Back

Comparing trait syndromes between Taiwanese subtropical terrestrial and epiphytic ferns and lycophytes at the species and community level

Helsen, K.; Lin, T.-Y.; Zeleny, D.

2021-09-06 ecology
10.1101/2021.09.06.459074 bioRxiv
Show abstract

Background and AimsWhile functional trait-trait and trait-environment relationships are well studied in angiosperms, it is less clear if similar relationships, such as the leaf economics spectrum (LES), hold for ferns, and whether they differ between terrestrial and epiphytic fern communities. We used vegetation data collected along an elevation gradient in Taiwan to explore these relationships. MethodsWe measured nine leaf traits for 47 terrestrial and 34 epiphytic fern species across 59 vegetation plots along an elevation gradient in the subtropical forest of Northern Taiwan. We explored trait-trait and trait-environment relationships at both the species and community levels for both growth habits, while accounting for phylogenetic relationships. Key ResultsEpiphytes differed from terrestrial ferns in species- and community-level trait values, mainly reflecting responses to higher drought and nutrient stress. The angiosperm LES was reflected in the trait-trait correlations of terrestrial ferns and less expressively in epiphytes. This pattern suggests that mainly water rather than nutrient availability shapes epiphytic trait patterns. Trait-trait analysis on raw trait data and on independent contrasts vary in some ways. Trait-environment relationships were similar for several drought-related traits across both species groups. ConclusionsThis study illustrates that fern trait patterns are not entirely equivalent for epiphytic and terrestrial species or communities and should not be extrapolated across growth habits or between the species and community levels. Phylogenetic constraints may influence the trait-environment response of epiphytic species.

Matching journals

The top 5 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.