Biology subjects

Garayburu-Caruso, V.

Publications and source records attributed to Garayburu-Caruso, V..

5 recordsLinked to original sources

bioRxiv2025

Harnessing artificial intelligence to automate environmental predictions

Predicting heterogeneous and non-linear processes remains a fundamental challenge in Earth sciences. Here, we present an artificial intelligence (AI)-guided framework that iteratively combines predictive modeling with targeted field sampling to rapidly improve environmental predictions. We demonstrate our workflow by predicting oxygen consumption, a key process of stream metabolism, across the contiguous United States (CONUS). Our approach consisted of 18 iterative loops of measurements and models, combining distributed participatory field sampling, lab analysis, automated machine learning (ML) predictions, and error and distinctiveness analyses to autonomously guide the next sampling at optimal site locations. Through our approach, we increased the predictive power of sediment oxygen consumption across CONUS by over fifteenfold between the first and last iteration. Relative to our last sampling iteration, our first sampling missed sites with high rates and underestimated median oxygen consumption rates by 68%. In addition to identifying areas of high oxygen consumption rates, iterations enabled refinement of laboratory and data handling methods, and engagement with a broad community of field researchers. We conclude that AI-guided iterative loops between targeted sampling and predictive modeling are a powerful and efficient approach for improving predictions of heterogeneous environmental processes.

ecology↗

bioRxiv2024

Sediment-associated processes drive spatial variation in ecosystem respiration in the Yakima River basin

Hyporheic zones (HZ) can contribute substantially to total stream ecosystem respiration (ERtot). HZ-focused process-based models may, therefore, effectively predict ERtot across sites, yet this remains untested under variable environmental conditions. Here we evaluate whether spatial variation in HZ respiration predicted via a process-based model explains spatial variation in field-estimates of ERtot across 33 sites in the Yakima River basin in Washington State, USA. We found that HZ respiration predictions did not explain spatial variation in field estimates of ERtot. To investigate further, we partitioned ERtot contributions into water column respiration (ERwc) and sediment-associated respiration (ERsed). ERsed contributed >50% of ERtot at 88% of sites, though relative contributions varied substantially. Despite this dominance, modeled HZ respiration explained neither spatial variation in ERtot nor in ERsed, suggesting that the HZ model alone does not capture the drivers of sediment-associated respiration across these sites. Instead, ERsed spatial variation was primarily explained by gross primary production, stream slope, velocity, and total dissolved nitrogen rather than median grain size, a primary control of HZ respiration predicted by the process-based model. Consistent with recent studies, our results indicate that improving basin-scale ERtot predictions requires integrating hydrologic and biogeochemical processes across hyporheic, benthic, and water column zones.

ecology↗

bioRxiv2021

Implications of sample treatment on characterization of the riverine environmental metabolome

High-resolution mass spectrometry techniques are widely used in the environmental sciences to characterize natural organic matter and, when utilizing these instruments, researchers must make multiple decisions regarding sample pre-treatment and the instrument ionization mode. To identify how these choices alter organic matter characterization and resulting conclusions, we analyzed a collection of 17 riverine samples from East River, CO (USA) under four PPL-based Solid Phase Extraction (SPE) treatment and electrospray ionization polarity (e.g., positive and negative) combinations: SPE (+), SPE (-), non-SPE (-), and non-SPE (+). The greatest number of formula assignments were achieved with SPE-treated samples due to the removal of compounds that could interfere with ionization. Furthermore, the SPE (-) treatment captured the most formulas across the widest chemical compound diversity. In addition to a reduced number of assigned formulas, the non-SPE datasets resulted in altered thermodynamic interpretations that could cascade into incomplete assumptions about the availability of organic matter pools for microbial heterotrophic respiration. Thus, we infer that the SPE (-) treatment is the best single method for characterizing environmental organic matter pools unless the focus is on lipid-like compounds, in which case we recommend a combination of SPE (-) and SPE (+) to adequately characterize these molecules. SynopsisWe provide data-driven sample treatment and ionization mode recommendations to researchers who aim to use high-resolution mass spectrometry to characterize environmental organic matter. Abstract Art O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

ecology↗

bioRxiv2020

Representing Organic Matter Thermodynamics in Biogeochemical Reactions via Substrate-Explicit Modeling

Predictive biogeochemical modeling requires data-model integration that enables explicit representation of the sophisticated roles of microbial processes that transform substrates. Data from high-resolution organic matter (OM) characterization are increasingly available and can serve as a critical resource for this purpose, but their incorporation into biogeochemical models is often prohibited due to an over-simplified description of reaction networks. To fill this gap, we proposed a new concept of biogeochemical modeling--termed substrate-explicit modeling--that enables parameterizing OM-specific oxidative degradation pathways and reaction rates based on the thermodynamic properties of OM pools. The resulting kinetic models are characterized by only two parameters regardless of the complexity of OM profiles, which can greatly facilitate the integration with reactive transport models for ecosystem simulations by alleviating the difficulty in parameter identification. For every detected organic molecule in a given sample, our approach provides a systematic way to formulate reaction kinetics from chemical formula, which enables the evaluation of the impact of OM character on biogeochemical processes across conditions. In a case study of two sites with distinct OM thermodynamics, our method not only predicted oxidative degradation to be primarily driven by thermodynamic efficiency of OM consistent with experimental rate measurements, but also revealed previously unknown critically important aspects of biogeochemical reactions, including their condition-specific response to carbon and/or oxygen limitations. Lastly, we showed that the proposed substrate-explicit modeling approach can be synergistically combined with enzyme-explicit approach to provide improved predictions. This result led us to present integrative biogeochemical modeling as a unifying framework that can ideally describe the dynamic interplay among microbes, enzymes, and substrates to address advanced questions and hypotheses in future studies. Altogether, the new modeling concept we propose in this work provides a foundational platform for unprecedented predictions of biogeochemical and ecosystem dynamics through enhanced integration with diverse experimental data and extant modeling approaches.

systems biology↗

bioRxiv2020

Carbon limitation leads to thermodynamic regulation of aerobic metabolism

Organic matter (OM) metabolism in freshwater ecosystems is a critical source of uncertainty in global biogeochemical cycles, yet aquatic OM cycling remains poorly understood. Here, we present the first work to explicitly test OM thermodynamics as a key regulator of aerobic respiration, challenging long-held beliefs that organic carbon and oxygen concentrations are the primary determinants of respiration rates. We pair controlled microcosm experiments with ultrahigh-resolution OM characterization to demonstrate a clear relationship between OM thermodynamic favorability and aerobic respiration under carbon limitation. We also demonstrate a shift in the regulation of aerobic respiration from OM thermodynamics to nitrogen content when carbon is in excess, highlighting a central role for OM thermodynamics in aquatic biogeochemical cycling particularly in carbon-limited ecosystems. Our work therefore illuminates a structural gap in aquatic biogeochemical models and presents a new paradigm in which OM thermodynamics and nitrogen content interactively govern aerobic respiration.

ecology↗