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The Dawn of Dynamic Hypergraph Modelling in Ecology

ORCID_LOGO based on reviews by Catherine Matias and 1 anonymous reviewer
A recommendation of:

A single changing hypernetwork to represent (social-)ecological dynamics

Codes used in this study


Submission: posted 03 November 2023, validated 04 November 2023
Recommendation: posted 16 January 2024, validated 27 January 2024
Cite this recommendation as:
Sueur, C. (2024) The Dawn of Dynamic Hypergraph Modelling in Ecology. Peer Community in Network Science, 100148. 10.24072/pci.networksci.100148


The study of Gaucherel et al. (2024) represents a groundbreaking shift in the field of ecosystem representation and management (DeFries and Nagendra 2017), offering a comprehensive and innovative approach that emphasises the importance of dynamic and complex models to accurately understand and preserve our ecosystems.

At the heart of this pioneering work is the introduction of advanced representational methods such as interaction networks and hypergraphs (Bretto 2013; Golubski et al. 2016), which mark a significant departure from traditional static models. These novel representations are adept at capturing the intricate, multi-component interactions within ecosystems, thereby providing a much more nuanced and interconnected view of ecological systems.

This approach is particularly innovative as it moves beyond the simplicity of previous models, offering a dynamic, fluid, and interconnected perspective of ecological dynamics that is more reflective of the real-world complexity of these systems. Furthermore, the study proposes the integration of social networks with ecological ones (Sosa et al. 2021; Sueur 2023), acknowledging the profound impact that human activities have on natural systems (Afana 2021; Elmqvist et al. 2021; Pelé et al. 2021). This interdisciplinary approach is pioneering in its attempt to bridge the gap between social and ecological studies, underscoring the interconnectedness of natural and human systems and highlighting the need for a holistic approach to ecosystem management (Stokols et al. 2013; Stone-Jovicich 2015).

The significance of the study lies not only in its methodological innovations but also in the implications it holds for the field of ecology and environmental management. By employing these advanced methodologies, the study provides a more thorough understanding of ecosystems. By considering a wide range of components and their interactions, these models offer insights into the complex dynamics of ecosystems, which are crucial for developing effective conservation and management strategies. This comprehensive approach is particularly important in an era where ecosystems are increasingly threatened by a variety of factors, including climate change, habitat destruction, and pollution (Mantyka‐pringle et al. 2012; Trathan et al. 2015).

Additionally, the dynamic nature of the proposed models, especially the use of hypergraphs, facilitates the adaptive management of ecosystems. By accurately representing the changing interactions and components within these systems, these models enable managers and policymakers to respond more effectively to ecological changes, ensuring that conservation efforts are both effective and timely (Ascough Ii et al. 2008; Fischer et al. 2009; McKinley et al. 2017). Moreover, the advanced modelling techniques proposed by the study have the potential to significantly improve predictive capabilities regarding ecosystem dynamics. Understanding the complex interactions and the long-term dynamics of ecosystems allows for better anticipation of future changes and challenges, a crucial aspect in a rapidly changing world where ecosystems are under constant threat.

In conclusion, this study marks a significant advancement in the field of ecological representation and management. Its innovative approach in utilising complex models like hypergraphs and integrating social and ecological networks provides a more comprehensive, dynamic, and nuanced understanding of ecosystems. Such innovations are crucial in an era of rapid environmental change and increasing anthropogenic pressures.

By enhancing our ability to understand, predict, and manage ecosystem dynamics, this study lays the groundwork for more effective conservation strategies and ecosystem management practices. It underscores the need for a holistic approach to understanding and preserving our natural world, recognising the intricate and interconnected nature of ecosystems and the pivotal role humans play within them.


Afana R (2021) Ecocide, Speciesism, Vulnerability: Revisiting Positive Peace in the Anthropocene. In: Standish K, Devere H, Suazo A, Rafferty R (eds) The Palgrave Handbook of Positive Peace. Springer, Singapore, pp 1-18

Ascough Ii J, Maier H, Ravalico J, Strudley M (2008) Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecol Model 219:383-399

Bretto A (2013) Hypergraph theory. Introd Math Eng Cham Springer 1:

DeFries R, Nagendra H (2017) Ecosystem management as a wicked problem. Science 356:265-270

Elmqvist T, Andersson E, McPhearson T, et al (2021) Urbanization in and for the Anthropocene. Npj Urban Sustain 1:6

Fischer J, Peterson GD, Gardner TA, et al (2009) Integrating resilience thinking and optimisation for conservation. Trends Ecol Evol 24:549-554

Gaucherel C, Cosme M, Noûs C, Pommereau F (2024) A single changing hypernetwork to represent (social-)ecological dynamics. bioRxiv, 2023.10.30.564699, ver. 3 peer-reviewed and recommended by Peer Community in Network Science.

Golubski AJ, Westlund EE, Vandermeer J, Pascual M (2016) Ecological networks over the edge: hypergraph trait-mediated indirect interaction (TMII) structure. Trends Ecol Evol 31:344-354

Mantyka‐pringle CS, Martin TG, Rhodes JR (2012) Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta‐analysis. Glob Change Biol 18:1239-1252

McKinley DC, Miller-Rushing AJ, Ballard HL, et al (2017) Citizen science can improve conservation science, natural resource management, and environmental protection. Biol Conserv 208:15-28

Pelé M, Georges J-Y, Matsuzawa T, Sueur C (2021) Editorial: Perceptions of Human-Animal Relationships and Their Impacts on Animal Ethics, Law and Research. Front Psychol 11:

Sosa S, Jacoby D, Lihoreau M, Sueur C (2021) Animal social networks: Towards an integrative framework embedding social interactions, space and time. Methods Ecol Evol 12:4-9

Stokols D, Lejano RP, Hipp J (2013) Enhancing the Resilience of Human-Environment Systems: a Social Ecological Perspective. Ecol Soc 18:

Stone-Jovicich S (2015) Probing the interfaces between the social sciences and social-ecological resilience: insights from integrative and hybrid perspectives in the social sciences. Ecol Soc 20:

Sueur C (2023) Socioconnectomics: Connectomics Should Be Extended to Societies to Better Understand Evolutionary Processes. Sci 5:5.

Trathan PN, García‐Borboroglu P, Boersma D, et al (2015) Pollution, habitat loss, fishing, and climate change as critical threats to penguins. Conserv Biol 29:31-41

Conflict of interest:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article. The authors declared that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.
The SESASA project (ERA-Net LEAP367 Agri n°465)


Evaluation round #1

DOI or URL of the preprint:

Version of the preprint: 1

Author's Reply, 15 Jan 2024

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Montpellier, January the 8th, 2024

To PCI Network Science Recommender: Prof. Cedric Sueur

Re: Manuscript re-submission 

Dear Recommender - Editor, 

Attached is our revised research paper for your publication. 

We modified the initial version of our paper. This paper now follows the comments of the reviewer. In particular, we shifted both Appendices into the main text at the most appropriate location. We corrected some of the captions and added the suggested references. We also re-edited the whole paper and clarified several explanations. 

We are therefore confident that the conceptual and methodological facets of the paper convey general messages for PCI Network Science and would resonate strongly in your publication.

We welcome any questions and comments that you may have.


Decision by ORCID_LOGO, posted 01 Jan 2024, validated 03 Jan 2024

The manuscript by Gaucherel and colleagues offers an insightful conceptual approach to ecological systems analysis using hypernetworks and Petri nets, emphasizing non-dyadic interactions. Acknowledging the reviewer's concerns, it's essential to highlight that while the paper revisits previously developed concepts by the authors, it aims to contextualize these within ecological systems analysis, a perspective that might be novel for many in the field. To address the critique of lacking new methodological contributions, the authors could elaborate on the specific implications and potential applications of these concepts in current ecological research. Clarifying the role of network visualization in determining node centrality is crucial to avoid misinterpretation. The authors should consider integrating the comprehensive explanations from the appendix into the main text, enhancing the manuscript's accessibility and clarity. Correcting minor typographical errors and enriching the text with more explicit literature references will further strengthen the paper's academic rigor. With these revisions, the manuscript promises to be a valuable guide for researchers in ecological studies exploring the utility of hypernetworks and Petri nets, presenting a clearer view of its practical applications while cautiously delineating the limitations of network visualization in analysis. We look forward to a revised submission for PCI Network Science that addresses these suggestions.

Reviewed by anonymous reviewer 1, 15 Dec 2023

Gaucherel and colleagues provide a conceptual overview of how hypernetwork approaches coupled with methodologies developed in computer science (e.g., Petri nets) can be used to better describe and analyze the topology and dynamics of ecological systems. Recognizing that many ecological interactions are non-dyadic in nature, hypergraphs that allow for such higher-order interactions are introduced as a useful description of an ecological system. The authors suggest using Petri nets to capture the relationships between ecosystem components (e.g., species, social insect castes) and processes. Thus, each process encompasses a directed hyperedge with the relevant components (represented as nodes) as inputs and outputs. This arrangement naturally leads to a bipartite network conception that can likewise be cast in the form of a hypergraph. This representation can be used to study both short-term flows and long-term topological dynamics.

The paper overall is well-written and interesting. It does not present any new data or analyses, but uses the authors’ previous investigations as a case study to explore how hypernetworks and Petri nets can be used to facilitate basic and applied ecological research. I believe the paper will be useful for introducing researchers to these tools and approaches, as well as the types of questions and analyses that they address.

The only substantive change that I would suggest is moving some of the material in the appendix, particularly L14-43, into the main text. I found the information that it contains to be very helpful in clarifying several points in the paper (e.g., the meaning of the double- vs. single-edged nodes, the meaning of white vs. black arcs, how the transition diagram was constructed). Especially for those readers unfamiliar with the authors’ previous work on these topics, integrating this information into the main text will substantially enhance its readability.

Minor line-by-line comments:

L21: Is ‘renewed’ the right word here? Novel, perhaps?

L142: ‘representing’ is misspelled.

L167-177: A relevant paper when considering nested network structures, including those that span multiple organization levels, is: Montiglio P.-O. et al., 2020, Behav. Ecol. 31, 279-286.

L193-194: As a caveat, it may be useful to note somewhere that the tools available to analyze hypernetworks are still somewhat limited, especially for non-specialist practitioners, relative to those for dyadic, graph-based networks.

L273: ‘hexagon’ is misspelled.

L277-296: It would be helpful to walk the reader through a few brief examples in plain words that show how Fig. 4 was constructed and how it can be interpreted. For example, what characterizes the state of the initial hexagon? What processes lead to states 14 and 4.

Figure 4: What do the numbers within nodes represent? Are they simply arbitrary labels of different states?

Reviewed by ORCID_LOGO, 31 Dec 2023