Posted by: oikosasa | January 27, 2014

To live in a multi-predatory landscape of fear

Having one predator chasing you is scary enough, but what about having two? Hunting in different habitats? Lucky me not being  roe-deer! Read more in the Oikos Early View paper “Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans” by Karen Lone and colleagues. Below is Karen’s summary of the paper:

The challenge of managing large carnivores in a multiple-use landscape in Norway has motivated a large research effort to understand carnivore ecology and their impact on livestock and other wildlife, in addition to extensive monitoring. I was lucky to be able to use some of the data collected in this larger framework to investigate predator-prey interactions and the landscapes of risk for roe deer. Our goal was to look at the effect of multiple predators preying on a single prey species. Roe deer have a natural predator in lynx, and a functional predator in hunters. We investigated how predation risk from these two predators related with habitat characteristics by comparing kill sites to sites used by live roe deer, and anticipated that they produced conflicting landscapes of risk.

LiDAR data from one field plot with radius ca 28m in a 3D perspective – points are colored by height above the ground, so vegetation hits stand out in warmer colors than ground hits.

LiDAR data from one field plot with radius ca 28m in a 3D perspective – points are colored by height above the ground, so vegetation hits stand out in warmer colors than ground hits.

In this paper we try to use Light Detection and Ranging (LiDAR) data to predict risk. We had access to a LiDAR dataset obtained by airborne laser scanning the entire 900km2 study area. As well as giving spatially extensive information, it also gives a lot of detail: the point cloud of height measurements (points at which laser beam was reflected) gives a nice visual impression of vegetation structure (see figure). Especially important LiDAR variables in our analysis of risk were laser echoes from the 0.5-2m height segment, corresponding to the density of the understory vegetation. The final predictive maps of predation risk are based on LiDAR data, a terrain model and a map of roads.

Our study site Hallingdalen, a valley in central Norway, is a multiple-use landscape.

Our study site Hallingdalen, a valley in central Norway, is a multiple-use landscape.

Both LiDAR data and field data provided the same findings – that predation risk from lynx was higher in denser habitat, and increasing with distance from roads. Conversely, the risk of being killed by a hunter was higher in more open habitat and closer to roads, indicating that roe deer face a trade-off between the two predators along these gradients. With regards to some habitat characteristics, the risk gradient aligned for lynx and hunter – both inferred greater risk in more rugged terrain. From the spatial predictions, we found that only 1% of the area had low predation risk from both predators. In other words, when we considered two predators together rather than each on their own, the roe deer had almost no refuges where they can escape predation altogether. Our study raises questions of how roe deer adapt their behavior, if at all, to reconcile the risk landscapes they face, and whether the temporal variations between their two predators may be the key to avoiding mortality.

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