Methods for implementing integrated step-selection functions with incomplete data.

Area of Science:

• Ecology
• Wildlife Biology
• Movement Ecology

Background:

• Integrated step-selection analyses (iSSAs) are standard for studying animal habitat and movement preferences.
• iSSAs require regularly timed animal location data, which is often unavailable due to tracking irregularities.
• Current practices often discard valuable data with temporal irregularities, limiting model scope.

Purpose of the Study:

• To evaluate alternative methods for integrated step-selection analyses (iSSAs) that accommodate irregular time intervals in animal tracking data.
• To compare the performance of these novel approaches against a baseline method that ignores temporal irregularities.
• To demonstrate the benefits of utilizing additional data points from temporally irregular datasets.

Main Methods:

• Developed and tested four alternative approaches to handle temporal irregularities in animal location data within iSSAs.
• Conducted a simulation study to compare the performance of the alternative methods against a baseline approach.
• Applied the methods to a case study involving spotted hyena (Crocuta crocuta) movement data.

Main Results:

• The alternative approaches demonstrated potential improvements in model performance compared to the baseline method.
• Leveraging data with temporal irregularities can lead to more robust analyses of movement and habitat selection.
• The case study illustrated the practical benefits of the proposed methods.

Conclusions:

• Researchers can improve the accuracy and scope of integrated step-selection analyses by incorporating methods that account for temporal irregularities.
• These findings encourage the use of more complete datasets, even those with missing locations.
• The developed methods offer a more comprehensive understanding of animal behavior and habitat use.