{"id":1131,"date":"2022-07-12T11:57:21","date_gmt":"2022-07-12T10:57:21","guid":{"rendered":"https:\/\/research.reading.ac.uk\/lemontree\/?p=1131"},"modified":"2022-10-14T05:38:01","modified_gmt":"2022-10-14T04:38:01","slug":"timing-is-everything-lemontree-science-meeting-june-2022","status":"publish","type":"post","link":"https:\/\/research.reading.ac.uk\/lemontree\/timing-is-everything-lemontree-science-meeting-june-2022\/","title":{"rendered":"TIMING IS EVERYTHING- LEMONTREE SCIENCE MEETING- JUNE 2022"},"content":{"rendered":"

The focus for our June Science meeting, was phenology and phenological strategies as part of Challenge 1: \u00a0Optimality at the leaf and plant levels<\/em>,<\/strong> where we plan to develop a comprehensive \u201coptimal trait theory\u201d predicting the leading dimensions of variation in the functional morphology of plants.<\/p>\n

Change in phenological events is one of the clearest indicators of climate change and there are already significant changes being seen in the natural world, from leaves and flowers appearing earlier, earlier phytoplankton blooms in lakes and oceans, earlier departure of migratory birds in spring\/ later migration in autumn and earlier appearance of mammals from winter hibernations. Altered phenological patterns can drastically impact trophic interactions and led to ecosystem imbalance.<\/p>\n

Understanding how phenological events may change with climate change is therefore timely (pun intended). Indicators from trees such as leaf out in spring and leaf fall in autumn can help us build a better understanding of our changing climate and how we can model this.<\/p>\n

In our meeting, we heard updates on their research from four team members who are working on phenological projects as part of LEMONTREE.<\/p>\n

\u00a0<\/strong><\/p>\n

PREDICTING LEAF AREA INDEX AT PEAK SEASON AND ITS ENVIRONMENTAL CONTROLS WITH A NOVEL CONSTRAINT EEO FRAMEWORK. <\/strong><\/h2>\n

Ziqi Zhu (Tsinghua University, <\/strong>LPICEA<\/strong><\/a>, Supervisor Dr Han Wang)<\/strong><\/p>\n

Recent increases in vegetation cover, observed over much of the world, reflect increasing CO2<\/sub> globally and warming in cold areas. However, the strength of the response to both CO2<\/sub> and warming appears to be declining. Here we examine changes in vegetation cover on the Tibetan Plateau over the past 35 years.<\/p>\n

Although the climate trends are similar across the Plateau, drier regions have become greener by 0.31\u00b10.14% yr\u22121 while wetter regions have become browner by 0.12\u00b10.08% yr\u20131. This divergent response is predicted by a universal model of primary production accounting for optimal carbon allocation to leaves, subject to constraint by water availability.<\/p>\n

Rising CO2<\/sub> stimulates production in both greening and browning areas; increased precipitation enhances growth in dry regions, but growth is reduced in wetter regions because warming increases below-ground allocation costs. The declining sensitivity of vegetation to climate change reflects a shift from water to energy limitation.<\/p>\n

\"\"
Figure 1. Peak vegetation growth responses across the Tibetan plateau (left) with changes in precipitation (right).<\/figcaption><\/figure>\n

 <\/p>\n

DOES INCREASING GROWING SEASON PHOTOSYNTHESIS REALLY LEAD TO LEAF SENESCENCE.<\/strong><\/h2>\n

Xinchen Lu (University of California, Berkely, supervisor Prof Trevor Keenan)<\/strong><\/p>\n

Xinchen gave a talk on his recent research that examined the relationships between growing season photosynthesis and end of growing season (EOS). Using observations from 40 eddy-covariance flux tower sites in temperate and boreal ecosystems in the northern hemisphere with more than 10 years of observations (594 site-years). Additional ground observations of phenology, satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), and three leaf senescence models were used to test the extent of a relationship between growing season photosynthesis and end of season senescence.<\/p>\n

The results suggest that there is no significant negative relationship between growing season photosynthesis and observed leaf senescence, flux-inferred EOS estimates, or remotely sensed phenological metrics, in most ecosystems.\u00a0 On the contrary, while they found negative effects of summer air temperatures and autumn vapor pressure deficit on EOS, more productive growing seasons were typically related to a later, not earlier, EOS. Their results were recently published in Global Change Biology (https:\/\/doi.org\/10.1111\/gcb.16104<\/a>).<\/p>\n

\"\"
Figure 2: Projections of future delays in autumn senescence dates with increasing air temperature, as estimated by three different models.<\/figcaption><\/figure>\n

FIRST STEPS TOWARDS A GLOBAL PHENOLOGY MODEL USING PHENOCAM DATA<\/strong><\/h2>\n

Boya Zhou (Imperial College London, supervisor Prof Iain Colin Prentice)<\/strong><\/p>\n

Boya introduced a new global phenology model simulation scheme based on eco-evolutionary optimality theory, and the feasibility of testing this model with the Phenocam<\/a> database. Leaf phenology is closely related to photosynthesis as reflected by gross primary productivity (GPP). On the one hand, GPP is dependent on leaf area index (LAI) because leaves are essential for photosynthesis. On the other hand, GPP determines the carbon allocated to leaves because plant leaf phenology strategies are to gain a competitive advantage in photosynthesis.<\/p>\n

Under an unchanging environment, these two processes will eventually reach a steady state, at which point LAI and GPP should show a linear relationship. This linear relationship, together with a simple light use efficiency algorithm of MOD17, could form a closed set of equations about steady-state LAI and GPP.<\/p>\n

A semi-prognostic phenology model was developed by Xin et al. (2020), which simulates the time series of vegetation LAI but requires the input of maximum satellite LAI data. Boya’s study aims to improve the existing semi-prognostic phenology model in two aspects:<\/p>\n