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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.

The eddy covariance method is widely used to investigate fluxes of energy, water, and carbon dioxide at landscape scales, providing important information on how ecological systems function. Flux measurements quantify ecosystem responses to environmental perturbations and management strategies, including nature‐based climate‐change mitigation measures. However, due to the high cost of conventional instrumentation, most eddy covariance studies employ a single system, limiting spatial representation to the flux footprint. Insufficient replication may be limiting our understanding of ecosystem behavior. To address this limitation, we deployed eight lower‐cost eddy covariance systems in two clusters around two conventional eddy covariance systems in the Chihuahuan Desert of North America for a period of 2 years. These dryland settings characterized by large temperature variations and relatively low carbon dioxide fluxes represented a challenging setting for eddy covariance. We found very good closure of energy and water balance across all systems (within ±9% of unity). We found very good correspondence between the lower‐cost and conventional systems' fluxes of sensible heat (with concordance correlation coefficient (CCC) of ≥0.87), latent energy (evapotranspiration; CCC ≥ 0.89), and useful correspondence in the net ecosystem exchange ((NEE); with CCC ≥ 0.4) at the daily temporal resolution. Relative to the conventional systems, the low‐frequency systems were characterized by a higher level of random error, particularly in the NEE fluxes. Lower‐cost systems can enable wider deployment affording better replication and sampling of spatiotemporal variability at the expense of greater measurement noise that might be limiting for certain applications. Replicated eddy covariance observations may be useful when addressing gaps in the existing monitoring of critical and underrepresented ecosystems and for measuring areas larger than a single flux footprint.

Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjustedR²of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similarwithinbut differentamong,plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha⁻¹. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe.