Abstract

BackgroundMeadow ecosystems have important ecological functions and support socioeconomic services, yet are subject to multiple stressors that can lead to rapid degradation. In the Sierra Nevada of the western USA, recreational pack stock (horses and mules) use in seasonally wet mountain meadows may lead to soil trampling and meadow degradation, especially when soil water content is high and vegetation is developing.MethodsIn order to improve the ability to predict meadow vulnerability to soil disturbance from pack stock use, we measured soil resistance (SR), which is an index of vulnerability to trampling disturbance, at two spatial scales using a stratified-random sampling design. We then compared SR to several soil and vegetation explanatory variables that were also measured at the two spatial scales: plant community type (local scale) and topographic gradient class (meadow scale).ResultsWe found that local-scale differences in drivers of SR were contingent on the meadow scale, which is important because multiple spatial scale evaluation of ecological metrics provides a broader understanding of the potential controls on ecological processes than assessments conducted at a single spatial scale. We also found two contrasting explanatory models for drivers of SR at the local scale: (1) soil gravimetric water content effects on soil disaggregation and (2) soil bulk density and root mass influence on soil cohesion. Soil resistance was insufficient to sustain pack stock use without incurring soil deformation in wet plant communities, even when plant cover was maximal during a major drought.ConclusionsOur study provides new information on seasonally wet meadow vulnerability to trampling by pack stock animals using multi-scale drivers of SR, including the contrasting roles of soil disaggregation, friction, and cohesion. Our work aims to inform meadow management efforts in the Sierra Nevada and herbaceous ecosystems in similar regions that are subject to seasonal soil saturation and livestock use.

Highlights

  • Meadows in the western United States of America provide ecosystem services and socioeconomic benefits, such as high plant diversity, critical habitat for wildlife, sediment and water storage and filtration, nutrient cycling, and flood attenuation, and are often popular recreational destinations (Roche et al 2012; Russo et al 2012; Norton et al 2014)

  • Three sets of explanatory variables were determined to be collinear (r > 0.75, Table 1): gravimetric water content (GWC) was collinear with water holding capacity (WHC) (r = 0.88) and bulk density (BD) (r = − 0.86), and root mass areal density (RMAD) were collinear with root content (r = 0.87)

  • Using linear mixed-effects regression (LMER) analysis, we identified five best explanatory mixed models based on non-collinear, parsimonious combinations of explanatory variables with the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) model criterion (Table 2)

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Summary

Introduction

Meadows in the western United States of America provide ecosystem services and socioeconomic benefits, such as high plant diversity, critical habitat for wildlife, sediment and water storage and filtration, nutrient cycling, and flood attenuation, and are often popular recreational destinations (Roche et al 2012; Russo et al 2012; Norton et al 2014). High elevation meadows are popular destinations for day hikers, backpackers, and recreational or administrative users with pack stock animals (horses and mules) due to their scenic beauty, iconic mountain vistas, close proximity to water, and availability of high-quality summer forage. These important ecosystems are vulnerable to degradation, especially when soils are wet immediately after snowmelt and vegetation is first developing in early summer (Cole et al 1987). In the Sierra Nevada of the western USA, recreational pack stock (horses and mules) use in seasonally wet mountain meadows may lead to soil trampling and meadow degradation, especially when soil water content is high and vegetation is developing

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