A methodological approach to identify cheap and accurate indicators for biodiversity assessment: application to grazing management and two grassland bird species.

In response to environmental threats, numerous indicators have been developed to assess the impact of livestock farming systems on the environment. Some of them, notably those based on management practices have been reported to have low accuracy. This paper reports the results of a study aimed at assessing whether accuracy can be increased at a reasonable cost by mixing individual indicators into models. We focused on proxy indicators representing an alternative to the direct impact measurement on two grassland bird species, the lapwing Vanellus vanellus and the redshank Tringa totanus. Models were developed using stepwise selection procedures or Bayesian model averaging (BMA). Sensitivity, specificity, and probability of correctly ranking fields (area under the curve, AUC) were estimated for each individual indicator or model from observational data measured on 252 grazed plots during 2 years. The cost of implementation of each model was computed as a function of the number and types of input variables. Among all management indicators, 50% had an AUC lower than or equal to 0.50 and thus were not better than a random decision. Independently of the statistical procedure, models combining management indicators were always more accurate than individual indicators for lapwings only. In redshanks, models based either on BMA or some selection procedures were non-informative. Higher accuracy could be reached, for both species, with model mixing management and habitat indicators. However, this increase in accuracy was also associated with an increase in model cost. Models derived by BMA were more expensive and slightly less accurate than those derived with selection procedures. Analysing trade-offs between accuracy and cost of indicators opens promising application perspectives as time consuming and expensive indicators are likely to be of low practical utility.