Kranstauber, Bart


Suchergebnisse Publikationen

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Density estimation using camera trap surveys : the random encounter model

2014, Rowcliffe, J. Marcus, Carbone, Chris, Kays, Roland, Kranstauber, Bart, Jansen, Patrick A.

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Here today, gone tomorrow : saving migratory animals

2011, Kays, Roland, Blake, Stephen, Cruz, Sebastian, Fiedler, Wolfgang, Kranstauber, Bart, Proanio, Carolina, Weinzierl, Rolf, Wikelski, Martin

Extinct might be a word you associate with animals that lived long ago, like the dinosaurs, but did you know that over 18,000 species are classified as "threatened" (susceptible to extinction) today? Scientists involved in wildlife conservation have a tough job; they're in charge of determining what needs to be done to prevent a species from becoming extinct. Habitat, food supply, and impacts of local human populations are just a few of the factors these scientists take into account. It's a lot to keep track of for a single location, but the job becomes even harder when it's a migratory animal. In this science project, you'll get a firsthand look at their job. You'll access real data about migratory birds and use satellite images to analyze their habitats, then come up with a conservation plan to protect the species from extinction.


Quantifying levels of animal activity using camera trap data

2014, Rowcliffe, J. Marcus, Kays, Roland, Kranstauber, Bart, Carbone, Chris, Jansen, Patrick A.

1. Activity level (the proportion of time that animals spend active) is a behavioural and ecological metric that can provide an indicator of energetics, foraging effort and exposure to risk. However, activity level is poorly known for free-living animals because it is difficult to quantify activity in the field in a consistent, cost-effective and non-invasive way.
2. This article presents a new method to estimate activity level with time-of-detection data from camera traps (or more generally any remote sensors), fitting a flexible circular distribution to these data to describe the underlying activity schedule, and calculating overall proportion of time active from this.
3. Using simulations and a case study for a range of small- to medium-sized mammal species, we find that activity level can reliably be estimated using the new method.
4. The method depends on the key assumption that all individuals in the sampled population are active at the peak of the daily activity cycle. We provide theoretical and empirical evidence suggesting that this assumption is likely to be met for many species, but may be less likely met in large predators, or in high-latitude winters. Further research is needed to establish stronger evidence on the validity of this assumption in specific cases; however, the approach has the potential to provide an effective, non-invasive alternative to existing methods for quantifying population activity levels.

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A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement

2012-07, Kranstauber, Bart, Kays, Roland, LaPoint, Scott, Wikelski, Martin, Safi, Kamran

1. The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its movement path rather than individual points and accounts for temporal autocorrelation and high data volumes. However, the BBMM assumes unrealistic homogeneous movement behaviour across all data.

2. Accurate quantification of the utilization distribution is important for identifying the way animals use the landscape.

3. We improve the BBMM by allowing for changes in behaviour, using likelihood statistics to determine change points along the animal's movement path.

4. This novel extension, outperforms the current BBMM as indicated by simulations and examples of a territorial mammal and a migratory bird. The unique ability of our model to work with tracks that are not sampled regularly is especially important for GPS tags that have frequent failed fixes or dynamic sampling schedules. Moreover, our model extension provides a useful one-dimensional measure of behavioural change along animal tracks.

5. This new method provides a more accurate utilization distribution that better describes the space use of realistic, behaviourally heterogeneous tracks.