Our goal was to predict road culvert passability, as defined by culvert outlet drop and outlet water velocity, for three fish swimming groups using remotely collected environmental variables that have been shown to influence the passability of road culverts.
We generated four boosted regression tree models, one for road culvert outlet drop and one each for the three culvert outlet water velocities, and predicted the probability of impassable road culverts on low-order streams based on the models. Independent variables in the modelsincluded the upstream area draining to the culvert, slope at the culvert, stream segment gradient and stream reach gradient.
Gradient of the stream segment was the most important predictor in the outlet drop model, while upstream drainage area was the most important predictor in the three water velocity models. A majority of road culverts on low-order streams are estimated to be passable even for weaker swimming fishes. Moderate to highly impassable road culverts are distributed across many low-order streams throughout the basin, but particular regions are predicted to have higher densities than others due to topography.
Predicted passability of road culverts by migratory fish is related to natural gradients in topography and stream size. While the probability of any particular culvert being impassable is low, the vast number of culverts in the basin means that, together, they could pose a greater challenge to migratory fish than dams. Our modelling framework could be used in any region where culverts are prevalent in the riverscape. The resulting estimates of passability to fishes can guide surveys towards the most problematic hydrological regions and structures and contribute to broad-scale prioritization of barrier removals to restore ecological connectivity.
