Emerging constraints of photosynthesis (including chlorophyll fluorescence), respiration and transpiration at ecosystem to global scales
Gross photosynthetic CO2 uptake is the single largest component of the global carbon cycle and a crucial variable for monitoring and understanding global biogeochemical cycles and fundamental ecosystem services. Nowadays routine measurements of the net biosphere-atmosphere CO2 exchange are conducted at the ecosystem scale in a large variety of ecosystem types across the globe. Gross photosynthetic and ecosystem respiratory fluxes are then typically inferred from the net CO2 exchange and used for benchmarking of terrestrial biosphere models or as backbones for upscaling exercises. Uncertainty in the responses of photosynthesis and respiration to the climate and environmental conditions is a major source of uncertainty in predictions of ecosystem-atmosphere feedbacks under climate change. On the other hand, transpiration estimates both at ecosystem to global scales are highly uncertain with estimates ranging from 20 to 90 % of total evapotranspiration. The most important bottleneck to narrow down the uncertainty in transpiration estimates is the fact that direct measurements of transpiration are uncertain and techniques like eddy covariance measure only the total evapotranspiration.
During the last decade, technological developments in field spectroscopy, including remote and proximal sensing of sun-induced fluorescence, as well as in isotope flux measurements and quantum cascade lasers have enabled alternative approaches for constraining ecosystem-scale photosynthesis, respiration and transpiration. On the other hand, a variety of approaches have been developed to directly assess the gross fluxes of CO2 and transpiration by using both process based and empirical models, and machine learning techniques.
In this session, we aim at reviewing recent progress made with novel approaches of constraining ecosystem gross photosynthesis, respiration and transpiration and at discussing their weaknesses and future steps required to reduce the uncertainty of present-day estimates. To this end, we are seeking contributions that use emerging constrains to improve the ability to quantify respiration and photosynthesis processes, transpiration and water use efficiency, at scales from leaf to ecosystem and global. Particularly welcome are studies reporting advancements and new developments in CO2 and evapotranspiration flux partitioning from eddy covariance data, the use of carbonyl sulfide, stable isotopes approaches and sun-induced fluorescence.
The increasing frequency and amplitude of extreme climatic events may decline ecosystem productivity and disturb the global carbon cycle. Recent and upcoming advances in remote sensing technology, such as hyperspectral reflectance and chlorophyll sun-induced fluorescence (SIF) missions, are boosting research on the monitoring of vegetation responses to heat and drought stress. To understand the impacts of stress on vegetation and the corresponding optical signals that can be sensed from space, it is essential to monitor the continuous dynamics of ecosystem carbon and water fluxes and optical signal responses to environmental changes on the ground.
We collected a unique dataset of synergistic observations of remote sensing and carbon-water flux measurements from multiple field sites of different vegetation types. This dataset elucidated variations of physiology, fluxes, and optical signals, including SIF and spectral vegetation indices. For example, in light-sensitive beech forests in Germany, we found that photoprotection is generally active. Gross primary productivity (GPP) and surface conductance (Gs) clearly decreased when heatwaves occurred. On the contrary, chlorophyll content changed only marginally, which was reflected by minimal changes in the chlorophyll index at red edge (CIred). The photochemical reflectance index (PRI), related to non-photochemical quenching (NPQ) via xanthophyll´s cycle, was sensitive to flash heat stress and related to vapor pressure deficit (VPD). But for longer and lower intensity of stress in another event, PRI only changed marginally. SIF was more sensitive to incident radiation (PPFD), but did not decrease with increasing air temperature (Ta) and VPD. However, SIF yield (the ratio of SIF and absorbed photosynthetically active radiation) decreased significantly during the heatwave. In contrast, in the light and heat-tolerant rice paddy in China, we observed that vegetation did not show negative effects at the early growing stage (nutritive growth) during an extreme heatwave (Ta>35 ̊C). Due to the high relative humidity (from evaporated water), VPD remained low despite the high temperatures. GPP increased slightly accompanied by a small decrease of Gs as VPD slightly increased. SIF, SIF yield, and PRI noticeably increased with increasing CIred, indicating that heat might have accelerated the physiology rather than stressed plants in the rice paddy, which could be due to an overall higher temperature optimum compared to the European beach forest.
Our results demonstrate that water supply shortage combined with heat waves can cause immediate down-regulation of photosynthesis and that the new remote sensing missions could detect this vegetation response. However, if the water supply is abundant during the heatwave, responses of both physiological and remote sensing parameters may not be sensitive to heat stress. Due to species and ecosystem differences in terms of heat resistance, the global response of vegetation remains hard to predict indicating the need to remotely monitor these responses in order to improve process-based models. The outcomes of this work will possibly provide new insights on the utilization of novel optical remote sensing information for vegetation monitoring during extreme events.
How to cite:
Zhang, Q., El-Madany, T., Pacheco-Labrador, J., Biriukova, K., Brümmer, C., Buchmann, N., Damm, A., Dechant, B., Delorme, J.-P., Ju, W., Paul-Limoges, E., Rossini, M., Ryu, Y., Schrader, F., Wohlfahrt, G., Yakir, D., Zhang, X., Zhang, Y., and Migliavacca, M.: Monitoring vegetation responses to heatwaves using novel remote sensing techniques, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10448, https://doi.org/10.5194/egusphere-egu22-10448, 2022.
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Although monitoring the health of the crops grown in our fields is almost as old as plant production itself, it is only now that technological development (remote and proximal sensing) enables us to look at the links between different processes. In Kartal, Hungary, measurements of the physiological performance of plants were carried out in crop fields in two consecutive years on five clearly discernible spots; in May 2020 on winter wheat and in June 2021 on sunflower. The five patches were selected on the basis of their distinct NDVI values from satellite imagery and/or canopy closure and identified using GPS coordinates. The following parameters were measured or derived: soil moisture, leaf area index (LAI), sun-induced fluorescence (SIF), vegetation indices (FCVI, PRI, MTCI, WI, NDRE), plant water, chlorophyll, and carotenoid content. To quantify the latter, three leaves from the plants on each patch were processed.
Our aim was to look for differences in the function and condition (stress state) of plants in patches that were markedly different. Field observations reflected the results of our instrumental measurements. For example, the average LAI values of the patch with the apparently tallest and greenest, i.e. most vital wheat plants, were more than twice as high as those obtained from patches that were visually either nutrient deficient or stressed (low and yellow plants). Similar conclusions could be drawn from the PRI values. The lowest value (-0.063) was derived from the patch with yellowish plants, while the highest (0.0235) was in the greenest patch. Principal component analysis (PCA) of the variables reflected slight differences between the two observed stands as well because yellowish, stressed vegetations were not found in the sunflower stand, even though the canopy closure was markedly different. PCA loadings for SIF B (at 685 nm in the O2 B-band) oriented in the direction of scores from the yellow plant patches, while SIF A (at 761 nm in the O2 A-band) oriented in the direction of scores from the healthy, green plant patches in the wheat stand, reflecting potential reabsorption by chlorophylls in healthy, larger canopy patches. Loadings of the two SIF metrics did not differ in orientation in the sunflower stand, both of them oriented in the direction of smaller canopy closure and smaller physiological activity plots, that is, plots characterized with a larger share of the excitation energy loss as fluorescent light emission.
Moreover, the SIF values measured with the Piccolo Doppio spectrometer were compared with the values obtained by using radiative transfer model simulations with the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. By comparing our data with a simplified test condition, we can draw conclusions about the sensitivity of the model for a set of input parameters.
How to cite:
Mészáros, Á., Fóti, S., Balogh, J., Pintér, K., Nagy, Z., and Bene, K.: Characteristics of sun-induced fluorescence in monocot and dicot crop patches with different NDVI and canopy closure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11051, https://doi.org/10.5194/egusphere-egu22-11051, 2022.
Solar energy absorbed by chlorophyll molecules of plants is either used for the carboxylation of CO2 (i.e. photosynthesis), dissipated (in a regulated and unregulated fashion) as heat or re-emitted at higher wavelengths as fluorescence. Proximal or remote sensing measurements of the so-called sun-induced chlorophyll fluorescence (SIF) are thus thought to offer a non-obtrusive approach for quantifying a key ecosystem carbon cycle component and metric of plant health, gross primary productivity (GPP).
In contrast to SIF, which is a relatively new approach, active measurements of chlorophyll fluorescence using the pulse amplitude modulation (PAM) technology represent a well-established standard approach. Up to date, few studies have combined active and passive (i.e. SIF) chlorophyll fluorescence measurements and the link between active chlorophyll fluorescence metrics and SIF is thus poorly understood, hampering progress in the physiological interpretation of the SIF signal.
The overarching goal of this study is to improve our physiological understanding of the SIF signal. To this end we jointly quantified ecosystem-scale passive (i.e. SIF) and leaf-scale active chlorophyll fluorescence. Measurements were conducted during the vegetation period (March to November 2021) at a mountain Scots Pine (Pinus sylvestris L.) forest close to the village of Obermieming (Austria).
Two sets of automated PAM fluorometers (MoniPAM, Walz, Germany) were installed from a walk-up tower to measure active leaf-scale chlorophyll fluorescence (ChlF) on branches in the sunlit top of the canopy and in the more shaded sub-canopy zone of the forest. Additionally, SIF at the canopy scale was measured via a high-resolution spectrometer system (FloX, JB Hyperspectral Devices, Germany) and the eddy covariance method was used to determine net ecosystem CO2 exchange.
Preliminary results show an increase of SIF yield (SIF/aPAR) with warming temperature throughout summer followed by a steady decrease starting in autumn. A major heatwave event in June resulted in a decrease of the SIF and PSII yields, leading to an increase in NPQ. An overall decrease of PSII efficiency for shaded leaves compared to sun-exposed ones was detected, with significant reductions throughout decreasingly colder days in spring and autumn for both groups. Shaded leaves responded with an overall higher investment into NPQ mechanisms. In contrast, an increased yield of ﬂuorescence and constitutive thermal energy dissipation (f,D) in sun-exposed leaves could indicate higher heat dissipation for the exposed group.
Further analyses will use active chlorophyll fluorescence metrics for studying seasonal variability in SIF and SIF yield and their relationship to ecosystem-scale GPP.
How to cite:
Schwarz, M., Hammerle, A., Julitta, T., Migliavacca, M., and Wohlfahrt, G.: Seasonal dynamics of active and passive chlorophyll fluorescence in a mountain Scots Pine (Pinus sylvestris L.) forest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11439, https://doi.org/10.5194/egusphere-egu22-11439, 2022.
The assessment of large-scale vegetation functioning is essential to improve cropland productivity and monitor natural ecosystem health. The development of remote sensing (RS) technologies over decades made such assessments possible from field- to global-scale. Nevertheless, commonly used reflectance-based RS methods are often not sensitive enough to timely inform preventive or corrective actions. Recent advances on the RS of solar-induced chlorophyll fluorescence (SIF) have opened opportunities for novel approaches of earlier stress detection since SIF was found to be closely linked to photosynthesis. The forthcoming FLuorescence EXplorer (FLEX) satellite mission of the European Space Agency (ESA, to be launched) will offer timely non-aggregated global-scale SIF data at 300 m spatial resolution. Such pixel size, even though unique and accurate enough to monitor processes at biome level, may not be suitable to assess field scale processes. Therefore, the development of methodologies to downscale satellite-SIF information is currently of utmost interest since allowing to increase the spatial resolution of origin observations. A first step to comprehend the characteristics that possible approaches must meet is to understand the magnitude of the spatial variability within a coarse pixel footprint across representative vegetation types. Our study consequently aims to understand the spatial variability within the footprint of a FLEX pixel. We particularly analyze the spatial dynamics of SIF via the near infrared reflectance of vegetation (NIRv) data derived from Sentinel 2, World View- and Geo Eye- (10.0 m, 0.30, 0.40 m pixel-1, respectively) that was suggested as proxy for SIF in absence of environmental stress. With Sentinel 2 based NIRv we focus on four ecosystems, including small and large scale agriculture, pampa and savannah, with World View- and Geo Eye based NIRv, we investigate rain and coniferous forests. The very high resolution of World View- and Geo Eye was required to compute the variograms of forests since they were affected by a nugget effect when using Sentinel-2 images. Investigated ecosystems represent the most abundant vegetation types that the FLEX mission will cover. We also assessed the relation between the spatial dependencies (approximated by the lag of calculated semi-variograms) and the average object size in all the ecosystems. We found largest spatial dependencies (400-600 m) in large-scale agriculture, pampa and savannah and contrasting lower (<10 m) in forests. Spatial dependencies of small-scale agricultural scenes were in a middle position with approximately 100 m. Moreover, the spatial dependencies were found to be significantly (p = 0.023) linked to the average object size of the ecosystems. This demonstrates the importance of flexible downscaling methods, e.g. in a fractals-based direction (Quiros et al., in press).
How to cite:
Quiros-Vargas, J., Siegmann, B., Damm, A., Krieger, V., Muller, O., and Rascher, U.: Spatial dependency of Solar-induced Chlorophyll Fluorescence (SIF)-emitting objects in the footprint of a FLuorescence EXplorer (FLEX) pixel: a SIF-downscaling perspective, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12671, https://doi.org/10.5194/egusphere-egu22-12671, 2022.
When it comes to monitoring the health status of ecosystems, satellite-based remote sensing approaches hit a sweet spot in terms of global spatial coverage and temporal resolution. Conventional optical remote sensing approaches, however, offer limited potential for the early detection of ecosystem stress, as changes in ecosystem structure and function often need to be substantial in order to be detectable from the reflectance in the visible and near-infrared range of the energy spectrum.
Satellite-based remote sensing of sun-induced chlorophyll fluorescence (SIF) offers much greater potential to that end. Chlorophyll fluorescence is generated when solar energy absorbed by chlorophyll inside plant leaves is not used for photosynthesis or dissipated as heat, but is instead emitted at a slightly higher wavelength. Chlorophyll fluorescence thus results from fine-tuned changes in chlorophyll energy partitioning and SIF provides a highly sensitive optical signal, which allows the early detection of plant stress before symptoms become apparent in classical optical remote sensing indices.
Our previous work, however, has shown that in order to correctly diagnose whether or not plants are exposed to stress, SIF needs to be quantified jointly with the energy that is dissipated as heat and that this process can be accurately quantified on the basis of reflectance changes around the green peak, exploited by the so-called photochemical reflectance index (PRI). SIF data have become available from a few satellite platforms during the past couple of years, however their spatio-temporal resolution and signal-to-noise ratio is still unsatisfactory. A major step forward in data quality is expected from the upcoming ESA Earth Explorer mission FLEX, scheduled to launch in mid-2023.
The overarching goal of the AustroSIF project is to make present and future satellite-based sun-induced chlorophyll fluorescence measurements a sensitive and reliable means for the early detection of ecosystem stress by combining remotely sensed SIF and PRI. To that end we propose to simulate satellite measurements using ground-based, proximal sensing of active and passive chlorophyll fluorescence and hyperspectral reflectance. These measurements will be conducted in the field covering a wide range of ecosystems typical for Austria. Available satellite products will be used to test this approach at larger spatial and temporal scales. Process-based models will be used to disentangle the underlying drivers.
This contribution will detail the project structure and approach and provide first, preliminary results obtained during the first project year.
How to cite:
Wohlfahrt, G., Hammerle, A., Duveiller, G., and Migliavacca, M.: Early stress detection in Austrian ecosystems with sun-induced chlorophyll fluorescence (AustroSIF), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4223, https://doi.org/10.5194/egusphere-egu22-4223, 2022.
Reducing water losses in agriculture needs a solid understanding of when evaporation (E) losses occur and how much water is used through crop transpiration (T). Partitioning ecosystem T is however challenging, and even more so when it comes to short-statured crops, where many standard methods cannot be applied. In this study, we combined biometeorological measurements with a SPA-Crop model to estimate T and E at a Swiss cropland over two crop seasons with winter cereals. We compared our results with two recent data-driven approaches: the Transpiration Estimation Algorithm (TEA) and the underlying Water Use Efficiency (uWUE).
Our results showed that the available energy reaching the soil through the crop canopy can highly vary depending on growth and climatic conditions. Despite large differences in the productivity of both years, the T to evapotranspiration (ET) ratio had relatively similar seasonal and diurnal dynamics, and averaged to 0.72 and 0.73 for both crop seasons. Our measurements combined with a SPA-Crop model provided T estimates similar to the TEA method, while the uWUE method underestimated T even when the soil and leaves were dry. T was strongly related to the leaf area index, but additionally varying due to climatic conditions. The most important climatic drivers controlling T were found to be the photosynthetic photon flux density (R2=0.84 and 0.87), and vapor pressure deficit (R2=0.86 and 0.70). Our results suggest that site-specific studies can help establish T/ET ratios, as well as identify dominant climatic drivers, which could then be used to partition T from reliable ET measurements.
How to cite:
Paul-Limoges, E., Revill, A., Maier, R., Buchmann, N., and Damm, A.: Insights for the Partitioning of Ecosystem Evaporation and Transpiration in Short-Statured Croplands , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3702, https://doi.org/10.5194/egusphere-egu22-3702, 2022.
For the first time, we present a comparison of atmospheric transport models for Carbonyl Sulfide (COS), a promising photosynthesis tracer, within the framework of the ongoing Atmospheric Tracer Transport Model Intercomparison Project (TransCom). Seven atmospheric transport models participated in the inter-comparison experiment and provided simulations of COS mixing ratios in the troposphere over a 10-year period (2010–2019), using prescribed state of the art surface fluxes for each component of the atmospheric COS budget (i.e., linked to vegetation, soil, ocean, fire and industry). The main goals of TransCom-COS are (a) to investigate the roles of the transport uncertainty and emission distribution in simulating the spatio-temporal variability of COS mixing ratio in the troposphere and (b) to assess the sensitivity of simulated tropospheric COS mixing ratio to the seasonal variability of the COS terrestrial fluxes. Models were run with the same prior emissions and without chemistry to isolate differences due to transport. Two COS flux scenarios were compared: one using a biospheric flux with a monthly time resolution and the other one using a biospheric flux with a tri-hourly time resolution. In addition, we investigated the sensitivity of the simulated concentrations to different biospheric fluxes and to indirect oceanic emissions through DMS. The modelled COS mixing ratios were assessed against observations from in situ surface stations, aircraft and ground based FTIR stations.
Using the state of the art surface fluxes for each component of the COS budget, preliminary results indicate that all transport models fail to capture the surface latitudinal distribution of COS. The COS mixing ratios are underestimated by at least 50 ppt in the tropics, pointing to a missing tropical source. In summer, the mixing ratios are overestimated by at least 50 ppt above 40N, pointing to a likely missing sink in the high northern latitudes during this period. The surface variability of COS mixing ratios is more sensitive to transport models than to a change in biospheric fluxes (two estimates based on different global Land Surface Models). Regarding the seasonal mean latitudinal profiles, the model spread is greater than 60 ppt above 40N in boreal summer and in the vicinity of anthropogenic sources. Regarding the seasonal amplitude, the model spread reaches 50 ppt at 6 sites out of 15, compared to an observed seasonal amplitude of 100 ppt. All models simulated a too late minimum by 2 to 3 months at northern sites ALT, BRW owing to likely errors in the seasonal cycle in the ocean emissions. Finally, the temporal resolution of the biospheric fluxes (monthly versus tri-hourly) has a small impact (less than 20 ppt) on the mean seasonal cycle at stations from the NOAA network.
How to cite:
Remaud, M., Abadie, C., Belviso, S., Cartwright, M., Cho, A., Kooijmans, L., Krol, M., Lennartz, S., Ma, J., Maignan, F., Niwa, Y., Palm, M., Patra, P., Peylin, P., and Roedenbeck, C.: Simulation of atmospheric COS mixing ratio : Evaluating the impact of transport and emission distribution on COS tropospheric variability using ground-based, aircraft, and FTIR data , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9394, https://doi.org/10.5194/egusphere-egu22-9394, 2022.
In search for a constraint on the gross primary productivity (GPP) on ecosystem level, the sulfur containing gas carbonyl sulfide (COS) shows great promise. COS takes a very similar route into the leaf as carbon dioxide (CO2), that is through the leaf boundary layer, the stomata and the mesophyll until COS is fully catalyzed in a one-way reaction by the enzyme carbonic anhydrase (CA), whereas CO2 reaches its endpoint at RuBisCO within the chloroplast stroma. For the calculation of the GPP based on COS fluxes, a beforehand knowledge about the ratio of the deposition velocities of COS to CO2, also called leaf relative uptake (LRU), is needed. Differences in the LRU between plants and under different environmental conditions might hinder a straightforward usage of this approach.
To investigate the LRU and its dependencies on the involved conductances, we conducted eddy covariance measurements of COS, CO2 and H2O at an agricultural field in Ariis, Italy. At this field, 2 different varieties of soybean were planted in adjacent plots. One was the commercial variety Eiko, to which we will refer to as the green “wild type” (WT). The other was a chlorophyll deficient golden/yellow variety called MinnGold (MG). Due to a lack of rain, all plots were irrigated 2 and 3 weeks into our 4 week campaign.
Despite having a reduced chlorophyll content, MG was able to match and even exceeded the rate of photosynthesis of the WT during our observation period. While the GPP was similar for both varieties during the first week, we observed a higher decline for WT in week 2 due to a naturally occurring drought. Even after the irrigation of both plots, the GPP of MG recovered faster. We also observed considerably higher COS uptake by MG during the whole campaign. The resulting LRU under high light conditions was also consequently higher for MG (1.41) than for WT (0.97). We calculated the aerodynamic, boundary layer, stomatal and internal conductance for both varieties and grouped the values into 4 phases: pre-drought, drought, rewetting and recovery. Based on these values and a linear perturbation analysis, we identified the internal conductance as the largest driver for the different LRUs between the two varieties.
Our results indicate that the stomatal conductance is not the only controlling factor for the LRU and that the mesophyll conductance can’t be neglected. We also show, the LRU response to drought differs between plants, even at the level of varieties.
How to cite:
Spielmann, F. M., Kitz, F., Hammerle, A., Gerdel, K., Alberti, G., Peressotti, A., Delle Vedove, G., and Wohlfahrt, G.: The leaf-internal conductance to COS – a party crasher for the leaf relative uptake rate?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7166, https://doi.org/10.5194/egusphere-egu22-7166, 2022.
Many model uncertainties results from parameter tuning to compensate for errors in model outputs. A number of studies have focused on the analysis of uncertainties in modelled gross primary production (GPP), particularly with regard to the representation of soil moisture stress. GPP is often overestimated by models during dry periods in water-limited regions, and this bias increases during drought events. Soil moisture stress functions are widely applied to correct this. However, soil moisture stress is not always the direct constraining factor on GPP, and the functions adopted by models do not correspond to accepted mechanisms. We have used eco-evolutionary optimality principles, via the so-called P model, to estimate carbon uptake at sites where leaf area index (LAI) was routinely measured. We used observational networks (including FLUXNET) and Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) data from satellites. By comparing modelled and observed GPP we determined whether there is a significant difference between model performance during the dry and wet seasons, or between energy- and water-limited sites. We found that the soil moisture stress function used in one version of the P models essentially compensates for uncertainties in fAPAR data from satellites, especially in grasslands and other areas subject to seasonal drought. This situation is problematic, since soil moisture is a driver or modulator of other ecosystem processes, including soil evaporation and runoff generation. A possible way forward involves implementing phenological components dependent on soil and atmospheric conditions. The new challenge this poses is to apply eco-evolutionary optimality principles to model the seasonal time course of LAI, which is often poorly simulated by complex ecosystem models.
How to cite:
Gan, W., Nóbrega, R., and Prentice, I. C.: Analysis of vegetation modelling uncertainties due to soil moisture stress during droughts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8303, https://doi.org/10.5194/egusphere-egu22-8303, 2022.
Bryophytes and lichens usually dominate the ground floor layer of boreal forests and tundra, and contribute up to 50% of ecosystem gross CO2 exchange (Bisbee et al. 2001; Goulden & Crill 1997). While Sphagnum spp. are the most important contributors in wetland C uptake, feathermosses and lichens play a significant role in drained habitats (Nilsson & Wardle 2005; O’Connell et al. 2003; Bjerke et al. 2013). Given their important ecological roles in such widespread biomes, it is surprising that still a few studies have attempted to understand the intrinsic factors that control moss-lichen cover carbon exchange dynamics specifically under ongoing climate change in high latitudes.
The aim of this work was to determine photoassimilation activity the widespread species of moss-lichen stratum during the growing season. The study has been conducted in Central Siberia near Zotino tall tower observatory (ZOTTO, 60 ° N, 89 ° E) in lichen- and feathermoss-dominated pine forests. The intensity of CO2 photoassimilation of ground vegetation dominants (Cladonia stellaris O., Cladonia rangiferina L., Cetraria islandica L., Pleurozium schreberi W. ex B., Hylocomium splendens H., Dicranum scoparium H.) was determined in situ by infrared gas analyzer Walz GFS-3000 (Heinz Walz GmbH, Effeltrich, Germany) during the most part of a growing season (from June to September).
Bryophytes demonstrated more intense photosynthetic activity throughout the growing season. From June to September, among the studied moss species, the highest values of photoassimilation were observed for P. schreberi, and the lowest for H. splendens. The maximum values were recorded in August for all studied species and amounted to 4.36 ± 0.13 μmol / m2 / s, and the lowest values were recorded in June to 1.4± 0.08 μmol / m2 / s . Among lichens, C. stellaris was the most photosynthetically active, and C. rangiferina showed the least CO2 photoassimilation rates. Moss-lichen layer dominants maintained relatively high photoassimilation activity throughout the growing season.
The research was funded by Krasnoyarsk Regional Fund of Science within the framework of the project № 2021 102007845 and RFBR, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project number 44-243003.
How to cite:
Polosukhina, D., Makhnykina, A., and Prokushkin, A.: Photoassimilation rates of sub-arctic moss and lichens species in pine ecosystems of the Central Siberia , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3346, https://doi.org/10.5194/egusphere-egu22-3346, 2022.
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