Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects.We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives. Geosphere-Biosphere Program (IGBP) and DIVERSITAS, the TRY database (TRY-not an acronym, rather a statement of sentiment; https ://www.try-db.org; Kattge et al., 2011) was proposed with the explicit assignment to improve the availability and accessibility of plant trait data for ecology and earth system sciences. The Max Planck Institute for Biogeochemistry (MPI-BGC) offered to host the database and the different groups joined forces for this community-driven program. Two factors were key to the success of TRY: the support and trust of leaders in the field of functional plant ecology submitting large databases and the long-term funding by the Max Planck Society, the MPI-BGC and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, which has enabled the continuous development of the TRY database.

Soils underpin terrestrial ecosystem functions, but they face numerous anthropogenic pressures. Despite their crucial ecological role, we know little about how soils react to more than two environmental factors at a time. Here, we show experimentally that increasing the number of simultaneous global change factors (up to 10) caused increasing directional changes in soil properties, soil processes, and microbial communities, though there was greater uncertainty in predicting the magnitude of change. Our study provides a blueprint for addressing multifactor change with an efficient, broadly applicable experimental design for studying the impacts of global environmental change.

All ecological disciplines consider temporal dynamics, although relevant concepts have been developed almost independently. We here introduce basic principles of temporal dynamics in ecology. We figured out essential features that describe temporal dynamics by finding similarities among about 60 ecological concepts and theories. We found that considering the hierarchically nested structure of complexity in temporal patterns (i.e. hierarchical complexity) can well describe the fundamental nature of temporal dynamics by expressing which patterns are observed at each scale. Across all ecological levels, driver-response relationships can be temporally variant and dependent on both shortand long-term past conditions. The framework can help with designing experiments, improving predictive power of statistics, and enhancing communications among ecological disciplines. The Need for Basic Principles of Temporal DynamicsAll ecological disciplines consider temporal dynamics with major paradigms shifting from one to another: equilibrium (see Glossary) to nonequilibrium, and stationary to nonstationary (Box 1). Understanding temporal dynamics is becoming more important in the Anthropocene. Several time-related concepts and statistics have emerged recently [1][2][3][4]. Nevertheless, ecology still lacks basic principles that underlie all studies relevant to temporal dynamics [5], and the exchange of knowledge about temporal dynamics among subdisciplines is limited [6,7].Recently developed concepts include, for example, temporal ecology [5], abrupt shifts in ecological systems [8], ecological memory [3], lag hypothesis for community dynamics [9], and asymptotic environmentally determined trajectories [1]. These were proposed almost independently of each other. However, they all consider that driver-response relationships are not necessarily constant through time, but they depend on the recent and historical past. This perspective brings together various concepts to figure out the essence of temporal dynamics across ecological and temporal scales. HighlightsTemporal dynamics are inherently complex.Concepts and techniques have flourished to understand ecological temporal dynamics in recent years.A key finding of recent studies is that driver-response relationships are not necessarily constant through time, but rather, that they are conditioned by the recent and historical past.Basic principles of temporal dynamics need to be summarized to increase the understanding and predictability of complex temporal dynamics in ecology and evolution.

Citation: Ryo, M., and M. C. Rillig. 2017. Statistically reinforced machine learning for nonlinear patterns and variable interactions. Ecosphere 8(11):e01976. 10. 1002/ecs2.1976 Abstract. Most statistical models assume linearity and few variable interactions, even though real-world ecological patterns often result from nonlinear and highly interactive processes. We here introduce a set of novel empirical modeling techniques which can address this mismatch: statistically reinforced machine learning. We demonstrate the behaviors of three techniques (conditional inference tree, model-based tree, and permutation-based random forest) by analyzing an artificially generated example dataset that contains patterns based on nonlinearity and variable interactions. The results show the potential of statistically reinforced machine learning algorithms to detect nonlinear relationships and higher-order interactions. Estimation reliability for any technique, however, depended on sample size. The applications of statistically reinforced machine learning approaches would be particularly beneficial for investigating (1) novel patterns for which shapes cannot be assumed a priori, (2) higher-order interactions which are often overlooked in parametric statistics, (3) context dependency where patterns change depending on other conditions, (4) significance and effect sizes of variables while taking nonlinearity and variable interactions into account, and (5) a hypothesis using parametric statistics after identifying patterns using statistically reinforced machine learning techniques.

Flows of water, soil, litter, and anthropogenic materials in and around rivers lead to the mixing of their resident microbial communities and subsequently to a resultant community distinct from its precursors. Consideration of these events through a new conceptual lens, namely, community coalescence, could provide a means of integrating physical, environmental, and ecological mechanisms to predict microbial community assembly patterns better in these habitats. Here, we review field studies of microbial communities in riverine habitats where environmental mixing regularly occurs, interpret some of these studies within the community coalescence framework and posit novel hypotheses and insights that may be gained in riverine microbial ecology through the application of this concept. Particularly in the face of a changing climate and rivers under increasing anthropogenic pressures, knowledge about the factors governing microbial community assembly is essential to forecast and/or respond to changes in ecosystem function. Additionally, there is the potential for microbial ecology studies in rivers to become a driver of theory development: riverine systems are ideal for coalescence studies because regular and predictable environmental mixing occurs. Data appropriate for testing community coalescence theory could be collected with minimal alteration to existing study designs.

Root traits are often thought to be analogues of leaf traits along the plant economics spectrum. But evolutionary pressures have most likely shaped above- and belowground patterns differentially. Here, we aimed to identify the most important aboveground traits for explaining root traits without an a priori focus on known concepts. We measured morphological root traits in a glasshouse experiment on 141 common Central European grassland species. Using random forest algorithms, we built predictive models of six root traits from 97 aboveground morphological, ecological and life history traits. Root tissue density was best predicted by leaf dry matter content, whereas traits related to root fineness were best predicted by diaspore mass: the heavier the diaspore, the coarser the root system. Specific leaf area (SLA) was not an important predictor for any of the root traits. This study confirms the hypothesis that root traits are more than analogues of leaf traits within a plant economics spectrum. The results reveal a novel ecological pattern and highlight the power of root data to close important knowledge gaps in trait-based ecology.

BackgroundpH is frequently reported as the main driver for prokaryotic community structure in soils. However, pH changes are also linked to “spillover effects” on other chemical parameters (e.g., availability of Al, Fe, Mn, Zn, and Cu) and plant growth, but these indirect effects on the microbial communities are rarely investigated. Usually, pH also co-varies with some confounding factors, such as land use, soil management (e.g., tillage and chemical inputs), plant cover, and/or edapho-climatic conditions. So, a more comprehensive analysis of the direct and indirect effects of pH brings a better understanding of the mechanisms driving prokaryotic (archaeal and bacterial) community structures.ResultsWe evaluated an agricultural soil pH gradient (from 4 to 6, the typical range for tropical farms), in a liming gradient with confounding factors minimized, investigating relationships between prokaryotic communities (16S rRNA) and physical–chemical parameters (indirect effects). Correlations, hierarchical modeling of species communities (HMSC), and random forest (RF) modeling indicated that both direct and indirect effects of the pH gradient affected the prokaryotic communities. Some OTUs were more affected by the pH changes (e.g., some Actinobacteria), while others were more affected by the indirect pH effects (e.g., some Proteobacteria). HMSC detected a phylogenetic signal related to the effects. Both HMSC and RF indicated that the main indirect effect was the pH changes on the availability of some elements (e.g., Al, Fe, and Cu), and secondarily, effects on plant growth and nutrient cycling also affected the OTUs. Additionally, we found that some of the OTUs that responded to pH also correlated with CO2, CH4, and N2O greenhouse gas fluxes.ConclusionsOur results indicate that there are two distinct pH-related mechanisms driving prokaryotic community structures, the direct effect and “spillover effects” of pH (indirect effects). Moreover, the indirect effects are highly relevant for some OTUs and consequently for the community structure; therefore, it is a mechanism that should be further investigated in microbial ecology.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0482-8) contains supplementary material, which is available to authorized users.

Summary 1. River flow alterations due to climate change and increasing water usage affect freshwater biodiversity including fish species richness. Here, we statistically explored the relationships of fish species richness to 14 ecologically relevant flow metrics as well as basin area and latitude in 72 rivers worldwide. 2. The statistical models best supported by the data included three variables with positive coefficients (mean river discharge, basin area and the maximum proportion of no‐flooding period) and three variables with negative coefficients (latitude, coefficients of variation in the frequency of low flow and the Julian date of annual minimum flow). 3. The model outputs have provided the first empirical indication that specific low‐ and high‐flow characteristics may be important in explaining variations in basin‐scale fish species richness. Our findings can be useful in identifying high‐risk basins for conservation of fish species diversity. 4. The results not only support the adoption of mean discharge as a predictor, but also suggest the importance of basin area in predicting basin‐scale fish species richness around the world.