Interactions between ecosystem properties and land use clarify spatial strategies to optimize trade-offs between agriculture and species conservation

Digging into the roots: understanding direct and indirect drivers of ecosystem service trade-offs in coastal grasslands via plant functional traits

Recent empirical and theoretical approaches have called for an understanding of the processes underpinning ecosystem service provision. Environmental gradients have shown effects on key plant functional traits that subsequently explain ecosystem properties of several systems. However, little is known concerning how associations between plant functional traits, including both below- and aboveground plant components, predict ecosystem properties and independently measured final ecosystem services. Here, we modeled (1) the responses of the leaf and plant economics spectrum, Plant size axis, and root growth to environmental gradients and (2) how associations between plant functional traits explain trade-offs and synergies between multiple ecosystem properties and final services. Forty-four plots were studied in a coastal marsh landscape of the German North Sea Coast. We used a partial least square structural equation model approach to test the hypothesized model. We found (1) a negative covariation between plant traits pertaining to a size axis and traits explaining both plant growth (roots and stems) and the leaf economics spectrum; (2) this trade-off responded significantly to the land use gradient and nutrient availability, which were both strongly driven by the groundwater gradient; (3) this trade-off explained an initial major trade-off between carbon stocks, at one extreme of the axis, and both the habitat value to conserve endangered plants and forage production for meat and dairy products at the other extreme. However, a secondary trade-off between nature conservation value and forage production, explained by a trade-off between leaf economics spectrum and plant growth in response to the land use intensity gradient, was also found.

Novel model coupling approach for resilience analysis of coastal plant communities

Resilience is a major research focus covering a wide range of topics from biodiversity conservation to ecosystem (service) management. Model simulations can assess the resilience of, for example, plant species, measured as the return time to conditions prior to a disturbance. This requires process-based models (PBM) that implement relevant processes such as regeneration and reproduction and thus successfully reproduce transient dynamics after disturbances. Such models are often complex and thus limited to either short-term or small-scale applications, whereas many research questions require species predictions across larger spatial and temporal scales. We suggest a framework to couple a PBM and a statistical species distribution model (SDM), which transfers the results of a resilience analysis by the PBM to SDM predictions. The resulting hybrid model combines the advantages of both approaches: the convenient applicability of SDMs and the relevant process detail of PBMs in abrupt environmental change situations. First, we simulate dynamic responses of species communities to a disturbance event with a PBM. We aggregate the response behavior in two resilience metrics: return time and amplitude of the response peak. These metrics are then used to complement long-term SDM projections with dynamic short-term responses to disturbance. To illustrate our framework, we investigate the effect of abrupt short-term groundwater level and salinity changes on coastal vegetation at the German Baltic Sea. We found two example species to be largely resilient, and, consequently, modifications of SDM predictions consisted mostly of smoothing out peaks in the occurrence probability that were not confirmed by the PBM. Discrepancies between SDM- and PBM-predicted species responses were caused by community dynamics simulated in the PBM and absent from the SDM. Although demonstrated with boosted regression trees (SDM) and an existing individual-based model, IBC-grass (PBM), our flexible framework can easily be applied to other PBM and SDM types, as well as other definitions of short-term disturbances or long-term trends of environmental change. Thus, our framework allows accounting for biological feedbacks in the response to short- and long-term environmental changes as a major advancement in predictive vegetation modeling.