{'fraction_non_alphanumeric': 0.013912075681691708, 'fraction_numerical': 0.0011129660545353367, 'mean_word_length': 5.8106060606060606, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Multistate Ornstein-Uhlenbeck approach for practical estimation of movement and resource selection around central places

Home range dynamics and movement are central to a species’ ecology and strongly mediate both intra- and interspecific interactions. Numerous methods have been introduced to describe animal home ranges, but most lack predictive ability and cannot capture effects of dynamic environmental patterns, such as the impacts of air and water flow on movement. Here, we develop a practical, multi-stage approach for statistical inference into the behavioral mechanisms underlying how habitat and dynamic energy landscapes—in this case how airflow increases or decreases the energetic efficiency of flight—shape animal home ranges based around central places. We validated the new approach using simulations, then applied it to a sample of 12 adult golden eagles Aquila chrysaetos tracked with satellite telemetry. The application to golden eagles revealed effects of habitat variables that align with predicted behavioral ecology. Further, we found that males and females partition their home ranges dynamically based on uplift. Specifically, changes in wind and sun angle drove differential space use between sexes, especially later in the breeding season when energetic demands of growing nestlings require both parents to forage more widely. This method is easily implemented using widely available programming languages and is based on a hierarchical multistate Ornstein-Uhlenbeck space use process that incorporates habitat and energy landscapes. The underlying mathematical properties of the model allow straightforward computation of predicted utilization distributions, permitting estimation of home range size and visualization of space use patterns under varying conditions.