Thiebault, A., Dubroca, L., Mullers, R. H. E., Tremblay, Y., & Pistorius, P. A. (2018). “M2B” package in R: Deriving multiple variables from movement data to predict behavioural states with random forests. Methods Ecol. Evol., 9(6), 1548–1555.
Résumé: 1. The behaviour of individuals affect their distributions and is therefore fundamental in determining ecological patterns. While, the direct observation of behaviour is often limited due to logistical constraints, collection of movement data has been greatly facilitated through the development of bio-logging. Movement data obtained through tracking instrumentation may potentially constitute a relevant proxy to infer behaviour. 2. To infer behaviour from movement data is a key focus within the “movement ecology” discipline. Statistical learning constitutes a number of methods that can be used to assess the link between given variables from a fully informed training dataset and then predict the values on a non-informed variable. We chose the random forest algorithm for its high prediction accuracy and its ease of implementation. The strength of random forest partly lies in its ability to handle a very large number of variables. Our methodology is accordingly based on the derivation of multiple predictor variables from movement data over various temporal scales, to capture as much information as possible from changes and variations in movement. 3. The methodology is described in four steps, using examples on foraging seabirds and fishing vessels for illustration. The models showed very high prediction accuracy (92%-97%), thereby confirming the influence of behaviour on movement decisions and demonstrating the ability to derive multiple variables from movement data to predict behaviour with random forests. 4. The codes developed for this methodology are published in the “M2B” (Movement to Behaviour) R package, available at https://CRAN.R-project.org/package=m2b. They can be used and adapted to datasets where movement was sampled from a wide range of taxa, sampling schemes or tracking devices. Observations are needed for a subset of the data, but once the model is trained, it can be used on any dataset with similar movement data.
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Triki, H. Z., Laabir, M., Lafabrie, C., Malouche, D., Bancon-Montigny, C., Gonzalez, C., et al. (2017). Do the levels of industrial pollutants influence the distribution and abundance of dinoflagellate cysts in the recently-deposited sediment of a Mediterranean coastal ecosystem? Sci. Total Environ., 595, 380–392.
Résumé: We studied the relationships between sediment industrial pollutants concentrations, sediment characteristics and the dinoflagellate cyst abundance within a coastal lagoon by investigating a total of 55 sampling stations within the Bizerte lagoon, a highly anthropized Mediterranean ecosystem. The sediment of Bizerte lagoon is characterized by a high dinocyst abundance, reaching a maximum value of 2742 cysts.g(-1) of dry sediment. The investigated cyst diversity was characterized by the presence of 22 dominant dinocyst morphotypes belonging to 11 genera. Two dinoflagellate species dominated the assemblage: Alexandrium pseudogonyaulax and Protoperidinium claudicans, representing 29 to 89% and 5 to 38% of the total cyst abundance, respectively, depending on the station. Seven morphotypes belonging to potentially toxic species were detected, including Alexandrium minutum, A. pseudogonyaulax, Alexandrium catenella/tamarense species complex, Lingulodinium polyedrum, Gonyaulax cf. spinifera complex, Prorocentrum micans and Protoceratium reticulatum. Pearson correlation values showed a positive correlation (alpha = 0.05) between cyst abundance and both water content and fine silt sediment content. Clustering revealed that the highest abundance of cysts corresponds to stations presenting the higher amounts of heavy metals. The simultaneous autoregressive model (SAM) highlighted a significant correlation (alpha = 0.05) between cyst accumulation and two main factors: sediment water content and sediment content for several heavy metals, including Hg, Cd, Cu, Ni and Cr. These results suggest that the degree of heavy metal pollution could influence cyst accumulation patterns. (C) 2017 Elsevier B.V. All rights reserved.
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Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., et al. (2018). Outstanding Challenges in the Transferability of Ecological Models. Trends Ecol. Evol., 33(10), 790–802.
Résumé: Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.
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