CamoEvo: An open access toolbox for artificial camouflage evolution experiments

This is the final version. Available on open access from Wiley via the DOI in this recordData archiving: The dryad doi is https://doi.org/10.5061/dryad.08kprr54d. All data for Box 1 can be found on dryad and our GitHub. Downloads and handbooks for CamoEvo and its genetic algorithm ImageGA can also be found on our GitHub.Camouflage research has long shaped our understanding of evolution by natural selection, and elucidating the mechanisms by which camouflage operates remains a key question in visual ecology. However, the vast diversity of color patterns found in animals and their backgrounds, combined with the scope for complex interactions with receiver vision, presents a fundamental challenge for investigating optimal camouflage strategies. Genetic algorithms (GAs) have provided a potential method for accounting for these interactions, but with limited accessibility. Here, we present CamoEvo, an open-access toolbox for investigating camouflage pattern optimization by using tailored GAs, animal and egg maculation theory, and artificial predation experiments. This system allows for camouflage evolution within the span of just 10-30 generations (∼1-2 min per generation), producing patterns that are both significantly harder to detect and that are optimized to their background. CamoEvo was built in ImageJ to allow for integration with an array of existing open access camouflage analysis tools. We provide guides for editing and adjusting the predation experiment and GA as well as an example experiment. The speed and flexibility of this toolbox makes it adaptable for a wide range of computer-based phenotype optimization experiments.Natural Environment Research Council (NERC

Habitat geometry rather than visual acuity limits the visibility of a ground-nesting bird's clutch to terrestrial predators

This is the final version. Available on open access from Wiley via the DOI in this record. Data availability statement: The dryad doi: https://datadryad.org/stash/share/2yJJ6xuGiKF7Ey6H2y1rAwyqJVGYFdBgFisTb4NN34I. All data and plots for the main text and supplementary material can be found within our dryad archive. ImageJ scripts for running RNL and 3D analyses with the MICA toolbox and ImageJ can be downloaded from our GitHub: https://github.com/GeorgeHancock471/3D_RNL_Tools.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 behavior is relevant to their conservation concerns. 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 modeling in northern lapwings, 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 for 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.Natural Environment Research Council (NERC)Game and Wildlife Conservation Trus