Abstract
BackgroundIdentifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals.MethodsUsing a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events.ResultsFor white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events.ConclusionsCaution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.
Highlights
Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community
There was no evidence that either a Utilization distribution (UD) volume threshold a Hidden Markov model (HMM) based on single-sex location data was able to identify Male-female interaction event (MFIE)
For locations identified as occurring during an MFIE, the standardized UD volume spanned 2–91% for males (Fig. 4A and B) and 1–97% for females (Fig. 4C and D)
Summary
Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. Along with a proliferation of datasets containing positional data on individual animals, researchers have developed a wide variety of tools and statistical models to visualize, quantify, and predict animal movement and space-use [37]. Some of these methods focus on a specific aspect of movement ecology, which is the identification of the underlying, latent, behavioral state of an individual that results in variation in movement and space-use quantities [29]. The combination of rich location-based datasets, accessible but complex statistical methods, and the absence of supporting data for validation purposes can create the perfect storm for a mismatch between the desired inference and the limitations of the data and statistical model
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