Understanding the functional significance of shelter for animal populations requires knowledge of the behavioural mechanisms that govern the dynamics of shelter use. Exploitation of shelters may be impeded by mutual interference, yet interference competition can be difficult to distinguish from exploitation competition. We used bullheads (Cottus gobio) as a model system of mobile fish to investigate the effect of intraspecific competition on shelter use. A series of field experiments was conducted under controlled conditions of shelter availability and population density. For each experiment, the location of each individual fish was observed over a period of 10 days. We then constructed a continuous-time Markov-chain model for the movement of fish between shelters and the open stream, which explicitly parameterised exploitation competition and interference competition for shelter and which accounted for two different size-classes of fish. By using a stochastic rather than a deterministic model, we were able to account for the distribution of fish across shelters, and not just the average occupation. Analysis of the model showed strong evidence of A. J. H. Davey exploitation competition, which was highly dependent on body size, and an increased departure rate from shared shelters. Over and above exploitation, interference competition limited the ability of unsheltered fish to colonise vacant shelters at high population densities. Different formulations of the interference competition were compared using the Akaike Information Criterion. The formulation that best fitted the observations modelled interference competition as an increasing function of average shelter occupancy rather than population density per se.
[1] Runoff-runon occurs when spatially variable infiltration capacities result in runoff generated in one location potentially infiltrating downslope in an area with higher infiltration capacity. The runoff-runon process is invoked to explain field observations of runoff ratios that decline with plot length and steady-state infiltration rates that increase gradually with rainfall intensity. To illustrate the influence of spatial variability and runoffrunon on net infiltration and runoff generation, we use both (i) field rainfall simulation on soil with a high infiltration capacity and (ii) numerical rainfall-runoff-runon simulations over a spatially variable area. Numerical simulations have shown that the spatial variability of soil infiltration properties affects surface runoff generation; however, it has proven difficult to represent analytically the associated runoff-runon process. We argue that given some simplifying assumptions, the runoff-runon phenomenon can be represented by queuing theory, well known in the literature on stochastic processes. Using this approach we report simple analytic expressions derived from the queuing literature that quantify the total runoff under steady-state conditions from a spatially variable tilted 2-D plane, and the runoff produced by the area connected with the lower boundary of the plane under these conditions. These quantities are shown to be equal to the waiting time and the ''sampled'' busy period, respectively, of a first-in first-out queue with exponentially distributed arrivals and service times. The queuing theory model is shown to be consistent with field observations of runoff; however, the approach requires some simplifying assumptions and restrictions that may negate the benefits of the reported analytic solutions.Citation: Jones, O. D., G. J. Sheridan, and P. N. Lane (2013), Using queuing theory to describe steady-state runoff-runon phenomena and connectivity under spatially variable conditions, Water Resour. Res., 49,[7487][7488][7489][7490][7491][7492][7493][7494][7495][7496][7497]
Soil erosion events following fire can wash sediment and ash into streams and reservoirs, contaminating water supplies for cities and towns. These risks are real, yet difficult to quantify, constraining the optimal selection of preventative and remedial options such as prescribed burning and investment in water treatment infrastructure. Non-stationary climate and fire regimes resulting from climate change add to this difficulty. What is the chance of a water supply becoming unusable due to fire? Will this increase with climate change? Will prescribed burning increase or decrease this risk? Answering these questions is challenging because both fire and rainfall regimes are already complex processes to model individually. Considering the interaction between these two processes substantially increases the complexity of the modeling problem.
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