Eritja R., Ruiz-Arrondo I., Delacour-Estrella S., Schaffner F., Álvarez-Chachero J., Bengoa M., Puig M.-A., Melero-Alcíbar R., Oltra A., Bartumeus F. (2019) First detection of Aedes japonicus in Spain: An unexpected finding triggered by citizen science. Parasites and Vectors. 12: 0-0.LinkDoi: 10.1186/s13071-019-3317-y
Background: Aedes japonicus is an invasive vector mosquito from Southeast Asia which has been spreading across central Europe since the year 2000. Unlike the Asian Tiger mosquito (Aedes albopictus) present in Spain since 2004, there has been no record of Ae. japonicus in the country until now. Results: Here, we report the first detection of Ae. japonicus in Spain, at its southernmost location in Europe. This finding was triggered by the citizen science platform Mosquito Alert. In June 2018, a citizen sent a report via the Mosquito Alert app from the municipality of Siero in the Asturias region (NW Spain) containing pictures of a female mosquito compatible with Ae. japonicus. Further information was requested from the participant, who subsequently provided several larvae and adults that could be classified as Ae. japonicus. In July, a field mission confirmed its presence at the original site and in several locations up to 9 km away, suggesting a long-time establishment. The strong media impact in Asturias derived from the discovery raised local participation in the Mosquito Alert project, resulting in further evidence from surrounding areas. Conclusions: Whilst in the laboratory Ae. japonicus is a competent vector for several mosquito-borne pathogens, to date only West Nile virus is a concern based on field evidence. Nonetheless, this virus has yet not been detected in Asturias so the vectorial risk is currently considered low. The opportunity and effectiveness of combining citizen-sourced data to traditional surveillance methods are discussed. © 2019 The Author(s).
Bartumeus F., Oltra A., Palmer J.R.B. (2018) Citizen Science: A Gateway for Innovation in Disease-Carrying Mosquito Management?. Trends in Parasitology. : 0-0.LinkDoi: 10.1016/j.pt.2018.04.010
Traditional methods for tracking disease-carrying mosquitoes are hitting budget constraints as the scales over which they must be implemented grow exponentially. Citizen science offers a novel solution to this problem but requires new models of innovation in the public health sector. © 2018 Elsevier Ltd
Farina, S., Oltra, A., Boada, J., Bartumeus, F., Romero, J., Alcoverro, T. (2017) Generation and maintenance of predation hotspots of a functionally important herbivore in a patchy habitat mosaic. Functional Ecology. : 0-0.LinkDoi: 10.1111/1365-2435.12985
Palmer, J.R.B., Oltra, A., Collantes, F., Delgado, J.A., Lucientes, J., Delacour, S., Bengoa, M., Eritja, R., Bartumeus, F. (2017) Citizen science provides a reliable and scalable tool to track disease-carrying mosquitoes. Nature Communications. 8: 0-0.LinkDoi: 10.1038/s41467-017-00914-9
Garriga J., Palmer J.R.B., Oltra A., Bartumeus F. (2016) Expectation-maximization binary clustering for behavioural annotation. PLoS ONE. 11: 0-0.LinkDoi: 10.1371/journal.pone.0151984
The growing capacity to process and store animal tracks has spurred the development of new methods to segment animal trajectories into elementary units of movement. Key challenges for movement trajectory segmentation are to (i) minimize the need of supervision, (ii) reduce computational costs, (iii) minimize the need of prior assumptions (e.g. simple parametrizations), and (iv) capture biologically meaningful semantics, useful across a broad range of species. We introduce the Expectation-Maximization binary Clustering (EMbC), a general purpose, unsupervised approach to multivariate data clustering. The EMbC is a variant of the Expectation-Maximization Clustering (EMC), a clustering algorithm based on the maximum likelihood estimation of a Gaussian mixture model. This is an iterative algorithm with a closed form step solution and hence a reasonable computational cost. The method looks for a good compromise between statistical soundness and ease and generality of use (by minimizing prior assumptions and favouring the semantic interpretation of the final clustering). Here we focus on the suitability of the EMbC algorithm for behavioural annotation of movement data. We show and discuss the EMbC outputs in both simulated trajectories and empirical movement trajectories including different species and different tracking methodologies. We use synthetic trajectories to assess the performance of EMbC compared to classic EMC and Hidden Markov Models. Empirical trajectories allow us to explore the robustness of the EMbC to data loss and data inaccuracies, and assess the relationship between EMbC output and expert label assignments. Additionally, we suggest a smoothing procedure to account for temporal correlations among labels, and a proper visualization of the output for movement trajectories. Our algorithm is available as an R-package with a set of complementary functions to ease the analysis. © 2016 Garriga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Gutiérrez-Roig M., Sagarra O., Oltra A., Palmer J.R.B., Bartumeus F., Díaz-Guilera A., Perelló J. (2016) Active and reactive behaviour in human mobility: The influence of attraction points on pedestrians. Royal Society Open Science. 3: 0-0.LinkDoi: 10.1098/rsos.160177
Human mobility is becoming an accessible field of study, thanks to the progress and availability of tracking technologies as a common feature of smart phones. We describe an example of a scalable experiment exploiting these circumstances at a public, outdoor fair in Barcelona (Spain). Participants were tracked while wandering through an open space with activity stands attracting their attention. We develop a general modelling framework based on Langevin dynamics, which allows us to test the influence of two distinct types of ingredients on mobility: reactive or context-dependent factors, modelled by means of a force field generated by attraction points in a given spatial configuration and active or inherent factors, modelled from intrinsic movement patterns of the subjects. The additive and constructive framework model accounts for some observed features. Starting with the simplest model (purely random walkers) as a reference, we progressively introduce different ingredients such as persistence, memory and perceptual landscape, aiming to untangle active and reactive contributions and quantify their respective relevance. The proposed approach may help in anticipating the spatial distribution of citizens in alternative scenarios and in improving the design of public events based on a facts-based approach. © 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License.
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