Modeling, Data Handling, and Analysis: Practical and Fundamental Consequences in Ecology and Evolution

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Background

Modeling has been primarily developed into physical sciences and more recently by life and social sciences. Within the latter, two main difficulties were encountered: the complexity of living and social systems and the variabilities of data. The efficiency of modeling methods in these fields has nevertheless been clearly demonstrated, and yet further development of these methods is needed.

Even if advances are regularly recorded, weaknesses persist which teach us as much about the methods as about their fields of application: how should a model, an analysis method, and the results they produce be read and interpreted? This question is very clearly raised by the growth in the number and diversity of data, with major differences between scales and ways of assessment. Furthermore, how do we address the avalanche of publications and approach meta-analyses? Eventually, the links with experimental approaches must be clearly explicit, even for studies in evolution.

Statistics is mainly characterized by the development of “context-free” methods, principally based on linear models and Euclidian geometry (descriptive data analysis) and theory of probabilities (inferential statistics). Non-linearities are rarely accounted for. Nevertheless, the introduction of a priori knowledge about the field of application can improve its efficiency. For instance, Bayesian statistics already allow for the consideration of context.

In modeling, the formulation of the models is the first logical step. However, in the physical sciences, they are most often instantiations of general formulas, mathematical expressions of physical "laws". On the other hand, in the life sciences, specific formulations are often necessary, leading to a greater diversity of models. For example, several expressions are commonly used to represent the growth of organisms. For social dimensions, other formalisms than mathematical ones, such as multi-agent models from computer science, are increasingly used. Nevertheless, there are still imperfections in the evaluation and even the interpretation of the differences between the responses of the models and the real data.

This raises the question of variability. In the physical sciences and for smooth phenomena, it is classically treated within the "error calculation". However, the case of irregular, chaotic, or intermittent regimes can no longer be ignored. Even more so for biological and social systems, variability may be necessary for their functioning, first for their survival, but above all for their evolution. Today, the question is: can this variability be generated by the living entities themselves, for example during the reproduction phase? The underlying processes would then be both products and drivers of evolution. What are the consequences, for instance in cellular division or at the ecological level in terms of resilience? Most of these processes still need to be identified, studied, and modeled.

From frontier to frontier, the methodological entry point, especially modeling and data analysis coupled with bioinformatics and experimental approaches, leads us to examine fundamental problems in ecology and evolution, as well as to solve practical problems in applied domains such as agronomy, biomedicine, or biotechnologies and to aid the development of new practices such as evolutionary medicine.

We welcome the submission of Original Research, Methods, Reviews, Opinion, and Perspective pieces aimed at the contribution of methodologies, but also new technologies to study fundamental questions in Ecology and Evolution, including practical issues, e.g. for agriculture, medicine, or management of ecosystems. Misuses examples are also to be examined. We welcome the submission of articles including but not limited to the following themes:
• Evolutionary medicine
• Meta-analysis to analyze ecological data
• Modeling of Biodiversity dynamics
• Evolutionary and ecological experiments in the laboratory
• Identification and modeling of biological processes generating variability (e.g. chaotic processes), the role of stochasticity in evolution, in pathological and ecological dynamics
• Biological exploration of new environments, adapted technologies et methodologies (e.g., deep ocean, underground biodiversity, subglacial lakes and non-evolving populations, exobiology)
• Multiscaling and levels of biological organization
• Studying and modeling epidemics and ecological dynamics including behaviors of actors, human or not

Keywords: methods in ecology and evolution, statistical ecology

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