Elsevier

Journal for Nature Conservation

Volume 12, Issue 4, 10 December 2004, Pages 229-235
Journal for Nature Conservation

Identifying potential breeding sites for the stone curlew (Burhinus oedicnemus) in the UK

https://doi.org/10.1016/j.jnc.2004.07.002Get rights and content

Summary

The stone curlew (Burhinus oedicnemus) is one of the rarest breeding birds in the UK with an estimated breeding population of under 300 pairs. In the UK a marked decrease in the numbers has occurred, mainly due to habitat change or loss, resulting in the bird no longer breeding on many of its traditional nesting grounds. Using a Geographic Information System we define potential breeding areas in a landscape known to historically support a stone curlew breeding population. With an understanding of the birds’ abiotic nesting requirements, we model optimal locations for potential breeding sites for the bird, using known historical breeding data to verify the selection process. In total, we identify 424 parcels of land (2866 hectares) providing the critical habitat requirements for the stone curlew in the overall study area, 323 (1958 hectares) of which correspond with the known historic breeding distribution of the bird. We conclude that the modelling approach outlined is valuable in the identification of potential breeding sites for the stone curlew and that the model could be operated in tandem with agri-environment payments such as those offered within the Countryside Stewardship Scheme, in an attempt to focus these payments upon areas which will deliver maximum nature conservation potential. In addition we suggest that, subject to appropriate data availability, the approach outlined be applied to other species of high-conservation concern, particularly those which are currently under consideration for introduction or re-introduction programmes.

Introduction

In conservation management strategies inferring potential distributions of some species based upon knowledge of their life history, rather than measuring them directly may be necessary, as many species are either isolated, secretive/cryptic, or have a low population density. Similarly, with poor or limited data it is difficult to build the logistic regression models employed in some species management strategies (Austin, Thomas, Houston, & Thompson, 1996).

One of the most widespread applications of Geographic Information Systems (GIS) in ecology is for mapping and classification of ecological resources (Wadsworth & Treweek, 1999). For example, Avery and Haines-Young (1990) used GIS and satellite imagery to predict and map potential habitat for dunlin (Calidris alpina); Austin et al. (1996) used GIS and remote sensing to predict the spatial distribution of buzzard (Buteo buteo) nesting sites; Lauver, Busby, and Whistler (2002) employed the use of a GIS to develop a habitat suitability model for the loggerhead shrike (Lanius ludovicianus) using land cover information; and, Figala, Prchalova, and Tester (2001) developed a GIS model to assess the levels of decline of grey partridge (Perdix perdix) nesting habitat in the Czech Republic. More recently, GIS have been used in an attempt to manage landscapes to the benefit of habitat expansion programmes for rare habitats with which threatened bird assemblages are associated (Bayliss, Helyar, Lee, & Thompson, 2003) and to model multi-species avian conservation scenarios (Bayliss, Simonite, & Thompson, 2002).

Many factors influence the distribution of a species, and it is not possible to quantify all of these components of a species niche (Gough & Rushton, 2000). It is therefore necessary to create a simplified representation of the species niche by identifying those factors that are considered to have the greatest influence on their distribution. Potential distribution maps can be derived by combining knowledge of relationships between species and habitats derived from the literature, with remotely sensed data on the distribution of land cover and other ecological components. In this research, we explore the possibility of predicting potential breeding locations for the stone curlew (Burhinus oedicnemus) in the Chilterns Natural Area, a former breeding area for the species, using a GIS to provide a simplified representation of potential breeding areas in a landscape known to historically support a stone curlew breeding population. We overlay several different data sets in order to determine and predict the location of suitable habitats for the stone curlew at the landscape scale. With an understanding of these abiotic requirements, the primary objective of this research was to create a GIS database which could subsequently be used to model optimal locations for potential breeding sites for the bird, using known historical breeding data to verify the selection process.

There is significant evidence of a long-term reduction in the numbers of stone curlew in the UK with an estimated decline of 85% between the 1940s and 1980s (Parslow, 1973; Sharrock, 1976). Consequently, the stone curlew is classified as one of the most threatened breeding birds in the UK and is also a species of conservation concern in Europe (Tucker & Heath, 1994). The stone curlew is largely confined in the UK to the Breckland of East Anglia and the Wessex downland where it historically nested on short-cropped turf containing areas of bare ground (Fig. 1). The conversion of these areas to other land uses, together with the cessation of grazing on the remaining grasslands by sheep or rabbits, has significantly reduced the extent of suitable habitats across the traditional range of the stone curlew.

In order to achieve substantial population growth, historical breeding areas will need to be colonised if the population increases identified in the UK Government biodiversity action plans are to be met, maintained and hopefully exceeded. The Chilterns Natural Area provides an obvious geographical gap in their present range as historically the stone curlew was once a widespread summer breeding visitor (Alexander, 1947; Aplin, 1889; Clark Kennedy, 1865; Hartert & Jourdain, 1920; Glue & Morgan, 1974; Price, 1947).

The Chilterns Natural Area consists of parts of the counties of Bedfordshire, Buckinghamshire, Hertfordshire and Oxfordshire and comprises a mixture of landscapes including farmland, woodland and open downland. The major feature is a northwest facing chalk escarpment rising to some 250 m, behind which the dip slope is cut by a series of deeply dissected valleys (Lee, Elton, & Thompson, 1999).

In the UK, stone curlew breeding sites are found on semi-natural grassland, heathlands and arable farmland. Breeding densities for the species are higher on semi-natural grasslands and heathlands, but because of the greater availability of suitable arable land there are now higher numbers of stone curlew found breeding on arable land than on semi-natural grassland in the UK. The key to the success of increasing the population of stone curlews in the UK is therefore to focus effort on breeding locations on arable land, as there is much more of this habitat available than of semi-natural grassland or heathland areas.

Section snippets

Methods

The GIS nest site predictive model utilised variables critical to the likelihood of stone curlew nesting attempts (Table 1).

Results

The final map (Fig. 4) shows the whole of the Chilterns Natural Area and parcels of land coloured in dark-grey which are:

  • greater than 1 km from a major road or motorway,

  • on arable land,

  • on arable land which has at least 30 hectares of unimproved grassland within 1 km,

  • on the preferred free draining soils groups,

  • on a slope less than 15°,

  • greater than 2 hectares in size.

This represents the land parcels in the Chilterns Natural Area which are deemed potential stone curlew breeding sites based upon

Discussion and conclusions

By identifying a number of abiotic requirements of the stone curlew, and modelling these and baseline environmental data within a GIS, a relatively small proportion of a landscape historically known to contain breeding stone curlew was identified as meeting the abiotic breeding requirements of the bird. There are however, still a number of factors that will influence breeding success and many of these can only be identified from field investigation. These factors are constraints which will

Acknowledgements

The authors would like to thank Andrew Stanbury of the RSPB for the information regarding former breeding distribution and Mary Webb of Oxford Brookes University for the aerial photography interpretation. Neil Bailey is funded under a joint NERC/ESRC initiative, Julian Bayliss is funded by the Esme Fairburn Foundation and John Lee is funded by the BBSRC. The National Soil Map is licensed from the National Soil Resource Institute and is reproduced with permission.

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