Evolution of foraging behaviour induces variable complexity-stability
relationships in a multiplex community with multiple interaction types
- Lin Wang,
- Ting Wang,
- Xiao-Wei Zhang,
- Jin-Bao Liao,
- Rui Wang
Abstract
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Early ecological theory predicts that complex ecological networks are
unstable and are unlikely to persist, despite many empirical studies of
such complexity in nature. This inconsistency has fascinated ecologists
for decades. To resolve complexity-stability relationships, coupling
population dynamics and trait dynamics is considered to be an important
way to understand the long-term stability of ecological community
assemblages. However, incorporating adaptive processes into ecologically
realistic networks with both antagonistic and mutualistic interactions
is still a challenge. Here, we explored an adaptive food-web model to
evaluate how the evolution of foraging preference (behaviour trait) to
determine the relationship between network complexity (e.g.,
connectance) and stability (e.g., community persistence) in a multiplex
community with multiple interaction types (MEST:
mutualist-exploiter-specialist predator-top predator). Our theoretical
results showed: (i) adaptive foraging of the top predator contributes to
the stability of mutualism and intermediate intensity of foraging
adaptation can lead to chaotic dynamics in a four-species MEST
community; (ii) the connectance-stability relationship may show positive
monotonic, negative monotonic, peaked and double-peaked patterns in
general MEST communities, while the double-peaked pattern is only
obtained when both the adaptation intensity and interspecific
competition are high. Moreover, we infer that foraging adaptation of the
top predator may alter positive and/or negative feedback loops
(trait-mediated indirect effects) to affect the stability of the MEST
community. Finally, our theoretical predictions may be consistent with
both the negative monotonic complexity-stability relationship revealed
in freshwater communities and the peaked pattern revealed in marine
communities. Our adaptive dynamics framework may provide an effective
way to address the complexity-stability debate in real ecosystems.