Managing uplands for biodiversity: Do agri-environment schemes deliver benefits for breeding lapwing Vanellus vanellus?


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  1. Within Europe, agri-environment schemes are the key delivery mechanism for biodiversity conservation outside protected areas. Schemes have a range of land management options designed to deliver outcomes for target habitats or species.
  2. Breeding waders form an important part of the biodiversity of upland grasslands, and in the UK, there are multiple land management options within agri-environment schemes designed to benefit waders. We assessed whether such options improve the suitability of breeding habitat and population dynamics for a declining wader, the lapwing Vanellus vanellus.
  3. The suitability of nesting and chick-rearing habitat was better on land with agri-environment scheme management, and breeding densities and productivity increased with habitat suitability.
  4. The lapwing populations declined during this study, and trends did not differ between agri-environment and non-agri-environment scheme land. Productivity was below that required for population stability, although there was evidence of higher productivity on agri-environment scheme land in later years.
  5. Agri-environment management consisted of multiple land management options that varied in delivery of suitable habitat, breeding densities and success. The best management options were all in England on land benefiting from specific management advice or with rough grazing and grazed pasture agri-environment scheme options.
  6. Synthesis and applications. Despite considerable investment and positive effects of agri-environment schemes on habitat quality, populations of lapwing in the UK uplands have declined because of inadequate productivity. For species with complex requirements, populations are only likely to increase when all of these requirements are provided. Appropriately targeted habitat management, delivered through agri-environment schemes, can play an important role in improving habitat quality and increasing landscape diversity. However, when populations are limited by something other than habitat quality, for example, predation, then habitat management alone is unlikely to recover populations. Increasing evidence suggests that predation impacts are also likely to be important for ground-nesting species such as lapwing. Predator management may therefore need to be integrated with habitat measures where predation is limiting breeding success and population recovery.


Agri-environment schemes (AES) are the main European policy response to the loss of biodiversity resulting from agricultural intensification (Donald, Green & Heath 2001). Schemes are designed and applied at a national scale resulting in a mixture of schemes covering 20% of EU farmland (Donald & Evans 2006). Schemes pay farmers to modify their farming practices to provide environmental benefits using two general approaches: (i) simple low-cost management over extensive areas aimed at widespread farmer participation and (ii) higher-maintenance management targeted to specific species or habitats but available to fewer farmers. Substantial financial resources are required to support these schemes (projected EU spend 2007–2013 €34·54 billion; Kleijn et al. 2011), and while some studies demonstrate benefits (e.g. O'Brien & Wilson 2011; Perkins et al. 2011; Baker et al. 2012), results can be mixed both between studies and between species or groups within studies (Kleijn & Sutherland 2003). Despite this, optimizing biodiversity gain and ecological benefits of AES remains a current policy challenge (Sutherland et al. 2006). In the UK, AES were introduced in 1987 with Environmentally Sensitive Areas (ESAs), and there has always been a strong emphasis on wildlife conservation (Kleijn & Sutherland 2003). There has been an evolution of AES over time, but what is important for biodiversity, is the availability of different land management options and their implications for habitat or species conservation.

In the UK, the bird assemblage of upland habitats is of international conservation importance (Thompson et al. 1995). Here, we use a 5-year study of the habitat and population response of lapwing Vanellus vanellus (L.) to AES management on enclosed upland grassland of the UK. We monitored four areas, two in north England and one each in Wales and Northern Ireland, each with specific land management options aimed at lapwings, breeding waders or upland ground-nesting bird assemblages.

Most studies of the efficacy of AES measure the number or density of animals at a single point in time or at relatively small spatial scales (Kleijn & Sutherland 2003). The only existing study of the benefits of AES for upland species covered one study area (346 fields) in a single year and showed that the abundance of upland specialist birds was higher where the proportion of ESA grassland in the surrounding area was higher (Dallimer et al. 2010). Uniquely, our study assesses spatial and temporal trends in the response of upland breeding lapwings to AES over large areas of the UK uplands by asking whether AES deliver habitat, population size and productivity benefits. The lapwing is red listed because of significant declines (−48% 1970–2009: Eaton et al. 2011) but is still a widespread farmland species and AES could therefore be an important tool for population recovery. Recent studies on Scottish farmland have shown that agri-environment measures are capable of slowing, and in some cases, reversing declines of waders but only at the farm scale (O'Brien & Wilson 2011). The importance of enclosed upland grassland as a breeding habitat for waders and the UK investment in AES management on 77 303 ha for upland grassland birds (Natural England 2009) means it is important to understand the efficacy of agri-environment management as a tool for recovery of upland lapwing populations.

Materials and methods

Project Areas and Site Selection

This study consisted of four upland project areas that were distinct geographically and important for grassland-breeding waders (Fig. 1). Here, upland grassland was enclosed grassland below the moorland line and was typically rush pasture, rough grazing, hay meadows or grassland that has been heavily improved. Over 4–5 years, we monitored the consequences for lapwing habitat quality and demographic rates of four projects designed around focussed delivery of lapwing management using AES management options and advice (2006–2010 Bowland and 2007–2010 Pennines, Wales and N. Ireland). The grassland management options available within AES that were present in each field and year were extracted from maps and data provided by Natural England, the Welsh Assembly Government and the Department of Agriculture and Rural Development in Northern Ireland (Table 1). Management options were aimed specifically at lapwings, breeding waders or ground-nesting birds, including breeding waders. Species-rich semi-natural grassland within Higher Level Stewardship (HLS) was the exception, because breeding waders were not mentioned in the option aims. However, meadow options within other AES were aimed at ground-nesting birds so we could justify including HLS meadow options (Table 1). As different AES have been available to farmers at different times, land had been in agri-environment management for a variable number of years [average no. years: ESA = 15·4, Countryside Stewardship = 5·6, HLS = 1·3, Countryside Management Scheme = 1·4, Tir Gofal (TG; AES in Wales) = 4·8]. In England, because of evolving schemes, there was some movement of fields in, out and between AES, with 2·3% of fields leaving schemes and 15·6% moving scheme (9·3% entering HLS, 1·3% from CSS to HLS, 5% from ESA into HLS). Some fields entered AES part-way through the study (n = 108), providing opportunity to assess before and after effects of AES. Projects aimed to have a number of sites (whole or part-farms) with and without agri-environment management (non-AE and AE) and targeted advice by project officers that could either be alone or in combination with AE management (AE+). Importantly, advice from staff at the Royal Society for the Protection of Birds (RSPB 2012) would be tailored to individual farm needs but would typically involve specific lapwing or grassland management advice, and occasionally, assistance was given to deliver specialist management techniques (e.g. rush control and scrape creation). Projects therefore had a variable number of sites and fields in the different AE management categories (Table S1, Supporting information).

Table 1. For each land management option, the agri-environment scheme (AES), scheme option codes and description and the project these apply to (P, Pennines; B, Bowland; W, Wales; NI, Northern Ireland). Analyses at the option level required some scheme options to be combined into land management options to give an adequate sample size; Nmax1 refers to the maximum number of fields in each scheme option or land management option (Nmax2) from any year
Land management options (AES)Scheme option code and description (project)Target species N max1 N max2 Max area in projects (ha)
  1. Target species are BW, breeding waders; LA, lapwing; GNB, ground-nesting birds. Area in projects is the maximum area of land in each combined option. Scheme: ESA, Environmentally Sensitive Area; CSS, Countryside Stewardship; ELS, Entry Level Stewardship; HLS, Higher Level Stewardship; TG, Tir Gofal; CMS, Countryside Management Scheme; RSPB, Royal Society for the Protection of Birds.

Tier 1 (ESA)Tier 1B: Meadows, pastures and allotments (P)BW2232231100
Tier 2 (ESA)Tier 2A/B: Herb-rich meadows, pastures and allotments (P)BW2727149
Meadows (CSS/HLS)HLS H3: Hay meadows (B, P)GNB326109
CSS UH1: Upland hay meadows (B, P)GNB8  
HLS HK6/7/8: Species-rich semi-natural grassland (B, P)GNB15  
Grazed pasture (CSS)CSS UP1: Upland grazed pastures (B, P)GNB4141460
Rough grazing (CSS/HLS)CSS UP2: Upland rough grazing (B, P)GNB378664
HLS HL7/8: Rough grazing for birds (B, P)BW75  
Rush (ELS)ELS EK4/EL4: Management of rush pastures (B, P)BW111162
Lapwing option (TG)TGL: Tir Gofal lapwing option (W)LA2121104
Lapwing/wader (CMS)CMS-LA: CMS lapwing option (NI)LA4953245
CMS-BW: CMS breeding wader option (NI)BW4 25
RSPB (none)RSPB-advised management in the absence of AES (B, W)LA6363366
Figure 1.

Map showing the distribution of study sites in England, Wales and Northern Ireland. Smaller images to the right show each project area on a larger scale. Note that scales vary.

In the Pennines, there were large areas of land in ESA, and in Wales, as the project progressed, increasing efforts by project staff to influence farm management for lapwing meant the sample of sites with no fields in management was inadequate for comparisons at the site scale (Table S1, Supporting information). Only Bowland had an adequate sample of sites in all three categories in each year. For the Pennines and Wales, AE and AE+ were combined (AE[+]) and in Northern Ireland all sites with AE received advice (AE+). Within projects, comparisons were therefore: Bowland = non-AE/AE/AE+, Pennines and Wales = non-AE/AE[+] and Northern Ireland = non-AE/AE+.

In each project area, farms were initially selected on the basis of breeding lapwing occupancy. Farmers were then approached to assess willingness to participate, which was high, and to gather information on AES status. The subset of farms selected gave the best fit between getting the desired number of farms with and without AES and having a good geographical spread. Adjacent sites were a minimum of 500 m apart, although this rule was relaxed if sites were separated by habitat inhospitable to waders (e.g. woodland). Average (±SE) site size and number of fields was 53·0 ± 3·2 and 10·8 ± 0·6 ha. For sites with AES management, average area and number of fields in AES was 31·1 ± 2·3 and 5·1 ± 0·4 ha.

Grassland Field Habitat Surveys

The condition of nine habitat features known to be important for breeding lapwings was assessed on the same day as lapwing surveys were carried out, using the methods outlined in Table 2. Thus, habitat condition of every field was assessed up to five times during each season. To assess whether habitat suitability for nesting and chick rearing varied in relation to AES, we created a composite field-level score of habitat suitability using the optimum conditions for lapwing (Table 2). Each habitat characteristic was scored as optimal (1) or suboptimal (0). For three features (grazing pressure, sward height and wet features), lapwings require different characteristics for nesting and chick rearing, and therefore, we used data from different visits to assess suitability for nesting and chick rearing (Table 2). For each field, we then summed the scores for the six features that do not vary between nesting and chick rearing, and we added the scores for grazing pressure, sward height and wet features for nesting and chick rearing separately (Table 2).

Table 2. The nine field characteristics that are important for breeding lapwings and the references that evidence their importance. For each characteristic, the description outlines the methods used to collect these data and the optimal and suboptimal conditions for breeding lapwings. Optimal and suboptimal conditions considered important for both nesting and chick rearing are not highlighted, those for nesting only and chick rearing only are in light and mid-grey, respectively
Field characteristicDescriptionOptimalSub-optimalReferences
Grassland typeOn visit 1, fields were classed as rush pasture, rough grazing, improved pasture, hay or silageRush pasture or rough grazingHay/sileage/heavily improved grasslandBaines (1989, 1990, 1990)
Field sizeArea (ha) of each field measured in MapinfoLarger than average (mn 4·9 ha)Smaller than averageStillman et al. (2006), MacDonald & Bolton (2008b) and Milsom et al. (2000)
Field aspectOn visit 1, the % of the field boundary that was <1·5 m tallwas estimated (no boundary, grass verge, fence or low hedge)OPEN (>75% boundary <1·5 m tall)RESTRICTED (<75% boundary <1·5 m tall)Stillman et al. (2006) and MacDonald & Bolton (2008b)
Predator perchesOn visit 1, the presence of any vantage points that could be used by avian predators was recorded (e.g. trees, telegraph poles, overhead wires)ABSENTPRESENTStillman et al. (2006), Berg, Lindberg & Kallebrink (1992), MacDonald & Bolton (2008b) and Milsom et al. (2000)
Stock typeOn each visit, stock type was recorded as cattle, sheep, horses or mixed (>1 stock type)CATTLE or MIXEDSHEEP or HORSESHart et al. (2002)
Grazing pressureOn each visit, each type of stock was countedNEST: NONE or LIGHTNEST: MODERATE or INTENSIVEMilsom et al. (2000) and Hart et al. (2002)
Livestock units (LU) = cattle*0·7 + calves*0·2 + sheep*0·08 + horses*1·0 (Chesterton 2006)CHICK: LIGHT to MODERATECHICK: NONE or INTENSIVE
NEST = mean LU visit 1–3
CHICK = mean LU visit 3–5
Grazing pressure categories: NONE (0 LU), LIGHT (≤0·75 LU), MODERATE (0·76–1·5 LU) and INTENSIVE (>1·5 LU)
Rush coverMaximum (visits 1–3) % of the field covered by soft rush to nearest 10%<30%>30%Milsom et al. (2000) and O'Brien (2001)
Sward heightShort, mixed or long: 75% of the field has vegetation height that is ankle height or less (SHORT) or taller than ankle height (LONG) or <75% of the field either SHORT or LONG (MIXED)NEST (visit 1): SHORTNEST (visit 1): MIXED or LONGMilsom et al. (2000) and O'Brien (2001)
CHICK (visit 3): SHORT or MIXEDCHICK (visit 3): LONG
Wet featuresPresence of features containing water (ditches, flood water, permanent pool, scrape, flush, moving water) and their accessibility (no steep edges) for foraging chicksNEST (visit 1–3): PRESENTNEST (visit 1–3): ABSENTMilsom et al. (2000), O'Brien (2001), McKeever (2003) and Eglington et al. (2010)

Estimating Lapwing Population Size and Productivity

We used Bolton et al. (2011) five-visit method to estimate breeding population size and productivity. Each year, bird survey periods were visit 1 (24 March–15 April), visit 2 (16 April–6 May), visit 3 (7 May–31 May), visit 4 (1 June–19 June) and visit 5 (20 June–8 July) and successive visits were at least 1 week apart but usually 2–3 weeks apart. Sites that contained no breeding lapwing on visits 1–3, or where all breeding lapwing had failed and departed by visit 3, did not receive further visits and in the latter case were recorded as zero productivity. The number of breeding pairs on each site was the maximum count (sum of all fields) between 15 April and the 31 May, typically visits 2 and 3, divided by two and rounded up. Site productivity (chicks fledged pair−1) was the summed count of all well feathered and fledged chicks from all visits (referred to as large chicks), divided by the number of pairs. To examine trends between field-scale habitat and AES data and our measures of population size and success, we used the maximum field count to calculate field pairs as above. However, field-scale measures of absolute productivity are problematic because large chicks are potentially highly mobile and occasionally occurred in fields where no pairs were recorded. Hence, we used a measure of relative productivity that indicates the importance of each field within a site by using the number of large chicks counted in each field, offset by the number of pairs within the site. Using large chicks as the measure of success could over emphasize field conditions for older chicks. However, in fields containing nesting lapwings, counts of large chicks increase with the number of pairs (slope = 0·47 ± 0·15, F1,1977 = 33·61, P < 0·0001), which suggests that good nesting fields also tend to be the better chick-rearing fields despite the fact that there will be some movement between fields.

Data Analysis

Our hypothesis was that agri-environment management would have beneficial effects on lapwing populations and our questions were therefore:

  1. Are fields with agri-environment management more suitable for lapwings, and over what time-scale do effects become apparent?
  2. Does lapwing density and productivity increase with habitat suitability?
  3. Does agri-environment management improve lapwing population trends and productivity?
  4. Do some land management options deliver better habitat and population prospects for lapwings?

We used generalized linear mixed models (GLMM) using the GLIMMIX procedure in sas v9.2 (SAS Institute Inc. 2000). As all of our response variables were counts (number of optimum habitat attributes, pairs and large chicks), we specified a Poisson distribution with logarithmic link function and denominator degrees of freedom were estimated using the Kenward–Roger method (Kenward & Roger 1997). For the four questions outlined previously, Table 3 provides full details of the models specified to answer these questions. Offset terms account for the likelihood of more pairs in large sites or fields (LN site area) and more large chicks in sites with many pairs (LN site pairs). Site or field nested within site were fitted as random effects in site and field-scale analyses, respectively. However, in some field-scale analyses, site was used as the random term because the covariance parameter estimate was zero. By accounting for the fact that observations from the same site or field are correlated, these random effects account for the repeated measures over multiple years. A small number of sites and fields changed AES category, and these were allocated to the new AES category without any time-lag because experimental work has demonstrated that changes in habitat quality have an immediate effect on the settlement of breeding lapwing (Eglington et al. 2009). Even though a small number of sites and fields changed status, the structure of the analysis means that information is being recovered from both between sites and fields (random effect) and within sites and fields (residual effects); therefore, estimates of effects and standard errors (e.g. AES) are adjusted appropriately (Brown & Prescott 2006). Where categorical variables were important in models, values were obtained through the back transformation of least-square means and the statistical significance of differences between categories was examined with difference in least-square means tests.

Table 3. For the four key questions (bold), the response variables, spatial scale, offset terms, random and fixed effects included in each analysis (continuous variables in italics)
ResponseScale:offset:randomFixed and interaction effects
  1. AES, agri-environment schemes; Non-AE, land without agri-environment; AE, land with agri-environment; AE+, agri-environment with advice; AE[+], combined AE and AE+.

  2. a

    Analyses uses a subset of 108 fields that entered AES part-way through the study.

  3. b

    For the land management options, see Table 1.

Question Are fields with agri-environment management more suitable for lapwings and over what timescale do effects become apparent?
Nest habitat suitabilityField:none:siteProject + Year + AES (non-AE vs. AE[+]) + Project × AES + Year × AES + Project × Year × AES
Field:none:field (site)As above but AES (AE vs. AE+)
Field:none:field (site)Projecta + Years in AE[+] (0, 1, 2, 3)
Chick habitat suitabilityField:none:field (site)Project + Year + AES (non-AE vs. AE[+]) + Project × AES + Year × AES + Project × Year × AES
Field:none:field (site)As above but AES (AE vs. AE+)
Field:none:field (site)Projecta + Years in AE[+] (0, 1, 2, 3)
Question Does lapwing density and productivity increase with habitat suitability?
No. pairsField:field area:siteProject + Year + Habitat (NEST) + Habitat × Project + Habitat × Year + Habitat × Project × Year
No. lg chicksField:site pairs:siteAs above but Habitat (CHICK)
Question Does agri-environment management improve lapwing population trends and productivity?
No. pairsField:site area:siteProject + Year + AES (non-AE vs. AE[+]) + Year × AES + Area managed + Area managed × Year
No. lg chicksField:site pairs:siteAs above
Question Do some land management options deliver better habitat and population prospects for lapwings?
Nest habitat suitabilityField:none:field (site)Year + Land management options (13 levels)b
Chick habitat suitabilityField:none:field (site)As above
No. pairsField:field area:siteAs above
No. lg chicksField:site pairs:field (site)As above

When answering the question of whether agri-environment management improves lapwing population trends or productivity, we tested for evidence of lapwing populations changing over time in a linear fashion. We used the Laplace method to approximate the likelihood allowing the use of likelihood ratio tests (LRTs) and found that fitting year as a categorical effect significantly improved model performance compared to a model with year as a linear effect (inline image, P = 0·01). We also used LRTs to consider whether including Bowland, in overall analyses of effects of AES, would represent a loss of information as AES delivery was more complex in Bowland. However, there was no support for any improvements in model fit when AES was fitted as a three-level factor (non-AE, AE, AE+) compared with a two-level factor (non-AE, AE[+]; lapwing density: inline image, P = 0·52; productivity: inline image, P = 0·96) so all subsequent models included all projects and were fit with AES as a two-level factor. Within sites, there was some variation between years in the area of land (ha) with AES management, so we included this variable and its interaction with year.


Are Fields with Agri-Environment Management More Suitable for Lapwings, and over What Time-Scale do Effects Become Apparent?

Overall, AE fields were more suitable for lapwing nesting and chick rearing compared with non-AE fields after controlling for project and year effects [nest, F1,5863 = 158·87, P < 0·0001, non-AE = 4·78, (95% CI's 4·65–4·92), AE = 5·43, (5·26–5·59); chick, F1,2206 = 74·74, P < 0·0001, non-AE = 3·50, (3·40–3·60), AE = 4·32, (4·15–4·49)]. This effect was consistent across projects except in the Pennines where differences were not significant (project*AES: nest, F3,5821 = 41·33, P < 0·0001; chick, F3,2204 = 13·22, P < 0·0001; Fig. 2). Using fields that switched from non-AE to AE[+] during the study (n = 108) and controlling for project effects, habitat suitability varied in relation to the number of years in AE[+] (nest: F3,370·1 = 4·08, P = 0·007; chick: F3,355 = 3·68, P = 0·01) Specifically, there was no difference between non-AE years and the first year in AE[+], but second and subsequent years had better and similar habitat suitability (Fig. 2c).

Figure 2.

The suitability of fields for lapwing (±95% CIs), comparing (a) nest and (b) chick habitat suitability (score out of nine) between land with (AE[+]) and without (non-AE) agri-environment in each project area and (c) nest and chick habitat suitability (nest = white, chick = dark grey) in the years before (0) and after AE[+] (1–3) for fields that entered agri-environment schemes (AES) part-way through the study. Within groups of bars [projects (a), (b), nest or chick in (c)], different letters denote significant differences (P < 0·05).

Within AES fields, nest habitat suitability was better in AE+ fields, but there was no difference in chick habitat suitability, after controlling for project and year effects [nest, F1,1289 = 6·91, P = 0·009, AE = 5·06, (4·90–5·22), AE+ = 5·33, (5·18–5·49); chick, F1,1586 = 2·47, P = 0·12, AE = 4·09, (3·89–4·30), AE+ = 4·27, (4·08–4·46)]. However, these effects varied between projects (project*AES: nest, F2,1185 = 6·26, P = 0·002; chick, F2,1511 = 3·29, P = 0·04) with AE+ fields having higher nest (Pennines) and chick (Pennines and Bowland) habitat suitability than AE fields. Despite habitat suitability being better in AES fields, on average 3–4 and 4–5 of the nine key nest and chick habitat elements, respectively, were missing from AES fields and only 9·6% and 2·0% of AES fields (n = 528) achieved a score of eight or more for nest and chick habitat suitability, respectively.

Does Lapwing Density and Productivity Increase with Habitat Suitability?

Lapwing densities and relative productivity increased with increasingly suitable nest and chick habitat, respectively, after accounting for difference between projects and years (nest habitat: F1,5258 = 37·88, P < 0·0001; chick habitat: F1,5002 = 157·69, P < 0·0001). Effects of nest habitat on densities varied between projects with no significant effect on densities in the Pennines and Bowland, and positive effects elsewhere (nest habitat*project: F3,5321 = 11·16, P < 0·0001; Fig. 3a). The strength of effects of chick habitat on productivity varied between projects but in all cases were positive (chick habitat*project: F3,5002 = 18·77, P < 0·0001; Fig. 3b).

Figure 3.

Trends in (a) lapwing densities and (b) productivity in relation to the suitability of habitat for nesting and chick rearing (solid lines indicate significant trends). Pennines [nest: t6023 = −0·89, P = 0·37, slope −0·03 (−0·11 to 0·04); chick: t5002 = 14·98, P < 0·0001, slope 0·64 (0·56–0·72)], Bowland [nest: t6053 = 0·67, P = 0·50, slope 0·03 (−0·05 to 0·11); chick: t5002 = 4·89, P < 0·0001, slope 0·24 (0·14–0·33)], Northern Ireland [nest, t4539 = 2·64, P = 0·008, slope 0·21 (0·05–0·36); chick: t4640 = 5·81, P < 0·0001, slope 0·64 (0·42–0·86)] and Wales [nest, t6018 = 3·81, P = 0·0001, slope 0·27 (0·13–0·41); chick: t5002 = 7·77, P < 0·0001, slope 0·78 (0·56–0·99)].

Does Agri-Environment Management Improve Lapwing Population Trends and Productivity?

After controlling for differences between projects (F3,150·38 = 33·33, P < 0·0001; Fig. S1, Supporting information), breeding densities differed between sites with and without AES [non-AES = 0·06 (0·05–0·08), AE[+] = 0·08 (0·07–0·10); F1,545·2 = 6·11, P = 0·01]. The area under AES management did not influence densities (F1,541·7 = 0·62, P = 0·43). There was strong evidence that densities declined over time (F4,437·9 = 9·65, P < 0·0001; Fig. 4a), with an overall population decline of 34·4% (32·9–35·8) but no evidence of differences in population trends between land with and without AES (F3,436·5 = 0·77, P = 0·51).

Figure 4.

Trends (±SE) in (a) lapwing densities over time and (b) annual productivity over time and between land with (AE[+] = grey) and without (non-AE = white) agri-environment. Letters denote significant differences (P < 0·05).

After controlling for differences between projects (F3,137·2 = 5·39, P = 0·002; Fig. S1, Supporting information), productivity varied between years, between land with and without AES and there was evidence of annual productivity varying in relation to AES with significantly higher productivity in AE[+] sites in 2009 and 2010 (year: F3,476 = 10·13, P < 0·0001; AES: F1,476 = 20·67, P < 0·0001; AES*year: F3,476 = 7·56, P < 0·0001; Fig. 4b). The area under AE management had no overall effect on productivity but varied between years with a positive effect in 2008 and a negative effect in 2009 [area: F1,181·7 = 0·65, P = 0·42; year: F3,476 = 11·20, P < 0·0001, 2008: slope = 0·006 (0·0003–0·01), t274·2 = 1·97, P = 0·05; 2009: slope = −0·01 (−0·01 to −0·003), t411·8 = −2·88, P = 0·004; other years P < 0·05].

Do Some Land Management Options Deliver Better Habitat and Population Prospects for Lapwings?

Agri-environment management was delivered through multiple schemes and options in addition to RSPB-advised management and non-AE land (Table 1). The suitability of habitat for nesting and chick rearing, lapwing-breeding density and relative productivity varied significantly between land management options after allowing for annual variation in these parameters (nest: F12,3355 = 29·69, P < 0·0001; chick: F12,4239 = 26·22, P < 0·0001; density: F12,1719 = 22·54, P < 0·0001; productivity: F12,3285 = 10·82, P < 0·0001). To assess the overall value to lapwings of each management option and to compare these with non-AE land in each country, we ranked each by the four parameters: suitability for nesting and chick rearing, density and relative productivity. Country-specific non-AE land and management options varied significantly in their median ranking (χ2 = 33·3, d.f. = 12, P = 0·0009; Fig. 5). The options that ranked highest overall were RSPB-advised management in England, rough grazing (CSS/HLS) and grazed pasture (CSS). Of the options available in England, all management options were ranked higher than non-AE land (Fig. 5). In Wales, there was little difference between RSPB-advised management, lapwing option in TG and non-AE land, ranked 10th, 11th and 12th respectively, whereas in Northern Ireland, the lapwing/wader CMS option was ranked 4th compared with non-AE land at 13th (Fig. 5).

Figure 5.

For management options available through agri-environment schemes (AES) on upland grasslands, Royal Society for the Protection of Birds (RSPB)-advised management in England and Wales (see Table 1) and land without agri-environment (non-AE) in each country, the mean (±SE) estimates for nest (white) and chick habitat suitability (light grey), lapwing breeding densities (pairs ha−1, mid-grey) and relative productivity (fledged chicks site pair−1, dark grey). Options ordered from best to worst (left to right) by the average rank (Kruskal–Wallis test). Note habitat suitability and densities have been scaled by 100 and 10, respectively.


This study provides evidence that lapwing populations declined by 8·6% p.a. across our study areas in the UK uplands and that these declines are driven by poor breeding success. Although there was no evidence of differences in population trends between land with and without AES management, breeding productivity was better on AES land in the latter 2 years but still below the threshold required for population stability (0·6–0·8 chicks pair−1; MacDonald & Bolton 2008a). It may be that the positive effects of AES on productivity were increasing over time, although it is not known whether this trend for improved productivity has continued. Overall, agri-environment management had a positive effect on habitat suitability, and habitat suitability positively influenced breeding densities and productivity. However, agri-environment management was delivered through a range of land management options that varied considerably in the extent to which they provided the requirements of breeding lapwings, and this may therefore explain the lack of an overall effect of AES. For example, some management options are no better than land without any AES (e.g. TG lapwing option) and some options attracted high densities of birds which then produced few young (e.g. meadows). However, even the best of these land management options are not compensating for the generally poor levels of breeding success of lapwings across the uplands of north England, Wales and Northern Ireland.

Causes of Poor Breeding Success

We measured productivity using simple field surveys, but we did not undertake detailed monitoring of nest and chick survival and therefore could not record causes of breeding failure. The suitability of breeding habitat is important because densities and productivity increased with habitat suitability. However, predation is the main cause of breeding failure for waders in the uplands (Parr 1993; Grant et al. 1999; Fletcher et al. 2010) and low-breeding success is the main demographic factor driving the decline of lapwing (Peach, Thompson & Coulson 1994). Habitat suitability could be affecting lapwing populations directly (e.g. inadequate provision of chick foraging resources) or interacting with predation such that nests or chicks are more susceptible to predation in less suitable habitat (Baines 1990; van der Wal & Palmer 2008). For example, lapwings nesting in unimproved grassland have higher breeding success than those on improved grassland (Baines 1989). Two experiments provide evidence for the role of predation in limiting lapwing populations, (i) removing crows and foxes improved breeding success in lowland areas with high densities of these predators (Bolton et al. 2007) and (ii) lapwings showed the largest positive population response to predator removal on upland moorland in England (Fletcher et al. 2010). However, in northern England, where there have been increases in grouse moor management and therefore associated predator control activities (Tharme et al. 2001; Yallop et al. 2006), lapwing productivity was still below the threshold level for population stability in most years (see Fig. S1b, Supporting information). In the Pennines, an area where predator control for grouse management is commonplace, the suitability of chick habitat had the strongest effect on productivity. This suggests that when predator control relaxes predation pressure, then habitat suitability may become the limiting factor. Predator management in the absence of improvements in habitat quality is therefore unlikely to improve productivity to a level sufficient to recover populations.

Improving Habitat to Improve Productivity?

Densities and productivity could be increased by improving habitat suitability, and lapwings nesting at high densities also achieve higher productivity (Baines 1990; Eglington et al. 2008; MacDonald & Bolton 2008b). Gains in productivity are particularly apparent once the habitat suitability index reaches six or above and increasing chick habitat suitability from six to eight could, on average, yield a 3·4-fold increase in productivity. Although habitat suitability was better on land managed with AES, only 20·3% and 11·9% of fields had nest and chick habitat suitability, respectively, of six or above. Clearly, there are many elements of habitat delivery that could be improved (see Fig. S2, Supporting information). Fewer than 50% of fields had the optimum grassland type, size, and aspect and the absence of vantage points for predators. These features are difficult to influence once an AES agreement is set up, and thus, suitable fields need to be targeted at the outset as these factors can influence predation rates (e.g. Baines 1990; Berg, Lindberg & Kallebrink 1992; MacDonald & Bolton 2008b).

Cattle or mixed grazing systems are optimal for breeding waders because they produce a varied sward height and structure and have higher abundance and availability of invertebrates (Vickery et al. 2001). Only 34% of fields were used for cattle or mixed grazing. Sheep grazing is prevalent in the uplands (Fuller & Gough 1999), and while intensive sheep grazing may not be optimal, the loss of cattle from many farms (DEFRA 2012) means sheep may be the only option, their impact dependent on careful stock management, timing and intensity. Some 28% of AES fields had too much rush compared with only 13% of non-AES fields. This may be because there is a tendency for farmers to enter their least-productive land into AES, and then, this is exacerbated by a reduction in agricultural activity over time rendering the vegetation composition and structure less attractive for waders (O'Brien & Wilson 2011). Studies in the lowlands show that lapwing chicks require wet features that provide invertebrate-rich foraging habitats, and these have positive effects on chick condition (Eglington et al. 2010). While there have been no such studies on upland grassland, lapwing families were commonly associated with wet features, yet the provision of wet features could be improved as only 63% and 37% of AES and non-AES fields, respectively, contained wet features suitable for chick foraging.

While our study focused on only one species of upland wader, there is extensive evidence to show that some other grassland waders are also in decline in the UK uplands (Sim et al. 2005), and while the wader species differ in their habitat requirements, it is plausible that processes affecting lapwings in this study could be similarly affecting the other species.

Challenges for Species Recovery Using AES

There are a number of challenges in using AES as a tool for species recovery. Many studies have found positive effects of AES on target species but low and fragmented levels of AES intervention may limit usefulness as a tool for population recovery (e.g. O'Brien & Wilson 2011; Perkins et al. 2011; Baker et al. 2012). For example, c. 50% of the lapwing population on Scottish farmland would need to benefit from AES to stabilize the population decline, yet <2% of the population was benefitting from AES (O'Brien & Wilson 2011). Across Europe, resources for top-level AES are likely to become increasingly limited exacerbating the problem of low and fragmented delivery. It is therefore increasingly important to establish which AES management options are beneficial to the species of interest and then to apply those options in landscapes where a large percentage of the population can be provided with a high level of continuous AES land. In landscapes intensified for agriculture, land-use diversity is often poor and AES options can diversify the landscape, increasing the suitability of such landscapes for species with complex requirements. For example, the Swiss agri-environment option, low-input grassland, has similar butterfly species richness to conventional grassland but low input grassland has more specialist species because reduced intensity and timing of management means the complex needs of those species are met (Aviron et al. 2007). Furthermore, when species have complex requirements, monitoring of AES can identify where existing options are failing to provide those needs, thus providing opportunities for adaptive management. For example, a study of the efficacy of AES management for corn buntings Emberiza calandra found that adaptation of an existing AES option to provide safe nesting habitat through late mowing of silage fields changed the population trend from stable to increasing (Perkins et al. 2011). The population-limiting effects of predation, especially on ground-nesting species such as waders, have already been discussed. It is mentioned again here because it could limit the benefits of AES, which generally aim to deliver improved habitats. Understanding more about the processes that limit the species we are trying to recover using AES will be fundamental to implementing an appropriate package of measures to promote population recovery. However, in situations where predation impacts are limiting populations, a targeted approach to habitat improvement using AES resources, in combination with predator management, may be necessary for securing population recovery.


Dave Elliott and Lorna Whiteside for their huge contributions to the Welsh and Irish projects. Funding from RSPB, Natural England's Action for Birds in England, Forest of Bowland AONB and the SITA Trust through the Enriching Nature Programme. Although too numerous to thank individually, the landowners, farmers and surveyors, without whom this project would have been impossible. Natural England, the Welsh Assembly Government and the Department of Agriculture and Rural Development for AE data. Jeremy Wilson, Pat Thompson, David Douglas, Lucy Malpas and anonymous referees are thanked for valuable comments on the manuscript.