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Plants and mycorrhizal fungi form mutualistic relationships that affect how resources flow between organisms and within ecosystems. Common mycorrhizal networks (CMNs) could facilitate preferential transfer of carbon and limiting nutrients, but this remains difficult to predict. Do CMNs favour fungal resource acquisition at the expense of plant resource demands (a fungi-centric view), or are they passive channels through which plants regulate resource fluxes (a plant-centric view)? We used stable isotope tracers (13CO2 and 15NH3), plant traits, and mycorrhizal DNA to quantify above- and below-ground carbon and nitrogen transfer between 18 plant species along a 520-km latitudinal gradient in the Pacific Northwest, USA. Plant functional type and tissue stoichiometry were the most important predictors of interspecific resource transfer. Of ‘donor’ plants, 98% were 13C-enriched, but we detected transfer in only 2% of ‘receiver’ plants. However, all donors were 15N-enriched and we detected transfer in 81% of receivers. Nitrogen was preferentially transferred to annuals (0.26 ± 0.50 mg N per g leaf mass) compared with perennials (0.13 ± 0.30 mg N per g leaf mass). This corresponded with tissue stoichiometry differences. Synthesis Our findings suggest that plants and fungi that are located closer together in space and with stronger demand for resources over time are more likely to receive larger amounts of those limiting resources. Read the free Plain Language Summary for this article on the Journal blog.

Abstract: Hyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C4), Chloridoideae (C4), and Pooideae (C3). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf‐level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite‐based spectrometers.

Remote sensing (RS) can be an efficient monitoring method to assess the ecological impacts of restoration. Yet, it has been used relatively little to monitor post‐restoration changes in boreal forestry‐drained peatlands, and particularly the linkages between changes in RS and plant species remain vague. To understand this gap, we utilize data from the Finnish peatland restoration monitoring network spanning 150 sites and a 10‐year post‐restoration monitoring period. We employ Bayesian joint species distribution models (Hierarchical Modeling of Species Communities) to study (1) the changes in optical Sentinel‐2 and Landsat satellite spectral signatures, (2) whether the RS variables improve predictions of vascular plant and moss species and functional type occurrence and cover, and (3) what kinds of associations exist between RS variables and plant species or functional types. Our results show that peatland restoration increases the reflectance of red and near‐infrared (NIR) bands in sparsely treed pine mire forests and open mires but not in densely treed spruce mire forests. Impacts on other tested RS variables consisting of moisture and greenness indices are less clear. Additionally, RS variables increase species‐ or functional type‐specific predictive power only modestly, and there are few clear links between the changes in RS variables and species or functional‐type occurrence and cover. We suggest that red and NIR reflectance can be used as satellite‐based indicators for peatland restoration success and further studies are required to develop usable methods for detecting species‐specific changes with RS. [ABSTRACT FROM AUTHOR]

Nitrogen (N) cycling in organic tundra soil is characterised by pronounced seasonal dynamics and strong influence of the dominant plant functional types. Such patterns in soil N-cycling have mostly been investigated by the analysis of soil N-pools and net N mineralisation rates, which, however, yield little information on soil N-fluxes. In this study we investigated microbial gross N-transformations, as well as concentrations of plant available N-forms in soils under two dominant plant functional types in tundra heath, dwarf shrubs and mosses, in subarctic Northern Sweden. We collected organic soil under three dwarf shrub species of distinct growth form and three moss species in early and late growing season. Our results showed that moss sites were characterised by significantly higher microbial N-cycling rates and soil N-availability than shrub sites. Protein depolymerisation, the greatest soil N-flux, as well as gross nitrification rates generally did not vary significantly between early and late growing season, whereas gross N mineralisation rates and inorganic N availability markedly dropped in late summer at most sites. The magnitude of the seasonal changes in N-cycling, however, clearly differed among plant functional types, indicating interactive effects of seasonality and plant species on soil N-cycling. Our study highlights that the spatial variation and seasonal dynamics of microbial N transformations and soil N availability in tundra heath are intimately linked with the distinct influence of plant functional types on soil microbial activity and the plant species-specific patterns of nutrient uptake and carbon assimilation. This suggests potential strong impacts of future global change-induced shifts in plant community composition on soil N-cycling in tundra ecosystems. [ABSTRACT FROM AUTHOR]

Opportunities to enhance ecosystem functions exist in human‐dominated working landscapes, but understanding of key drivers is often lacking in these novel environments. In Suisun Marsh, California, USA, impounded managed wetlands designed to promote waterfowl incidentally support higher plankton densities—a key resource for imperilled pelagic fishes—than adjacent tidal habitats. Managed wetland operations could produce plankton at critical periods for fish and the aquatic food‐web more broadly, but drivers of plankton production in managed wetlands are poorly understood.We proposed that decaying vegetation resulting from controlled flooding in managed wetlands is important for stimulating plankton blooms, but that the effect varies by plant species and functional type. We conducted a mesocosm experiment to test the effects of different inundated plants on plankton production, including three forb species, three emergent graminoid species and a control treatment without added plant material.Forbs promoted larger phytoplankton blooms and higher zooplankton production—by an order of magnitude—than both emergent graminoids and the control treatment. Emergent graminoids supported larger phytoplankton blooms, but not higher zooplankton production, than the control treatment. Phytoplankton blooms exhibited pulse dynamics in all treatments, with densities increasing initially and crashing by the end of the experiment. Phytoplankton blooms in forb and emergent treatments lasted similar durations, counter to the expectation that emergent graminoids would support diminished production over a relatively prolonged period. Among zooplankton taxa, Daphnia magna and rotifers were strongly associated with all three forbs species while the nonnative cyclopoid copepod Limnothoina tetraspina were associated with cattails (Typha domingensis) and the control. Forbs temporarily depleted dissolved oxygen concentrations during the first week of the experiment, suggesting a tradeoff by which decaying forbs boost plankton production at risk of initial hypoxia.Our results suggest that seasonal drying and flooding operations in managed wetlands are likely to enhance plankton production because dry periods promote forb growth and subsequent flooding introduces labile forb material to the aquatic food web. Results also suggest that seasonal managed‐wetlands act as novel floodplain‐analogues, by which flood pulses drive productivity. Further research is needed to quantify production benefits and tradeoffs to the aquatic ecosystem at the landscape scale and to identify optimal management regimes that will help conserve imperilled species. [ABSTRACT FROM AUTHOR]

Nitrogen deposition continues to change grassland plant community composition particularly in more mesic systems; however, whether these altered plant communities will respond differently to other global change factors remains to be seen. Here, we explore how nutrient-altered tallgrass prairie responds to drought. Seven years of nutrient treatments (control, nitrogen (N), phosphorus (P), and N + P) resulted in significantly different plant communities. Within this experimental context we imposed a 3-year drought followed by 3 years of recovery from drought. The response of plant functional types depended on the nutrient treatment. During recovery years, C4 grasses recovered in the first year in all treatments but the N + P treatment, where instead annual grasses increased. These differential responses during recovery resulted in greater shifts in community composition in the N + P treatment compared with the controls. Despite the effects on community composition, we found no interaction between nutrient treatment and drought treatment on species richness or evenness and standing biomass during drought or recovery. We found drought induced shifts in plant functional groups led to the composition of previously droughted N + P plot becoming more dominated by annual grasses during the recovery years, likely creating a lasting legacy of drought. [ABSTRACT FROM AUTHOR]

Mediterranean ecosystems are predicted to undergo longer and more intense summer droughts. The mechanisms underlying the response of herbaceous communities to such drier environments should be investigated to identify the resilience thresholds of Mediterranean rangelands. A 5‐year experiment was conducted in deep and shallow soil rangelands of southern France. A rainout shelter for 75 days in summer imposed drier and warmer conditions. Total soil water content was measured monthly to model available daily soil water. Aboveground net primary production (ANPP), forage quality, and the proportion of graminoids in ANPP were measured in spring and autumn. Plant senescence and plant cover were assessed in summer and spring, respectively. The experimental years were among the driest ever recorded at the site. Therefore, manipulated summer droughts were drier than long‐term ambient conditions. Interactions between treatment, community type, and experimental year were found for most variables. In shallow soil communities, spring plant cover decreased markedly with time. This legacy effect, driven by summer plant mortality and the loss of perennial graminoids, led to an abrupt loss of resilience when the extreme water stress index exceeded 37 mm 10 day−1, characterized by a reduction of spring plant cover below 50% and a decreased ANPP in rainy years. Conversely, the ANPP of deep soil communities remained unaffected by increased summer drought, although the presence of graminoids increased and forage nutritive value decreased. This study highlights the role of the soil water reserve of Mediterranean plant communities in modulating ecosystem responses to chronically intensified summer drought. Communities on deep soils were resilient, but communities on shallow soils showed a progressive, rapid, and intense degradation associated with a loss of resilience capacity. Notably, indexes of extreme stress were a better indicator of tipping points than indexes of integrated annual stress. Considering the role of soil water availability in other herbaceous ecosystems should improve the ability to predict the resilience of plant communities under climate change. [ABSTRACT FROM AUTHOR]

Terrestrial vegetation is a crucial component of the Earth system, and its changes not only represent one of the most distinct aspects of climate change but also exert significant feedback within the climate system by exchanging energy, moisture, and carbon dioxide. To quantitatively and mechanistically study climate-vegetation feedback, numerical vegetation models have been developed on the theory of ecophysiological constraints on plant functional types. The models eventually can simulate vegetation distribution and succession across different spatial and temporal scales, and associated terrestrial carbon cycle processes by categorizing vegetation into biomes according different plant functional types and their associated environmental factors. Here we review the developing history of vegetation models and provide recent advances and future directions. Before 21st century, static vegetation models, as developed statistical models, can only simulate equilibrated characteristics of vegetation distribution. In last several decades, Dynamic Global Vegetation Models (DGVMs) have been developed to simulate instantaneous responses of vegetation to climate change and associated dynamics, and can be coupled with Earth system models to investigate interactions among atmosphere, ocean, and land. DGVMs are also widely applied to investigate the dynamics accounting for changes in the geographic distribution patterns of land surface vegetation at different spatial and temporal scales and to assess the impacts of terrestrial carbon and water fluxes and land use changes. We suggest that future vegetation modeling could integrate with machine learning, and explore vegetation transient response and feedback as well as impacts of process hierarchies and human activities on climate and ecosystem. [ABSTRACT FROM AUTHOR]

Summary: Strong disturbances may induce ecosystem transitions into new alternative states that sustain through plant–soil interactions, such as the transition of dwarf shrub‐dominated into graminoid‐dominated vegetation by herbivory in tundra. Little evidence exists on soil microbial communities in alternative states, and along the slow process of ecosystem return into the predisturbance state.We analysed vegetation, soil microbial communities and activities as well as soil physico‐chemical properties in historical reindeer enclosures in northernmost Finland in the following plot types: control heaths in the surrounding tundra; graminoid‐dominated; 'shifting'; and recovered dwarf shrub‐dominated vegetation inside enclosures.Soil fungal communities followed changes in vegetation, whereas bacterial communities were more affected by soil physico‐chemical properties. Graminoid plots were characterized by moulds, pathotrophs and dark septate endophytes. Ericoid mycorrhizal and saprotrophic fungi were typical for control and recovered plots. Soil microbial communities inside the enclosures showed historical contingency, as their spatial variation was high in recovered plots despite the vegetation being more homogeneous.Self‐maintaining feedback loops between plant functional types, soil microbial communities, and carbon and nutrient mineralization act effectively to stabilize alternative vegetation states, but once predisturbance vegetation reestablishes itself, soil microbial communities and physico‐chemical properties return back towards their predisturbance state. [ABSTRACT FROM AUTHOR]

Dams worldwide have significantly altered the composition of riparian forests. However, research on the functional traits of dominant herbs experiencing flooding stress due to dam impoundment remains limited. Given the high plasticity of leaf traits and their susceptibility to environmental influences, this study focuses on riparian herbs along the Three Gorges Hydro‐Fluctuation Zone (TGHFZ). Specifically, it investigates how six leaf physiological traits of leading herbs—carbon, nitrogen, phosphorus, and their stoichiometric ratios—adapt to periodic flooding in the TGHFZ using cluster analysis, one‐way analysis of variance (ANOVA), multiple comparisons, Pearson correlation analysis, and principal component analysis (PCA). We categorized 25 dominant herb species into three plant functional types (PFTs), noting that species from the same family tended to fall into the same PFT. Notably, leaf carbon content (LCC) exhibited no significant differences across various PFTs or altitudes. Within riparian forests, different PFTs employ distinct adaptation strategies: PFT‐I herbs invest in structural components to enhance stress resistance; PFT‐II, mostly comprising gramineous plants, responds to prolonged flooding by rapid growth above the water; and PFT‐III, encompassing nearly all Compositae and annual plants, responds to prolonged flooding with vigorous rhizome growth and seed production. Soil water content (SWC) emerges as the primary environmental factor influencing dominant herb growth in the TGHFZ. By studying the response of leaf physiological traits in dominant plants to artificial flooding, we intend to reveal the survival mechanisms of plants under adverse conditions and lay the foundation for vegetation restoration in the TGHFZ. [ABSTRACT FROM AUTHOR]

This study integrates phenological data with Plant Functional Types (PFTs) to map biodiversity and ecosystem dynamics. It introduces Phenological Functional Types (PhFTs) by combining remote sensing with ground-based observations, enhancing vegetation modeling’s temporal resolution. Analysing 130 tree species in Shoolpaneshwar Wildlife Sanctuary reveals unique PhFTs with distinct phenological signatures linked to ecological functions. Using advanced remote sensing, especially the Enhanced Vegetation Index (EVI), enables detailed temporal and spatial analyses across large geographic scales. Cluster analysis and Fuzzy C-Means (FCM) clustering categorize phenological stages, highlighting phenological diversity’s role in ecosystem services and conservation. The study identifies significant phenological shifts likely due to climate change, impacting ecosystem structure and function. It underscores the importance of multidisciplinary approaches in addressing ecological issues, contributing insights to global change biology and landscape ecology for biodiversity conservation and sustainable ecosystem management. [ABSTRACT FROM AUTHOR]

Summary: Land surface phenology (LSP), the characterization of plant phenology with satellite data, is essential for understanding the effects of climate change on ecosystem functions. Considerable LSP variation is observed within local landscapes, and the role of biotic factors in regulating such variation remains underexplored.In this study, we selected four National Ecological Observatory Network terrestrial sites with minor topographic relief to investigate how biotic factors regulate intra‐site LSP variability. We utilized plant functional type (PFT) maps, functional traits, and LSP data to assess the explanatory power of biotic factors for the start and end of season (SOS and EOS) variability.Our results indicate that PFTs alone explain only 0.8–23.4% of intra‐site SOS and EOS variation, whereas including functional traits significantly improves explanatory power, with cross‐validation correlations ranging from 0.50 to 0.85. While functional traits exhibited diverse effects on SOS and EOS across different sites, traits related to competitive ability and productivity were important for explaining both SOS and EOS variation at these sites.These findings reveal that plants exhibit diverse phenological responses to comparable environmental conditions, and functional traits significantly contribute to intra‐site LSP variability, highlighting the importance of intrinsic biotic properties in regulating plant phenology. [ABSTRACT FROM AUTHOR]

Stomata play a critical role in surface–atmosphere exchange by controlling the flux of water and CO2 between the leaf and the atmosphere. However, the driving factors for the vital parameter, the marginal water cost of the carbon gain (λ), are poorly understood in the subalpine regions. Therefore, we studied λ in subalpine plants at all across altitudes. There was a parabolic pattern in λ of trees with increasing elevation, the highest at 2700 m asl and 3500 m asl for the broadleaf trees and the coniferous trees, respectively, while the λ of species of herbs and shrub decreased with elevation. For all species, λ were higher during the mid-growing season than during the early and late growing seasons under the same conditions. Mean λ values were higher in herbs and shrubs than in trees, indicating a more conservative strategy for water use in trees than in herbs and shrubs in forest communities. Furthermore, a higher λ value of the broadleaf tree than of the coniferous tree suggests that angiosperm trees use water more profligately than gymnosperm trees. Environmental factors had opposite effects on λ for herbs, shrubs, and trees. Soil conditions were positively correlated with λ for herbs and shrubs, but negatively for trees. Vegetation factors negatively influenced λ for herbs and shrubs, while no significant relationship was found with trees. From the results of the structural equation model, the improved empirical models for the simulation of stomatal conductance(gs) simulation based on the optimal stomatal behavior theory can accurately estimate the gs of the main species in subalpine forest communities. [ABSTRACT FROM AUTHOR]

Summary: Tropical forest root characteristics and resource acquisition strategies are underrepresented in vegetation and global models, hampering the prediction of forest–climate feedbacks for these carbon‐rich ecosystems. Lowland tropical forests often have globally unique combinations of high taxonomic and functional biodiversity, rainfall seasonality, and strongly weathered infertile soils, giving rise to distinct patterns in root traits and functions compared with higher latitude ecosystems. We provide a roadmap for integrating recent advances in our understanding of tropical forest belowground function into vegetation models, focusing on water and nutrient acquisition. We offer comparisons of recent advances in empirical and model understanding of root characteristics that represent important functional processes in tropical forests. We focus on: (1) fine‐root strategies for soil resource exploration, (2) coupling and trade‐offs in fine‐root water vs nutrient acquisition, and (3) aboveground–belowground linkages in plant resource acquisition and use. We suggest avenues for representing these extremely diverse plant communities in computationally manageable and ecologically meaningful groups in models for linked aboveground–belowground hydro‐nutrient functions. Tropical forests are undergoing warming, shifting rainfall regimes, and exacerbation of soil nutrient scarcity caused by elevated atmospheric CO2. The accurate model representation of tropical forest functions is crucial for understanding the interactions of this biome with the climate. [ABSTRACT FROM AUTHOR]

Background and aims: It is widely accepted that soil water repellency (SWR) is mainly caused by hydrophobic compounds from plants and microbes. The relation between these hydrophobic compounds, which are defined as SWR biomarkers, and SWR has been rarely known and the knowledge of their sources remains controversial. We aimed to select key SWR biomarkers predicting SWR and to trace their origin. Methods: Topsoils under/around five dominant plant species (DPS) belonging to various plant functional types (PFTs) on the Chinese Loess Plateau were sampled, together with plant samples, i.e., plant leaves and roots. A sequential extraction procedure and hydrolysis approach were applied to obtain three organic fractions: dichloromethane (DCM)/ methanol (MeOH) soluble fraction (D), DCM/MeOH soluble fraction of isopropanol/ammonia solution (IPA/NH3) extract (AS), and DCM/MeOH insoluble fraction of IPA/NH3 extract (AI), which were analyzed by gas chromatography-mass spectrometry (GC–MS). Results: The cutin concentrations in leaves and soils of evergreen trees with higher WDPT values were 10–60 times and 35.39–78.77% higher than those of other PFTs, respectively. However, the root-derived suberin in the soils under evergreen trees, deciduous trees, and shrubs were 19.55–62.69% lower than that around the legumes and grasses. Correlation analysis further revealed that cutin was positively correlated with SWR (r2 = 0.24, p < 0.001), whereas suberin was not (p > 0.05). Conclusion: After considering the SWR behavior during extraction and the possible origin of the compounds, we concluded that leaf-derived cutin appears to have the greatest effect on SWR. [ABSTRACT FROM AUTHOR]

Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment‐, individual root‐, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation. We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition, p <.001) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients, p <.001). Furthermore, whole root system conductance increased significantly with root system age across several crop species (p <.01), causing very large variation in the data (>2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state‐of‐the‐art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species. These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database (https://roothydraulic-properties.shinyapps.io/database/), fostering scientific exchange. [ABSTRACT FROM AUTHOR]

An effective way to reduce complexity in ecological modelling is by grouping species that share similar characteristics into functional groups or types. Often, the creation of plant functional groups (PFGs) is carried out for each case study in an ad-hoc way using a small set of traits. This limits the transferability of these PFGs to other geographical areas or study systems. We propose a novel generic framework to generate PFGs that considers the most important ecological dimensions, is applicable to case studies globally, and that emerges from patterns of functional redundancy across species. Based on most relevant and measured plant characteristics, we designed a multi-step process that includes: i) data harmonisation and missing values imputation; ii) species clustering based on multiple characteristics encompassing the main ecological dimensions featured in plant community ecological models (i.e., dispersal, competition, and demography) and iii) the combination of ecological dimension-specific groups into comprehensive PFGs. We demonstrate this framework by applying it to a global dataset of plant characteristics including a functional traits dataset and a plant-soil co-occurrence dataset for 19,102 species. Lastly, to test the ability of generated PFGs to summarise species’ functional variation within plant communities, we correlate taxonomical and functional diversity indices calculated at the species and at the PFGs level across a global dataset of plant communities (sPlotOpen). Our framework generated 465 global, robust data-driven PFGs with non-overlapping combinations of traits for each ecological dimension divided by growth form. The validation returned positive correlation values between PFGs and species-level diversity metrics, supporting the ability of the obtained PFGs to capture functional and taxonomic diversity patterns across a variety of plant communities worldwide. The framework allows for the easy integration of newly available species characteristics data. The obtained global PFGs, covering all main known ecological processes and environmental conditions at small resolution, can increase the predictive power and accuracy of process-based models and help furthering varying-scale ecological studies.

Leaf functional traits (LFTs) are highly plastic and essential to plant resource acquisition and utilization. However, the collinearity of environmental factors and differences in the response of distinct functional types generate challenges in clarifying the contributions of environment variables to LFTs. This study selected 28 species from four forests in northern China with similar mean annual precipitation (MAP) to explore how other dissimilar environmental factors influence LFTs after controlling for the most influential factor. Specifically, we examined the effect of geographical (latitude, longitude, and altitude) and non-precipitation environmental factors (mean annual temperature (MAT), aridity index (AI), and soil pH) on leaf dry matter content (LDMC), and leaf carbon (LCC), nitrogen (LNC), and phosphorus (LPC) concentration, and pH. The collinearity of environmental factors on LFTs can be attributed to the regulation of moisture and temperature by geographical factors. Therefore, the remaining geo-environmental factors still played a significant role in LFT variation after excluding the most influential factor, which assist LFTs to maintain the original pattern of change across the environmental gradient. As latitude, longitude, and AI decreased, MAT increased, LCC and LPC decreased, LNC first increased, then decreased, and LDMC and leaf pH did not change significantly. The trends and intensity of LFTs variation varied between functional types. Significant differences in correlation were observed in LPC-to-LDMC and LPC-to-LNC across all regions for each species. Trait-trait relationships were inconsistent between life forms (herbs and woody plants) for leaf pH-to-nutrient concentration (C, N, P), LCC-to-LPC, and LCC-to-LDMC. Environmental variables were larger contributors to LFTs (especially LNC, LPC, and LDMC) variation than plant functional types (PFTs), while LCC and leaf pH were largely dependent of PFTs. These patterns result from plant adaptations to multiple variables, and a single environmental factor (even the core element that generated the collinearity of environmental factors such as MAP) in northern China cannot dominant geographic patterns among LFTs. This study sheds light on plant adaptation strategies to environmental change, deepening our understanding of ecosystem function and functional coordination.

Weed responses in disturbance-prone agroecosystems are linked to specific plant traits that enable their persistence. Understanding how weeds adapt to thrive in these systems in response to herbicide application is important for farmers to improve weed management for enhanced crop productivity. In this study, we investigated the functional traits and types of weed species able to persist within fields of glyphosate-tolerant maize in the Oliver Tambo District of the Eastern Cape Province, South Africa. This was accomplished by exploring the abundance patterns, composition, and richness of specific weed traits and functional types. Frequency measures (%) were used to identify indicator species. A data set comprising 42 indicator weed species and 11 predefined disturbance traits from 28 fields of glyphosate-tolerant maize was considered for functional analysis. Clusters were identified according to the grouping of weed species based on their trait scores, which revealed ten plant functional types (PFTs). Disturbances associated with post-emergence (after ploughing, sowing, and herbicide application) act as filters that select for weed species with traits such as life span, life form, growth form, photosynthetic pathway, carbon storage, and nitrogen-fixing ability to colonise fields. Trait richness did not differ significantly across maize fields. Our results highlighted the functional types and traits that are favourable to weed resistance and survival, and these need to be considered when developing different herbicide application protocols. By understanding which traits are favourable for weed survival post-emergence, farmers can apply targeted weed management to safeguard maize productivity. In addition, successful control of weeds will contribute to landscape-targeted herbicide applications that are less harmful to the environment. [ABSTRACT FROM AUTHOR]

For over 40 years, Plant Functional Types (PFTs) have been used to discretize the ∼400,000 species of terrestrial plants into "similar" classes. Within Earth System Models (ESMs), PFTs simplify terrestrial biosphere modeling in combination with soil information and other site characteristics. However, in flux analysis studies, PFT schemes are often implemented as the sole analytical lens to clarify complex behavior. This usage assumes that PFTs adequately enable a mapping between climate inputs and flux outputs. Here, we show that random forest models, trained using aggregated climate and flux measurements from 245 eddy‐covariance sites, cannot accurately predict PFT groupings, regardless of the nature of the PFT scheme. Similarly, PFTs provide negligible benefit when using site climate to predict site flux regimes and vice versa. While use of PFT classifications is convenient, our results suggest they do not aid analytical skill, which has important implications for future terrestrial flux studies. Plain Language Summary: To understand how the land surface behaves, we often divide plants into a small number (20 or less) of "similar" groups, such as evergreen forests, or grasslands, known as Plant Functional Types (PFTs). The idea is that landscapes with similar large‐scale characteristics will behave in the same way. In land surface models, these PFT groups determine how the simulated plants react to the climate in combination with soil information and other characteristics, yet analysis of observations often use PFT groups alone to try to explain variations in results between different experimental sites. We use machine learning to show that while PFTs might be visually compelling, they do not necessarily represent behavior groupings and might actually hide real world behavior if used for analysis. As such, we suggest that future studies instead try to look at more specific site characteristics when trying to explain analysis results. Key Points: Plant Functional Types (PFTs), as often used in land flux studies, are not easily empirically associated with site climate and/or flux regimesA broad selection of alternative vegetation/land cover classifications do not offer greater predictabilityThe disconnect between PFTs and climate/flux regimes has implications for modeling and analysis of terrestrial systems [ABSTRACT FROM AUTHOR]

Abstract Dams worldwide have significantly altered the composition of riparian forests. However, research on the functional traits of dominant herbs experiencing flooding stress due to dam impoundment remains limited. Given the high plasticity of leaf traits and their susceptibility to environmental influences, this study focuses on riparian herbs along the Three Gorges Hydro‐Fluctuation Zone (TGHFZ). Specifically, it investigates how six leaf physiological traits of leading herbs—carbon, nitrogen, phosphorus, and their stoichiometric ratios—adapt to periodic flooding in the TGHFZ using cluster analysis, one‐way analysis of variance (ANOVA), multiple comparisons, Pearson correlation analysis, and principal component analysis (PCA). We categorized 25 dominant herb species into three plant functional types (PFTs), noting that species from the same family tended to fall into the same PFT. Notably, leaf carbon content (LCC) exhibited no significant differences across various PFTs or altitudes. Within riparian forests, different PFTs employ distinct adaptation strategies: PFT‐I herbs invest in structural components to enhance stress resistance; PFT‐II, mostly comprising gramineous plants, responds to prolonged flooding by rapid growth above the water; and PFT‐III, encompassing nearly all Compositae and annual plants, responds to prolonged flooding with vigorous rhizome growth and seed production. Soil water content (SWC) emerges as the primary environmental factor influencing dominant herb growth in the TGHFZ. By studying the response of leaf physiological traits in dominant plants to artificial flooding, we intend to reveal the survival mechanisms of plants under adverse conditions and lay the foundation for vegetation restoration in the TGHFZ.

Leaf chlorophyll content (LCC) is crucial in plant physiology and ecological research. Although several LCC products have recently been generated at a regional or global scale, understanding their accuracy is still a concern in the scientific community. We intercompared and analyzed five existing LCC products (MuSyQ LCC, MODIS LCC, MERIS LCC, GLCC, and GLOBMAP MERIS LCC) over China in terms of spatial continuity and spatiotemporal consistency over seven plant functional types. The products of 2011 and 2019 over China were used in this study. Research findings indicate (1) the 30 m-resolution MuSyQ LCC has the highest accuracy compared to field LCC of cropland and grassland types, with an RMSE of 19.5 μg/cm2, while MODIS LCC product demonstrates a more robust fit to the measured LCC, with an R2 of 0.341. (2) Interpolation of products with lower spatial resolution e.g. MODIS LCC, MERIS LCC, and GLOBMAP MERIS LCC, generally improves spatial continuity. The non-interpolated 30 m MuSyQ LCC exhibits good regional continuity due to its high spatial resolution. The lowest spatial continuity is found over shrubs for all products. (3) MODIS LCC and MuSyQ LCC of 2019 demonstrate high overall spatial consistency and exhibit the highest correlation over cropland sites. MODIS LCC and GLOBMAP MERIS LCC of 2011 demonstrate high temporal consistency over deciduous forests, evergreen forests, grasslands, and shrubs sites. The most robust overall temporal consistency is exhibited among all products in the deciduous needleleaved forest, followed by evergreen needleleaved forest and grassland. The findings of this research are essential for improving leaf chlorophyll content inversion algorithms and for understanding and better use of LCC products in land surface models.

Abstract Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment‐, individual root‐, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation. We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition, p 2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state‐of‐the‐art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species. These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database (https://roothydraulic-properties.shinyapps.io/database/), fostering scientific exchange.

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.

Understanding how the complexities of biodiversity are maintained on islands has long been one of the central issues studied in conservation biology. With the global onset of anthropogenically driven biodiversity changes, the influence of anthropogenic activities challenges the applicability of the widely accepted Equilibrium Theory of Island Biogeography (ETIB). This study aimed to test how island area, isolation (MD: distance to the mainland, and ND: distance to the nearest island), and human activities (habitat heterogeneity, and proportion of road area relative to island area to refer to the human impact on island) separately and jointly affect the species diversity of vascular plants. The number of plant species with different growth forms (woody plants, herbs, and lianas) was calculated on 35 islands in Sanyang Wetland, which is a human-dominated wetland near the highly populated city of Wenzhou, China. We also evaluated the relative importance of these variables on the number of species using a random forest analysis. The number of species increased with increasing island area and habitat heterogeneity and decreased with increasing isolation for different growth forms. A significant, positive relationship was observed between the number of species and the proportion of road area for herbs, but not for woody plants and lianas. Habitat heterogeneity was the most important predictor for all plants, the proportion of road area was the most important predictor for herbs, and MD was the most important predictor for woody plants and lianas. These results indicate that ETIB remains relevant in the context of human disturbance, but the relative importance of ecological processes on species diversity differs significantly among plant growth forms. Therefore, we should consider plant functional groups when testing multifactorial effects on species diversity on human-dominated islands.

This study integrates phenological data with Plant Functional Types (PFTs) to map biodiversity and ecosystem dynamics. It introduces Phenological Functional Types (PhFTs) by combining remote sensing with ground-based observations, enhancing vegetation modeling’s temporal resolution. Analysing 130 tree species in Shoolpaneshwar Wildlife Sanctuary reveals unique PhFTs with distinct phenological signatures linked to ecological functions. Using advanced remote sensing, especially the Enhanced Vegetation Index (EVI), enables detailed temporal and spatial analyses across large geographic scales. Cluster analysis and Fuzzy C-Means (FCM) clustering categorize phenological stages, highlighting phenological diversity’s role in ecosystem services and conservation. The study identifies significant phenological shifts likely due to climate change, impacting ecosystem structure and function. It underscores the importance of multidisciplinary approaches in addressing ecological issues, contributing insights to global change biology and landscape ecology for biodiversity conservation and sustainable ecosystem management.

Abstract For over 40 years, Plant Functional Types (PFTs) have been used to discretize the ∼400,000 species of terrestrial plants into “similar” classes. Within Earth System Models (ESMs), PFTs simplify terrestrial biosphere modeling in combination with soil information and other site characteristics. However, in flux analysis studies, PFT schemes are often implemented as the sole analytical lens to clarify complex behavior. This usage assumes that PFTs adequately enable a mapping between climate inputs and flux outputs. Here, we show that random forest models, trained using aggregated climate and flux measurements from 245 eddy‐covariance sites, cannot accurately predict PFT groupings, regardless of the nature of the PFT scheme. Similarly, PFTs provide negligible benefit when using site climate to predict site flux regimes and vice versa. While use of PFT classifications is convenient, our results suggest they do not aid analytical skill, which has important implications for future terrestrial flux studies.

Understanding how the complexities of biodiversity are maintained on islands has long been one of the central issues studied in conservation biology. With the global onset of anthropogenically driven biodiversity changes, the influence of anthropogenic activities challenges the applicability of the widely accepted Equilibrium Theory of Island Biogeography (ETIB). This study aimed to test how island area, isolation (MD: distance to the mainland, and ND: distance to the nearest island), and human activities (habitat heterogeneity, and proportion of road area relative to island area to refer to the human impact on island) separately and jointly affect the species diversity of vascular plants. The number of plant species with different growth forms (woody plants, herbs, and lianas) was calculated on 35 islands in Sanyang Wetland, which is a human-dominated wetland near the highly populated city of Wenzhou, China. We also evaluated the relative importance of these variables on the number of species using a random forest analysis. The number of species increased with increasing island area and habitat heterogeneity and decreased with increasing isolation for different growth forms. A significant, positive relationship was observed between the number of species and the proportion of road area for herbs, but not for woody plants and lianas. Habitat heterogeneity was the most important predictor for all plants, the proportion of road area was the most important predictor for herbs, and MD was the most important predictor for woody plants and lianas. These results indicate that ETIB remains relevant in the context of human disturbance, but the relative importance of ecological processes on species diversity differs significantly among plant growth forms. Therefore, we should consider plant functional groups when testing multifactorial effects on species diversity on human-dominated islands. [ABSTRACT FROM AUTHOR]

Summary: Drylands of the southwestern United States are rapidly warming, and rainfall is becoming less frequent and more intense, with major yet poorly understood implications for ecosystem structure and function. Thermography‐based estimates of plant temperature can be integrated with air temperature to infer changes in plant physiology and response to climate change. However, very few studies have evaluated plant temperature dynamics at high spatiotemporal resolution in rainfall pulse‐driven dryland ecosystems.We address this gap by incorporating high‐frequency thermal imaging into a field‐based precipitation manipulation experiment in a semi‐arid grassland to investigate the impacts of rainfall temporal repackaging.All other factors held constant, we found that fewer/larger precipitation events led to cooler plant temperatures (1.4°C) compared to that of many/smaller precipitation events. Perennials, in particular, were 2.5°C cooler than annuals under the fewest/largest treatment.We show these patterns were driven by: increased and consistent soil moisture availability in the deeper soil layers in the fewest/largest treatment; and deeper roots of perennials providing access to deeper plant available water. Our findings highlight the potential for high spatiotemporal resolution thermography to quantify the differential sensitivity of plant functional groups to soil water availability. Detecting these sensitivities is vital to understanding the ecohydrological implications of hydroclimate change. [ABSTRACT FROM AUTHOR]

Climate warming and projected increase in summer droughts puts northern peatlands under pressure by subjecting them to a combination of gradual drying and extreme weather events. The combined effect of those on peatland functions is poorly known. Here, we studied the impact of long‐term water level drawdown (WLD) and contrasting weather conditions on leaf phenology and biomass production of ground level vegetation in boreal peatlands. Data were collected during two contrasting growing seasons from a WLD experiment including a rich and a poor fen and an ombrotrophic bog. Results showed that WLD had a strong effect on both leaf area development and biomass production, and these responses differed between peatland types. In the poor fen and the bog, WLD increased plant growth, while in the rich fen, WLD reduced the growth of ground level vegetation. Plant groups differed in their response, as WLD reduced the growth of graminoids, while shrubs and tree seedlings benefited from it. In addition, the vegetation adjusted to the lower WTs, was more responsive to short‐term climatic variations. The warmer summer resulted in a greater maximum and earlier peaking of leaf area index, and greater biomass production by vascular plants and Sphagnum mosses at WLD sites. In particular, graminoids benefitted from the warmer conditions. The change towards greater production in the WLD sites in general and during the warmer weather in particular, was related to the observed transition in plant functional type composition towards arboreal vegetation. [ABSTRACT FROM AUTHOR]

Current challenges of functional responses in plant communities to climate change call for multi‐factorial experiments. Moreover, studies on climate change should focus on below‐ground responses since absorptive roots largely control soil C allocation and resource acquisition. Thus, we aimed to understand biomass allocation and traits of absorptive roots in young mesocosm grasslands subjected to simultaneous manipulation of three components of climate change.We tested grassland biomass and root traits under climate change while manipulating functional composition. Using 64 mesocosms with designed grasslands within four chambers of a controlled‐environment facility ('ecotron'), we simulated two contrasting IPCC climate change scenarios for elevated [CO2] and temperature ('eCO2' and 'eT'). We applied normal vs. reduced precipitation of early summer in Central Europe. We also tested the effect of functional composition by varying the proportion of grasses and forbs in the communities. Specifically, we quantified above‐ and below‐ground biomass, root diameter (RD), root tissue density (RTD), specific root length (SRL), and root length density (RLD).Functional composition played a significant role in biomass allocation of the grasslands, with grass‐dominated communities producing more below‐ground biomass than forb‐dominated ones, and the opposite pattern registered above‐ground. Below‐ground biomass did not respond to climate change factors, whereas root trait values responded significantly during early establishment of the grasslands. A higher RD indicated a more conservative strategy under reduced precipitation, while eT and eCO2 led to higher RTD. We detected interactive effects between climate change and functional composition on root traits. Moreover, root biomass primarily occupied the upper soil layer, while a warm and CO2‐rich environment promoted root allocation to the lower soil layer. Grass‐dominated communities quickly colonized all available soil volume, while forb‐dominated ones accumulated more root biomass in the upper soil layer.In the mesocosm grasslands, root trait variation rather than root biomass reflected below‐ground adjustments to climate change. Furthermore, functional composition and the associated trait diversity modulated biomass allocation. Thus, establishing plant communities that are more resilient to climate change must consider the functional and taxonomic composition of the seed mixtures designed to restore urban grasslands. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Process-based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass-dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C3 grasses and (sub)tropical C4 grasses. Incorporation of species-level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), situated between species-level trait data and PFT-level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.

Peatlands are one of the most significant terrestrial carbon pools, and the processes behind the carbon cycle in peatlands are strongly associated with different vegetation patterns. Handheld spectroradiometer data has been widely applied in ecological research, but there is a lack of studies on peatlands assessing how the temporal and spectral resolution affect the detectability of vegetation patterns. We collected field spectroscopy and vegetation inventory data at two northern boreal peatlands, Lompolojänkkä and Halssiaapa, between late May and August 2019. We conducted multivariate random forest regressions to examine the appropriate periods, benefits of multi-temporal data, and optimal spectral bandwidth and sampling interval for detecting plant communities and the two-dimensional (2D) %-cover, above-ground biomass (AGB) and leaf area index (LAI) of seven plant functional types (PFTs). In the best cross-site regression models for detecting plant community clusters (PCCs), R2 was 42.6–48.0% (root mean square error (RMSE) 0.153–0.193), and for PFT 2D %-cover 53.9–69.8% (RMSE 8.2–17.6%), AGB 43.1–61.5% (RMSE 86.2–165.5 g/m2) and LAI 46.3–51.3% (RMSE 0.220–0.464 m2/m2). The multi-temporal data of the whole season increased R2 by 13.7–24.6%-points and 10.2–33.0%-points for the PCC and PFT regressions, respectively. There was no single optimal temporal window for vegetation pattern detection for the two sites; in Lompolojänkkä the early growing season between late May and mid-June had the highest regression performance, while in Halssiaapa, the optimal period was during the peak season, from July to early August. In general, the spectral sampling interval between 1 to 10 nm yielded the best regression performance for most of the vegetation characteristics in Lompolojänkkä, whereas the optimal range extended to 20 nm in Halssiaapa. Our findings underscore the importance of fieldwork timing and the use of multi-temporal and hyperspectral data in detecting vegetation in spatially heterogeneous landscapes.

Diversity of Plant Functional Types (PFTs) along a sediment core drilled in Lonar Crater Lake, Central India, is analysed, based on previously published pollen data for a total of 115 depth levels and covering the past 9.2 kyr. Our results support concepts of a dominantly humid period persisting until ca. 5 ka, succeeded by a significantly drier phase with weaker monsoon, including various prominent drying pulses. Throughout the Holocene, plant diversity at Lonar was composed of a variable proportion of herbaceous PFTs (ca. 40–60%) and mainly angiosperm woody PFTs. Changes in herbaceous diversity account for a considerable part of the observed PFT data variability. PCA analysis reveals a total of four partly alternating diversity associations (DAs) interpretable in terms of vegetation spanning from drier Savanna and dry deciduous forest to (semi-) evergreen woody vegetation, mainly recorded in older strata. It is shown that the succession of the DAs is largely triggered by climate change inferred from other proxies. Correlation analysis with geochemical data testifies that the diversity signal obtained is largely unaffected by sedimentary processes and detrital inflow, highlighting the potential of this archive to reflect 'true' diversity signals, rather than artefacts of sedimentary processes. While warm and humid conditions promoted diversity, drying and weak monsoon tended to cause diversity loss. In contrast, the relative diversity of dry herbs increased during drier periods. Short-term variability in our data is evident in varying diversity percentages of single PFTs and their ratios. Changing ratios of dry on mesic herbs, the relation of shrub and tree diversity, tropical on temperate PFTs, and total plant diversity are likely related to solar cycles. Evidence for recurrent cooling episodes through solar forcing comes from the observed coincidence of Grand Solar Minima and minima in the tropical/temperate PFT ratio. [ABSTRACT FROM AUTHOR]

A dynamic vegetation model was adapted to a mixed forest of deciduous broad‐leaved trees and evergreen conifers in Hokkaido, the northernmost main island of Japan, located within a transition zone between cool‐temperate and sub‐boreal forest ecosystems. In this forest, elevation and a terrestrial‐wetness index affect tree biomass and the percentage of conifers. We maximized agreement between this observed pattern and model output by calibrating parameters that control drought and excessive soil‐moisture tolerance, establishment rate, and background mortality rate. In the simulation, biomass increased as a single peak curve with simulation year, while the percentage of conifers increased until the end of 200 simulation years. The 75‐year simulated forest was most comparable to the adapted forest, consistent with the average frequency of catastrophic storm disturbances in Hokkaido. The model also reconstructed a reasonable succession pattern based on temperature and soil moisture. Therefore, the model mechanistically reconstructed the mixed forest via spatial niche segregation and succession after catastrophic disturbances. However, the model did not reconstruct the percentages of broad‐leaved trees and conifers of forests prior to the disturbance in independent validation plots, demonstrating that additional processes should be considered in future models. [ABSTRACT FROM AUTHOR]

Accurate estimation and forecasts of net biome CO2 exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. Prior efforts to improve NBE predictions have predominantly focused on increasing models' structural realism (and thus complexity), but parametric error and uncertainty are also key determinants of model skill. Here, we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe, pitting the traditional plant functional type (PFT)‐based approach against a novel top‐down, machine learning‐based "environmental filtering" (EF) approach. To do so, we simulate these contrasting methods for parameter assignment within a flexible model–data fusion framework of the terrestrial carbon cycle (CARDAMOM) at a global scale. In the PFT‐based approach, model parameters from a small number of select locations are applied uniformly within regions sharing similar land cover characteristics. In the EF‐based approach, a pixel's parameters are predicted based on underlying relationships with climate, soil, and canopy properties. To isolate the role of parametric from structural uncertainty in our analysis, we benchmark the resulting PFT‐based and EF‐based NBE predictions with estimates from CARDAMOM's Bayesian optimization approach (whereby "true" parameters consistent with a suite of data constraints are retrieved on a pixel‐by‐pixel basis). When considering the mean absolute error of NBE predictions across time, we find that the EF‐based approach matches or outperforms the PFT‐based approach at 55% of pixels—a narrow majority. However, NBE estimates from the EF‐based approach are susceptible to compensation between errors in component flux predictions and predicted parameters can align poorly with the assumed "true" values. Overall, though, the EF‐based approach is comparable to conventional approaches and merits further investigation to better understand and resolve these limitations. This work provides insight into the relationship between terrestrial biosphere model performance and parametric uncertainty, informing efforts to improve model parameterization via PFT‐free and trait‐based approaches. [ABSTRACT FROM AUTHOR]

Questions: Does grazing management shape the proportion of plant guilds (grasses, legumes, forbs and sedges) in mountain grasslands?Which properties of grazing management have the greatest effect on the proportion of plant guilds: grazer diversity/identity or grazing intensity?Are the effects of grazing management on guild proportion modified by other environmental variables that explain plant guild distribution at broad spatial scales? Location: Mountains in the Pyrenees. Methods: We modelled the proportion of grasses, legumes, forbs and sedges using data from the PASTUS database (n = 96), which contains a wide range of environmental and management conditions due to the high variety of environmental conditions in mountain grasslands in the Pyrenees. We used a machine‐learning algorithm to find those variables that best explained the proportions of each plant guild. We focussed on the differences between the levels of grazing intensity and the grazing species included in the model, and on detecting interactions between grazing variables and climate, topography and soil conditions. Results: The proportion of forbs and grasses strongly depended on the grazing livestock species at broad spatial scales. Only soil pH showed a higher overall explanatory power on guild distribution. In general, forbs were favoured in cattle‐ and grasses in sheep‐grazed grasslands, the latter also being favoured on acidic soils, while forbs were favoured in more alkaline soil conditions. However, the effects of those factors (grazing species and soil pH) were modulated through interactions with several other environmental variables, including soil Mg, K and P, and terrain slope. In contrast, grazing intensity was a minor driver of guild distribution. Conclusions: Our results provide information about the relationship between plant functional diversity, indicated by the different plant guild proportions, and grazing management in the Pyrenean grasslands. This information could be useful for developing hypotheses for future experimental studies and for designing policies to improve the management of mountain grasslands. [ABSTRACT FROM AUTHOR]

Weed responses in disturbance-prone agroecosystems are linked to specific plant traits that enable their persistence. Understanding how weeds adapt to thrive in these systems in response to herbicide application is important for farmers to improve weed management for enhanced crop productivity. In this study, we investigated the functional traits and types of weed species able to persist within fields of glyphosate-tolerant maize in the Oliver Tambo District of the Eastern Cape Province, South Africa. This was accomplished by exploring the abundance patterns, composition, and richness of specific weed traits and functional types. Frequency measures (%) were used to identify indicator species. A data set comprising 42 indicator weed species and 11 predefined disturbance traits from 28 fields of glyphosate-tolerant maize was considered for functional analysis. Clusters were identified according to the grouping of weed species based on their trait scores, which revealed ten plant functional types (PFTs). Disturbances associated with post-emergence (after ploughing, sowing, and herbicide application) act as filters that select for weed species with traits such as life span, life form, growth form, photosynthetic pathway, carbon storage, and nitrogen-fixing ability to colonise fields. Trait richness did not differ significantly across maize fields. Our results highlighted the functional types and traits that are favourable to weed resistance and survival, and these need to be considered when developing different herbicide application protocols. By understanding which traits are favourable for weed survival post-emergence, farmers can apply targeted weed management to safeguard maize productivity. In addition, successful control of weeds will contribute to landscape-targeted herbicide applications that are less harmful to the environment.

AimPlant functional groups are widely used in community ecology and earth system modelling to describe trait variation within and across plant communities. However, this approach rests on the assumption that functional groups explain a large proportion of trait variation among species. We test whether four commonly used plant functional groups represent variation in six ecologically important plant traits.LocationTundra biome.Time periodData collected between 1964 and 2016.Major taxa studied295 tundra vascular plant species.MethodsWe compiled a database of six plant traits (plant height, leaf area, specific leaf area, leaf dry matter content, leaf nitrogen, seed mass) for tundra species. We examined the variation in species-level trait expression explained by four traditional functional groups (evergreen shrubs, deciduous shrubs, graminoids, forbs), and whether variation explained was dependent upon the traits included in analysis. We further compared the explanatory power and species composition of functional groups to alternative classifications generated using post hoc clustering of species-level traits.ResultsTraditional functional groups explained significant differences in trait expression, particularly amongst traits associated with resource economics, which were consistent across sites and at the biome scale. However, functional groups explained 19% of overall trait variation and poorly represented differences in traits associated with plant size. Post hoc classification of species did not correspond well with traditional functional groups, and explained twice as much variation in species-level trait expression.Main conclusionsTraditional functional groups only coarsely represent variation in well-measured traits within tundra plant communities, and better explain resource economic traits than size-related traits. We recommend caution when using functional group approaches to predict tundra vegetation change, or ecosystem functions relating to plant size, such as albedo or carbon storage. We argue that alternative classifications or direct use of specific plant traits could provide new insights for ecological prediction and modelling.

Peatland ecosystem restoration following oil mining activities in Alberta, Canada, aims at reestablishing crucial peatland functions, such as wildlife habitat, water storage and filtration, peat accumulation, and carbon sequestration. To reinstate peatland functions, characteristic hydrological conditions are necessary to support the establishment and growth of characteristic wetland vegetation. Following in situ oil sands well pad disturbances in the Peace River and Cold Lake Oil Sands regions in Alberta, we evaluated the efficiency of peatland restoration approaches including different groundwork and revegetation techniques. Groundwork techniques included the complete removal (CR) or partial removal (PR) of the former in situ well pads' mineral fill and revegetation included the spontaneous revegetation via natural ingress of diaspores from nearby peatlands, or managed revegetation via planting of Carex aquatilis, Larix laricina, and Salix lutea. We assessed the plant species composition, biochemical and hydrological properties of all study areas, including restored peatland areas, an unrestored area, and reference areas (REF) for comparison. Ten years post‐restoration, in the restored areas the mean total plant cover was 57% with an average of 35 vascular plant and bryophyte species, while in REF 68% mean total plant cover and an average of 64 plant species were recorded. Respectively, characteristic peatland species contributed to 61 and 100% of the species composition. PR and hydrological connection to the adjacent peatland resulted in near surface water table and the highest peatland plant species diversity, while CR promoted the formation of a shallow open water area. [ABSTRACT FROM AUTHOR]

Bright sunshine duration (BSD) data are required for simulating biomes using process-based vegetation models. However, monthly global paleoclimate datasets that can be used in paleo data–model comparisons do not necessarily contain BSD or radiation data. Considering the theoretical and practical aspects, the scheme of Yin, X. (1999) is here recommended to estimate monthly time series of relative BSD using only monthly climate and location data. As a case study for the Carpathian Region, the efficiency of both the original and a variant of that scheme is analysed in this paper. The alternative scheme has high applicability in paleoenvironmental studies. Comparison of the estimated and observed BSD data shows that from May to August, the value of relative root mean squared error in more than 90 percent of the study area does not exceed the threshold of 20 percent, indicating an excellent performance of the original estimation scheme. It is also found that though the magnitude of overestimation for the alternative algorithm is significant in the winter period, the proposed method performs similarly well in the growing season as the original. Furthermore, concerning modelling the distribution of biomes, simulation experiments are performed to assess the effects of modifying some configuration settings: (a) the generation of relative BSD data, and (b) the algorithm used to create quasi-daily weather data from the monthly values. Under both the recent humidity conditions of the study region and the spatial resolution of the climate dataset used, the results can be considered sufficiently robust, regardless of the configuration settings tested. Thus, using monthly temperature and precipitation climatologies, the spatial distribution of biomes can be properly simulated with the configuration settings proposed here

Understanding variation in leaf functional traits-including rates of photosynthesis and respiration and concentrations of nitrogen and phosphorus-is a fundamental challenge in plant ecophysiology. When expressed per unit leaf area, these traits typically increase with leaf mass per area (LMA) within species but are roughly independent of LMA across the global flora. LMA is determined by mass components with different biological functions, including photosynthetic mass that largely determines metabolic rates and contains most nitrogen and phosphorus, and structural mass that affects toughness and leaf lifespan (LL). A possible explanation for the contrasting trait relationships is that most LMA variation within species is associated with variation in photosynthetic mass, whereas most LMA variation across the global flora is associated with variation in structural mass. This hypothesis leads to the predictions that (i) gas exchange rates and nutrient concentrations per unit leaf area should increase strongly with LMA across species assemblages with low LL variance but should increase weakly with LMA across species assemblages with high LL variance and that (ii) controlling for LL variation should increase the strength of the above LMA relationships. We present analyses of intra- and interspecific trait variation from three tropical forest sites and interspecific analyses within functional groups in a global dataset that are consistent with the above predictions. Our analysis suggests that the qualitatively different trait relationships exhibited by different leaf assemblages can be understood by considering the degree to which photosynthetic and structural mass components contribute to LMA variation in a given assemblage.

The light use efficiency (LUE) approach is a well-established method for estimating gross primary productivity (GPP) over large areas using Earth observation data. The present study aims to determine maximum light use efficiency (LUEmax) values specific to the northwest Himalayan foothills of India. It also aims to estimate the spatio-temporal variability of GPP from 2001 to 2020 using remote sensing data in combination with eddy covariance data in the LUE-based model. The model was parameterized using different sets of default and calculated parameters. The study showed that the use of PFT-specific LUEmax and temperatures increased the accuracy of the model predictions. On validation, the LUE-based model predicted GPP showed R² = 0.82 for moist deciduous and R² = 0.83 for dry deciduous PFTs. The study revealed that with rigorous model parameterization, RS data can be used in an LUE-based model to achieve accurate spatio-temporal estimates of GPP. [ABSTRACT FROM AUTHOR]

Climate warming is inducing widespread vegetation changes in Arctic tundra ecosystems, with the potential to alter carbon and nutrient dynamics between vegetation and soils. Yet, we lack a detailed understanding of how variation in vegetation and topography influences fine‐scale temperatures ("microclimate") that mediate these dynamics, and at what resolution vegetation needs to be sampled to capture these effects. We monitored microclimate at 90 plots across a tundra landscape in western Greenland. Our stratified random study design covered gradients of topography and vegetation, while nested plots (0.8–100 m2) enabled comparison across different sampling resolutions. We used Bayesian mixed‐effect models to quantify the direct influence of plot‐level topography, moisture and vegetation on soil, near‐surface and canopy‐level temperatures (−6, 2, and 15 cm). During the growing season, colder soils were predicted by shrub cover (−0.24°C per 10% increase), bryophyte cover (−0.35°C per 10% increase), and vegetation height (−0.17°C per 1 cm increase). The same three factors also predicted the magnitude of differences between soil and above‐ground temperatures, indicating warmer soils at low cover/height, but colder soils under closed/taller canopies. These findings were consistent across plot sizes, suggesting that spatial predictions of microclimate may be possible at the operational scales of satellite products. During winter, snow cover (+0.75°C per 10 snow‐covered days) was the key predictor of soil microclimate. Topography and moisture explained little variation in the measured temperatures. Our results not only underline the close connection of vegetation and snow with microclimate in the Arctic tundra but also point to the need for more studies disentangling their complex interplay across tundra environments and seasons. Future shifts in vegetation cover and height will likely mediate the impact of atmospheric warming on the tundra soil environment, with potential implications for below‐ground organisms and ecosystem functioning. [ABSTRACT FROM AUTHOR]

Decomposition of plant litter is a fundamental ecological process, integral to soil formation, soil organic matter chemistry, and biogeochemical cycling. However, much of our understanding of decay dynamics focuses on rates of litter mass loss and therefore carbon dynamics, with relatively less exploration of the chemical nature of litter decomposition during which the degradation of litter structural and metabolic compounds into fragments are either metabolized or ultimately incorporated into soil humus. Our understanding of the patterns of changes in litter chemistry throughout decomposition is incomplete, as few studies have measured chemical content beyond initial litter chemistry and throughout decay, and particularly not chemistry beyond carbon and nitrogen. The existing literature also reports idiosyncratic instances of litter chemical convergence and divergence. We used archived litter decomposition samples and data from across the U.S. Long-Term Ecological Research Network to investigate the trajectory of a comprehensive array of litter chemistry, including nutrient, structural, and metabolic parameters, across a wide variety of plant functional types and ecosystems, throughout the first 70% of mass loss. Our results do not yield a universally common pattern of litter chemical trajectories across all functional types and regions, and very limited evidence of convergence or divergence in chemistry over time, mostly within the nutrient elements. We provide details about the behavior of individual chemical parameters to functional type and region over decay. Changes in plant communities driven by global change may alter nutrient cycling and SOM formation through persistence or divergence on litter chemistry inputs. [ABSTRACT FROM AUTHOR]

Ombrotrophic peatlands are important long-term sinks for atmospheric carbon as plant productivity exceeds litter decomposition. Changes in plant community composition may alter decomposition rates through alterations in microbial communities and activity. Such plant community driven changes in decomposition rates may however differ between microhabitats. Nevertheless, the microhabitat-context-dependency of plant community composition effects on decomposition remains poorly understood. We used a long-term (> 10 year) plant removal experiment to study how vascular plant functional types (PFTs, i.e. graminoids and ericoids) influence decomposition processes in wet lawns and hummocks. We employed the Tea Bag Index (TBI) as an indicator for early litter decomposition and carbon stabilization and assessed the potential activity of five hydrolytic extracellular enzymes (EEAs) as indicators for microbial activity. PFT removal had no effect on the TBI decomposition rate constant (k), nor on the stabilization factor (S). Yet, k increased slightly when both PFTs were absent. In the lawns, we observed higher values of k and S as compared to hummocks. PFT composition influenced four out of five hydrolytic EEAs that can drive decomposition. Yet, this influence was non-pervasive and microhabitat dependent. In wet lawns, PFT removal generally increased enzyme activities, while opposite trends were detected in the hummocks. Our results suggest an important role for vegetation change, through their influence on enzyme activity, along the lawn-hummock gradient in regulating decomposition processes in northern peatlands. This implies that potential consequences of vegetation changes on organic matter turnover, hence the peatland carbon sink function, cannot be generalized across peatland microhabitats. [ABSTRACT FROM AUTHOR]