Grassland-breeding waders: identifying key habitat requirements for management


Jennifer Smart, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK (fax + 44 1603 592250; e-mail


  • 1Habitat loss and degradation of wetland ecosystems, principally through large-scale drainage and conversion to arable farmland, have been implicated in the widespread, dramatic declines of breeding waders across Europe. Managing the remaining wetlands to reverse these declines will require a detailed understanding of their habitat requirements.
  • 2In the UK, grazing marshes are key components of the remaining wetlands in both coastal and inland sites, and the structure of grazing marsh habitat can differ between these locations. Redshank Tringa totanus is a declining wader species that breeds in both marsh types. We quantified the habitat features that influence redshank selection of breeding and nest site locations, across coastal and inland marshes, in eastern England.
  • 3On both marsh types, breeding location and breeding densities within fields were positively related to the lengths of pool edge and all wet features, respectively. Nest site location was principally influenced by vegetation characteristics, with soil penetrability also important on inland sites but proximity to wet features and vegetation type at the nest important on coastal sites. Hatching probability was higher when the surrounding soils were more penetrable.
  • 4Synthesis and applications. The wet features of critical importance for breeding redshank are common on coastal marshes and can be deliberately established on inland sites. Coastal marshes are often rare and frequently threatened by dynamic coastal processes, whereas inland marshes are more abundant but largely unsuitable for breeding waders at present. These analyses highlight the scope for improving the management of inland marshes for breeding redshank. As habitat suitable for breeding redshank frequently supports a range of other wader species, this information can also direct management efforts to improve breeding wader populations in the wider countryside.


The conservation of wetland ecosystems is of global importance because of dramatic declines in both the overall area and quality of remaining wetlands. Wetland ecosystems support a high diversity of species (Frayer et al. 1983; Dahl 1990) but have declined over the last century by about 50% (IUCN 2005;, accessed 3 March 2005) and are amongst the most degraded of ecosystems (Amezaga, Santamaria & Green 2002). Historical wetland losses have resulted primarily from human activities (Brinson & Malvarez 2002; Kingsford & Thomas 2002) and future wetland loss is likely to accelerate as a consequence of climate change and sea level rise. By 2080, 22% of the world's coastal wetlands could be lost as a direct result of accelerated sea level rise (Nicholls, Hoozemans & Marchand 1999). In addition, increasing frequency and amplitude of extreme summer temperatures may have detrimental effects on water availability (Briggs & Hossell 1995; Thomson et al. 2001).

Breeding wader species are an important component of the biodiversity of wetlands, and many of these species have undergone dramatic declines in the last 30 years (Piersma 1986; Brindley et al. 1998; Wilson et al. 2005). In England, breeding redshank Tringa totanus, lapwing Vanellus vanellus and snipe Gallinago gallinago have declined by 29%, 38% and 61%, respectively, over the last 20 years, and 64% of all grassland breeding waders have become restricted to a few key areas (Wilson et al. 2005).

Given the widespread, dramatic declines in breeding wader populations and their grassland breeding habitats, identifying the key habitat requirements of these species is critical in improving management of the remaining grassland. This is particularly true of inland grazing marshes as their coastal counterparts are vulnerable to sea level rise and loss through managed realignment (Evans et al. 2004; Nicholls 2004), and the European Union (EU) Habitats Directive requires compensation for habitat loss of designated sites within Europe (European Commission 1992). Large areas of inland grazing marsh are currently managed as commercial, agricultural farmland and support few breeding waders. These sites have the potential to provide suitable breeding wader habitat with appropriate management. On coastal grasslands, multispecies studies have indicated the influence of shallow wet areas and vegetation structure on the distribution of breeding waders and foraging adults and chicks of several species (Vickery et al. 1997; Milsom et al. 2000, 2002). There have been no similar studies of breeding wader requirements on inland grazing marshes, and the relative quality of inland and coastal breeding habitats is currently unknown.

In this study, we focused on the redshank, a wader species that breeds in a range of habitats throughout Europe and is listed as a species of European conservation concern (Heath et al. 2000). The aim of this study, for both inland and coastal grazing marsh, was to explore the relative impact on breeding redshank of variation in the key aspects of grassland management. On grasslands, vegetation structure, soil moisture levels and the extent and type of shallow wet features are all determined by grazing regimes and the manipulation of water tables and surface water. We quantified the relative importance of these habitat features for the presence and density of breeding redshank, the location of nests within fields and the success of individual nesting attempts.

Materials and methods

study sites

The study was carried out in eastern England from March to July 2003 on three areas of coastal grazing marsh located on the north Norfolk coast (Holkham, N52:57:37, E0:47:51; Burnham Overy, N52:57:40, E0:46:04; Burnham Norton, N52:58:00, E0:42:58) and two areas of inland grazing marsh within the Broads environmentally sensitive area (Buckenham Marshes, N52:35:31, E1:28:05; Berney Marshes, N52:35:14, E1:37:48). Coastal grazing marsh was defined as any area of grazing marsh with an area of intertidal mudflat or saltmarsh (which can provide important foraging opportunities; Lourenço, Granadiero & Palmeirim 2005) adjacent to it, whereas the inland sites were 2 km and 12 km from these intertidal habitats.

Fields were selected primarily on the basis of their historical redshank breeding density (Table 1). Redshank breeding density on each field was assessed using breeding wader surveys carried out by reserve staff from 2000 to 2002. Fields were then categorized as high- (≥ 0·2 pairs ha−1 or breeding redshank present in all years) or low- (< 0·2 pairs ha−1 or no breeding redshank in at least 1 year in 3) density fields. This resulted in 48 fields being selected, 24 each from coastal and inland sites (Table 1). The number of pairs of redshank nesting in each field was estimated using the standard method developed by Green, Johnson & Collins (1984) and Green (1986). Densities in the fields in 2003 were highly consistent with those in 2000–02 (r = 0·67, n= 48, P < 0·001).

Table 1.  The number of fields with high and low redshank breeding densities used in each study site. Fields were categorized according to the mean (± SE) annual redshank breeding density (pairs ha−1) from 2000 to 2002, and the mean number of years in which redshank were absent from these fields (2000–02) is shown. See text for details
HabitatSiteDensity categoryNo. of fieldsMean (± SE) densityMean no. of absences (2000–02)
High60·62 ± 0·150·5
BuckenhamLow60·11 ± 0·031·5
High60·50 ± 0·140
CoastalBurnham NortonLow40·31 ± 0·061
High40·49 ± 0·030
Burnham OveryLow40·02 ± 0·012·75
High40·48 ± 0·090
High40·54 ± 0·080

grazing marsh habitat surveys

Surveys of habitat structure at each site were carried out between 24 March and 30 April 2003, the period over which redshank select nesting locations. Each field was divided into a grid of 50 × 50-m squares and surveys were carried out in between two and eight randomly selected squares per field, with the number of squares dependent on the area of the field. The mean (± SD) percentage of each field surveyed was 26 ± 11%. On grazing marshes, habitat structure in field edges (c. 10 m from the ditch) often differs from the remainder of the marsh because of the elevated nature of ditch edges resulting from ditch management. Square selection was therefore stratified such that the number of squares that included a field edge was determined by the proportion of the field area that was represented by field edge.

A total of 247 squares was surveyed, comprising 121 in 24 coastal fields and 126 in 24 inland fields. Within each habitat square, vegetation characteristics, ground conditions and water features were all measured (Table 2). At each of five equally spaced locations, moving diagonally from one corner of the square to the other, five replicate measures of vegetation height and soil penetrability and one measure of surface wetness were recorded. In each quarter of the square the ground cover was categorized and the percentage cover of each category was estimated (Table 2). The mean of these four measures of percentage cover of each category was then calculated for each habitat square. Three components of vegetation height were recorded because the transect method provided estimates of mean vegetation height but did not always record patches of tall or short vegetation within the square. All wet features within each square were categorized as ditches, rills, foot drains or pools (for definitions see Table 2). The length of the edge of each wet feature within the square was measured, the slope of the edge was recorded as either steep (> 60°) or shallow (< 60°) and features containing water were recorded as wet. The distance to each wet feature from the centre of the square was estimated in 5-m categories. In addition, in a 1-m wide transect from a randomly selected corner of the square to the centre marker, counts were made of the number of four different types of tussock: Juncus (rushes), Carex (sedges), Deschampsia spp. and other tussock-forming grasses.

Table 2.  Name, unit of measurement and description of each of the variables recorded during surveys of fields and nest sites of breeding redshank, on coastal and inland grazing marshes in 2003. The analysis column indicates variables used for the different analyses (f, field scale; p, principal components analysis; n, nest scale; ns, nest success)
Habitat variableUnitAnalysisDescription
Vegetation heightcmf,p,n,nsRecorded to the nearest 0·5 cm, using a sward stick with a polystyrene disc (diameter 24 cm and mass 28·5 g)
Soil penetrabilitykg m−2f,p,n,nsVicksburg penetrometer measurements of the force to drive a metal rod (diameter 7·5 mm) to a depth of 10 cm
Surface wetnessCategoryf,pFive categories: 1, water present; 2, water wells up underfoot only; 3, no water but slight dampness underfoot; 4, soil damp to touch; 5, dry ground
Water% coverf,pStanding water
Bare wet ground% coverf,pUnvegetated damp or wet ground
Bare dry ground% coverf,p,n,nsUnvegetated dry ground
Short vegetation% coverf,p,nVegetation < 10 cm
Tall vegetation% coverf,p,nVegetation ≥ 10 cm
Juncus spp.% coverf,pAll rush species
Linear wet feature lengthmf,pLength of (a) foot drains (shallow ditches created through management, usually linear and restricted to inland sites) or (b) rills (remnants of saltmarsh creeks, usually shallow and meandering and always confined to coastal sites)
Poolmf,pLength of permanent or temporary flooded areas within fields
Total wet feature lengthmf,pSummed length of all ditches, foot drains, rills and pools
TussocksNo.f,p,n,nsTotal number of tussocks in area
Grazing intensityLU ha−1 day−1f,pNumber of livestock units per hectare per day
Distance to wet featuremn,nsDistance from the centre of a habitat or nest square to the nearest wet feature
Wet feature typeCategoryn,nsType of nearest wet feature (rill, foot drain, ditch or pool)
Nest tussock typeCategorynsJuncus spp., grass spp., Deschampsia or sedge spp.
Nest vegetation heightcmnsVegetation height of the nest tussock, recorded with a sward stick (as above)

The structure of the habitat within fields is strongly influenced by the type and intensity of livestock grazing. On each field, the number of livestock was counted every 3–4 days, from late March until the end of June 2003. Different types and ages of livestock were recorded separately and the numbers were converted into livestock unit (LU) days using published figures for livestock grazing (SAC 1992).

redshank nesting densities

Nest locations were marked and revisited at 3–4-day intervals to assess survival; these visit rates have been shown not to affect nest success in similar species (Fletcher, Warren & Baines 2005). Observers were careful to avoid trampling the vegetation around nests and directions of approach were varied. Maximum length and breadth of all eggs were measured to the nearest 0·1 mm and weight was measured to the nearest 0·1 g; the method of Green (1986) was used to estimate hatch date from these measures. Nests were classified as successful if at least one chick hatched and in all cases observers were present at each nest as it hatched. Failed nests were classified according to egg remains: deserted nests had a full clutch of intact eggs, trampled nests had flattened eggs in the nest and signs of cattle activity around the nest, and predated nests were either empty well before the hatch date or contained remains of predated eggs.

The habitat characteristics around each nest site were measured within a week of locating each nest and usually on the same day that the nest was found. Within a 10 × 10-m area around each nest, all of the parameters recorded in the 50 × 50-m habitat squares (described above) were measured, with the following modifications. (i) Vegetation height, soil penetrability and surface wetness were measured at three locations: around the nest and at two diagonally opposite corners of the square. (ii) Vegetation height at the nest was also measured by placing the sward stick in the nest cup. (iii) The tussock type of the nest itself was recorded and selection for a particular nest tussock type was explored using the Jacobs’ preference index (Jacobs 1974), which ranges from +1 (total selection) to −1 (total avoidance). (iv) The distance from the nest cup to the nearest wet feature was recorded in addition to wet feature type.

data analyses

Our analyses focused on the relative importance of variation in vegetation structure, soil moisture levels and shallow wet features on the presence, density and success of breeding redshank at three spatial scales. A range of analytical techniques was employed. (i) At the large scale (field scale), logistic regression analysis was used to identify the characteristics of fields where breeding redshank were either present or absent. A combination of principal components analysis and multiple regression analysis was then used to identify the habitat characteristics determining the density of breeding redshank within occupied fields. (ii) At a smaller spatial scale (nest sites), logistic regression analysis was used to identify the habitat characteristics important for nest-site selection. (iii) At an even smaller spatial scale (within nest sites), the habitat characteristics of the nest cup were compared with the habitat immediately surrounding the nest, using anova, non-parametric tests or G-test comparisons of Jacobs’ preference indices (Jacobs 1974), where appropriate. Finally, logistic regression analysis was used to explore the habitat characteristics influencing nest success.


key factors influencing the presence of breeding redshank in fields

To assess the features of grazing marshes that influence the presence of breeding redshank, logistic regression models were developed that incorporated the suite of habitat variables recorded on each field. There was significant collinearity amongst five of the habitat variables, all of which reflected the availability of water within fields: total wet feature length (m), surface wetness, water cover, bare wet ground cover and linear wet feature length (r > 0·72, P < 0·001 in all cases). To assess the effect of each of the collinear variables on the final model, five logistic regression analyses were run, each including only one of the collinear habitat variables. None of these five variables was retained in any of the models, and there was no difference in the remaining variables which were retained in the final model of each of these analyses.

The probability of individual fields being occupied by breeding redshank was significantly greater when there was a greater length of pool edge (Fig. 1a) and a greater percentage cover of Juncus spp. (Fig. 1b). The inclusion of these two parameters in the model allowed the presence of breeding redshank to be correctly classified in 89% of fields and the absence of redshank in 81% of fields (log-likelihood ratio 40·1, inline image = 25·7, P < 0·001). The presence of breeding redshank was not influenced by any of the other variables listed in Table 2. The blocking factor identifying whether sites were located on coastal or inland habitats did not significantly improve the model fit, but was retained within the model to ensure that any habitat-specific variance was accounted for.

Figure 1.

The probability of fields containing breeding redshank in relation to (a) the mean length of pool edge (Wald 7·7, d.f. = 1, P= 0·006) and (b) the (arc-sine square-root transformed) mean percentage cover of Juncus spp. (Wald 6·9, d.f. = 1, P= 0·009). Bars show the mean (± SE) proportion of occupied fields and lines show the fitted logistic regression curves.

key factors influencing redshank density on grazing marshes

Across all the coastal grazing marsh sites, the number of pairs in each field recorded during surveys was strongly correlated with the peak (maximum number active at the same time) number of nests (r = 0·91, slope = 1·1, n = 24, P < 0·001). On the inland sites, the number of pairs was also significantly correlated with the number of nests at one site (Berney, r= 0·81, slope = 1·03, n = 13, P < 0·001) but not quite at the other (Buckenham, r = 0·55, slope = 0·22, n = 12, P = 0·07). On four of the 12 fields at Buckenham, the number of pairs of redshank recorded during surveys was much lower than the peak number of nests located, suggesting that the survey had severely underestimated nesting densities on these fields. To overcome this discrepancy, the relationship between the peak number of nests and the number of pairs per field on all sites (excluding Buckenham) was used to estimate the probable number of pairs in each field on Buckenham, given the peak number of nests found on each field (Fig. 2; peak number of nests on the 11 fields at Buckenham ranged between one and six).

Figure 2.

The relationship between the peak number of redshank nests found and the number of pairs of redshank recorded during surveys on 37 fields on coastal and inland grazing marshes in 2003 (y = 1·10x + 0·26, R2 = 0·81, n= 37, P < 0·001).

To include all habitat variables in an initial exploration of variation in redshank breeding densities, we ran three unrotated principal components analyses. Each analysis included a suite of habitat variables indicative of (i) surface water conditions, (ii) soil water conditions and (iii) vegetation characteristics of fields occupied by breeding redshank. The first component in each analysis explained 70%, 93% and 64% of the variance in the surface water, soil water and vegetation characteristics, respectively (Table 3). Positive values of the surface water component indicated fields with increasing length of wet features. Negative values of the soil water component were indicative of fields with highly penetrable soils, high surface wetness and higher percentage cover of Juncus and bare wet ground. Positive values of the vegetation component were characteristic of a low percentage cover of short vegetation. The principal component scores were then used in a multiple regression analysis to explore the habitat characteristics likely to be important in determining redshank breeding densities. The surface water component, which described the increasing length of wet features, was the only significant factor influencing redshank density (R2 = 0·52, n= 27, P < 0·001, slope = 0·26). As total wet feature length was very strongly correlated (r = 0·98; Table 3) with the surface water principal component, Fig. 3 shows the positive relationship between total wet feature length and redshank density.

Table 3.  Results of three principal components analyses of three aspects of habitat structure (surface water, soil water and vegetation) on 27 fields with breeding redshank. The loading of each habitat variable on each component is shown and all loadings > 0·6 and < −0·6 are shown in bold. See Table 2 for variable definitions
Principal components analysis % of varianceSurface water 69·7Soil water 92·6Vegetation 63·8
Surface water variables
Linear wet features0·81  
Total wet features0·98  
Soil water variables
Soil penetrability  0·99 
Surface wetness  0·72 
Juncus spp. –0·67 
Bare wet ground –0·63 
Bare dry ground  0·63 
Vegetation variables
Vegetation height  −0·24
Short vegetation   0·99
Tall vegetation  −0·16
Tussocks   0·55
Grazing intensity   0·14
Figure 3.

The relationship between redshank breeding density and the total length of wet features (foot drains, rills, pools and ditches) in fields occupied by breeding redshank on grazing marshes (y = 0·005x + 0·052, R2 = 0·47, n= 27, P < 0·001).

key factors influencing the location of redshank nests within fields

For both habitats, a logistic regression model showed that nests were present significantly more often in areas with taller vegetation (Fig. 4), greater cover of tall vegetation and fewer tussocks (Table 4). The probability of nest occurrence was higher in areas with a greater cover of short vegetation on inland sites, but a lower cover of short vegetation on coastal sites (Table 4). This result may at first seem contradictory; however, tall vegetation is rare on coastal grassland (Fig. 4), and the few patches that exist are strongly selected by redshank. In contrast, tall vegetation is common on inland grassland, and redshanks appear to select mosaics of short and tall vegetation. On inland sites, decreasing soil penetrability increased the probability of nest occurrence, and on coastal sites the probability of a habitat square containing a nest increased if the nearest wet feature was either a rill or a pool (Table 4). On inland sites, the model successfully classified the presence of a nest in 72% of cases and the absence of a nest in 97% of cases; overall 89% of cases were correctly identified (log-likelihood ratio 104·7, inline image= 116·2, P < 0·001). On coastal sites, the model successfully classified the presence of a nest in 71% of cases and the absence of a nest in 98% of cases; overall 93% of cases were correctly identified (log-likelihood ratio 67·1, inline image = 85·7, P < 0·001).

Figure 4.

The influence of vegetation height on the probability of 10 × 10-m field sections containing a redshank nest on (a) inland (Wald 21·26, d.f. = 1, P < 0·001) and (b) coastal (Wald 16·17, d.f. = 1, P < 0·001) grazing marsh. Bars show the mean (± SE) proportion of habitat square with nests and lines show the fitted logistic regression curves.

Table 4.  A logistic regression model of the effect of habitat structure variables (see Table 2) on the probability of a redshank nest occurring in 10 × 10-m field sections, on coastal and inland grazing marshes in 2003
HabitatHabitat variableDirectionWaldd.f.P
InlandVegetation height+21·261< 0·001
Short vegetation+13·741< 0·001
Tall vegetation+12·661< 0·001
Tussocks12·621< 0·001
Soil penetrability 5·491< 0·05
CoastalVegetation height+16·171< 0·001
Nearest wet feature+13·293< 0·01
Tall vegetation+11·361< 0·001
Short vegetation 7·641< 0·01
Tussocks 7·171< 0·01

nest site characteristics

On both coastal and inland sites, vegetation height at the nest cup (mean ± SE, inland 17·9 ± 6·6 cm, coastal 14·1 ± 3·8 cm) was significantly taller than both the vegetation immediately around the nest (inland 8·8 ± 4·7 cm; coastal 6·0 ± 2·7 cm) and the vegetation in the remainder of the 10 × 10-m area around the nest (inland 8·1 ± 3·2 cm, F2,166 = 58·4, P < 0·001; coastal 5·7 ± 2·2 cm, F2,89 = 74·8, P < 0·001).

Penetrability of the ground immediately around the nest and in the surrounding 10 × 10-m area did not differ significantly on inland sites (t57 = −0·73, NS). However, on coastal grazing marsh the ground immediately around the nest was significantly more penetrable (137·8 kg m−2 ± 7·2) compared with the surrounding area (187·8 ± 10·2, t26 = −4·6, P < 0·001).

The surface wetness of the area immediately around the nest and in the surrounding 10 × 10-m area did not differ significantly for either coastal grazing marsh (Wilcoxon signed rank test, z= −1·4, n= 31, NS) or inland grazing marsh (Wilcoxon signed rank test, z = −0·4, n= 57, NS).

On coastal sites, redshank avoided tussocks of Juncus spp. (Jacobs’ index −1) and showed a slight preference for nesting in grass tussocks (Jacobs’ index 0·11, G2 = 23·5, P < 0·001). On inland sites, redshank showed a slight but non-significant preference for nesting in Juncus (Jacobs’ index 0·28) and grass tussocks (Jacobs index 0·26) and avoidance of Deschampsia (Jacobs index −0·59) and sedges (Jacobs index −0·2, G3 = 6·64, P= 0·08).

effects of habitat structure on hatching success

A total of 86 nests was monitored (inland n = 55, coastal n= 31). The daily survival rate of nests did not differ between inland and coastal habitats (daily survival rate ± 95% confidence interval (CI), inland 92·7 ± 2·2%, coastal 92·0 ± 3·1%). In total, 21 nests hatched and 65 failed (predated n= 55, deserted n= 6. livestock trampled n= 4). The probability of redshank nests hatching successfully was higher when the soil surrounding the nest was more penetrable (Fig. 5). The model successfully classified hatching in only 10% of cases but the model successfully classified predation in 95% of cases. This indicated that the majority of predation of these nests took place on less penetrable soils but successful nests occurred on a range of soil penetrability conditions.

Figure 5.

The influence of soil penetrability on the probability of redshank nests hatching successfully (Wald 6·39, d.f. = 1, P= 0·011). Bars show the mean (± SE) proportion of nests that were successful and lines show the fitted logistic regression curves. Soil penetrability is the force required to penetrate the soil to a given depth.


The scale at which a study is carried out reveals different features about the habitat requirements of species. On coastal and inland grazing marshes, both field selection and the density of breeding redshank within fields were determined principally by the length of wet features in fields (Figs 1 and 3). In contrast, nest site selection appeared to be driven by vegetation characteristics, in particular the presence of taller vegetation (Fig. 4), and nest hatching success was higher in areas with more penetrable soils (Fig. 5). These analyses were based on data from 2003. However, in the subsequent two years nests were frequently located within 10 m of previous locations, indicating that nest-site selection is repeatable between years.

water resources and breeding redshank

We have demonstrated the importance of shallow wet features in determining both the presence (Fig. 1) and abundance (Fig. 3) of redshank breeding on inland and coastal grazing marsh. It is probable that water has a number of essential functions within grazing marshes that are important to redshank and other breeding waders. (i) Water and the associated wet features appear to be an important feeding habitat for both adults and young. Wet features that retain water through spring and into summer can provide a source of aquatic invertebrate prey (Ausden, Sutherland & James 2001). In addition, Milsom et al. (2002) showed that adult redshank preferred to feed in wet rills than open grassland or dry rills. (ii) Soil fauna are more accessible to foraging waders when water levels are just below the surface of the soil (Ausden, Sutherland & James 2001). (iii) Surface and soil water may reduce vegetation growth, resulting in a shorter, more open sward in the late breeding season, which is more suitable for foraging waders (Ausden, Sutherland & James 2001). (iv) The presence of water and therefore suitable breeding conditions into June and July could prolong the breeding season and may increase the probability of pairs renesting following clutch loss, providing the potential for increased productivity (Beintema & Muskens 1987; Green 1988). (v) Water could affect the distribution and movements of predators of wader nests such as stoats Mustela erminia and foxes Vulpes vulpes (Green 1986; Schmitt, Ratcliffe & Smart 2000; Seymour et al. 2003; Seymour, Harris & White 2004). Ground predators may also make use of drier areas as corridors to move around, within and between fields. This may in part explain why nests were more likely to hatch in soft ground conditions, given that wetter areas tended to have the more penetrable soils.

vegetation structure and breeding redshank

Vegetation characteristics were important in determining the presence of redshank nests (Fig. 4). Vegetation structure can be manipulated through management, although there are costs and benefits associated with the various means of achieving this. During the breeding season, vegetation height will depend on factors such as whether a field is being used for hay, silage or grazing, levels of fertilizer inputs, soil moisture content, local climate and previous management (Vickery et al. 1997, 2001; Vickery & Gill 1999). Changes in land use, in particular increases in the area of grassland under silage production and decreases in the area of cattle-grazed grassland, have been related to recent decreases in the breeding densities of redshank (Vickery et al. 2001; Wilson et al. 2005).

Grazing can provide a means of manipulating vegetation structure and the timing, intensity and choice of grazing animals directly affect the characteristics of grass swards and the potential benefits to ground nesting birds (Vickery et al. 2001). Cattle-grazing is more likely to produce longer and more heterogeneous swards suitable for redshank, compared with sheep that produce close cropped homogeneous swards of c. 3 cm. However, grazing also carries a cost, as levels of nest trampling can be high. Trampling of 35–70% of wader nests has been reported at grazing densities of c. 2·5 cattle ha−1 (Green 1986; Beintema & Muskens 1987). These detrimental effects can be minimized through manipulation of the timing and density of grazing. For example, for inland grazing marsh fields that were cattle-grazed during May (the peak nesting period for redshank), only 7% of redshank nests were trampled with a stocking density of < 0·7 cows ha−1 (J. Smart, unpublished data). Thus there may be a fine balance between the nest-loss cost of grazing and the benefits gained from managing the sward characteristics to the benefit of both nesting and foraging waders.

implications for management

The importance of wet grassland habitats for breeding wader populations, and for breeding and wintering wildfowl, is recognized across a range of nature conservation organizations and designations, in agri-environment schemes and in the UK's Biodiversity Action Plans (English Nature 2005;, accessed 3 March 2005). These action plans have specific species and habitat targets; currently the wet grassland target aims to maintain the 300 000 ha of existing floodplain and coastal grazing marsh, rehabilitate another 10 000 ha and create 2500 ha of new marshes (Anonymous 1995). The analyses presented here allow the probable changes in breeding redshank populations resulting from different management strategies to be explored.

The results of this study, in particular the close relationship between the distribution of breeding redshank and the presence of shallow wet features, may also go some way to explaining both the declines in breeding redshank and the success of nature reserves at maintaining populations of breeding waders. It is probable that a combination of land drainage and conversion of grassland to arable cultivation (Robinson & Sutherland 2002) has resulted in the dry, homogeneous grazing marshes with dense grass swards that are common in the wider countryside today, and which are rarely used by breeding waders. In contrast, the management of nature reserves for breeding waders frequently aims to reverse these processes through, for example, reverting arable land to grassland, rehabilitating existing grasslands by reducing or removing chemical inputs, grazing at low densities and recreating wet features, often with water level control structures to maintain areas of open water throughout the breeding season (Benstead et al. 1997). Nature reserves, however, form a very small proportion of the total grazing marsh resource, and agri-environment schemes, such as the environmentally sensitive area scheme in the UK, often aim to promote grassland management that falls somewhere between the extremes of nature reserves and intensive agricultural grassland areas. Land managed under these schemes is either designed to enhance the environment (i.e. those promoting maintenance of higher water levels and delaying grazing) or to maintain its current state. Enhancement schemes are more successful at conserving breeding waders than maintenance schemes (Ausden & Hirons 2002), but success is dependent on the uptake of enhancement schemes by farmers in the wider countryside (Wilson, Ausden & Milsom 2004; Wilson et al. 2005).

future challenges

There are many challenges facing the future management of the wet grassland resource. Agri-environment schemes, as a tool for conserving biodiversity, are commonplace in Europe and 24·3 billion euros have been invested in these schemes since 1994 (Kleijn & Sutherland 2003). However, there has been a lack of robust studies evaluating the effectiveness of such schemes (Kleijn & Sutherland 2003). In the UK, significant changes to current agri-environment schemes are underway, some of which target management for breeding waders. This presents both a challenge and an opportunity for scientifically robust monitoring at the outset, to test which management practices are both cost-effective and successful at delivering the goals of the schemes.

Climate change is also likely to have a significant impact on breeding waders. Projected increases in sea level (Hulme, Turnpenny & Jenkins 2002) are likely to result in a loss of intertidal habitats such as saltmarshes, while efforts to mitigate for saltmarsh loss through managed realignment may also result in a loss of coastal grazing marsh (Nicholls 2001; Watkinson, Gill & Hulme 2004). This could mean that the relative importance of coastal grazing marsh, inland grazing marsh and saltmarsh to breeding waders is likely to change in the future. There is growing concern over the annual rate of saltmarsh loss (1·9 and 10·0 ha year−1) in eastern England (Cooper, Skrzypczak & Burd 2000), and there are predictions for a complete loss of saltmarsh from three estuaries in eastern England by 2050 (Cottle, Pethick & Dalton 2002).

Mitigation for the probable losses of saltmarsh and coastal grazing marsh resulting from coastal management decisions may not always be possible in the coastal zone because of spatial limits and socio-economic considerations. Mitigation, through the rehabilitation and creation of inland freshwater grassland, may consequently become an important option in the conservation of breeding wader populations (Smart & Gill 2003). Climate change is also likely to present managers of freshwater grasslands with increasing challenges. Increasing average temperatures coupled with an increased frequency of high summer temperatures and very dry summers (Hulme, Turnpenny & Jenkins 2002; Watkinson, Gill & Hulme 2004) could result in changes in both vegetation characteristics and water balance (Briggs & Hossell 1995; Thomson et al. 2001). Availability of water in the breeding season is a key habitat component for breeding wader species and reductions in the amount of water available and increases in evapotranspiration are likely to lead to water deficits and increasing problems for wetland managers.

synthesis and applications

Understanding the breeding habitat requirements of a species is a vital component of targeting management to provide improved breeding conditions. The management measures that we highlight for breeding redshank are very likely to benefit other wading bird species, as sites that support redshank generally also support other species. For example, all 36 grassland sites in East Anglia that support breeding redshank also support breeding lapwing, whereas 34 sites support breeding lapwing only (J. Smart, unpublished data), suggesting that management for redshank is also appropriate for lapwing but the reverse is not true. Intensively managed nature reserves appear to be successful at conserving breeding wader populations and the role of these sites as centres for population expansion is important. However, a major goal of nature conservation is to re-establish breeding waders in the wider countryside. The analyses presented here indicate that management that aims to attract breeding waders at the field scale should incorporate shallow pools with a length of pool edge in excess of 50 m. Within fields, breeding densities are influenced not just by pools but by all wet features, including engineered features such as foot drains; our analyses indicate that a doubling of total wet feature length can result in a doubling of breeding densities. Nest success is less easy to influence with habitat management; however, the provision of areas of soft, penetrable soils for nesting may reduce levels of predation, at least at these nesting densities. We would recommend the use of experiments to explore the minimum level of management of these habitat features required to increase breeding wader populations in the wider countryside.


We are grateful to English Nature and the Royal Society for the Protection of Birds for allowing access onto their nature reserves. Kirsty Coutts provided invaluable assistance in the field and Mark Bolton and two anonymous referees provided helpful comments on this manuscript. This work was funded by the Natural Environment Research Council.