Journal
ECOLOGY
Volume 95, Issue 4, Pages 833-844Publisher
WILEY
DOI: 10.1890/13-1010.1
Keywords
species interaction; complementarity; community ecology; mutualism; multiple predator effects; connectance webs; antagonism; functional webs; energy-flow webs; functional group; mutualistic network; diversity
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Funding
- Achievement Rewards for College Scientists (ARCS) Foundation
- Center for Population Biology
- NSF [DEB 1011635, DEB 0918267, DEB 1145588, OCE 0850707, OCE 1234345]
- Division Of Environmental Biology
- Direct For Biological Sciences [1145588] Funding Source: National Science Foundation
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Most organisms interact with multiple mutualistic species that confer different functional benefits, yet current conceptual frameworks do not fully address this complexity. A network approach considers multiple mutualistic interactions within a functional type and has been largely nonmechanistic, with little attention to the fitness consequences of specific interactions. Alternatively, consumer-resource approaches have explicitly characterized the mechanisms and fitness consequences of resource exchange, but have not been extended to functionally divergent partners. First, we merge these approaches using graphical models to define the multiple mutualist effects (MMEs) that occur when a focal species has multiple partner mutualists. This approach mirrors food web research that has been advanced by studies of multiple predator effects as well as by detailed investigations of modules nested within larger networks. Second, we define the pathways through which a focal mutualist and two or more partner species could interact, reviewing examples of MMEs that span a range from positive to negative fitness effects. Third, given the potential for nonadditivity demonstrated by the existing literature, we pose new hypotheses for species-interaction outcomes by examining factors such as the extent of overlap in rewards exchanged among partners and their resulting network topologies. Our synthesis illustrates how the consideration of MMEs can improve the ability to predict the outcomes of losses or gains of mutualisms from ecosystems.
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