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We introduce multiple refinements to the standard method for assessing CEO effects on performance, variance partitioning methodology, more accurately contextualizing CEOs' contributions. Based on a large 20-year sample, our new 'CEO in Context' technique points to a much larger aggregate CEO effect than is obtained from typical approaches. As a validation test, we show that our technique yields estimates of CEO effects more in line with what would be expected from accepted theory about CEO influence on performance. We do this by examining the CEO effects in subsamples of low-, medium-, and high-discretion industries. Finally, we show that our technique generates substantially different—and we argue more logical—estimates of the effects of many individual CEOs than are obtained through customary analyses.

• Close coupling between water loss and carbon dioxide uptake requires coordination of plant hydraulics and photosynthesis. However, there is still limited information on the quantitative relationships between hydraulic and photosynthetic traits. • We propose a basis for these relationships based on optimality theory, and test its predictions by analysis of measurements on 107 species from 11 sites, distributed along a nearly 3000-m elevation gradient. • Hydraulic and leaf economic traits were less plastic, and more closely associated with phylogeny, than photosynthetic traits. The two sets of traits were linked by the sapwood to leaf area ratio (Huber value, v H). The observed coordination between v H and sapwood hydraulic conductivity (K S) and photosynthetic capacity (V cmax) conformed to the proposed quantitative theory. Substantial hydraulic diversity was related to the trade-off between K S and v H. Leaf drought tolerance (inferred from turgor loss point, −Ψtlp) increased with wood density, but the trade-off between hydraulic efficiency (K S) and −Ψtlp was weak. Plant trait effects on v H were dominated by variation in K S, while effects of environment were dominated by variation in temperature. • This research unifies hydraulics, photosynthesis and the leaf economics spectrum in a common theoretical framework, and suggests a route towards the integration of photosynthesis and hydraulics in land-surface models.

While there are now a number of theoretical models predicting how consistent individual differences in behaviour may be generated and maintained, so far, there are few empirical tests. The social niche specialization hypothesis predicts that repeated social interactions among individuals may generate among-individual differences and reinforce within-individual consistency through positive feedback mechanisms. Here, we test this hypothesis using groups of the social spider Stegodyphus mimosarum that differ in their level of familiarity. In support of the social niche specialization hypothesis, individuals in groups of spiders that were more familiar with each other showed greater repeatable among-individual variation in behaviour. Additionally, individuals that were more familiar with each other exhibited lower within-individual variation in behaviour, providing one of the first examples of how the social environment can influence behavioural consistency. Our study demonstrates the potential for the social environment to generate and reinforce consistent individual differences in behaviour and provides a potentially general mechanism to explain this type of behavioural variation in animals with stable social groups.

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

Emergence of the metacommunity concept has made a substantial contribution to better understanding of the community composition and dynamics in a regional context. However, long-term field data for testing of available metacommunity models are still scarce, and the extent to which these models apply to the real world remains unknown. Tests conducted so far have largely sought to fit data on the entire regional set of species to one of several metacommunity models, implicitly assuming that all species operate similarly over the same set of sites. However, species differ in their habitat use. These differences can, in the most general terms, be expressed as a gradient of habitat specialization (ranging from habitat specialists to habitat generalists). We postulate that such differences in habitat specialization will have implications for metacommunity dynamics. Specifically, we predict that specialists respond more to local processes and generalists respond to regional spatial processes. We tested these predictions using natural microcosm communities for which long-term (nine-year) environmental and population dynamics data were available. We used redundancy analysis to determine the proportion of variation explained by environmental and spatial factors. We repeated this analysis to explain variation in the entire regional set of species, in generalist species only, and in specialists only. We further used ANOVA to test for differences in the proportions of explained variation. We found that habitat specialists responded primarily to environmental factors and habitat generalists responded mainly to spatial factors. Thus, from the metacommunity perspective, the dynamics of habitat specialists are best explained by a combination of species sorting and mass effects, while that of habitat generalists are best explained by patch dynamics and neutral models. Consequently, we infer that a natural metacommunity can exhibit complicated dynamics, with some groups of species (e.g., habitat specialists) governed according to environmental processes and other groups (e.g., habitat generalists) governed mainly by dispersal processes.

Recognizing others’ social interactions is a crucial human ability. Using simple stimuli, previous studies have shown that social interactions are selectively processed in the superior temporal sulcus (STS), but prior work with movies has suggested that social interactions are processed in the medial prefrontal cortex (mPFC), part of the theory of mind network. It remains unknown to what extent social interaction selectivity is observed in real world stimuli when controlling for other covarying perceptual and social information, such as faces, voices, and theory of mind. The current study utilizes a functional magnetic resonance imaging (fMRI) movie paradigm and advanced machine learning methods to uncover the brain mechanisms uniquely underlying naturalistic social interaction perception. We analyzed two publicly available fMRI datasets, collected while both male and female human participants (n = 17 and 18) watched two different commercial movies in the MRI scanner. By performing voxel-wise encoding and variance partitioning analyses, we found that broad social-affective features predict neural responses in social brain regions, including the STS and mPFC. However, only the STS showed robust and unique selectivity specifically to social interactions, independent from other covarying features. This selectivity was observed across two separate fMRI datasets. These findings suggest that naturalistic social interaction perception recruits dedicated neural circuity in the STS, separate from the theory of mind network, and is a critical dimension of human social understanding.

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by comparing their relative contribution to the spatial variety of LST. This study adopted the ordinary least squares regression, spatial autoregression and variance partitioning methods to investigate the relationship between 2D/3D building patterns and summertime LST across 2016–2017 in Shanghai. The 2D and 3D building patterns in this study were quantified by four 2D and six 3D metrics. The results showed that: (1) During the daytime, 2D/3D building metrics had significant correlation with LST. However, 3D building patterns played a significant role in predicting LST. They explained 51.0% and 10.2% of the variance in LST, respectively. (2) The building coverage ratio, building density, mean building projection area, the standard deviation of building height, and mean building height highly correlated with LST. Specifically, the building coverage ratio was the main predictor, which was obviously positively correlated with LST. The correlation of building density and average projected area with LST was positive and significant, while the correlation of building height standard deviation and average building height with LST was negative. The increase in average height and standard deviation of buildings and the decrease in building coverage ratio, average projected area, and density of buildings, can effectively improve the urban thermal environment at the census tract level. (3) Spatial autocorrelation analysis can elaborate the spatial relationship between building patterns and LST. The findings from our research will provide important insights for urban planners and decision makers to mitigate urban heat island problems through urban planning and building design.

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

Herbaceous plants are a key component of tropical forests. Previous work indicates that herbs contribute substantially to the species richness of tropical plant communities. However, the processes structuring tropical herb diversity, and how they contrast with woody communities, have been underexplored. Within the understory of a 50-ha forest dynamics plot in central Panama, we compared the diversity, distribution, and abundance of vascular herbaceous plants with woody seedlings (i.e., tree and lianas <1 cm DBH and ≥ 20 cm tall). Beta-diversity was calculated for each community using a null model approach. We then assessed the similarity in alpha and beta-diversity among herbs, tree seedlings, and liana seedlings. Strengths of habitat associations were measured using permutational ANOVA among topographic habitat-types. Variance partitioning was then used to quantify the amount of variation in species richness and composition explained by spatial and environmental variables (i.e., topography, soils, and shade) for each growth form. Species richness and diversity were highest for tree seedlings, followed by liana seedlings and then herbs. In contrast, beta-diversity was 16-127% higher for herbs compared to woody seedlings, indicating higher spatial variation in this stratum. We observed no correlation between local richness or compositional uniqueness of herbs and woody seedlings across sites, indicating that different processes control the spatial patterns of woody and herbaceous diversity and composition. Habitat associations were strongest for herbs, as indicated by greater compositional dissimilarity among habitat types. Likewise, environmental variables explained a larger proportion of the variation in species richness and composition for herbs than for woody seedlings (richness = 25%, 14%, 12%; composition = 25%, 9%, 6%, for herbs, trees, and lianas, respectively). These differences between strata did not appear to be due to differences in lifespan alone, based on data from adult trees. Our results point to contrasting assembly mechanisms for herbaceous and woody communities, with herbs showing stronger niche-derived structure. Future research on tropical herbaceous communities is likely to yield new insights into the many processes structuring diverse plant communities.

• Plant functional traits regulate ecosystem functions but little is known about how cooccurring gradients of land use and edaphic conditions influence their expression. We test how gradients of logging disturbance and soil properties relate to community-weighted mean traits in logged and old-growth tropical forests in Borneo. • We studied 32 physical, chemical and physiological traits from 284 tree species in eight 1 ha plots and measured long-term soil nutrient supplies and plant-available nutrients. Logged plots had greater values for traits that drive carbon capture and growth, whilst oldgrowth forests had greater values for structural and persistence traits. Although disturbance was the primary driver of trait expression, soil nutrients explained a statistically independent axis of variation linked to leaf size and nutrient concentration. Soil characteristics influenced trait expression via nutrient availability, nutrient pools, and pH. • Our finding, that traits have dissimilar responses to land use and soil resource availability, provides robust evidence for the need to consider the abiotic context of logging when predicting plant functional diversity across human-modified tropical forests. The detection of two independent axes was facilitated by the measurement of many more functional traits than have been examined in previous studies.