Open Call for Papers: Special Feature on Realising the Promise of Large Data and Complex Models

In an era of rapid global change, ecologists are increasingly asked to provide answers to big, urgent questions of global concern. On the one hand, responding to such requests seems increasingly feasible – given the rapid increase in the ability to collect ecological data at ever-higher spatio-temporal scales, new, unsolved questions can be tackled and increasingly realistic models can be developed, pushing the boundaries of the questions which can be answered. However, large datasets and complex models can lead to ”big” trouble, in terms of handling and manipulating the data, in addition to fitting complex models to data and interpreting the output. 

There is hence an exciting and timely potential, and the motivation for this Special Feature, for new methodological developments, through an improved collaboration between ecologists, statisticians, computer scientists and mathematical modellers. We here invite submissions, comprising research articles, and application papers, providing novel solutions to one or more of the following specific topics, within the overall topic of efficiently modelling, analysing and interpreting modern ecological datasets:

Visualising large multivariate datasets

Visualising complex, multi-dimensional data, such as bioacoustic and multivariate biologging/sensor data, or geographic/spatial data, has become increasingly challenging and at the same time crucially needed, to aid model development and interpretation, and reproducibility of research. We call for case studies and successful methods that can be applied to large, complex data and multi-dimensional parameter space, and provide indications for improving reproducibility of research.

Complexity, interpretability and predictive ability

It is possible to fit a wealth of complex models to large data sets; however, where does the line get drawn between fitting a model for complexity’s sake and because it is actually required for an understanding of the dynamics exhibited by the data? In which cases could a simple model actually tell more? How should we discriminate between different models dependent on what questions are of interest? Novel solutions and practical approaches would benefit many researchers.

Computational efficiency and scalability for hierarchical models

Thanks to technological innovations and data sharing initiatives, the amount of spatial, temporal and/or individual-based information is increasing exponentially (e.g. bioacoustics data, sensor data, citizen science data, etc.). Fitting hierarchical/state-space models to such ecological datasets presents many challenges including, for example, model fitting, computational time, model design, and interpretability. We call for applications and novel solutions papers, overcoming these challenges.

Modern technological advances and data challenges

New forms of data are emerging, particularly as new technology and data collection techniques evolve. These advances lead to new modelling techniques and data analytic approaches. Not least the rise of Citizen science projects and related data collection initiatives, which are collecting an unprecedented amount of data, but are typically not collected using a structured design (opportunistic or preferential sampling) and hence are severely biased. We call for novel methods overcoming the computational and bias-related issues of such data.

Contributing to the Special Feature

The focus of the Special Feature is on providing practical solutions to the challenges of analysing modern large datasets and/or complex models. In this context, “large” is defined in the sense that fitting a given model to the data using standard model-fitting tools becomes computationally slow or even infeasible. Similarly, “complex” models refers to ecological models for which there are challenges in terms of specifying a biologically realistic model and/or fitting the model to the data using modern computational techniques. Conversely, papers focusing on describing the potential of new or big data to particular areas are not appropriate. Similarly, submissions that present case studies or applications using existing methods, or are incremental additions of existing methods, are outside of the scope of this Special Feature. 

We invite the submission of research articles, reviews and application-driven papers. Please highlight in your cover letter that you would like your contribution to be considered for this Special Feature. Manuscripts will be subject to the same rigorous peer review process as any other submission, and must meet our requirements of novelty and broad relevance for an audience of ecologists and/or evolutionary biologists. If not considered appropriate for the Special Feature, manuscripts will be considered as a normal submission.

Guest Edited by Luca Börger, Laura Graham, Ruth King, Rachel McCrea

Pre-submission enquiries are not necessary, but any questions can be directed to:

The deadline for submission: 01 July 2021

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