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High photosynthetic capacity of Sahelian C3 and C4 plants

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Sibret, T; Verbruggen, W; Peaucelle, M; Verryckt, LT; Bauters, M; Combe, M; Boeckx, P; Verbeeck, H

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2021

PHOTOSYNTHESIS RESEARCH

147

161-175

The semi-arid ecosystems of the African Sahel play an important role in the global carbon cycle and are among the most sensitive ecosystems to future environmental pressures. Still, basic data of photosynthetic characteristics of Sahelian vegetation are very limited, preventing us to properly understand these ecosystems and to project their response to future global changes. Here, we aim to study and quantify key leaf traits, including photosynthetic parameters and leaf nutrients (N-leaf and P-leaf), of common C-3 and C-4 Sahelian plants (trees, lianas, and grasses) at the Dahra field site (Senegal). Dahra is a reference site for grazed semi-arid Sahelian savannah ecosystems in carbon cycle studies. Within the studied species, we found that photosynthetic parameters varied considerably between functional types. We also found significant relationships between and within photosynthetic parameters and leaf traits which mostly differed in their slopes from C-3 to C-4 plants. In agreement with the leaf economic spectrum, strong relationships (R-2 = 0.71) were found between SLA and N-leaf whereby C-3 and C-4 plants showed very similar relationships. By comparing our data to a global dataset of plant traits, we show that measured Sahelian plants exhibit higher photosynthetic capacity (A(sat)) compared to the non-Sahelian vegetation, with values that are on average a fourfold of the global average. Moreover, Sahelian C-3 plants showed photosynthetic nutrient use efficiencies that were on average roughly twice as high as global averages. We interpreted these results as the potential adaptation of Sahelian plants to short growing season lengths via an efficient nutrient allocation to optimize photosynthesis during this period. Our study provides robust estimates of key functional traits, but also traits relationships that will help to calibrate and validate vegetation models over this data-poor region.

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