Collective foraging has been shown to benefit organisms in environments where food is patchily distributed, but whether this is true in the case where organisms do not rely on long-range communications to coordinate their collective behaviour has been understudied. To address this question, we use the tractable laboratory model organism Caenorhabditis elegans , where a social strain ( npr-1 mutant) and a solitary strain (N2) are available for direct comparison of foraging strategies. We first developed an on-lattice minimal model for comparing collective and solitary foraging strategies, finding that social agents benefit from feeding faster and more efficiently simply owing to group formation. Our laboratory foraging experiments with npr-1 and N2 worm populations, however, show an advantage for solitary N2 in all food distribution environments that we tested. We incorporated additional strain-specific behavioural parameters of npr-1 and N2 worms into our model and computationally identified N2's higher feeding rate to be the key factor underlying its advantage, without which it is possible to recapitulate the advantage of collective foraging in patchy environments. Our work highlights the theoretical advantage of collective foraging owing to group formation alone without long-range interactions and the valuable role of modelling to guide experiments. This article is part of the theme issue ‘Multi-scale analysis and modelling of collective migration in biological systems'.

Collective foraging has been shown to benefit organisms in environments where food is patchily distributed, but whether this is true in the case where organisms do not rely on longrange communications to coordinate their collective behaviour has been understudied. To address this question, we use the tractable laboratory model organism Caenorhabditis elegans, where a social strain (npr-1 mutant) and a solitary strain (N2) are available for direct comparison of foraging strategies. We first developed an on-lattice minimal model for comparing collective and solitary foraging strategies, finding that social agents benefit from feeding faster and more efficiently simply due to group formation. Our laboratory foraging experiments with npr-1 and N2 worm populations, however, show an advantage for solitary N2 in all food distribution environments that we tested. We incorporated additional strainspecific behavioural parameters of npr-1 and N2 worms into our model and computationally identified N2's higher feeding rate to be the key factor underlying its advantage, without which it is possible to recapitulate the advantage of collective foraging in patchy environments. Our work highlights the theoretical advantage of collective foraging due to group formation alone without long-range interactions, and the valuable role of modelling to guide experiments.

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<p>Projections of the future development of the Antarctic Ice Sheet still exhibit a large degree of uncertainty due to difficulties in constraining parameters of ice-flow models such as basal boundary conditions. Deriving better estimates of these parameters from radargrams could greatly improve model accuracy, but integration of inferred radar attributes into ice-flow models is not yet widespread.</p> <p>Here, we develop a radar forward modeling framework that is geared to train a machine learning workflow (likely simulation-based inference) to extract radar attributes such as the internal stratigraphy and basal boundary conditions (e.g., frozen vs. wet) from radar data. The workflow starts with ice-dynamic forward models predicting physically sound stratigraphies and internal/basal temperatures for synthetic flow settings using shallow ice, shallow shelf and higher order ice-flow models. This is then used as input to the radar simulator (here gprMax), which calculates the radar image produced by such a stratigraphy. To do so, we match the synthetic permittivities of the modeled stratigraphy with statistical properties known from ice-core logging data and prescribe temperature dependent attenuation via an Arrhenius relation. gprMax is optimized for acceleration using GPUs which can be efficiently employed when solving the FDTD discretized Maxwell equations. Currently, 200 m wide and 500 m deep sections can be simulated on a single NVIDIA GeForce RTX 2070 Super graphics card within 390 minutes. The runtime can be substantially improved in a HPC environment. In order to obtain radar simulations comparable with observations, we also add system specific noise and contributions from volume scattering with variable surface roughness. Here, we focus on 50 MHz pulse radar for which we have many observational counterparts. However, the workflow is designed to encompass multiple ice-dynamic settings and different radar frequencies.</p> <p>The application of physical forward models will result in physically meaningful radargrams which are indistinguishable from observations. This provides a tool to create datasets for training machine learning workflows for inference without the limitations of hand-labeled data.</p>

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<p>The internal ice stratigraphy as imaged by radar is an integrated archive of the atmospheric- oceanographic, and ice-dynamic history that the ice sheet has experienced. It provides an observational constraint for ice flow modeling that has been used for instance to predict age-depth relationships at prospective ice-coring sites in Antarctica’s interior. The stratigraphy is typically more disturbed and more difficult to image in coastal regions due to faster ice flow. Yet, knowledge of ice stratigraphy across ice shelf grounding lines and further seawards is important to help constrain ocean-induced melting and associated stability.</p><p>Here, we present preliminary results of synthesizing information from radar stratigraphic characteristics from airborne and ground-based radar surveys that have been collected for specific projects starting from the 1990s onwards focusing on ice marginal zones of Antarctica. The key data is based on airborne surveys from the German Alfred Wegener Institute’s polar aircrafts equipped with a 150 MHz radar. In the meantime this system has been replaced by an ultra-wide band 150-520 MHz radar. The older data will provide a baseline with extensive coverage that can be used for model calibration and change detection over time. We aim to provide metrics of the radio stratigraphy (e.g. shape and slope of internal reflection horizons) as well as classified prevalent stratigraphy types that can be used to calibrate machine learning approaches such as simulation based inference. The data obtained will be integrated in coordination efforts within the SCAR AntArchitecture Action Group.</p>