Leaf functional traits and resource use strategies facilitate the spread of invasive plant Parthenium hysterophorus across an elevational gradient in western Himalayas.

Parthenium hysterophorus L. (Asteraceae) is a highly prevalent invasive species in subtropical regions across the world. It has recently been seen to shift from low (subtropical) to high (sub-temperate) elevations. Nevertheless, there is a dearth of research investigating the adaptive responses and the significance of leaf functional traits in promoting the expansion to high elevations. The current study investigated the variations and trade-offs among 14 leaf traits (structural, photosynthetic, and nutrient content) of P. hysterophorus across different elevations in the western Himalayas, India. Plots measuring 20 × 40 m were established at different elevations (700 m, 1100 m, 1400 m, and 1800 m) to collect leaf trait data for P. hysterophorus. Along the elevational gradient, significant variations were noticed in leaf morphological parameters, leaf nutrient content, and leaf photosynthetic parameters. Significant increases were observed in the specific leaf area, leaf thickness, and chlorophyll a, total chlorophyll and carotenoid content, as well as leaf nitrogen and phosphorus content with elevation. On the other hand, there were reductions in the amount of chlorophyll b, photosynthetic efficiency, leaf dry matter content, leaf mass per area, and leaf water content. The trait-trait relationships between leaf water content and dry weight and between leaf area and dry weight were stronger at higher elevations. The results show that leaf trait variability and trait-trait correlations are very important for sustaining plant fitness and growth rates in low-temperature, high-irradiance, resource-limited environments at relatively high elevations. To summarise, the findings suggest that P. hysterophorus can expand its range to higher elevations by broadening its functional niche through changes in leaf traits and resource utilisation strategies.

Scaling of leaf area with biomass in trees reconsidered: constant metabolically active sapwood volume per unit leaf area with height growth.

Hypoallometric (slope<1) scaling between metabolic rate and body mass is often regarded as near-universal across organisms. However, there are compelling reasons to question hypoallometric scaling in woody plants, where metabolic rate=leaf area. This leaf area must provide carbon to the metabolically active sapwood volume (VMASW). Within populations of a species, variants in which VMASW increases per unit leaf area with height growth (e.g. ⅔ or ¾ scaling) would have proportionally less carbon for growth and reproduction as they grow taller. Therefore, selection should favor individuals in which, as they grow taller, leaf area scales isometrically with shoot VMASW (slope=1). Using tetrazolium staining, we measured total VMASW and total leaf area (LAtot) across 22 individuals of Ricinus communis and confirmed that leaf area scales isometrically with VMASW, and that VMASW is much smaller than total sapwood volume. With the potential of the LAtot-VMASW relationship to shape factors as diverse as the crown area-stem diameter relationship, conduit diameter scaling, reproductive output, and drought-induced mortality, our work suggests that the notion that sapwood increases per unit leaf area with height growth requires revision.

Optimization of leaf area index measurement method and correction of green plot ratio formula based on regional plant characteristics-a study in Chongqing, China.

The quantification of green space green plot ratio (GPR) is mostly based on estimation formulas, and the leaf area index (LAI) estimation values in these estimation formulas have not been well verified by measured LAI values, resulting in errors and uncertainties in GPR quantification results. This study aims to address this gap by measuring the LAI of 113 regional plants in Chongqing, China, following a standardized measurement path for digital hemispherical photography (DHP). The results indicate that the optimal relative exposure value (REV) was - 1 under overcast conditions and - 2 under sunny and cloudy conditions. Among the threshold algorithms for hemispherical images, the Intermodes algorithm in ImageJ was the best. The LAI of regional plants is highest in summer, followed by spring and autumn, and lowest in winter. Tree height (h) and crown width (w) are key factors affecting LAI, but the LAI also varies with plant species. Overall, the LAI of evergreen trees is higher than that of deciduous trees. The LAI of evergreen trees and shrubs with a height shorter than 5 m is the largest, and that of deciduous trees and shrubs with a crown width larger than 8 m is the largest. The study further verified that the existing GPR estimation formula exhibited large errors in Chongqing, while there was a strong correlation (R2 = 0.973) between the GPR estimation value and the measured value. A conversion formula was developed to reduce estimation biases, and the corrected formula is capable of estimating GPR values more accurately when actual LAI measurements are insufficient. Overall, this study verifies the significance of measuring localized LAI values, promotes the understanding of LAI suitability for GPR calculations, and provides an empirical formula for GPR estimation in Chongqing, China.

UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe.

Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data.

Evidence for Reductions in Physical and Chemical Plant Defense Traits in Island Flora.

Reduced defense against large herbivores has been suggested to be part of the "island syndrome" in plants. However, empirical evidence for this pattern is mixed. In this paper, we present two studies that compare putative physical and chemical defense traits from plants on the California Channel Islands and nearby mainland based on sampling of both field and common garden plants. In the first study, we focus on five pairs of woody shrubs from three island and three mainland locations and find evidence for increased leaf area, decreased marginal leaf spines, and decreased concentrations of cyanogenic glycosides in island plants. We observed similar increases in leaf area and decreases in defense traits when comparing island and mainland genotypes grown together in botanic gardens, suggesting that trait differences are not solely driven by abiotic differences between island and mainland sites. In the second study, we conducted a common garden experiment with a perennial herb-Stachys bullata (Lamiaceae)-collected from two island and four mainland locations. Compared to their mainland relatives, island genotypes show highly reduced glandular trichomes and a nearly 100-fold reduction in mono- and sesquiterpene compounds from leaf surfaces. Island genotypes also had significantly higher specific leaf area, somewhat lower rates of gas exchange, and greater aboveground biomass than mainland genotypes across two years of study, potentially reflecting a broader shift in growth habit. Together, our results provide evidence for reduced expression of putative defense traits in island plants, though these results may reflect adaptation to both biotic (i.e., the historical absence of large herbivores) and climatic conditions on islands.

Ability of Different Growth Indicators to Detect Salt Tolerance of Advanced Spring Wheat Lines Grown in Real Field Conditions.

Plant growth indicators (GIs) are important for evaluating how different genotypes respond to normal and stress conditions separately. They consider both the morphological and physiological components of plants between two successive growth stages. Despite their significance, GIs are not commonly used as screening criteria for detecting salt tolerance of genotypes. In this study, 36 recombinant inbred lines (RILs) along with four genotypes differing in their salt tolerance were grown under normal and 150 mM NaCl in a two-year field trial. The performance and salt tolerance of these germplasms were assessed through various GIs. The analysis of variance showed highly significant variation between salinity levels, genotypes, and their interaction for all GIs and other traits in each year and combined data for two years, with a few exceptions. All traits and GIs were significantly reduced by salinity stress, except for relative growth rate (RGR), net assimilation rate (NAR), and specific leaf weight (SLW), which increased under salinity conditions. Traits and GIs were more correlated with each other under salinity than under normal conditions. Principal component analysis organized traits and GIs into three main groups under both conditions, with RGR, NAR, and specific leaf area (SLA) closely associated with grain yield (GY) and harvest index, while leaf area duration (LAD) was closely associated with green leaf area (GLA), plant dry weight (PDW), and leaf area index (LAI). A hierarchical clustering heatmap based on GIs and traits organized germplasms into three and four groups under normal and salinity conditions, respectively. Based on the values of traits and GIs for each group, the germplasms varied from high- to low-performing groups under normal conditions and from salt-tolerant to salt-sensitive groups under salinity conditions. RGR, NAR, and LAD were important factors determining genotypic variation in GY of high- and low-performing groups, while all GIs, except leaf area duration (LAR), were major factors describing genotypic variation in GY of salt-tolerant and salt-sensitive groups. In conclusion, different GIs that reveal the relationship between the morphological and physiological components of genotypes could serve as valuable selection criteria for evaluating the performance of genotypes under normal conditions and their salt tolerance under salinity stress conditions.

Fast-slow traits predict competition network structure and its response to resources and enemies.

Plants interact in complex networks but how network structure depends on resources, natural enemies and species resource-use strategy remains poorly understood. Here, we quantified competition networks among 18 plants varying in fast-slow strategy, by testing how increased nutrient availability and reduced foliar pathogens affected intra- and inter-specific interactions. Our results show that nitrogen and pathogens altered several aspects of network structure, often in unexpected ways due to fast and slow growing species responding differently. Nitrogen addition increased competition asymmetry in slow growing networks, as expected, but decreased it in fast growing networks. Pathogen reduction made networks more even and less skewed because pathogens targeted weaker competitors. Surprisingly, pathogens and nitrogen dampened each other's effect. Our results show that plant growth strategy is key to understand how competition respond to resources and enemies, a prediction from classic theories which has rarely been tested by linking functional traits to competition networks.

Intraspecific variability of leaf form and function across habitat types.

Trait-based ecology has already revealed main independent axes of trait variation defining trait spaces that summarize plant adaptive strategies, but often ignoring intraspecific trait variability (ITV). By using empirical ITV-level data for two independent dimensions of leaf form and function and 167 species across five habitat types (coastal dunes, forests, grasslands, heathlands, wetlands) in the Italian peninsula, we found that ITV: (i) rotated the axes of trait variation that define the trait space; (ii) increased the variance explained by these axes and (iii) affected the functional structure of the target trait space. However, the magnitude of these effects was rather small and depended on the trait and habitat type. Our results reinforce the idea that ITV is context-dependent, calling for careful extrapolations of ITV patterns across traits and spatial scales. Importantly, our study provides a framework that can be used to start integrating ITV into trait space analyses.

Experimental analysis of PM2.5 reduction characteristics between Korean red pine (Pinus densiflora) and sawtooth oak (Quercus acutissima) saplings under different densities and arrangement structures.

As global air pollution, particularly fine particulate matter (PM2.5), has become a major environmental problem, various PM2.5 mitigation technologies including green infrastructure have received significant attention. However, owing to spatial constraints on urban greening, there is a lack of management plans for urban forests to efficiently mitigate PM2.5. In this study, we assessed the PM2.5 reduction capabilities of Pinus densiflora (Korean red pine) and Quercus acutissima (sawtooth oak) by measuring the changes of PM2.5 concentrations using an experimental chamber system. In addition, the PM2.5 reduction efficiency in 90 min (PMRE90) and the amount of PM2.5 reduction per leaf area (PMRLA) were compared based on arrangement structures and density levels. The results showed that the PM2.5 reduction by plants was significantly greater than that of the control experiment without any plants, and an additional reduction effect of approximately 1.38 times was induced by a 1.5 m s-1 air flow. The PMRE90 of Korean red pine was the highest at medium density. In contrast, the PMRE90 of sawtooth oak was the highest at high density. The PMRLA of both species was highest at low densities. The different responses of the species to total reduction were well explained by total leaf area (TLA). The PMRE90 of both species was positively correlated with TLA. The PMRLA of sawtooth oak was approximately 2.3 times greater than that of Korean red pine. However, there were no significant differences in both PMRE90 and PMRLA between the arrangement structures. Our findings reveal the potential mechanisms of vegetation in reducing PM2.5 according to arrangement structure and density. This highlights the importance of efficiently using urban green spaces with spatial constraints on PM2.5 mitigation in the future.

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches.

Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R 2 = 0.83), followed by the RF model (R 2 = 0.82), with the PLSR model exhibited the lowest accuracy (R 2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.

Potassium deficiency reduces grapevine transpiration through decreased leaf area and stomatal conductance.

Plants require potassium (K) to support growth and regulate hydraulics. Yet, K's effects on transpiration are still speculated. We hypothesized that K deficiency would limit grapevine water uptake by limiting canopy size and stomatal conductance (gs). Hence, we constructed large (2 m3) lysimeters and recorded vine transpiration for three years (2020-2022) under three fertilization application rates (8, 20, or 58 mg K L-1 in irrigation). Maximal K availability supported transpiration up to 75 L day-1, whereas K-deficient vines transpired only 60 L day-1 in midsummer. Limited vine growth and canopy size mainly accounted for reduced transpiration under low K conditions. Hence, considering K demand in addition to supply, we compared K deficiency effects on vines bearing 20 or 50 fruit clusters and found that reduced gs further limited transpiration when yields were high. Although fruits were strong K sinks, high yields did not alter K uptake because lower vegetative growth countered the additional K demands. Potassium deficiency leads to lower transpiration and productivity. Yet, internal mineral allocation compensates for fruit K uptake and masks biochemical indices or physiological proxies for K deficiency. Thus, decision support tools should integrate mineral availability, seasonal growth, and yield projections to determine grapevine water demands.

The influence of stand composition and season on canopy structure and understory light environment in different subtropical montane Pinus massoniana forests.

Canopy structure and understory light have important effects on forest productivity and the growth and distribution of the understory. However, the effects of stand composition and season on canopy structure and understory light environment (ULE) in the subtropical mountain Pinus massoniana forest system are poorly understood. In this study, the natural secondary P. massoniana-Castanopsis eyrei mixed forest (MF) and P. massoniana plantation forest (PF) were investigated. The study utilized Gap Light Analyzer 2.0 software to process photographs, extracting two key canopy parameters, canopy openness (CO) and leaf area index (LAI). Additionally, data on the transmitted direct (Tdir), diffuse (Tdif), and total (Ttot) radiation in the light environment were obtained. Seasonal variations in canopy structure, the ULE, and spatial heterogeneity were analyzed in the two P. massoniana forest stands. The results showed highly significant (P < 0.01) differences in canopy structure and ULE indices among different P. massoniana forest types and seasons. CO and ULE indices (Tdir, Tdif, and Ttot) were significantly lower in the MF than in the PF, while LAI was notably higher in the MF than in the PF. CO was lower in summer than in winter, and both LAI and ULE indices were markedly higher in summer than in winter. In addition, canopy structure and ULE indices varied significantly among different types of P. massoniana stands. The LAI heterogeneity was lower in the MF than in the PF, and Tdir heterogeneity was higher in summer than in winter. Meanwhile, canopy structure and ULE indices were predominantly influenced by structural factors, with spatial correlations at the 10 m scale. Our results revealed that forest type and season were important factors affecting canopy structure, ULE characteristics, and heterogeneity of P. massoniana forests in subtropical mountains.

Creating a new yellow and blue combination transparent film for Panax ginseng C.A. Meyer growth based on orthogonal designs.

Panax ginseng C.A. Meyer originates from old-growth forest environments, where the light intensity and spectrum reaching the forest bed are influenced by the canopy and humidity. In farmlands, suitable light intensity for cultivation is achieved by controlling the light transmission rate using shading nets, while light quality is regulated by a cover of yellow or blue transparent film. Such films have a light quality distinct from that produced by old-growth forests. Herein, a large composite film was developed by alternating small pieces of yellow and blue transparent film. An orthogonal array was used to evaluate the influence of the small transparent film area (STFA), yellow transparent film (YTF) number, and blue transparent film (BTF) number on the associated changes in ginseng in a range of fluorescence-, photosynthesis-, morphology-, and crop quality-related factors. Our results showed that light intensity was influenced primarily by STFA, which caused an overall decrease, while the light quality ratio was affected primarily by YTF number, which increased the proportion of red light and decreased that of blue light, with corresponding influence on different growth parameters. Based on these observations, an improved yellow and blue combination transparent film (YBCTF) with the following characteristics was established: STFA: 15 × 15 cm, YTF: two pieces, and BTF: three pieces. The improved YBCTF facilitated efficient light energy use by the plants, and led to an increase in leaf area, the per leaf photosynthetic rate, dry root weight, and the per root single ginsenoside yield. The findings present a relatively low-cost approach for optimising the light environment of ginseng cultivated in farmland and other crops in large-scale agricultural settings.

In situ short-term responses of Amazonian understory plants to elevated CO2.

The response of plants to increasing atmospheric CO2 depends on the ecological context where the plants are found. Several experiments with elevated CO2 (eCO2) have been done worldwide, but the Amazonian forest understory has been neglected. As the central Amazon is limited by light and phosphorus, understanding how understory responds to eCO2 is important for foreseeing how the forest will function in the future. In the understory of a natural forest in the Central Amazon, we installed four open-top chambers as control replicates and another four under eCO2 (+250 ppm above ambient levels). Under eCO2, we observed increases in carbon assimilation rate (67%), maximum electron transport rate (19%), quantum yield (56%), and water use efficiency (78%). We also detected an increase in leaf area (51%) and stem diameter increment (65%). Central Amazon understory responded positively to eCO2 by increasing their ability to capture and use light and the extra primary productivity was allocated to supporting more leaf and conducting tissues. The increment in leaf area while maintaining transpiration rates suggests that the understory will increase its contribution to evapotranspiration. Therefore, this forest might be less resistant in the future to extreme drought, as no reduction in transpiration rates were detected.

Structural diversity is better associated with forest productivity than species or functional diversity.

Understanding the relationship between biodiversity and productivity can be advanced by improving metrics used to quantify biodiversity. Structural diversity, that is, variation of size and form of plant organs, is an emerging biodiversity metric. However, compared with the other biodiversity metrics, its relative importance in specific components of forest productivity, for example, recruitment of new individuals, biomass net change after accounting for mortality, is largely unknown, particularly across a large spatial scale with multiple influential gradients. To address the knowledge gap, we used USDA Forest Service Forest Inventory and Analysis (FIA) data across the southcentral USA from 2008 to 2017. We calculated forest biomass increments due to recruitment and growth and net change in biomass. Then, we quantified the effects of a range of abiotic and biotic variables on the biomass increments and net change. Our results showed that (1) Structural diversity was negatively associated with the two biomass increments and net change in biomass. The negative effects were supported by increased occurrences of insects and diseases with greater structural diversity. (2) Compared with species and functional diversity, structural diversity showed a better association with biomass increments and net change, suggested by its larger absolute values of standardized coefficients, and the effects of structural diversity were negative in contrast to species diversity. (3) The effects of structural diversity, stand age, and elevation differed between natural and planted forests that may stem from the differences in stand development and species composition between the two forest types. Together, structural diversity may represent an important dimension of biodiversity impacts on plant productivity, which could be related to the exacerbated disturbances with greater structural diversity.

Soil warming affects sap flow and stomatal gas exchange through altering functional traits in a subtropical forest.

Climate warming influences the structure and function of ecosystems. However, the mechanisms of plant water use and gas exchange responses to climate warming have been less studied, especially from the perspective of different functional traits. We conducted a field experiment to investigate how soil warming (+2 °C) affects sap flow and stomatal gas exchange through plant functional traits and nutrient characteristics in a subtropical forest. We measured stomatal gas exchange of trees (Acacia auriculiformis and Schima superba) and shrubs (Castanea henryi and Psychotria asiatica), and monitored long-term sap flow of both tree species. Besides, plant leaf nutrient contents, functional traits, and soil nutrients were also studied. It is demonstrated that soil warming significantly increased maximum sap flow density (Js_max, 35.1 %) and whole-tree transpiration (EL, 46.0 %) of A. auriculiformis, but decreased those of S. superba (15.6 % and 14.9 %, respectively). Warming increased the photosynthetic rate of P. asiatica (18.0 %) and water use efficiency of S. superba (47.2 %). Leaf nutrients and stomatal anatomical characteristics of shrubs were less affected by soil warming. Soil warming increased (+42.7 %) leaf K content of A. auriculiformis in dry season. Decomposition of soil total carbon, total nitrogen, and available nitrogen was accelerated under soil warming, and soil exchangeable Ca2+ and Mg2+ were decreased. Trees changed stomatal and anatomic traits to adapt to soil warming, while shrubs altered leaf water content and specific leaf area under soil warming. Warming had a greater effect on sap flow of trees, as well as on their leaf gas exchange (total effect: -0.27) than on that of shrubs (total effect: 0.06). In summary, our results suggest that the combination of functional and nutrient traits can help to better understand plant water use and gas exchange responses under climate warming.

Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation.

Machine learning (ML) techniques offer a promising avenue for improving the integration of remote sensing data into mathematical crop models, thereby enhancing crop growth prediction accuracy. A critical variable for this integration is the leaf area index (LAI), which can be accurately assessed using proximal or remote sensing data based on plant canopies. This study aimed to (1) develop a machine learning-based method for estimating the LAI in rice and soybean crops using proximal sensing data and (2) evaluate the performance of a Remote Sensing-Integrated Crop Model (RSCM) when integrated with the ML algorithms. To achieve these objectives, we analyzed rice and soybean datasets to identify the most effective ML algorithms for modeling the relationship between LAI and vegetation indices derived from canopy reflectance measurements. Our analyses employed a variety of ML regression models, including ridge, lasso, support vector machine, random forest, and extra trees. Among these, the extra trees regression model demonstrated the best performance, achieving test scores of 0.86 and 0.89 for rice and soybean crops, respectively. This model closely replicated observed LAI values under different nitrogen treatments, achieving Nash-Sutcliffe efficiencies of 0.93 for rice and 0.97 for soybean. Our findings show that incorporating ML techniques into RSCM effectively captures seasonal LAI variations across diverse field management practices, offering significant potential for improving crop growth and productivity monitoring.

Divergent trajectories of future global gross primary productivity and evapotranspiration of terrestrial vegetation in Shared Socioeconomic Pathways.

Understanding the future trends of carbon and water fluxes between terrestrial ecosystems and the atmosphere is crucial for predicting Earth's climate dynamics. This study employs an advanced numerical approach to project global gross primary productivity (GPP) and evapotranspiration (ET) from 2001 to 2100 under various climate scenarios based on Shared Socioeconomic Pathways (SSPs). To improve predictions of vegetation dynamics, we introduce a novel model (CoLM-PVPM), an enhancement of the Common Land Model version 2014 (CoLM2014), incorporating a prognostic vegetation phenology model (PVPM). Compared to CoLM2014 that relies on satellite-based leaf area index (LAI) inputs, CoLM-PVPM predicts LAI time series using climate variables. Model validation using historical data from 2001 to 2010 demonstrates PVPM in capturing spatiotemporal variations in satellite LAI. Our modeling results indicate that annual averaged LAI and total GPP increase under SSP1-2.6 but decrease under SSP2-4.5, SSP3-7.0, and SSP5-8.5 by 2100. By comparison, annual total ET consistently increases under all SSP scenarios by 2100. Global annual averaged LAI is highly correlated with annual total GPP in all scenarios, while its correlation with annual total ET weakens in SSP2-4.5, SSP3-7.0, and SSP5-8.5. Global annual total vapor pressure deficit (VPD) and precipitation are highly correlated with annual total ET in all scenarios. As emission levels increase, the negative correlation between annual total VPD and GPP strengthens, while the correlation between annual total precipitation and GPP weakens. This research presents an improved model for predicting terrestrial vegetation processes and underscores the importance of low carbon emission scenarios in maintaining carbon-water balances in specific regions.

Nutrients from spawning salmon influence leaf area, tissue density, and nitrogen-15 in riparian plant leaves.

Nutrient subsidies have significant impacts on ecosystems by connecting disjunct habitats, often through long-distance animal migrations. Salmon migrations on the North Pacific coasts provide these kinds of nutrient subsidies from senescent fish at the end of their life cycle, which can have significant ecological effects on terrestrial species. This can include impacts on individuals, populations, and communities, where shifts in community composition towards plant species that indicate nitrogen-rich soils have been documented. We investigated the effects of variation in salmon spawning density on the leaf traits of four common riparian plant species on the central coast of British Columbia, Canada. We found that all plant species had higher foliar salmon-derived nitrogen on streams with a higher spawning density. Three of the four species had larger leaves, and one species also had higher leaf mass per area on streams with more salmon. However, we found no differences in leaf greenness or foliar percent nitrogen among our study streams. These results demonstrate that nutrient subsidies from spawning salmon can have significant impacts on the ecology, morphology, and physiology of riparian plants, which lends support to a mechanism by which certain plants are more common on productive salmon streams.