Sub-Saharan social-ecological systems are undergoing changes in environmental conditions, including modifications in rainfall pattern and biodiversity loss. Consequences of such changes depend on complex causal chains which call for integrated management strategies whose efficiency could benefit from ecosystem dynamic modeling. However, ecosystem models often require lots of quantitative information for estimating parameters, which is often unavailable. Alternatively, qualitative modeling frameworks have proved useful for explaining ecosystem responses to perturbations, while only requiring qualitative information about social-ecological interactions and events and providing more general predictions due to their validity for wide ranges of parameter values. In this paper, we propose the Ecological Discrete-Event Network (EDEN), an innovative qualitative dynamic modeling framework based on “if-then” rules generating non-deterministic dynamics. Based on expert knowledge, observations, and literature, we use EDEN to assess the effect of permanent changes in surface water and herbivores diversity on vegetation and socio-economic transitions in an East African savanna. Results show that water availability drives changes in vegetation and socio-economic transitions, while herbivore functional groups have highly contrasted effects depending on the group. This first use of EDEN in a savanna context is promising for bridging expert knowledge and ecosystem modeling.

Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, ranging from state-and-transition models to assembly graphs. Model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology apart from precursory applications. This article proposes to address this situation, through an inventory of existing ecological STGs and an accessible presentation of the model-checking methodology. This overview is illustrated by the application of model-checking to assess the dynamics of a vegetation pathways model. We select management scenarios by model-checking Computation Tree Logic formulas representing management goals and built from a proposed catalogue of patterns. In discussion, we sketch bridges between existing studies in ecology and available model-checking frameworks. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarify and compare them.

Ecosystem modeling is a complex and multidisciplinary modeling problem which emerged in the 1950s. It takes advantage of the computational turn in sciences to better understand anthropogenic impacts and improve ecosystem management. For that purpose, ecosystem simulation models based on difference or differential equations were built. These models were relevant for studying dynamical phenomena and still are. However, they face important limitations in data-poor situations. As a response, several formal and non-formal qualitative dynamical modeling approaches were independently developed to overcome some limitations of the existing methods. Qualitative approaches allow studying qualitative dynamics as relevant abstractions of those provided by quantitative models (e.g., response to press perturbations). Each modeling framework can be viewed as a different assemblage of properties (e.g., determinism, stochasticity or synchronous update of variable values) designed to satisfy some scientific objectives. Based on four stated objectives commonly found in complex environmental sciences ((1) grasping qualitative dynamics, (2) making as few assumptions as possible about parameter values, (3) being explanatory and (4) being predictive), our objectives were guided by the wish to model complex and multidisciplinary issues commonly found in ecosystem modeling. We then discussed the relevance of existing modeling approaches and proposed the ecological discrete-event networks (EDEN) modeling framework for this purpose. The EDEN models propose a qualitative, discrete-event, partially synchronous and possibilistic view of ecosystem dynamics. We discussed each of these properties through ecological examples and existing analysis techniques for such models and showed how relevant they are for environmental science studies.

The emergence of cultural traditions has long been considered to depend on sophisticated social learning mechanisms, particularly conformist transmission exhibiting a disproportionate bias towards majority behaviours. We challenge this assumption by demonstrating that gene-culture coevolutionary dynamics can fundamentally alter the conditions required for tradition formation, especially in the context of mate choice. Using simulation models of mate choice where female preferences are socially transmitted and male traits are genetically inherited, we show that the interaction between cultural and genetic transmission creates novel evolutionary dynamics. In a first model (the ‘Cultural Model’) representing classical cultural evolution scenarios, we show that traditions emerge only when conformity disproportionately amplifies majority behaviours. In a second model (the ‘Gene-Culture Model’), we introduce a distinction between learned behaviours and realized behaviours and show that when mate choice depends on both conformist learning and male trait availability, even simple social learning strategies can establish persistent traditions. In this context, majority exaggeration is no longer a necessary property of social learning for traditions to arise. This occurs because positive frequency-dependent interactions between preference and trait dynamics provide an additional mechanism that reinforces majority behaviours beyond conformist learning. Our findings have broader implications for cultural evolution theory, suggesting that many behaviours that depend on resource availability may exhibit tradition-like patterns under less stringent social learning conditions than previously assumed. By distinguishing between preference and choice, and demonstrating the importance of gene-culture interactions, this study advocates for more integrated approaches to understanding cultural evolution that consider the interplay between multiple inheritance systems.

Sub-Saharan social-ecological systems are undergoing changes in environmental conditions, including modifications in rainfall pattern and biodiversity loss. Consequences of such changes depend on complex causal chains which call for integrated management strategies whose efficiency could benefit from ecosystem dynamic modelling. However, ecosystem models often require lots of quantitative information for estimating parameters, which is often unavailable. Alternatively, qualitative modelling frameworks have proved useful for explaining ecosystem response to perturbations, while requiring fewer information and providing more general predictions. However, current qualitative methods have some shortcomings which may limit their utility for specific issues. In this paper, we propose the Ecological Discrete-Event Network (EDEN), an innovative qualitative dynamic modelling framework based on "if-then" rules which generates many alternative event sequences (trajectories). Based on expert knowledge, observations and literature, we use this framework to assess the effect of permanent changes in surface water and herbivores diversity on vegetation and socio-economic transitions in an East African savanna. Results show that water availability drives changes in vegetation and socio-economic transitions, while herbivore functional groups had highly contrasted effects depending on the group. This first use of EDEN in a savanna context is promising for bridging expert knowledge and ecosystem modelling.

Sub-Saharan savanna ecosystems are undergoing transitions such as bush encroachment, desertification or agricultural expansion. Such shifts and persistence of land cover are increasingly well understood, especially bush encroachment which is of major concern in pastoral systems. Although dominant factors can explain such transformations, they often result from intertwined causes in which human activities play a significant role. Therefore, in this latter case, these issues may require integrated solutions, involving many interacting components. Ecosystem modelling has proved appropriate to support decision-makers in such complex situations. However, ecosystem models often require lots of quantitative information for estimating parameters and the precise functional form of interactions is often unknown. Alternatively, in rangeland management, States-and-Transitions Models (STMs) have been developed to organize knowledge about system transitions and to help decision-makers. However, these conceptual diagrams often lack mathematical analyzing tools, which strongly constrains their complexity. In this paper, we introduce the Ecological Discrete-Event Network (EDEN) modelling approach for representing the qualitative dynamics of an East-African savanna as a set of discrete states and transitions generated from empirical rules. These rules are derived from local knowledge, field observations and scientific literature. In contrast with STMs, EDEN generates automatically every possible states and transitions, thus enabling the prediction of novel ecosystem structures. Our results show that the savanna is potentially resilient to the disturbances considered. Moreover, the model highlights all transitions between vegetation types and socio-economic profiles under various climatic scenarios. The model also suggests that wildlife diversity may increase socio-economic resistance to seasonal drought. Tree-grass coexistence and agropastoralism have the widest ranges of conditions of existence of all vegetation types and socio-economic profiles, respectively. As this is a preliminary use of EDEN for applied purpose, analysis tools should be improved to enable finer investigation of desirable trajectories. By translating local knowledge into ecosystem dynamics, the EDEN approach seems promising to build a new bridge between managers and modellers.