Autres appellations ou acronymes

IBTS_species_model

Date (Publication)

2009-12-31T00:00:00

Identificateur

CHARM_MERNMER_EGG_GLM_MOD_ERR_R

Forme de la présentation

Carte numérique

Autres informations de référence

Source CHARM Consortium

Reconnaissance

IFREMER

Reconnaissance

CHARM consortium

Etat

Finalisé

Contact pour la ressource

Nom de l'organisation	Nom de la personne	Adresse e-mail	Rôle
Ifremer	Yves Verin	yves.verin@ifremer.fr	Point de contact
Ifremer	Sandrine Vaz	andre.carpentier@ifremer.fr	Point de contact

Fréquence de mise à jour

Lorsque nécessaire

Thèmes Sextant

• /Milieu biologique/Espèces/Espèces d'intérêt halieutique

Discipline

• Species data set

• CHARM

GEMET - INSPIRE themes, version 1.0

• Répartition des espèces

external.theme.gemet

• ressource halieutique

Cadre Réglementaire - SIMM

• Directive Cadre Stratégie pour le Milieu Marin (DCSMM)

DCSMM : Descripteurs

• D1: Biodiversité - Céphalopodes

Sous-regions marines

• Channel-North Sea

Limitation d'utilisation

research-only

Contraintes d'accès

Licence

Autres contraintes

Has to be cited this way in maps : "Source CHARM Consortium"

Autres contraintes

Has to be cited this way in bibliography : "Carpentier A, Martin CS, Vaz S (Eds.), 2009. Channel Habitat Atlas for marine Resource Management, final report / Atlas des habitats des ressources marines de la Manche orientale, rapport final (CHARM phase II). INTERREG 3a Programme, IFREMER, Boulogne-sur-mer, France. 626 pp. & CD-rom"

Type de représentation spatiale

Raster

Dénominateur de l'échelle

1000

Langue de la ressource

Français

Langue de la ressource

English

Encodage des caractères

Utf8

Catégorie ISO

• Biologie, faune et flore
• Océans
• Environnement

Description de l'environnement de travail

Microsoft Windows XP ; ESRI ArcGIS 9.x

Identifiant géographique

Eastern English Channel

Nom du système de référence

/ 4326

Dimensions

2

Noms des axes

Axe - X

Nombre de pixel

279

Résolution

0.009  degree

Noms des axes

Axe - Y

Nombre de pixel

167

Résolution

0.009  degree

Type de raster

Surface

Disponibilité des paramètres de transformation

Non

Ressource en ligne

Protocole	Adresse Internet	Nom
OGC:WMS	https://sextant.ifremer.fr/services/wms/wms_charm	CHARM_MERNMER_EGG_GLM_R
OGC:WMS	https://sextant.ifremer.fr/services/wms/wms_charm	CHARM_MERNMER_EGG_GLM_MOD_ERR_R
WWW:LINK	http://www.ifremer.fr/charm/
COPYFILE		Preferential habitat
COPYFILE		Model error

Niveau

Jeu de données

Généralités sur la provenance

The survey concerns the Dover Strait to the southern half of the North Sea. Its aim is to estimate fish abundance and distribution and to compute recruitment indices (abundance of juveniles) for the fish species exploited in the North Sea. Since 2006, French IBTS used a continuous fish egg pumping device called CUFES allowing the collection of information on the spatial distribution of winter spawning areas and on the spawning habitat of several important species.

Description

GLM describes the mean response of a species abundance or presence probability according to environmental conditions (figure 2). In this type of model, a linear prediction is related to the mean of the response variable through a link function (e.g. identity function for a normally distributed variable, or logit function for binary data). Corresponding habitat models required a two step modelling procedure. The presence probability of the considered species as a function of environmental factors is first modelled using presence-absence data, independently from abundance data. Then, the response in terms of abundance is modelled in case of presence only. The species¿ habitat can finally be predicted by combining the presence-absence model with the model of abundance response in case of presence. This procedure allows circumventing the problem of atypical distribution of count data which include numerous observations with value zero, which is common in species abundance data (Stefánsson, 1996; Barry & Welsh, 2002). In this study, model selection was carried out by initially fitting a complete model including all available explanatory variables (continuous parameters were introduced as second order polynomials, nominal variables as factors and all first order interactions between environmental parameters were considered; note that interactions were not tested for the egg developmental stage). The selected environment predictors were: temperature, salinity, bed shear stress, depth, chlorophyll a concentration (only for the egg stage), fluorescence (only for the larval stage) and seabed sediment type. Although the first six factors were regarded as continuous covariables, sediment type (mud, fine sand, coarse sand, gravel and pebble) was introduced in the model as a categorical factor. The GLM model was optimised through backward selection based on Chi-square or F-test significance tests (Venables & Ripley, 2002). This approach was taken rather than Akaike Information Criterion reduction (or AIC; Akaike, 1974) to be coherent with quantile regression selection procedure which is also based on significance tests. For presence-absence data, binomial modelling with logit link function was chosen to obtain a prediction of the probability of presence of the species considered. For non-null abundance data (i.e. removing zero values), the data was log-transformed to achieve normality, and gaussian modelling with identity link function was used to predict positive density on a log scale. The predicted probability of presence was then multiplied with the positive density prediction, to obtain the final predicted value of abundance (Stefánsson, 1996). Quantile Regression (RQ) belongs to the family of regression approaches that also includes simple li-near and multiple regression (Koenker, 2005). In RQ, any part of the data distribution may be modelled rather than the mean (a quantile q describing the value greater or equal to q% of the observed data, or in other words the upper bound of q% of the observed data). The study of the upper-bound of response data (typically abundance between 0.75 and 0.95 quantiles) as a function of environmental factor (figure 3) allows estimating their limiting effects on a species distribution (Cade et al., 1999; Hiddink & Kaiser, 2005). As for GLM modelling (see §4.1), the selected environment predictors were: temperature, salinity, bed shear stress, depth, chlorophyll a concentration (only for the egg stage) and fluorescence (only for the larval stage) as continuous covariables and seabed sediment type as factor. Model selection with RQ is made complicated by the large number of candidate models that can be estimated over a range of different quantiles (i.e. one model per quantile): in other words, the model selection includes both the selection of explanatory variables and that of the quantile at which there are considered. Model selection was carried out by initially fitting a model to all available explanatory variables (continuous parameters were introduced as second order polynomials, nominal variables as factors and all first order interactions between environmental parameters were considered; note that interactions were not tested for the egg stage). The selection procedure used is that proposed by Vaz et al. (2008). RQ models were estimated at five quantile intervals, from the 75th to the 95th. Using a backwards elimination procedure, significance tests of all polynomials and interactions were performed and the variable associated with the largest average p-value across the five quantiles, contingent on being greater than 0.05, was selected to be removed from the model. The reduced model hence obtained was then re-run across all five quantiles, and additional variables were removed according to the same rule. Main effects were tested only when associated interactions and second order polynomials had been eliminated. The process of backward elimination was stopped when all remaining variables were significant (p < 0.05) at least for one quantile (Vaz et al., 2008), this quantile being selected as the representative quantile. In case the resulting model was found to have all variables significant over more than one quantile, the highest of these quantiles was chosen, as the more representative of the limiting effect imposed by the environmental variables over the species abundance. For each species considered, the equation of the final habitat model was used to recode digital maps of the environmental factors with the predicted abundance (or presence probability) of the species, using the Raster Calculator tool, thereby producing a habitat map. Prior to this and for each survey, digital (raster) maps of the environmental parameters had been limited to the ranges of values observed during the surveys, so as to avoid extrapolating outside the model development bounds. The resulting habitat maps were further centred and standardised, so that the resulting maps ranged between 0 and 1, thereby permitting an easier comparison amongst results from different stage, species or season (notable exceptions are the habitat maps based on binary data, and the larval stage habitat maps). The spatial distribution of the model error ratios was mapped for each model, the value of 1 corresponding to the maximum possible prediction error. The model prediction error can thus be interpreted as a percentage of model uncertainty.

Description

IBTS survey, scientific survey of IFREMER

Métadonnées

Identifiant de la fiche

09f625b6-20c0-4951-b810-a1d6aa2da9ad XML

Langue

English

Jeu de caractères

Utf8

Identifiant de la fiche de métadonnées parent

Zones fonctionnelles halieutiques d'importance (ZFHi) - Manche

55b44ab3-0b69-4bc4-8d4d-0ad2e022005c

Type de ressource

Jeu de données

Date des métadonnées

2024-01-24T16:05:34.709Z

Nom du standard de métadonnées

ISO 19115:2003/19139 - SEXTANT

Version du standard de métadonnées

1.0

Contact

Nom de l'organisation	Nom de la personne	Adresse e-mail	Rôle
Ifremer	Fanny Lecuy	sextant@ifremer.fr	Point de contact

Autre langue

LanguageCode	CharacterEncoding
Français	Utf8
Anglais	Utf8