Abstract. Making accurate estimations of daily and annual Rs fluxes is key for understanding the carbon cycle process and projecting effects of climate change. In this study we used high-frequency sampling (24 measurements per day) of Rs in a temperate rainforest during 1 year, with the objective of answering the questions of when and how often measurements should be made to obtain accurate estimations of daily and annual Rs. We randomly selected data to simulate samplings of 1, 2, 4 or 6 measurements per day (distributed either during the whole day or only during daytime), combined with 4, 6, 12, 26 or 52 measurements per year. Based on the comparison of partial-data series with the full-data series, we estimated the performance of different partial sampling strategies based on bias, precision and accuracy. In the case of annual Rs estimation, we compared the performance of interpolation vs. using non-linear modelling based on soil temperature. The results show that, under our study conditions, sampling twice a day was enough to accurately estimate daily Rs (RMSE  <  10 % of average daily flux), even if both measurements were done during daytime. The highest reduction in RMSE for the estimation of annual Rs was achieved when increasing from four to six measurements per year, but reductions were still relevant when further increasing the frequency of sampling. We found that increasing the number of field campaigns was more effective than increasing the number of measurements per day, provided a minimum of two measurements per day was used. Including night-time measurements significantly reduced the bias and was relevant in reducing the number of field campaigns when a lower level of acceptable error (RMSE  <  5 %) was established. Using non-linear modelling instead of linear interpolation did improve the estimation of annual Rs, but not as expected. In conclusion, given that most of the studies of Rs use manual sampling techniques and apply only one measurement per day, we suggest performing an intensive sampling at the beginning of the study to determine minimum daily and annual frequencies of sampling.

2018. Carbon fluxes from a temperate rainforest site in southern South America reveal a very sensitive sink. Ecosphere 9(4):

No abstract

Background: Quillaja saponaria Mol., Cryptocarya alba Mol. Looser, and Lithraea caustica Molina Hook et Arn., are common sclerophyllous species in Mediterranean Central Chile. Mesophyll conductance, g m , may strongly limit photosynthesis in these semiarid environments. Results: Simultaneous measurements of gas exchange and chlorophyll fluorescence were carried out in 45 nursery plants from these species to determine diffusional and biochemical limitations to photosynthesis. Values of stomatal conductance, g s , were greater than those of mesophyll conductance, g m , while their ratio (g m /g s ) was not influenced by species being on average 0.47. Relative limitations posed by mesophyll conductance to photosynthesis, L m , (0.40 ± 0.02) were high compared to those imposed by stomata, L s (0.07 ± 0.01). The average CO 2 concentration in the intercellular air spaces (C i ) was 32 μmol mol −1 lower than in the atmosphere (C a ), while the average CO 2 concentration in the chloroplasts (C c ) was 131 μmol mol −1 lower than C i independent of species. Maximal rates of Rubisco carboxylation, V cmax , and maximal electron transport rates driving regeneration of RuBP, J max , ranged from 13 to 66 μmol CO 2 m −2 s −1 and from 33 to 148 μmol electrons m −2 s −1 , respectively, and compare well to averages for C 3 plants.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; Making accurate estimations of daily and annual &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;s&lt;/sub&gt; fluxes is key for understanding the carbon cycle process and projecting effects of climate change. In this study we used high-frequency sampling (24-measurements per day) of &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;s&lt;/sub&gt; in a temperate rainforest during one year, with the objective of answering the questions of when and how often measurements should be made to obtain accurate estimations of daily and annual &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;s&lt;/sub&gt;. In this aim, we randomly selected data to simulate samplings of 1, 2, 4 or 6 measurements per day (distributed either during the whole day or only during daytime) combined with 4, 6, 12, 26 or 52 measurements per year. Based on the comparison of partial-data series with the full-data series, we estimated the performance of different partial sampling strategies based on bias, precision and accuracy. In the case of annual &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;s&lt;/sub&gt; estimation, we compared the performance of interpolation vs. using non-linear modelling based on soil temperature. The results show that, under our study conditions, sampling twice a day was enough to accurately estimate daily &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;s&lt;/sub&gt; (RMSE&amp;#8201;&lt;&amp;#8201;10&amp;#8201;% of average daily flux), even if both measurements were done during daytime. The highest reduction in RMSE for the estimation of annual &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;s&lt;/sub&gt; was achieved when increasing from 4 to 6 measurements per year, but reductions were still relevant when further increasing the frequency of sampling. In conclusion, we found that increasing the number of field campaigns was more effective than increasing the number of measurements per day, provided a minimum of two measurements per day was used. Including nighttime measurements significantly reduced the bias and was relevant in reducing the number of field campaigns when a lower level of acceptable error (RMSE&amp;#8201;&lt;&amp;#8201;5&amp;#8201;%) was established. Using non-linear modelling instead of linear interpolation did improve the estimation of annual &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;s&lt;/sub&gt; but not as expected. Given that most of the studies of &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;s&lt;/sub&gt; use manual sampling techniques and apply only one measurement per day, we suggest making an intensive sampling at the beginning of the study to determine minimum daily and annual frequencies of sampling.&lt;/p&gt;