DOI: 10.5061/dryad.wm37pvmtb
title: Habitat geometry rather than visual acuity limits the visibility of a ground-nesting bird's clutch to terrestrial predators

description: The nests of ground-nesting birds rely heavily on camouflage for their
 survival, and predation risk, often linked to ecological changes from
 human activity, is a major source of mortality. Numerous ground-nesting
 bird populations are in decline, so understanding the effects of
 camouflage on their nesting behaviour is of relevance to their
 conservation concern. Habitat three-dimensional (3D) geometry together
 with predator visual abilities, viewing distance, and viewing angle
 determine whether a nest is either visible, occluded or too far away to
 detect. While this link is intuitive, few studies have investigated how
 fine-scale geometry is likely to help defend nests from different predator
 guilds. We quantified nest visibility based on 3D occlusion, camouflage,
 and predator visual modelling in northern lapwing, Vanellus vanellus, on
 different land management regimes. Lapwings selected local backgrounds
 that had a higher 3D complexity at a spatial scale greater than their
 entire clutches compared to local control sites. Importantly, our findings
 show that habitat geometry – rather than predator visual acuity –
 restricts nest visibility to terrestrial predators, and that their field
 habitats perceived by humans as open are functionally closed with respect
 to a terrestrial predator searching for nests on the ground. Taken
 together with lapwings’ careful nest site selection, our findings
 highlight the importance of considering habitat geometry for understanding
 the evolutionary ecology and management of conservation sites for
 ground-nesting birds.
description: Data Collection Sites All images and 3D
 scans were collected from two separate locations monitored by the Game and
 Wildlife Conservation Trust (GWCT); the Avon Valley in Hampshire
 [50.93105,-1.78462] and Burpham in Sussex [50.87198, -0.51812].
 Predation Data Nest predation status was
 determined using nest temperature loggers (iButtons) and weekly nest
 checks from the date of discovery to the point of failure or hatching
 following the methods of Hartman and Oring (Hartman & Oring,
 2006). Predated eggshell fragments or disappearance of clutches/eggs prior
 to egg weight estimated hatch dates were encoded as predation
 events.  Nest and Null Photography and
 Scanning From March to Mid-June of 2021 and 2022, we
 photographed 115 lapwing nests and 3D scanned 83. The nests were scanned
 with an ASUS Zenfone AR using the Matterport Scenes app from a height of
 1.2m. Scans and photographs were taken from a height of 1.2 metres at a
 flat 90o (vertical) angle from the ground. For each nest, an additional
 nest-less photo and scan were taken at a distance of 1-2 metres (4 paces)
 from the nest, by backtracking in the direction of the approach to avoid
 further trampling the surrounding area. These additional photos and scans
 were used as a paired null for each nest. Photographs
 of the nests and nulls were taken using a chart colour-calibrated Sony
 A6000 with a Baader venus-u 52mm UV filter and the camera’s own visible
 light filter. A 7% and 93% uniform (λ 200–700nm) reflectance standard was
 placed in situ for each photograph. As the lighting environment was highly
 variable due to changes in solar angle and weather, all photos were taken
 with a 1m2 pop-out NEEWER diffuser sheet at times greater than 2 hours
 from dawn and dusk to prevent patterns from shadows changing the luminance
 and colour measurements of the clutches and their backgrounds. Photographs
 were converted to standardised multispectral images using the ‘generate
 multispectral image’ function within the MICA toolbox v2.2.2 for
 ImageJ. Dataset Generation 3D
 measures and colour measures for the scans and MSPEC images were generated
 using custom scripts for ImageJ available on our GitHub: The data frames for Z (depth) energy variation, 3D transects, colour metrics and occlusion metrics were combined with habitat and nest ID data using excel with additional filtering of damaged photographs and one non-subject species (redshank) within our R code.
description: Running the statistical analyses requires the R code and data
 frames for 3D and colour measures, stored in the same folder layout as
 provided for the zip. Simply set the working directory for the R script to
 the same folder. <br><br>Example 3D scans and .mspecs are also
 provided, requiring ImageJ and version 2.2.0 to open. All scripts for our
 3D analyses are provided on our GitHub: https://github.com/GeorgeHancock471/3D_RNL_Tools. To open our .mspecs requires the installation of a custom version of "_Load_Multispectral_Image.txt", which allows for RAW UV and human visible spectrum images with different rotations to be combined. Copy and paste the .txt file provided alongside our .mspecs into the plugins/micaToolbox/ folder of ImageJ.