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Trade-offs in above and belowground biomass allocation influencing seedling growth in a tropical forest

Citation

Umaña, Maria Natalia et al. (2020), Trade-offs in above and belowground biomass allocation influencing seedling growth in a tropical forest, Dryad, Dataset, https://doi.org/10.5061/dryad.bk3j9kd93

Abstract

1. Plants allocate biomass to different organs in response to resource variation for maximizing performance, yet we lack a framework that adequately integrates plant responses to the simultaneous variation in above and belowground resources. Although traditionally, the optimal partition theory (OPT) has explained patterns of biomass allocation in response to a single limiting resource, it is well known that in natural communities multiple resources limit growth. We study trade-offs involved in plant biomass allocation patterns and their effects on plant growth under variable below and aboveground resources –light, soil N, and P– for seedling communities.

2. We collected information on leaf, stem, and root mass fractions for more than 1,900 seedlings of 97 species paired with growth data and local-scale variation in abiotic resources from a tropical forest in China.

3. We identified two trade-off axes that define the mass allocation strategies for seedlings – allocation to photosynthetic vs. non-photosynthetic tissues and allocation to roots over stems – that responded to the variation in soil P and N and light. Yet, the allocation patterns did not always follow predictions of OPT in which plants should allocate biomass to the organ that acquires the most limiting resource. Limited soil N resulted in high allocation to leaves at expense of non-photosynthetic tissues, while the opposite trend was found in response to limited soil P. Also, co-limitation in above and belowground resources (light and soil P) led to mass allocation to stems at expense of roots. Finally, we found that growth increased under high light availability and soil P for seedlings that either invested more in photosynthetic over non-photosynthetic tissues or/and that allocated mass to roots at expense of stem.

4. Synthesis: Biomass allocation patterns to above and belowground tissues are described by two independent trade-offs that allow plants to have divergent allocation strategies (e.g., high root allocation at expense of stem or high leaf allocation at expense of allocation to non-photosynthetic tissues) and enhance growth under variable resources. Identifying the trade-offs driving biomass allocation is important to disentangle plant responses to the simultaneous variation in resources in diverse forest communities.

Methods

For each seedling plot, we sampled 50 g of top-soil (0-10 cm) immediately after the trait data collection was finished. The soil was collected from each of the corners and at the center of each 1-m2 plot, then, mixed-together in a single sample and processed in the lab. Soil samples were posteriorly air-dried and sieved (2 mm). Total nitrogen (g/kg) was determined by total combustion using an auto-analyzer (Dumas combustion method, Vario MAX CN, Germany) and total phosphorus (g/kg) by inductively coupled plasma atomic emission spectrometry (ICP-AES). All the soil analyses were conducted at the Biogeochemical Laboratory at Xishuangbanna Tropical Botanical Garden. 

To assess light conditions in the understory, we took hemispherical photographs at the center of each seedling plot. The pictures were taken at 1 m above the ground using a Nikon FC-E8 lens and a Nikon Coolpix 4500 camera (Nikkor, Nikon, Japan). All photographs were taken before sunrise between March and April 2014 and posteriorly analyzed using Gap Light Analyzer software (Frazer, Canham, & Lertzman, 2000) (http://www.caryinstitute.org/science-program/our-scientists/dr-charles-d-canham/gap-light-analyzer-gla).

Usage Notes

Metadata:

canopy.openness: Canopy openness (%)

TN: Total nitrogen (%)

TP: total phosphorus

Funding

National Science Foundation, Award: DEB-1046113

Strategic Priority Research Program of Chinese Academy of Sciences, Award: XDB31000000

Strategic Priority Research Program of Chinese Academy of Sciences, Award: XDB31000000

National Key Research and Development Program of China, Award: 2016YFC0500202

National Science Foundation, Award: U1902203

Chinese Academy of Sciences, Award: Y4ZK111B01

Strategic Priority Research Program of Chinese Academy of Sciences, Award: XDB31000000