Florian Hartig

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Mechanistic simulation models in macroecology and biogeography: state-of-art and prospects

Juliano Sarmento Cabral, Luis Valente & Florian Hartig

Ecography, 40(2), 267–280 (2017)
Cite this
@article{cabral2017mechanistic,
  author = {Juliano Sarmento Cabral and Luis Valente and Florian Hartig},
  title = {Mechanistic simulation models in macroecology and biogeography: state-of-art and prospects},
  journal = {Ecography},
  volume = {40},
  number = {2},
  pages = {267--280},
  year = {2017},
  doi = {10.1111/ecog.02480},
}

DOI: 10.1111/ecog.02480
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Funding

This study was supported through funding by sDiv, the Synthesis Centre of iDiv (DFG FZT 118); Alexander von Humboldt Foundation; Brandenburg Ministry for Science, Research and Culture; DFG (Research grant) (VA 1102/1-1).

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Abstract

Macroecology and biogeography are concerned with understanding biodiversity patterns across space and time. In the past, the two disciplines have addressed this question mainly with correlative approaches, despite frequent calls for more mechanistic explanations. Recent advances in computational power, theoretical understanding, and statistical tools are, however, currently facilitating the development of more system‐oriented, mechanistic models. We review these models, identify different model types and theoretical frameworks, compare their processes and properties, and summarize emergent findings. We show that ecological (physiology, demographics, dispersal, biotic interactions) and evolutionary processes, as well as environmental and human‐induced drivers, are increasingly modelled mechanistically; and that new insights into biodiversity dynamics emerge from these models. Yet, substantial challenges still lie ahead for this young research field. Among these, we identify scaling, calibration, validation, and balancing complexity as pressing issues. Moreover, particular process combinations are still understudied, and so far models tend to be developed for specific applications. Future work should aim at developing more flexible and modular models that not only allow different ecological theories to be expressed and contrasted, but which are also built for tight integration with all macroecological data sources. Moving the field towards such a ‘systems macroecology’ will test and improve our understanding of the causal pathways through which eco‐evolutionary processes create diversity patterns across spatial and temporal scales.

What the paper shows and why it matters

(AI-generated)

Macroecology and biogeography ask why biodiversity is distributed as it is across space and time. They have answered mostly with correlative statistics, which describe patterns but struggle to identify causes, and with simple classical theories, most built on a few processes, typically at one level of organisation. Simplicity and an easy link to data arguably explain the success of the classical theories. But when a theory leaves out processes, a misfit to data is hard to read as evidence against its core assumptions; a flexible theory can fit well without showing that its processes matter; and predictions under environmental change need the essential processes to be included. Mechanistic simulation models, programs that run hypothesised processes step by step, offer an alternative, yet most were built for specific purposes. The problem the paper takes on is how they can become a general tool for testing theories causally and predicting biodiversity.

The paper, a review of mechanistic simulation models in macroecology and biogeography, argues that explaining and predicting biodiversity causally requires models that simulate several ecological and evolutionary processes together, and that the field should build them as flexible, modular system models linked to data, which it calls ‘systems macroecology’. It supports this by surveying published models, grouped by process, from physiology to evolution and human drivers. A wide range of these processes was already being modelled, but usually only a few at a time, and some combinations had rarely been studied. Combining processes produces complex and frequently contrasting patterns, and models that unify several theories behave differently from the ‘pure’ theories; the paper takes this as evidence that simple theories are hard to falsify. It names four obstacles: scaling up, calibrating with data, validating, and balancing complexity.

The paper’s route is to join existing models into modular ones with exchangeable parts and standardised inputs and outputs, and to connect them to large biodiversity databases through modern statistical inference methods such as approximate Bayesian computation. Such models would let competing theories be contrasted in one framework, alongside simpler analyses. Its long-term hope is convergence on system models that synthesise what has been learned, perhaps in place of one general theory. Connolly et al. (2017) cited the paper among reviews that treat ‘mechanistic’ and ‘process-based’ as synonyms, and proposed distinguishing them.

References