Statistical Approaches for Hidden Variables in Ecology. Nathalie Peyrard
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Название: Statistical Approaches for Hidden Variables in Ecology
Автор: Nathalie Peyrard
Издательство: John Wiley & Sons Limited
Жанр: Социология
isbn: 9781119902782
isbn:
This book is not designed to be read from front to back, but rather as a resource on which ecologists working with models or statisticians working in the field of ecology may draw. Chapters are arranged in order of ecological scale, from individuals up to ecosystems, providing an initial interpretive framework. Another approach would be to consider the nature of the hidden variable being modeled. One final approach would be to examine different statistical models: some models are used in several chapters, in connection with questions on different scales, and using different estimation methods.
Table I.1 gives a summary of the contents of the different chapters and is designed to help readers identify material which is of interest to them.
I.5. Directions for further perspectives
The examples described above, along with those presented in the following chapters, highlight the immense flexibility of latent variables models. These models, involving one or more latent layers, provide a rich framework for the description of complex dependency structures, and/or for the approximation of a mechanistic description of the phenomena involved.
However, it is important to note that the most sophisticated models are almost always the most complex in terms of inference. It would be wrong to assume that inference simply “happens”, whatever the statistical approach (frequentist, Bayesian, etc.). At the time of writing, there is no fully generic approach suitable for use with all models, and this is unlikely to change in the near future. Even the best-established algorithms (EM, MCMC, etc.) require users to have a good understanding of the underlying principles in order to guide and control their behavior, and/or to adjust the algorithm as needed. This need for adjustment is clearly visible in the chapters of this book.
To conclude this introduction, we wish to highlight two areas for further research in ecology, drawing on statistical modeling of hidden variables, which are not covered in this book but which show promise: namely the integration (or combination) of data from multiple sources, and the use of participative scientific data.
Table I.1. Chapters and contents
Several works have recently been published on the integration of data from multiple sources in the field of ecology (Miller et al. 2019; Isaac et al. 2020). The aim of the authors is to systematically improve the precision of estimated data, potentially decreasing sample size, and to enable the estimation of parameters that cannot be approximated by any other means. Data integration generally involves a hierarchical modeling approach in which the hidden variable is present in all of the sources used in its estimation.
Data from participative scientific activity has also received increasing attention in the literature in recent years (Dickinson et al. 2012; McKinley et al. 2017). This is due to the increasing availability of the data, and to the fact that information can now be collected across an increasingly broad spatial and temporal scale. Participative data sources are a fascinating subject of study in statistical ecology, raising a number of new challenges in terms of spatial bias in sampling, or variations in participant expertise. Once again, a clear distinction between the ecological processes embodied by the hidden variables and the associated observation methods is essential in order to develop a full response to the ecological question.
I.6. References
Dickinson, J.L., Shirk, J., Bonter, D., Bonney, R., Crain, R.L., Martin, J., Phillips, T., Purcell, K. (2012). The current state of citizen science as a tool for ecological research and public engagement. Frontiers in Ecology and the Environment, 10(6), 291–297.
Isaac, N.J., Jarzyna, M.A., Keil, P., Dambly, L.I., Boersch-Supan, P.H., Browning, E., Freeman, S.N., Golding, N., Guillera-Arroita, G., Henrys, P.A., Jarvis, S., Lahoz-Monfort, J., Pagel, J., Pescott, O.L., Schmucki, R., Simmonds, E.G., O’Hara, R.B. (2020). Data integration for large-scale models of species distributions. Trends in Ecology & Evolution, 35(1), 56–67.
McKinley, D.C., Miller-Rushing, A.J., Ballard, H.L., Bonney, R., Brown, H., Cook-Patton, S.C., Evans, D.M., French, R.A., Parrish, J.K., Phillips, T.B., Ryan, S.F., Shanley, L.A., Shirk, J.L., Stepenuck, K.F., Weltzin, J.F., Wiggins, A., Boyle, O.D., Briggs, R.D., Chapin, S.F., Hewitt, D.A., Preuss, P.W., Soukup, M.A. (2017). Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation, 208, 15–28.
Miller, D.A.W., Pacifici, K., Sanderlin, J.S., Reich, B.J. (2019). The recent past and promising future for data integration methods to estimate species’ distributions. Methods in Ecology and Evolution, 10(1), 22–37.
1 1 https://oliviergimenez.github.io/code_livre_variables_cachees/.
1
Trajectory Reconstruction and Behavior Identification Using Geolocation Data
Marie-Pierre ETIENNE1 and Pierre GLOAGUEN2
1 Institut Agro, Agrocampus Ouest, CNRS, IRMAR – UMR 6625, Rennes, France
2 Paris-Saclay University, AgroParisTech, INRAE, UMR MIA-Paris, France
1.1. Introduction
The study of movement in ecology has taken off in recent years, driven by questions relating to the determinisms of individual movement. Interest in the ecology of movement has been largely fueled by the emergence and development of GPS technologies over the last 20 years, helped along by the creation of numerous databases made up of individual trajectories. These observations, on fine spatial and temporal levels, can be used to study the behavior of individuals in relation to their living environment. A variety of trajectory models have been developed and applied with the aim of reconstructing these behaviors and understanding the underlying determinisms. In this chapter, we shall present two latent variable models, widely used in movement ecology for trajectory analysis. Each model corresponds to a specific objective: the reconstruction of real trajectories with the removal of any geolocation errors, and the identification of different behaviors in the course of movement.
1.1.1. Reconstructing a real trajectory from imperfect observations
Trajectory data are frequently marred by errors for a variety of reasons (satellite accessibility issues, geolocation errors, etc.). This results in noisy observations of the real position of the animal, which is itself unknown. The hidden variable is, therefore, the real position and the observed variable is the noisy version. In Figure 1.1, we can see that some recorded positions of a Cape dolphin, tracked using the Argos system, are actually on land – a situation which is evidently improbable. This observation almost certainly corresponds to noisy data concerning the actual position of the tracked individual.