How more sophisticated leaf biomass simulations can increase the realism of modelled animal populations

Jens Krause, Mike Harfoot, Selwyn Hoeks, Peter Anthoni, Calum Brown, Mark Rounsevell and Almut Arneth

Ecological Modelling, 2022, vol. 471, issue C

Abstract: Animal biodiversity, and its key roles in ecosystem state and functioning, is facing critical challenges in the wake of anthropogenic activities. It is urgently necessary to improve understanding of the interconnections between animals and the vegetation within ecosystems. Process-based modelling has shown to be a mighty tool in making assessments on ecological processes. We assess the effect of different vegetation models on simulated animal biodiversity by replacing the vegetation module within Madingley, a multi-trophic model of functional diversity with LPJ-GUESS, a dynamic global vegetation model. We compare the output metrics of the model system to Madingley’s default version for four ecosystem types around the globe and analyse whether the realism of the simulation results increased as a result of the coupling between Madingley and LPJ-GUESS. Simulated animal populations react to the coupling by shifting towards smaller individuals with a higher abundance. General shifts in body mass and animal distributions can be traced back to ecological processes, allowing in-depth analysis of heterotrophic responses to changes in leaf biomass. We also derive power-law relationships for herbivory to NPP and herbivore biomass to NPP and conclude that the coupled model system simulates animal populations that follow reasonable power-laws which are similar to power-laws derived from empirical data. Our results indicate that developing process-based model systems is a viable way to assess multi-trophic interconnections between animal populations and the ecosystems vegetation.

Keywords: Modelling animal populations in terrestrial ecosystem; Advances/refinement in methods for ecological modelling (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:471:y:2022:i:c:s0304380022001697

DOI: 10.1016/j.ecolmodel.2022.110061

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