TY - JOUR
T1 - Spatiotemporal organization of ant foraging from a complex systems perspective
AU - Cristín, Javier
AU - Fernández-López, Pol
AU - Lloret-Cabot, Roger
AU - Genovart, Meritxell
AU - Méndez, Viçenc
AU - Bartumeus, Frederic
AU - Campos, Daniel
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/6/4
Y1 - 2024/6/4
N2 - We use complex systems science to explore the emergent behavioral patterns that typify eusocial species, using collective ant foraging as a paradigmatic example. Our particular aim is to provide a methodology to quantify how the collective orchestration of foraging provides functional advantages to ant colonies. For this, we combine (i) a purpose-built experimental arena replicating ant foraging across realistic spatial and temporal scales, and (ii) a set of analytical tools, grounded in information theory and spin-glass approaches, to explore the resulting data. This combined approach yields computational replicas of the colonies; these are high-dimensional models that store the experimental foraging patterns through a training process, and are then able to generate statistically similar patterns, in an analogous way to machine learning tools. These in silico models are then used to explore the colony performance under different resource availability scenarios. Our findings highlight how replicas of the colonies trained under constant and predictable experimental food conditions exhibit heightened foraging efficiencies, manifested in reduced times for food discovery and gathering, and accelerated transmission of information under similar conditions. However, these same replicas demonstrate a lack of resilience when faced with new foraging conditions. Conversely, replicas of colonies trained under fluctuating and uncertain food conditions reveal lower efficiencies at specific environments but increased resilience to shifts in food location.
AB - We use complex systems science to explore the emergent behavioral patterns that typify eusocial species, using collective ant foraging as a paradigmatic example. Our particular aim is to provide a methodology to quantify how the collective orchestration of foraging provides functional advantages to ant colonies. For this, we combine (i) a purpose-built experimental arena replicating ant foraging across realistic spatial and temporal scales, and (ii) a set of analytical tools, grounded in information theory and spin-glass approaches, to explore the resulting data. This combined approach yields computational replicas of the colonies; these are high-dimensional models that store the experimental foraging patterns through a training process, and are then able to generate statistically similar patterns, in an analogous way to machine learning tools. These in silico models are then used to explore the colony performance under different resource availability scenarios. Our findings highlight how replicas of the colonies trained under constant and predictable experimental food conditions exhibit heightened foraging efficiencies, manifested in reduced times for food discovery and gathering, and accelerated transmission of information under similar conditions. However, these same replicas demonstrate a lack of resilience when faced with new foraging conditions. Conversely, replicas of colonies trained under fluctuating and uncertain food conditions reveal lower efficiencies at specific environments but increased resilience to shifts in food location.
KW - Animals
KW - Ants/physiology
KW - Behavior, Animal/physiology
KW - Computer Simulation
KW - Feeding Behavior/physiology
KW - Models, Biological
KW - Social Behavior
KW - Spatio-Temporal Analysis
UR - http://www.scopus.com/inward/record.url?scp=85195245680&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c5fe67a1-28b7-3da2-aa19-c89510c8ba16/
U2 - 10.1038/s41598-024-63307-1
DO - 10.1038/s41598-024-63307-1
M3 - Article
C2 - 38834710
AN - SCOPUS:85195245680
SN - 2045-2322
VL - 14
JO - SCIENTIFIC REPORTS
JF - SCIENTIFIC REPORTS
M1 - 12801
ER -