The development and application of remote sensing to monitor sand dune habitats

Sand dunes are complex systems that contain several habitats, often as mosaics or transitions between types. Several of these habitats are afforded protection under European Legislation and in the UK nationally within Special Areas of Conservation (SAC) and Sites of Special Scientific Interest (SSSI). Natural England has a statutory duty to report to Europe on the conservation status and condition of sand dunes; and is required to report to the UK Government on designated sites. To achieve this we have sought ways of capturing, analysing and interpreting data on the extent and location of sand dune habitats. This requires an ability to be able to obtain data over large areas of coastline in an efficient way. Natural England and Environment Agency Geomatics have worked collaboratively for over 16 years, sharing data and ecological knowledge. In 2012 work started to evaluate the use of remote sensing to map UK BAP and Annex I sand dune habitats. A methodology has now been developed and tested to map sand dune habitats. The key objective was to provide an operational tool that will help to map these habitats and understand change on sites around England. This has been achieved through analysis of LIDAR and Compact Airborne Spectrographic Imager (CASI) data using Object Orientated Image Analysis. Quality Control (QC) and accuracy assessments have shown this approach to be successful and 11 sites have been mapped to date. These techniques are providing a new approach to monitoring change in coastal vegetation communities and informing management of protected sites.     

Impact of host community composition on Lyme disease risk

The drivers of variable disease risk in complex multi-host disease systems have proved very difficult to identify. Here we test a model that explains the entomological risk of Lyme disease (LD) in terms of host community composition. The model was parameterized in a continuous forest tract at the Cary Institute of Ecosystem Studies (formerly the Institute of Ecosystem Studies) in New York State, U.S.A. We report the results of continuing longitudinal observations (10 years) at the Cary Institute, and of a shorter-term study conducted in forest fragments in LD endemic areas of Connecticut, New Jersey, and New York, USA. Model predictions were significantly correlated with the observed nymphal infection prevalence (NIP) in both studies, although the relationship was stronger in the longer-term Cary Institute study. Species richness was negatively, albeit weakly, correlated with NIP (logistic regression), and there was no relationship between the Shannon diversity index (H') and NIP. Although these results suggest that LD risk is in fact dependent on host diversity, the relationship relies explicitly on the identities and frequencies of host species such that conventional uses of the term biodiversity (i.e., richness, evenness, H') are less appropriate than are metrics that include species identity. This underscores the importance of constructing interaction webs for vertebrates and exploring the direct and indirect effects of anthropogenic stressors on host community composition.