | Phenotypic relationship between test results of Swedish warmblood horses as 4-year-olds and longevity[END_REF].

The aim of this study was to determine the factors affecting longevity in jumping competition and to estimate the heritability of this trait using survival analysis to include the resulting breeding evaluations into the breeding plans of the French Stud Book.

MATERIALS AND METHODS

Animal Care and Use Committee approval was not obtained for this study because the data were obtained from an existing database (Fédération Française d'Equitation, Lamotte Beuvron, France, and Les Haras Nationaux, Saumur, France).

Data

In France, show jumping competitions take place principally from March to October. The participation of each horse in competitions depends on the wishes of the rider, the availability of the horse, and the targeted championship. The sporting season is annual. A horse may begin competing at 4 yr of age or older. When a horse is no longer used for competition, it simply disappears from the jumping data the following years. All results in jumping competition have been recorded since 1972 by the French Equestrian Federation (Lamotte Beuvron, France). The results from official jumping competitions between 1972 to 2008 were used in this study to analyze longevity. The lifetime of the horse was defined as the time spent in competition, measured in years. For example, a horse with a first competition at 4 yr of age and its last competition at 8 yr of age had a lifetime of 5 yr. Culling was defined as the end of the competing life of the horse, whereas a possible longer use for breeding or leisure is not considered. The study included data from 209,296 horses with an overall 987,879 yr of performance. The lifetimes of horses still in competition in 2008 were considered as rightcensored records. There were no left-censored records because only horses born in or after 1968, having begun competing in or after 1972, were included in the analysis. There were 47,766 horses with censored lifetimes (21.9%).

Survival Analysis

Because the measure of time was discrete, the model described by [START_REF] Prentice | Regression analysis of grouped survival data with application to breast cancer data[END_REF] was considered. [START_REF] Ducrocq | Extension of survival analysis models to discrete measures of longevity[END_REF] demonstrated the equivalence of such a model with a fully parametric model in which the baseline is estimated at every discrete time point and the definition of hazard is modified to take into account the discrete time scale. This attribute allowed them to include this model in their survival kit package [START_REF] Ducrocq | The survival kit v3.0, a package for large analysis of survival data[END_REF], which was used here. Let A j be the time interval (1 yr): A j = [a j-1 ,a j ], j = 1...m, a 0 = 0, a m = +∞, and λ the hazard function:

λ λ ( , ) ()exp( ' ), t t i i z z = 0 β
where λ o ( ) t is the baseline hazard function and z i is the design vector for explanatory variables β for the horse i. Culling or censoring during the time interval A j is denoted t j . We have the following:

S t j i k k j i ( , ) , exp z z =     = - ∏ α β β 1 1
where S is the survivor function and

α λ k a a x x k k = -           - ∫ exp () . 0 1 d
The hazard function during a given time interval is similarly written as λ α ( , ) .

exp( ' )

t j i j i z z = - 1 β
The variances of random effects included in β were obtained from their marginal posterior density and the parameters (α j , β) were estimated by maximizing the joint posterior density, given the variance.

Culling Factors

The following factors affecting the competitive lifetime were included in the model: i. Region of birth with 22 levels based on the administrative regions in France. ii. Month of birth with 6 levels: January and February were grouped together, as well as July to December; the most frequent month of birth was April (36%). iii. Year of recording with 36 levels from 1972 to 2007. iv. Age at first competition with 6 levels: 4, 5, 6, 7, 8, 9 yr and more. The most frequent age at first competition was 4 yr (40%), followed by 5 yr (27%) and 6 yr (14%). Only 19% of horses began competing at an older age. v. Level of performance. The ability of the horse to perform well in competition is a major effect influencing the longevity of the horse in competition. To measure this, we used the traditional measurement of performance, which has been in use in the French breeding evaluation system for many years. This measurement is the natural logarithm of the sum of earnings in competition during the sport season (i.e., during the year; [START_REF] Langlois | Estimation of the breeding value of sport horses on the basis of their earnings in French equestrian competitions[END_REF]. Earnings are allocated to horses according to the technical level of the competition and their rank among the top 25% of all horses. Horses that did not earn money in the year were considered in a single class of nonearning horses. This measurement was corrected for year, sex, and age effects. The effect of performance varied throughout the years of the competing lifetime of the horse because it depended on time. The level of the performance effect was the level achieved the previous year. This choice was made because the measurement of performance depended on the total number of starts per year. The duration of continued competing for a given year depends directly on culling, which causes the 2 factors to merge. This was avoided by using performances from the previous year. For each year (except when the first year was studied), there were 12 levels of performance: ln(earnings) of previous year classified into 10 classes (<μ -2σ, from μ -2σ to μ = 2σ by 0.5 σ unit, >μ + 2σ), previous year without performance (except when the second year of competition was studied), previous year without earnings (but with some starts).

The performance effect was combined with sex (mare, stallion, gelding) and the number of years in competition (1, 2, 3, 4, and more). So that in total, 96 levels were used: 3 for the first year (only sex, no previous performance), 27 for the second year (no previous year without starts and performances above 1 SD pulled together due to the decreased variability of performances for the first year of competition), 30 for the third year (same levels as for the second year plus 3 levels for each sex having a previous year without starts), and 36 for the fourth and following years (all variations in performance). vi. Random sire and maternal grand-sire effect. A total of 16,668 sires and maternal grand-sires were identified, and 23,413 sires including ancestors were used in the analysis to account for all sire-maternal grand-sire relationships. There were 7,244 sires with progeny and grand progeny, 4,497 with only progeny and 4,927 with only grand progeny. There were no sires missing and only 5% of maternal grand-sires, so the completeness of pedigree was good. The mean family size was 17.8 progeny by sire (from 1 to 1,001), 16.3 grand progeny (from 1 to 1,156).

Family size decreased exponentially: only 9% of sires had 50 or more progeny, and 69% of sires had less than 10 progeny.

Heritability

Heritability was defined according to [START_REF] Yazdi | Heritability, reliability of genetic evaluations and response to selection in proportional hazard models[END_REF]. Assuming a proportion of uncensored records p, the heritability was the equivalent heritability that can be used to directly compute reliability of sires using the index of selection:

h p s s 2 2 2 4 1 = + σ σ .

RESULTS

Baseline Functions

Baseline functions were illustrated for a reference horse with the following factors: the region of birth, Basse Normandie; month of birth, April; age at first start, 4 yr old; gelding; with corrected ln(earnings) at 0.5 SD below the mean ( , ).

µ σ µ - [ ] /2
Figure 1 shows the survival function, the density function, and the hazard function. The hazard function decreased between the first and second year in competition, indicating that the first year in competition is crucial for the horse. Subsequently, the hazard function remained stable between the second and third year and increased with time spent in competition, illustrating the aging effect. The relative risk obtained the first year (13% of horses culled after having begun competition) was reached again only after the fifth year. The half-life spent in competition (50% of horses still present) was 6.0 yr.

Fixed Effects

All fixed effects included in the model were tested using the sequential log-likelihood ratio test and were highly significant (P < 0.001). The largest increase of log-likelihood was obtained when adding the year effect, the age at first competition effect, and the level of competition effect.

The region of birth effect was moderate; the relative risk varied from 0.86 to 1.03 (Supplemental Table 1; http://jas.fass.org/content/vol89/issue10/). The number of births per region was not correlated with the region effect. In each case it showed the influence of a particular territory. The influence of the month of birth on the relative risk was low: from 1.00 to 1.04 with an advantage for horses born in April (Supplemental Table 2; http://jas.fass.org/content/vol89/is-sue10/). The influence of year was important with a relative risk varying from 0.54 to 1.37 and a large edge effect (Figure 2 Whereas the previously stated effects dealt with environmental conditions, other effects referred to the choices of the rider in the management of his/her horse and the ability of the horse to compete. The age at first competition had a great influence on longevity in favor of horses that began at 4 yr of age (Figure 3). The younger the horse started competing, the longer it stayed in competition. The half-life was 6.0 yr in competition for horses beginning at 4 yr of age vs. 4.8 yr of age for horses that began at age 6 (with the same mean performance). The level of performance had a great impact on longevity with variations depending on sex and the number of years already spent in competition. For the first year of competition, the relative risk was computed only according to sex because no previous performance was available to measure the ability in jumping competition. The relative risk of culling was greater (P = 10 -7 ) for mares (1.29) and stallions (1.17) when compared with geldings (Supplemental Table 3; http:// jas.fass.org/content/vol89/issue10/). Figure 4 shows the relative risk for the second year of performance and Figure 5 for the fourth and later years. The greater relative risk observed for females compared with stallions and geldings occurred throughout the career of the horse (with 1, 2, 3, or more years in competition). From the second year of performance onward, the risk to be culled decreased when performance increased, with a plateau of performances more than 1 SD above the mean. This decrease of relative risk with the increase in performance was more pronounced for horses after their third year in competition than for the second year. The very best horses (>2 SD above the mean) had a slightly greater relative risk than good horses, especially for stallions the second year of performance. A year of participation in competition but no earnings corresponded to a low performance with greater relative risk of culling. However, a year of absence from jumping competition seemed to have no effect on the decision of culling the year afterward (relative risk between 1.04 and 1.15).

Heritability and Breeding Values

The sire variance component was estimated at 0.0257 (SD = 0.0015); the equivalent heritability [START_REF] Yazdi | Heritability, reliability of genetic evaluations and response to selection in proportional hazard models[END_REF] was 0.08 with P = 0.781, the proportion of uncensored records. There were 3,303 sires for which the accuracy was greater than 0.60, and among them, 262 were used for breeding in 2008. The mean relative risk for these 3,303 sire effects was 0.98, with a SD of 0.11, and maximum and minimum values of 1.54 and 0.70, respectively. Sires were mostly of advanced age: 77% were born before 1983 and only 2% (51 sires) were born after 1992. Figure 6 shows the survival curve of progeny from the best sire used for breeding in 2008 and from the worst sire for longevity.

DISCUSSION

Choice of Model and Data Adequacy

The aim of this study was to determine the genetic factors of functional longevity. The definition of functional longevity was the robustness of a horse for a given jumping ability. The trait analyzed was the length of the competing life in jumping competition.

The principal flaw of competition data is the lack of information on the causes of culling. The end of competitive life may be death, invalidity for competition for multiple reasons including diseases, beginning of a breeding career (particularly for mares), or exportation to continue jumping competition abroad. Competing may also be stopped temporarily or even permanently due to factors that are not related to the individual horse, but to the rider (e.g., lack of time because of professional activities, moving to a place with limited training opportunities). Accordingly, it was not always easy to distinguish between involuntary (e.g., death, invalidity) and voluntary culling (e.g., breeding activities, opportunities of the rider). The knowledge of the reproductive life of mares (date of foaling) would not solve the problem because even mares with musculoskeletal problems that make them inappropriate for competition could reproduce and are used for reproduction. Concerning the rider effect, there is no way to record the reasons of the rider for leaving competition. Because the majority of riders in jumping are nonprofessional, many of the reasons for culling may be voluntary but not taken into account in the model. The only effect that may account for the collective behavior of riders was the year effect (e.g., when the overall economic situation may influence the sport industry). The reasons for riders stopping competition led to an overestimation of the residual variance and an underestimation of heritability. For this reason, EBV may be biased, but the personal decisions of the rider had no reason to be linked to the sire of the horse they ride. The reliability of the EBV of the sire should be less than expected and associated with greater heritability. Similar cases are expected with farm data in other species. Important bias was expected only if the decision of the rider depended on the quality of the horse as is the case with exportations. However, exportations were not recorded in the database, so only the correlation between the probability of being exported and quality of the horse for jumping were taken into account by the addition of the level of performance effect in the model. If exportations are more frequent for the progeny of a sire for unknown reasons and not related to ability, then correction is not possible and bias in the EBV may be expected.

In comparison with the first French study on longevity [START_REF] Ricard | Analysis of factors affecting length of competitive life of jumping horses[END_REF], the model has been greatly improved by including both the performance measured the year preceding the event of failure or censoring and the interactions due to the sex of the horse. In the previous study, the model for performance was based on 2 effects: the number of starts and yearly earnings. The competing lifetime was precorrected against the yearly earning effect obtained with the full model and then reanalyzed without including the number of starts to obtain an estimate for heritability. This avoided any confusion between the level of the earnings obtained and possible culling. In the model used here, use of the number of starts is no longer necessary because it measures the performance of the horse for the year preceding the studied time interval. In addition, and contrary to the previous study from which mares had been excluded, the inclusion of interactions due to the sex of the horse meant that the special status of mares due to the impossibility of simultaneously breeding and competing could be assessed.

Management of Horses

Choices relative to the management of horses have a great impact on their longevity in competition. Horses beginning their career at 4 yr of age stayed in competition longer than horses beginning at 5 yr of age, and those horses stayed longer than horses that began at 6 yr of age. This contradicts the common belief of riders that the use of young horses in competition handicaps their health. Early habituation to competition may therefore be advantageous for the career of the horse. This habituation concerned horses beginning at 4 or 5 yr of age because they have a similar relative risk; the increase of relative risk was more important after the age of 6 yr. We assumed that this habituation was carried out with care and was not too intensive. However, at a given age rather than at a given number of years spent in competition, the survival probability for horses having started competition later was still greater than for those having started earlier, although the former spent less time in competition.

The influence of the level of performance was also important. As expected, horses with poor performance had a greater risk of culling than horses with better performance. This may be the result of the voluntary choice of the rider (he/she does not want to continue competing with a horse that is unsuccessful) or indicate that the horse has experienced health problems that make it no longer competitive and result in culling. This effect was moderate the first year in competition (maximum risk ratio according to sex: 1.84) but became important after the third year spent in competition (maximum risk ratio according to sex: 2.57). The relative risk of mares being culled was always greater than that for stallions or geldings because they can always be used for breeding. Accordingly, their longevity in competition depended less on their own ability: the range of relative risks according to performance was less for mares than for geldings. In addition, the relative risk increased for mares with good performance because good sport mares were more susceptible to be used for breeding (when they reached performances of 0.5 SD above the mean; i.e., among the best 30%). An increase of the relative risk was also observed for very good sport stallions and geldings. The explanation may be that these good young horses were exported and disappeared from French data or, for stallions, that they were used exclusively for breeding (even if stallions can be used simultaneously for breeding and competition). Another explanation may be that very good performances put greater strain on their locomotory system and result in increased involuntary culling for health problems. The relative risk was always less for geldings because they can only be used in competition. Surprisingly, 1 yr without participation or without earnings had a very different effect on the relative risk. A year of absence from competition had no effect on the decision to cull the year afterward. This indicated that either the absence was not due to health problems or that any health problems were solved after a year of rest. On the contrary, a year of participation but no earnings, and therefore no success in competition, always led to a greater relative risk of culling, as with very bad performances.

Selection

The trait longevity in competition showed genetic variability, even though causes for culling were not considered when it was defined. As an illustration, Figure 6 shows that, after 7 yr in competition, 50% of the progeny of the best sire were still in competition for their eighth year (thus aged 12 yr or more) against 27% for the worst sire when all other factors were kept equal. This is not a negligible effect, especially for jumping ability. There were no similar results found in the literature, except for a previous French study [START_REF] Ricard | Analysis of factors affecting length of competitive life of jumping horses[END_REF] which gave similar results for the variance of the sire effect (0.0261). However, calculation of the EBV is not the only factor required to define the selection plan. According to the biological characteristics of the species and testing structures (field test for young horses or competition tests), selection of sires in sport horse breeding is essentially based on their own performance and performance of contemporary relatives [START_REF] Dubois | Optimization of breeding schemes for sport horses[END_REF]. The EBV for longevity stemming from results of the progeny of the sire in competition are obtained too late to affect genetic progress and to be used to select stallions having already produced many progeny. That is why most people try to develop indirect criteria to select for lon-gevity, or in reality against early retirement. [START_REF] Wallin | Phenotypic relationship between test results of Swedish warmblood horses as 4-year-olds and longevity[END_REF] found that better conformation score (best 10%), good leg note (best 75%), and high orthopedic status (best 36%) measured at 4 yr old in the Swedish Riding Horse Quality Test were associated with a decreased relative risk of culling during the entire life of the horse (in and out of competition). [START_REF] Wallin | Phenotypic relationship between test results of Swedish warmblood horses as 4-year-olds and longevity[END_REF] suggested that these traits should be used for selecting young horses. [START_REF] Stock | Survey on the development of Hanoverian warmblood horses, selected for sale at auction in 1991 to 1998[END_REF] found that the prevalence of radiographic findings on young horses sold in auctions influenced the number of later participations in competition, even if there was no effect on successful participation. [START_REF] Ducro | Influence of foot conformation on duration of competitive life in a Dutch warmblood horse population[END_REF] found a negative effect of uneven feet on the duration of competitive life especially in Elite Jumping (risk ratio 0.52 for even feet). All these reports showed the importance of limb conformation, which may be detected at an early stage, for the longevity of horses. However, the EBV for longevity in competition, as proposed in this paper, shows a greater reliability due to a larger data set (more than 200,000 horses against about 2,000 to 14,000 horses in the other studies), a reduced proportion of censoring (22% against sometimes 73%), and included more voluntary and involuntary reasons for culling. In competition data, large data sets are often available and proportion of censoring is low and may be even less as shown by Árnason (2006), which found only 15% of censoring for 32,504 racing careers of Swedish Standardbred. Causes other than the musculoskeletal system, which may be anticipated by analysis of limb conformation, represented about 40% of causes of death in [START_REF] Wallin | Estimates of longevity and causes of culling and death in Swedish warmblood and coldblood horses[END_REF]. The inclusion of voluntary causes of culling enabled us to assess functional longevity rather than true longevity. Although obtaining highly accurate EBV for longevity is difficult at early ages, the use of even less accuracy EBV is an improvement over individual phenotypic observations of structural characteristics. An optional approach could be to combine the observation of structural traits with the quality of longevity performance data to produce more accurate EBV for longevity. In near future, late EBV calculation may no longer be a problem because it can provide the basis for genomic selection [START_REF] Meuwissen | Prediction of total genetic value using genome-wide dense marker maps[END_REF]. With the help of EquineSNP50 BeadChip (Illumina Inc., San Diego, CA), the data for older sires may be used to predict accurate breeding values for young candidates, both stallions and females. However, as many are finding out, databases containing both genomic scan data and individual performance data (such as longevity) of considerable size are needed to establish the relationships between genomic information and longevity.

Conclusion

Competition data provide a good tool to measure the jumping longevity for sport horses and EBV for sires. Although the trait did not detail the causes of involuntary culling, genetic variability was found and resulted in suggestions relating to management. Horses that started competition late and low performance horses had greater relative risk of culling. Heritability was 0.10. Breeding values were computed, and even if high accuracy was obtained only late in the life of the sire, their use will be emphasized in the future with the help of genomic selection.

  ). The low relative risk observed with the earliest data records was due to samples including only young horses before 1976 because data contained only horses born from 1968 onward. The high relative risks observed in 2006 and even the more so in 2007 are explained as follows: horses in competition in 2007 but not in 2008 were considered culled in 2007 although they may compete again in 2009 after only 1 yr without performances. Up to 2005, 1 or 2 yr of absence was not considered as culling but as years without performance before further participation in competition. These years were included in the time spent in competition. Such temporary pauses in competition were considered as the end of the competing life of the horse for 2006 and 2007 and thus explain the larger estimates observed for the level of culling. When the edge effects are ignored, the tendency was a slight decrease of the relative risk as a function of years (from 1.06 to 0.94), showing an increased environmental resistance of the horses, with

Figure 1 .

 1 Figure 1. Survival, density, and hazard functions of longevity in jumping competition for a reference horse [region of birth, Basse Normandie; month of birth, April; age at first start, 4 yr old; gelding; and with corrected ln(earnings) between the mean and one-half SD below the mean.

Figure 2 .

 2 Figure 2. The figure shows the effect of year of performance on the relative risk of culling.

Figure 3 .

 3 Figure 3. The figure shows the effect of age at first competition on the relative risk of culling (SE < 0.02).

Figure 4 .

 4 Figure 4. The figure shows the effect of level of performance in jumping competition the previous year on the relative risk of culling during the second year in competition according to sex (µ = mean).

Figure 5 .

 5 Figure 5. The figure shows the effect of level of performance in jumping competition the previous year on the relative risk of culling during the fourth and following years in competition according to sex (µ = mean).

Figure 6 .

 6 Figure 6. The figure shows the survival function for reference progeny from the best and worst sire for longevity used in breeding in 2008.

Research was supported by Les Haras Nationaux (Saumur, France).

Corresponding author: anne.ricard@toulouse.inra.fr Received February 1, 2011. Accepted May 2, 2011.

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K Elkjaer

M.-L Ancker

H Gustafsson

N C Friggens

A Waldmann

L Mølbak

H Callesen

Chr Hansen

Uterine bacterial flora in postpartum Danish Holstein dairy cows determined using DNA-based fingerprinting: Correlation to uterine condition and calving management

Keywords:

a b s t r a c t

The overall aim of this study was to describe uterine bacterial flora during the postpartum period in Danish Holstein cows using the Terminal Restriction Fragment Length Polymorphism (T-RFLP) method. This method produces a pattern of nucleic acid fragments from the microorganisms present, reflecting the "fingerprint" of the actual microbial flora. As well as characterizing changes in flora with time from calving and between herds, data were examined for strong relations between uterine bacterial flora, calving management and uterine condition. In total 125 Holstein cows from five herds were included, and for each cow calving management was recorded. Cows were clinically examined on average 8 (range 0-19) and 28 (range 22-38) days after calving, and a uterine sample was taken for bacterial identification using T-RFLP. Milk samples were taken weekly for progesterone analysis. Bacteria were found in all cows at both examinations, and the flora was composed of many species, including species not traditionally reported to be present in the bovine uterus. The bacterial composition differed according to days from calving and herd. In all five herds Fusobacterium necrophorum, Pseudomonas/Acinetobacter and Bacteroides/Sphingobacterium/Prevotellaceae were among the most common at both examinations. In four herds there was a percentage decrease of F. necrophorum from first to second examination, and in all herds there was a percentage increase of Pseudomonas/Acinetobacter from first to second examination. No differences in bacterial flora were found between cows with different uterine scores, which were influenced by herd, calving difficulty and retained placenta.

© 2013 Elsevier B.V. All rights reserved.

Introduction

In cows with a normal puerperium, prevalence and consequences of bacteria in the uterine lumen is debated. In some studies bacterial cultures were found in a high proportion of puerperal cows [START_REF] Elliott | Uterus of the cow after parturition: bacterial content[END_REF][START_REF] Griffin | Non-specific uterine infection and bovine fertility. I. Infection patterns and endometritis during the first seven weeks post-partum[END_REF][START_REF] Hussain | Postpartum uterine flora following normal and abnormal puerperium in cows[END_REF] while in other studies they were not [START_REF] Bekana | Intrauterine bacterial findings and hormonal profiles in post-partum cows with normal puerperium[END_REF][START_REF] Fredriksson | Intrauterine bacterial findings and release of PGF2-alpha in the postpartum dairy cow[END_REF][START_REF] Noakes | Bacterial flora of the uterus of cows after calving on two hygienically contrasting farms[END_REF]. Prevalence rates of Streptococci spp. and Staphylococci spp. may be greater in cows with uncomplicated postpartum (p.p.) period, indicating a sort of 'normal' uterine flora [START_REF] Huszenicza | Uterine bacteriology, resumption of cyclic ovarian activity and fertility in postpartum cows kept in large-scale dairy herds[END_REF]. From cows with abnormal uterine conditions, bacteria as Escherichia coli, Arcanobacterium pyogenes and gram negative anaerobes (e.g. Bacteroides spp. and Fusobacterium spp.) are more often isolated [START_REF] Santos | Metagenomic analysis of the uterine bacterial microbiota in healthy and metritic postpartum dairy cows[END_REF][START_REF] Sheldon | Uterine diseases in cattle after parturition[END_REF][START_REF] Werner | Relationship between bacteriological findings in the second and fourth week postpartum and uterine infection in dairy cows considering bacteriological results[END_REF]; such bacterial flora is characterized as "contamination" or "infection" [START_REF] Sheldon | Influence of uterine bacterial contamination after parturition on ovarian dominant follicle selection and follicle growth and function in cattle[END_REF]. Among pathogenic bacteria, A. pyogenes is often related to a serious effect on reproduction [START_REF] Griffin | Non-specific uterine infection and bovine fertility. I. Infection patterns and endometritis during the first seven weeks post-partum[END_REF][START_REF] Huszenicza | Uterine bacteriology, resumption of cyclic ovarian activity and fertility in postpartum cows kept in large-scale dairy herds[END_REF][START_REF] Williams | Clinical evaluation of postpartum vaginal mucus reflects uterine bacterial infection and the immune response in cattle[END_REF]. In some studies the bacterial flora in the uterus of cattle changes in composition and amount within the first weeks after calving [START_REF] Bekana | Intrauterine bacterial findings and hormonal profiles in post-partum cows with normal puerperium[END_REF][START_REF] Dolezel | Bacterial contamination of the uterus in cows with various clinical types of metritis and endometritis and use of hydrogen peroxide for intrauterine treatment[END_REF][START_REF] Hussain | Postpartum uterine flora following normal and abnormal puerperium in cows[END_REF]. It remains to be shown if knowledge of the uterine bacterial status of a cow during the first two weeks p.p. can be used to indicate her risk of a prolonged p.p. anovulation.

Bacteria have traditionally been classified using bacterial culture. However, as some bacteria are easier to grow than others [START_REF] Amann | Phylogenetic identification and in situ detection of individual microbial cells without cultivation[END_REF], and as some will be overwhelmed by others, a culture period can shift the flora from time of sampling to time of reading the culture result. [START_REF] Elliott | Uterus of the cow after parturition: bacterial content[END_REF] stated that six different growth-media should be used to detect all organisms in the bovine uterus at various times after calving, but this has rarely, if ever, been done in subsequent studies [START_REF] Bekana | Intrauterine bacterial findings and hormonal profiles in post-partum cows with normal puerperium[END_REF][START_REF] Griffin | Non-specific uterine infection and bovine fertility. I. Infection patterns and endometritis during the first seven weeks post-partum[END_REF][START_REF] Noakes | Bacterial flora of the uterus of cows after calving on two hygienically contrasting farms[END_REF]. Several culture-independent methods for identifying and characterizing the microbiological communities of different environments are therefore now applied, also in studies of the bovine uterus [START_REF] Santos | Metagenomic analysis of the uterine bacterial microbiota in healthy and metritic postpartum dairy cows[END_REF][START_REF] Sheldon | Specific strains of Escherichia coli are pathogenic for the endometrium of cattle and cause pelvic inflammatory disease in cattle and mice[END_REF][START_REF] Silva | Genomic and phenotypic characterization of Escherichia coli isolates recovered from the uterus of puerperal dairy cows[END_REF]. One such method is the Terminal Restriction Fragment Length Polymorphisms (T-RFLP) method that produces a pattern of nucleic acid fragments (TRFs) from all microorganisms in the sample, reflecting the "fingerprint" of the actual microbial flora [START_REF] Kitts | Terminal restriction fragment patterns: a tool for comparing microbial communities and assessing community dynamics[END_REF]. If this does not result in a clear identification of the organism, DNA cloning and sequencing may be the next step. The final result is a picture of the bacteria present in the sample, including those difficult to grow [START_REF] Amann | Phylogenetic identification and in situ detection of individual microbial cells without cultivation[END_REF][START_REF] Kalmbach | Isolation of new bacterial species from drinking water biofilms and proof of their in situ dominance with highly specific 16S rRNA probes[END_REF].

One important factor for infection risk may be hygienic conditions and cow management around calving, including cleanliness of the calving pen and routines regarding calving assistance. Very few studies have focused on this [START_REF] Noakes | Bacterial flora of the uterus of cows after calving on two hygienically contrasting farms[END_REF][START_REF] Potter | Risk factors for clinical endometritis in postpartum dairy cattle[END_REF], and how management practices affect uterine bacterial flora is not known for certain. Furthermore, the extent to which the uterine bacteria composition depends on the cow's herd has not been studied.

The overall aim of this study was to describe the uterine bacterial flora during the p.p. period in Danish Holstein cows using the T-RFLP method. The hypothesis was that a certain bacteria composition, assessed by the T-RFLP method, was associated with the appearance of the vaginal discharge. As well as characterizing changes in flora with time from calving and between herds, data were examined for strong relations between the uterine bacterial flora, calving management and uterine condition.

Materials and methods

Study farms

The trial was conducted from October 2008 to January 2009 in five herds with Danish Holstein cows. Four of the five herds were commercial farms, selected within the same veterinary practice with a standardized herd health agreement with weekly veterinary visits and routine examinations of fresh cows. The four commercial herds were chosen from a larger pool of herds according to occurrence of metritis (to obtain a variation in pathological picture), and based on the willingness of the herd owners to participate (to obtain the best possible registrations). Two herds (Herds 1 and 2) with a lesser incidence of metritis (7.9% and 12.6%, respectively) and two herds (Herds 3 and 4) with a greater incidence of metritis (21.4% and 20.9%, respectively) were chosen. The fifth herd was the research farm connected to Aarhus University and had a lesser metritis incidence at 6.0%. The herd size was 140-400 cows, and the milk yield was 9500-11,000 kg ECM/cow per year.

Animals and records

The first 25 cows that calved in each of the five herds after the start of the trial period were selected for this study, so a total of 125 cows of Parities 1-8 were included in the study. To standardize the clinical procedures and reporting of findings, all participants received standard instructions at the beginning of the study. All individual cow data were recorded directly for this study using a special protocol or through the Danish Central Cattle Database.

Calving management was evaluated by the farmer for every calving by the following parameters: use of calving pen, number of cows in the pen at calving (single or more), whether clean bedding was provided in the pen before calving, whether cleaning or disinfection of the pen was conducted before calving, and the time the cow spent in the calving pen before calving (coded as: 1 = moved to calving pen immediately before calving; 2 = moved 12 h before calving; 3 = moved 24 h before calving, 4 = moved more than 24 h before calving).

Calving date, calving difficulty and calf survival as well as time to fetal membrane detachment were recorded by the farmer. Calving difficulty is a mandatory registration for Danish farmers and is entered into the Danish Central Cattle Database. Farmers recorded calving difficulty using one of the five categories: 1 = easy calving with no assistance, 2 = easy calving, but with the farmer's assistance, 3 = difficult calving without veterinary assistance but with farmer's assistance, 4 = difficult calving with veterinary assistance, 5 = cesarean section. For evaluating calf survival two categories were used: 1 = living calf, 2 = stillborn or dead within the first 24 h after delivery. All cows gave birth to single calves and without any cesarean section.

Retention of the fetal membranes for more than 12 h was treated by manual removal of the placenta, and antibiotics were only used if the cow was ill, based on lack of appetite and increased rectal temperature (>39.5 • C). Systemic treatment was with oxytetracycline and was preferred to avoid placing antibiotics directly into the uterine lumen. If the cow had a retained placenta, the uterine sample was taken immediately before removal of the placenta. In total six cows were treated for retained placenta, and these cows were distributed across all herds. Metritis was defined according to the definitions of puerperal metritis and clinical metritis by [START_REF] Sheldon | Defining postpartum uterine disease in cattle[END_REF]. Only cows defined as having puerperal metritis (abnormally enlarged uterus, watery foul-smelling discharge with signs of systemic illness and fever >39.5 • C within 21 days after calving) were treated, and antibiotics were only used systemically. Metritis treatment was with oxytetracycline and, if needed, non-steroidal anti-inflammatory drugs. If a uterine sample was to be taken on the same day as treatment, this was always done immediately before the treatment. In total 11 cows were treated for metritis (two in Herd 1, three in Herd 2, three in Herd 3, two in Herd 4, one in Herd 5). Four of these cows were also among those treated for retained placenta. All treated cows remained in the study with all treatments being recorded. In total, 24 cows were treated with antibiotics (for any reason) in the period from calving to first examination, and 17 cows were treated in the period between the first and the second examination.

Clinical examinations

All cows were clinically examined twice after calving by three herd veterinarians (one in Herd 1 and 3, one in Herd 2 and 4, one in Herd 5). The first examination was made on average 8 days p.p. (range 0-19 days) with the majority (85%) of the cows being examined between 5 and 11 days p.p. At this time all fresh cows were examined by the herd veterinarian according to the standardized procedure specified in the Danish law regarding herd health agreements. According to this law, fresh cows must be examined once in the period 5-19 days after calving, and they must, among other things, have a scoring of uterine discharge according to a legally specified scoring system. The uterine scores are the only registrations addressed in this paper. This uterine scoring system is a combined evaluation of amount, color, smell, pus content and consistency of the vaginal discharge (Table 1), and the principles of this scoring system are comparable to those in the scoring system by [START_REF] Williams | Clinical evaluation of postpartum vaginal mucus reflects uterine bacterial infection and the immune response in cattle[END_REF]. The uterine score is given by scoring discharge manually withdrawn from the vagina. If this discharge smells abnormal, a score of at least 5 must be given.

The cows had their second examination on average 28 days p.p. (range 22-38 days) with the majority (72%) of the cows examined between 26 and 30 days p.p. At this examination only a uterine sample was taken. The average number of days between the two examinations was 19 (range 11-30 days) with the interval being 19-23 days for the majority of cows (66%).

Uterine condition

At the first examination, each cow was given one of the 10 uterine scores (Table 1). For data analysis cows were Plenty amounts of gray/yellow seromucous secretion -no abnormal smell. 5

Poor/plenty amounts of purulent secretion -difference in moisture and color -smells abnormal. 6

Increasing amounts of secretion -difference in texture and color -smells abnormal. 7

Increasing amounts of secretion -beginning to look red-brownish -stinks. 8

Plenty amounts of grayish secretion -stinks. 9

Large amounts of brown-yellow/brown secrete -typically a retained placenta -very foul smell.

Modified and translated from http://www.landbrugsinfo.dk/Kvaeg/Filer/ nysr vej klinisk.pdf (in Danish).

grouped into the three overall uterine conditions: good (uterine score 0 and 1; 58 cows (49%)); medium (uterine score 2, 3 and 4; 40 cows (34%)); bad (uterine score 5, 6, 7, 8 and 9; 20 cows (17%)).

Uterine sample collection

Uterine samples were taken from all cows at both first and second clinical examination, using an instrument developed at the Estonian University of Life Sciences. The instrument consisted of an outer guiding tube and an inner rod mounted with a small sterile brush (Uterobrush, Medscand Medical AB, Malmö, Sweden). The brush has a small ball at its outer end, which fits into the guiding tube cap, thereby closing the tube and avoiding contamination of the brush during its insertion through vagina and cervix. Before use, the whole instrument was sterilized by boiling for 10 min. At each sampling the cow was restrained and its vulvar lips were cleaned using a dry paper towel. By rectal manipulation of the cervix, the instrument was then guided through the cervix into the uterus lumen, where the brush was protruded and rotated several times to collect material. The brush was then withdrawn into the guiding tube to ensure uncontaminated collection of uterine discharge. After sampling, the brushes were aseptically separated from the device into separate plastic containers marked with individual cow numbers. The brushes were frozen (-19 • C) within 3 h from sampling, and were later transported on dry ice to the lab. After each use the instrument was cleaned in lukewarm soap water, a new Uterobrush was mounted and the whole instrument was sterilized by boiling.

Uterine sample analysis

For DNA extraction from the uterine samples they were mixed with phosphate-buffered saline, followed by centrifugation at 20,000 × g for 10 min at 5 • C. From each sample, equal amounts of DNA were extracted. The supernatant was discarded and the pellet re-suspended in 200 l phosphate-buffered saline and 350 l solution A from Easy DNA kit (Invitrogen, Paisley, UK) added together with 400 mg 100 m Zirkonia-silica beads (BioSpec Products Inc., Bartlesville, US). The bacterial cells were then broken in a mini beadbeater (Biospec Products Inc.) for 4 min. Hereafter the DNA was extracted with the Easy DNA kit as described by the manufacturer. The DNA concentration was measured by optical density and the DNA was adjusted to a concentration of 5 g of DNA/ml.

A polymerase chain reaction (PCR) was performed with fluorescence tagged primers for 16S rRNA gene, and the PCR products were purified as previously described [START_REF] Mølbak | Increased amount of Bifidobacterium thermacidophilum and Megasphaera elsdenii in the colonic microbiota of pigs fed a swine dysentery preventive diet containing chicory roots and sweet lupine[END_REF]. The PCR products were digested with the restriction enzyme HhaI to yield fluorescently labeled terminal restriction fragments (TRFs). The TRFs were analyzed by polyacrylamide gel electrophoresis according to the procedure described in [START_REF] Mølbak | Increased amount of Bifidobacterium thermacidophilum and Megasphaera elsdenii in the colonic microbiota of pigs fed a swine dysentery preventive diet containing chicory roots and sweet lupine[END_REF].

Identification of TRFs with naming of the bacteria was done by cloning and sequencing the DNA in two samples from each herd, one from a cow with uterine score 0, and one from a cow with a greater uterine score (5-9). The number of samples was limited because of economic considerations. Cloning and sequencing was done as described previously [START_REF] Mølbak | Increased amount of Bifidobacterium thermacidophilum and Megasphaera elsdenii in the colonic microbiota of pigs fed a swine dysentery preventive diet containing chicory roots and sweet lupine[END_REF].

The TRF profiles and 16S rRNA gene sequences were analyzed using the software BioNumerics (Applied Maths, Kortrijk, Belgium). The 16S rRNA gene sequences have been assigned to their taxonomic names in RDP classifier (http://rdp.cme.msu.edu). Based on 99% similarity, representative operational taxonomic units were created and were submitted to the National Center for Biotechnology Information (NCBI), where they received their respective accessions numbers. In Section 3 of this manuscript, the table includes both bacterial names and their respective TRF sizes. If specific naming was not completed this is indicated as undetermined (UD) and only the TRF size is stated.

Milk sampling and analysis

Milk samples from each cow were taken by the farmer once a week from calving to 8 weeks p.p. In the two herds using milking parlors, samples were taken at morning milking where milk from the individual cow was stripped directly from the teat into a plastic collection tube after removal of the milking equipment. In the two herds using robot milking, the milk samples were taken at varying times after milking, but also here milk was stripped directly from the teat into the plastic collection tube. In the fifth herd, proportional whole milk samples were collected automatically during milking in the robotic milking system. All milk samples from each cow were frozen (-19 • C) for later analysis.

Milk progesterone was determined using the Ovucheck ELISA-kit (Biovet Inc., St. Hyacinthe (Oc), J2S BW2 Canada). Milk samples were pipetted, diluted and distributed using a Biomek 2000, Laboratory Automation Workstation, Beckman Coulter. Milk samples (here 25 l, diluted 1 + 2 with water) were handled according to the manufacturer's instructions. Plates were read using a spectrophotometer/fluorometer (Fluostar, BMG Labtechnologies, 575 nm). Analyses were performed in 96 well plates with two times seven standards (0-30 ng/ml) locally made from milk from ovariectomized cows and ethanolic progesterone solutions, and with two times two control samples (3 and 15 ng/ml) used for every analysis and plate. Inter-assay precision was 18.6 and 13.0 CV%, for lesser and greater control samples, respectively (n = 38; n = 2), while intraassay precision was 10.6 and 8.1 CV% for lesser and greater control samples, respectively (n = 2; n = 38). The accuracy of lesser control (3 ng/ml) was 2.35 ± 0.74 (SD) (n = 76), and for greater control (15 ng/ml) it was 15.75 ± 1.69 (SD) (n = 76).

Milk progesterone profiles

The analysis of milk samples from each cow resulted in progesterone profiles that were used to determine the length of p.p. anovulation. The cow was defined to have started estrous cycling, when the progesterone concentration was above 4 ng/ml [START_REF] Friggens | Prediction of the reproductive status of cattle on the basis of milk progesterone measures: model description[END_REF] for two consecutive samples, where the day of the second milk sample above 4 ng/ml was set as the actual starting day, due to the previously mentioned variation in milk sampling method in the herds.

Based on the cow's actual starting day of estrous cycling, the length of p.p. anestrous was calculated. A new variable was then made to define the length of the p.p. anovulation as normal or long. The definition was made by looking at the 90th percentile of length of p.p. anestrous, which was 56 days in the cows in this study. A p.p. anestrous ≤56 days was therefore defined as normal, whereas a length of more than 56 days was defined as long.

Statistical analysis

Of the 125 cows in this study, uterine samples from seven cows contained too little material to yield a sufficient result in the T-RFLP, so these cows were removed from all the analyses. The registrations regarding calving management were occasionally missing due to unknown causes, so these analyses were made on lesser number of cows. Lastly, 12 cows were removed from the analyses where length of p.p. anovulation was used, due to missing progesterone samples.

For the statistical analyses the program 'R' (R Development Core Team, 2010) was used, and the level of significance was 5%. In analyses regarding bacterial composition, principal component analysis (PCA) was applied, using the prcomp function. The raw data were composed of each cow's percentage-wise distributions of its different TRF sizes, so the sum of percentages for TRFs for each cow was 100. This dataset contained 186 different TRF sizes, so PCA was chosen for this dataset, because it is a useful method to reduce this very high number of variables [START_REF] Ringner | What is principal component analysis?[END_REF], and because it is a strong tool for comparison of complex microbial communities [START_REF] Abdo | Statistical methods for characterizing diversity of microbial communities by analysis of terminal restriction fragment length polymorphisms of 16S rRNA genes[END_REF][START_REF] Dollhopf | Interpreting 16S rDNA T-RFLP data: application of self-organizing maps and principal component analysis to describe community dynamics and convergence[END_REF][START_REF] Kaplan | 16S Ribosomal DNA terminal restriction fragment pattern analysis of bacterial communities in feces of rats fed Lactobacillus acidophilus NCFM[END_REF]. In addition to reduce the dimension of the original dataset, an advantage of the PCA is that you obtain a dataset with variables (PCs) that are uncorrelated with each other, due to the orthogonal construction. This is in contrast to the original dataset, where it must be assumed that there is some correlation between the variables.

Construction of principal components

The original variables (TRF sizes) were used to construct a new set of variables, the so-called principal components (PCs). A subset of these PCs can then be used for further analysis. Each individual uterine sample contributed to the construction of each PC. In a PC the original variables are weighted in such a way that the resulting component explains as much of the variation in the original dataset as possible. Therefore the first PC (PC1) explains the most variation of the original data, and the second PC (PC2, orthogonal to PC1) explains as much as possible of the remaining variation, not explained by PC1. Subsequent PCs are constructed in the same way, thereby explaining less and less of the variation in the original dataset. The same number of PCs is constructed as there are variables in the original dataset (here 186), but to reduce the dimension of the dataset, only the first few PCs are used for further analyses.

In the present manuscript a combination of two criteria for determining the number of PCs chosen for further analysis were applied [START_REF] Mardia | Multivariate Analysis[END_REF]: first the chosen PCs together must explain at least 90% of the total variation in the original dataset, and second each chosen component must explain at least 1% of the total variation. In the present study both of the criteria led to the same number of PCs chosen in each analysis.

Following the PCA a new dataset containing the chosen number of PCs was constructed, now with a smaller dimension than the original dataset. Each of the PCs can be seen as a new measure for composition of TRF sizes, in other words bacterial composition. In the subsequent analyses, the PCs can be evaluated together with the loadings for each PC, where the loadings indicate the weight of the original variables, in this case TRF sizes representing specific bacteria, in the composition of the PC.

Analysis of principal components

Testing for differences in the profile of bacterial species present in the different individual samples (hereafter referred to as bacteria composition) between the first and the second examination was done as follows: a PCA was performed as described above. The PCA resulted in a dataset with 20 PCs chosen (PC1 to PC20). Following this an analysis of the effect of examination number was made for each PC separately, starting with PC1, then PC2 and onwards. The analysis was made using a linear regression with the respective PC as the response variable and examination number as the explanatory variable.

For the main outcome (i.e. bacterial composition), the linear regression included the explanatory variables herd, days from calving, calving difficulty, retained placenta, use of clean bedding, length of p.p. anovulation and whether the uterine condition was classified as good, medium or bad. As the initial analysis showed that there was an effect of examination number on bacterial composition, the present analysis was made for each examination number separately. The original dataset was split into two, according to examination number, and separate PCAs were conducted for first and second examination, respectively, resulting in two new datasets. The dataset regarding the first examination consisted of 20 chosen PCs (PC1 to PC20), and the dataset regarding the second examination consisted of 15 chosen PCs (PC1 to PC15). Each of the chosen PCs was used as the response variable in linear regressions with the above-mentioned explanatory variables, starting with PC1, then PC2 and onwards.

To evaluate if antibiotic treatments had any effect on the bacterial composition in the uterus, separate linear regressions were built with the PCs as the response variable and treated/not treated as the explanatory variable. The scenarios considered were: (i) difference in bacteria composition at the first examination for cows treated/not treated in the period from calving to first examination; (ii) difference in bacteria composition at the second examination for cows treated/not treated in the period from calving to first examination; (iii) difference in bacteria composition at the second examination for cows treated/not treated in the period from first to second examination. In all the mentioned analyses Bonferroni's correction for multiple tests was used to avoid the problem that calculating numerous correlations increases the risk of erroneously concluding the presence of a significant correlation.

Additional analyses

To test which variables had an effect on the response variable (uterine score, which is a class variable from 0 to 9 (Table 1)), multinomial log-it models were applied using the multinom function. The explanatory variables tested were herd, calving difficulty, retained placenta, parity and herd × calving difficulty. The interaction herd × calving difficulty was included in the analysis because the scoring of calving difficulty was possibly influenced by the observer, i.e. to which herd the cow belonged. The same method was used in analyzing which variables had an effect on the variable calving difficulty, as this is also a class variable. The explanatory variables analyzed here were herd, parity, milk fever, use of calving pen, number of cows in calving pen at the time of calving and time spent in calving pen before calving.

Results

Bacterial flora

Bacteria were found in all cows at both first and second examination. The initial analyses were performed to investigate possible differences in bacteria composition between examinations. The examination number had an effect on PC1 and PC7 (P = 0.002 and P = 0.025, respectively), whereas the analyses for the remaining PCs showed no significant effect of examination number. The first PC (PC1) accounted for 29.1% of the total variation in the profile of bacterial species present in the different individual samples (referred to as bacterial composition). From the loadings concerning this PC it was found, that the bacteria Pseudomonas/Acinetobacter and Fusobacterium necrophorum were given the greatest weight. The seventh PC (PC7) only accounted for 3.75% of the total variation in the original dataset regarding bacteria composition. From this analysis it can be assumed that there were differences in bacteria composition between the first and second examination, and the following analyses were, therefore, done separately for the first and the second examination.

Bacterial flora at first and second examination

In the data-set regarding the first examination the effects of the variables herd, days from calving, clean bedding, calving difficulty, retained placenta, antibiotic treatment before first examination, length of p.p. anovulation, and uterine condition (good/medium/bad) on each chosen PC were analyzed. The only significant variable was herd, which was significant in the analyses regarding PC6 and PC12. Together, these 2 PCs accounted for 7.3% of the total variation in bacteria composition, with PC6 accounting for 5.0% and PC12 accounting for 2.3%. From the loadings it was seen that PC6 mainly gave weight to E. coli, while PC12 mainly gave weight to a bacterium with the TRF size 230.6 that was not found in the clone library (data not shown).

To investigate whether the herd effect was due to different histories of metritis incidence between the five herds (high or low), further investigation of the linear model was made. This showed that the significant herd effect on PC6 was due only to a significant difference between Herd 2 and the remaining herds and not between any other herds. The significant herd effect on PC12 was due only to significant differences between Herd 3 and the remaining herds and not between any other herds. Therefore, the herd effect was not due to history of disease incidence, as there then would have been no differences between the low incidence herds (1, 2 and 5), and no differences between the high incidence herds (3 and 4).

In the data regarding the second examination the effects of the variables herd, days from calving, clean bedding, calving difficulty, retained placenta, antibiotic treatment before first examination, antibiotic treatment from first to second examination, and length of p.p. anovulation on each chosen PC was tested. Again herd was the only significant variable in the linear model for PC2 and PC11 that accounted for 15.6% and 1.6% of the total variation, respectively. Investigation of the loadings showed that PC2 mainly gave weight to Arcanobacterium. As for the first examination, the herd effect was investigated further, showing that the effect of herd on PC2 was due to differences between Herd 1 and the rest, while the effect on PC11 was due to differences between Herd 5 and the rest. Again the herd effect was not found to be due to historical metritis incidence.

Change in bacterial flora between first and second examination

To get an indication of the number of bacterial species present in the uterus of the p.p. cows in this study, the number of TRFs was used as a proxy for the number of bacterial species, and was investigated for each examination in each herd. There was a large variation in number of TRFs in the herds (from 1 to 50), with an average number from both examinations of 21 ± 1.4, 11 ± 1.2, 16 ± 1.1, 15 ± 1.0 and 8 ± 0.6 in Herd 1, 2, 3, 4 and 5, respectively. No clear differences in number of TRFs were found between first and second examinations. The numbers given here illustrate that a large and variable number of bacterial species was found in each herd and at both examinations. The most common bacteria from each herd at each examination are shown in Table 2.

In all five herds the bacteria F. necrophorum, Pseudomonas/Acinetobacter and Bacteroides/Sphingobacterium/Prevotellaceae were among the most frequent at both first and second examination. From the first to the second examination there was a decrease in percentage of F. necrophorum in all herds except Herd 4, while there was an increase in percentage of Pseudomonas/Acinetobacter in all five herds.

Influence of calving management

The initial analysis of variance showed that there was no significant effect of calving difficulty on bacterial composition at either first or second examination. Then effect of calving difficulty was tested with uterine score as the response variable. The explanatory variables tested were herd, calving difficulty, retained placenta, parity and herd x calving difficulty. The model with uterine score as response variable and all explanatory variables could be reduced stepwise, and only the effect of herd was significant for uterine score (P < 0.001). If uterine score as a response variable was replaced by its subgroups good, medium and bad, both herd (P < 0.001), calving difficulty (P = 0.016) and retained placenta (P = 0.003) had an effect.

An analysis was made to test which variables had an effect on the parameter calving difficulty. The explanatory variables tested were herd, parity, milk fever, use of calving pen, number of cows in calving pen at the time of calving and time spent in calving pen before calving. The model containing all explanatory variables was reduced stepwise, and only herd and time spent in calving pen before calving had an effect on the parameter for calving difficulty (P < 0.001 and P = 0.006, respectively).

Discussion

This is one of the first studies where uterine bacterial flora in p.p. dairy cows has been described based on identification of the bacterial DNA using the T-RFLP method [START_REF] Santos | Metagenomic analysis of the uterine bacterial microbiota in healthy and metritic postpartum dairy cows[END_REF]. This method has advantages compared to traditional bacterial culture as it detects DNA and, therefore, indicates all bacteria present in a sample, regardless of their growth potential, and not only those bacteria that can be cultured on a given medium [START_REF] Amann | Phylogenetic identification and in situ detection of individual microbial cells without cultivation[END_REF][START_REF] Kalmbach | Isolation of new bacterial species from drinking water biofilms and proof of their in situ dominance with highly specific 16S rRNA probes[END_REF]. Still, the T-RFLP method does not always determine the exact species of bacteria directly from the output, even when applying further analyses such as cloning and sequencing [START_REF] Abdo | Statistical methods for characterizing diversity of microbial communities by analysis of terminal restriction fragment length polymorphisms of 16S rRNA genes[END_REF]. In the present research, this is illustrated by the obtained TRF size of 206.48, that could correspond to Pseudomonas, which was found to have TRF size 205 by cloning and sequencing, or it could correspond to Acinetobacter, which was found to have TRF size 208 by cloning and sequencing. Due to this, both potential bacterial names are given, and the same bacteria name can occur with different corresponding TRF sizes. a Bacteria are ranked so the 3 most common are at the top in alphabetical order, followed by the remainder of the bacteria, also in alphabetical order. The percentages can be compared within herd.

b The bacteria name is based on produced clone library. c UD indicates that the bacterium was found by T-RFLP, but undetermined in the actual performed cloning.

To establish the exact species it would have been necessary to do next-generation sequencing on the amplicons of the 16S rRNA gene or the microbiome. Due to economic considerations the exact bacterial names were not pursued.

Uterine flora in relation to herd and time after calving

The bacterial flora was found to differ between the five herds in the present study. Such a herd difference has not been shown before, and this difference is of importance to consider both when interpreting results from uterine samplings and when selecting antibiotic treatments, as treatments possibly should differ among herds.

The findings here that the uterine flora in the p.p. cow consists of many species are well in line with other studies [START_REF] Bekana | Intrauterine bacterial findings in postpartum cows with retained fetal membranes[END_REF][START_REF] Huszenicza | Uterine bacteriology, resumption of cyclic ovarian activity and fertility in postpartum cows kept in large-scale dairy herds[END_REF][START_REF] Kask | Bacteriological and histological investigation of the postpartum bovine uterus in two Estonian dairy herds[END_REF][START_REF] Sheldon | Influence of uterine bacterial contamination after parturition on ovarian dominant follicle selection and follicle growth and function in cattle[END_REF]. A recent study also using culture-independent methods for describing and comparing uterine bacterial composition in Holstein dairy cows showed, that the bacterial diversity in uteri was greater and more complex than previously described using traditional culture [START_REF] Santos | Metagenomic analysis of the uterine bacterial microbiota in healthy and metritic postpartum dairy cows[END_REF]. This is also demonstrated in the present study but on an even larger number of cows. In the present study, bacteria were present in 100% of cows at the second examination approximately 28 days after calving. This is a greater prevalence compared to previous studies [START_REF] Huszenicza | Uterine bacteriology, resumption of cyclic ovarian activity and fertility in postpartum cows kept in large-scale dairy herds[END_REF][START_REF] Sheldon | Uterine diseases in cattle after parturition[END_REF], and the discrepancy might very well be a reflection of the use of the T-RFLP method compared to bacterial culture. Culture-independent methods of analysis generally detect a much greater number of microorganisms than culture [START_REF] Santos | Metagenomic analysis of the uterine bacterial microbiota in healthy and metritic postpartum dairy cows[END_REF][START_REF] Zoetendal | Molecular ecological analysis of the gastrointestinal microbiota: a review[END_REF] due to limitations in the culturing techniques [START_REF] Zoetendal | Molecular ecological analysis of the gastrointestinal microbiota: a review[END_REF]. Therefore culture-independent methods are now widely used for studying complex microbial communities in many different species [START_REF] Mølbak | Increased amount of Bifidobacterium thermacidophilum and Megasphaera elsdenii in the colonic microbiota of pigs fed a swine dysentery preventive diet containing chicory roots and sweet lupine[END_REF][START_REF] Rampersaud | Microbiota of the upper and lower genital tract[END_REF][START_REF] Santos | Metagenomic analysis of the uterine bacterial microbiota in healthy and metritic postpartum dairy cows[END_REF]. It must be noted that as T-RFLP also detects dead or damaged bacteria, however, not all the bacteria found might necessarily cause the disease. The fraction of dead bacteria, however, must be assumed to be minor and to be a part of the same population as the live bacteria.

At first examination, on average 8 days after calving, the most common bacterium in all herds was F. necrophorum. This organism is an obligate anaerobe, inhabits mucous membranes in both humans and animals, and is known to cause diseases in cattle such as interdigital phlegmon, necrotic laryngitis and necrotic vaginitis, usually as part of a mixed infection. It is also commonly isolated from the uterus of cattle, especially after a complicated calving with e.g. retained placenta and/or uterine infections [START_REF] Bekana | Intrauterine bacterial findings and hormonal profiles in post-partum cows with normal puerperium[END_REF][START_REF] Huszenicza | Uterine bacteriology, resumption of cyclic ovarian activity and fertility in postpartum cows kept in large-scale dairy herds[END_REF][START_REF] Santos | Metagenomic analysis of the uterine bacterial microbiota in healthy and metritic postpartum dairy cows[END_REF]. It was therefore quite surprising, that F. necrophorum was found to such a large degree even in healthy animals in this study. One reason could be, that F. necrophorum is more pathogenic when found together with A. pyogenes [START_REF] Ruder | Uterine infections in the postpartum cow. 2. Possible synergistic effect of Fusobacterium necrophorum and Corynebacterium pyogenes[END_REF], a bacterium that was not often found in the present study.

At second examination the most common bacteria were Pseudomonas/Acinetobacter in four of the five herds, whereas F. necrophorum was still the most common bacteria in Herd 4. Pseudomonas/Acinetobacter are strict aerobe gram-negative bacteria, widespread in the environment. Acinetobacter is mostly known to be a potential pathogen, occasionally reported to cause disease in immune-compromised humans [START_REF] Bergogne-Berezin | Acinetobacter spp. as nosocomial pathogens: microbiological, clinical, and epidemiological features[END_REF]. Pseudomonas is, however, known to be involved in a variety of diseases. The finding in the present study that such bacteria, rarely reported to cause uterine disease, are so common in the uterus of cattle is in agreement with the results of [START_REF] Santos | Metagenomic analysis of the uterine bacterial microbiota in healthy and metritic postpartum dairy cows[END_REF].

Arcanobacterium is frequently found in uterine samples from dairy cows and impairs later reproduction [START_REF] Bonnett | Endometrial biopsy in Holstein-Friesian dairy cows. III. Bacteriological analysis and correlations with histological findings[END_REF][START_REF] Griffin | Non-specific uterine infection and bovine fertility. I. Infection patterns and endometritis during the first seven weeks post-partum[END_REF][START_REF] Jadon | Prevalence of aerobic and anaerobic uterine bacteria during peripartum period in normal and dystocia-affected buffaloes[END_REF][START_REF] Takács | Bacterial contamination of the uterus after parturition and its effect on the reproductive performance of cows on large-scale dairy farms[END_REF]. Arcanobacterium was not often found in the present study, and the low prevalence of Arcanobacterium might also be the reason for no relation between bacterial flora and length of p.p. anovulation. Further, there were no specific bacteria or flora in cows with pathological, malodorous uterine discharge (score ≥ 5) in the present study, as has been indicated by others [START_REF] Griffin | Non-specific uterine infection and bovine fertility. I. Infection patterns and endometritis during the first seven weeks post-partum[END_REF][START_REF] Huszenicza | Uterine bacteriology, resumption of cyclic ovarian activity and fertility in postpartum cows kept in large-scale dairy herds[END_REF][START_REF] Williams | Clinical evaluation of postpartum vaginal mucus reflects uterine bacterial infection and the immune response in cattle[END_REF]. In another analysis, using the data from this study (Mølbak et al., 2011, unpublished), uterine scores were used to divide the cows into healthy (≤3) and sick (≥4), and the analysis was made solely on the relative presence or absence of the different bacteria. With these definitions, there was a greater prevalence of F. necrophorum in the sick animals (uterine score ≥4). This illustrates the importance of disease definitions for the conclusions attained.

In the present analysis there was a difference in bacterial flora between first and second examination. This was expected, since variation in bacterial flora with time from calving is also shown in other studies [START_REF] Bekana | Intrauterine bacterial findings and hormonal profiles in post-partum cows with normal puerperium[END_REF][START_REF] Griffin | Non-specific uterine infection and bovine fertility. I. Infection patterns and endometritis during the first seven weeks post-partum[END_REF][START_REF] Hussain | Postpartum uterine flora following normal and abnormal puerperium in cows[END_REF]. There seems to be an overabundance of gram-negative bacteria in the cows in our study relative to other reports. A shift from a gramnegative to gram-positive flora over the first 4-5 weeks postpartum has previously been described [START_REF] Hussain | Postpartum uterine flora following normal and abnormal puerperium in cows[END_REF]. More anaerobic or facultative anaerobic bacteria were also detected in the present study at first examination compared to second examination. Others have found an anaerobic flora up to 8 weeks p.p. [START_REF] Bekana | Intrauterine bacterial findings in postpartum cows with retained fetal membranes[END_REF][START_REF] Bekana | Intrauterine bacterial findings and hormonal profiles in post-partum cows with normal puerperium[END_REF][START_REF] Kask | Bacteriological and histological investigation of the postpartum bovine uterus in two Estonian dairy herds[END_REF]. The findings in the present study could indicate that the uterine environment shifts from anaerobic to aerobic during the involution process, something to consider if bacterial cultures are to be made.

Connection between uterine flora and uterine scoring system used in Denmark

In the present study, there was not any relationship between the uterine flora, determined by the T-RFLP method, and the uterine scoring system used in Denmark. This is supported by a recent study, where only a slight agreement between major uterine bacterial pathogens and score of vaginal discharge was found [START_REF] Mcdougall | Relationships between cytology, bacteriology and vaginal discharge scores and reproductive performance in dairy cattle[END_REF]. This is in contrast to [START_REF] Williams | Clinical evaluation of postpartum vaginal mucus reflects uterine bacterial infection and the immune response in cattle[END_REF], however, where scoring of the vaginal discharge did reflect uterine bacterial flora. For the analyses we grouped the uterine scores into three groups of uterine condition that were comparable to those of [START_REF] Williams | Clinical evaluation of postpartum vaginal mucus reflects uterine bacterial infection and the immune response in cattle[END_REF], but even with this reduction in the number of classes in the Danish uterine scoring system, no relation to bacterial flora was found. This is an aspect to consider for future analyses on the Danish scoring system. A reason for not seeing any relations could be that we did not have an indication of the exact amount of bacteria present, as the T-RFLP method is merely semi-quantitative [START_REF] Osborn | An evaluation of terminalrestriction fragment length polymorphism (T-RFLP) analysis for the study of microbial community structure and dynamics[END_REF]. Thus, shifts in bacteria populations with uterine score may be masked by the continued presence of species in non-significant but detectable quantities. This semi-quantitative aspect of the T-RFLP method may be a limitation in respect to examining bacterial population shifts. This would call for the combination of the T-RFLP method with another method better suited to quantify bacteria amount, such as quantitative real time PCR with general bacterial primers or species specific primers [START_REF] Matsuki | Quantitative PCR with 16S rRNA-gene-targeted species-specific primers for analysis of human intestinal Bifidobacteria[END_REF].

Finally, it should be mentioned that another contributor to the non-significant relations between vaginal discharge and uterine bacterial flora could be that vaginal discharge may not always reflect inflammation in the uterus, but rather be due to cervicitis or vaginitis [START_REF] Dubuc | Definitions and diagnosis of postpartum endometritis in dairy cows[END_REF].

Important management variables

In the present study, there was no significant effects of management factors on uterine bacterial composition indicating that, although there may be effects of factors such as calving difficulty, these management factors did not have so pronounced effects that they could be detected with the present sample size of 125 cows. Indeed, from our analyses it appears that the more assistance required at calving, the greater risk of having a poor uterine score. This seems plausible, as manipulations of the calf in the birth canal must increase the risk of introducing bacteria [START_REF] Bruun | Risk factors for metritis in Danish dairy cows[END_REF] and might also damage the soft tissues in the birth canal, thereby increasing the inflammatory response. Furthermore we could not see any effects of the use of fresh, clean bedding in the pen before calving on bacteria composition. The reason for this could be that the hygienic standards in the participating herds and particularly in the calving pens were already high. These previous findings are consistent with others [START_REF] Noakes | Bacterial flora of the uterus of cows after calving on two hygienically contrasting farms[END_REF][START_REF] Potter | Risk factors for clinical endometritis in postpartum dairy cattle[END_REF], where the hygienic environment had no influence on either the quantitative or qualitative uterine bacterial flora or prevalence of endometritis. One explanation might be that the effect of hygiene is hidden in the herd parameter. Some of the herd differences might also come from the fact that different persons were involved in the data registration [START_REF] Lastein | Veterinary decision making in relation to metritis-a qualitative approach to understand the background for variation and bias in veterinary medical records[END_REF], but still it was very important for us to study these factors under typical commercial conditions. Furthermore, a recent study documented moderate intra-observer repeatability of scoring vaginal discharge, and showed that the experience of the investigator had no effect on the scoring [START_REF] Leutert | Evaluation of vaginoscopy for the diagnosis of clinical endometritis in dairy cows[END_REF].

Conclusion

The bacterial flora was composed of many different species, and composition was dependent on herd and time from calving. For all five herds the bacteria F. necrophorum, Pseudomonas/Acinetobacter and Bacteroides/Sphingobacterium/Prevotellaceae were among the most common at both first and second examination. Neither calving difficulty nor calving management was significant for bacterial composition. The uterine condition, assessed by the Danish uterine score system, was influenced by herd, calving difficulty and retained placenta, but was not related to uterine bacterial flora. Denmark) for excellent assistance during uterine sample and DNA analysis. The authors also thank the participating farmers, veterinarians and technical staff for their involvement, as well as statistical consultant Malene Juul Rasmussen (Agrotech A/S, Institute for Agri Technology and Food Innovation, Denmark) for help on the statistical analyses. The study was financed by the Danish Ministry of Science, Technology and Innovation and the Knowledge Centre for Agriculture, Cattle. Support for the development and production of the instruments for uterine sample collection was provided by the Estonian Science Foundation and the Estonian Ministry of Agriculture. The funding sources had no involvement in any parts of the study.

Table 1

 1 Outline of the clinics defining the uterine score system used routinely on cows 5-20 days after calving in Denmark.

	Uterine	Clinic
	score
	0	None or a very small amount of clear mucous secretion -
		no smell.
	1	A very small amount of bloody mucous secretion -no
		smell.
	2	Small amount of bloody mucous/gray secretion -no smell.
	3	Plenty amounts of bloody seromucous/gray-yellow
		secretion -scabs on the tail -no smell.
	4

Table 2

 2 The percentagewise most common bacteria a at first (avg. 8 days p.p.) and second (avg. 28 days p.p.) clinical examination in the 5 herds.

	Bacteria name b	TRF size	Herd 1		Herd 2		Herd 3		Herd 4		Herd 5
			Examination	Examination	Examination	Examination	Examination
			First	Second	First	Second	First	Second	First	Second	First	Second
	Pseudomonas/Acinobacter	206.48	8.6	11.0	4.9	23.5	7.1	18.5	9.0	15.3	18.2	26.0
	Fusobacteriumnecrophorum	82.10	10.6	3.1	19.9	10.6	18.3	11.8	16.2	17.9	21.5	10.2
	Sphingobacterium/Bacteroides/Prevotellaceae	100.20	7.1	9.9	5.0	6.4	4.8	4.1	9.3	3.7	7.0	8.2
	Arcanobacterium	483.65	4.5	4.3
	Brevundimonas	331.90									5.4	6.4
	Comamonas	567.26						4.9		6.6	5.1	8.2
	E. coli	373.15			9.2
	Peptostreptococcus	62.79					4.8
	Sphingobacterium/Bacteroides	104.51		4.1	13.1
	Stentrophomonas	215.96				4.4		4.9				9.0
	Streptococcus	583.74	6.0				7.8	2.7
	UD c	98.49				8.1			7.6	7.1
	UD c	200.80							4.5	6.1
	UD c	230.60					5.6	2.4
	Veillonella	593.77	6.2

Acknowledgements

The authors thank Kitt Dupont and Tim Kåre Jensen (National Veterinary Institute, Technical University of