Estimating utilization distributions from fitted step-selection functions

By J. Signer, J. Fieberg, Tal Avgar. 2017

116 citations recorded by the source corpus.

Habitat-selection analyses are often used to link environmental covariates, measured within some spatial domain of assumed availability, to animal location data that are assumed to be independent. Step-selection functions (SSFs) relax this independence assumption, by using a conditional model that explicitly acknowledges the spatiotemporal dynamics of the availability domain and hence the temporal dependence among successive locations. However, it is not clear how to produce an SSF-based map of the expected utilization distribution. Here, we used SSFs to analyze virtual animal movement data generated at a fine spatiotemporal scale and then rarefied to emulate realistic telemetry data. We then compared two different approaches for generating maps from the estimated regression coefficients. First, we considered a naive approach that used the coefficients as if they were obtained by fitting an unconditional model. Second, we explored a simulation-based approach, where maps were generated using stochastic simulations of the parameterized step-selection process. We found that the simulation-based approach always outperformed the naive mapping approach and that the latter overestimated home-range size and underestimated local space-use variability. Differences between the approaches were greatest for complex landscapes and high sampling rates, suggesting that the simulation-based approach, despite its added complexity, is likely to offer significant advantages when applying SSFs to real data.

Subfields: Ecology, Small Animals

View Paper: https://www.semanticscholar.org/paper/7c3b1c0e0a0ebf2991961402c7969aa47a2cc685

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. J. Signer
2. Tal Avgar
3. J. Fieberg
4. Mariana M. P. B. Fuentes
5. K. Laidre
6. S. Boutin
7. S. Maxwell
8. A. C. Burton

In Small Animals

1. J. Signer
2. Tal Avgar
3. J. Fieberg
4. K. Laidre
5. A. C. Burton
6. S. Boutin
7. E. Neilson
8. Kate Broadley

Citations in the corpus

• Marine mammal conservation: over the horizon (2021, methodology): “…impacts of such assumptions and would benefit from combining multiple data sources to evaluate methods (Mikkelsen et al. 2016, Woodman et al. 2019), and linking inference from the typical HPM landscape-scale models with models of individual movement paths (Signer et al. 2017, Michelot et al. 2019).”
• Animal movement affects interpretation of occupancy models from camera‐trap surveys of unmarked animals (2018, methodology): “…above-stated relationship between r and b. Alternatively, our model can be viewed as a discrete approximation of a circular bivariate Ornstein-Uhlenbeck model (often used to model animal ranging behavior; Blackwell 1997), which has a well-known Gaussian steady-state solution (Signer et al. 2017).”
• Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses (2018, methodology): “In a final step, we simulated space-use from the fitted model m1 to obtain a model-based estimate of the animal’s utilization distribution (UD; Avgar et al. , 2016; Signer et al. , 2017).”