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.
Highlights
Habitat suitability models for plants and animals often focus on course-grained abiotic habitat characteristics at the expense of microhabitat factors and/or biotic interactions that can be important for structuring the use of space [1,2]
While integrated terrain and occupancy models would find utility across a number of fields in ecology, we demonstrate its use by applying it to the study of conspecific attraction in colonial seabirds
The point cloud consisted of 113,338,579 points in 3D space (5,667 points per square meter over an area of 66,395.90 m2), which were generalised to a mesh containing 2,495,043 vertices and 4,988,477 faces
Summary
Habitat suitability models for plants and animals often focus on course-grained abiotic habitat characteristics at the expense of microhabitat factors and/or biotic interactions that can be important for structuring the use of space [1,2]. Despite their ubiquity and importance for spatial ecology, the scale and extent of data used to explore relationships between organisms and the space they occupy are often dictated by the availability of environmental data rather than. Computer Vision Based Ultra-Fine Scale Habitat Suitability
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
