Summary1. Theory predicts that the processes generating biodiversity after disturbance will change during succession. Comparisons of phylogenetic and functional (alpha and beta) diversity with taxonomic diversity can provide insights into the extent to which community assembly is driven by deterministic or stochastic processes, but comparative approaches have yet to be applied to successional systems. 2. We characterized taxonomic, phylogenetic and functional plant (alpha and beta) diversity within and between four successional stages in a > 270-year-long arable-to-grassland chronosequence. Null models were used to test whether functional and phylogenetic turnover differed from random expectations, given the levels of species diversity. 3. The three facets of diversity showed different patterns of change during succession. Between early and early-mid succession, species richness increased but there was no increase in functional or phylogenetic diversity. Higher than predicted levels of functional similarity between species within the early and early-mid successional stages, indicate that abiotic filters have selected for sets of functionally similar species within sites. Between late-mid and late succession, there was no further increase in species richness, but a significant increase in functional alpha diversity, suggesting that functionally redundant species were replaced by functionally more dissimilar species. Functional turnover between stages was higher than predicted, and higher than within-stage turnover, indicating that different assembly processes act at different successional stages. 4. Synthesis. Analysis of spatial and temporal turnover in different facets of diversity suggests that deterministic processes generate biodiversity during post-disturbance ecosystem development and that the relative importance of assembly processes has changed over time. Trait-mediated abiotic filtering appears to play an important role in community assembly during the early and early-mid stages of arable-to-grassland succession, whereas the relative importance of competitive exclusion appears to have increased towards the later successional stages. Phylogenetic diversity provided a poor reflection of functional diversity and did not contribute to inferences about underlying assembly processes. Functionally deterministic assembly suggests that it may be possible to predict future post-disturbance changes in biodiversity, and associated ecosystem attributes, on the basis of species' functional traits but not phylogeny.

Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414-2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson's diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 mˆ4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400-700 nm) and to vegetation structural properties, such as above-ground biomass (700-1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity.

No abstract

Sweet potato plants were grown with or without Glomus intraradices in split-root pots with adjacent root compartments containing a soil with a low availability of phosphate. One fungal tube, from which root growth was excluded, was inserted into each root compartment. During 4 weeks before harvest, the soil moisture level in either both or only one of the two root-compartments of each pot was decreased. Controls remained well watered. Low soil moisture generally had a negative effect on the amount of extraradical mycelium of G. intraradices extracted from the fungal tubes. Sporulation in the fungal tubes was much higher compared with the soil in the root compartment, but remained unaffected by the soil moisture regime. Concentrations of P in extraradical mycelium were much lower than usually found in plants and fungi, while P concentrations in associated mycorrhizal host plant tissues were in an optimum range. This suggests efficient transfer of P from the extraradical mycelium to the host plant. Despite the negative effect of a low soil moisture regime on extraradical G. intraradices development, the symbiosis indeed contributed significantly to P uptake of plants exposed to partial rootzone drying. The possibility that extraradical arbuscular mycorrhizal fungal development was limited by P availability under dry soil conditions is discussed.

Self-reporting techniques, such as data logging or a diary, are frequently used in long-term studies, but prone to subjects' forgetfulness and other sources of inaccuracy. We conducted a six-week self-reporting study on smartphone usage in order to investigate the accuracy of self-reported information, and used logged data as ground truth to compare the subjects' reports against. Subjects never recorded more than 70% and, depending on the requested reporting interval, down to less than 40% of actual app usages. They significantly overestimated how long they used apps. While subjects forgot self-reports when no automatic reminders were sent, a high reporting frequency was perceived as uncomfortable and burdensome. Most significantly, self-reporting even changed the actual app usage of users and hence can lead to deceptive measures if a study relies on no other data sources. With this contribution, we provide empirical quantitative long-term data on the reliability of self-reported data collected with mobile devices. We aim to make researchers aware of the caveats of self-reporting and give recommendations for maximizing the reliability of results when conducting large-scale, long-term app usage studies.