| Summary: | Population cycles in small rodents have attracted attention from ecologists for more than a century. This spectacular phenomenon is crucial for the functioning of many northern food-webs and has intrigued ecologist because of its lessons for general ecology. Knowledge about the rodent cycle has, however, been hampered by the lack of reliable monitoring methods both for rodents and some of their assumed interactants (e.g. the small mustelids). In resent decades, camera traps have become widely used in ecology as they provide a cost-efficient and non-invasive method for wildlife monitoring. In this thesis, consisting of four studies, I will investigate how camera trap tunnels tailored for small mammals can enhance rodent monitoring. First, in study I, I together with colleagues conducted the first large scale assessment of the applicability of tunnel-based camera traps to estimate population parameters in a small mammal community, including during a long Arctic winter. We showed that the camera trap provide estimates of rodent occupancy also under the snow during winter. Further we give recommendation on micro-scale placement of the traps to maximize technical functionality in order to avoid loss of data. Then, in study II, we expand on dynamic occupancy models for interacting species by including two nested spatial scales. This allows for camera trap-based investigation of the rodent-mustelid interaction on both a local and a landscape scale. Features of this interaction are assumed to be a key to understand the cause(s) of the rodent population cycles. In study III, we apply the statistical framework developed in study II to a dataset derived from the long-term monitoring program Climate-ecological Observatory for Arctic Tundra (COAT). Our results show that presence of mustelids increased the extinction probability of rodents on both a local and a landscape scale. Furthermore, we demonstrate a clear habitat dependence and indications of a season-dependency in the rodent-mustelid interaction strength. Finally, in ...
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