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Abstract Pinus edulis Engelm. is a short-stature, drought-tolerant tree species that is abundant in piñon-juniper woodlands throughout semiarid ecosystems of the American Southwest. P. edulis is a model species among ecophysiological disciplines, with considerable research focus given to hydraulic functioning and carbon partitioning relating to mechanisms of tree mortality. Many ecological studies require robust estimates of tree structural traits such as biomass, active sapwood area, and leaf area. We harvested twenty trees from Central New Mexico ranging in size from 1.3 to 22.7 cm root crown diameter (RCD) to derive allometric relationships from measurements of RCD, maximum height, canopy area (CA), aboveground biomass (AGB), sapwood area (AS), and leaf area (AL). Total foliar mass was measured from a subset of individuals and scaled to AL from estimates of leaf mass per area. We report a strong nonlinear relationship to AGB as a function of both RCD and height, whereas CA scaled linearly. Total AS expressed a power relationship with RCD. Both AS and CA exhibited strong linear relationships with AL (R2 = 0.99), whereas RCD increased nonlinearly with AL. We improve on current models by expanding the size range of sampled trees and supplement the existing literature for this species. Study Implications: Land managers need to better understand carbon and water dynamics in changing ecosystems to understand how those ecosystems can be sustainably used now and in the future. This study of two-needle pinon (Pinus edulis Engelm.) trees in New Mexico, USA, uses observations from unoccupied aerial vehicles, field measurements, and harvesting followed by laboratory analysis to develop allometric models for this widespread species. These models can be used to understand plant traits such biomass partitioning and sap flow, which in turn will help scientists and land managers better understand the ecosystem services provided by pinon pine across North America. 

Abstract Terrestrial evapotranspiration is the second‐largest component of the land water cycle, linking the water, energy, and carbon cycles and influencing the productivity and health of ecosystems. The dynamics of ET across a spectrum of spatiotemporal scales and their controls remain an active focus of research across different science disciplines. Here, we provide an overview of the current state of ET science across in situ measurements, partitioning of ET, and remote sensing, and discuss how different approaches complement one another based on their advantages and shortcomings. We aim to facilitate collaboration among a cross‐disciplinary group of ET scientists to overcome the challenges identified in this paper and ultimately advance our integrated understanding of ET. 

Abstract Extensive ecological research has investigated extreme climate events or long‐term changes in average climate variables, but changes in year‐to‐year (interannual) variability may also cause important biological responses, even if the mean climate is stable. The environmental stochasticity that is a hallmark of climate variability can trigger unexpected biological responses that include tipping points and state transitions, and large differences in weather between consecutive years can also propagate antecedent effects, in which current biological responses depend on responsiveness to past perturbations. However, most studies to date cannot predict ecological responses to rising variance because the study of interannual variance requires empirical platforms that generate long time series. Furthermore, the ecological consequences of increases in climate variance could depend on the mean climate in complex ways; therefore, effective ecological predictions will require determining responses to both nonstationary components of climate distributions: the mean and the variance. We introduce a new design to resolve the relative importance of, and interactions between, a drier mean climate and greater climate variance, which are dual components of ongoing climate change in the southwestern United States. The Mean × Variance Experiment (MVE) adds two novel elements to prior field infrastructure methods: (1) factorial manipulation of variance together with the climate mean and (2) the creation of realistic, stochastic precipitation regimes. Here, we demonstrate the efficacy of the experimental design, including sensor networks and PhenoCams to automate monitoring. We replicated MVE across ecosystem types at the northern edge of the Chihuahuan Desert biome as a central component of the Sevilleta Long‐Term Ecological Research Program. Soil sensors detected significant treatment effects on both the mean and interannual variability in soil moisture, and PhenoCam imagery captured change in vegetation cover. Our design advances field methods to newly compare the sensitivities of populations, communities, and ecosystem processes to climate mean × variance interactions. 

Abstract Understanding the complex and unpredictable ways ecosystems are changing and predicting the state of ecosystems and the services they will provide in the future requires coordinated, long‐term research. This paper is a product of a U.S. National Science Foundation funded Long Term Ecological Research (LTER) network synthesis effort that addressed anticipated changes in future populations and communities. Each LTER site described what their site would look like in 50 or 100 yr based on long‐term patterns and responses to global change drivers in each ecosystem. Common themes emerged and predictions were grouped into state change, connectivity, resilience, time lags, and cascading effects. Here, we report on the “state change” theme, which includes examples from the Georgia Coastal (coastal marsh), Konza Prairie (mesic grassland), Luquillo (tropical forest), Sevilleta (arid grassland), and Virginia Coastal (coastal grassland) sites. Ecological thresholds (the point at which small changes in an environmental driver can produce an abrupt and persistent state change in an ecosystem quality, property, or phenomenon) were most commonly predicted. For example, in coastal ecosystems, sea‐level rise and climate change could convert salt marsh to mangroves and coastal barrier dunes to shrub thicket. Reduced fire frequency has converted grassland to shrubland in mesic prairie, whereas overgrazing combined with drought drive shrub encroachment in arid grasslands. Lastly, tropical cloud forests are susceptible to climate‐induced changes in cloud base altitude leading to shifts in species distributions. Overall, these examples reveal that state change is a likely outcome of global environmental change across a diverse range of ecosystems and highlight the need for long‐term studies to sort out the causes and consequences of state change. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about state changes as an important driver of ecosystem structure, function, services, and futures.