Newswise — In a groundbreaking research paper featured in the Proceedings of the National Academy of Sciences, researchers Gabriel Gellner, Kevin McCann from the University of Guelph, and Alan Hastings (SFI External Professor at UC Davis) have taken a fresh and innovative approach to modeling food webs. Unlike traditional methods that attempt to replicate complex and stable ecosystems using simplified species interactions, the authors have adopted a novel inverse technique. Their method assumes the existence of these ecosystems and then works backward to characterize the food webs that can support such assumptions. This remarkable approach represents a significant advancement in addressing a fundamental ecological question: how does biodiversity contribute to the stability of ecosystems? By exploring this inverse perspective, the study provides valuable insights into how natural systems might respond to the increasing disturbances caused by human activities.
According to Hastings, instead of beginning with the challenging task of measuring how species affect one another, the researchers start with the number of each species present and decipher how they interact in a way that explains their coexistence. This unique approach opens up new possibilities for understanding the intricacies of ecological dynamics and sheds light on the delicate balance that sustains diverse ecosystems.
Throughout generations, Earth's ecosystems have displayed surprisingly stable dynamics, yet understanding the factors behind this stability has puzzled ecologists. In the pursuit of answers, Lord Robert May, a former Chair of the SFI Science Board, turned to economic theory and introduced the community matrix. This mathematical tool serves to describe the intricate relationships among species within an ecosystem. By using species interactions as a foundation, the matrix aims to elucidate the role of diversity and complexity in maintaining ecosystem stability. While this approach proves valuable by considering all food web interactions, it falls short due to its reliance on overly simplistic assumptions about how organisms interact with one another. Consequently, several models based on this technique suggest that stability decreases as biodiversity increases, which contradicts what we observe in stable ecosystems.
Nevertheless, comprehending the mechanisms that enable large and complex ecosystems to persist is of utmost importance. Failing to grasp the stabilizing forces within these ecosystems leaves us ill-equipped to preserve them in the face of ever-increasing chaos, such as severe weather events, rampant wildfires, or the proliferation of invasive species. To safeguard our planet's ecosystems, we must deepen our understanding of their stability and resilience.
The success of the inverse approach lies in its incorporation of crucial biological constraints into the model. One such constraint is the feasibility constraint, which ensures that only real interactions are represented within the model. This means that the simulated food webs align with actual ecological relationships observed in nature. Additionally, the model considers an energetic constraint, which dictates that a predator's meal cannot yield more energy than the energy expended during the hunt. This constraint is based on the well-known ecological principle that only a fraction (typically 10-20%) of an organism's energy is transferred to its consumer in a food chain.
Hastings emphasizes the significance of these biological constraints in the model, stating, "We see numerous diverse ecosystems across the world. By incorporating the proper biological information into the model, we can effectively simulate large and diverse ecosystems and gain insights into why they exhibit stability." This approach offers a valuable means of understanding and explaining the mechanisms that underpin the stability of various ecosystems in the natural world.
The authors emphasize that the inverse approach presents significant theoretical advantages compared to May's classical approach, which was introduced over 40 years ago. While May's approach operated within a statistical universe, the novel property of the inverse approach is that it allows a focus solely on the collection of webs that correspond to realistic and feasible solutions.
May's community matrix had a profound impact on ecological theory for almost half a century. In a similar manner, just as May drew inspiration from economics to rethink diversity-stability relationships, Hastings and his coauthors now draw on recent advancements in genomics for their innovative approach. The authors firmly believe that their inverse method holds immense potential for driving theoretical advancements in the field of ecology and related disciplines.
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