Alejandro Cueva

Environmental observatory networks are fundamental in advancing scientific understanding of biogeochemical processes. FLUXNET is a global network of regional eddy covariance networks that measure ecosystem‐scale exchanges of greenhouse gases (e.g., CO ₂ , CH ₄ , H ₂ O) and energy between the biosphere and the atmosphere. MexFlux is the eddy covariance network of Mexico, a megadiverse country with many underrepresented ecosystems within FLUXNET. This study evaluates the spatial representativeness of MexFlux by assessing its ability to capture the statistical and spatial heterogeneity of annual gross primary productivity (GPP) and evapotranspiration (ET) within Mexico. We tested four network configurations: the historical distribution of MexFlux sites (MexFlux‐H, n = 33), an expanded network with 20 additional sites (MexFlux + 20, 53 sites), MexFlux sites with publicly available data (MexFlux‐P, n = 20), and a hypothetical optimized design with only 25 sites (MexFlux25, n = 25). Results show that MexFlux‐H and MexFlux‐P overrepresent regions with GPP values between 250 and 600 gC m ⁻² yr ⁻¹ and ET of 200–1,200 mm yr ⁻¹ . MexFlux + 20 demonstrates that adding 20 strategically located sites improves the representativeness of MexFlux while preserving the historical distribution of the network. The configuration of MexFlux25 highlights that a few but strategically distributed sites are an alternative way to enhance the representativeness of the network. Mountain regions, tropical forests, and urban sites may remain underrepresented in any network configuration, highlighting the challenges of monitoring efforts in this country. Our framework integrates distributions, copulas, semivariograms, and upscaling to highlight the value of a multidimensional assessment of spatial representativeness, which applies to other regional FLUXNET networks.

Decomposition is the transformation of dead organic matter into its inorganic constituents. In most biomes, decomposition rates can be accurately predicted with simple mathematical models, but these models have long under‐predicted decomposition in globally extensive drylands. We posit that the exposed surface conditions characteristic of drylands make litter decomposition uniquely subject to microsite‐specific environmental controls and spatially variable microbial communities. As such, decomposition in dryland ecosystems—which are characterized by extremes in temporal heterogeneity of climate conditions and spatial heterogeneity of vegetation cover with corresponding microclimate variability—is a prime example of a macrosystems process that can be addressed by merging field data with new predictive process models operating across a hierarchical continuum of spatial scales and process resolutions. A macrosystems approach offers promise to reconcile model‐measurement discrepancies by integrating observations and experiments across multiple scales, from microsites (e.g. shrub sub‐canopy or intercanopy) to regions (e.g. across a 100s of km² study site with complex topography, precipitation and temperature) and ultimately to a continental perspective (e.g. North American drylands). Recent developments in technology and data availability position the scientific community to integrate laboratory, field, modelling and remote sensing approaches across a hierarchical range of scales to capture the spatiotemporal distribution of litter and environmental conditions needed to predict decay dynamics at the micro‐to‐macroscale. This multi‐scale approach promises a path forward to resolving a longstanding disconnect between measured data and modelled processes in dryland litter decomposition. Dryland litter decomposition presents an excellent case study for resolving spatially and temporally complex biogeochemical dynamics through a hierarchical, multidisciplinary macrosystems approach. We focus on dryland litter decomposition, but the hierarchical, multidisciplinary macrosystems approach we outline shows great potential for resolving other spatially and temporally complex biogeochemical processes across a wide range of ecosystems. Read the free Plain Language Summary for this article on the Journal blog.

Soil biota generates carbon that exports vertically to the atmosphere (CO₂) and transports laterally to streams and rivers (dissolved organic and inorganic carbon, DOC and DIC). These processes, together with chemical weathering, vary with flow paths across hydrological regimes; yet an integrated understanding of these interactive processes is still lacking. Here we ask: How and to what extent do subsurface carbon transformation, chemical weathering, and solute export differ across hydrological and subsurface structure regimes? We address this question using a hillslope reactive transport model calibrated using soil CO₂ and water chemistry data from Fitch, a temperate forest at the ecotone boundary of the Eastern temperate forest and mid‐continent grasslands in Kansas, USA. Model results show that droughts (discharge at 0.08 mm/day) promoted deeper flow paths, longer water transit time, carbonate precipitation, and mineralization of organic carbon (OC) into inorganic carbon (IC) (∼98% of OC). Of the IC produced, ∼86% was emitted upward as CO₂ gas and ∼14% was exported laterally as DIC into the stream. Storms (8.0 mm/day) led to carbonate dissolution but reduced OC mineralization (∼88% of OC) and promoted DOC production (∼12% of OC) and lateral fluxes of IC (∼53% of produced IC). Differences in shallow‐versus‐deep permeability contrasts led to smaller difference (2 producers and vertical carbon transporters under dry conditions, and as active DOC producers and lateral carbon transporter under wet conditions.

The North American Monsoon season (June–September) in the Sonoran Desert brings thunderstorms and heavy rainfall. These rains bring cooler temperature and account for roughly half of the annual precipitation making them important for biogeochemical processes. The intensity of the monsoon rains also increase flooding in urban areas and rely on green infrastructure (GI) stormwater management techniques such as water harvesting and urban rain gardens to capture runoff. The combination of increased water availability during the monsoon and water management provide a broad moisture regime for testing responses in microbial metabolism to natural and managed soil moisture pulses in drylands. Soil microbes rely on atmospheric hydrogen (H₂) as an important energy source in arid and semiarid landscapes with low soil moisture and carbon availability. Unlike mesic ecosystems, transient water availability in arid and semiarid ecosystems has been identified as a key limiting driver of microbe-mediated H₂ uptake. We measured soil H₂ uptake in rain gardens exposed to three commonly used water harvesting practices during the monsoon season in Tucson AZ, USA. In situ static chamber measurements were used to calculate H₂ uptake in each of the three water harvesting treatments passive (stormwater runoff), active (stored rooftop runoff), and greywater (used laundry water) compared to an unaltered control treatment to assess the effects of water management practices on soil microbial activity. In addition, soils were collected from each treatment and brought to the lab for an incubation experiment manipulating the soil moisture to three levels capturing the range observed from field samples. H₂ fluxes from all treatments ranged between −0.72 nmol m⁻² s⁻¹ and −3.98 nmol m⁻² s⁻¹ over the monsoon season. Soil H₂ uptake in the greywater treatment was on average 53% greater than the other treatments during pre-monsoon, suggesting that the increased frequency and availability of water in the greywater treatment resulted in higher H₂ uptake during the dry season. H₂ uptake was significantly correlated with soil moisture (r = −0.393, p = 0.001, df = 62) and temperature (r = 0.345, p = 0.005, df = 62). Our findings suggest that GI managed residential soils can maintain low levels of H₂ uptake during dry periods, unlike unmanaged systems. The more continuous H₂ uptake associated with GI may help reduce the impacts of drought on H₂ cycling in semiarid urban ecosystems.

Although drylands cover >40% of the land surface, models of ecosystem gross primary productivity (GPP) generally have been designed for mesic temperate ecosystems. Arguably, GPP models often lack a good representation of vegetation phenology, particularly not estimating the ecosystem effects of the prolonged foliage senescence which may be common in drylands. To estimate daily GPP for a water‐limited Mediterranean shrubland, we propose a simple framework (GPP_mod) using light use efficiency, a spectral vegetation index derived from digital cameras, and five meteorological variables, including an index of functional senescence of foliage (i.e., heat degree‐days). We tested the model with different combinations of meteorological variables but without senescence, using 1 year's data. The best formulation showed good agreement with GPP derived from eddy covariance (GPP_EC; r² = 0.53, RMSE = 0.77). However, including the foliage senescence parameter significantly improved model performance (r² = 0.74, RMSE = 0.49), especially during the fall season. In the following year, we validated the parameters: The overall GPP_mod and GPP_EC comparison yielded an r² = 0.78. We postulate that models that mainly rely on meteorological variables or greenness indices could yield an overestimation of annual GPP between 24% and 90%, while models including the foliage senescence parameter reduced that bias by 10% to 34%. Our results highlight the importance of incorporating the phenology of foliage senescence in models regarding productivity in drylands or dry sclerophyll ecosystems.