A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests

Artificial intelligence for geoscience: Progress, challenges, and perspectives

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.

A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one-third of terrestrial gross primary productivity (GPP). However, the continental-scale leaf phenology-photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC-LUE) and proposed a novel leaf age-dependent LUE model (denoted as LA-LUE model). At the site level, the LA-LUE model (average R2 = .59, average root-mean-square error [RMSE] = 1.23 gC m-2 day-1) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 = .18; average RMSE = 1.87 gC m-2 day-1). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R2 = .80; average RMSE = 1.74 gC m-2 day-1), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 = .64; average RMSE = 1.90 gC m-2 day-1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 = .78; average RMSE = 1.88 gC m-2 day-1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite-based LUE model and provides a feasible implementation for mapping the continental-scale GPP seasonality over the entire TEFs.

Storms facilitate airborne DNA from leaf fragments outside the main tree pollen season

Bioaerosols are useful indicators of plant phenology and can demonstrate the impacts of climate change on both local and regional scales (e.g. pollen monitoring/flowering phenology). Analysing bioaerosols with eDNA approaches are becoming more popular to quantify the diversity of airborne plant environmental DNA (eDNA) and flowering season of plants and trees. Leaf abscission from broadleaved trees and other perennial species can also indicate the status of plant health in response to climate. This happens primarily during autumn in response to seasonal growth conditions and environmental factors, such as changing photoperiod and reduced temperatures. During this period biological material is released in larger quantities to the environment. Here, rural bioaerosol composition during late summer and autumn was captured by MiSEQ sequencing of the rRNA internal transcribed spacer 2 (ITS2) region, a common marker for taxonomic variation. Meteorological parameters were recorded from a proximal weather station. The composition of atmospheric taxa demonstrated that deciduous tree DNA forms part of the bioaerosol community during autumn and, for several common broadleaved tree species, atmospheric DNA abundance correlated to high wind events. This suggests that both flowering and autumn storms cause bioaerosols from deciduous trees that can be detected with eDNA approaches. This is an aspect that must be considered when eDNA methods are used to analyse either pollen or other fragments from trees.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10453-024-09826-w.

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

The Sanjiang Plain has the greatest concentration of freshwater marshes in China. Marshes in this area play a key role in adjusting the regional carbon cycle. As an important quality parameter of marsh ecosystems, vegetation aboveground biomass (AGB) is an important index for evaluating carbon stocks and carbon sequestration function. Due to a lack of in situ and long-term AGB records, the temporal and spatial changes in AGB and their contributing factors in the marsh of Sanjiang Plain remain unclear. Based on the measured AGB, normalized difference vegetation index (NDVI), and climate data, this study investigated the spatiotemporal changes in marsh AGB and the effects of climate variation on marsh AGB in the Sanjiang Plain from 2000 to 2020. Results showed that the marsh AGB density and annual maximum NDVI (NDVI_max) had a strong correlation, and the AGB density could be accurately calculated from a power function equation between NDVI_max and AGB density (AGB density = 643.57 × NDVI max 4 . 2474 ). According to the function equation, we found that the AGB density significantly increased at a rate of 2.47 g·C/m²/a during 2000-2020 in marshes of Sanjiang Plain, with the long-term average AGB density of about 282.05 g·C/m². Spatially, the largest increasing trends of AGB were located in the north of the Sanjiang Plain, and decreasing trends were mainly found in the southeast of the study area. Regarding climate impacts, the increase in precipitation in winter could decrease the marsh AGB, and increased temperatures in July contributed to the increase in the marsh AGB in the Sanjiang Plain. This study demonstrated an effective approach for accurately estimating the marsh AGB in the Sanjiang Plain using ground-measured AGB and NDVI data. Moreover, our results highlight the importance of including monthly climate properties in modeling AGB in the marshes of the Sanjiang Plain.

Aboveground Biomass of Wetland Vegetation Under Climate Change in the Western Songnen Plain

Understanding the spatiotemporal dynamics of aboveground biomass (AGB) is crucial for investigating the wetland ecosystem carbon cycle. In this paper, we explored the spatiotemporal change of aboveground biomass and its response to climate change in a marsh wetland of western Songen Plain by using field measured AGB data and vegetation index derived from MODIS datasets. The results showed that the AGB could be established by the power function between measured AGB density and the annual maximum NDVI (NDVImax) of marsh: Y = 302.06 × NDVImax 1.9817. The averaged AGB of marshes showed a significant increase of 2.04 g⋅C/m2/a, with an average AGB value of about 111.01 g⋅C/m2 over the entire western Songnen Plain. For the influence of precipitation and temperature, we found that the annual mean temperature had a smaller effect on the distribution of marsh AGB than that of the total precipitation in the western Songnen Plain. Increased precipitation in summer and autumn would increase AGB by promoting marshes' vegetation growth. In addition, we found that the minimum temperature (Tmin) and maximum temperatures (Tmax) have an asymmetric effect on marsh AGB on the western Songnen Plain: warming Tmax has a significant impact on AGB of marsh vegetation, while warming at night can non-significantly increase the AGB of marsh wetland. This research is expected to provide theoretical guidance for the restoration, protection, and adaptive management of wetland vegetation in the western Songnen Plain.

Tropical tall forests are more sensitive and vulnerable to drought than short forests

Our limited understanding of the impacts of drought on tropical forests significantly impedes our ability in accurately predicting the impacts of climate change on this biome. Here, we investigated the impact of drought on the dynamics of forest canopies with different heights using time-series records of remotely sensed Ku-band vegetation optical depth (Ku-VOD), a proxy of top-canopy foliar mass and water content, and separated the signal of Ku-VOD changes into drought-induced reductions and subsequent non-drought gains. Both drought-induced reductions and non-drought increases in Ku-VOD varied significantly with canopy height. Taller tropical forests experienced greater relative Ku-VOD reductions during drought and larger non-drought increases than shorter forests, but the net effect of drought was more negative in the taller forests. Meta-analysis of in situ hydraulic traits supports the hypothesis that taller tropical forests are more vulnerable to drought stress due to smaller xylem-transport safety margins. Additionally, Ku-VOD of taller forests showed larger reductions due to increased atmospheric dryness, as assessed by vapor pressure deficit, and showed larger gains in response to enhanced water supply than shorter forests. Including the height-dependent variation of hydraulic transport in ecosystem models will improve the simulated response of tropical forests to drought.

Estimating ecological sustainability in the Guangdong-Hong Kong-Macao Greater Bay Area, China: Retrospective analysis and prospective trajectories

In recent decades, rapid urbanization and intensified global climate change have resulted in a significant difference of environment and resources distribution on space, which would cause trouble for accurate assessment of regional ecological sustainable development, especially in the large urban agglomerations. The parameters used in previous assessment methods have normally ignored spatial heterogeneity, leading to deviations in the evaluation accuracies against the context above. By incorporating remote sensing technology, this study proposed an improved emergy ecological footprint (EEF) method and a novel ecological sustainability index to comprehensively analyze the variability of ecological security states (ESS) from 1994 to 2018 in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and to predict its sustainable growth potential based on a combined factorial decomposition and scenario analysis. Results showed that the pixel-based emergy analysis revealed significant heterogeneity over time and space under the impact of climate change and intense land use activities during the study period. The emergy carrying capacity per capita (ecc) and the emergy ecological footprint per capita (eef) also showed a significant difference between the nine cities in the GBA. In addition, the traditional EEF method, which does not consider the spatiotemporal variation, has indeed overestimated the GBA's ecc by 15% compared with our results. The ESS of the GBA gradually worsened from slight insecurity in the 1990s to moderate insecurity in 2018. If the current trends in socio-economic activities and climate change continue according to the RCP8.5 scenario in the IPCC, the ESS of the GBA will reach the extreme insecurity state in 2050. However, our scenarios show that industrial structure adjustment, energy structure optimization, and especially biological resource conservation can reduce the EFI by approximately 6.52%, 23.4%, and 30.6%, respectively. Consequently, effective implementation of the above measures can limit the increase both in emergy ecological deficit and emergy ecological footprint intensity (EFI) and, together, contribute to a higher security status in the GBA in 2050.

The Influence of Ozone on Net Ecosystem Production of a Ryegrass–Clover Mixture under Field Conditions

In order to understand the effect of phytotoxic tropospheric ozone (O3) on terrestrial vegetation, we quantified the impact of current O3 concentration ([O3]) on net ecosystem production (NEP) when compared to the conditions of the pre-industrial era. We compared and tested linear mixed-effects models based on [O3] and stomatal O3 flux (Fsto). The managed ryegrass–clover (Lolium perenne and Trifolium pratense) mixture was grown on arable land in the Czech Republic, Central Europe. Values of [O3] and Fsto were measured and calculated based on resistance analogy, respectively, while NEP was calculated from eddy covariance CO2 fluxes. We found the Fsto-based model more precise when compared to measured NEP. High Fsto was found even at low [O3], while broad summer maximum of [O3] was not necessarily followed by significant NEP decline, due to low soil water content leading to a low stomatal conductivity and Fsto. Comparing to low pre-industrial O3 conditions, current levels of O3 resulted in the reduction of cumulative NEP over the entire growing season, up to 29.7 and 13.5% when the [O3]-based and Fsto-based model was applied, respectively. During the growing season, an O3-induced reduction of NEP ranged between 13.1% in May and 26.2% in July when compared to pre-industrial Fsto levels. Looking to the future, high [O3] and Fsto may lead to the reduction of current NEP by approximately 13.3% on average during the growing season, but may increase by up to 61–86.6% in autumn, indicating further O3-induced acceleration of the senescence. These findings indicate the importance of Fsto and its inclusion into the models estimating O3 effects on terrestrial vegetation. The interaction between environmental factors and stomatal conductance is therefore discussed in detail.