Modelling important areas for breeding waders as a tool to target conservation and minimise conflicts with land use change

Abstract

Breeding waders are high profile species of conservation concern because of their declining populations. Their future depends on the outcomes of land-use policy and local management decisions. However, the low spatial resolution of extensive data on wader occurrence is poorly suited to directing conservation initiatives or to minimising detrimental impacts arising from land-use changes such as forest expansion. We used statistical models to produce high-resolution maps of predicted wader abundance in Britain and tested whether these were sufficiently accurate to be used for decision making. Random forest regression trees were developed using Bird Atlas data modelled with a range of environmental data sets to predict the relative abundances of ten species of breeding wader across Britain at 1-km square resolution. Similar analytical frameworks could usefully be applied in other geographical areas.Correlations with Bird Atlas metrics and other independent data indicate that these predictive models worked best for Curlew Numenius arquata and Oystercatcher Haematopus ostralegus. Model performance was poorer for species with more restricted distributions and/or habitat requirements, such as Greenshank Tringa nebularia and Ringed Plover Charadrius hiaticula. The precision of model predictions was also limited by the rescaling of data to 1-km square resolution.To facilitate practical and consistent interpretation by stakeholders influencing the status of breeding waders, model outputs were categorised into five discrete strata of relative abundance. These strata were used to produce sensitivity maps (available at https://app.bto.org/wader-map/), primarily to inform high-level policy decisions. The model outputs were also used to investigate broad-scale associations of breeding wader distribution with landscapes and land uses. These show that high proportions of populations (relative to their extent) are supported by areas with comparatively low predation risk (e.g. islands and grouse moors) and also by nature reserves. At more local scales, the sensitivity maps can be used to help decide on the levels of scrutiny required to assess development proposals (e.g. for establishment of new tree planting).