This document is a scholarly article discussing the potential of higher-order network(**) approaches in understanding animal communication. The authors argue that these approaches can provide new insights across various social contexts and help address new research questions. They also suggest that animal communication networks can inspire and test new theoretical models in network science.
The authors highlight the value of interdisciplinary collaboration, particularly between biologists and network scientists. They believe that such collaboration can lead to new methodological advancements and help answer questions that would otherwise be out of reach. For instance, they discuss the potential of incorporating individual heterogeneity (differences in how individuals receive and respond to signals) and developing new models for the temporal dynamics of higher-order networks.
The document also delves into the study of rhythmic animal behaviors, focusing on the capacity of groups of individuals to synchronize their behavior with precision in real time. This is seen as a key trait of our species, and the authors suggest that understanding this could provide insights into our biological and evolutionary background.
The authors also discuss the potential of animal vocal communication networks to inspire the development of new theoretical models. They suggest that developing approaches to incorporate individual heterogeneity into theoretical models of higher-order social and communication networks could have important implications for understanding their dynamics.
In terms of funding, the authors acknowledge support from various sources, including the James S. McDonnell Foundation, the National Science and Engineering Research Council of Canada, the European Union’s Horizon 2020 research and innovation programme, and the Royal Society University Research Fellowship.
Overall, the document presents a compelling case for the potential of higher-order network approaches in advancing our understanding of animal communication and behavior. It highlights the value of interdisciplinary collaboration and suggests that this could lead to new methodological advancements and insights into our own evolutionary background.
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**In the context of this scholarly article, “higher-order network” refers to a type of network representation that goes beyond dyadic (pairwise) interactions to represent multibody interactions involving two or more individuals at a time. This is particularly relevant in the study of animal communication where interactions are not always dyadic. For instance, acoustic signals often have multiple simultaneous receivers, or receivers integrate information from multiple signallers. Higher-order network approaches, such as hypergraphs, simplicial sets, and simplicial complexes, can capture these complex interactions and provide new insights into animal communication.
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This document is a scholarly article discussing the potential of higher-order network(**) approaches in understanding animal communication. The authors argue that these approaches can provide new insights across various social contexts and help address new research questions. They also suggest that animal communication networks can inspire and test new theoretical models in network science.
The authors highlight the value of interdisciplinary collaboration, particularly between biologists and network scientists. They believe that such collaboration can lead to new methodological advancements and help answer questions that would otherwise be out of reach. For instance, they discuss the potential of incorporating individual heterogeneity (differences in how individuals receive and respond to signals) and developing new models for the temporal dynamics of higher-order networks.
The document also delves into the study of rhythmic animal behaviors, focusing on the capacity of groups of individuals to synchronize their behavior with precision in real time. This is seen as a key trait of our species, and the authors suggest that understanding this could provide insights into our biological and evolutionary background.
The authors also discuss the potential of animal vocal communication networks to inspire the development of new theoretical models. They suggest that developing approaches to incorporate individual heterogeneity into theoretical models of higher-order social and communication networks could have important implications for understanding their dynamics.
In terms of funding, the authors acknowledge support from various sources, including the James S. McDonnell Foundation, the National Science and Engineering Research Council of Canada, the European Union’s Horizon 2020 research and innovation programme, and the Royal Society University Research Fellowship.
Overall, the document presents a compelling case for the potential of higher-order network approaches in advancing our understanding of animal communication and behavior. It highlights the value of interdisciplinary collaboration and suggests that this could lead to new methodological advancements and insights into our own evolutionary background. user
**In the context of this scholarly article, “higher-order network” refers to a type of network representation that goes beyond dyadic (pairwise) interactions to represent multibody interactions involving two or more individuals at a time. This is particularly relevant in the study of animal communication where interactions are not always dyadic. For instance, acoustic signals often have multiple simultaneous receivers, or receivers integrate information from multiple signallers. Higher-order network approaches, such as hypergraphs, simplicial sets, and simplicial complexes, can capture these complex interactions and provide new insights into animal communication.