Autres appellations ou acronymes

YFS_species_model

Date (Publication)

2009-12-31T00:00:00

Identificateur

CHARM_MICRKIT_NURS_RQ_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
Center for Environment, Fisheries & Aquaculture Science	Steve Mackinson	steve.mackinson@cefas.co.uk	Fournisseur
Ifremer	Sandrine Vaz	sandrine.vaz@ifremer.fr	Fournisseur
CHARM Consortium	CHARM Consortium	sandrine.vaz@ifremer.fr	Gestionnaire

Fréquence de mise à jour

Lorsque nécessaire

Thèmes Sextant

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

Discipline

• espèces

• CHARM

GEMET - INSPIRE themes, version 1.0

• Habitats et biotopes

external.theme.gemet

• ressource halieutique

Thème

• zone fonctionnelle halieutique

Cadre Réglementaire - SIMM

• Directive Cadre Stratégie pour le Milieu Marin (DCSMM)

DCSMM : Descripteurs

• D1: Biodiversité - Céphalopodes

• D1: Biodiversité - Poissons

Sous-regions marines

• /Métropole/Manche mer du Nord

Thématiques - SIMM

• /Activités et Usages/Pêche et Aquaculture

• /Etat du Milieu/Espèces

• /Etat du Milieu/Habitats

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

2500

Langue de la ressource

Français

Langue de la ressource

English

Encodage des caractères

Utf8

Catégorie ISO

• Biologie, faune et flore

Description de l'environnement de travail

Microsoft Windows XP ; ESRI ArcGIS 9.x

Identifiant géographique

Eastern English Channel

Date de début

1977-01-01

Date de fin

2006-12-31

Nom du système de référence

WGS 84 (EPSG:4326)

Dimensions

2

Noms des axes

Axe - X

Nombre de pixel

489

Résolution

0.009  degree

Noms des axes

Axe - Y

Nombre de pixel

278

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_MICRKIT_NURS_RQ_R
OGC:WMS	https://sextant.ifremer.fr/services/wms/wms_charm	CHARM_MICRKIT_NURS_RQ_MOD_ERR_R
WWW:LINK	http://www.ifremer.fr/charm/
COPYFILE		Probable habitat
COPYFILE		Model error

Niveau

Jeu de données

Généralités sur la provenance

Combinations of the French YFS and the British YFS.

Description

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

FYFS plus BYFS surveys

Métadonnées

Identifiant de la fiche

d79a8904-9773-4fcc-9297-d02a0f154065 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

2024-01-24T16:07:14.024Z

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