We apply modern regression methods (adapted to spatial, temporal, or spatio-temporal data) such as genralized linear (mixed) models or generalized additive (mixed) models to estimate linear and nonlinear trends and population sizes, and to visualize distribution patterns. These models can be adapted to various types of data, such as presence-absence data, count data, catch-recatch data and presence-only data.
Recent developments in wildlife telemetry and tracking technologies have led to an impressive growth in the volume
of GPS and telemetry tracking data. However, the data gathered by these methods represents 'presence-only-data' with high spatio-temporal autocorrelation, making the statistical
analysis of these data challening.
Our research involves the statistical analysis of these tracking data using various approaches:
Does the animal have a significant preference for certain habitats? Does the population significantly avoid certain anthropogenic
structures (such as wind turbines)? These questions can be answered analysing the data using appropriate regression techniques specialized for tracking data. We have further developed
and adapted a newly emerging and increasingly used class of models called 'point process models' (PPM's) for the analysis of animal
tracking data. These models overcome the statistical disadvantages of the traditionally used 'pseudo-absence models' and can be combined with mixed or additive modelling. Furthermore we
frequently apply Step-Selection Analysis (SSA) methods and Hidden Markov Models (HMMs).
Which regions are used most frequently used by an animal or a population? Several methods can be used to estimate and visualize home ranges, but each method makes certain assumptions about the data. We can help to choose and apply the most appropriate method for your data and research question.
Animals usually show different behavioral states (e.g., 'foraging', 'resting', or 'migration'). Automatic categorization into these states based on GPS / telemetry tracking data can be of great interest, e.g. because each of these states is related to specific covariates. However, several possible methods can be used to achieve this, with each again requiring certain assumptions regarding the data to be met. We can advise you on and apply the most appropriate method based on your data and research question.
Tracking data are sometimes either sparse or show strong uncertainties (e.g. Argos-based data). However, we can use various methods (such as state-space-models) to interpolate the animal's most likely true track, e.g., combined with subsequent appropriate regression analyses.
To determine if an anthropogenic structure significantly reduces animal density it is necessary to distinguish between the true impact of the structure and natural density fluctuations in space and time.
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