Phenotypic plasticity contributes more to the variations in nutrient resorption than genetic differentiation in a grassland dominant
Zhang, Zuxin et al. (2022), Phenotypic plasticity contributes more to the variations in nutrient resorption than genetic differentiation in a grassland dominant, Dryad, Dataset, https://doi.org/10.5061/dryad.n8pk0p2zf
1. Phenotypic plasticity and genetic differentiation are the two important processes determining the leaf nutrient resorption among and within plant species, which is critical for understanding the adaptability of plants. However, relative contributions of these two processes have never been quantified at a large geographical scale.
2. Here, we investigated intraspecific variations in nutrient resorption among 14 Stipa breviflora populations along a latitude gradient in 2018 and 2019. Furthermore, we sow seeds from these populations in two common gardens at different latitudes, and examined the variations in nutrient resorption.
3. Our results showed that nitrogen and phosphorus resorption efficiency (NRE and PRE) among S. breviflora populations in nature were positively related to latitude, while this trend disappeared in the common gardens. The heritability of NRE and PRE was 11.45 % and 16.78 %, respectively. These results suggested that phenotypic plasticity contributed much more than genetic variation to nutrient resorption of S. breviflora. Moreover, the structural equation modeling (SEM) suggested that latitude indirectly affected nutrient resorption mainly by altering soil nutrients. With the increasing of latitude, soil nutrients decreased while nutrient resorption increased. This suggested the main process regulating nutrient resorption is negative feedback to soil nutrient availability.
4. Our study provides new insights into the role of nutrient resorption in plant adaptations to geographic variations.
Inner Mongolia Autonomous Region Major Project, Award: 2020ZD0021
Gansu Provincial Science and Technology Major Project, Award: 19ZD2NA002
National Natural Science Fund, Award: 31672473