{"id":2534,"date":"2025-07-21T09:02:24","date_gmt":"2025-07-21T08:02:24","guid":{"rendered":"https:\/\/research.reading.ac.uk\/lemontree\/?p=2534"},"modified":"2025-07-21T09:40:52","modified_gmt":"2025-07-21T08:40:52","slug":"testing-eco-evolutionary-theory-how-tropical-forest-traits-respond-with-dryness","status":"publish","type":"post","link":"https:\/\/research.reading.ac.uk\/lemontree\/testing-eco-evolutionary-theory-how-tropical-forest-traits-respond-with-dryness\/","title":{"rendered":"Testing Eco-Evolutionary Theory: How Tropical Forest Traits vary with Dryness"},"content":{"rendered":"

By Huanyuan Zhang-Zheng<\/h2>\n
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Mole National Park, Ghana<\/figcaption><\/figure>\n

 <\/p>\n

We have seen many global analyses of plant traits. Admittedly, many of them are intriguing and insightful. However, there is a reoccurring issue evident in most global analysis, is that some of the results are merely showing a difference between tundra and tropical forest, or a difference between desert bushes and tropical forests. These environments are so ecologically distinct that it\u2019s not especially surprising or informative to find that they differ. The question is: what do we learn beyond confirming the obvious?<\/p>\n

As an ecologist, it is always fun to discuss how plants can adapt to living environments through evolution, especially for plants community geographically close but with contrasting environments. We are also more excited about finding something that we can clearly explain the driving factors, in this case, due to change in aridity. You\u2019ve probably heard it be said \u201cLook how different the vegetation is here, just an hour\u2019s drive and everything has changed\u201d.<\/em> These kinds of natural transitions give us a unique opportunity to ask why and how plants differ.<\/p>\n

Eco-Evolutionary Optimality theory (EEO), developed by this lab and many collaborators, considers the role of vapour pressure deficit (VPD) (a measurement of atmospheric dryness). Alongside temperature, VPD is one of the most critical variables in EEO. Afterall, we know that plants suffer from aridity and are suffocated from inundation. However, to date, we haven’t published a paper to systematically review and articulate how EEO expects each plant functional trait to vary from wet to dry places, holding other variables unchanged.<\/p>\n

I am delighted to share that our newly published paper in Communications Biology<\/em><\/a> addresses this gap. Rather than start with field data, we started with theoretical expectations from EEO. The introduction and method section of this study explained these theories in detail but to summarise:<\/p>\n

In tropical vegetation, plants in drier environments experience higher VPD, leading to less stomatal openness, thus lower leaf internal CO2<\/sub> (ci\/ca). Drier environments often mean less cloud and thus more sunlight (measured by PPFD), allowing plants to make greater use of available light. To utilize more energy during the growing season, plants require higher maximum rates of photosynthetic electron transport (Jmax<\/sub>) and electron transport-limited rate of CO2<\/sub> assimilation\u00a0(Aj). But since photosynthesis is co-limited by Aj and Rubisco-limited assimilation (Ac), plants must also increase Vcmax (the maximum rate of Rubisco carboxylation) so they don\u2019t waste their high Aj potential. These changes collectively raise dark respiration and light-saturated assimilation rates (Asat400 and Asat2000).<\/p>\n

 <\/p>\n

To test these predictions, we collected plant functional trait data along a 250km wet-to-dry gradient in Ghana, spanning three nature reserves. Whilst this isn\u2019t a global meta-analysis, rather a focussed, regional study, nevertheless, we showed that field measurements do corroborate the above photosynthesis-related expectations.<\/p>\n

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Figure 1. There are seven study one-hectare plots, grouped in three sites (ANK, BOB, and KOG), from wet (left, dark blue) to dry (right, light blue). We show field photos taken in January 2022 (dry season) for plot a ANK03, b BOB01, and c KOG02. We also show d\u00a0mean annual air temperature (MAT, \u00b0C);\u00a0e\u00a0vapor pressure deficit (VPD, kPa),\u00a0f\u00a0daytime sub-canopy VPD (kPa),\u00a0g\u00a0mean annual precipitation (MAP, mm\/year) and\u00a0h\u00a0soil volumetric water content at 12\u2009cm depth (Soil moisture) as annual mean (%). VPD, MAT, and MAP are measured by meteorological stations. Sub-canopy VPD is daytime vapor pressure deficit beneath the canopy measured during June\u2013December 2014 using a handheld hygrometer (see \u201cMethods\u201d). The figure also shows\u00a0i\u00a0the percentage of deciduous trees (%) and\u00a0j\u00a0the percentage of evergreen trees (%) at each plot. The sum of deciduous and evergreen is less than 100% because we are uncertain about the phenology of a handful of species. Image (c) has been reproduced from a previous publication<\/figcaption><\/figure>\n

 <\/p>\n

 <\/p>\n

One may expect that, along the wet-dry gradient, plant functional traits associated with water transportation would show remarkable pattern. However, it is not as clear as photosynthesis traits. Some hydraulic traits show some data-theory inconsistencies, especially those associated with xylem efficiency safety trade-off. We conducted a PCA combining photosynthetic and hydraulic traits, and found that deciduousness was a major factor structuring trait variation. Photosynthetic traits varied more clearly across the gradient than hydraulic traits.<\/p>\n

Why might this be? We don\u2019t fully know but it is likely that the hydraulic traits here are stem and twigs traits, which are less sensitive to environments. Plants may have a range of options to connect leaves and roots.<\/span>\u00a0Interestingly, We did find clear patterns in turgor loss point and Huber value (both linked to leaf hydraulics). We didn\u2019t measure root traits, but we suspect these would show stronger responses to aridity and have earmarked this as an area for future research. Indeed, if you think about the water infrastructure in dry vs. wet regions, pipe size doesn\u2019t necessarily change, so maybe the same applies to tree stems?<\/p>\n

 <\/p>\n

EEO theory is often applied in (or even proposed for) vegetation modelling. What would we suggest to modelers based on this study? It seems important and feasible to incorporate the dynamic variation in \u00a0Vcmax<\/sub>, Jmax<\/sub>, and ci\/ca in response to VPD. Huber value may also be incorporated with regards to carbon allocation. However, modelling hydraulic traits, at least in our study sites, appears to be mission impossible.<\/p>\n

 <\/p>\n

\"\" \"\" \"\"<\/p>\n

Photos: Left: Dr Huanyuan Zhang-Zheng (on the right) in Bobiri forests, with research assistant Kelvin Anim-Adjei (on the left); middle: Dr Huanyuan Zhang-Zheng in Bobiri forests, Ghana; Right: Dr Huanyuan Zhang-Zheng at the entrance of Bobiri forests, Ghana<\/p>\n

 <\/p>\n

You can read the full paper here:<\/p>\n

Zhang-Zheng, H., Malhi, Y., Zieminska, K., Gvozdevaite, A., Peprah, T., Boakye, M., Adu-Bredu, S., Aguirre- Gutierrez, J., Moore, S., Sandoval, D., Tang, M., Prentice, I.C. Menor, I.O. (2025). A test of ecophysiological theories on tropical forest functional traits along a VPD gradient. Commun Biol<\/em>\u00a08<\/strong>, 1031 (2025). https:\/\/doi.org\/10.1038\/s42003-025-08420-1<\/a><\/p>\n

 <\/p>\n

 <\/p>\n

This blog was written by Colin Prentice\u2019s former Master’s student. Whilst the work was not funded by LEMONTREE, several team members were involved in this research as it is closely related to our aims. We appreciate the continued collaboration with Huanyuan Zhang-Zheng.<\/p>\n","protected":false},"excerpt":{"rendered":"

By Huanyuan Zhang-Zheng   We have seen many global analyses of plant traits. Admittedly, many of them are intriguing and insightful. 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