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Summary Steady‐state photosyntheticCO2responses (A/Cicurves) are used to assess environmental responses of photosynthetic traits and to predict future vegetative carbon uptake through modeling. The recent development of rapidA/Cicurves (RACiRs) permits faster assessment of these traits by continuously changing [CO2] around the leaf, and may reveal additional photosynthetic properties beyond what is practical or possible with steady‐state methods.Gas exchange necessarily incorporates photosynthesis and (photo)respiration. Each process was expected to respond on different timescales due to differences in metabolite compartmentation, biochemistry and diffusive pathways. We hypothesized that metabolic lags in photorespiration relative to photosynthesis/respiration andCO2diffusional limitations can be detected by varying the rate of change in [CO2] duringRACiR assays. We tested these hypotheses through modeling and experiments at ambient and 2% oxygen.Our data show that photorespiratory delays cause offsets in predictedCO2compensation points that are dependent on the rate of change in [CO2]. Diffusional limitations may reduce the rate of change in chloroplastic [CO2], causing a reduction in apparentRACiR slopes under highCO2ramp rates.MultirateRACiRs may prove useful in assessing diffusional limitations to gas exchange and photorespiratory rates.
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bayesTPC: Bayesian inference for thermal performance curves in R
Abstract Reliable predictions of ectotherm responses to climatic warming are important because many of these organisms perform important roles that can directly impact human society.Thermal performance curves (TPCs) provide useful information on the physiological constraints that limit the capacity of these temperature‐sensitive organisms to exist and grow.NLS pipelines for fitting TPCs are widely available, but these approaches rely on assumptions that can yield unreliable parameter estimates.We presentbayesTPC, anRpackage for fitting TPCs to trait responses using thenimblelanguage and machinery as the underlying engine for Markov Chain Monte Carlo.bayesTPCaims to support the adoption of Bayesian approaches in thermal physiology, and promote TPC fitting that adequately quantifies uncertainty.
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- PAR ID:
- 10577295
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 16
- Issue:
- 4
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 687-697
- Size(s):
- p. 687-697
- Sponsoring Org:
- National Science Foundation
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