3 research outputs found
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Beyond ecosystem modeling: a roadmap to community cyberinfrastructure for ecological data‐model integration
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundationsof community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is key to meeting the pressing needs of science and society in the 21st century
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Enhancing crop growth in rooftop farms by repurposing CO2 from human respiration inside buildings
Peer reviewed: TrueAcknowledgements: We would like to thank Sidney Hare and Tony Pham for their assistance collecting samples. The entire community at the Boston University Academy, especially Paige Brewster, Travis Bain, and Cynthia Taylor, also were essential in carrying out this project. This content also appeared in the PhD thesis titled Enhancing plant growth and carbon harvesting for sustainable agriculture (Buckley, 2020).Integrating cities with the surrounding environment by incorporating green spaces in creative ways would help counter climate change. We propose a rooftop farm system called BIG GRO where air enriched with carbon dioxide (CO2) produced through respiration from indoor spaces is applied through existing ventilation systems to produce a fertilization effect and increased plant growth. CO2 measurements were taken inside 20 classrooms and at two exhaust vents on a rooftop at Boston University in Boston, MA. Exhausted air was directed toward spinach and corn and plant biomass and leaf number were analyzed. High concentrations of CO2 persisted inside classrooms and at rooftop exhaust vents in correlation with expected human occupancy. CO2 levels averaged 1,070 and 830 parts per million (ppm), reaching a maximum of 4,470 and 1,300 ppm CO2 indoors and at exhaust vents, respectively. The biomass of spinach grown next to exhaust air increased fourfold compared to plants grown next to a control fan applying atmospheric air. High wind speed from fans decreased growth by approximately twofold. The biomass of corn, a C4 plant, experienced a two to threefold increase, indicating that alternative environmental factors, such as temperature, likely contribute to growth enhancement. Enhancing growth in rooftop farms using indoor air would help increase yield and help crops survive harsh conditions, which would make their installation in cities more feasible.</jats:p
Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis
The leaf economic spectrum is a widely studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of plant functional types (PFTs). We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (specific leaf area [SLA], N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (dark respiration [Rd], maximum RuBisCo carboxylation rate [Vc,max], maximum electron transport rate [Jmax]) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, this covariance was an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.</p