Biology subjects

Langa, J. A.

Publications and source records attributed to Langa, J. A..

4 recordsLinked to original sources

bioRxiv2026

Coupling demography and interactions reshapes the structural stability of ecological communities

The assembly of ecological communities depends on both species' demography and the interactions among them. Ecological theory assumes that both ecological processes vary independently. However, empirical evidence demonstrates otherwise. How the structural stability of ecological communities changes when species' intrinsic growth rates and interactions are coupled remains unknown. To address this knowledge gap, we present several seemingly disconnected examples, such as the stress-gradient hypothesis, the covariance between environment and competition, higher-order interactions, and niche packing, that actually fall along a continuum of changes ranging from positive to negative interactions. Although the specific details of each scenario differ, they all share a common feature. When demography and interactions are coupled in multispecies models of population dynamics, the limits of structural stability in ecological communities shift non-linearly, and the domain of viability increases or decreases compared to a non-coupling scenario. We mathematically demonstrate that whether these changes are positive or negative depends on how the strength of intraspecific interactions shifts relative to interspecific interactions as species' growth rates increase. These new non-linear relationships imply two important shifts of thinking. Theoretically, the definition of structural stability must be generalized and its estimation requires the use of probabilistic measures based on ecological limits. Empirically, if abiotic and biotic barriers operate simultaneously, we need to account for such non-linear processes that can significantly modify population dynamics. Otherwise, we will not make accurate predictions of how ecological communities are assembled and biodiversity is maintained.

ecology↗

bioRxiv2024

Cooperation maximizes biodiversity

Cooperation, the mutual benefit that individuals of different species obtain when they interact together, is ubiquitous in nature. Despite their importance, most of all current ecological theories have been formalized focusing on negative interactions such as competition or predation. The role of cooperation, or other types of positive interactions including facilitation and mutualism, has not been fully addressed, or, if so, always in combination with negative interactions. This fact limits our understanding of the unique features by which cooperation as opposed to competition promotes biodiversity. To address this gap, we introduce here cooperation into structural stability, a general framework to understand how species interactions and environmental variability determine the long-term persistence of species within communities. Compared to a pure competitive case, cooperation promotes three distinctive features. First, cooperation increases the opportunities for species to coexist. This feature increases the persistence of species with contrasted phylogenetic, functional, and demographic strategies that the environment would otherwise filter. Second, cooperation creates intertwined biodiversity where the existence of some species begets the presence of others. Third, cooperation promotes multistability by changing the dynamics of community assembly due to variations in environmental conditions. In conclusion, we present a fully operational framework to understand the unique ecological roles of cooperation in nature. It indicates that cooperation as opposed to competition maximizes the maintenance of biodiversity.

ecology↗

bioRxiv2021

Cooperation enhances structural stability in mutualistic systems

Dynamical systems on graphs allow to describe multiple phenomena from different areas of Science. In particular, many complex systems in Ecology are studied by this approach. In this paper we analize the mathematical framework for the study of the structural stability of each stationary point, feasible or not, introducing a generalization for this concept, defined as Global Structural Stability. This approach would fit with the proper mathematical concept of structural stability, in which we find a full description of the complex dynamics on the phase space due to nonlinear dynamics. This fact can be analyzed as an informational field grounded in a global attractor whose structure can be completely characterized. These attractors are stable under perturbation and suppose the minimal structurally stable sets. We also study in detail, mathematically and computationally, the zones characterizing different levels of biodiversity in bipartite graphs describing mutualistic antagonistic systems of population dynamics. In particular, we investigate the dependence of the region of maximal biodiversity of a system on its connectivity matrix. On the other hand, as the network topology does not completely determine the robustness of the dynamics of a complex network, we study the correlation between structural stability and several graph measures. A systematic study on synthetic and biological graphs is presented, including 10 mutualistic networks of plants and seed-dispersal and 1000 random synthetic networks. We compare the role of centrality measures and modularity, concluding the importance of just cooperation strength among nodes when describing areas of maximal biodiversity. Indeed, we show that cooperation parameters are the central role for biodiversity while other measures act as secondary supporting functions. Author summaryWe introduce the concept of Global Structural Stability as a proper mathematical concept to fully understand biodiversity in some ecological systems. Our concept retakes the definitions in the classical works of R. Thom [1] and Andronov-Pontryagin [2]. Moreover, there exists a close relation between the structure of a complex network, described as a graph, and its associated dynamics. Mutualistic networks introduce cooperation links between two groups of species, as plant and pollinators or seed-dispersal. The understanding of organizational aspects leading to maximizing biodiversity is one of the more important research areas in Theoretical and Applied Ecology. In this work we introduce a systematic study on different graph measures in order to identify optimal organization for maximal biodiversity (defined as structural stability). Our results conclude that, for mutualistic systems, the strength in cooperation parameters are the core fact, i.e., cooperation is the real fact optimizing biodiversity among other possible structural configurations.

ecology↗

bioRxiv2019

Dynamical informational structures characterize the different human brain states of wakefulness and deep sleep

The dynamical activity of the human brain describes an extremely complex energy landscape changing over time and its characterisation is central unsolved problem in neuroscience. We propose a novel mathematical formalism for characterizing how the landscape of attractors sustained by a dynamical system evolves in time. This mathematical formalism is used to distinguish quantitatively and rigorously between the different human brain states of wakefulness and deep sleep. In particular, by using a whole-brain dynamical ansatz integrating the underlying anatomical structure with the local node dynamics based on a Lotka-Volterra description, we compute analytically the global attractors of this cooperative system and their associated directed graphs, here called the informational structures. The informational structure of the global attractor of a dynamical system describes precisely the past and future behaviour in terms of a directed graph composed of invariant sets (nodes) and their corresponding connections (links). We characterize a brain state by the time variability of these informational structures. This theoretical framework is potentially highly relevant for developing reliable biomarkers of patients with e.g. neuropsychiatric disorders or different levels of coma.

neuroscience↗