Back

Decomposing intraspecific phenotypic variation: implications for species and functional diversity

Worthy, S. J.; Umana, M. N.; Zhang, C.; Lin, L.; Cao, M.; Swenson, N. G.

2023-01-21 ecology
10.1101/2023.01.20.524990 bioRxiv
Show abstract

Researchers have a history of seeking explanation for and understanding of diversity patterns. High-dimensional trait-based trade-offs have been hypothesized as important for maintaining species and functional diversity. These relationships have primarily been investigated at the community-level, despite the importance of intraspecific variation to diversity maintenance. The goal of this research is to determine if alternative phenotypes are present within species and the impacts of this on diversity in a tropical seedling community in China. We ask 1) do trait combinations found across species, at the community-level, also exist within species?; 2) how consistent are alternative phenotypes and their contributions to growth across species?; and 3) how do findings align with species co-occurrence patterns? We model species-specific growth with individual-level trait measurements, environmental data, and their interactions, allowing for identification of intraspecific alternative phenotypes and quantification of the contribution of variables to growth. We find that two of three species have intraspecific alternative phenotypes. Specifically, individuals within these species share a trait combination, but how they combine the traits differs depending on the type and level of soil nutrients. Furthermore, we find that similarity among species in alternative phenotypes and variables that contribute most to growth may lead to negative spatial co-occurrence of species. Overall, we find that multiple traits or interactions between traits and the environment drive species-specific strategies for growth. These results highlight how individuals are highly variable, with phenotypically different individuals having similar growth performance, and suggest how high species and functional diversity can be maintained in communities.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.