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.

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.

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.

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.