Video recording and vegetation classification elucidate sheep foraging ecology in species-rich grassland.

Stephen J G Hall,Elis Vollmer,Robert G H Bunce,David R Arney

doi:10.1002/ece3.8172

Abstract

Factors influencing grazing behavior in species‐rich grasslands have been little studied. Methodologies have mostly had a primary focus on grasslands with lower floristic diversity.We test the hypothesis that grazing behavior is influenced by both animal and plant factors and investigate the relative importance of these factors, using a novel combination of video technology and vegetation classification to analyze bite and step rates.In a semi‐natural, partially wooded grassland in northern Estonia, images of the vegetation being grazed and records of steps and bites were obtained from four video cameras, each mounted on the sternum of a sheep, during 41 animal‐hours of observation over five days. Plant species lists for the immediate field of view were compiled. Images were partnered by direct observation of the nearest‐neighbor relationships of the sheep. TWINSPAN, a standard vegetation classification technique allocating species lists to objectively defined classes by a principal components procedure, was applied to the species lists and 25 vegetation classes (15 open pasture and 10 woodland) were identified from the images.Taking bite and step rates as dependent variables, relative importance of animal factors (sheep identity), relative importance of day, and relative importance of plant factors (vegetation class) were investigated. The strongest effect on bite rates was of vegetation class. Sheep identity was less influential. When the data from woodland were excluded, sheep identity was more important than vegetation class as a source of variability in bite rate on open pasture.The original hypothesis is therefore supported, and we further propose that, at least with sheep in species‐rich open pastures, animal factors will be more important in determining grazing behavior than plant factors. We predict quantifiable within‐breed and between‐breed differences, which could be exploited to optimize conservation grazing practices and contribute to the sustainability of extensive grazing systems.

Highlights

Many landscapes of high biodiversity and cultural significance are intimately associated with traditional livestock systems (Bunce et al, 2018a, 2018b; Plieninger et al, 2015; Redecker et al, 2002)
We test the hypothesis that grazing behavior is influenced by both animal and plant factors and investigate the relative importance of these factors, using a novel combination of video technology and vegetation classification to analyze bite and step rates
The original hypothesis is supported, and we further propose that, at least with sheep in species-rich open pastures, animal factors will be more important in determining grazing behavior than plant factors

Summary

| INTRODUCTION

Many landscapes of high biodiversity and cultural significance are intimately associated with traditional livestock systems (Bunce et al, 2018a, 2018b; Plieninger et al, 2015; Redecker et al, 2002). It is clear from quantitative genetics that some behavioral components of grazing behavior are heritable (Fogarty et al, 2009; Snowder et al, 2001), supporting the contention that grazing behavior is an outcome of genotype–environment interaction (Osoro et al, 1999; Rook, Dumont, et al, 2004) Nongenetic animal factors such as individual learning and social interactions (Provenza & Balph, 1990; Sibbald et al, 2008) modulated by traditional herding practices (Meuret & Provenza, 2015) will be important. From a long-term study in a species-rich alpine grassland, Mysterud and Austrheim (2016) hypothesized that the “dietary composition” of sheep arises from a combination of “sheep preferences” and “immediate environment.” We have tested this hypothesis in a diverse vegetation environment, but with a more behavioral approach. This investigation of a hypothesis evaluates the use of a novel combination of ecological and behavioral approaches

| METHODS

| Objective classification

Findings

| DISCUSSION