Data-based, synthesis-driven: Setting the agenda for computational ecology

Computational thinking is the integration of algorithms, software, and data, tosolve general questions in a field. Computation ecology has the potential totransform the way ecologists think about the integration of data and models. Asthe practice is gaining prominence as a way to conduct ecological research, itis important to reflect on what its agenda could be, and how it fits within thebroader landscape of ecological research. In this contribution, we suggest areasin which empirical ecologists, modellers, and the emerging community ofcomputational ecologists could engage in a constructive dialogue to build on oneanother's expertise; specifically, about the need to make predictions frommodels actionable, about the best standards to represent ecological data, andabout the proper ways to credit data collection and data reuse. We discuss howtraining can be amended to improve computational literacy.

Data-based, synthesis-driven: setting the agenda for computational ecology

Computational thinking is the integration of algorithms, software, and data, to solve general questions in a field. Computation ecology has the potential to transform the way ecologists think about the integration of data and models. As the practice is gaining prominence as a way to conduct ecological research, it is important to reflect on what its agenda could be, and how it fits within the broader landscape of ecological research. In this contribution, we suggest areas in which empirical ecologists, modellers, and the emerging community of computational ecologists could engage in a constructive dialogue to build on one another’s expertise; specifically, about the need to make predictions from models actionable, about the best standards to represent ecological data, and about the proper ways to credit data collection and data reuse. We discuss how training can be amended to improve computational literacy.

Digging deeper on “deep” learning: A computational ecology approach

We propose an alternative approach to “deep” learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibility. Cross-fertilization of learning processes across multiple domains is the fundamental feature of human intelligence that must inform “new” artificial intelligence.

Thinking with the Animal-Hacker

In the depths of the Cumbria hills a dairy cow changes its route to stare deep into the camera lens of the ‘Environmental Virtual Observatory’ (EVO) (www.evo-uk.org). Downstream at 15 minute intervals organic matter is pushed through turbidity probes, sometimes causing the computation to glitch and upload its own movement into a data storage warehouse. In this muddy, messy situation of the EVO there is something lurking, something which might be described as the ‘Animal-Hacker’ the non-human animal, an entity that exploits the computational ecology, reconfigures it in an act of what Donna Haraway would describe as “worlding”.

Computational ecology as an emerging science

It has long been recognized that numerical modelling and computer simulations can be used as a powerful research tool to understand, and sometimes to predict, the tendencies and peculiarities in the dynamics of populations and ecosystems. It has been, however, much less appreciated that the context of modelling and simulations in ecology is essentially different from those that normally exist in other natural sciences. In our paper, we review the computational challenges arising in modern ecology in the spirit of computational mathematics, i.e. with our main focus on the choice and use of adequate numerical methods. Somewhat paradoxically, the complexity of ecological problems does not always require the use of complex computational methods. This paradox, however, can be easily resolved if we recall that application of sophisticated computational methods usually requires clear and unambiguous mathematical problem statement as well as clearly defined benchmark information for model validation. At the same time, many ecological problems still do not have mathematically accurate and unambiguous description, and available field data are often very noisy, and hence it can be hard to understand how the results of computations should be interpreted from the ecological viewpoint. In this scientific context, computational ecology has to deal with a new paradigm: conventional issues of numerical modelling such as convergence and stability become less important than the qualitative analysis that can be provided with the help of computational techniques. We discuss this paradigm by considering computational challenges arising in several specific ecological applications.