Login / Signup

Temporal variation in the effect of ants on the fitness of myrmecophilic plants: seasonal effect surpasses periodic benefits.

Karla MoniqueGeane Rodrigues de SouzaEduardo Soares CalixtoEstevao Alves Silva
Published in: Die Naturwissenschaften (2022)
Plants provide extrafloral nectar, which is a food resource taken by ants, especially aggressive species that may act as plant guards. To our knowledge, no study has been conducted to concurrently investigate the fluctuation of plant fitness over its whole reproductive season, recording and comparing both short periods (different samplings during the plant's reproductive season) and the season/pooled data (all fruits produced during the reproductive season). Here, by assigning plants to either ant-present or absent treatments, we investigated the influence of the protective foliage-dwelling ant, Camponotus crassus, on the flower bud and fruit production of four extrafloral nectaried plants (Ancistrotropis firmula, Bionia coriacea, Cochlospermum regium, and Peixotoa tomentosa) throughout their annual reproductive season. Periodic samples in the field revealed a large variation in plant reproduction throughout the season; the increases in buds and fruits were not constantly higher in plants with ants, and in fact, plants without ants had more reproductive structures sometimes. Nonetheless, the examination of the pooled data, i.e., cumulative number of flower buds and fruits produced during the reproductive season, revealed the plants with ants produced more flower buds and fruits (e.g., up to two-fold greater in A. firmula) compared to ant-absent treatments. Our results indicate the effects of ants on plant reproduction are not constant over time, but the net benefits to plants with ants are reflected in increased fruit production. Therefore, the investigations of the benefit of ants on plants should consider the whole plant's reproductive season rather than single samplings within plant reproduction period.
Keyphrases
  • physical activity
  • cell wall
  • electronic health record
  • mass spectrometry
  • big data
  • machine learning
  • high resolution
  • risk assessment
  • open label
  • artificial intelligence
  • plant growth
  • data analysis