Qualitative Modeling for Bridging Expert-Knowledge and Social-Ecological Dynamics of an East African Savanna

Abstract

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.

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

“…This is precisely what we modeled in several studies from a chosen EN of temperate and tropical social-ecological systems (Mao et al, 2021;Cosme et al, 2022). By using specific tools, it is also possible to formalize specific questions about the dynamics displayed in the STG, without explicitly computing them, a relevant advantage when it becomes too large to be fully computed (Fages et al, 2004;Largouët et al, 2012;C.…”

BACKLAND: spatially explicit and high‐resolution pollen‐based BACKward LAND‐cover reconstructions

“…However, by integrating and spatialising the LOVE estimates, BACKLAND indirectly integrates the effect of these drivers on vegetation composition and configuration. Future studies would be needed to compare the outputs of this static diachronic approach with those of other types of pattern‐based (Houet et al 2012a, b2012) or process‐based (Gaucherel and Pommereau 2019, Gaucherel et al 2020, Cosme et al 2022) modeling approaches, in order to evaluate and interpret the convergences and divergences of the modeled trajectories. Such comparisons will allow a better overall understanding of the Bassiès socio‐ecosystem dynamics (Houet et al 2010b, Gritti et al 2013).…”

Petri Nets Semantics of Reaction Rules (RR)

“…For instance, in [5][6][7][8], we have used tina [1] to compute explicit state-spaces from the ppn semantics of rr since tina supports transitions priorities. We also have used the epn semantics in [4,12] through a translation of extended Petri nets into gal systems [19,Sec. 5] in order to compute symbolic state-spaces using libddd and its-tools [18,19].…”

“…From the proved equivalences, we also have that the marking graph of an epn translated from a normal rr system is equivalent to the marking graph of the ppn translated from the elementarisation of the normal rr system (upper-most gray dotted edge in the diagram from Figure 2). Finally, note that there exists a translation from epn to ppn that is well known in the Petri net community 4 and corresponds exactly to what we use in the current paper: safe places are translated into pairs of complementary places, which allows to implement reset and inhibitor arcs as regular arcs, while read arcs are implemented with side-loops.…”

“…The framework eden has been developed and used for ecological studies for more than five years [4][5][6][7][8]12]. It provides tools and methods to formally model ecosystems, and analyse them through an interactive method that lets the users explore their models dynamics and draw understanding progressively.…”