Developmental biomechanics and age polyethism in leaf-cutter ants.

Many social insects display age polyethism: young workers stay inside the nest, and only older workers forage. This behavioural transition is accompanied by genetic and physiological changes, but the mechanistic origin of it remains unclear. To investigate if the mechanical demands on the musculoskeletal system effectively prevent young workers from foraging, we studied the biomechanical development of the bite apparatus in Atta vollenweideri leaf-cutter ants. Fully matured foragers generated peak in vivo bite forces of around 100 mN, more than one order of magnitude in excess of those measured for freshly eclosed callows of the same size. This change in bite force was accompanied by a sixfold increase in the volume of the mandible closer muscle, and by a substantial increase of the flexural rigidity of the head capsule, driven by a significant increase in both average thickness and indentation modulus of the head capsule cuticle. Consequently, callows lack the muscle force capacity required for leaf-cutting, and their head capsule is so compliant that large muscle forces would be likely to cause damaging deformations. On the basis of these results, we speculate that continued biomechanical development post eclosion may be a key factor underlying age polyethism, wherever foraging is associated with substantial mechanical demands.

An agent-based model shows zombie ants exhibit search behavior.

Parasites can alter the behavior of animals. Such alterations could be a byproduct of infection or actively controlled and directed by the parasite. Ants infected with zombie ant fungi (Ophiocordyceps sp.) show behavioral changes culminating in the ant dying while biting into vegetation. To investigate the influence of the parasite on behavioral changes, we created an agent-based model that provides a prediction of how fungal infected ants move before death. The model shows how alterations in movement, such as an increased turning rate, within the normal range of ant behavior, can lead a host from the nest to the underside of a leaf. This demonstrates the simplicity in how such behavioral changes could evolve, as the fungal parasite could benefit from the natural behavior of the host, contesting a hypothesis of highly directed manipulation.

A new zombie ant behavior unraveled: Aggregating on tree trunks.

Hosts can be manipulated by parasites to move to locations advantageous for onward transmission. To investigate the role of behavioral manipulation in creating transmission hotspots, we studied the distribution of zombie turtle ants in the Amazon rainforest. The turtle ant Cephalotes atratus nests and mostly forages in the canopy, but is found at the base of trees when infected with the zombie ant fungus Ophiocordyceps kniphofioides. We found 626 infected cadavers on 14.8% of 162 trees sampled. Cadavers were highly aggregated on the surface of the trees, explained by behavioral observations indicating infected ants as slightly attracted to zombie ant cadavers on a tree. From 1,726 h of camera footage, we recorded the removal of three zombie ant cadavers by live ants. The number of removals compared to the density of infected individuals indicates the base of a tree as an escape from the evolved ability of social insects to recognize and treat disease inside the nest, allowing the parasite to continuously remain in the environment.

Large deformation diffeomorphic mapping of 3D shape variation reveals two distinct mandible and head capsule morphs in Atta vollenweideri leaf-cutter worker ants.

Ants are crucial ecosystem engineers, and their ecological success is facilitated by a division of labour among sterile "workers". In some ant lineages, workers have undergone further morphological differentiation, resulting in differences in body size, shape, or both. Distinguishing between changes in size and shape is not trivial. Traditional approaches based on allometry reduce complex 3D shapes into simple linear, areal, or volume metrics; modern approaches using geometric morphometrics typically rely on landmarks, introducing observer bias and a trade-off between effort and accuracy. Here, we use a landmark-free method based on large deformation diffeomorphic metric mapping (LDDMM) to assess the co-variation of size and 3D shape in the mandibles and head capsules of Atta vollenweideri leaf-cutter ants, a species exhibiting extreme worker size-variation. Body mass varied by more than two orders of magnitude, but a shape atlas created via LDDMM on µ-CT-derived 3D mesh files revealed only two distinct head capsule and mandibles shapes-one for the minims (body mass < 1 mg) and one for all other workers. We discuss the functional significance of the identified 3D shape variation, and its implications for the evolution of extreme polymorphism in Atta.

replicAnt: a pipeline for generating annotated images of animals in complex environments using Unreal Engine.

Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To help overcome these limitations, we developed replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation. We also show that it increases the subject-specificity and domain-invariance of the trained networks, thereby conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.

Learning from algorithm-generated pseudo-annotations for detecting ants in videos.

Deep learning (DL) based detection models are powerful tools for large-scale analysis of dynamic biological behaviors in video data. Supervised training of a DL detection model often requires a large amount of manually-labeled training data which are time-consuming and labor-intensive to acquire. In this paper, we propose LFAGPA (Learn From Algorithm-Generated Pseudo-Annotations) that utilizes (noisy) annotations which are automatically generated by algorithms to train DL models for ant detection in videos. Our method consists of two main steps: (1) generate foreground objects using a (set of) state-of-the-art foreground extraction algorithm(s); (2) treat the results from step (1) as pseudo-annotations and use them to train deep neural networks for ant detection. We tackle several challenges on how to make use of automatically generated noisy annotations, how to learn from multiple annotation resources, and how to combine algorithm-generated annotations with human-labeled annotations (when available) for this learning framework. In experiments, we evaluate our method using 82 videos (totally 20,348 image frames) captured under natural conditions in a tropical rain-forest for dynamic ant behavior study. Without any manual annotation cost but only algorithm-generated annotations, our method can achieve a decent detection performance (77% in [Formula: see text] score). Moreover, when using only 10% manual annotations, our method can train a DL model to perform as well as using the full human annotations (81% in [Formula: see text] score).

Automated tracking and analysis of ant trajectories shows variation in forager exploration.

Determining how ant colonies optimize foraging while mitigating pathogen and predator risks provides insight into how the ants have achieved ecological success. Ants must respond to changing resource conditions, but exploration comes at a cost of higher potential exposure to threats. Fungal infected cadavers surround the main foraging trails of the carpenter ant Camponotus rufipes, offering a system to study how foragers behave given the persistent occurrence of disease threats. Studies on social insect foraging behavior typically require many hours of human labor due to the high density of individuals. To overcome this, we developed deep learning based computer vision algorithms to track foraging ants, frame-by-frame, from video footage shot under the natural conditions of a tropical forest floor at night. We found that most foragers walk in straight lines overlapping the same areas as other ants, but there is a subset of foragers with greater exploration. Consistency in walking behavior may protect most ants from infection, while foragers that explore unique portions of the trail may be more likely to encounter fungal spores implying a trade-off between resource discovery and risk avoidance.