Predicting stochastic community dynamics in grasslands under the assumption of competitive symmetry

Abstract

Community dynamics is influenced by multiple ecological processes such as environmental spatiotemporal variation, competition between individuals and demographic stochasticity. Quantifying the respective influence of these various processes and making predictions on community dynamics require the use of a dynamical framework encompassing these various components. We here demonstrate how to adapt the framework of stochastic community dynamics to the peculiarities of herbaceous communities, by using a short temporal resolution adapted to the time scale of competition between herbaceous plants, and by taking into account the seasonal drops in plant aerial biomass following winter, harvesting or consumption by herbivores. We develop a hybrid inference method for this novel modelling framework that both uses numerical simulations and likelihood computations. Applying this methodology to empirical data from the Jena biodiversity experiment, we find that environmental stochasticity has a larger effect on community dynamics than demographic stochasticity, and that both effects are generally smaller than observation errors at the plot scale. We further evidence that plant intrinsic growth rates and carrying capacities are moderately predictable from plant vegetative height, specific leaf area and leaf dry matter content. We do not find any trade-off between demographical components, since species with larger intrinsic growth rates tend to also have lower demographic and environmental variances. Finally, we find that our model is able to make relatively good predictions of multi-specific community dynamics based on the assumption of competitive symmetry. Quantifying the respective influence of these various processes and making predictions on community dynamics require the use of a dynamical framework encompassing these various 30components. We here demonstrate how to adapt the framework of stochastic community dynamics to the peculiarities of herbaceous communities, by using a short temporal resolution 32 adapted to the time scale of competition between herbaceous plants, and by taking into account the seasonal drops in plant aerial biomass following winter, harvesting or 34 consumption by herbivores. We develop a hybrid inference method for this novel modelling framework that both uses numerical simulations and likelihood computations. Applying this 36 methodology to empirical data from the Jena biodiversity experiment, we find that environmental stochasticity has a larger effect on community dynamics than demographic 38 stochasticity, and that both effects are generally smaller than observation errors. We further evidence that plant intrinsic growth rates and carrying capacities are moderately predictable 40 from plant vegetative height, specific leaf area and leaf dry matter content. We do not find any trade-off between demographical components, since species with larger intrinsic growth 42 rates tend to also have lower demographic and environmental variances. Finally, we find that our model is able to make ...

Disentangling the processes driving plant assemblages in mountain grasslands across spatial scales and environmental gradients

“…Plot level trait divergence occurred particularly at high elevations, presumably indicating spatial partitioning rather than limiting similarity as the underlying mechanism. Our findings suggest that approaches studying and modelling assembly processes, such as stacked species distribution models (Dubuis et al, 2011) or mechanistic models of community assembly (DeMalach, Zaady, Weiner, Kadmon, & Cahill, 2016;Lohier, Jabot, Weigelt, Schmid, & Deffuant, 2016;Shipley, Vile, & Garnier, 2006), should consider the interaction of biotic and abiotic factors along environmental gradients, particularly when examining communities at fine spatial scales. Furthermore, the detection of spatial partitioning in this study calls for high-resolution abiotic data to allow understanding and forecasting of community and biodiversity patterns at fine scales.…”

Dos and don'ts when inferring assembly rules from diversity patterns

“…Very importantly, the parameterized model could also be used to account for transient dynamics (P10) and to make predictions, so far largely unattained aims in large‐scale community ecology. Examples of such mechanistic models exist already (Cazelles, Mouquet, Mouillot, & Gravel, 2016; Kalyuzhny, Kadmon, & Shnerb, 2015; Lohier, Jabot, Weigelt, Schmid, & Deffuant, 2016), but many processes and process combinations are still understudied in this young research field (Cabral et al, 2017). Interestingly, the development and application of such mechanistic models in community ecology will benefit greatly from the solutions outlined here (S1–S4) because these provide a range of partly independent diversity patterns (e.g., trait versus phylogenetic patterns, abundance weighted patterns, small versus large‐scale patterns), an indispensable requisite for inverse parameterization (Grimm et al, 1996).…”

Fine‐scale functional metacommunity dynamics: Analysing the role of disturbance‐driven environmental variability in grasslands

“…Besides, the importance of ecological drift (i.e. random temporal changes in species abundances) is poorly documented in grasslands (but see Martorell & Freckleton, 2014; Lohier et al, 2016) and only at inter‐annual time scales, not on shorter seasonal scales. Beyond the study of dispersal, drift and environmental filtering processes in isolation, the relative role of these three processes on fine spatial (within grasslands) and temporal (within season) community dynamics still requires more attention (Pinto et al, 2014; Bittebiere et al, 2020).…”

“…The magnitude of ecological drift at intra‐annual time scales has not been studied to our knowledge. Analyses at inter‐annual time scales of an experimentally sown grassland have demonstrated a limited impact of ecological drift compared to environmental filtering (Lohier et al, 2016). Besides, the magnitude of ecological drift decreases with the size of the community (Lande et al, 2003), which is not expected to strongly vary between mowing and grazing treatments.…”

“…According to Vellend (2016), four fundamental processes, namely speciation, selection, dispersal and drift, influence community structure at a wide range of scales. While speciation acts at large spatio‐temporal scales to shape the regional species pool (Cornell & Harrison, 2014), the three other processes are likely to impact grassland plant community dynamics at various spatial and temporal scales, including fine ones (Questad & Foster, 2008; Fridley et al, 2011; Lohier et al, 2016).…”