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Structural equation modeling reveals determinants of fitness in a cooperatively breeding bird.

Michela BusanaFranz J WeissingMartijn HammersJoke BakkerHannah L DugdaleSara Raj PantDavid S RichardsonTerrence A BurkeJan Komdeur
Published in: Behavioral ecology : official journal of the International Society for Behavioral Ecology (2021)
Even in well-studied organisms, it is often challenging to uncover the social and environmental determinants of fitness. Typically, fitness is determined by a variety of factors that act in concert, thus forming complex networks of causal relationships. Moreover, even strong correlations between social and environmental conditions and fitness components may not be indicative of direct causal links, as the measured variables may be driven by unmeasured (or unmeasurable) causal factors. Standard statistical approaches, like multiple regression analyses, are not suited for disentangling such complex causal relationships. Here, we apply structural equation modeling (SEM), a technique that is specifically designed to reveal causal relationships between variables, and which also allows to include hypothetical causal factors. Therefore, SEM seems ideally suited for comparing alternative hypotheses on how fitness differences arise from differences in social and environmental factors. We apply SEM to a rich data set collected in a long-term study on the Seychelles warbler ( Acrocephalus sechellensis ), a bird species with facultatively cooperative breeding and a high rate of extra-group paternity. Our analysis reveals that the presence of helpers has a positive effect on the reproductive output of both female and male breeders. In contrast, per capita food availability does not affect reproductive output. Our analysis does not confirm earlier suggestions on other species that the presence of helpers has a negative effect on the reproductive output of male breeders. As such, both female and male breeders should tolerate helpers in their territories, irrespective of food availability.
Keyphrases
  • body composition
  • physical activity
  • healthcare
  • mental health
  • human health
  • magnetic resonance
  • gene expression
  • magnetic resonance imaging
  • big data
  • single cell
  • genetic diversity
  • deep learning