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| Marandel, Florianne; Lorance, Pascal; Trenkel, Verena M.. |
| The thornback ray (Raja clavata) in the Bay of Biscay is presumed to have declined during the 20th Century. To evaluate this decline and estimate biomass trajectories, a hypothetical catch time series was created for the period 1903-2013. A Bayesian state-space biomass production model with a Schaefer production function was fitted to the hypothetical catch time series and to a shorter research vessel Catch Per Unit Eeffort (CPUE) time series (1973-2013, with missing years). A censored likelihood made it possible to obtain biomass estimates without a CPUE time series or only with an estimate of biomass depletion. A simulation-estimation approach showed a high sensitivity of results to the prior for the intrinsic growth rate. The model provided biomass... |
| Tipo: Text |
Palavras-chave: Population dynamics; Stock assessment; Data poor; Censored data; Bayes; Thornback ray; State-space model. |
| Ano: 2016 |
URL: http://archimer.ifremer.fr/doc/00359/47032/46947.pdf |
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| Woillez, Mathieu; Fablet, Ronan; Tran-thanh Ngo,; Lalire, Maxime; Lazure, Pascal; De Pontual, Helene. |
| Numerous methods have been developed to geolocate fish from data storage tags. Whereas demersal species have been tracked using tide-driven geolocation models, pelagic species which undertake extensive migrations have been mainly tracked using light-based models. Here, we present a new HMM-based model that infers pelagic fish positions from the sole use of high-resolution temperature and depth histories. A key contribution of our framework lies in model parameter inference (diffusion coefficient and noise parameters with respect to the reference geophysical fields—satellite SST and temperatures derived from the MARS3D hydrodynamic model), which improves model robustness. As a case study, we consider long time series of data storage tags (DSTs) deployed on... |
| Tipo: Text |
Palavras-chave: Fish movement; Archival tagging; Migration; Population structure; Hidden Markov Model (HMM); State-space model. |
| Ano: 2016 |
URL: http://archimer.ifremer.fr/doc/00300/41097/40270.pdf |
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| Vermard, Youen; Rivot, Etienne; Mahevas, Stephanie; Marchal, Paul; Gascuel, Didier. |
| Recent advances in technologies have lead to a vast influx of data on movements, based on discrete recorded position of animals or fishing boats, opening new horizons for future analyses. However, most of the potential interest of tracking data depends on the ability to develop suitable modelling strategies to analyze trajectories from discrete recorded positions. A serious modelling challenge is to infer the evolution of the true position and the associated spatio-temporal distribution of behavioural states using discrete, error-prone and incomplete observations. In this paper, a Bayesian Hierarchical Model (HBM) using Hidden Markov Process (HMP) is proposed as a template for analyzing fishing boats trajectories based on data available from... |
| Tipo: Text |
Palavras-chave: Bayesian Hierarchical Models; Hidden Markov Model; State-space model; VMS; Fleet behaviour; Fishing effort. |
| Ano: 2010 |
URL: http://archimer.ifremer.fr/doc/00009/11993/9342.pdf |
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| Tandeo, Pierre; Ailliot, Pierre; Autret, Emmanuelle. |
| Satellites provide important information on many meteorological and oceanographic variables. State-space models are commonly used to analyse such data sets with measurement errors. In this work, we propose to extend the usual linear and Gaussian state-space to analyse time series with irregular time sampling, such as the one obtained when keeping all the satellite observations available at some specific location. We discuss the parameter estimation using a method of moment and the method of maximum likelihood. Simulation results indicate that the method of moment leads to a computationally efficient and numerically robust estimation procedure suitable for initializing the Expectation-Maximisation algorithm, which is combined with a standard numerical... |
| Tipo: Text |
Palavras-chave: State-space model; Irregular sampling; Ornstein-Uhlenbeck process; EM algorithm; Sea surface temperature. |
| Ano: 2011 |
URL: http://archimer.ifremer.fr/doc/00039/15047/12441.pdf |
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| Oliveira,Marcio Paulo de; Tavares,Maria Hermínia Ferreira; Uribe-Opazo,Miguel Angel; Timm,Luis Carlos. |
| Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Autocorrelation; Cross correlation; Linear regression; State-space model; Soil and plant properties. |
| Ano: 2011 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832011000100009 |
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| Gimenez, Olivier; Buckland, Stephen T.; Morgan, Byron J. T.; Bez, Nicolas; Bertrand, Sophie; Choquet, Remi; Dray, Stephane; Etienne, Marie-pierre; Fewster, Rachel; Gosselin, Frederic; Merigot, Bastien; Monestiez, Pascal; Morales, Juan M.; Mortier, Frederic; Munoz, Francois; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M.; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; De Valpine, Perry; Rexstad, Eric. |
| 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... |
| Tipo: Text |
Palavras-chave: Citizen science; Hidden Markov model; Hierarchical model; Movement ecology; Software package; Spatially explicit capture-recapture; Species distribution modelling; State-space model. |
| Ano: 2014 |
URL: https://archimer.ifremer.fr/doc/00249/36026/35298.pdf |
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| Mcclintock, Brett T.; Langrock, Roland; Gimenez, Olivier; Cam, Emmanuelle; Borchers, David L.; Glennie, Richard; Patterson, Toby A.; Coulson, Tim. |
| Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or ‘hidden’. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise... |
| Tipo: Text |
Palavras-chave: Behavioural ecology; Community ecology; Ecosystem ecology; Hierarchical model; Movement ecology; Observation error; Population ecology; State-space model; Time series. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00655/76692/77831.pdf |
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