Alternate title

YFS_species_model

Date (Publication)

2009-12-31T00:00:00

Citation identifier

CHARM_MICRKIT_NURS_RQ_MOD_ERR_R

Presentation form

Digital map

Other citation details

Source CHARM Consortium

Credit

IFREMER

Credit

CHARM consortium

Status

Completed

Point of contact

Organisation name	Individual name	Electronic mail address	Role
Center for Environment, Fisheries & Aquaculture Science	Steve Mackinson	steve.mackinson@cefas.co.uk	Resource provider
Ifremer	Sandrine Vaz	sandrine.vaz@ifremer.fr	Resource provider
CHARM Consortium	CHARM Consortium	sandrine.vaz@ifremer.fr	Custodian

Maintenance and update frequency

As needed

Thèmes Sextant

• /Biological Environment/Species/Fish Species of Commercial Interest

Discipline

• Species data set

• CHARM

GEMET - INSPIRE themes, version 1.0

• Habitats and biotopes

external.theme.gemet

• ressource halieutique

Theme

• essential fish habitat

Cadre Réglementaire - SIMM

• Marine Strategy Framework Directive (MSFD)

DCSMM : Descripteurs

• D1: Fish and Cephalopods

• D1: Biodiversité - Poissons

Sous-regions marines

• /Metropolitan France/Channel-North Sea

Thématiques - SIMM

• /Uses and Human Activities/Fisheries and aquaculture

• /Environmental Status/Species

• /Environmental Status/Habitats

Use limitation

research-only

Access constraints

License

Other constraints

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

Other constraints

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"

Spatial representation type

Grid

Denominator

2500

Language

Français

Language

English

Character set

UTF8

Topic category

• Biota

Environment description

Microsoft Windows XP ; ESRI ArcGIS 9.x

Geographic identifier

Eastern English Channel

Begin date

1977-01-01

End date

2006-12-31

Reference system identifier

WGS 84 (EPSG:4326)

Number of dimensions

2

Dimension name

Column

Dimension size

489

Resolution

0.009  degree

Dimension name

Row

Dimension size

278

Resolution

0.009  degree

Cell geometry

Area

Transformation parameter availability

No

OnLine resource

Protocol	Linkage	Name
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

Hierarchy level

Dataset

Statement

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

Metadata

File identifier

d79a8904-9773-4fcc-9297-d02a0f154065 XML

Metadata language

English

Character set

UTF8

Parent identifier

Zones fonctionnelles halieutiques d'importance (ZFHi) - Manche

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

Hierarchy level

Dataset

Date stamp

2024-01-24T16:07:14.024Z

Metadata standard name

ISO 19115:2003/19139 - SEXTANT

Metadata standard version

1.0

Metadata author

Organisation name	Individual name	Electronic mail address	Role
Ifremer	Fanny Lecuy	sextant@ifremer.fr	Point of contact

Other language

Language	Character encoding
French	UTF8
English	UTF8