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Light foraging by trees is a fundamental process shaping forest communities. In heterogeneous light environments this behavior is expressed as plasticity of tree growth and the development of structural asymmetries. We studied the relative influence of neighborhood structure and directional solar radiation on horizontal asymmetry of tree crowns in late‐successional high latitude (67–68°N) forests in northern Fennoscandia. We described crown asymmetries as crown vectors (i.e. horizontal vectors from stem center to crown center), which we obtained from canopy maps based on crown perimeter measurements in the field. To disentangle the influence of the two main determinants, inter‐tree competition and directionality of above‐canopy solar radiation at high latitudes, we applied circular statistical models, utilizing cylindrical distributions, to these data consisting of orientations and intensities of crown asymmetry. At the individual tree level, our model predicted crown asymmetry vectors from the current stand structure, and the predictions became better when the intensity of asymmetry (i.e. crown vector length) was higher. Competition was the main determinant of crown asymmetry for 2/3 of trees, and the model predictions improved when we incorporated the directionality of solar radiation. At the stand‐level, these asymmetries had resulted in a small increment of the projected canopy area and an increased regularity of spatial structure. Our circular statistical modelling approach provided a quantitative evaluation of the relative importance of directionality of solar radiation and neighborhood stand structure, showing how both of these factors play a role in formation of crown asymmetries in high latitude forests. This approach further demonstrated the applicability of circular statistical modeling in ecological studies where the response variable has both orientation and intensity.

Detecting vulnerability of humid tropical forests to multiple stressors

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 132:191-201 (1996) - doi:10.3354/meps132191 Planktonic primary production and microbial respiration measured by 14C assimilation and dissolved oxygen changes in coastal waters of the Antarctic Peninsula during austral summer: implications for carbon flux studies Arístegui J, Montero MF, Ballesteros S, Basterretxea G, van Lenning K Oxygen consumption and production and carbon fixation by micro-organisms were measured simultaneously in coastal surface waters near the Antarctic Peninsula. Although there was a good qualitative agreement between the oxygen and carbon measurements, total daily water-column integrated carbon incorporation measured by radiocarbon uptake in the particulate fraction underestimated net community production measured by the oxygen method by 29 to 54% (using a photosynthetic quotient of 1.5). Unaccounted-for exudation of dissolved organic carbon during the 14C uptake experiments may explain this discrepancy. Respiratory carbon losses by micro-organisms (largely phytoplankton) ranged between 10 and 50% of gross production, the highest values corresponding to the more productive stations. These estimates are, however, slightly conservative, since they refer to the upper 30 m of the water column, corresponding approximately to the euphotic zone in this region. Our results show that microbial respiration is an important part of the carbon flux of coastal Antarctic plankton. Unless it is considered in carbon flux models, the contribution of higher trophic levels to the carbon fluxes in marine food webs may be seriously overestimated. Primary production . Microbial respiration . Carbon and oxygen fluxes . Coastal Antarctic waters Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 132. Publication date: February 29, 1996 Print ISSN:0171-8630; Online ISSN:1616-1599 Copyright © 1996 Inter-Research.

Tropical forests account for approximately one-fourth of the global terrestrial carbon sink, playing an important role in the Earth’s carbon cycle. Importantly, mainland Southeast Asia has the densest vegetation surface but its ecohydrology is historically understudied due to the paucity of field observations and modelling studies. Leveraging on existing flux tower data, remote sensing products, and the mechanistic ecohydrological model T&C, we provide an enhanced understanding of carbon and water exchanges in mainland Southeast Asia. The T&C model is tested to reproduce various ecosystem types of Southeast Asia, including tropical evergreen forests, subtropical deciduous forests, savannas, rubber plantations, and rice fields. The flux tower data including gross primary productivity (GPP) and evapotranspiration (ET) along with remote sensing data of leaf area index and other vegetation indexes, allow us to better refine and constrain model simulations.  With the integration of data and model, we provide a comprehensive picture of spatiotemporal patterns and key drivers of carbon and water fluxes in mainland Southeast Asia. Our findings highlight a strong latitudinal gradient in carbon fluxes and ET associated with seasonality of rainfall as well as an important role of vapour pressure deficit (VPD) and soil moisture content with different responses in wet and dry years. Direct effects of temperature and precipitation are relatively smaller when compared to VPD and soil moisture in driving changes of carbon and water fluxes. These findings, combined with our model framework, pave the road to more accurate predictions of ecohydrological variables in the relatively understudied region of mainland Southeast Asia.

Leaf photosynthesis and respiration are two of the largest carbon fluxes between the atmosphere and biosphere. Although experiments examining the warming effects on photosynthetic and respiratory thermal acclimation have been widely conducted, the sensitivity of various ecosystem and vegetation types to warming remains uncertain. Here we conducted a meta-analysis on experimental observations of thermal acclimation worldwide. We found that the optimum temperature for photosynthetic rate (Topt) and the maximum rate of carboxylation of Rubisco (ToptV) in tropical forest plants increased by 0.51°C and 2.12°C per 1°C of warming, respectively. Similarly, Topt and the optimum temperature for maximum electron transport rate for RuBP regeneration (ToptJ) in temperate forest plants increased by 0.91°C and 0.15°C per 1°C of warming, respectively. However, reduced photosynthetic rates at optimum temperature (Aopt) were observed in tropical forest (17.2%) and grassland (16.5%) plants, indicating that they exhibited limited photosynthetic thermal acclimation to warming. Warming reduced respiration rate (R25) in boreal forest plants by 6.2%, suggesting that respiration can acclimate to warming. Photosynthesis and respiration of broadleaved deciduous trees may adapt to warming, as indicated by higher Aopt (7.5%) and Topt (1.08°C per 1°C of warming), but lower R25 (7.7%). We found limited photosynthetic thermal acclimation in needleleaved evergreen trees (-14.1%) and herbs (-16.3%), both associated with reduced Aopt. Respiration of needleleaved deciduous trees acclimated to warming (reduced R25 and temperature sensitivity of respiration (Q10)); however, broadleaved evergreen trees did not acclimate (increased R25). Plants in grasslands and herbaceous species displayed the weakest photosynthetic acclimation to warming, primarily due to the significant reductions in Aopt. Our global synthesis provides a comprehensive analysis of the divergent effects of warming on thermal acclimation across ecosystem and vegetation types, and provides a framework for modeling responses of vegetation carbon cycling to warming.

Conversion of a tropical forest into agroforest alters the fine root-related carbon flux to the soil

ECOSENSE is a Collaborative Research Centre (CRC) funded by the German Research Foundation DFG to develop and test novel environmental sensing techniques and apply them to enhance our understanding and modelling of water and carbon fluxes in forest ecosystems. Specifically, with a distributed sampling approach, ECOSENSE studies the impact of forest heterogeneity in space and time in response to hydro-climatic stresses (Werner et al. 2024). Core to the project is the ECOSENSE forest, located in the foothills of the Black Forest in Germany (48.2685 N, 7.8782 E). The forest primarily consists of Beech ( Fagus sylvatica ) and Douglas-fir ( Pseudotsuga menziesii ). Two 30 m canopy access towers, one in a F. sylvatica and the other in a P. menziesii plot, and a 46 m tall eddy-covariance tower, which exceeds the canopy height by 20 m, form the core infrastructure. The integrated tower-based eddy covariance measurements are complemented with in-situ and remote sensing sensor networks distributed throughout the forest. The sensor networks provide simultaneous data on soil, litter, subcanopy, tree and leaf conditions. Cameras, drones and LiDAR systems record spectral and morphometric properties from seconds to seasons. In this contribution, we highlight how tower-based eddy-covariance flux measurements, which spatially integrated carbon and water fluxes above the mixed forest, benefit from massively distributed sensor networks to account for identifying the horizontally and vertically heterogeneous processes at the soil, tree and leaf-level. This will allow for the detection and attribution of “hot spots” and “hot moments”. We show the potential of integrating sensor networks and flux tower measurements, using data from the first year of continuous and parallel measurements at the ECOSENSE forest. 400 near-surface sensors and 20 vertical depth profiles continuously measure soil water content and soil temperature dynamics with unprecedented spatial resolution across the flux tower footprint. Sensors are deployed in different tree species plots and strategically measure gradients close to tree stems vs. further away. In addition at 13 sites, continuous soil carbon-dioxide concentrations are measured. Sub-canopy eddy covariance systems at 2 m height measure soil carbon and water fluxes in an area dominated by F. sylvatica and in an area dominated by P. menziesii . As an example of the potential of this dataset, we show how ecosystem-integrated carbon fluxes can be linked to the horizontal distribution of soil respiration, gross primary productivity (GPP), soil moisture and temperature, and assess the relative importance of soil water in controlling and dominating ecosystem fluxes under the same climatic forcing. At the tree level, a distributed sensor network consisting of 54 sap flow sensors (heat-pulse-method), 34 point dendrometers and 18 micro tensiometers provide tree-specific information on water fluxes that will be linked to tower-based evapotranspiration measurements. At the leaf-level, little branchlets of P. menziesii and leaves of F. sylvatica are equipped with novel, lightweight and non-invasive cuvettes (12), on six different trees while measuring temperature and humidity inside the cuvette, in ambient air, and on the leaf surface. The cuvettes are connected via PTFE-tubing to an automated air sampling system to continuously measure leaf CO2 assimilation, 13C discrimination and transpiration in the sun and shade canopy. Additionally, biogenic volatile organic compound (BVOC) emissions from the cuvettes and from nine vertical canopy layers are sampled campaign-wise and analysed via GC-MS. To measure continuous active chlorophyll fluorescence at the leaf-level, multiple MICRO-PAM measuring heads are attached in the sun canopy of P. menziesii (n = 3) and F. sylvatica (n = 3), directly accessible from the tower platforms. The measuring heads acquired all chlorophyll fluorescence parameters, leaf-level photosynthetically active radiation and leaf temperature at 15-minute intervals. We use this information to compare leaf- and ecosystem-level water use efficiencies and test different methods to partition ecosystem eddy fluxes into GPP. Also leaf-level BVOC fluxes are compared to ecosystem-level fluxes determined by a vertical gradient system and an experimental BVOC relaxed eddy accumulation system on the 46 m tower. Leaf-level active chlorophyll fluorescence sensors are compared against continuous tower-based spectroradiometric measurements (FLOX) to calculate canopy-integrated sun-induced fluorescence signals, which again are linked to eddy-covariance derived GPP. Distributed sensors are also installed to track leaf structural and leaf water content dynamics. Water-related leaf angle variations during day and night are recorded as image time series from 24 cameras and analysed using a deep learning approach (AngleCam). Additionally, six GNSS antennas, installed on the ground below the canopy and one above on the tower to track vegetation optical depth, provide high-temporal-resolution estimates on the variation of total leaf foliage area and canopy water content. We conduct monthly repetitive laser scanning as well as spectral imaging observations using unmanned aerial vehicles to gather high-resolution data on vegetation structure and vitality within the central eddy footprint area. This data allows us to continuously track specific tree properties such as tree heights and enables the analysis of seasonal changes and stress-induced variations, which is another link to longer-term tower flux measurement trends in the future. All data is collected on-site and transmitted in near-real time to a central database and visualization platforms. The data are used to initiate, drive, evaluate and further develop physiologically-based ecosystem models. In summary, combining integrative tower measurements with microscale sensor networks allows us to observe ecosystem processes across scales and time.

Contrasting responses of grassland water and carbon exchanges to climate change between Tibetan Plateau and Inner Mongolia

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.

The relative importance of abiotic and biotic factors in shaping forest biomass stocks and fluxes remains a controversial issue. Here, using data gathered from 39 1 ha plots located in flooded and terra firme mature tropical lowland forests of the Amazon and Orinoquia regions of Colombia, we evaluated the importance of climate, soil fertility, and flooding, as well as tree taxonomic/phylogenetic diversity and forest structural properties, in determining the aboveground biomass stocks (AGB; Mg ha −1 ) and aboveground woody productivity (AWP; Mg ha −1 year −1 ). Using information‐theoretic multimodel inference and variance partitioning we found that forest structural features such as the number of trees with diameter at breast height ≥ 70 cm, and wood density, are the main drivers of variation in AGB. However, taxonomic diversity also contributes to AGB because it is associated with more large trees in these forests. In contrast, the key drivers of AWP in these forests were soil P and Mg concentrations, with no significant effects of diversity indices. These findings emphasize the need to include major soil cations other than N and P (e.g. Mg) in experimental studies to improve our understanding about the extent to which soil fertility can modulate increases in forest AWP due to climate change. Terra firme forests had higher AGB stocks than flooded forests, but both had similar AWP; and we found similar results for the drivers of AGB and AWP between flooded and terra firme forests. Our results provide limited evidence for strong effects of plant diversity on AGB or AWP. Therefore, we call for caution on generalizations of nature‐based initiatives aiming to preserve diversity based on maximizing carbon stocks and productivity, due to the complex nature of the processes controlling carbon accumulation and carbon fluxes in tropical forests.

The seasonality of forest leaf area index and photosynthetic activity is an important element of ecosystem function. The ecohydrological models representing water and carbon fluxes in tropical forests need regional-scale spatial information on forest leaf phenology. We aimed to evaluate the utility of the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very high resolution radiometer (AVHRR) imagery for monitoring evergreen and deciduous forest phenology in lowland Cambodia. The temporal sequences of the normalized difference vegetation index (NDVI) data were constructed from ten-day composite imagery acquired from May 2001 to April 2002. A local maximum fitting (LMF) technique that combined time-series filtering and local curve-fitting techniques was used to extract cloud-free data. With this approach, different phenology between evergreen and deciduous forests was clearly observed during the dry season: Deciduous forests exhibited marked and spatially uniform loss and (later) gain in new leaf area, whereas evergreen forests showed lesser and spatially/temporally heterogeneous changes in leaf phenology. A significant finding was that about 30% of evergreen forests shed detectable quantities of leaves at two times during a dry season (i.e., in the early, and late, dry season). In summary, the capability of composite NOAA/AVHRR NDVI data sets processed with the LMF technique was established as a viable approach for monitoring heterogeneous forest phenology at regional scales during the tropical dry season, although it is apparent that further improvements in cloud clearing are required if the approach is to be useful also in the wet season.

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

BackgroundReliable information about the spatial distribution of aboveground biomass (AGB) in tropical forests is fundamental for climate change mitigation and for maintaining carbon stocks. Recent AGB maps at continental and national scales have shown large uncertainties, particularly in tropical areas with high AGB values. Errors in AGB maps are linked to the quality of plot data used to calibrate remote sensing products, and the ability of radar data to map high AGB forest. Here we suggest an approach to improve the accuracy of AGB maps and test this approach with a case study of the tropical forests of the Yucatan peninsula, where the accuracy of AGB mapping is lower than other forest types in Mexico. To reduce the errors in field data, National Forest Inventory (NFI) plots were corrected to consider small trees. Temporal differences between NFI plots and imagery acquisition were addressed by considering biomass changes over time. To overcome issues related to saturation of radar backscatter, we incorporate radar texture metrics and climate data to improve the accuracy of AGB maps. Finally, we increased the number of sampling plots using biomass estimates derived from LiDAR data to assess if increasing sample size could improve the accuracy of AGB estimates.ResultsCorrecting NFI plot data for both small trees and temporal differences between field and remotely sensed measurements reduced the relative error of biomass estimates by 12.2%. Using a machine learning algorithm, Random Forest, with corrected field plot data, backscatter and surface texture from the L-band synthetic aperture radar (PALSAR) installed on the on the Advanced Land Observing Satellite-1 (ALOS), and climatic water deficit data improved the accuracy of the maps obtained in this study as compared to previous studies (R2 = 0.44 vs R2 = 0.32). However, using sample plots derived from LiDAR data to increase sample size did not improve accuracy of AGB maps (R2 = 0.26).ConclusionsThis study reveals that the suggested approach has the potential to improve AGB maps of tropical dry forests and shows predictors of AGB that should be considered in future studies. Our results highlight the importance of using ecological knowledge to correct errors associated with both the plot-level biomass estimates and the mismatch between field and remotely sensed data.

In order to discuss the values and daily variation characteristics of heat storage fluxes in a tropical seasonal rain forest in Xishuangbanna, the sensible and latent heat storage flux within air column, canopy heat storage flux, energy storage by photosynthesis and ground heat storage above the soil heat flux plate, as well as the ratios of these heat storage fluxes to the net radiation in the cool-dry, hot-dry and rainy season were compared and analyzed based on the observation data of carbon fluxes, meteorological factors and biomass within this tropical seasonal rain forest from January 2003 to December 2004. The findings showed that heat storage terms ranged significantly in the daytime and weakly in the nighttime, and the absolute values of sensible and latent heat storage fluxes were obviously greater than other heat storage terms in all seasons. In addition, the absolute values of total heat storage fluxes reached the peak in the hot-dry season, then were higher in the rainy season, and reached the minimum in the cool-dry season. The ratios of heat storage fluxes to net radiation generally decreased with time in the daytime, moreover, the sensible and latent heat storage dominated a considerable fraction of net radiation, while other heat storage contents occupied a smaller fraction of the net radiation and the peak value was not above 3.5%. In the daytime, the ratios of the total heat storage to net radiation were greater and differences in these ratios were distinct among seasons before 12:00, and then they became lower and differences were small among sea-sons after 12:00. The energy closure was improved when the storage terms were considered in the energy balance, which indicated that heat storage terms should not been neglected. The energy closure of tropical seasonal rain forest was not very well due to effects of many factors. The results would help us to further understand energy transfer and mass exchange between tropical forest and atmosphere. Moreover, they would supply a research basis for studying energy closure at other places.

Understanding of the extent of acclimation of light-saturated net photosynthesis (An ) to temperature (T), and associated underlying mechanisms, remains limited. This is a key knowledge gap given the importance of thermal acclimation for plant functioning, both under current and future higher temperatures, limiting the accuracy and realism of Earth system model (ESM) predictions. Given this, we analysed and modelled T-dependent changes in photosynthetic capacity in 10 wet-forest tree species: six from temperate forests and four from tropical forests. Temperate and tropical species were each acclimated to three daytime growth temperatures (Tgrowth ): temperate - 15, 20 and 25°C; tropical - 25, 30 and 35°C. CO2 response curves of An were used to model maximal rates of RuBP (ribulose-1,5-bisphosphate) carboxylation (Vcmax ) and electron transport (Jmax ) at each treatment's respective Tgrowth and at a common measurement T (25°C). SDS-PAGE gels were used to determine abundance of the CO2 -fixing enzyme, Rubisco. Leaf chlorophyll, nitrogen (N) and mass per unit leaf area (LMA) were also determined. For all species and Tgrowth , An at current atmospheric CO2 partial pressure was Rubisco-limited. Across all species, LMA decreased with increasing Tgrowth . Similarly, area-based rates of Vcmax at a measurement T of 25°C (Vcmax25 ) linearly declined with increasing Tgrowth , linked to a concomitant decline in total leaf protein per unit leaf area and Rubisco as a percentage of leaf N. The decline in Rubisco constrained Vcmax and An for leaves developed at higher Tgrowth and resulted in poor predictions of photosynthesis by currently widely used models that do not account for Tgrowth -mediated changes in Rubisco abundance that underpin the thermal acclimation response of photosynthesis in wet-forest tree species. A new model is proposed that accounts for the effect of Tgrowth -mediated declines in Vcmax25 on An , complementing current photosynthetic thermal acclimation models that do not account for T sensitivity of Vcmax25 .