- NSF-PAR ID:
- 10086348
- Date Published:
- Journal Name:
- Journal of ecology
- Volume:
- 106
- ISSN:
- 0022-0477
- Page Range / eLocation ID:
- 1910-1924
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Different populations of plant species can adapt to their local pollinators and diverge in floral traits accordingly. Floral traits are subject to pollinator‐driven natural selection to enhance plant reproductive success. Studies on temperate plant systems have shown pollinator‐driven selection results in floral trait variation along elevational gradients, but studies in tropical systems are lacking. We analyzed floral traits and pollinator assemblages in the Neotropical bee‐pollinated taxon
Costus guanaiensis var.tarmicus across four sites along a steep elevational gradient in Peru. We found variations in floral traits of size, color, and reward, and in the pollinator assemblage along the elevational gradient. We examined our results considering two hypotheses, (1) local adaptation to different bee assemblages, and (2) the early stages of an evolutionary shift to a new pollinator functional group (hummingbirds). We found some evidence consistent with the adaptation ofC. guanaiensis var.tarmicus to the local bee fauna along the studied elevational gradient. Corolla width across sites was associated with bee thorax width of the local most frequent pollinator. However, we could not rule out the possibility of the beginning of a bee‐to‐hummingbird pollination shift in the highest‐studied site. Our study is one of the few geographic‐scale analyses of floral trait and pollinator assemblage variation in tropical plant species. Our results broaden our understanding of plant‐pollinator interactions beyond temperate systems by showing substantial intraspecific divergence in both floral traits and pollinator assemblages across geographic space in a tropical plant species. -
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Location Veracruz, Mexico.
Taxon Leaf‐litter ants.
Methods We sampled 320 m2of leaf litter spread across eight equally spaced sites from sea level to 3500 m of elevation. We used regression models to predict α‐diversity patterns with climatic (temperature and precipitation) and spatial (geometric constraints) variables. We also assessed, through multiple regression based on distance matrices (MRM), the relative importance of habitat filtering and dispersal limitations for shaping total dissimilarity (βsor), turnover (βsim) and nestedness (βnes).
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