Background:The popularity of tri-axial accelerometer data loggers to quantify animal activity through the analysis of signature traces is increasing. However, there is no consensus on how to process the large data sets that these devices generate when recording at the necessary high sample rates. In addition, there have been few attempts to validate accelerometer traces with specific behaviours in non-domesticated terrestrial mammals. We fitted a collar with a tri-axial accelerometer to a tame captive Eurasian badger (Meles meles). The animal was allowed to move freely in an outside enclosure and artificial sett whilst movements were recorded using a video camera. Data were analysed using custom-written software in terms of magnitude of movement, posture and periodicity using spectral analysis, a principal component analysis, the k-nearest neighbour algorithm and a decision tree to facilitate the automated classification of behaviours. Findings:We have demonstrated that various discrete behaviours (walking, trotting, snuffling and resting) can be differentiated using tri-axial accelerometer data. Classification accuracy ranged between 77.4% and 100% depending on the behaviour and classification method employed. Conclusions:These results are an important step in defining how accelerometer data code for the behaviour of free-ranging mammals. The classification methods outlined here have the potential to be used in the construction of a behavioural database and to generate behaviour-time budgets of hitherto unparalleled detail for wild animals. This would be invaluable for studies of nocturnal, subterranean or difficult-to-observe species that are particularly sensitive to human intrusion.

The prevalence and diversity of plant parasitic nematodes in Northern Ireland cereal and grassland was determined from 191 agricultural fields. A total of 18 nematode genera were detected, including economically important pests, Meloidogyne spp., Heterodera spp. and Pratylenchus spp., each of which were above economic damage thresholds in a significant proportion of the sites (92.4%, 70% and 28.6%, respectively). The detection of the root knot nematode, Meloidogyne minor (6% prevalence), was significant given its recent emergence across the turf grass sector and the prospect of M. minor becoming a common agricultural pest. Analyses of nematode prevalence and abundance highlighted significant associations with grass and cereals, soil types, soil grade (proxy for soil quality) and rainfall levels. Specifically, nematode populations varied between the two major soils (brown earths and gleys), while significant trends for increased nematode diversity and greater prevalence of both Meloidogyne and Pratylenchus with increasing rainfall were also observed. Multivariate analyses were performed to determine interactive effects and the relative importance of the factors affecting nematode populations. Notably, rainfall, in combination with either crop type or soil grade, had a significant effect on nematode abundance and diversity. The findings suggest significant changes in nematode populations have occurred over the last several decades and the possibility that these are linked to changing climate and cropping practices are discussed, as well as future concerns for plant parasitic nematode management.

No abstract

No abstract

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.