Suchergebnisse

Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy probability of the contributing individual species. The formulation is flexible and applicable to different taxa. It possesses several advantages, including the ability to accommodate the sporadic unavailability of data, incorporate variation in the estimation precision of the individual species' indices when appropriate, and allow the direct incorporation of smoothing over time. Furthermore, model fitting is straightforward in Bayesian and classical implementations, the latter adopting either efficient Hidden Markov modelling or the Kalman filter. Conveniently, the same algorithms can be adopted for cases based on abundance or occupancy data—only the subsequent interpretation differs. The procedure removes the need for bootstrapping which can be prohibitive. We recommend which of two alternatives to use when taxa are fully or partially sampled. The performance of the new approach is demonstrated on simulated data, and through application to three diverse national UK data sets on butterflies, bats and dragonflies. We see that uncritical incorporation of index standard errors should be avoided.

Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy probability of the contributing individual species. The formulation is flexible and applicable to different taxa. It possesses several advantages, including the ability to accommodate the sporadic unavailability of data, incorporate variation in the estimation precision of the individual species' indices when appropriate, and allow the direct incorporation of smoothing over time. Furthermore, model fitting is straightforward in Bayesian and classical implementations, the latter adopting either efficient Hidden Markov modelling or the Kalman filter. Conveniently, the same algorithms can be adopted for cases based on abundance or occupancy data—only the subsequent interpretation differs. The procedure removes the need for bootstrapping which can be prohibitive. We recommend which of two alternatives to use when taxa are fully or partially sampled. The performance of the new approach is demonstrated on simulated data, and through application to three diverse national UK data sets on butterflies, bats and dragonflies. We see that uncritical incorporation of index standard errors should be avoided.

Due to concerns over negative impacts on insect pollinators, the European Union has implemented a moratorium on the use of three neonicotinoid pesticide seed dressings for mass-flowering crops. We assessed the effectiveness of this policy in reducing the exposure risk to honeybees by collecting 130 samples of honey from bee keepers across the UK before (2014: N = 21) and after the moratorium was in effect (2015: N = 109). Neonicotinoids were present in about half of the honey samples taken before the moratorium, and they were present in over a fifth of honey samples following the moratorium. Clothianidin was the most frequently detected neonicotinoid. Neonicotinoid concentrations declined from May to September in the year following the ban. However, the majority of post-moratorium neonicotinoid residues were from honey harvested early in the year, coinciding with oilseed rape flowering. Neonicotinoid concentrations were correlated with the area of oilseed rape surrounding the hive location. These results suggest mass flowering crops may contain neonicotinoid residues where they have been grown on soils contaminated by previously seed treated crops. This may include winter seed treatments applied to cereals that are currently exempt from EU restrictions. Although concentrations of neonicotinoids were low (<2.0 ng g-1), and posed no risk to human health, they may represent a continued risk to honeybees through long-term chronic exposure.

Due to concerns over negative impacts on insect pollinators, the European Union has implemented a moratorium on the use of three neonicotinoid pesticide seed dressings for mass flowering crops. We assessed the effectiveness of this policy in reducing the exposure risk to honeybees by collecting 130 samples of honey from bee keepers across the UK before (2014: N = 21) and after the moratorium was in effect (2015: N = 109). Neonicotinoids were present in about half of the honey samples taken before the moratorium, and they were present in over a fifth of honey samples following the moratorium. Clothianidin was the most frequently detected neonicotinoid. Neonicotinoid concentrations declined from May to September in the year following the ban. However, the majority of post-moratorium neonicotinoid residues were from honey harvested early in the year, coinciding with oilseed rape flowering. Neonicotinoid concentrations were correlated with the area of oilseed rape surrounding the hive location. These results suggest mass flowering crops may contain neonicotinoid residues where they have been grown on soils contaminated by previously seed treated crops. This may include winter seed treatments applied to cereals that are currently exempt from EU restrictions. Although concentrations of neonicotinoids were low (<2.0 ng g-1), and posed no risk to human health, they may represent a continued risk to honeybees through long-term chronic exposure.