Semantic Scholar Open Access 2022 12 sitasi

How to approach the study of syndromes in macroevolution and ecology

M. Sinnott‐Armstrong Rocío Deanna Chelsea Pretz Sukuan Liu Jesse C. Harris +3 lainnya

Abstrak

Abstract Syndromes, wherein multiple traits evolve convergently in response to a shared selective driver, form a central concept in ecology and evolution. Recent work has questioned the existence of some classic syndromes, such as pollination and seed dispersal syndromes. Here, we discuss some of the major issues that have afflicted research into syndromes in macroevolution and ecology. First, correlated evolution of traits and hypothesized selective drivers is often relied on as the only evidence for adaptation of those traits to those hypothesized drivers, without supporting evidence. Second, the selective driver is often inferred from a combination of traits without explicit testing. Third, researchers often measure traits that are easy for humans to observe rather than measuring traits that are suited to testing the hypothesis of adaptation. Finally, species are often chosen for study because of their striking phenotypes, which leads to the illusion of syndromes and divergence. We argue that these issues can be avoided by combining studies of trait variation across entire clades or communities with explicit tests of adaptive hypotheses and that taking this approach will lead to a better understanding of syndrome‐like evolution and its drivers.

Topik & Kata Kunci

Penulis (8)

M

M. Sinnott‐Armstrong

R

Rocío Deanna

C

Chelsea Pretz

S

Sukuan Liu

J

Jesse C. Harris

A

Amy Dunbar-Wallis

S

Stacey D. Smith

L

L. Wheeler

Format Sitasi

Sinnott‐Armstrong, M., Deanna, R., Pretz, C., Liu, S., Harris, J.C., Dunbar-Wallis, A. et al. (2022). How to approach the study of syndromes in macroevolution and ecology. https://doi.org/10.1002/ece3.8583

Akses Cepat

Lihat di Sumber doi.org/10.1002/ece3.8583
Informasi Jurnal
Tahun Terbit
2022
Bahasa
en
Total Sitasi
12×
Sumber Database
Semantic Scholar
DOI
10.1002/ece3.8583
Akses
Open Access ✓