Julia Chacón‐Labella

Plant functional traits, particularly leaf mass per area (LMA), are critical links between changes in plant diversity and the biosphere’s exchanges of energy and matter. Despite their importance, a predictive understanding of the biosphere is hampered by spatial and temporal gaps in trait observations. Here, we introduce iLMA, a novel remote sensing index capable of capturing LMA spatial variation across major terrestrial biomes (R2 = 0.63, RMSE = 13.6%) and LMA temporal changes, enabling a continuous mapping of LMA even at high resolution (30 m). Our global mapping revealed LMA decreased 10-11% between 1985 to 2024, largely driven by increasing temperatures, while LMA change in some regions (e.g. tropical South America) is also heavily influenced by land-use change. Due to LMA connections with carbon metabolism in ecosystems, a global decrease in LMA suggests possible, and largely unexplored, consequences to the terrestrial carbon sink and climate feedbacks.

Measuring ecosystem gas fluxes is crucial to understanding the dynamics of ecosystem water and energy cycling. A common method to measure ecosystem gas fluxes (CO ₂ , CH ₄ , N ₂ O) and to compare experimental treatments is the use of closed‐loop chambers. However, the output data require extensive processing before analysis, making the workflow prone to bias and limiting comparability between studies. While there are various methods to process flux data, they lack reproducibility and automation. The fluxible R package provides a reproducible way to process raw gas concentration data from closed‐loop chambers into an analysis‐ready dataset of ecosystem gas fluxes. The processing steps include (1) separating the measurements, (2) fitting a linear, quadratic, or exponential model, (3) assessing the quality of the fit, (4) plotting the fluxes for visual inspection and (5) calculating the fluxes. In addition to reproducibility, fluxible focuses on homogeneity and automation in data processing, thus reducing the time investment for users while improving comparability across studies.

Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce thetraitstrapR package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow,trait_fill, allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

The COVID‐19 crisis has forced researchers in Ecology to change the way we work almost overnight. Nonetheless, the pandemic has provided us with several novel components for a new way of conducting science. In this perspective piece, we summarize eight central insights that are helping us, as early career researchers, navigate the uncertainties, fears, and challenges of advancing science during the COVID‐19 pandemic. We highlight how innovative, collaborative, and often Open Science‐driven developments that have arisen from this crisis can form a blueprint for a community reinvention in academia. Our insights include personal approaches to managing our new reality, maintaining capacity to focus and resilience in our projects, and a variety of tools that facilitate remote collaboration. We also highlight how, at a community level, we can take advantage of online communication platforms for gaining accessibility to conferences and meetings, and for maintaining research networks and community engagement while promoting a more diverse and inclusive community. Overall, we are confident that these practices can support a more inclusive and kinder scientific culture for the longer term.

As Open Science practices become more commonplace, there is a need for the next generation of scientists to be well versed in these aspects of scientific research. Yet, many training opportunities for early career researchers (ECRs) could better emphasize or integrate Open Science elements. Field courses provide opportunities for ECRs to apply theoretical knowledge, practice new methodological approaches, and gain an appreciation for the challenges of real‐life research, and could provide an excellent platform for integrating training in Open Science practices. Our recent experience, as primarily ECRs engaged in a field course interrupted by COVID‐19, led us to reflect on the potential to enhance learning outcomes in field courses by integrating Open Science practices and online learning components. Specifically, we highlight the opportunity for field courses to align teaching activities with the recent developments and trends in how we conduct research, including training in: publishing registered reports, collecting data using standardized methods, adopting high‐quality data documentation, managing data through reproducible workflows, and sharing and publishing data through appropriate channels. We also discuss how field courses can use online tools to optimize time in the field, develop open access resources, and cultivate collaborations. By integrating these elements, we suggest that the next generation of field courses will offer excellent arenas for participants to adopt Open Science practices.

Summary: Roots are assumed to play a major role in structuring soil microbial communities, but most studies exploring the relationships between microbes and plants at the community level have only used aboveground plant distribution as a proxy. However, a decoupling between belowground and aboveground plant components may occur due to differential spreading of plant canopies and root systems. Thus, soil microbe–plant links are not completely understood. Using a combination of DNA metabarcoding and spatially explicit sampling at the plant neighbourhood scale, we assessed the influence of the plant root community on soil bacterial and fungal diversity (species richness, composition and β‐diversity) in a dry Mediterranean scrubland. We found that root composition and biomass, but not richness, predict unique fractions of variation in microbial richness and composition. Moreover, bacterial β‐diversity was related to root β‐diversity, while fungal β‐diversity was related to aboveground plant β‐diversity, suggesting that plants differently influence both microbial groups. Our study highlights the role of plant distribution both belowground and aboveground, soil properties and other spatially structured factors in explaining the heterogeneity in soil microbial diversity. These results also show that incorporating data on both plant community compartments will further our understanding of the relationships between soil microbial and plant communities.

Plant diversity fosters productivity in natural ecosystems. Biodiversity effects might increase agricultural yields at no cost in additional inputs. However, the effects of diversity on crop assemblages are inconsistent, probably because crops and wild plants differ in a range of traits relevant to plant–plant interactions. We tested whether domestication has changed the potential of crop mixtures to over‐yield by comparing the performance and traits of major crop species and those of their wild progenitors under varying levels of diversity. We found stronger biodiversity effects in mixtures of wild progenitors, due to larger selection effects. Variation in selection effects was partly explained by within‐mixture differences in leaf size. Our results indicate that domestication might disrupt the ability of crops to benefit from diverse neighbourhoods via reduced trait variance. These results highlight potential limitations of current crop mixtures to over‐yield and the potential of breeding to re‐establish variance and increase mixture performance.

Most work on plant community ecology has been performed above ground, neglecting the processes that occur in the soil. DNA metabarcoding, in which multiple species are computationally identified in bulk samples, can help to overcome the logistical limitations involved in sampling plant communities belowground. However, a major limitation of this methodology is the quantification of species’ abundances based on the percentage of sequences assigned to each taxon. Using root tissues of five dominant species in a semi‐arid Mediterranean shrubland (Bupleurum fruticescens, Helianthemum cinereum, Linum suffruticosum, Stipa pennata and Thymus vulgaris), we built pairwise mixtures of relative abundance (20%, 50% and 80% biomass), and implemented two methods (linear model fits and correction indices) to improve estimates of root biomass. We validated both methods with multispecies mixtures that simulate field‐collected samples. For all species, we found a positive and highly significant relationship between the percentage of sequences and biomass in the mixtures (R² = .44–.66), but the equations for each species (slope and intercept) differed among them, and two species were consistently over‐ and under‐estimated. The correction indices greatly improved the estimates of biomass percentage for all five species in the multispecies mixtures, and reduced the overall error from 17% to 6%. Our results show that, through the use of post‐sequencing quantification methods on mock communities, DNA metabarcoding can be effectively used to determine not only species’ presence but also their relative abundance in field samples of root mixtures. Importantly, knowledge of these aspects will allow us to study key, yet poorly understood, belowground processes.

Summary: Most ecological theories that aim to explain coexistence in megadiverse communities employ a set of three rules to describe the stochastic geometry of biodiversity: (i) individuals exhibit intraspecific clustering; (ii) species abundances vary according to a log‐normal distribution and (iii) the spatial arrangement between species is independent. The first two rules have received strong empirical support, but the third remains largely unexplored. To address this deficiency, we evaluated the independent species arrangement rule in a species‐rich shrubland and its potential drivers, that is, the levels of species richness and intraspecific clustering exhibited by a given species at different scales, and the relative abundance of such species in the community. We found that interspecific associations were rare and that independence was positively related to species richness and intraspecific clustering, but negatively related to relative species abundances. Synthesis. Our results agree with the independent species arrangement rule and they provide empirical support for the stochastic geometry of biodiversity. In the context of species‐rich plant communities, the likelihood of two species encountering is very small. However, our study demonstrated a novel feature of this context, where both intraspecific clustering (due limitations on dispersal) and relative species abundances play fundamental roles in determining the probability of two species encountering and interacting, especially at very fine spatial scales.