What it takes to invade grassland ecosystems: traits, introduction history and filtering processes.

Whether the success of alien species can be explained by their functional or phylogenetic characteristics remains unresolved because of data limitations, scale issues and weak quantifications of success. Using permanent grasslands across France (50 000 vegetation plots, 2000 species, 130 aliens) and building on the Rabinowitz's classification to quantify spread, we showed that phylogenetic and functional similarities to natives were the most important correlates of invasion success compared to intrinsic functional characteristics and introduction history. Results contrasted between spatial scales and components of invasion success. Widespread and common aliens were similar to co-occurring natives at coarse scales (indicating environmental filtering), but dissimilar at finer scales (indicating local competition). In contrast, regionally widespread but locally rare aliens showed patterns of competitive exclusion already at coarse scale. Quantifying trait differences between aliens and natives and distinguishing the components of invasion success improved our ability to understand and potentially predict alien spread at multiple scales.

Dynamics of Weeds in the Soil Seed Bank: A Hidden Markov Model to Estimate Life History Traits from Standing Plant Time Series.

Predicting the population dynamics of annual plants is a challenge due to their hidden seed banks in the field. However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes. In agroecosystems, most weed seeds survive during unfavourable seasons and persist for several years in the seed bank. This causes difficulties in making accurate predictions of weed population dynamics and life history traits (LHT). Consequently, it is very difficult to identify management strategies that limit both weed populations and species diversity. In this article, we present a method of assessing weed population dynamics from both standing plant time series data and an unknown seed bank. We use a Hidden Markov Model (HMM) to obtain estimates of over 3,080 botanical records for three major LHT: seed survival in the soil, plant establishment (including post-emergence mortality), and seed production of 18 common weed species. Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values. The results showed that the LHT provided by the HMM enabled fairly accurate estimates of weed populations in different crops. There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal). The relationships between estimated LHTs and that between the estimated LHTs and the ecological characteristics of weeds provided insights into weed strategies. For example, a common strategy to cope with agricultural practices in several weeds was to produce less seeds and increase germination rates. This knowledge, especially of LHT for each type of crop, should provide valuable information for developing sustainable weed management strategies.

Vegetation ecology meets ecosystem science: Permanent grasslands as a functional biogeography case study.

The effect of biodiversity on ecosystem functioning has been widely acknowledged, and the importance of the functional roles of species, as well as their diversity, in the control of ecosystem processes has been emphasised recently. However, bridging biodiversity and ecosystem science to address issues at a biogeographic scale is still in its infancy. Bridging this gap is the primary goal of the emerging field of functional biogeography. While the rise of Big Data has catalysed functional biogeography studies in recent years, comprehensive evidence remains scarce. Here, we present the rationale and the first results of a country-wide initiative focused on the C3 permanent grasslands. We aimed to collate, integrate and process large databases of vegetation relevés, plant traits and environmental layers to provide a country-wide assessment of ecosystem properties and services which can be used to improve regional models of climate and land use changes. We outline the theoretical background, data availability, and ecoinformatics challenges associated with the approach and its feasibility. We provide a case study of upscaling of leaf dry matter content averaged at ecosystem level and country-wide predictions of forage digestibility. Our framework sets milestones for further hypothesis testing in functional biogeography and earth system modelling.