Effects of tracking data biases in species distribution models for large scale studies

MegaMove members have led an important paper on analysing tracking data bias to estimate species distributions. The study Quantifying effects of tracking data bias on species distribution models, which was published in the journal Methods in Ecology and Evolution, used simulated data emulating the movement of marine predators to test the effects of different types of tracking data when predicting species distributions.

Telemetry, or tracking, datasets assist in the conservation of threatened species by increasing our knowledge of where these animals go. This study assessed the effects of common biases in telemetry data and provided ideas for how to alleviate some of these effects. The team found that the results of species distribution models can be affected by tagging location bias, and when using short tracks, which can result from tag failure, battery depletion, or damage to the tag’s antenna.

We found that replacing short tracks with longer tracks, or with tracks from other locations, can assist reducing the effects of these biases and improve model performance. This study provides a reference to effectively use tracking datasets obtained with different tag types for different species and habitats when predicting large scale species distributions.