Global patterns of forest autotrophic carbon fluxes

Global soil respiration estimation based on ecological big data and machine learning model

Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R2 = 0.78216, root mean squared error (RMSE) = 285.8964 gCm-2y-1, and mean absolute error (MAE) = 180.4186 gCm-2y-1, which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs.

Impact of meteorological variability on diurnal and seasonal net ecosystem productivity in a desert riparian forest ecosystem

The desert riparian forests are susceptible to meteorological changes and contribute significantly to the net ecosystem productivity (NEP) variations of arid ecosystems. However, the responsive patterns of their NEP variations to the meteorological variabilities remain inadequately comprehended. To address this gap, we utilized seven years of eddy covariance flux measurements in a representative desert riparian forest to investigate the NEP variations and its response to changing meteorological factors across diverse temporal scales. The results revealed significant periodic variations in half-hourly NEP, with dominant cycles spanning from five hours to one year, with a principal oscillation period of one day. Key meteorological factors including global solar radiation (Rg), relative humidity (RH), air temperature (Ta), soil temperature (Ts), and vapor pressure deficit (VPD) exhibited synchronization with NEP on daily scales. This synchronization, coupled with the observed one-day periodic NEP variations, provides robust evidence supporting the existence of a circadian rhythm in the ecosystem carbon exchange of desert riparian forest regulated by meteorological conditions. Seasonal patterns were significant in the impact of Rg phase, Ta diurnal amplitude, and VPD diurnal amplitude on NEP diurnal amplitude and phase. The NEP diurnal amplitude significantly, directly, and positively affected daily NEP in both the dormant and growing seasons, whereas its phase yielded significant negative effects (P< 0.05). The averages, amplitudes, and phases of diurnal meteorological conditions controlled the daily NEP by regulating NEP diurnal amplitude and phase. These findings provide evidence that the variability in circadian rhythms, caused by the increase in diurnal Ta and VPD, significantly impact the daily NEP at an ecosystem scale. This study enriches our comprehension of the meteorological mechanisms governing diurnal and seasonal carbon uptake dynamics within desert riparian forests, providing fresh insights into the direct and indirect roles of climate change in shaping patterns of ecosystem carbon exchange.

It's only natural: Plant respiration in unmanaged systems

Respiration plays a key role in the terrestrial carbon cycle and is a fundamental metabolic process in all plant tissues and cells. We review respiration from the perspective of plants that grow in their natural habitat and how it is influenced by wide-ranging elements at different scales, from metabolic substrate availability to shifts in climate. Decades of field-based measurements have honed our understanding of the biological and environmental controls on leaf, root, stem, and whole-organism respiration. Despite this effort, there remain gaps in our knowledge within and across species and ecosystems, especially in more challenging-to-measure tissues like roots. Recent databases of respiration rates and associated leaf traits from species representing diverse biomes, plant functional types, and regional climates have allowed for a wider-lens view at modeling this important CO2 flux. We also re-analyze published data sets to show that maximum leaf respiration rates (Rmax) in species from around the globe are related both to leaf economic traits and environmental variables (precipitation and air temperature), but that root respiration does not follow the same latitudinal trends previously published for leaf data. We encourage the ecophysiological community to continue to expand their study of plant respiration in tissues that are difficult to measure and at the whole plant and ecosystem levels to address outstanding questions in the field.

Effects of climate and plant functional types on forest above-ground biomass accumulation

BACKGROUND

Forest above-ground biomass (AGB) accumulation is widely considered an important tool for mitigating climate change. However, the general pattern of forest AGB accumulation associated with age and climate gradients across various forest functional types at a global scale have remained unclear. In this study, we compiled a global AGB data set and applied a Bayesian statistical model to reveal the age-related dynamics of forest AGB accumulation, and to quantify the effects of mean annual temperature and annual precipitation on the initial AGB accumulation rate and on the saturated AGB characterizing the limit to AGB accumulation.

RESULTS

The results of the study suggest that mean annual temperature has a significant positive effect on the initial AGB accumulation rate in needleleaf evergreen forest, and a negative effect in broadleaf deciduous forest; whereas annual precipitation has a positive effect in broadleaf deciduous forest, and negative effect in broadleaf evergreen forest. The positive effect of mean annual temperature on the saturated AGB in broadleaf evergreen forest is greater than in broadleaf deciduous forest; annual precipitation has a greater negative effect on the saturated AGB in deciduous forests than in evergreen forests. Additionally, the difference of AGB accumulation rate across four forest functional types is closely correlated with the forest development stage at a given climate.

CONCLUSIONS

The contrasting responses of AGB accumulation rate to mean annual temperature and precipitation across four forest functional types emphasizes the importance of incorporating the complexity of forest types into the models which are used in planning climate change mitigation. This study also highlights the high potential for further AGB growth in existing evergreen forests.

Global patterns of allometric model parameters prediction

Variations in biomass-carbon of forest can substantially impact the prediction of global carbon dynamics. The allometric models currently used to estimate forest biomass face limitations, as model parameters can only be used for the specific species of confirmed sites. Here, we collected allometric models LnW = a + b*Ln(D) (n = 817) and LnW = a + b*Ln(D²H) (n = 612) worldwide and selected eight variables (e.g., mean annual temperature (MAT), mean annual precipitation (MAP), altitude, aspect, slope, soil organic carbon (SOC), clay, and soil type) to predict parameters a and b using Random Forest. LnW = a + b*Ln(D), drove mainly by climate factors, showed the parameter a range from - 5.16 to - 0.90 [VaR explained (model evaluation index): 66.21%], whereas parameter b ranges from 1.84 to 2.68 (VaR explained: 49.96%). Another model LnW = a + b*Ln(D²H), drove mainly by terrain factors, showed the parameter a range from - 5.45 to - 1.89 (VaR explained: 69.04%) and parameter b ranges from 0.43 to 1.93 (VaR explained: 69.53%). Furthermore, we captured actual biomass data of 249 sample trees at six sites for predicted parameters validation, showing the R² (0.87) for LnW = a + b*Ln(D); R² (0.93) for LnW = a + b*Ln(D²H), indicating a better result from LnW = a + b*Ln(D²H). Consequently, our results present four global maps of allometric model parameters distribution at 0.5° resolution and provides a framework for the assessment of forest biomass by validation.

Warm springs alter timing but not total growth of temperate deciduous trees

As the climate changes, warmer spring temperatures are causing earlier leaf-out^1-3 and commencement of CO₂ uptake^1,3 in temperate deciduous forests, resulting in a tendency towards increased growing season length³ and annual CO₂ uptake^1,3-7. However, less is known about how spring temperatures affect tree stem growth^8,9, which sequesters carbon in wood that has a long residence time in the ecosystem^10,11. Here we show that warmer spring temperatures shifted stem diameter growth of deciduous trees earlier but had no consistent effect on peak growing season length, maximum growth rates, or annual growth, using dendrometer band measurements from 440 trees across two forests. The latter finding was confirmed on the centennial scale by 207 tree-ring chronologies from 108 forests across eastern North America, where annual ring width was far more sensitive to temperatures during the peak growing season than in the spring. These findings imply that any extra CO₂ uptake in years with warmer spring temperatures^4,5 does not significantly contribute to increased sequestration in long-lived woody stem biomass. Rather, contradicting projections from global carbon cycle models^1,12, our empirical results imply that warming spring temperatures are unlikely to increase woody productivity enough to strengthen the long-term CO₂ sink of temperate deciduous forests.

Regional Contributions and Climate Attributions to Interannual Variation of Global Net Ecosystems Production by an ECOSYSTEM Processed Model Driven by Remote Sensing Data over the Past 35 Years

Global climate change has significantly affected terrestrial carbon sinks. Net ecosystem production (NEP) plays a critical role in the global carbon cycle. However, interannual variability (IAV) of the NEP and its regional contributions and climate attributions are not well-understood on a global scale. This study used a diagnostic model driven by remote sensing leaf area index (LAI) to investigate the NEP IAV and analyze regional and climate contributions on a global scale from 1982 to 2016. We found large NEP IAV during the study period, with the NEP detrended anomaly ranging from −2.3 Pg C in 1998 to 1.6 Pg C in 2013 at a global scale. Furthermore, 63.7% and 34.1% of the areas showed positive and negative contributions to NEP IAVs globally, respectively. Evergreen broadleaf forest (EBF) contributed the most (31.1%) to NEP IAV, followed by cropland (21.7%) and grassland (20.8%). Temperature played the most critical roles in the global NEP IAV, with a contribution of 45.5%. However, the partial correlation between NEP and temperature was negative, and the correlation with precipitation was positive in most areas of the globe, indicating that global warming is not conducive to the global carbon sink, but abundant rainfall is important for the global carbon cycle. This study suggests that, to increase the global carbon sink, we should pay more attention to tropical forests (EBFs) and highlight the importance of water availability.

Distribution of biomass dynamics in relation to tree size in forests across the world

Tree size shapes forest carbon dynamics and determines how trees interact with their environment, including a changing climate. Here, we conduct the first global analysis of among-site differences in how aboveground biomass stocks and fluxes are distributed with tree size. We analyzed repeat tree censuses from 25 large-scale (4-52 ha) forest plots spanning a broad climatic range over five continents to characterize how aboveground biomass, woody productivity, and woody mortality vary with tree diameter. We examined how the median, dispersion, and skewness of these size-related distributions vary with mean annual temperature and precipitation. In warmer forests, aboveground biomass, woody productivity, and woody mortality were more broadly distributed with respect to tree size. In warmer and wetter forests, aboveground biomass and woody productivity were more right skewed, with a long tail towards large trees. Small trees (1-10 cm diameter) contributed more to productivity and mortality than to biomass, highlighting the importance of including these trees in analyses of forest dynamics. Our findings provide an improved characterization of climate-driven forest differences in the size structure of aboveground biomass and dynamics of that biomass, as well as refined benchmarks for capturing climate influences in vegetation demographic models.

Climate change impacts plant carbon balance, increasing mean future carbon use efficiency but decreasing total forest extent at dry range edges

Carbon use efficiency (CUE) represents how efficient a plant is at translating carbon gains through gross primary productivity (GPP) into net primary productivity (NPP) after respiratory costs (R_a ). CUE varies across space with climate and species composition, but how CUE will respond to climate change is largely unknown due to uncertainty in R_a at novel high temperatures. We use a plant physiological model validated against global CUE observations and LIDAR vegetation canopy height data and find that model-predicted decreases in CUE are diagnostic of transitions from forests to shrubland at dry range edges. Under future climate scenarios, we show mean growing season CUE increases in core forested areas, but forest extent decreases at dry range edges, with substantial uncertainty in absolute CUE due to uncertainty in R_a . Our results highlight that future forest resilience is nuanced and controlled by multiple competing mechanisms.