Three-dimensional barricading of a predatory trap reduces predation and enhances prey capture

Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing

Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here, we provide a detailed analysis of the heterogeneous graph structures of spider webs and use deep learning as a way to model and then synthesize artificial, bioinspired 3D web structures. The generative models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) an analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation; 2) a discrete diffusion model with full neighbor representation; and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bioinspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose an algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles toward integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties.

Unique behavioural modifications in the web structure of the cave orb spider Meta menardi (Araneae, Tetragnathidae)

In the last decade there has been a renewed interest in the study of behavioural adaptations to environmental constraints with a focus on adaptations to challenging habitats due to their reduced ecological complexity. However, behavioural studies on organisms adapted to nutrient poor subterranean habitats are few and far between. Here, we compared both morphological traits, in terms of relative leg lengths, and behavioural traits, captured in the geometry of the spider web, between the cave-dwelling spider, Meta menardi, and two aboveground species from the same family (Tetragnathidae); Metellina mengei and Tetragnatha montana. We found that the webs of the cave spider differed significantly from the two surface-dwelling species. The most dramatic difference was the lack of frame threads with the radii in the webs instead attaching directly to the surrounding rock, but other differences in relative web size, web asymmetry and number of capture spiral threads were also found. We argue that these modifications are likely to be adaptations to allow for a novel foraging behaviour to additionally capture walking prey within the vicinity of the web. We found only limited evidence for morphological adaptations and suggest that the cave orb spider could act as a model organism for studies of behaviour in energy-poor environments.

Imaging and analysis of a three-dimensional spider web architecture

Spiders are abundantly found in nature and most ecosystems, making up more than 47 000 species. This ecological success is in part due to the exceptional mechanics of the spider web, with its strength, toughness, elasticity and robustness, which originate from its hierarchical structures all the way from sequence design to web architecture. It is a unique example in nature of high-performance material design. In particular, to survive in different environments, spiders have optimized and adapted their web architecture by providing housing, protection, and an efficient tool for catching prey. The most studied web in literature is the two-dimensional (2D) orb web, which is composed of radial and spiral threads. However, only 10% of spider species are orb-web weavers, and three-dimensional (3D) webs, such as funnel, sheet or cobwebs, are much more abundant in nature. The complex spatial network and microscale size of silk fibres are significant challenges towards determining the topology of 3D webs, and only a limited number of previous studies have attempted to quantify their structure and properties. Here, we focus on developing an innovative experimental method to directly capture the complete digital 3D spider web architecture with micron scale resolution. We built an automatic segmentation and scanning platform to obtain high-resolution 2D images of individual cross-sections of the web that were illuminated by a sheet laser. We then developed image processing algorithms to reconstruct the digital 3D fibrous network by analysing the 2D images. This digital network provides a model that contains all of the structural and topological features of the porous regions of a 3D web with high fidelity, and when combined with a mechanical model of silk materials, will allow us to directly simulate and predict the mechanical response of a realistic 3D web under mechanical loads. Our work provides a practical tool to capture the architecture of sophisticated 3D webs, and could lead to studies of the relation between architecture, material and biological functions for numerous 3D spider web applications.