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1. Predicting the intensity of partially observed data from a revisited kriging for point processes

Author

Gabriel, Edith, Bonneu, Florent, Monestiez, Pascal, and Chadoeuf, Joel

Subjects
Statistics - Methodology
Abstract

We consider a stationary and isotropic spatial point process whose a realisation is observed within a large window. We assume it to be driven by a stationary random field $U$. In order to predict the local intensity of the point process, $\lambda (x|U)$, we propose to define the first- and second-order characteristics of a random field, defined as the regularized counting process, from the ones of the point process and to interpolate the local intensity by using a kriging adapted to the regularized process., Comment: arXiv admin note: text overlap with arXiv:1409.6441

Published
2016

2. Predicting the intensity of partially observed data from a revisited kriging for point processes

Author

Gabriel, Edith, Bonneu, Florent, Monestiez, Pascal, and Chadoeuf, Joel

Subjects
Statistics - Methodology
Abstract

We consider a stationary and isotropic spatial point process, whose a realisation is observed within a large window. In order to predict its local intensity, we propose to define the first- and second-order characteristics of a random field, defined as the regularized counting process, from the ones of the point process and to interpolate the intensity by using a revisited kriging of the regularized process.

Published
2014

3. Combining multiple visual surveys to model the habitat of deep-diving cetaceans at the basin scale : Large-scale modelling of deep-diving cetacean habitats

Author

Virgili, Auriane, Authier, Matthieu, Boisseau, Oliver, Cañadas, Ana, Claridge, Diane, Cole, Tim, Corkeron, Peter, Dorémus, Ghislain, David, Léa, Di-Méglio, Nathalie, Dunn, Charlotte, Dunn, Tim E., García-Barón, Isabel, Laran, Sophie, Lauriano, Giancarlo, Lewis, Mark, Louzao, Maite, Mannocci, Laura, Martínez-Cedeira, José, Palka, Debra, Panigada, Simone, Pettex, Emeline, Roberts, Jason J., Ruiz, Leire, Saavedra, Camilo, Santos, M. Begoña, Van Canneyt, Olivier, Bonales, José Antonio Vázquez, Monestiez, Pascal, and Ridoux, Vincent

Published
2019

4. Statistical ecology comes of age

Author

Gimenez, Olivier, Buckland, Stephen T, Morgan, Byron JT, Bez, Nicolas, Bertrand, Sophie, Choquet, Rémi, Dray, Stéphane, Etienne, Marie-Pierre, Fewster, Rachel, Gosselin, Frédéric, Mérigot, Bastien, Monestiez, Pascal, Morales, Juan M, Mortier, Frédéric, Munoz, François, Ovaskainen, Otso, Pavoine, Sandrine, Pradel, Roger, Schurr, Frank M, Thomas, Len, Thuiller, Wilfried, Trenkel, Verena, de Valpine, Perry, and Rexstad, Eric

Subjects
Aetiology, 2.5 Research design and methodologies (aetiology), Animals, Biodiversity, Ecology, Models, Statistical, citizen science, hidden Markov model, hierarchical model, movement ecology, software package, spatially explicit capture-recapture, species distribution modelling, state-space model, spatially explicit capture–recapture, state–space model, Biological Sciences, Evolutionary Biology
Abstract

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Published
2014

33. Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?

Author

Virgili, Auriane, primary, Hedon, Laura, additional, Authier, Matthieu, additional, Calmettes, Beatriz, additional, Claridge, Diane, additional, Cole, Tim, additional, Corkeron, Peter, additional, Dorémus, Ghislain, additional, Dunn, Charlotte, additional, Dunn, Tim E., additional, Laran, Sophie, additional, Lehodey, Patrick, additional, Lewis, Mark, additional, Louzao, Maite, additional, Mannocci, Laura, additional, Martínez-Cedeira, José, additional, Monestiez, Pascal, additional, Palka, Debra, additional, Pettex, Emeline, additional, Roberts, Jason J., additional, Ruiz, Leire, additional, Saavedra, Camilo, additional, Santos, M. Begoña, additional, Van Canneyt, Olivier, additional, Bonales, José Antonio Vázquez, additional, and Ridoux, Vincent, additional

Published
2021
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34. A real‐world implementation of a nationwide, long‐term monitoring program to assess the impact of agrochemicals and agricultural practices on biodiversity

Author

Andrade, Camila, Villers, Alexandre, Balent, Gerard, Bar-Hen, Avner, Chadoeuf, Joël, Cylly, Daniel, Cluzeau, Daniel, Fried, Guillaume, Guillocheau, Sarah, Pillon, Olivier, Porcher, Emmanuelle, Tressou, Jessica, Yamada, Ohri, Lenne, Nicolas, Jullien, Jérôme, Monestiez, Pascal, Centre d'Ecologie et des Sciences de la COnservation (CESCO), Muséum national d'Histoire naturelle (MNHN)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Biostatistique et Processus Spatiaux (BioSP), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Station Biologique de Paimpont CNRS UMR 6653 (OSUR), Université de Rennes 1 (UR1), Laboratoire de la Santé des Végétaux, Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Direction Générale de l'Alimentation (DGAL), Ministère de l'agriculture, de l'agroalimentaire et de la forêt, Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Unité Phytopharmacovigilance et Observatoire des résidus de pesticides (UPO), Direction de l'Evaluation des Risques (DER), Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES)-Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR), Laboratoire de la santé des végétaux (LSV), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Agence française pour la biodiversité

Subjects
Ecology, nontarget species, farmland biodiversity, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, agricultural practices, monitoring program, QH540-549.5, Original Research
Abstract

Biodiversity has undergone a major decline throughout recent decades, particularly in farmland. Agricultural practices are recognized to be an important pressure on farmland biodiversity, and pesticides are suspected to be one of the main causes of this decline in biodiversity. As part of the national plan for reduction of pesticides use (Ecophyto), the French ministry of agriculture launched the 500 ENI (nonintended effects) monitoring program in 2012 in order to assess the unintended effects of agricultural practices, including pesticide use, on biodiversity represented by several taxonomic groups of interest for farmers. This long‐term program monitors the biodiversity of nontargeted species (earthworms, plants, coleoptera, and birds), together with a wide range of annual data on agricultural practices (crop rotation, soil tillage, weed control, fertilizers, chemical treatments, etc.). Other parameters (e.g., landscape and climatic characteristics) are also integrated as covariates during the analyses. This monitoring program is expected to improve our understanding of the relative contribution of the different drivers of population and community trends. Here, we present the experience of setting up the 500 ENI network for this ambitious and highly complex monitoring program, as well as the type of data it collects. The issue of data quality control and some first results are discussed. With the aim of being useful to readers who would like to set up similar monitoring schemes, we also address some questions that have arisen following the first five years of the implementation phase of the program., We present a national monitoring program on biodiversity and agricultural practices, the 500 ENI monitoring program. Its objectives are to detect changes in biodiversity indicators and simultaneous changes in agricultural practices, including pesticide use. Such multi‐taxa long‐term monitoring at national scale, focused on both biodiversity and agricultural practices, has no equivalent in Europe.

Published
2021
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35. Jointly estimating spatial sampling effort and habitat suitability for multiple species from opportunistic presence‐only data

Author

Botella, Christophe, Joly, Alexis, Bonnet, Pierre, Munoz, François, Monestiez, Pascal, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Biostatistique et Processus Spatiaux (BioSP), Consortium Floris'Tic, ANR-18-CE02-0010,EcoNet,Modèles statistiques avancés pour les réseaux écologiques(2018), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
U10 - Informatique, mathématiques et statistiques, Analyse de données, [SDV]Life Sciences [q-bio], Participation communautaire, Distribution spatiale, F70 - Taxonomie végétale et phytogéographie, Distribution des populations, Botanique, Plante sauvage, Participation publique, [SDE]Environmental Sciences, Échantillonnage, [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; 1. Building reliable species distribution models (SDMs) from presence-only information requires a good understanding of the spatial variation in the sampling effort. However, in most cases, the sampling effort is unknown, leading to biases in SDMs. This study proposes a method to jointly estimate the parameters of sampling effort and species densities to avoid such biases. The method is particularly suited to the analysis of massive but highly heterogeneous presence-only data. 2. The proposed method is based on estimating the variation in sampling effort over units of a spatial mesh in parallel with the environmental density of multiple species using a marked Poisson process model. Based on simulations with realistic settings, we examined the performance and robustness of parameter estimations. We also analysed a large-scale citizen science dataset with highly heterogeneous sampling (Pl@ntNet), including around 300,000 occurrences of 150 plant species. 3. We found that sampling effort was correctly estimated when the true sampling effort was constant within the cells of a spatial mesh. Estimation bias arose when sampling effort and environmental drivers strongly covaried within cells. Otherwise, the inference was correct and robust to sampling variation within cells. Running the model on real occurrences of 150 plant species provided an estimated map of relative sampling effort for 15% of French territory. We also found that the density estimated for an exotic invasive plant was consistent with prior data. 4. This is the first method jointly estimating species densities depending on environment, and sampling effort as an explicit spatial function, from occurrence data of multiple species. An asset of the method is that a few frequently observed species greatly contribute to correctly estimate sampling effort, thereby improving density estimation of all other species. This approach can thus provide reliable SDM for This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Published
2021
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39. Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection

Author

Botella, Christophe, Joly, Alexis, Monestiez, Pascal, Bonnet, Pierre, Munoz, François, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Biostatistique et Processus Spatiaux (BioSP), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Evolutionary Processes, Speciation, Science, Distribution des populations, Research and Analysis Methods, Mathematical and Statistical Techniques, Bias, Curve fitting, Species delimitation, Probability density, Species interactions, Modélisation environnementale, Evolutionary Biology, Ecology, U10 - Informatique, mathématiques et statistiques, Environmental geography, Cryptic speciation, Ecology and Environmental Sciences, L60 - Taxonomie et géographie animales, Biology and Life Sciences, F70 - Taxonomie végétale et phytogéographie, Biodiversity, Models, Theoretical, Probability Theory, Simulation and modeling, Physical Sciences, [SDE]Environmental Sciences, Medicine, Mathematical Functions, Software, Mathematics, Research Article, Ecological niches
Abstract

International audience; The use of naturalist mobile applications have dramatically increased during last years, and provide huge amounts of accurately geolocated species presences records. Integrating this novel type of data in species distribution models (SDMs) raises specific methodological questions. Presence-only SDM methods require background points, which should be consistent with sampling effort across the environmental space to avoid bias. A standard approach is to use uniformly distributed background points (UB). When multiple species are sampled, another approach is to use a set of occurrences from a Target-Group of species as background points (TGOB). We here investigate estimation biases when applying TGOB and UB to opportunistic naturalist occurrences. We modelled species occurrences and observation process as a thinned Poisson point process, and express asymptotic likelihoods of UB and TGOB as a divergence between environmental densities, in order to characterize biases in species niche estimation. To illustrate our results, we simulated species occurrences with different types of niche (specialist/generalist, typical/marginal), sampling effort and TG species density. We conclude that none of the methods are immune to estimation bias, although the pitfalls are different: For UB, the niche estimate fits tends towards the product of niche and sampling densities. TGOB is unaffected by heterogeneous sampling effort, and even unbiased if the cumulated density of the TG species is constant. If it is concentrated, the estimate deviates from the range of TG density. The user must select the group of species to ensure that they are jointly abundant over the broadest environmental sub-area.

Published
2020
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40. Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?

Author

Virgili, Auriane, Hedon, Laura, Authier, Matthieu, Calmettes, Beatriz, Claridge, Diane, Cole, Tim, Corkeron, Peter, Dorémus, Ghislain, Dunn, Charlotte, Dunn, Tim E., Laran, Sophie, Lehodey, Patrick, Lewis, Mark, Louzao, Maite, Mannocci, Laura, Martínez-cedeira, José, Monestiez, Pascal, Palka, Debra, Pettex, Emeline, Roberts, Jason J., Ruiz, Leire, Saavedra, Camilo, Santos, M. Begoña, Van Canneyt, Olivier, Bonales, José Antonio Vázquez, Ridoux, Vincent, Virgili, Auriane, Hedon, Laura, Authier, Matthieu, Calmettes, Beatriz, Claridge, Diane, Cole, Tim, Corkeron, Peter, Dorémus, Ghislain, Dunn, Charlotte, Dunn, Tim E., Laran, Sophie, Lehodey, Patrick, Lewis, Mark, Louzao, Maite, Mannocci, Laura, Martínez-cedeira, José, Monestiez, Pascal, Palka, Debra, Pettex, Emeline, Roberts, Jason J., Ruiz, Leire, Saavedra, Camilo, Santos, M. Begoña, Van Canneyt, Olivier, Bonales, José Antonio Vázquez, and Ridoux, Vincent

Abstract

In habitat modelling, environmental variables are assumed to be proxies of lower trophic levels distribution and by extension, of marine top predator distributions. More proximal variables, such as potential prey fields, could refine relationships between top predator distributions and their environment. In situ data on prey distributions are not available over large spatial scales but, a numerical model, the Spatial Ecosystem And POpulation DYnamics Model (SEAPODYM), provides simulations of the biomass and production of zooplankton and six functional groups of micronekton at the global scale. Here, we explored whether generalised additive models fitted to simulated prey distribution data better predicted deep-diver densities (here beaked whales Ziphiidae and sperm whales Physeter macrocephalus) than models fitted to environmental variables. We assessed whether the combination of environmental and prey distribution data would further improve model fit by comparing their explanatory power. For both taxa, results were suggestive of a preference for habitats associated with topographic features and thermal fronts but also for habitats with an extended euphotic zone and with large prey of the lower mesopelagic layer. For beaked whales, no SEAPODYM variable was selected in the best model that combined the two types of variables, possibly because SEAPODYM does not accurately simulate the organisms on which beaked whales feed on. For sperm whales, the increase model performance was only marginal. SEAPODYM outputs were at best weakly correlated with sightings of deep-diving cetaceans, suggesting SEAPODYM may not accurately predict the prey fields of these taxa. This study was a first investigation and mostly highlighted the importance of the physiographic variables to understand mechanisms that influence the distribution of deep-diving cetaceans. A more systematic use of SEAPODYM could allow to better define the limits of its use and a development of the model that would si

Published
2021
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41. Fishing behaviours and fisher effect in decision‐making processes when facing depredation by marine predators

Author

Janc, Anaïs, Guinet, Christophe, Pinaud, David, Richard, Gaetan, Monestiez, Pascal, Tixier, Paul, Janc, Anaïs, Guinet, Christophe, Pinaud, David, Richard, Gaetan, Monestiez, Pascal, and Tixier, Paul

Abstract

Fishers aim to optimise cost–benefit ratios of their behaviour when exploiting resources. Avoidance of interactions with marine predators (i.e. their feeding on catches in fishing gear, known as depredation) has recently become an important component of their decisions. How fishers minimise these interactions whilst maximising fishing success is poorly understood. This issue is addressed in a sub-Antarctic, long-line fishery confronted with extensive depredation by sperm whales Physeter macrocephalus and killer whales Orcinus orca by examining a 15-year data set. Whereas a broad range of behaviours was identified from spatio-temporal and operational descriptors, none combined high fishing success with low frequency of interactions. With experience, fishers favoured exploitation of productive patches with high frequencies of interactions over avoidance behaviours. Such decisions, although potentially optimal in the short term, are likely to intensify pressures on fish stocks and impact depredating whales. Therefore, the present study provides additional evidence to inform management decisions pertaining to the coexistence between fisheries and marine predators.

Published
2021
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42. Marine Citizen Science: Current State in Europe and New Technological Developments

Author

García-Soto, Carlos, Seys, J. J. C., Zielinski, O., Busch, J. A., Luna, S. I., Báez, José Carlos, Domegan, C., Dubsky, K., Kotynska-Zielinska, I., Loubat, P., Malfatti, Francesca, Mannaerts, G., Monestiez, Pascal, van der Meeren, G. I., Gorsky, G., García-Soto, Carlos, Seys, J. J. C., Zielinski, O., Busch, J. A., Luna, S. I., Báez, José Carlos, Domegan, C., Dubsky, K., Kotynska-Zielinska, I., Loubat, P., Malfatti, Francesca, Mannaerts, G., Monestiez, Pascal, van der Meeren, G. I., and Gorsky, G.

Abstract

Marine citizen science is emerging with promising opportunities for science, policy and public but there is still no comprehensive overview of the current state in Europe. Based on 127 projects identified for the North Sea area we estimate there might be as much as 500 marine and coastal citizen science projects running in Europe, i.e., one marine citizen science project per 85 km of coastline, with an exponential growth since 1990. Beach-based projects are more accessible and hence most popular (60% of the projects), and the mean duration of the projects is 18–20 years. Current trends, topics, organizers, aims, and types of programme in terms of participation are presented in this overview. Progress in marine citizen science is specially enabled and promoted through technological developments. Recent technological advances and best practise examples are provided here, untapping the potential of smart mobile apps, do-it-yourself (DIY) technologies, drones, and artificial intelligence (AI) web services

Published
2021

48. Species recommendation using intensity models and sampling bias correction (GeoLifeCLEF2019: Lof of Lof team)

Author

Monestiez, Pascal, Botella, Christophe, Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Linda Cappellato, Nicola Ferro, David E. Losada, Henning Müller, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD [France-Sud]), and Biostatistique et Processus Spatiaux (BIOSP)

Subjects
predictive power, evaluation, Species Distribution Models, model performance, GeoLifeCLEF 2019, prediction, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, benchmark, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, environmental data, LifeCLEF, presence-only data, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, biodiversity, species recommenda- tion, methods comparison
Abstract

International audience; This paper presents three algorithms for species spatial rec-ommendation in the context of the GeoLifeCLEF 2019 challenge. Wesubmitted three runs to this task, all based on the estimation of speciesenvironmental intensities through Poisson processes models: The first isdirectly derived from MAXENT method used for species distributionmodels. The second method is a modification that uses sites were speciesobserved as background points in MAXENT to correct for spatial sam-pling bias due to heterogeneous sampling in the training occurrences. Thelast method jointly estimates species and sampling intensities to correctfor sampling bias. The best run was the MAXENT method which wasranked 14 over 44 runs with a top30 accuracy of 0.111 on the test setwhile the worst performing method was LOF with an accuracy of 0.086(ranked 19)

Published
2019

50. Sperm whale ship strikes in the Pelagos Sanctuary and adjacent waters: assessing and mapping collision risks in summer

Author

Dimeglio, Chloé, David, Léa, Monestiez, Pascal, Groupement d'Intérêt Scientifique pour les Mammifères Marins de Méditerranée, Partenaires INRAE, Institut EcoOcean, Biostatistique et Processus Spatiaux (BioSP), and Institut National de la Recherche Agronomique (INRA)

Subjects
modelling, marine protected area, ship strike, distribution, Animal Science and Zoology, sperm whale, Aquatic Science, mediterranean sea, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Ecology, Evolution, Behavior and Systematics
Abstract

International audience; Collisions with large vessels potentially present a major conservation issue for sperm whales in the Pelagos Sanctuary in the northwestern Mediterranean Sea. The exact numbers and locations of ship strikes remain largely unknown at present. In this study, sightings of sperm whales in summer (June–September) were gathered for the period between 1998 and 2008 from nine French and Italian organisations, together covering an area of more than 68,000km. Working on a regular grid of 0.1° × 0.1° latitude/longitude cells, approximate surface density of the whales was calculated using kriging methods. A database of shipping lanes of ferries and merchant vessels was assembled, and traffic density was mapped (kilometres travelled per cell). The data was overlayed and multiplied, using ArcGis, to create a relative density surface map of sperm whales correlated with the relative density of maritime traffic. Several maps of collision risk were drawn up according to the speed class of the vessels. Of living animals that had been photo-identified, 9% had scars attributed to ship strike. Results show that sperm whales are at high risk from merchant vessels along the French and Italian continental coasts and at risk from conventional ferries on the east side of the islands of Corsica and Sardinia. It was calculated that 74 animals could be in a ship strike risk situation during the summer period in the Pelagos Sanctuary. Based on these results, mitigation measures already in place were reviewed and new measures to reduce ship strike risk are suggested.

Published
2019

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