Ultra-Fine Scale Spatially-Integrated Mapping of Habitat and Occupancy Using Structure-From-Motion

PLoS One. 2017 Jan 11;12(1):e0166773. doi: 10.1371/journal.pone.0166773. eCollection 2017.

Abstract

Organisms respond to and often simultaneously modify their environment. While these interactions are apparent at the landscape extent, the driving mechanisms often occur at very fine spatial scales. Structure-from-Motion (SfM), a computer vision technique, allows the simultaneous mapping of organisms and fine scale habitat, and will greatly improve our understanding of habitat suitability, ecophysiology, and the bi-directional relationship between geomorphology and habitat use. SfM can be used to create high-resolution (centimeter-scale) three-dimensional (3D) habitat models at low cost. These models can capture the abiotic conditions formed by terrain and simultaneously record the position of individual organisms within that terrain. While coloniality is common in seabird species, we have a poor understanding of the extent to which dense breeding aggregations are driven by fine-scale active aggregation or limited suitable habitat. We demonstrate the use of SfM for fine-scale habitat suitability by reconstructing the locations of nests in a gentoo penguin colony and fitting models that explicitly account for conspecific attraction. The resulting digital elevation models (DEMs) are used as covariates in an inhomogeneous hybrid point process model. We find that gentoo penguin nest site selection is a function of the topography of the landscape, but that nests are far more aggregated than would be expected based on terrain alone, suggesting a strong role of behavioral aggregation in driving coloniality in this species. This integrated mapping of organisms and fine scale habitat will greatly improve our understanding of fine-scale habitat suitability, ecophysiology, and the complex bi-directional relationship between geomorphology and habitat use.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Ecosystem*
  • Models, Biological*

Associated data

  • Dryad/10.5061/dryad.2j420

Grants and funding

This work was funded by the US National Science Foundation Office of Polar Programs (Award No. NSF/OPP-1255058) and the Institute of Advanced Computational Science at Stony Brook University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.