Perinatal mortality and severe morbidity in low and high risk term pregnancies in the Netherlands: prospective cohort study
2010

Department of Obstetrics
University Medical Center Utrecht
3584 EAUtrechtNetherlands

Department of Neonatology
University Medical Center Utrecht

Netherlands Perinatal Registry
Utrecht

Department of Pathology
University Medical Center Utrecht

Department of Obstetrics and Gynaecology
Diakonessenhospital, Utrecht

Department of Obstetrics and Gynaecology
Zuwe Hofpoort Hospital
WoerdenNetherlands

Department of Pediatrics
St Antonius Hospital
NieuwegeinNetherlands

Department of Obstetrics and Gynaecology
Admiraal de Ruyter Hospital
GoesNetherlands

Department of Obstetrics and Gynaecology
Meander Medical Center
AmersfoortNetherlands

Perinatal mortality and severe morbidity in low and high risk term pregnancies in the Netherlands: prospective cohort study
3415639201010.1136/bmj.c5639RESEARCH Annemieke C C Evers, PhD student, resident in gynaecology, 1 Hens A A Brouwers, neonatologist, 2 Chantal W P M Hukkelhoven, epidemiologist, 3 Peter G J Nikkels, pathologist, 4 Janine Boon, gynaecologist, 5 Anneke van Egmond-Linden, gynaecologist, 6 Jacqueline Hillegersberg, paediatrician, 7 Yvette S Snuif, gynaecologist, 8 Sietske Sterken-Hooisma, midwife secondary care, 9 Hein W Bruinse, professor of obstetrics, 1 Anneke Kwee, gynaecologist 1 Correspondence to: A C C Evers email@example.com Cite this as: BMJ
Objective To compare incidences of perinatal mortality and severe perinatal morbidity between low risk term pregnancies supervised in primary care by a midwife and high risk pregnancies supervised in secondary care by an obstetrician. Design Prospective cohort study using aggregated data from a national perinatal register. Setting Catchment area of the neonatal intensive care unit (NICU) of the University Medical Center in Utrecht, a region in the centre of the Netherlands covering 13% of the Dutch population. Participants Pregnant women at 37 weeks' gestation or later with a singleton or twin pregnancy without congenital malformations. Main outcome measures Perinatal death (antepartum, intrapartum, and neonatal) or admission to a level 3 NICU. Results During the study period 37 735 normally formed infants were delivered at 37 weeks' gestation or later. Sixty antepartum stillbirths (1.59 (95% confidence interval 1.19 to 1.99) per 1000 babies delivered), 22 intrapartum stillbirths (0.58 (0.34 to 0.83) per 1000 babies delivered), and 210 NICU admissions (5.58 (4.83 to 6.33) per 1000 live births) occurred, of which 17 neonates died (0.45 (0.24 to 0.67) per 1000 live births). The overall perinatal death rate was 2.62 (2.11 to 3.14) per 1000 babies delivered and was significantly higher for nulliparous women compared with multiparous women (relative risk 1.65, 95% confidence interval 1.11 to 2.45). Infants of pregnant women at low risk whose labour started in primary care under the supervision of a midwife had a significant higher risk of delivery related perinatal death than did infants of pregnant women at high risk whose labour started in secondary care under the supervision of an obstetrician (relative risk 2.33, 1.12 to 4.83). NICU admission rates did not differ between pregnancies supervised by a midwife and those supervised by an obstetrician. Infants of women who were referred by a midwife to an obstetrician during labour had a 3.66 times higher risk of delivery related perinatal death than did infants of women who started labour supervised by an obstetrician (relative risk 3.66, 1.58 to 8.46) and a 2.5-fold higher risk of NICU admission (2.51, 1.87 to 3.37). Conclusions Infants of pregnant women at low risk whose labour started in primary care under the supervision of a midwife in the Netherlands had a higher risk of delivery related perinatal death and the same risk of admission to the NICU compared with infants of pregnant women at high risk whose labour started in secondary care under the supervision of an obstetrician. An important limitation of the study is that aggregated data of a large birth registry database were used and adjustment for confounders and clustering was not possible. However, the findings are unexpected and the obstetric care system of the Netherlands needs further evaluation.

INTRODUCTION

Perinatal mortality is an important indicator of the quality of obstetric care. The Peristat I and II studies, based on data from 1999 and 2004, showed that the Netherlands has almost the highest perinatal mortality (11.4 and 10.0 per thousand) in Europe. [1][2][3][4][5] Several factors are mentioned as possible explanations for this high mortality, such as differences in registration and maternal characteristics of the Dutch childbearing population, restricted management of premature babies, and the absence of standard screening for congenital anomalies. The numbers of older mothers, multiple pregnancies, and mothers belonging to an ethnic minority are relatively high in the Netherlands. 5 6 However, this can only partly explain the high perinatal mortality. 7 Whether the Dutch obstetric care system contributes to this relatively high mortality remains unclear. 8 The Dutch system is different from all other obstetric care systems in Europe and is characterised by a well defined distribution between primary and secondary care. A distinction is made between women with a low risk of pathology and those with a higher risk. Independent midwives in primary care provide care only to women at low risk. Early in pregnancy, healthy women enter into the system at the primary care level. If pregnancy, childbirth, and the postpartum period are uncomplicated, the woman remains under the care of the midwife in primary care. The woman has the choice of a home or a hospital delivery, both under the supervision of her own midwife. If complications occur or risk factors arise during pregnancy or labour or in the postpartum period, the woman is referred to an obstetrician in secondary care. In some pregnancies, risk factors based on medical or obstetric history are already present early in pregnancy. In these cases, obstetric care starts directly at the secondary care level by the obstetrician. The indications for referral from primary to secondary care have been agreed by the professionals involved and are laid out in the "obstetric indication list." 9 In 2007 a prospective cohort study of severe morbidity and mortality of term fetuses or neonates, called ATNICID (Admission of Term Neonates to Intensive Care or Intrauterine Death), started in the centre of the Netherlands. This study had two aims. The first aim was to improve the quality of obstetric care by organising audits of all cases of perinatal mortality and severe morbidity of term infants (at least 37 weeks) without severe congenital malformations, chromosomal abnormalities, or metabolic diseases. The second aim was to gain insight into perinatal mortality and severe morbidity in relation to the Dutch obstetric care system. We chose to limit the analysis to normally formed term infants because this is a well defined cohort in which perinatal mortality is higher than in most European countries (Peristat II 10 ) and both midwives and obstetricians are involved in the care of these patients, depending on the risk factors present. In this paper we report on the perinatal death rate and severe perinatal morbidity of normally formed term infants from pregnancies that were supervised by midwives in primary care as no risk factors were initially present and from pregnancies that were supervised by an obstetrician in secondary care.

METHODS

We did a prospective cohort study, including cases in a prospective manner. We estimated the population at risk from retrospective registry data.

Study population Between 1 January 2007 and 31 December 2008 we prospectively collected data on all antepartum stillbirths, intrapartum stillbirths, neonatal deaths, and admissions to a level 3 neonatal intensive care unit (NICU) of term infants within the first seven days of life in the research area. We defined the research area as the catchment area of the NICU of the University Medical Center Utrecht in the centre of the Netherlands. This unit is one of 10 Dutch level 3 NICUs, which care for 13% of all neonates with need for intensive care in the Netherlands. All hospitals (one university hospital, seven non-university teaching hospitals, and four general hospitals) and midwife practices (n=56) that refer neonates to this NICU participated in the study. This part of the Netherlands is a geographically distinct area with approximately 21 000 deliveries a year.

Obstetric and neonatal variables

We systematically recorded obstetric and neonatal variables for each mother-infant pair. The researcher used the original medical records to collect information about the pregnancy, the course of delivery, and the state of the newborn at birth immediately after admission to the NICU. Neonatal data were recorded at the time of death or discharge of the neonate from the NICU.

Gestational age was determined on the basis of the last menstrual period in combination with early ultrasonography. We defined nulliparous women as women who had never given birth to a fetus of more than 16 weeks' gestation and multiparous women as those with one or more deliveries after 16 weeks' gestation. We defined an elective caesarean section as one for which no intention to deliver vaginally existed. According to the Dutch national obstetric guidelines, women at low risk can start labour under the supervision of a midwife in primary care (with a choice between birth at home or in hospital). Women at high risk start labour under the supervision of an obstetrician in hospital. We categorised obstetric care during labour into five overlapping groups: start of labour in primary care, start of labour in secondary care, delivery in primary care, referral from primary care to secondary care during labour, and delivery in secondary care.

An expert panel audited all cases. The panel consisted of eight members from different professions: a midwife working in primary care, a midwife working in secondary care, obstetricians, paediatricians, and a pathologist. They used clear definitions to classify deaths in a systematic way as antepartum, intrapartum, neonatal, or delivery related. They also decided which level of care the woman had received and determined the reason for admission of the neonate to the NICU.

Definitions

We defined antepartum stillbirth as a death of a fetus after 37 weeks' gestation before the onset of labour and intrapartum stillbirth as a stillbirth in which intrauterine fetal death occurred after the onset of labour but before birth. We defined delivery related neonatal death as the combination of intrapartum stillbirth and neonatal death caused by complications that occurred during labour (asphyxia or intrauterine infection). We did not include late neonatal deaths of infants after seven days of life in our perinatal death rates.

Data from birth registry Data collected on perinatal mortality and admission to the NICU were related to parity and level of care during pregnancy and delivery. Data on the total number of term births, parity, and place of delivery came retrospectively from the Netherlands perinatal registry and were based on postal codes (catchment area of the NICU) from 2007-8. About 95% of primary care data and 99% of secondary care data on pregnancy and delivery are entered in this database. Data on multiple pregnancies and elective caesarean sections also came from the Netherlands perinatal registry. We used these data to calculate perinatal death and admission rates. As intrapartum death and delivery related neonatal death are very uncommon after an elective caesarean delivery in the event of a term fetus without congenital anomalies, we excluded elective caesarean sections from the denominator for intrapartum and delivery related neonatal death.

Statistical analysis

We calculated incidences with the total number of term neonates born in the research area during the study period as the denominator. For antepartum death, the denominator included all fetuses alive at 37 weeks. For intrapartum death, we excluded antepartum deaths from the denominator; for NICU admission, the denominator included live births only.

We used SPSS 12.0 for Windows and Confidence Interval Analysis for statistical analysis. We calculated crude relative risks with 95% confidence intervals. We defined the group of women who started labour in secondary care as the reference group. We did a sensitivity analysis to investigate the robustness of our findings because of possible under-registration in primary care (approximately 5%). To be sure to capture all possible under-registration, we increased the denominator of the low risk group artificially by 10%. Because we included twins in our study and could only analyse them as separate infants rather than clusters (as information at the level of the individual mother was not available), we also did a sensitivity analysis without twins.

We did not include neonates referred from other regions to our NICU because of capacity problems in their own region during the study period in this study cohort. However, we did include neonates born in our research area who were admitted to another NICU because of capacity problems in our region. To get insight into the generalisability of our study results, we extracted data on the following variables from the database: parity (0 or ≥1), age (≥35 or <35), ethnicity (Western or non-Western), and socioeconomic class (five categories) for the region of the study population and for the remaining part of the Netherlands.

To guarantee anonymity for the participants, we collected data without knowledge of the personal characteristics (name, date of birth, address) of the woman, the obstetric caregiver, or the hospital. Therefore, we could not link our perinatal death and NICU cases to the national database, making a multivariate analysis to adjust for confounders and clustering of patients in hospitals and midwives' practices impossible. However, we compared the prevalence of several risk factors between women at low risk in primary care and women at high risk in secondary care, by using data from the national perinatal registry. We used χ 2 tests to see if significant differences existed. We considered a two sided P value of <0.05 to be significant.

RESULTS

During the study period, 37 735 normally formed infants were born at term in the catchment area of our NICU: 16 672 (44.2%) infants of nulliparous women (including 143 (0.9%) twin pregnancies) and 21 063 (55.8%) infants of multiparous women (including 226 (1.1%) twin pregnancies). Data on 91 (0.2%) infants were missing; we excluded these from further analysis. The figure shows that 18 686 (49.5%) infants were born to women who started labour in primary care as low risk, of whom 5492 (29.4%) were referred to secondary care during labour; 13 194 (35.0%) infants were born under the supervision of a midwife in primary care, and 24 450 (64.8%) infants were born under the supervision of a gynaecologist. Overall, an elective caesarean section was done in 2163 (5.7%) deliveries: 765 were nulliparous women (including 37 twin pregnancies), and 1398 were multiparous women (including 44 twin pregnancies).

In total, 60 antepartum deaths (1.59 (95% confidence interval 1.19 to 1.99) per 1000 babies delivered), 22 intrapartum deaths (0.58 (0.34 to 0.83) per 1000 babies delivered), and 210 admissions to the NICU (5.58 (4.83 to 6.33) per 1000 live births delivered) occurred, of which 17 (0.45 (0.24 to 0.67) per 1000 live births) neonates died (table 1). The overall perinatal death rate was 2.62 (2.11 to 3.14) per 1000 babies delivered, and this death rate was significantly higher for nulliparous women than for multiparous women (relative risk 1.65, 95% confidence interval 1.11 to 2.45). Of the 60 antepartum stillbirths, 37 occurred in primary care and 23 in secondary care. We could not calculate the incidence Twenty-two intrapartum stillbirths and 14 delivery related neonatal deaths occurred (tables 2 and 3). Infants of pregnant women at low risk had a significantly higher risk of delivery related perinatal death (relative risk 2.33, 1.12 to 4.83) (table 3), compared with infants of women at high risk whose labour started in secondary care under the supervision of an obstetrician. Infants of women who were referred to secondary care during labour had a 3.66 times higher risk of delivery related perinatal death than did infants of women who started labour in secondary care (relative risk 3.66, 1.58 to 8.46) (table 3).

Two hundred and thirty-eight neonates were admitted to the study NICU; 30 neonates were born in another region and were therefore excluded, and two neonates born in our region and admitted to other NICUs were included retrospectively. In total, we included 210 neonates, resulting in an overall incidence of admission to NICU of 5.58 (4.83 to 6.33) per 1000 live births. We did not exclude women who delivered by elective caesarean section from denominator numbers. Nulliparous women had a significantly higher risk than did multiparous women (8.03 (6.67 to 9.38) v 3.67 (2.85 to 4.49) per 1000 live births; relative risk 2.19, 1.65 to 2.89) of their child being admitted to the NICU. Half of the women (51%, n=107) started labour in primary care. Of these, 70% (n=75) were referred to secondary care during labour. In the end, 85% (n=179) delivered in secondary care. The incidence of admissions to the NICU was 2.43 per 1000 term births in primary care, 13.7 per 1000 term births if referral to secondary care during labour occurred, and 5.45 per 1000 term births managed exclusively in secondary care (table 4). Reasons for admission to the NICU were asphyxia (n=101, 48%), infection (32, 15%), respiratory distress (25, 12%), meconium aspiration syndrome (10, 5%), cerebral morbidity (16, 8%), seizures (5, 2%), and other reasons (21, 10%). In total, 17 (8%) neonates died in the neonatal period, 71% (n=12) because of asphyxia and 29% (n=5) because of an infection. Fourteen cases were classified as directly related to circumstances during labour. In two of the three non-delivery related deaths, asphyxia occurred before the start of labour. In the remaining case, the reason for admission to the NICU was a neonatal infection (Enterobacter meningitis).

Of the 26 delivery related deaths for which labour started in primary care, labour started at home in 17 (65%) cases and in the hospital in nine (35%) cases. For  the 107 admissions to the NICU, the equivalent numbers were 62 (58%) and 42 (42%).

The sensitivity analysis showed our findings to be robust when we increased the denominator of the low risk group by 10% (web appendix A) and when we excluded multiple gestations (web appendix B). Because we could not adjust for confounders, we compared the prevalence of several risk factors between women at low risk in primary care and women at high risk in secondary care, using data from the national perinatal registry. Analysis of the variables (parity, age, socioeconomic class, ethnicity, multiple pregnancy) showed that nulliparity, age 35 or over, non-Western ethnicity, low socioeconomic class, and multiple pregnancies were more prevalent among women at high risk in secondary care. We hypothesise that if we could have adjusted for these risk factors, the effect would have been even larger as the risk factors were more prevalent among women at high risk (web appendix C).

DISCUSSION

The main finding of this study is that the Dutch obstetric system that is based on risk selection and obstetric care at two levels may not be as effective as was once thought. The Dutch obstetric system itself possibly contributes to the high perinatal mortality compared with most European countries. We found that delivery related perinatal death was significantly higher among low risk pregnancies in midwife supervised primary care than among high risk pregnancies in obstetrician supervised secondary care. This difference was even greater among the cases that were referred from primary to secondary care during labour. Unfortunately, we were unable to adjust for confounding variables because we used aggregated data from a large birth registry database. However, the results are unlikely to have been overestimated, because risk factors such as low socioeconomic status, higher age, or non-Western ethnicity were more prevalent among the women at high risk.

As we explained in the results section, we could not compare antepartum fetal death at term between the two levels of care. However, 37 cases of antepartum stillbirths occurred in primary care and 23 cases in secondary care. At the start of labour, 49.5% of all women were supervised by a midwife and 50.2% by an obstetrician, so the incidence of antepartum stillbirth probably did not differ between the low risk group and the high risk group. With respect to severe morbidity in normal term infants (that is, admission to a level 3 neonatal intensive care unit) we found no difference between the low risk and high risk pregnancies. Five per 1000 babies were admitted in both groups, of which 8% died. The perinatal death rate after 37 weeks of gestation in normally formed term infants was 2.62 per 1000 babies. This is in accordance with the results of the Peristat II study comparing perinatal death rates between different countries in Europe. 10 This study reported a perinatal death rate of 3.2 per 1000 term babies in the Netherlands, including infants with congenital anomalies. Congenital abnormalities are assumed to contribute at least 0.4 per 1000 babies to the perinatal death rate, so the high perinatal mortality in the centre of the Netherlands seen in our study is similar to that in the Peristat II study. 10 Strengths and weaknesses of study A limitation of our study is that we used aggregated data from a large birth registry database to determine denominator numbers. Under-reporting could have influenced these numbers, as 95% of primary care data and 99% of secondary care data are entered in RESEARCH the national perinatal registry database. However, the results remained similar after a conservative sensitivity analysis. Furthermore, the overall perinatal mortality was comparable to the rates described in the Peristat II study. 10 Another limitation is that we could not adjust for risk factors because we used aggregated data and cases could not be linked to the database. However, we showed that known risk factors for adverse perinatal outcome (age 35 or over, low socioeconomic class, and non-Western ethnicity) are over-represented in secondary care. If we could adjust for these factors, we would expect an even greater difference in perinatal death rates between the two levels of care. Analysis of some risk factors showed that women with a higher socioeconomic class, Western ethnicity, and age above 35 were slightly over-represented in the study area compared with the rest of the Netherlands. However, the perinatal mortality for singletons in the province of Utrecht was virtually the same as that in the rest of the Netherlands -10.0 versus 10.2 per 1000 babies. A major strength of our study is that a multidisciplinary team consisting of obstetricians, midwives, neonatologists, and a pathologist classified all deaths. They used clear definitions to classify deaths as antepartum, intrapartum, neonatal, and delivery related in a systematic way. Under-reporting and misclassification of deaths and admissions to the NICU are therefore most unlikely. This is a major strength of our study compared with other studies that have used large databases.

Findings in relation to other studies

To our knowledge, this is the only study in the Netherlands to show a higher risk of delivery related perinatal mortality among women with the intention to deliver in primary care compared with women delivered in secondary care. Comparing intended home and hospital births in a cohort of 529 688 low risk pregnancies in primary care in the Netherlands, de Jonge et al recently found low rates of perinatal mortality (intrapartum and neonatal death before 7 days) and admission to the NICU. 11 They concluded that an intended home birth does not increase risks compared with an intended hospital birth in this population. However, women who were referred during labour were not analysed separately in this study, and no comparison was made with high risk pregnancies in secondary care. The incidences of delivery related perinatal mortality and NICU admissions were lower than the rates we found-0.5 versus 1.04 per 1000 babies delivered and 2.0 versus 5.6 per 1000. A possible explanation could be the retrospective use of a large database, in which data can be missing or filled in incorrectly. Misclassification of intrapartum deaths could be one of these factors. Absent fetal heart beat discovered during labour, after which the woman was referred to secondary care, could have been classified as antepartum deaths, whereas we classified these according to the course of labour into antepartum or intrapartum death.

Ravelli et al reported an overall perinatal mortality for normal term infants of 2.8 per 1000 babies delivered, almost the same as in our study. 12 However, they found a perinatal mortality in a population at low risk of 1.3 per 1000 (0.4 per 1000 for home births, 0.9 per 1000 in outpatient clinics (midwife led), and 2.4 per 1000 for intrapartum referral) and a higher perinatal mortality rate of 4.5 per 1000 in a high risk population. 12 This discordance between their study and ours may be explained by the fact that when antepartum stillbirths occur in primary care they are referred to the gynaecologist before labour starts and are then counted in the death rates of secondary care. Finally, Amelink-Verburg et al evaluated a cohort of more than 280 000 women at low risk and found a low perinatal (intrapartum and first day) mortality of 0.5 per 1000 babies. 13 However, in agreement with our study, they found a relative high mortality in women who where referred during labour on an emergency basis (fetal distress) of 10.7 per 1000 babies delivered. However, only 3.6% of women who started labour in primary care had to be referred as an emergency.

The authors of these studies all state that the Dutch obstetric care system, based on risk selection and obstetric care at two levels, works well. We challenge this statement, as we found that the perinatal death rate of women at low risk intended to deliver in primary care was higher than that of women at high risk delivering in secondary care. In particular, infants of women who were referred from primary to secondary care during labour had a more than 3.5-fold higher perinatal death rate compared with infants of women who started labour in secondary care. In 2007 40% of nulliparous women started labour in primary care, of whom 43% were referred to secondary care during labour; the equivalent figures for multiparous women were 47% and 13%.

Meaning of study Our in-depth analysis of the perinatal mortality of normal term infants has shown that delivery related deaths are higher in the so-called low risk pregnancies supervised by a midwife in primary care than in the high risk pregnancies supervised by an obstetrician in secondary care. This seriously questions the supposed effectiveness of the Dutch obstetric system that is based on risk selection and obstetric care at two levels. Of major concern is the fact that the highest mortality was among the infants of women who were referred from primary care to secondary care during labour because of an apparent complication. Hypothetically, this high mortality could have several causes. Delay can occur at three moments. Firstly, diagnosis in primary care can be delayed because the midwife is not always present during the first stage of labour and fetal heart beats are often checked only every two to four hours. Secondly, transport can delay treatment in case of an emergency. Finally, a delay can occur because the obstetrician underestimates the problem as the referred woman is a "low risk" patient. In addition, essential information can be lost during the referral. These factors should be subject to further investigation, especially to evaluate whether complications with the potential to lead to perinatal death can better be predicted.

On the other hand, one may wonder whether the obstetric care system in the Netherlands need to be critically revised. Less than 50% of nulliparous women start labour in primary care, of whom 43% are referred to secondary care during labour. For multiparous women, the figures are 45% and 13%. So the number of women who finally deliver in primary care is rather limited. Against the background of the observed perinatal mortality data, a close cooperation or even a fusion between the two levels of care may be the preferred solution. The role of the midwife should not be underestimated and is most likely one of the reasons that the rate of caesarean section is still the lowest in Europe.

Unanswered questions and future research

The main unanswered question remains whether the high perinatal death rate in the Netherlands compared with the rest of Europe can be lowered within the existing obstetric care system. In our opinion, this implies a critical assessment of the course of events that results in an adverse outcome-an assessment in which all professionals are involved. Such an audit is being arranged on a national basis in the Netherlands. In our ATNI-CID study, a detailed audit on factors associated with substandard care in cases with an adverse outcome is ongoing. Further research should be directed to different care systems in which midwives and obstetricians work much more closely together.

In summary, the Dutch obstetric care system is based on the assumptions that pregnant women and women in labour can be divided into a low risk group and a high risk group, that the first group of women can be supervised by a midwife (primary care) and the second group by an obstetrician (secondary care), and that women in the primary care group can deliver at home or in hospital with their own midwife. When complications occur or risk factors arise antenatally, during labour, or in the puerperium in primary care, the women is referred to secondary care.

We found that the perinatal death rate of normal term infants was higher in the low risk group than in the high risk group, so the Dutch system of risk selection in relation to perinatal death at term is not as effective as was once thought. This also implies that the high perinatal death rate in the Netherlands compared with other European countries may be caused by the obstetric care system itself, among other factors. A critical evaluation of the obstetric care system in the Netherlands is thus urgently needed.

We thank all midwives and gynaecologists in the study area for their participation in the study, collection of data, and valuable discussions at the audit meetings. We thank the Netherlands perinatal registry for permission to use data.

WHAT IS ALREADY KNOWN ON THIS TOPIC

Despite the high level of medical care in the Netherlands, the perinatal death rate is one of the highest in Europe The obstetric care system in the Netherlands is characterised by midwife led care for pregnant women at low risk and obstetrician led care for those at high risk Home birth is still practised in the low risk group, and 22% actually deliver at home

WHAT THIS STUDY ADDS

Infants of low risk pregnant women who started labour in primary care had a higher risk of delivery related perinatal death than did infants of high risk pregnant women who started labour in secondary care The obstetric care system in the Netherlands may contribute to the high perinatal mortality RESEARCH

Flow chart of complete cohort with respect to level of care during delivery. Numbers are total number of infants, including 369 twin pregnancies (738 children). P0=infants of nulliparous women; ≥P1=infants of multiparous women of antepartum stillbirth at either level of care because we did not have the denominator, as the perinatal registry contains only the level of care at the start of labour.Secondary care
(n=18 958, 50.2%)
P0 (n=8902, 53.4%)
≥P1 (n=10 056, 47.7%)

Total cohort
(n=37 735 children)
P0 (n=16 672, 44.2%)
≥P1 (n=21 063, 55.8%)

Referral during labour
(n=5492, 14.6%)
P0 (n=3815, 22.9%)
≥P1 (n=1677, 8.0%)

Primary care
(n=18 686, 49.5%)
P0 (n=7719, 46.3%)
≥P1 (n=10 967, 52.1%)

Secondary care
(n=24 450, 64.8%)
P0 (n=12 717, 76.3%)
≥P1 (n=11 733, 55.7%)

Primary care
(n=13 194, 35.0%)
P0 (n=3904, 23.4%)
≥P1 (n=9290, 44.1%)

Missing data (n=91, 0.2%)

At start
of labour

At delivery

Table 1 |
1Incidence of perinatal mortality among term infants without congenital malformations, comparing infants of nulliparous and multiparous womenOutcome
Total cohort
No of deaths

Incidence/1000
(95% CI)

Relative risk
(95% CI)

Antepartum stillbirth
37 735
60
1.59 (1.19 to 1.99)

Nulliparous
16 672
35
2.10 (1.40 to 2.79) 1.77 (1.06 to 2.95)

Multiparous
21 063
25
1.19 (0.72 to 1.65)
Reference

Intrapartum stillbirth
37 675
22
0.58 (0.34 to 0.83)

Nulliparous
16 637
14
0.84 (0.40 to 1.28) 2.21 (0.93 to 5.27)

Multiparous
21 038
8
0.38 (0.12 to 0.64)
Reference

Neonatal death
37 653
17
0.45 (0.24 to 0.67)

Nulliparous
16 623
7
0.42 (0.11 to 0.73) 0.89 (0.34 to 2.33)

Multiparous
21 030
10
0.48 (0.18 to 0.77)
Reference

Perinatal death
37 735
99
2.62 (2.11 to 3.14)

Nulliparous
16 672
56
3.36 (2.48 to 4.24) 1.65 (1.11 to 2.45)

Multiparous
21 063
43
2.04 (1.43 to 2.65)
Reference

Table 2 |
2Intrapartum death rate among term infants without congenital malformations, comparing infants of women whose labour started in primary care and those referred during labour with infants of women who started labour in secondary careVariables
Total *

Intrapartum
stillbirth †

Incidence/1000
(95% CI)

Relative risk
(95% CI)

Risk difference/1000
(95% CI)

Start of labour in primary care
18 686
18
0.96 (0.52 to 1.41)
4.03 (1.36 to 11.9)
0.72 (0.22 to 1.23)

Nulliparous
7 719
10
1.30 (0.49 to 2.10)
2.62 (0.82 to 8.37)
0.80 (0.14 to 1.74)

Multiparous
10 967
8
0.73 (0.22 to 1.23)
0.73 (0.22 to 1.23)

Start of labour in secondary care
16 739
4
0.24 (0.01 to 0.47)
Reference

Nulliparous
8 104
4
0.49 (0.01 to 0.98)

Multiparous
8 635
0
0

Referral to secondary care during labour
5 492
6
1.09 (0.22 to 1.97)
4.57 (1.29 to 16.2)
0.85 (0.05 to 1.76)

Nulliparous
3 815
4
1.05 (0.31 to 2.62)
2.12 (0.53 to 8.49)
0.56 (0.58 to 1.69)

Multiparous
1 677
2
1.19 (0.18 to 4.33)
1.19 (0.18 to 4.33)

Delivery in primary care
13 194
12
0.91 (0.40 to 1.42)
3.81 (1.23 to 11.8)
0.67 (0.11 to 1.24)

Nulliparous
3 904
6
1.54 (0.55 to 3.36)
3.11 (0.89 to 11.0)
1.04 (0.28 to 2.36)

Multiparous
9 290
6
0.65 (0.13 to 1.16)
0.65 (0.13 to 1.16)

Delivery in secondary care
22 231
10
0.45 (0.17 to 0.73)
Not calculated
Not calculated

Nulliparous
11 919
8
0.67 (0.21 to 1.14)

Multiparous
10 312
2
0.19 (0.07 to 0.46)

Missing
87

Total
35 512
22
0.62 (0.36 to 0.88)

*Elective caesarean sections excluded.
 †Intrapartum death diagnosed in primary care, after which woman was referred to secondary care.

Table 3 |
3Delivery related death rate among term infants without congenital malformations, comparing infants of women whose labour started in primary care and those referred during labour with infants of women who started labour in secondary careVariables
Total*

Delivery related
perinatal death

Incidence/1000
(95% CI)

Relative risk
(95% CI)

Risk difference/1000
(95% CI)

Start of labour in primary care
18 686
26
1.39 (0.86 to 1.93)
2.33 (1.12 to 4.83)
0.79 (0.14 to 1.44)

Nulliparous
7 719
13
1.68 (0.77 to 2.60)
2.27 (0.87 to 5.98)
0.94 (0.15 to 2.03)

Multiparous
10 967
13
1.19 (0.54 to 1.83)
2.56 (0.84 to 7.85)
0.72 (0.07 to 1.51)

Start of labour in secondary care
16 739
10
0.60 (0.23 to 0.97)
Reference

Nulliparous
8 104
6
0.74 (0.15 to 1.33)

Multiparous
8 635
4
0.46 (0.01 to 0.92)

Referral to secondary care during labour
5 492
12
2.18 (0.95 to 3.42)
3.66 (1.58 to 8.46)
1.59 (0.30 to 2.88)

Nulliparous
3 815
7
1.83 (0.79 to 3.72)
2.48 (0.83 to 7.37)
1.09 (0.39 to 2.58)

Multiparous
1 677
5
2.98 (0.92 to 6.90)
6.44 (1.73 to 23.9)
2.52 (0.13 to 5.17)

Delivery in primary care
13 194
14
1.06 (0.51 to 1.62)
1.78 (0.79 to 4.00)
0.46 (0.20 to 1.13)

Nulliparous
3 904
6
1.54 (0.55 to 3.36)
2.08 (0.67 to 6.43)
0.80 (0.57 to 2.16)

Multiparous
9 290
8
0.86 (0.27 to 1.46)
1.86 (0.56 to 6.17)
0.40 (0.35 to 1.15)

Delivery in secondary care
22 231
22
0.99 (0.58 to 1.40)
Not calculated
Not calculated

Nulliparous
11 919
13
1.09 (0.50 to 1.68)

Multiparous
10 312
9
0.87 (0.30 to 1.44)

Missing
87

Total
35 512
36
1.01 (0.68 to 1.34)

*Elective caesarean sections excluded.

Table 4 |
4Incidence of NICU admissions of term neonates, comparing infants of women whose labour started in primary care and those referred during labour with infants of women who started labour in secondary careVariables
Total*

NICU
admissions

Incidence/1000
(95% CI)

Relative risk
(95% CI)

Risk difference/1000
(95% CI)

Start of labour in primary care
18 668
107
5.73 (4.65 to 6.81)
1.05 (0.80 to 1.38)
0.28 (−1.23 to 1.79)

Nulliparous
7 709
73
9.47 (7.31 to 11.6)
1.40 (1.00 to 1.97)
2.70 (−0.06 to 5.45)

Multiparous
10 959
34
3.10 (2.06 to 4.14)
0.72 (0.46 to 1.13)
−1.18 (−2.83 to 0.47)

Start of labour in secondary care
18 894
103
5.45 (4.40 to 6.50)
Reference

Nulliparous
8 863
60
6.77 (5.06 to 8.48)

Multiparous
10 031
43
4.29 (3.01 to 5.57)

Referral to secondary care during
labour

5 486
75
13.7 (10.6 to 16.7)
2.51 (1.87 to 3.37)
8.22 (4.97 to 11.5)

Nulliparous
3 811
58
15.2 (11.5 to 19.6)
2.25 (1.57 to 3.22)
8.45 (4.20 to 12.7)

Multiparous
1 675
17
10.1 (5.92 to 16.2)
2.37 (1.35 to 4.14)
5.86 (0.90 to 10.8)

Delivery in primary care
13 182
32
2.43 (1.59 to 3.27)
0.45 (0.30 to 0.66)
−3.02 (−4.37 to −1.68)

Nulliparous
3 898
14
3.59 (2.01 to 6.04)
0.53 (0.30 to 0.95)
−3.18 (−5.72 to −0.64)

Multiparous
9 284
18
1.94 (1.04 to 2.83)
0.45 (0.26 to 0.78)
−2.35 (−3.91 to −0.79)

Delivery in secondary care
24 380
178
7.30 (6.23 to 8.37)
Not calculated
Not calculated

Nulliparous
12 674
118
9.31 (7.64 to 11.0)

Multiparous
11 706
60
5.13 (3.83 to 6.42)

Missing
91

Total
37 653
210
5.58 (4.83 to 6.33)

NICU=neonatal intensive care unit.
*Elective caesarean sections included.

BMJ | ONLINE FIRST | bmj.com

Contributors: AK, HWB, and HAAB initiated and coordinated the study. Contributors: AK, HWB, and HAAB initiated and coordinated the study.

ACCE coordinated logistics, data collection, and quality control of data. AK, CWPMH, and ACCE analysed the data. All authors actively participated in interpreting the results and revising the paper, which was written by ACCE, AK, HWB, and HAAB. AK is the guarantor. Funding: None. Competing interests: None declared. Ethical approval: Not needed for this type of study in the Netherlands. Data sharing: Dataset available from corresponding author at a.evers@umcutrecht. Consent was not obtained. A K Acce, Haab Hwb, Pgjn , involved in designing the study protocol. but presented data are anonymised and risk of identification is lowACCE, AK, HWB, HAAB, and PGJN were involved in designing the study protocol. ACCE coordinated logistics, data collection, and quality control of data. AK, CWPMH, and ACCE analysed the data. All authors actively participated in interpreting the results and revising the paper, which was written by ACCE, AK, HWB, and HAAB. AK is the guarantor. Funding: None. Competing interests: None declared. Ethical approval: Not needed for this type of study in the Netherlands. Data sharing: Dataset available from corresponding author at a.evers@umcutrecht. Consent was not obtained, but presented data are anonymised and risk of identification is low.

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EuroNatal International Audit Panel, EuroNatal Working Group. Differences in perinatal mortality and suboptimal care between 10 European regions: results of an international audit. J H Richardus, W C Graafmans, S P Verloove-Vanhorick, J P Mackenbach, BJOG. 110Richardus JH, Graafmans WC, Verloove-Vanhorick SP, Mackenbach JP, EuroNatal International Audit Panel, EuroNatal Working Group. Differences in perinatal mortality and suboptimal care between 10 European regions: results of an international audit. BJOG 2003;110:97-105.

Higher perinatal mortality in the Netherlands than in other European countries: the Peristat-II study. A D Mohangoo, S E Buitendijk, C W Hukkelhoven, A C Ravelli, Rijninksvan Driel, G C Tamminga, P , Ned Tijdschr Geneeskd. 152Mohangoo AD, Buitendijk SE, Hukkelhoven CW, Ravelli AC, Rijninks- van Driel GC, Tamminga P, et al. Higher perinatal mortality in the Netherlands than in other European countries: the Peristat-II study. Ned Tijdschr Geneeskd 2008;152:2718-27.

EURO-PERISTAT Working Group on Risk Factors. Do differences in maternal age, parity and multiple births explain variations in fetal and neonatal mortality rates in Europe? Results from the EURO-PERISTAT project. S Anthony, G W Jacobusse, K M Van Der Pal-De Bruin, S Buitendijk, J Zeitlin, Paediatr Perinat Epidemiol. 23Anthony S, Jacobusse GW, van der Pal-de Bruin KM, Buitendijk S, Zeitlin J, EURO-PERISTAT Working Group on Risk Factors. Do differences in maternal age, parity and multiple births explain variations in fetal and neonatal mortality rates in Europe? Results from the EURO-PERISTAT project. Paediatr Perinat Epidemiol 2009;23:292-300.

Measuring up to the best? International differences in perinatal mortality. P W Achterberg, Achterberg PW. Measuring up to the best? International differences in perinatal mortality. 2005. www.rivm.nl/bibliotheek/rapporten/ 270032001.pdf.

Obstetric care in the Netherlands under assessment again. J M Merkus, Ned Tijdschr Geneeskd. 152Merkus JM. Obstetric care in the Netherlands under assessment again. Ned Tijdschr Geneeskd 2008;152:2707-8.

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The European perinatal health report: comparing the health and care of pregnant women and newborn babies in Europe. J Zeitlin, A Mohangoo, M Cuttini, Europeristat Report Writing, Alexander S Committee, H Barros, J Epidemiol Community Health. 63Zeitlin J, Mohangoo A, Cuttini M, EUROPERISTAT Report Writing Committee, Alexander S, Barros H, et al. The European perinatal health report: comparing the health and care of pregnant women and newborn babies in Europe. J Epidemiol Community Health 2009;63:681-2.

Perinatal mortality and morbidity in a nationwide cohort of 529 688 low-risk planned home and hospital births. A De Jonge, Van Der Goes, By, A C Ravelli, M P Amelink-Verburg, B W Mol, J G Nijhuis, BJOG. 116De Jonge A, van der Goes BY, Ravelli AC, Amelink-Verburg MP, Mol BW, Nijhuis JG, et al. Perinatal mortality and morbidity in a nationwide cohort of 529 688 low-risk planned home and hospital births. BJOG 2009;116:1177-84.

Perinatal mortality in the Netherlands 2000-2006: risk factors and risk selection. Acj Ravelli, M Eskes, M Tromp, A M Van Huis, Eap Steegers, P Tamminga, Ned Tijdschr Geneesk. 152Ravelli ACJ, Eskes M, Tromp M, Van Huis AM, Steegers EAP, Tamminga P, et al. Perinatal mortality in the Netherlands 2000-2006: risk factors and risk selection. Ned Tijdschr Geneesk 2008;152:2728-33.

Evaluation of 280 000 cases in Dutch midwifery practices: a descriptive study. M P Amelink-Verburg, S P Verloove-Vanhorick, Rma Hakkenberg, Ime Veldhuijzen, Bennebroek Gravenhorst, J Buitendijk, S E , BJOG. 115Amelink-Verburg MP, Verloove-Vanhorick SP, Hakkenberg RMA, Veldhuijzen IME, Bennebroek Gravenhorst J, Buitendijk SE. Evaluation of 280 000 cases in Dutch midwifery practices: a descriptive study. BJOG 2007;115:570-8.


Long-term stability of grazing lawns in a small protected area, the Mountain Zebra National Park
13 Aug. 2013

Peter Novellie email@example.com
Conservation Services Division
South African National Parks
South Africa

Angela Gaylard
Conservation Services Division
South African National Parks
South Africa

PO Box 7870001PretoriaSouth Africa

Long-term stability of grazing lawns in a small protected area, the Mountain Zebra National Park
13 Aug. 20132DD519F7E6AC7229B1597F771160E2D510.4102/koedoe.v55i1.1108Received: 15 Aug. 2013 Accepted: 07 June 2013
We examined a heavily grazed plant community dominated by creeping grass species with the aim of, (1) determining its response to the exclusion of grazing and (2) its long-term persistence.This plant community was particularly favoured by wild ungulate species that prefer short grasses -blesbok (Damaliscus dorcas phillipsi), springbok (Antidorcas marsupialis) and black wildebeest (Connochaetes gnou).Exclusion of grazing by large herbivores by means of fencing resulted in the virtual disappearance of the creeping grasses and their replacement by tall tufted species.On plots that remained unfenced, the plant species composition was found to be little changed after an interval of more than 20 years.The number large stock unit equivalents (LSU) per ha carried by the plant community was used as a proxy for grazing intensity.Monitored for approximately 2 years at the start of the study, LSU per ha was found to greatly exceed levels recommended for commercial livestock production.This plant community conforms to a recently published definition of a grazing lawn, in that intense grazing promotes palatable, grazing-tolerant grass species.Conservation implications:The positive association between grazers and grazing-tolerant grass species evidently persisted for more than 20 years and there was no evidence of an increase in abundance of unpalatable plant species.Despite the small size of the park, which limited the extent of large herbivore movements, localised heavy grazing did not lead to range degradation.

Introduction

The term 'grazing lawn' has been widely used to describe patches that, as a result of frequent, intense grazing, become dominated by short, grazing-tolerant grass species (Archibald 2008;Archibald et al. 2005;Cromsigt & Olff 2008;McNaughton 1984;Stock, Bond & Van de Vijver 2009;Waldram, Bond & Stock 2007).In a detailed review of this phenomenon, Cromsigt and Kuijper (2011:212) defined a grazing lawn as 'a grazed or browsed patch in which intense foraging leads to increased resource availability and a proportional increase in palatable species in the patch'.Thus, grazing lawns are characterised by a positive association between grazers and grazingtolerant grasses; intensive grazing has the effect of increasing the productivity and quality of the food resource (Cromsigt & Kuijper 2011).It has been demonstrated (Archibald 2008;Cromsigt & Olff 2008;Stock et al. 2009;Waldram et al. 2007) that grazing lawns do not derive solely from existing matrices of abiotic factors, such as soil heterogeneity (although their distribution may be influenced by such factors), but are animal driven.Consequently, they can increase or decrease depending on fire, rainfall and grazing regime.

The formation of a shifting mosaic of grazing lawns together with areas of taller grass appears to be a key determinant of spatial heterogeneity of food resources that is necessary to maintain a diversity herbivore species (Cromsigt & Olff 2008;Fuhlendorf & Engle 2001;Pickett, Cadenasso & Benning 2003) and is important for maintaining biological diversity and stability in grasslands (Tilman & Downing 1994).From this perspective, patch-selective grazing and grazing lawn formation should be regarded as a desirable phenomenon in grassland systems that are managed with the objective of maintaining biodiversity and natural ecological processes.

However, concern has been expressed about possible negative effects of patch-selective grazing (Fuls 1992;Kellner & Bosch 1992).It is conceivable that, under certain conditions, protracted use of individual patches may reach a point where the positive association between grazers and palatable grasses breaks down and the lawn transforms to a state that can no longer support grazing.This may be a particular risk in small protected areas, where fences restrict the spatial scale over which grazing dynamics can play out (Owen-Smith 1983).Under these conditions, patch-selective grazing may not result in a stable (or at least resilient) mosaic of grazing lawns, but may instead cause progressive depletion of the grazing resource.

To avoid range deterioration in small protected areas, it has been proposed that stocking rates of wild ungulate species should be maintained in accordance with norms set for commercial livestock production (Meissner 1982).However, if grazing lawns are prone to transform to a different state, then the setting of stocking rates for the area as a whole may be ineffective in preventing such change (Kellner & Bosch 1992).

It is therefore important to understand the drivers of grazing lawn formation in small protected areas and to monitor the stability of the grazing lawn mosaic.The key question is whether the lawns retain their attractiveness to grazing ungulates over the long term, or whether they eventually transform to a state, either through soil loss or encroachment by unpalatable species, which no longer provides grazing.

The Mountain Zebra National Park (MZNP) is a relatively small protected area, which carries localised high densities of grazing ungulates, some of which strongly favour grazing lawns (Novellie 1990).This study was conducted on some of the most heavily used grazing lawns in the MZNP, examining (1) their long-term persistence and (2) response to the exclusion of grazing by means of fenced exclosures.

Research method and design

Setting

The MZNP is situated 24 km west of the town of Cradock in the Eastern Cape Province, South Africa.At the time when the observations commenced (1987), the park was only 6536 ha.In the late 1990s it was greatly expanded in size, extending over 28 412 ha at the conclusion of the study.Monthly minimum and maximum temperatures vary from 6 °C to 28 °C in summer (from September to March) and from 0 °C -20 °C in winter (from April to August) (Brown & Bezuidenhout 2000).Rainfall averages about 400 mm, with most (70%) falling in the summer months.Periodic light snow occurs during the winter months and frost is common between May and October.

The park lies in a transition zone, incorporating elements of three biomes, the Nama-Karoo, Grassland and Albany Thicket.It includes three of the vegetation types recognised by Mucina and Rutherford (2006): the Eastern Upper Karoo, Karoo Escarpment Grassland and Eastern Cape Escarpment Thicket.This study was conducted in Karoo Escarpment Grassland in a valley near the western boundary of the park, near two small impoundments called the Oudamme.Although fires occur periodically in the higher Karoo Escarpment Grassland of the park, the areas of grazing lawn seldom, if ever, burn.No fires occurred in the Oudamme Valley between 1987 and 2010.

Data on habitat use by grazing ungulates (Novellie 1990(Novellie , 1991) ) revealed that the Oudamme Valley was one of the most heavily grazed areas of the park during the late 1980s.This area was therefore selected for a more detailed assessment of the long-term stability of the grazing lawns.

Procedure

In March and April of 1987, four matched pairs of plots, each roughly 30 m by 30 m in size, were laid out in the heavily grazed areas of the Oudamme Valley.In each plot a 200-point survey for canopy spread cover was conducted (see Novellie & Strydom 1987 for further details).The points were arranged in eight rows of 25 each, with 1-m spacing between points and 1-m or 2-m spacing between rows.A canopy spread strike was recorded if a point fell within an imaginary line drawn around the perimeter of the canopy of a plant.For each strike the species of plant was recorded.One member of each pair of plots was then selected at random and a fence approximately 1.5 m high was erected around it to exclude grazing by large herbivores.The other member of each pair remained exposed to grazing.Using the same method, the surveys of two of the pairs of plots were repeated in June 2009.The surveys of the remaining two pairs were completed in June 2010.The plant species names reported in this article follow Klopper et al. (2006).Canonical correspondence analysis was used to investigate changes from 1987 to 2009-2010 in plant species composition in the fenced and unfenced plots (Ter Braak 1987).

Between 1987 and 2009-2010 no formal surveys were conducted, but the plots were visited every second or third year (by the first author) to get a general impression of the state of the vegetation.

An indication of the intensity of use of the habitat by large herbivores was obtained by conducting five to ten counts per month from 1987 to 1989.Ideally, the density of animals should have been sampled exactly within the four 30 m by 30 m plots that were left unfenced, but at this small scale a very large number of counts would have been needed to get a reliable indication of average animal density.It was therefore necessary to conduct the animal counts in larger-sized plots.Two plots, each 100 m by 100 m (1 ha) were identified in similar habitat, situated about 200 m apart from one another and adjacent to the four matched pairs of plots used for the exclosure study.The corners were marked with 1.8 m high steel fencing standards which could be seen through binoculars by an observer on the ridge overlooking the Oudamme Valley.Thus, the animals within the boundaries of the plots could be counted without disturbing them.

The canopy spread cover of plant species within the two 1 ha plots was determined by 200-point surveys, as described above.The 200-point surveys were replicated at randomly chosen locations within each 1 ha plot, ten surveys in one plot and eight in the other.(The results of these surveys are also reported by Novellie 1990).In total, one of the 1 ha plots was checked 199 times and the other 255 times.For comparison with Department of Agriculture stocking rate standards as applied during the 1980s, the average numbers of ungulates in each plot were converted to large stock unit equivalents (LSU), according to the method of Meissner (1982), as published in the Government Gazette (Republic of South Africa 1985).

Animal counts in the 1 ha plots were not continued after 1989, but the locations of animals plotted during the annual helicopter census (Gaylard 2011) provided a rough indication of use of the Oudamme Valley by large herbivores between 1997 and 2011.

Results

Table 1 shows the mean canopy spread cover of grass species in the four matched pairs of plots and each of the two 1 ha plots at the start of observations in 1987.The species composition was broadly similar across all plots confirming that they were representative of the same plant community.The most abundant grasses in terms of canopy spread cover were the creeping species Cynodon incompletus and Tragus koelerioides.The former grew in dense lawns around accumulations of dung but elsewhere occurred generally as sparse runners.In contrast, T. koelerioides was not closely associated with dung patches and occurred as sparse runners rather than lawns.The bunch grasses Merxmuellera disticha, Digitaria eriantha and Eragrostis curvula were next most abundant in terms of cover.Merxmuellera disticha was very patchy in distribution and the tall, dense tufts showed relatively little signs of grazing.In contrast, D. eriantha and E. curvula were widely distributed and virtually all tufts of these species were cropped to a height of around 5 cm.The annual grass Aristida congesta was widespread but its cover was generally low.The taller bunch grasses: Themeda triandra, Cymbopogon pospischilii and Heteropogon contortus were rare throughout the Oudamme Valley, the only specimens seen at that time were growing within the canopy of shrubs where they were protected against grazing.

Figure 1 and Figure 2 compare the fenced and matched unfenced plots in the years 1987 and 2009-2010.Only the most abundant and widespread grass (Figure 1) and dwarf shrub (Figure 2) species are shown, as the others were either too uncommon or too localised to draw conclusions as to their response to the exclusion of grazing.

The canonical correspondence analysis showed that species composition was significantly associated with treatments, with the strongest association for the fenced plots in 2009-2010 (F = 9.77; p = 0.002).The ordination diagram (Figure 3) shows how the species were clustered with reference to the treatments.Of the grass species, T. triandra, C. pospischilii and H. contortus formed a group that was very strongly associated with the exclosures as surveyed during 2009-2010.In clear contrast to this group, the exclosure plots were characterised by a lack of the annual grass A. congesta and the creeping grasses C. incompletus and T. koelerioides.The bunch grasses E. curvula and D. eriantha were in an intermediate position in that they were associated with all plots, although they both showed a relative increase in cover in the fenced plots in 2009-2010 relative to the other plots (Figure 1).In summary, the fenced plots in 2009-2010 showed a complete transformation in grass species composition; the creeping and annual species all but disappeared and taller bunch grass species were dominant (Figure 1).On the other hand, the unfenced plots showed relatively minor changes in grass species composition from 1987 to 2009-2010 (Figure 1).

In contrast to the grasses, none of the common dwarf shrub species showed a consistent response to fencing, instead differences are apparent between the survey years (Figure 2 and Figure 3).Chrysocoma ciliata is associated with both the fenced and unfenced plots in 1987, but in 2009-2010 it declined in all plots.Helichrysum dregeanum is associated with unfenced and fenced plots, but only in 2009-2010.Felicia fascicularis increased in the unfenced plots, but there was considerable between-plot variation in the case of the fenced plots.The reason for the changes in abundance in these dwarf shrub species from 1987 to 2009-2010 is unknown.

Over the period 1987-1989, both of the 1 ha plots were utilised mainly by short grass-grazers, blesbok (Damaliscus dorcas phillipsi), black wildebeest (Connochaetes gnou) and springbok (Antidorcas marsupialis).Mountain zebra (Equus zebra zebra) and ostrich (Struthio camelus) were observed there comparatively infrequently (Table 2).Red hartebeest (Acelaphus buselaphus), also common in the park at the time, were never observed in these plots.The stocking rate carried by Plot 1 was estimated at 9.7 ha per LSU, whilst that of Plot 2 was 1.8 ha per LSU.The difference in animal density between the two may have been caused by the fact that there was more surface rock in Plot 1; hence, the cover of short grass favoured by grazers was lower in Plot 1 (mean ± standard error: 16.0% ± 2.2%) than in Plot 2 (28.0%± 2.7%).For both plots, the stocking rates are considerably higher than the 14 ha per LSU recommended for the area by the Grootfontein Agricultural Development Institute (1986).

TABLE 1: Percentage canopy spread cover (mean ± standard error) of grass species compared between the study plots (four matching pairs of plots roughly 30 m by 30 m each) and Plots 1 and 2 (each 100 m by 100 m) which were used to determine the density of grazing ungulates using the area.

Species

Growth  The census results (Table 3) indicated that the Oudamme Valley continued to support fair numbers of large herbivores from 1997 to 2011.It was, however, not possible to be sure from the census results of the exact plant community being utilised, as the animals are likely to have moved around in    response to the helicopter.This is probably the reason why Table 3 indicates a relatively greater abundance of mountain zebra and mountain reedbuck, species which tend to favour the ridges around the valley rather than the valley bottom habitat in the 1 ha plots.For this reason, we did not attempt to estimate animal densities from the census results.

Discussion

The results described above are in accordance with evidence in the literature (Archibald 2008;Cromsigt & Olff 2008;Stock et al. 2009;Waldram et al. 2007) that grazing lawns are animal driven; if grazing is removed the creeping grasses give way to taller bunch grasses.

The change in species composition in the exclosures follows the successional sequence predicted by Vorster's (1982) veld condition index for the Karoo Region, which classifies T. triandra, C. pospischilii, H. contortus and D. eriantha as 'decreaser' species, which decrease in abundance if subjected to heavy grazing, whereas E. curvula, C. incompletus and T. koelerioides are 'increaser' species which are typically promoted by grazing.In terms of Vorster's index, which is designed as a guide to commercial pastoralists, the proliferation of the increaser species at the expense of decreaser species is regarded as a sign of deterioration of veld condition, something to be rectified by reducing stocking densities.This is obviously not true for natural systems, where grazing lawns of increaser species are the product of a positive association between consumers and the resource.This is evident from the fact that blesbok, springbok and black wildebeest showed a strong preference for communities dominated by increaser species (Novellie 1990) and occupied these habitat patches at relatively high densities.Moreover, the increaser species are highly palatable to the short-grass grazers, as is evident from the extent to which they were defoliated in the study area.

It is noteworthy that, although all the fenced plots were dominated by one or other bunch grass species, there were differences between the plots in the particular grass species that eventually came to dominate; in some it was T. triandra, in others C. pospischilii or D. eriantha.Given that the tufted grasses were extremely rare in the area before fencing, it seems likely that this was caused by chance variation between plots in the nearest sources of colonising seeds.

For this plant community (described as Lower Slope Degraded Dwarf Shrubland by Van der Walt 1980), the term 'grazing lawn' is perhaps a misnomer, because it is only C. incompletus that forms actual lawns and these are generally no more than a few square metres in area immediately surrounding dense patches of ungulate dung.Nevertheless, we regard it as appropriate to refer to the C. incompletus -T.koelerioides community as a whole -rather than specifically the Cynodon lawns -as a grazing lawn, as it conforms to Cromsigt and Kuijper's (2011:212) definition of a lawn in that 'intense foraging leads to increased resource availability and a proportional increase in palatable species in the patch'.

The most common dwarf shrub species associated with the grazing lawns, F. fascicularis and H. dregeanum, are regarded as being palatable to domestic stock by Vorster (1982) and, as noted, frequently showed signs of browsing in the Oudamme Valley.It is interesting to consider whether this community may qualify as a browsing as well as a grazing lawn (as defined by Cromsigt & Kuijper 2011).However, these dwarf shrubs persisted in the exclosures (Figure 2) so, unlike the creeping grasses, their presence does not appear to be dependent on grazing.

We did not quantitatively measure the rate at which the transformation in plant species composition within the exclosures took place, but the periodic inspections during the study period suggested that it occurred very slowly.Little change in species composition could be discerned during the first 5 years after construction of the exclosures (P.Novellie, pers.obs.).As noted, the tall grass species that came to dominate the exclosures were initially very uncommon in the area, so the slow transition may have been due to scarcity of seed.

In the MZNP, it appears that the mechanism driving the formation of these grazing lawns is the patch-selective grazing of blesbok, springbok and black wildebeest.The habit of the territorial males of these species of occupying on 'stamping grounds' and of defecating and urinating at specific localities (Du Plessis 1972;Lynch 1971;Novellie 1975Novellie , 1990;;Von Richter 1972), results in accumulations of dung and associated Cynodon lawns.Termite mounds are present in the Oudamme Valley and may also play a facilitating role in influencing the formation of grazing lawns (see Cromsigt & Olff 2008).Du Plessis (1972) describes how a blesbok male, introduced into a paddock, initially focused his grazing around the margins of a termite mound, eventually creating a patch of closely grazed grass.Megaherbivores, such as white rhino, have been demonstrated to play a key role in creating grazing lawns in Hluhluwe-iMfolozi Park (Cromsigt & Olff 2008;Waldram et al. 2007), but megaherbivores are absent from the MZNP.

Conclusion

Although there has been concern around the possibility that the grazing lawns may transform into an ungrazable state, it appears that the plant species composition on the grazed plots showed little change 20 years after the initial surveys, despite continued use of the area by the short-grass grazers.Not having conducted interim descending point surveys we are uncertain as to fluctuations in species composition that may have occurred during the 20 year interval.However, no sign of transformation could be detected during the visits to the study area made about every 2-3 years; in general, the appearance the plant species composition appeared to be very stable.There was never any indication of dominance by the species of annual grasses and unpalatable dwarf shrubs (e.g. A. congesta and C. ciliata) that were identified by Vorster (1982) as characterising the worst state of veld degradation in the Karoo Region.

Thus, no evidence could be detected of a breakdown of the positive association between grazers and the creeping grasses.However, this is not to say that it could not occur under certain circumstances.In small protected areas it is highly desirable to develop a predictive understanding of the relationship between the overall stocking rate maintained, on the one hand, and grazing lawn stability and/or resilience on the other.

FIGURE 1 :
1

FIGURE 2 :
2
FIGURE 2: Mean percentage canopy cover of some dwarf shrub species in four pairs of plots compared between 1987 and 2009-2010 in fenced and unfenced plots: (a) Felicia fascicularis (dwarf shrub), (b) Helichrysum dregeanum (dwarf shrub) and (c) Chrysocoma ciliata (dwarf shrub).

FIGURE 3 :
3
FIGURE 3: Ordination diagram of the canonical correspondence analysis.

form Study plots in 1987 (N = 8 plots) Plot 1 in 1987 (N = 10 plots) Plot 2 in 1988 (N = 8 plots)
Aristida congestaannual grass0.90 ± 0.640.40 ± 0.200.00 ± 0.00Cynodon incompletuscreeping grass7.90 ± 5.735.20 ± 1.796.90 ± 2.63Tragus koelerioidescreeping grass6.00 ± 2.077.40 ± 0.9113.40 ± 0.88Aristida diffusabunch grass0.30 ± 0.590.50 ± 0.173.10 ± 0.89Cymbopogon pospischiliibunch grass0.00 ± 0.000.00 ± 0.000.20 ± 0.09Digitaria erianthabunch grass3.30 ± 2.224.10 ± 1.115.50 ± 0.84Eragrostis curvulabunch grass2.40 ± 1.992.00 ± 0.295.00 ± 0.77Eragrostis obtusabunch grass0.60 ± 1.060.20 ± 0.070.10 ± 0.08Eustachys paspaloides-0.10 ± 0.180.10 ± 0.070.20 ± 0.09Heteropogon contortusbunch grass0.00 ± 0.000.00 ± 0.000.20 ± 0.13Merxmuellera distichabunch grass2.90 ± 5.469.90 ± 4.041.80 ± 0.85Microchloa caffrabunch grass0.80 ± 0.600.30 ± 0.120.80 ± 0.23Oropetium capensebunch grass0.00 ± 0.000.00 ± 0.000.00 ± 0.00Sporobolus fimbriatusbunch grass0.10 ± 0.180.20 ± 0.150.00 ± 0.00Themeda triandrabunch grass0.00 ± 0.000.10 ± 0.050.10 ± 0.08

TABLE 2 :
2
Mean numbers of large herbivore species counted in two 1 ha sized plots from 1987 to 1989, with large stock unit equivalents and stocking rate expressed as ha per large stock unit equivalent.
SpeciesPlot 1Plot 2Mean numberMean LSUMean numberMean LSUper countper countper countper countBlesbok0.160.0260.980.157Black wildebeest0.140.0411.260.364Springbok0.300.0240.040.003Mountain zebra0.020.0090.030.016Ostrich0.010.0040.010.003Number of counts255.00-199.00-Total LSU-0.103-0.544ha per LSU-9.700-1.800LSU, large stock unit equivalents.

TABLE 3 :
3
Numbers of different species counted during the annual helicopter census in the Oudamme Valley from 1997 to 2011.
YearBlesbokBlack wildebeestSpringbokMountain zebraRed hartebeest Mountain ReedbuckGrey rhebokEland19979171230280019982223670019990011101520200021834024302002152282601286320034309191000200431211702102200812479402450200902103208002010010401000932011032050040Mean6.5526.737.7312.180.6412.642.826.18
doi:10.4102/koedoe.v55i1.1108 http://www.koedoe.co.za
AcknowledgementsWe are grateful to Park Manager, Ettienne Fourie, for constructing and maintaining the exclosures in good condition over many years, to Section Ranger, Robyn Wood, and Student Ranger, Nonkazimlo Baca, for able assistance with the vegetation surveys and to Animal Ecologist, Guy Castley, for historical air census data.An earlier draft of the article was greatly improved by the comments of two anonymous reviewers.Competing interestsThe authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.Authors' contributions
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