Remotely Sensed Variables Predict Grassland Diversity Better at Scales Below 1,000 km as Opposed to Abiotic Variables That Predict It Better at Larger Scales DOI Creative Commons
Yujin Zhao, Bernhard Schmid, Zhaoju Zheng

et al.

Earth s Future, Journal Year: 2024, Volume and Issue: 12(11)

Published: Nov. 1, 2024

Abstract Global spatial patterns of vascular plant diversity have been mapped at coarse grain based on climate‐dominated environment–diversity relationships and, where possible, finer using remote sensing. However, for grasslands with their small sizes, the limited availability vegetation plot data has caused large uncertainties in fine‐grained mapping species diversity. Here we used survey from 1,609 field sites (>4,000 plots 1 m 2 ), remotely sensed (ecosystem productivity and phenology, habitat heterogeneity, functional traits spectral diversity), abiotic (water‐ energy‐related, characterizing environment) together machine learning autoregressive models to predict map grassland richness per 100 across Mongolian Plateau 500 resolution. Combining all variables yielded a predictive accuracy 69% compared 64% or 65% alone. Among variables, showed highest power (55%) estimation, followed by phenology (48%), (48%) heterogeneity (48%). When considering autocorrelation, explained 52% 41%. Moreover, Remotely provided better prediction smaller size (<∼1,000 km), while water‐ energy‐dominated macro‐environment were most important drivers dominated effects macro‐scale (>∼1,000 km). These findings indicate that characteristics provide similar predictions richness, they offer complementary explanations broad scales.

Language: Английский

Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches DOI Creative Commons
Benjamin Dechant, Jens Kattge, Ryan Pavlick

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114276 - 114276

Published: June 27, 2024

Foliar traits such as specific leaf area (SLA), nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies ecosystem functioning.Various global maps of these foliar have been generated using statistical upscaling approaches based on in-situ trait observations.Here, we intercompare upscaled at 0.5 • spatial resolution (six for SLA, five N, three P), categorize the used to generate them, evaluate with estimates from a database vegetation plots (sPlotOpen).We disentangled contributions different functional types (PFTs) quantified impacts plot-level metrics evaluation sPlotOpen: community weighted mean (CWM) top-of-canopy (TWM).We found that SLA N differ drastically fall into two groups are almost uncorrelated (for P only one group were available).The primary factor explaining differences between is use PFT information combined remote sensing-derived land cover products while other mostly relied environmental predictors alone.The corresponding exhibit considerable similarities patterns strongly driven by cover.The not PFTs show lower level similarity tend be individual variables.Upscaled both moderately correlated sPlotOpen data aggregated grid-cell (R = 0.2-0.6)when processing way consistent respective approaches, including metric (CWM or TWM) scaling grid cells without accounting fractional impact TWM CWM was relevant, but considerably smaller than information.The better reproduce between-PFT data, performed similarly capturing within-PFT variation.Our findings highlight importance explicitly within-grid-cell variation, which has implications applications existing future efforts.Remote sensing great potential reduce uncertainties related observations regression-based mapping steps involved upscaling.

Language: Английский

Citations

6
Global patterns of plant functional traits and their relationships to climate DOI Creative Commons
Jiaze Li, I. Colin Prentice

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: Sept. 13, 2024

Language: Английский

Citations

4
Prediction in trait-based ecology: global simulations of specific leaf area using a trait-based dynamic vegetation model DOI Creative Commons
Liam Langan, Teja Kattenborn, Christine Römermann

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 19, 2025

Abstract Predicting plant community functional traits is considered a ’Holy Grail’ of trait-based ecology because underpin ecosystem processes. Previous statistical, machine learning, and optimality approaches have produced global trait predictions. However, the utility vegetation models, which include demographic processes can represent diversity, remains unexplored at this scale. We use aDGVM2-LL, trait- individual-based dynamic model (DGVM). aDGVM2-LL simulates assembly, driven by natural selection, biotic, abiotic conditions; simulated specific leaf area (SLA) an emergent outcome assembly. examine: 1) how well simulate SLA examining deviations from data, 2) explore drivers strong deviations. Compared to GBIF-derived displays mean differences -2.9 (m2/kg)(GBIF range ca. 4 – 35 m2/kg), root square error (RMSE) 7.25, normalised absolute (nMAE) 26.54%. Published displayed with data between (mean : -4.83 2.67, RMSE: 4.41 6.68, nMAE: 13.41% 25.20%). Thus, are comparable published predictions while RMSEs nMAEs higher. Large mismatches occur in areas where incorrectly relative abundances deciduous vs. evergreen phenologies. Correcting phenological strongly reduces (-0.14 0.43), (5.85 5.90), (15.44% 20.61%). These results show that eco-evolutionary, process-based approach reasonably values, particularly when accurate. Our highlight general importance phenology for traits. The correct simulation phenologies crucial predict contemporary future SLA.

Language: Английский

Citations

0
Developing a predictive science of the biosphere requires the integration of scientific cultures DOI Creative Commons
Brian J. Enquist, Christopher P. Kempes,

Geoffrey B. West

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(19)

Published: April 19, 2024

Increasing the speed of scientific progress is urgently needed to address many challenges associated with biosphere in Anthropocene. Consequently, critical question becomes: How can science most rapidly large, complex global problems? We suggest that lag development a more predictive not only because so much complex, or we do have enough data, are doing experiments, but, large part, unresolved tension between three dominant cultures pervade research community. introduce and explain concept present novel analysis their characteristics, supported by examples formal mathematical definition/representation what this means implies. The operate, varying degrees, across all science. However, within biosciences, contrast some other sciences, they remain relatively separated, lack integration has hindered potential power insight. Our solution accelerating broader, enhance cultures. process integration—Scientific Transculturalism—recognizes push for interdisciplinary research, general, just enough. Unless these formally appreciated thinking iteratively integrated into discovery advancement, there will continue be numerous significant increasingly limit forecasting prediction efforts.

Language: Английский

Citations

3
Unraveling the relationship between environment and plant functional traits DOI
Meghna Krishnadas,

Bandaru Peddiraju,

Snehalatha Vadigi

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 59 - 86

Published: Jan. 1, 2025

Language: Английский

Citations

0
Plant traits shape global spatiotemporal variations in photosynthetic efficiency DOI
Yulin Yan, Bolun Li, Benjamin Dechant

et al.

Nature Plants, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Language: Английский

Citations

0
Improved global estimation of seasonal variations in C3 photosynthetic capacity based on eco-evolutionary optimality hypotheses and remote sensing DOI
Yihong Liu, Jing M. Chen, Mingzhu Xu

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 313, P. 114338 - 114338

Published: Aug. 8, 2024

Language: Английский

Citations

2
Spatial mapping of key plant functional traits in terrestrial ecosystems across China DOI Creative Commons
Nannan An, Nan Lü, Weiliang Chen

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(4), P. 1771 - 1810

Published: April 11, 2024

Abstract. Trait-based approaches are of increasing concern in predicting vegetation changes and linking ecosystem structures to functions at large scales. However, a critical challenge for such is acquiring spatially continuous plant functional trait maps. Here, six key traits were selected as they can reflect resource acquisition strategies functions, including specific leaf area (SLA), dry matter content (LDMC), N concentration (LNC), P (LPC), (LA) wood density (WD). A total 34 589 situ measurements 3447 seed species collected from 1430 sampling sites China used generate spatial maps (∼1 km), together with environmental variables indices based on two machine learning models (random forest boosted regression trees). To obtain the optimal estimates, weighted average algorithm was further applied merge predictions derive final The showed good accuracy estimating WD, LPC SLA, R2 values ranging 0.48 0.68. In contrast, both had weak performance LDMC, less than 0.30. Meanwhile, LA considerable differences between some regions. Climatic effects more important those edaphic factors distributions traits. Estimates northeastern Qinghai–Tibetan Plateau relatively high uncertainties due sparse samplings, implying need observations these regions future. Our could provide support trait-based allow exploration relationships characteristics 1 km resolution now available https://doi.org/10.6084/m9.figshare.22351498 (An et al., 2023).

Language: Английский

Citations

1
Leaf functional traits of Daphniphyllum macropodum across different altitudes in Mao’er Mountain in Southern China DOI Creative Commons
Zhangting Chen, Qiaoyu Li,

Zhaokun Jiang

et al.

Frontiers in Forests and Global Change, Journal Year: 2024, Volume and Issue: 7

Published: July 17, 2024

Investigating functional traits among mountain species with differing altitude requirements is integral to effective conservation practices. Our study aims investigate the structural and chemical characteristics of Daphniphyllum macropodum leaves at three altitudes (1100 m, 1300 1500 m) across southern China provide insight into changes in leaf (LFT) as well plant adaptations response changing environmental conditions. Leaf include thickness (LT), area (LA), specific (SLA), tissue density (LD), respectively, while properties carbon-nitrogen-phosphorus (C:N:P) contents ratios, such C/N, C/P, N/P. findings demonstrated significant effect on both (LT, SLA, LD) aspects (N, N/P) LFT. In particular, 1100 m differed greatly, having lower SLA values than m. Observable trends included an initial increase followed by a decline rose. Notable them were LT, LD, N, N/P locations. Traits significantly higher m; C/N displayed inverse trend, their lowest occurring Furthermore, this research various degrees variation LFT, exhibiting greater fluctuation traits. Robust correlations observed certain traits, SLA. interdependency relationships between N P interconnectedness. Redundancy analysis indicated that soil factors, specifically content, exerted strongest impact At D. employed acquisition strategies; however, strategies emerged, showing shift from conservative ones.

Language: Английский

Citations

0
Global shift in a key plant trait indicates a change in biosphere function DOI Open Access
César Hinojo-Hinojo, Teresa Bohner, Julia Chacón‐Labella

et al.

Published: May 3, 2024

In the face of climate change, understanding dynamic responses vegetation is crucial for predicting shifts in biosphere functioning. Plant functional traits, particularly leaf mass per area (LMA), are critical links between plant metabolism, to and broader exchanges energy matter within biosphere. Despite their importance, a comprehensive, predictive traits changes hampered by spatial temporal gaps trait observations. Here, we introduce novel remote sensing method global, continuous mapping LMA its historical shifts. Consistent with ecological theory widespread decrease global warming, our findings reveal reduction 6.5-7.6 % 1985 2019, primarily due increasing temperatures. This varies among biomes, evergreen conifer tropical forests showing most significant declines. Due connections carbon metabolism ecosystems, points quickening cycle, including largely unexplored contributions increased photosynthesis recent decades. Collectively, these results signal an ongoing profound transformation functioning resulting from climate-related traits.

Language: Английский

Citations

0