Study Motivation: Local-Scale Variation of Autumn Phenology
Autumn phenology, the timing of the transition of trees from their active growing phase to dormancy, is a crucial biological indicator of temperate forest ecosystems, particularly sensitive to climate change1. It plays a critical role in forest carbon sequestration capacity by affecting the length of the growing season, capturing widespread attention within the scientific community2.
At the China National Botanical Garden, where we work and live, we noted that trees of the same species exhibited notable differences in the timing of leaf senescence. This variability was intriguing because these trees were exposed to similar macroclimatic conditions. The fact that trees, despite being close together, were shedding their leaves at different times suggested that factors beyond species and macroclimatic might be at play. This observation led us to hypothesize that local-scale factors, possibly related to the trees’ microclimate or canopy structure, might be driving these differences. These questions motivated us to investigate the mechanisms regulating local-scale variation in autumn phenology on a broader, more natural scale.
Our Journey: Changbai Mountain (CBS) Temperate Forest Site
To investigate the role of local factors in autumn phenology, we selected the CBS site, a typical temperate forest in northern China, for our study. We collected various data, starting with high-frequency drone imagery to track leaf color changes over time. We also deployed sensors to measure temperatures inside and outside the canopy and used terrestrial light detection and ranging (lidar) to capture canopy structure metrics, such as plant area index and canopy height.
At the CBS temperate forest site, we collected drone imagery, microclimate measurements, and terrestrial lidar data.
Our data revealed significant local-scale variations in autumn phenology, even among trees of the same species, similar to our observations at the botanical garden. Additionally, we found strong relationships between canopy structure, microclimate, and autumn phenology. Excited by these results, we decided to expand our study to larger and more diverse temperate forests to explore whether the observed relationships were consistent.
Expanding Our Study: NEON Temperate Forest Sites
We extended our study to include data from five NEON temperate forest sites. NEON provides long-term ecological monitoring, which includes airborne lidar data and in-situ microclimate measurements. Additionally, PlanetScope satellite data, with its high spatiotemporal resolution, enabled us to monitor crown-scale autumn phenology. This rich dataset allowed us to test our hypothesis across diverse temperate forests.
The results mirrored those from the CBS site: canopy structure was significantly related to autumn phenology metrics, and their relationships remained consistent across different sites and years. These associations can be partially attributed to the regulatory effect of canopy structure on microclimate condition. Complex canopies, in particular, delay the start of autumn and shorten the duration of autumn in temperate forests by reducing light levels within the canopy and enhancing temperature buffering during the growing season. The light attenuation within the canopy may diminish photosynthetic intensity and reduce carbon uptake, especially for understory trees, potentially delaying leaf senescence3. Additionally, enhanced temperature buffering slows the accumulation of cold temperatures, reducing frost risk for plants and further delaying leaf senescence4.
A conceptual diagram of the influence of canopy structure on autumn phenology in temperate forests by mediating microclimate conditions.
Implications for Projecting Autumn Phenology under Climate Change
The influence of canopy structure on autumn phenology through microclimate mediation has rarely been considered in existing autumn phenology models. Incorporating the identified “canopy structure-microclimate-autumn phenology” pathway into existing models, such as CDD5 and TPM6, significantly improves prediction accuracy and reduces the delay in SOA predictions for the remainder of the century under shared socio-economic pathway (SSP)1-2.6, SSP3-7.0, and SSP5-8.5, with decreasing magnitude from high to low emission scenarios. Our findings emphasize that neglecting the “canopy structure-microclimate-autumn phenology” pathway may lead existing autumn phenology models to overestimate the delaying effects of macroclimate on SOA under future climate scenarios, potentially skewing our understanding of the responses and feedback mechanisms of temperate forests to global climate change.
Moving Forward
Our findings provide a new perspective to understand the mechanism driving the local-scale heterogeneity of autumn phenology in temperate forests, and offer a novel scientific basis to predict the responses of temperate forests to global climate change. We suggest that there is an urgent need to incorporate the observed “canopy structure-microclimate-autumn phenology” pathway into Earth system and vegetation models, especially considering the asynchronous changes of macroclimate and microclimate conditions in the backdrop of global climate changes.
However, predicting microclimate conditions at large scales is still difficult, and climate change is altering forest canopies, affecting their ability to regulate microclimate. Our future projections of autumn phenology assumed a constant canopy buffering effect on microclimate, which may not fully reflect future trends, limiting our understanding of how canopy structure, macroclimate, and microclimate together influence autumn phenology under climate change. Further research should focus on comprehensive assessments of macroclimate, microclimate, and canopy structure, and how they interact across space and time.
References
1. Piao, S. et al. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 25, 1922-1940 (2019).
2. Menzel, A. & Fabian, P. Growing season extended in Europe. Nature 397, 659-659 (1999).
3. Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066-1071 (2020).
4. Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).
5. Jeong, S. & Medvigy, D. Macroscale prediction of autumn leaf coloration throughout the continental United States. Glob. Ecol. Biogeogr. 23, 1245-1254 (2014).
6. Lang, W., Chen, X., Qian, S., Liu, G. & Piao, S. A new process-based model for predicting autumn phenology: How is leaf senescence controlled by photoperiod and temperature coupling? Agric. For. Meteorol. 268, 124-135 (2019).
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