Abstract
All communities may re-assemble after disturbance. Predictions for re-assembly outcomes are, however, rare. Here we model how fish communities in an extremely variable Australian desert river re-assemble following episodic floods and drying. We apply information entropy to quantify variability in re-assembly and the dichotomy between stochastic and deterministic community states. Species traits were the prime driver of community state: poor oxygen tolerance, low dispersal ability, and high fecundity constrain variation in re-assembly, shifting assemblages towards more stochastic states. In contrast, greater connectivity, while less influential than the measured traits, results in more deterministic states. Ecology has long recognised both the stochastic nature of some re-assembly trajectories and the role of evolutionary and bio-geographic processes. Our models explicitly test the addition of species traits and landscape linkages to improve predictions of community re-assembly, and will be useful in a range of different ecosystems.
Highlights
Patterns of community re-assembly following disturbance events can be critical in shaping the function of ecosystems[1,2,3,4,5]
Freshwater fish communities possess a diverse range of species traits; often occupying dynamic habitats with broad ranges of connectivity
The three species traits that were most important in predicting the probability of species occurrence were: oxygen tolerance, dispersal ability, and total fecundity (Table 1)
Summary
Patterns of community re-assembly following disturbance events can be critical in shaping the function of ecosystems[1,2,3,4,5]. Ephemeral rivers are considered to be the most hydrologically dynamic of all freshwater ecosystems[26,27,28] These habitats experience episodic flood events and extended periods of drought that can leave entire sub-catchments dry[28,29,30]. Information entropy is widely used to measure variation, or uncertainty, in the outcome of ecological and non-ecological assembly processes[31] It has, for example, been used in species distribution models[32,33], to quantify species diversity[34] (Shannon’s information entropy35,36) and to predict the role of environmental variability in determining biodiversity (Shipley’s maximum entropy;[5,15]). The adaptive cycle model focuses upon the processes of destruction and reorganisation providing a more complete view of system dynamics, organisation and resilience[39]
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