Modelling human influences on biodiversity at a global scale–A human ecology perspective

Human impacts contribute to the reduction of biodiversity. To describe biodiversity loss and identify its causes, biologists (in particular ecologists) use a wide range of models. Globalised human interventions cause most biodiversity losses. Therefore, global biodiversity is taken as the level of observation. This also allows for the appreciation of the world as an environmental entity. Because humans play a dual role in the context of biodiversity loss, their “parameterisation” in such models is particularly interesting. On the one hand human influences can be categorised as disruptive factors because they transcend the biological needs of the individual human. On the other hand humans are themselves part of the biosphere, so their impact on the environment and biodiversity must also be qualified as a natural phenomenon. What at first glance appears contradictory can very well be embraced by models and modelling approaches. Consequently, we review ten different (global) biodiversity models on how they grasp human influences that consist of stressors (pressures) and also drivers (societal needs). This work's main expectation is that the reviewed models make certain assumptions, include some aspects and exclude others. In our review, we regard them as heuristic models. Thus, it is not the detailed algorithms we are interested in, but how human activities are included and depicted within the models. The model review also deploys a human ecology perspective, which includes an evolutionary and societal angle and consciously investigates (1) human adaptions to the environment and (2) human alterations of the environment. While our review clearly shows the widely shared understanding for the need of evidence-based countermeasures to act responsibly, very few models offer concrete recommendations but instead mostly limit their advice to the technical level of modelling. We conclude that policy advice, science communication, and concrete measures need to be improved in biodiversity models.