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The use of trait-based approaches to understand ecological communities has increased in the past two decades because of their promise to preserve more information about community structure than taxonomic methods and their potential to connect community responses to subsequent effects of ecosystem functioning. Though trait-based approaches are a powerful tool for describing ecological communities, many important properties of commonly-used trait metrics remain unexamined. Previous work with simulated communities and trait distributions shows sensitivity of functional diversity measures to the number and correlation of traits used to calculate them, but these relationships have yet to be studied in actual plant communities with a realistic distribution of trait values, ecologically meaningful covariation of traits, and a realistic number of traits available for analysis. To address this gap, we used data from six grassland plant communities in Minnesota and New Mexico, USA to test how the number of traits and the correlation between traits used in the calculation of eight functional diversity indices impact the magnitude of functional diversity metrics in real plant communities. We found that most metrics were sensitive to the number of traits used to calculate them, but functional dispersion (FDis), kernel density estimation dispersion (KDE dispersion), and Rao's quadratic entropy (Rao's Q) maintained consistent rankings of communities across the range of trait numbers. Despite sensitivity of metrics to trait correlation, there was no consistent pattern between communities as to how metrics were affected by the correlation of traits used to calculate them. We recommend that future use of evenness metrics include sensitivity analyses to ensure results are robust to the number of traits used to calculate them. In addition, we recommend use of FDis, KDE dispersion, and Rao's Q when ecologically applicable due to their ability to produce consistent rankings among communities across a range of the numbers of traits used to calculate them. [ABSTRACT FROM AUTHOR]

The use of trait-based approaches to understand ecological communities has increased in the past two decades because of their promise to preserve more information about community structure than taxonomic methods and their potential to connect community responses to subsequent effects of ecosystem functioning. Though trait-based approaches are a powerful tool for describing ecological communities, many important properties of commonly-used trait metrics remain unexamined. Previous work in studies that simulate communities and trait distributions show consistent sensitivity of functional richness and evenness measures to the number of traits used to calculate them, but these relationships have yet to be studied in actual plant communities with a realistic distribution of trait values, ecologically meaningful covariation of traits, and a realistic number of traits available for analysis. Therefore, we propose to test how the number of traits used and the correlation between traits used in the calculation of functional diversity indices impacts the magnitude of eight functional diversity metrics in real plant communities. We will use trait data from three grassland plant communities in the US to assess the generality of our findings across ecosystems and experiments. We will determine how eight functional diversity metrics (functional richness, functional evenness, functional divergence, functional dispersion, kernel density estimation (KDE) richness, KDE evenness, KDE dispersion, Rao's Q) differ based on the number of traits used in the metric calculation and on the correlation of traits when holding the number of traits constant. Without a firm understanding of how a scientist's choices impact these metric, it will be difficult to compare results among studies with different metric parametrization and thus, limit robust conclusions about functional composition of communities across systems. [ABSTRACT FROM AUTHOR]

Causal inferences from experimental data are often justified based on treatment randomization. However, inferring causality from data also requires complementary causal assumptions, which have been formalized by scholars of causality but not widely discussed in ecology. While ecologists have recognized challenges to inferring causal relationships in experiments and developed solutions, they lack a general framework to identify and address them. We review four assumptions required to infer causality from experiments and provide design-based and statistically based solutions for when these assumptions are violated. We conclude that there is no clear demarcation between experimental and non-experimental designs. This insight can help ecologists design better experiments and remove barriers between experimental and observational scholarship in ecology. Causal inferences require causal assumptions. To formalize the assumptions required to draw causal inferences from experimental data, scholars have leveraged insights about causal inference in observational settings. Even carefully designed experiments may face challenges in satisfying four important causal assumptions. Ecologists sometimes acknowledge and address these challenges but do not have a cohesive framework for understanding them. When the validity of a causal assumption is questionable, ecologists can apply design-based and statistically based solutions. Despite popular wisdom, no clear demarcation exists between experimental and non-experimental designs. When inferring causal relationships from data, experimentalists need to be just as careful as non-experimentalists in assessing the validity of their assumptions. [ABSTRACT FROM AUTHOR]

In most plant communities, the net effect of nitrogen enrichment is an increase in plant productivity. However, nitrogen enrichment also has been shown to decrease species richness and to acidify soils, each of which may diminish the long‐term impact of nutrient enrichment on productivity. Here we use a long‐term (20 year) grassland plant diversity by nitrogen enrichment experiment in Minnesota, United States (a subexperiment within the BioCON experiment) to quantify the net impacts of nitrogen enrichment on productivity, including its potential indirect effects on productivity via changes in species richness and soil pH over an experimental diversity gradient. Overall, we found that nitrogen enrichment led to an immediate positive increment in productivity, but that this effect became nonsignificant over later years of the experiment, with the difference in productivity between fertilized and unfertilized plots decreasing in proportion to nitrogen addition‐dependent declines in soil pH and losses of plant diversity. The net effect of nitrogen enrichment on productivity could have been 14.5% more on average over 20 years in monocultures if not for nitrogen‐induced decreases in pH and about 28.5% more on average over 20 years in 16 species communities if not for nitrogen‐induced species richness losses. Together, these results suggest that the positive effects of nutrient enrichment on biomass production can diminish in their magnitude over time, especially because of soil acidification in low diversity communities and especially because of plant diversity loss in initially high diversity communities. [ABSTRACT FROM AUTHOR]

BACKGROUND: Vocational recovery is a concept that has grown out of the mental health recovery model and has been operationalized as a threshold for work capacity for persons working with serious mental illness (SMI). In this article, is it conceptualized as a process towards gainful employment and overall mental health recovery. OBJECTIVE: This article describes the early phases of the vocational recovery process of individuals with serious mental illness participating in a supported employment program and the factors that make up this process. This study describes the vocational recovery process and the mental health related recovery gains attained through employment. METHODS:Aconstructivist grounded theory approachwas employed in this study. The primary author conducted participant observations and conducted semi-structured interviews with 10 individuals at a supported employment program to develop a theory about a vocational process that is grounded in data. RESULTS: A phase like process emerged from the data, which moved individuals from prevocational to early vocational recovery to active vocational recovery process. Participants experienced both mental health and vocational recovery challenges and benefits as they moved through the supported employment program and substantial support from providers, family members, program director and peers emerged as a driving force to move individuals through the recovery process. CONCLUSION: Participants in this study rapidly placed into a supported employment program were able to move through their psychosocial barriers to employment and move towards an active vocational recovery process. The phase like progression suggests that appropriate interventions can be formulated that best meets the individual's vocational and mental health needs depending on where individuals are in their overall vocational recovery process. Follow up research is necessary. [ABSTRACT FROM AUTHOR]

The effects of altered nutrient supplies and herbivore density on species diversity vary with spatial scale, because coexistence mechanisms are scale dependent. This scale dependence may alter the shape of the species–area relationship (SAR), which can be described by changes in species richness (S) as a power function of the sample area (A): S = cAz, where c and z are constants. We analysed the effects of experimental manipulations of nutrient supply and herbivore density on species richness across a range of scales (0.01–75 m2) at 30 grasslands in 10 countries. We found that nutrient addition reduced the number of species that could co‐occur locally, indicated by the SAR intercepts (log c), but did not affect the SAR slopes (z). As a result, proportional species loss due to nutrient enrichment was largely unchanged across sampling scales, whereas total species loss increased over threefold across our range of sampling scales. [ABSTRACT FROM AUTHOR]

It is now well established that biodiversity plays an important role in determining ecosystem functioning and its stability over time. A possible mechanism for this positive effect of biodiversity is that more diverse plant communities have a greater capacity to respond to environmental changes through shifts in species dominance and composition. In our study, we utilized data from five long-term grassland biodiversity experiments located in North America (three studies) and Central Europe (two studies), in which plant species richness and global change drivers were manipulated simultaneously. The global change drivers included warming, drought, elevated atmospheric CO2 concentrations, elevated N inputs, or intensive management. Across drivers, functional change over time was significantly greater for communities of high plant diversity than that of low diversity because of a higher functional and phylogenetic richness and mostly associated with a dominance by species with a 'slow and tall' strategy. Community functional shifts in response to global change drivers were, however, relatively weak and mostly not influenced by diversity. The exception to this was warming, where diverse communities showed stronger shifts than species-poor communities. Our results confirm the hypothesis that diverse communities have a greater capacity for functional change than species-poor communities, particularly in their successional dynamics, but also potentially in their responses to environmental change. This capacity could underlie the positive biodiversity-stability relationship and buffer ecosystem responses to environmental change. [ABSTRACT FROM AUTHOR]

Recent studies have revealed many potential benefits of increasing plant diversity in natural ecosystems, as well as in agroecosystems and production forests. Plant diversity potentially provides a partial to complete substitute for many costly agricultural inputs, such as fertilizers, pesticides, imported pollinators and irrigation. Diversification strategies include enhancing crop genetic diversity, mixed plantings, rotating crops, agroforestry and diversifying landscapes surrounding croplands., Here we briefly review studies considering how increasing plant diversity influences the production of crops, forage, and wood, yield stability, and several regulating and supporting agroecosystem services. We also discuss challenges and recommendations for diversifying agroecosystems., There is consistently strong evidence that strategically increasing plant diversity increases crop and forage yield, wood production, yield stability, pollinators, weed suppression and pest suppression, whereas effects of diversification on soil nutrients and carbon remain poorly understood., Synthesis. The benefits of diversifying agroecosystems are expected to be greatest where the aims are to sustainably intensify production while reducing conventional inputs or to optimize both yields and ecosystem services. Over the next few decades, as monoculture yields continue to decelerate or decline for many crops, and as demand for ecosystem services continues to rise, diversification could become an essential tool for sustaining production and ecosystem services in croplands, rangelands and production forests. [ABSTRACT FROM AUTHOR]