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  1. The three‐dimensional (3D) physical aspects of ecosystems are intrinsically linked to ecological processes. Here, we describe structural diversity as the volumetric capacity, physical arrangement, and identity/traits of biotic components in an ecosystem. Despite being recognized in earlier ecological studies, structural diversity has been largely overlooked due to an absence of not only a theoretical foundation but also effective measurement tools. We present a framework for conceptualizing structural diversity and suggest how to facilitate its broader incorporation into ecological theory and practice. We also discuss how the interplay of genetic and environmental factors underpin structural diversity, allowing for a potentially unique synthetic approach to explain ecosystem function. A practical approach is then proposed in which scientists can test the ecological role of structural diversity at biotic–environmental interfaces, along with examples of structural diversity research and future directions for integrating structural diversity into ecological theory and management across scales. 
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    Free, publicly-accessible full text available February 1, 2024
  2. Abstract

    Gradient and scale are two key concepts in ecology and evolution that are closely related but inherently distinct. While scale commonly refers to the dimensional space of a specific ecological/evolutionary (eco–evo) issue, gradient measures the range of a given variable. Gradient and scale can jointly and interactively influence eco–evo patterns. Extensive previous research investigated how changing scales may affect the observation and interpretation of eco–evo patterns; however, relatively little attention has been paid to the role of changing gradients. Here, synthesizing recent research progress, we suggest that the role of scale in the emergence of ecological patterns should be evaluated in conjunction with considering the underlying environmental gradients. This is important because, in most studies, the range of the gradient is often part of its full potential range. The difference between sampled (partial) versus potential (full) environmental gradients may profoundly impact observed eco–evo patterns and alter scale–gradient relationships. Based on observations from both field and experimental studies, we illustrate the underlying features of gradients and how they may affect observed patterns, along with the linkages of these features to scales. Since sampled gradients often do not cover their full potential ranges, we discuss how the breadth and the starting and ending positions of key gradients may affect research design and data interpretation. We then outline potential approaches and related perspectives to better integrate gradient with scale in future studies.

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  3. Abstract

    Determining how local and environmental conditions affect community assembly processes is critical to understanding and preserving ecosystem functions. A combination of plant traits is required to capture the broad spectrum of strategies that species employ to respond to varying environmental conditions. The trait hypervolume (i.e.,n‐dimensional trait space) accurately describes such multi‐trait characteristics. Here we use hypervolume mismatch metric, defined as the difference between the observed trait hypervolume and the trait hypervolume inferred from local and/or regional species pools, to investigate plant community assembly. Our method suggests plant traits should be categorized a priori to quantify trait hypervolumes associated with environmental variation (i.e., resource utilization strategies). Using the plant trait data from North American and South African grassland communities, this hypervolume mismatch metric can be applied to different categories of traits and scales, thus providing new insights into community assembly processes. For example, the trait hypervolumes calculated from physiological traits (e.g., mean stomatal length, stomatal pore index, and mean stomatal density) were highly correlated with regional environmental factors. By contrast, local species pool factors explained a greater proportion of variation in hypervolumes estimated from leaf stoichiometric traits (e.g., leaf nitrogen [N] content, leaf carbon [C] content, and leaf C/N ratio). Therefore, this hypervolume mismatch framework can accurately identify the separate impacts of regional versus local species pools on community assembly across environmental gradients.

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  4. During the 1930s Dust Bowl drought in the central United States, species with the C3photosynthetic pathway expanded throughout C4-dominated grasslands. This widespread increase in C3grasses during a decade of low rainfall and high temperatures is inconsistent with well-known traits of C3vs. C4pathways. Indeed, water use efficiency is generally lower, and photosynthesis is more sensitive to high temperatures in C3than C4species, consistent with the predominant distribution of C3grasslands in cooler environments and at higher latitudes globally. We experimentally imposed extreme drought for 4 y in mixed C3/C4grasslands in Kansas and Wyoming and, similar to Dust Bowl observations, also documented three- to fivefold increases in C3/C4biomass ratios. To explain these paradoxical responses, we first analyzed long-term climate records to show that under nominal conditions in the central United States, C4grasses dominate where precipitation and air temperature are strongly related (warmest months are wettest months). In contrast, C3grasses flourish where precipitation inputs are less strongly coupled to warm temperatures. We then show that during extreme drought years, precipitation–temperature relationships weaken, and the proportion of precipitation falling during cooler months increases. This shift in precipitation seasonality provides a mechanism for C3grasses to respond positively to multiyear drought, resolving the Dust Bowl paradox. Grasslands are globally important biomes and increasingly vulnerable to direct effects of climate extremes. Our findings highlight how extreme drought can indirectly alter precipitation seasonality and shift ecosystem phenology, affecting function in ways not predictable from key traits of C3and C4species.

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  5. Abstract

    Satellite‐derived sun‐induced chlorophyll fluorescence (SIF) has been increasingly used for estimating gross primary production (GPP). However, the relationship between SIF and GPP has not been well defined, impeding the translation of satellite observed SIF to GPP. Previous studies have generally assumed a linear relationship between SIF and GPP at daily and longer time scales, but support for this assumption is lacking. Here, we used the GPP/SIF ratio to investigate seasonal variations in the relationship between SIF and GPP over the Northern Hemisphere (NH). Based on multiple SIF products and MODIS and FLUXCOM GPP data, we found strong seasonal hump‐shaped patterns for the GPP/SIF ratio over northern latitudes, with higher values in the summer than in the spring or autumn. This hump‐shaped GPP/SIF seasonal variation was confirmed by examining different SIF products and was evident for most vegetation types except evergreen broadleaf forests. The seasonal amplitude of the GPP/SIF ratio decreased from the boreal/arctic region to drylands and the tropics. For most of the NH, the lowest GPP/SIF values occurred in October or September, while the maximum GPP/SIF values were evident in June and July. The most pronounced seasonal amplitude of GPP/SIF occurred in intermediate temperature and precipitation ranges. GPP/SIF was positively related to temperature in the early and late parts of the growing season, but not during the peak growing months. These shifting relationships between temperature and GPP/SIF across different months appeared to play a key role in the seasonal dynamics of GPP/SIF. Several mechanisms may explain the patterns we observed, and future research encompassing a broad range of climate and vegetation settings is needed to improve our understanding of the spatial and temporal relationships between SIF and GPP. Nonetheless, the strong seasonal variation in GPP/SIF we identified highlights the importance of incorporating this behavior into SIF‐based GPP estimations.

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