Long‐term time series of legume cycles in a semi‐natural montane grassland: evidence for nitrogen‐driven grass dynamics?

Abstract

Summary Several dynamic models have shown that dynamics of legumes and grasses can result in periodic behaviour. These oscillations arise due to delays in nitrogen flows coupled with differences in ability to compete for light. However, long‐term time series on legume dynamics that could be used to test predictions of these models are almost non‐existent. We examine legume oscillations in a semi‐natural mountain grassland using a long‐term (Tilde; 30 years) data series on aboveground biomass of individual species and on nitrogen and phosphorus content over time. Using autocorrelation analysis, we show that there is a strong periodicity (with period of 8–9 years) of legume and grass biomass and nitrogen content in the grass biomass. These three variables are in fairly stable phase shifts relative to each other, with a grass peak followed by a peak in C : N ratio in grasses which is followed by a legume peak. Phosphorus content in either legume or grass biomass does not show synchronous cycling with legume or grass biomass or nitrogen content in grass. Fitting a dynamic linear model to the data showed that legumes affect nitrogen content in grasses, and grass biomass both affects and is affected by nitrogen content. In contrast, there is no negative effect of grasses on legumes, indicating some other process must be responsible for the legume decline. Manuring, which was occasionally applied to the plots, also does not seem to affect the cycling. Second‐order term for legumes showed some evidence of self‐inhibitory effects in legumes, but phosphorus content in legumes shows no support for phosphorus limitation. The most likely explanation of the legume decline should be sought elsewhere (pathogens, soil biota etc.). Synthesis. Long‐term data support the existing the claim that legume dynamics are the key driver of nitrogen dynamics in nutrient‐poor semi‐natural grasslands. Grasses benefit from the nutrient enrichment due to legume cycling, but are a passive element and do not play a role in legume limitation. Apart from the role of nutrient cycling, these legume‐driven nutrient dynamics also constitutes processes by which long‐term richness of meadows is maintained.

Long‐term effects of mulching, traditional cutting and no management on plant species composition of improved upland grassland in the Czech Republic

“…For example, the rapid increase in the shade‐intolerant (Grime et al., ) T. repens from its initial negligible cover was followed by successional decrease under repeated defoliation managements. High variation in the cover of T. repens between years have also been reported in other long‐term manipulative experiments (Gaisler et al., ; Pavlů et al., ) and are probably associated with nitrogen‐driven grass dynamics (Herben et al., ). On the other hand, the cover of Taraxacum spp.…”

A new variance ratio metric to detect the timescale of compensatory dynamics

“…For example, the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO) contribute to long‐term patterns of drought over the United States (McCabe et al 2004), and the El Niño–Southern Oscillation (ENSO) can also be related to drought or extreme rainfall (Yoon et al 2015). Long‐timescale nitrogen periodicity of 8–9 yr driven by legume cycles has also been reported (Herben et al 2017). Long‐term resource variation likely propagates through ecosystems, ultimately affecting aggregate community properties in ways that depend on species interactions and shared responses to drivers.…”

Directional trends in species composition over time can lead to a widespread overemphasis of year‐to‐year asynchrony

“…For example, trends could be the result of species responding differently to disturbance or to an increase in nutrient availability. Such opposite trends could be monotonic or following waves in time (Wu et al ., 2007), e.g., resulting from periodic climate events such as “El Niño”, or intrinsic cycling of particular functional groups such as legumes (Herben et al ., 2017). These results are partially expected because our datasets comprised natural or semi‐natural well‐established plant communities but included experimental conditions in which changes in abundance or composition of species are common.…”