DOI: 10.5061/dryad.q0n31
title: Data from: Are 2D space-use analyses adapted to animals living in 3D environments? A case study on a fish shoal

description: Methodologies enabling the monitoring of animal movement and behavior in
 3-dimensions (3D; x, y, z, the latter accounting for the vertical
 dimension) are becoming increasingly accessible and can be deployed on
 entire groups of animals inhabiting 3D habitats. When 2-dimensional (2D;
 x, y) space-use analyses are used on such groups, their spatial
 organization is represented as a planar projection of individuals’
 space-use. Movement on the vertical dimension is ignored and could biased
 ecological inference made from the spatial structure of the group. We used
 a digital imaging technique to track movements and feeding behavior of
 individual animals within a free-range aggregation of juvenile
 drift-feeding fish (Galaxias anomalus) in 3D and at fine spatiotemporal
 scales. We estimated spatiotemporal overlap of space-use and feeding
 territories between group-members using 2D (x, y) and 3D spatial analysis
 to: (1) describe the spatial structure of the group, (2) identify patterns
 of resource partitioning, and (3) investigate the relationship between
 space-use overlap and feeding behavior. We found that overlapping ratios
 of space-use and feeding territories were over-estimated in 2D, while 3D
 analysis of space-use provided evidence of spatial partitioning between
 group-members. We also found that, regardless of the computation used, the
 overlapping ratios of space-use were positively correlated with
 overlapping ratios of feeding territories while no effect was found on
 feeding activity. In conclusion, whilst 3D analysis provided valuable
 information on the spatial structure of a group, inferences on the
 ecological function of space-use can also be obtained from 2D analysis.
description: 3D_2D_overlap_manuscript
 dataset copy3D and 2D computations
 of Utilisation Distribution (UD) based on Kernel Density Estimates (KDE)
 as well as extra information. In this file, each observation is associated
 with its UD50 and UD95 computed in 2D and in 3D, as well as the area and
 volume of its feeding territory. Scaled values for 3D UDs are annotated by
 SoverL and SnonoverL and Sfeed_over and Sfeed_nonover for feeding
 territories. tini indicates the timestamp of the start of the observation
 in second, tend the end, monitor indicates the duration of the
 observation, dim the dimension of the computation (3D or 2D). feed_act
 indicates the number of prey captured per minutesite41_transRaw coordinates of recorded position of each observation made in video session 1.site42Raw coordinates of recorded position of each observation made in video session 2.site43_transRaw coordinates of recorded position of each observation made in video session 3.site44_v2_transRaw coordinates of recorded position of each observation made in video session 4.site45_transRaw coordinates of recorded position of each observation made in video session 5.Site41_feed_v2Raw coordinates of recorded feeding events associated with each observation made in video session 1site42_feedRaw coordinates of recorded feeding events associated with each observation made in video session 2.site43_feed_transRaw coordinates of recorded feeding events associated with each observation made in video session 3.site44_feed_transRaw coordinates of recorded feeding events associated with each observation made in video session 4.Site44_feed_v2_trans.csvsite45_feed_transRaw coordinates of recorded feeding events associated with each observation made in video session 5.