Spatial Modelling of fishing effort

Transcription

Spatial Fishing Effort Modelling Network
CICEM
SFEM : Conceptual & Methodological
Document
A. Bensch, F. Carocci, F. Corsi, L. Drapeau, G. Le
Corre, J. Morales
Workshop I : 11-14/04/2000 – Montpellier
Spatial Modelling of fishing effort
1. Introduction
Investigation of the spatial distribution of fishing effort in relation to
catch statistics seems rather promising both as a mean to address
management concern on the fishery's status and the general growing concern
of the conservation issues related to the biological conservation of the fishing
grounds and stocks.
Data availability appears to be the limiting factor to define high resolution
dynamic effort distribution models. Currently, the availability of data sets
(e.g. bathymetry and port location) enable the production of high resolution
spatial models with a decrease in the temporal resolution. A GIS context
offers solutions enabling collaboration between these models.
Besides the analysis of the accuracy of the existing models, developed within
the constrains mentioned above, this project envisages to assess further
evolution in the quality of the models, integrating other environmental
variables which will enable to enhance the temporal resolution of results. An
approach to possible enhancements has already been provided during the
COPEMED meeting in Fuengirola (Towards the Use of Geographic
Information Systems as a Decision Support Tool for the Management of
Mediterranean Fisheries-Informes y Estudios COPEMED n° 4 Junio 2000).
Further inputs to the model may come from the experiences on potential
activities identification in the areas subject to regulation factors (restriction,
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prohibition, constrains, attractiveness, …) and the localisation of fishing
grounds through statistical analysis of a rich data set resulting from surveys.
The implementation phase of the model framework will identify various
steps. They will operate, within the limits dictated by the model evolution
itself, and could be used as a support to GFCM working group on its fishing
effort advice policy as well as others internationals institutions.
Project main objectives :
1. to develop an exploratory methodology of spatial distribution of fisheries
effort integrating expert knowledge and fisheries data set,
2. ability to work on rich or poor data set
in terms of effort, catches,
environmental conditions, etc…,
3. to investigate the usefulness of the methodology with data set available
such as within the COPEMED area,
4. to identify and implement computer tools in order to facilitate the
application of the methodology.
Meeting objectives :
Based on documents provided during the stage I, the Workshop I helped in
the definition of one common development platform :
1. positioning of methods according to data sets richness from basic data set
situations (ports localisation, fleet size, bathymetry,...), to rich situations
(localised effort, environment data and/or remote sensing, ...), hypothesis
and constraints, outputs results, limits and domains of applications of each
approach.
2. Evaluation of a complementary approach, in order to create a new
approach.
3. Identification of commonalities, overlapping and integration consideration
of the complementary approaches to be tested.
4. Definitions of tasks - stage II (modules development, tests, data set
creation, and common approach feasibility analysis) and positioning of
each member.
5. Evaluation of technical solutions for the implementation of the
methodology.
2. Methodological concepts
The idea behind the conceptual model of fishing effort spatial allocation is the
definition of the basic geographical entities that contribute to the
identification of the area actually exploited by a fishing fleet segment. A geo-
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entity represents a set of rules, activities, practice and physical characteristics
which defines the spatial extend of a given fishery. Within an exploited area ,
further assessment of the distribution of fishing effort can be achieved
through modelling options such as the parametric and the non-parametric
approaches described later in this same report. (ref. Analyse the 2 models
available and integration).
It is fundamental to bear in mind that geo-entities are defined for fishing
segments and thus each set of geo-entities only applies to a specific and
homogeneous group of boats and represents the average conditions for that
group. For this reason, the temporal scale is not determined a priori but
results from the fishing segment's selection.
2.1 Fundamental Geo-Entities
To allow a proper modelling, the geo entities must be identified
according to the following two main criteria, a) a zone must be self
contained, that is it should explain exhaustively a major component of
the driving forces of spatial allocation of the fishing effort and b) it
must be describable in terms of combination of easily accessible map
layers.
Geo-ent it y 1
Geo-ent it y N
Geo-ent it y 2
Fishing ef f ort
allocat ion area
Figure 1
The first criteria ensures that the
relationship between fishing
effort allocation and the basic
zones can be summarized as the
intersection between the geoentities themselves (fig.1), while
the second criteria guarantees
that each one of the geo entities
can be geographically described.
Considering the dynamics of a fishing fleet, the area where the fishing
effort is allocated reflects essentially three main constraints :
a) the area must have sufficient stock density of the target species to
ensure adequate yields and thus attract fishermen
b) it must be accessible to the fleet, that is it must be within the
navigation range of the boats and must have specific environmental
characteristics that enable the fishing activity
c) the area must meet all legislative requirements both in space and
time.
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According to the above assessment of the dynamics of a fishing fleet
we have defined the following geo-entities:
ABUNDANCE ZONE
is the area, or the ensemble of areas, in which there is concentration of
the target species. It is the attractive area where, all other conditions
being the same, the boats of the fishing segment will tend to
concentrate.
Abundance zone is, to many extents, both an objective and a subjective
zone as it is determined by the perception that the fishermen have
(subjective) of the distribution of the resource (objective). It often reflects,
in the traditional component of the fishing activity, good fishing
grounds locations handed down from the parent to the son.
Along with the knowledge on the distribution of the resource, the
Abundance zone is determined by the following characteristics of the
fleet segment being assessed (ref. Fleet segment) :
Fishing gear, target species
Fishing strategy (or tactic) applied by the fishermen, fishing season
Age of the fishery
ACCESSIBLE ZONE
is the area, or the ensemble of the areas, that are both within the range
of action of the fishing segment and are compatible with its
exploitation strategy. The boat presence probability is positive within
the zone, for a trip duration quoting that the fleet definition criteria are
constant in time.
It reflects the fishing boat economic constraints to reach the most
productive zones (e.g. not too far from the market place), the
marketing strategy (e.g. multiple days fishing period vs. day by day
fishing), the type of fishing gear used in relation to the physical and
environmental conditions of the fishing ground (e.g. rocky sea bottom
types incompatible with trawl activity).
Although with different levels of importance, the following fleet
segment characteristics contribute to the identification of the Accessible
zone :
Port location
Boat type
Engine (type, power, speed, autonomy ...)
Equipment (navigational and stock search aids)
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Draught
Type and stocking capacity of captures
Gear
Fishing strategy (or tactic) applied by the fishermen, fishing season
Destination of catch
AUTHORIZED ZONE
is the area, or the ensemble of the areas, that is legally authorized for
the fishing segment. Includes exclusive exploitation zones, protected
areas, seasonally closed areas, military zones, navigation channels, etc.
Some of these areas are independent of the fleet segment characteristics
(they apply to all segments or even to all boats and ships), other are
specific to certain fleet segments and are determined by the type of
license that the fleet segment has (e.g. protection of Posidonia beds
from bottom trawling in Mediterranean areas).
The following diagram shows the causal relationship existing between
the fleet segment and the geo-entities that we have identified.
FLEET
SEGMENT
practice
is located
fishing strategy:
port
target species, gear, etc.
has
has
technical characteristics:
engine power, gear, market
strategy, etc.
license
seek
determine
determine
Abundance
zone
Accessible
zone
Authorized
zone
determine
Activity zone
ACTIVITY ZONE
is the composition of the above geo-entities and determines the
Activity zone which is defined as the area where the fishing activity
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takes place. The activity zone can be distinguished in two different
types: the Potential Activity zone and the Effective Activity zone.
An ideal estimation of the first one is the result of the deterministic
intersection of all the areas in which the fishing activity is authorized,
of the areas in which is economically sustainable, of those in which the
environmental and physical conditions are compatible with the fishing
activity. This question is a multivariate problem, therefore one or more
of the parameters can be "unfavourable". The Potential Activity zone
must be build like a probabilistic composition (Bayes), which exclude
only absolute inaccessible areas.
The second one is the area determined by the subjective interpretation
of the above constraints on the part of the fishermen. For instance
fishermen may be willing to take chances both in respect of the
enforcement of a specific authorized zone and in the avoidance of an
area in which the environmental condition can cause gear loss.
FACTOR ZONE
these zones enable an accurate definition of activity zones that modify
the fishing effort density distribution. Different parameters can define
a factor zone :
distance from port,
bathymetry,
fishing ground,
specific abundance,
legislation ...
and therefore produce different effects : attraction or repulsion ...
2.2 Fleet Segment
The fishing effort distribution modelling is applied to an homogeneous
set of boats referred as "fleet segment". Determination of the fleet
segment requires the selection of most discriminating variables
available for the boats according to effort distribution. Classification
and thresholds identification based on multivariate analysis will lead
to the calculation of the nominal effort by fleet segment an port.
The following non exhaustive list, describe information which can be
included as discriminating criteria for clustering fishing units in an
homogeneous segment fleet according to their fishing effort
localisation.
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Type of boat
Engine (type, power)
Radar
Draught
Storage facilities and capacities
Licence
Gear, target species, catches composition
Strategy (or tactic) adopted, fishing calendar
Production destination,
Age of fisheries
2.3 Available models analysis
Spatial distribution of fishing effort is investigated with two empirical
approaches. They both were presented during the COPEMED
Fuengirola Meeting (December 1998) and can be found in the annexes
of this report Fabio Carocci & John Caddy "GIS Application and the
Spatial allocation of fishing intensity from coastal ports" and Fabio
Corsi "Spatial distribution of fishing effort : modelling through
deductive methodology".
This kind of modelling deals in the first approach with drop with
distance of fishing effort exerted on a given fishing ground modelled
through "friction of distance" equation applied to local inshore fleet
activity. The idea is to simplify the situation and to consider the ports
as having an area of influence and a field of action over adjacent
grounds. The equation behind the model, is expressed by mean of the
following :
E x = PR ∗ e
æ x − v (T −T0 ) ö
−ç
S
è
2
Where :Ex is the fishing effort exerted at distance x from port
PR is the relative port fishing power (compared with reference
year R)
x
is distance from port
v
is the velocity with which fish resources are exhausted
S
is a dispersion parameter (distance scale)
T
is the time since onset of the fishery
T0
is the shift (delay) parameter representing the date of
starting the fishery
The parameter x (distance from port) is calculated as the growing
distance from the considered port(s), in other words the minimum
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distances measured according to the minimum number of cells that
must be traversed to move from that cell to the nearest source target.
Distance
The second approach is based on deductive modelling of use of space.
Knowing that at any given scale
Distance Score function
there
are
particular
25
environmental variables that are
20
of main interests to describe
15
fishing effort. For instance bottom
10
5
type and depth provide evidence
0
on the distribution when working
5
10
15
20
25
30
35
-5 0
at a very coarse resolution.
Score
Distance from the port can
provide insightful information at finer detail .The deductive model is
built according to the advice of a fishery expert. The expert is asked to
define percentage use of space (scoring function) according to distance
from the port and depth of fishing ground (bathymetry). The discrete
table is then fitted to a continuous function which serves as a
smoothing tool for the threshold defined by the expert.
Methods complementarity's
An extensive analysis of these modelling can show common points and
therefore was used to defined a global modelling approach of the
spatial effort distribution using parametric and/or non parametric
modelling. Both methods can be seen as defining a scoring function on
specific data sets. Either defined through expert knowledge (non
parametric) or a mathematical function( parametric) the underlying
process aims at associating a score or a preference level for different
spatial coverage. The methods uses distance from ports and
bathymetry as ancillary information layers. It can be seen that
according to the richness of the data sets, information layers like
bottom types, targeted species distributions, satellite images (SST,
chlorophyll..) can be processed the same way and contribute to
accurate modelling of fishing effort distribution. The integration of the
two methods leads to the definition of a "scoring function" on
multivariate information layers. This scoring function generalized the
categorical function (discrete) or continuous approaches according to
the level of knowledge on each variable. Use of a GIS tool is
compulsory to integrate multiformats information layers and to
provide efficient spatial indices.
2.4 Global Methodological proposal
The aim of the conceptual model is to derive the geo-entities outlined
in the previous paragraph as GIS data layers, which can be later
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integrated into the assessment of the spatial distribution of fishing
effort.
For the sake of classification and explanation we can distinguish
between geo-entities that can be directly placed on a map and those
that need to be modelled through correlated variables.
Generally speaking the use of correlated variables as a mean to
describe the spatial distribution of an ecological phenomenon is at the
basis of most of the GIS modelling approach. The idea is to use easily
obtainable data sets as proxy to the variable that needs to be modelled.
Here, the two main constraints that apply are cost of available data and
their significance (correlation) to describe the original variable.
The following is a tentative classification of the three basic geo-entities
we have identified in the conceptual model into the two basic
categories outlined above. It must be kept in mind that this
classification is extremely fuzzy and that each one of the geo-entities
can fall in either one of the above categories according to the scope (e.g.
spatial and temporal resolution and objective) of the final model.
2.4.1.1
Geo-entities inter-relations
Eventually the Accessible Zone is a geo-entity that needs to be
derived from correlated variables. It is the result of the integration
of many different parameters of both the fishing segment under
investigation and of the fishing area. For instance the economics of
the fishing activity is a function of the cost of the fishing and the
marketing strategy among other things. Well, both these variables
are quite difficult to be interpret spatially unless we use correlated
variables such as distance from the port which can be correlated to
the cost of reaching a specific part of the fishing ground in terms of
fuel cost and can be correlated to marketing strategy in terms of
time needed to make a round trip from the market place.
The Authorized Zone is a geo-entity that can be directly placed on
the map as it is generally defined by the overlay of all the
boundaries of some administrative units that are described in a
piece of legislation.
To build the layer of the authorized zone is a matter of interpreting
the available legislation to identify all the boundaries of all the
restricted and/or authorized zones that apply to the fishing
segment and to the area under investigation. Once these zones are
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identified a simple overlay can produce a Boolean GIS layer which
identifies the Authorized Zone.
The Abundance Zone is a geo-entity that falls somewhere in
between the two typologies outlined at the beginning of the
paragraph. It can be inferred from experimental surveys
(independent variable), a bio-ecology expertise ( compiled
knowledge upon the area and species habitat according to depth,
bottom types...) , fishing effort (under the assumption that fishing
activity will concentrate on areas of higher stock abundance). Each
method has its benefits and drawbacks, for instance the last two are
more cost effective but are less precise than the first; while the last,
in our particular case study, can introduce circularity in the
analysis.
2.4.2 Spatial-temporal resolution and variables selection process
When modelling a geo-entity with one or more variable there are
some basic rules that can be followed to select the most cost
effective variables. Obviously there are many variables that can be
correlated to the above geo entities and the exercise consists mainly
in the identification of the correlation function and the field of
applicability of the function itself.
In the following example we have chosen five variables (namely
distance from the port, depth weather, bottom types and fishermen
attitude) out of all possible variables, to show the kind of analysis
needed to assess the use of proxy variables.
We will consider four main topics in the selection process:
availability (as there is no real sense in building a model on
unavailable data), spatial and temporal resolution (as they define
the scope of our modelling effort) and ecological interpretation of
the inclusion of a specific variable in the model.
According to the hierarchical hypothesis at any given scale there
are particular environmental variables that drive the ecological
processes and considering fishing effort as an ecological process
that targets the fish population, each one of the variables becomes
increasingly important to describe fishing effort distribution
according to the spatial and temporal scale of the analysis.
For instance, both bottom type and depth provide good evidence of
the Accessible Zone in the temporal domain when analysed at a
very coarse resolution. The two variables account essentially for the
structural description of the fishing ground which indeed is rather
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invariant in time. On the other hand a detailed account of the
fishing ground structure can provide fine resolution information in
the spatial domain.
Similarly, distance from the port can be correlated to the economics
of fishing activity and can provide insightful information when the
analysis is conducted at finer detail compared to the previous
variables both in the temporal and in the spatial domains.
Weather can be correlated to an average resolution description of
the accessibility to the resource in space, while the highly repetitive
availability of data sets in the temporal domain makes it a variable
of choice when conducting analyses at a very fine temporal scale.
Finally to complete the list of
variables given above, the
hypothetical description of individual mood and attitude of
fishermen on a daily basis would provide the final answer to the
localization of fishing effort both in time and space.
The figure above summarises this concept showing how these
variables relate to fishing effort both in the time scale and in the
spatial scale. From the figure it is possible to derive a general idea
of the type and the extent of utilisation of a model obtained from
the different variables.
finer
Availability
Depth
Distance
from port
Weather
Bottom
types
coarser
Time scale
Spatial scale
finer
Fishermen
mood
finer
For a better understanding of the potential utilisation of the
different variables the present availability of each data set is also
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shown in the figure on the vertical axis, as we believe that this is
still the major constraint in the development of GIS models.
2.4.3
Available data sets and their use
Following the analysis outlined in the previous paragraph, our
group has identified a set of geographic and non geographic data
sets that can be integrated in the definition of the different geoentities. Please keep in mind that all these data sets need to be
integrated with the information on the fishing segment under
investigation to produce an adequate model of the different geoentities.
Data set
Accessible Zone
Authorised Zone
Abundance Zone
Allows computation of
Port
Defines
the
legal
distance from the port
location +
framework
for
the NO CORRELATION
which is the primary
Coast line
Authorised Zones
proxy for accessibility
Identify
geographic
Correlation
with
and
meteorological
environmental
structures that can limit
parameters derivable
NO CORRELATION
or
enhance
from satellite images
Satellite
accessibility (e.g. wind,
(e.g. plumes, primary
waves)
production etc.)
Satellite radar images can be a first approximation of the overall model
result through ship detection analyses
Ecological descriptor of
Correlation
with Identifies bathymetric resource
distribution
of
the limits of the Authorised based on the species
Bathymetry limitations
fishing gear
Zone
ecological
requirements
BASIC DATA SET
Allows the extraction of
authorised
zones
Legal
NO CORRELATION
based
on
the NO CORRELATION
definition
identification of the
limits as outlined in the
legislation
Species
distribution
Proxy for fishing gear
models based on the
Bottom
limits
(e.g.
rocky Defines protected sea
species
ecological
types
bottoms
unavailable bed types
needs (only applies to
for trawling)
some target species)
Fishing
First approximation of a the combination of the geo-entities based on
ground
existing knowledge
from expert
BASIC DATA SET
Defines
protected
Enables
direct
areas
base
on
Biomass
NO CORRELATION
assessment
of the
survey
sensitive area (e.g.
resource
distribution
nursery)
through interpolation
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The above table summarises the relationships existing between
the three geo-entities and the data sets analysed.
We have further analysed the data sets ranking them according
to their availability and they usefulness for the production of
high resolution models in space and time. The integration of the
two tables can help defining the scope of a model built with
specific data sets. Please note that rank 0 in the temporal
resolution column is used to identify data sets that can be
assumed to be invariant in time.
Data set
Availability
Resolution
OVERALL
Spatial Temporal
Port location + Coast line
1
1
0
1
Satellite
3
2
1
2
Maps
6
3
0
3
GEBCO
2
8
0
4
Legal definition
4
4
2
5
Bottom types
7
5
0
6
Fishing ground from expert
5
7
3
7
Biomass survey
8
5
4
8
Bathymetry
Here availability is define as ease of finding the data sets in a
format readily usable within the model (e.g. digitised maps for
the geographic data sets). Based on this criteria the port location
and the coast line are the most easy to find followed by satellite
images, which come in digital format from the origin. Both of
these data sets score very well also in the other two columns; the
spatial resolution achievable from the port location can be
increased to match the one need for a exhaustive description of
the fishing fleet segment while the same data set is assumed to
be invariant in time; similarly satellite maps have very high
spatial (even the 1.1 km2 of NOAA data set can be considered
very high for fishery applications) and temporal (measures can
be repeated even more than once per day) solutions.
For bathymetry we distinguish between a commonly available
data set (GEBCO) which ranks higher than satellite images for
availability and other digitised maps which are less common but
by far more accurate and with better spatial resolution;
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bathymetry, independently from which data set is used is
assumed to be invariant in time.
Legal definitions are possibly very easy to find although very
seldom in digital format, their spatial resolution depends on the
accuracy of the description and on the maps used to georeference them, on the other side their history can be traced very
accurately and this allows to rank them very high for temporal
resolution. Bottom types are extremely rare and although they
can add interesting information for the identification of the geoentities their availability lowers their overall ranking.
The opposite is true for the fishing ground information from
experts which is certainly more easy to find than the bottom
types but most of the times is limited to small portions of the
fishing ground, thus limiting its spatial resolution and its overall
score.
Finally the low overall ranking of the biomass surveys is due to
the extremely scarce availability of such surveys and on their
limited resolution both in space and time (e.g. most of the times
they only cover small portions of a fishery and are just a single
time picture of the situation). Nevertheless it is important to
note that this last piece of information is the basic data set for
the identification of the Abundance Zone and thus it may be
worth using whatever is available, at least as a control data set
to validate Abundance Zones derived from other data.
As a general comment the use of first four data sets (and with
satellite images being also optional) can produce an adequate
description of the three basic geo-entities outlined in the
conceptual model.
2.4.4 Addressing uncertainty
In a previous paragraph we have defined the Activity Zones as
being the result of the intersection of the three basic geo-entities
and we have also subdivided the Activity Zone into a Potential
and a Real Zone.
The deterministic overlay of the geo-entities can only address
the identification of the Potential Activity Zone, which is what
would happen if no individual variability was introduced by the
fishermen. The Real Activity Zone, being the result of the
interpretation of the constraints (both legal and environmental)
on the side of the fishermen, must then be addressed with the
uncertainty connected to human attitude and behaviour.
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This can be achieved with the introduction of fuzziness in the
identification of some of the limits of the geo-entities.
A
A
Authorized zones
A and B
B
Membership
function
B
Uncertainty levels
of inclusion into
the authorized
zones a or B
For instance, although an Authorized Zone can be defined
in an extremely precise way based on the text of the law
limits access to specific areas the perception of this limit
by the fishermen can be far less precise. Some fishermen
may be willing to take chances and fish within the
restricted area and some will accept the limitations
imposed by the law.
To account for this behaviour limits of the geo-entities can
be defined as belts of uncertainty representing the
likelihood levels of acceptance of the limit itself. Then is a
matter of using fuzzy algebra to combine the different
geo-entities and to address the identification of the Real
Activity Zone.
Currently we are not envisaging the use of uncertainty in the
model, although its integration will be considered in future
developments.
3. Spatial Fishing Effort Modelling
The present section will present an overview of the structure and
requirements of the first version of the prototype of the Spatial Fishing Effort
Modelling (SFEM) where the conceptual model described in previous sections
is transferred to computer modules. As stated before, the SFEM will be based
on the assumptions that the parametric and non-parametric approaches are
applied on a selected single segment of the fleet, operating from one single
port.
The SFEM will be constituted by managers and wizards, hereafter listed
(Figure 2) :
Input manager (geographical coverage management module)
Geographical wizard
Scoring wizard
Criteria wizard (constraints Builder / exploitation zone module)
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Parameters manager
Computation module
Results manager
It has been decided that, at the stage of the present prototype, the
implementation of the various modules will progress at different speed.
Consequently :
the Input manager will be developed as an ArcView extension
the criteria and scoring wizard will be developed independently and will
provide ready to use raster images for the Input Manager
the calculation, i.e., the application of the two approaches (parametric and
non-parametric ones), will be carried on independently as well
the results, modifications, savings and merging modules will be
developed in the future version of the prototype depending on the success
of the first version
It is important to note that each module will be equipped with a Wizard-style
help to guide users into each step of the input, processing and output flow of
information.
3.1 Input manager
For the first prototype, the "Input manager" module will be developed as
an application with dedicated time and will serve to manage the basic and
ancillary information needed as input of the SFEM. They consist in the
following :
-
Fleet info : it will provide an input framework to enter information on the
characteristics of a single fleet segment such as gear license, strategy and
tactic, port location, speed and autonomy. The fleet's characteristics are
essential to the SFEM. If there is no data on fleet's characteristics no SFEM
is applicable. These characteristics will then be summarized on what is
generally called the fleet power from a single port.
-
Port location: it identifies, in the space, the location of the port where the
fleet is based.
-
Coastline: together with the offshore limit, it defines in the space the area
of application of the SFEM.
-
Exploitation zone: it will be the result of a combination of geo-entities that
represent the different factors and constrains (criteria) that affect the
fishery activity under examination. Such criteria would be developed
under the conceptual definitions of the accessible zone, administrative
zone and abundance zone as defined before on this report. A single layer
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of all combined criteria will represent the exploited zone. For the
construction of such layer, a Criteria wizard will be developed.
-
Scoring layers: whether the non-parametric approach is selected or not, the
scoring layers represent an image containing continuous surface of the
independent variable used in the non-parametric approach. For the time
being, these layers are limited to one image of the bathymetry and
distance from the port of the exploitation zone.
For the first version of the prototype, the Input Module will provide
essentially the possibility to enter some basic fleet information, to
incorporate
an exploitation zone (but not to provide the Criteria
Wizard) and a series of Scored Layers (but not the Scoring Wizard) and
pass the information for the subsequent step, the Calculation Module.
3.2 The Wizards
a) Geographical wizard : this wizard will determine the best
geographical extend and spatial resolution for the data set. It could
be based either on users specifications or on computed parameters
to standardized the geographical information. A distance coverage
is then calculated using this format, also used to produce the
results.
b) Scoring wizard: the scoring layers are used in the Scoring Wizard
as references to help user to built the best parameters or scoring
function (for details on the parametric and non-parametric
approach, see previous sections). It is important to note at this point
that once the scoring function has been found, the Wizard will
either pass this information to the following Calculation Module or
it will create a new image that can be used as one of the geo-entities
forming the criteria for the definition of the exploitation zone.
c) Criteria wizard : based on the available data layers, the wizard will
guide the user to define factors and constrains which affect/define
the exploitation zone. The user can enter criteria using either
Boolean value of 0 or 1 or by delimiting each criterion using the
fuzzy logic. In the last case, the user can introduce fuzziness both
on the limits of each criterion or on its effect on the exploitation
zone. The result will be an image with a mosaic of possible
exploitation zone accessible by the fleet segment under
examination.
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3.3 Calculation Module
In the final version of the prototype, it will mainly execute the following
operations on the information delivered by the Input manager module
(see Figure 3):
Selecting the best resolution to be applied to all images, depending either
on the default value assigned by the software (i.e., ArcView) or according
to the highest resolution of the raster images used as inputs.
a) Transforming all inputs to a common raster format, after coregistration by re-sampling techniques for raster geo-entities
and projection and rasterization for vector geo-entities.
b) Applying the parametric values or scoring functions for one or
both approaches (the parametric and the non-parametric) on
each scoring layer.
c) Applying a standardization of results, in other words transform
the resulting image into a probabilistic image. The results can
then be refined if necessary.
d) Overlaying the exploitation zone by means of simple map
algebra operations (such as overlay by multiplication) or by
applying weighting factors.
e) Passing the results to the Result Module for further analysis on
the results.
3.4 Results Manager
The outcome management and the calculation process storage will be
handled by the Result Manager. It presents :
-
Fishing effort geographical coverage for the fleet segment analysed,
Metadata associated to each results coverages (keeping tracks of the
layers, scores, functions and parameters used),
Descriptive statistics to synthesised the results (i.e. : minimum
distance between 2 boats, concentration effort index ( area,
perimeter, ratios, …).
Future developments for this results manager could propose the
integration of different fleet segment results. These developments
would give an opportunity to figure out the global distribution of
effort for the fishery and further explore interactions between fleet
segment and scenarios.
Page 18
Prot ot ype organisat ion
One segment f leet / port
Apply scorei = fi (layeri) (i = 1 .. n coverages)
Exploitated Zone Mask
Standardisation
Overlay Multiply Weighting factors
Mandatory
Coast line
+
Comput at ion
Port locations
Fishing effort distribution maps
Optional
Bathymetry
Bottom types
Fishing grounds
SST images
Legal definition
Legal definition
Fleet Information : Port, fleet and segment
ident, nominal effort, speed, autonomy…
+
Scored coverages :
output from the
scoring wizard
Biomass surveys
…
I nput manager
Paramet ers manager
(mult iples f ormat s (N ))
Definition of common :
Geographic extension
Spatial resolution
Projection
(user defined or computed)
Exploitation zone :
output from the
calculation wizard
Result s manager
Scores functions fi for each coverage i (can
be fuzziness functions on limits for
exploitation zone)
Scoring function
Distance Score function
Exploitation zone construction via combination (Bayes) of constraints (boolean or
fuzzy)
Apply this unique raster
format to the ‘N’ coverages
Computation of the distance
coverage
Score
35
30
25
25
20
20
15
15
10
10
5
5
0
0
0
0
2
4
6
8
10
12
Bathymetry
f1
Geographical wizard
Constraints A
(ex : bathymetry)
Bathymetry
10
20
30
40
-5
…f
S c o re
N
Scoring wizard
Bottom types
Exploitation
zone
Constraints B
Ex : Fishings grounds
…
Crit eria wizard
Page 19
4. Technical specifications
4.1
Software requirements
A prototype will be developed in order to test efficiency of the models on
different data sets. The main requirement is to provide the end users with
an easy to use software. This main requirement lead to investigate what
customisable development tools are currently available and expertise
present among the network team.
4.2
Software functionalities
The software will provide an easy way to integrate vectors geographical
layers. These layers can be points layers (ports locations), lines
(bathymetry), polygons (bottom types...). It should be able to work on
images (raster layers) to deal with satellites imagery, and to provide
modelling operations tools (Bayes, fuzziness).
Different opportunities can be chosen, ranging from a methodological
booklet on a GIS software (Idrisi, ArcView, MapInfo...), to a complete
independent software developed in C/C++ or JAVA. Great developments
can be achieved from this late choice (Jshape, Java Imaging). The tool
should include these independent modules :
4.2.1
Geographical layer manager :
This manager will help to build the catalogue of available data layers
for a specific data sets. Through an friendly user interface, the user will
document available coverage. Compulsory layers will be asked for
(ports locations, coast line, bathymetry), and further layers will be
integrated according to the user needs and knowledge.
4.2.2
Geographical Properties wizard :
Once provided, all the coverage will be analysed in terms of spatial
extends, resolution, and projection. Common parameters will be
proposed to the user. At this step, a choice of an extend, a resolution,
and a projection will have to be made, in order to standardise the
coverage's for further processing. It has to be noticed that a proper
Page 19
projection is of main importance for the distance calculation process.
All GIS soft provide projection tools, no distances can be used as long
as data are not projected (geographical projections). For this reason, the
geographical wizard will then create the distance from ports layer. This
raster layer will not be provided by the user but will be calculated.
Many functions are available to calculate distances in a GIS, but instead
of calculating the actual distance from one point to another like an
Euclidian distance, we need to determine the shortest cost distance (or
accumulated travel cost) from each cell to the nearest cell in the set of
source cells. That cost distance functions apply distance not in
geographic units but in cost units.
The wizard will then provide an interactive way to visualize the data
sets standardised with descriptive statistics as maximum, minimum,
etc…for each layer
4.2.3
Scoring wizard :
This tool will have the standardised layers as inputs layers. For each
layer, the user will have opportunity to define the scoring function to
apply to the layer. The wizard will help to build the scores table which
will associated the scores to the levels of variable in case of a deductive
approach or will ask for the formula to apply to these levels in case of a
parametric approach. According to the development platform chosen
further development can be investigated as a automatic adjustment
modelling to find the continuous function which best fit the deductive
modelling. The Scoring Wizard will insure that each layer has its own
score function. This step is generic and can be used to defined
fuzziness function Fuzziness measures the imprecision which
characterises classes with no sharply defined boundaries. A
membership function(MF) defines the degree to which an object is
member of a set : Boolean, linear, sinusoidal (MF(z)=1/(1+a(z-c)2) (a
defines the shape, c the value of property z at the central concept)
Page 20
4.2.4
Criteria Wizard :
In order to build up the exploitation zone, this wizard will analysed
catalogued layers. Has it had been explained previously, the
exploitation zone is an overlay result of specific layers defining
abundance zones, accessible zones and authorized zones. It behaves as
a mask where the fishing effort allocation will be applied. It has to be
defined according to availability of the layers and will be automatised.
According to the richness of data set, a resulting exploitation zone will
be calculated and added to the catalogue data.
4.2.5
Parameters Manager :
At this stage, the system will checked availability of the different
coverage's and information about the fleet (nominal effort).
4.2.6
Calculation and Results
This module will undertake the layers combination and the
application of rules and scoring functions within each layer. The
calculation is the core of the soft and will require different
functionalities from the main GIS soft that will be selected. From the
analysis, we can assess that a full raster GIS will be required as
combination will be done under this format. Idrisi GIS is for this
purpose a very good solution latest version I32.01 access to fuzzy logic
procedures. Nevertheless, it lacks customisable function which is very
important in terms of development. A feasible solution will be to use
ArcView 3.2 GIS with its Spatial Analyst extension to be able to work
on raster layer. It's possible to developed specific Dynamic Library
Loading (DLL) in C/C++ to program specific function with the
provided ESRI DLL (GRIDIO.DLL).
5. Data requirements
Instance of compulsory dataset for the SFEM, as information coverages
for the Input Manager :
Page 21
Cadix - Case studie
7°40'
7°30'
7°20'
7°10'
7°00'
6°50'
6°40'
6°30'
6°20'
6°10'
6°00'
37°30'
37°30'
37°20'
SPAIN
Isla Cristina El Terrón
Ayamonte
Ú
Ê
ÚÊ
Ê
Ú
Ú
Ê
37°10'
Punta Umbría
37°20'
37°10'
37°00'
37°00'
36°50'
Ú Sanlucar de Barr
Ê
Ú
Ê
Harbours
Coastline
Bathymetry
10 - 50 m
50 - 100 m
100 - 200 m
200 - 1000 m
36°50'
36°40'
36°40'
36°30'
36°30'
36°20'
36°20'
N
W
7°40'
7°30'
7°20'
7°10'
7°00'
6°50'
6°40'
6°30'
6°20'
6°10'
E
6°00'
S
Five harbours with shared fishing grounds and the bathymetry.
7°50'
7°40'
7°30'
7°20'
7°10'
7°00'
6°50'
6°40'
6°30'
6°20'
6°10'
6°00'
37°30'
37°30'
37°20'
SPAIN
Ayamonte
ÚÊ
Ê
Ú
Ú
Ê
Ú
Ê
37°10'
Punta Umbría
37°20'
37°10'
37°00'
37°00'
36°50'
36°50'
ÚSanlucar de Barr
Ê
36°40'
36°40'
36°30'
36°30'
36°20'
36°20'
36°10'
7°50'
36°10'
7°40'
7°30'
7°20'
7°10'
7°00'
6°50'
6°40'
6°30'
6°20'
6°10'
Innerwaters
6 miles
12 miles
Nursery
Ê Harbours.shp
Ú
Coastline
Bathymetry
10 - 50
50 - 100
100 - 200
200 - 1000
N
W
E
6°00'
S
Administrative limits and protected areas
Page 22
7°50'
7°40'
7°30'
7°20'
7°10'
7°00'
6°50'
6°40'
6°30'
6°20'
6°10'
6°00'
37°30'
37°20'
37°30'
SPAIN
Ayamonte
Ú
Ê
Ú
Ê
Ú
Ê
Ú
Ê
Punta Umbría
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37°00'
S
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36°50'
37°20'
37°10'
37°00'
ÚSanlucar de B
Ê
36°50'
36°40'
36°40'
36°30'
36°30'
36°20'
36°20'
36°10'
7°50'
36°10'
7°40'
7°30'
7°20'
7°10'
7°00'
6°50'
6°40'
6°30'
6°20'
6°10'
Observations
Coastline
Bathymetry
10 - 50 m
50 - 100 m
100 - 200 m
200 - 1000 m
S
#
N
W
E
6°00'
S
Nominal effort for each harbor and distributed observations on the fishing
grounds.
6. Task scheduling
Task
Period or Deadline
Conceptual and Methodological Document
Technical Specifications for developments
Input Manager development
Calculation module (F. Corsi & F. Carocci)
Constraints (G. Le Corre and L. Drapeau)
Data identification (A. Bensch, J. Morales)
Data set preparation
Meeting preparation
2nd meeting
End of December 2000
Mid of Febrauary 2001
Mid March 2001
Mid March 2001
Mid February 2001
June to December 2000
May to December 2000
March 2001
April 2001
7. References
Caddy, J.F. and Carocci, F. 1999. The spatial allocation of fishing intensity by
port-based inshore fleets: a GIS application. Ices Journal of Marine Sciences,
56: pp 388-403
Page 23
Caddy, J.F. 1975. Spatial model for an exploited shellfish population, and its
application to the Georges Bank scallop fishery. J. Fish. Res. Bd. Canada, 32(8):
pp 1305-1328
FAO "Groupe de travail sur les modèles conceptuels de données appliqués
aux pêcheries de l'Afrique de l'Ouest" Dakar, Sénégal 30 Octobre-4 Novembre
1995.
8. Annexes
8.1 GEAM approach using the "proportional effort allocation" principle by
Fabio Carocci
8.2 Survey Data statistics for spatial fishing effort analysis Artisanal Fisheries
8.3 L’application “localisation de l’activité des flottilles et des moyens de
production”, et les concepts associés
8.4 Analyse territoriale de la réglementation des pêches Gildas Le Corre –
IFREMER - Sète
Page 24
Annex 1
GEAM approach using the "proportional
effort allocation" principle
Method Approach
Fabio Carocci
I.
THE
METHOD OBJECTIVE
1. Introduction
The present approach intends to develop a framework for aiding fishery
managers and coastal area planners in analysing the likely interactions of
ports, inshore fleets, and local non-migratory inshore stocks.
Objectives
1) inshore fishery: the main target of the present approach is the so-called
artisanal fishery, composed by port-based fleets, mainly targeting
demersal fishery, with a duration of one or more few days
2) fishing effort: the approach try to describe the depletion of fishing
resources by modelling the distribution of the fishing effort (or fishing
intensity on a given area or fishing grounds) based either on the
"friction of distance" approach or by means of the Gaussian Effort
Allocation ("GEAM") approach
3) spatial data integration: for further analysis, the model can be
integrated within more complex scenarios with spatial dynamic data
4) scenarios : the approach is also suggested as an aid to deciding on
different scenarios for the location of marine parks or fishery closure
areas as well as for installation of new ports on a given coastal area
The input and output data
The model relies on the acquisition of some basic spatial and numerical
data:
1) location of ports, available or easily achievable in most of the
commonly encountered situations
2) a digitised coastline at the most appropriate scale, available at low cost
or free of charge from different sources
3) some basic information on the composition of the fleet operating on
the given area; these information are usually available from fishery
surveys as number of vessels for each segment of fishing power
Page 25
4) a realistic bathymetry of the surrounding fishing grounds; these
information can be easily accessible, in most of the case, only at low
resolution levels but it may represent a good starting point
5) some data and its digital spatial representation either on the
distribution of resources or the distribution of recent fishing effort for
the resources in question; to obtain these information, normally not
always available or not easily accessible, two options can be adopted:
a) the distribution of effort can be determined either from a
knowledge of fishing grounds or the behaviour of a sample of
representative fishermen
b) if these data are not available, one can assume the "proportional
effort allocation", (Caddy, 1975) where effort is assumed to be
allocated locally by the fleet in proportional to the local biomass
distribution. In this way survey biomass can be used to tune the
model approximately, so that distance from the port to the
projected peak in effort distribution coincides roughly with the
distance to the peak biomass, or is adjusted to best coincide with
the overall biomass distribution.
As main output, the approach may give:
1) an image of the distribution of the fishing effort or fishing intensity on
a given fishing ground which best fit the known distribution of fishing
effort
2) one or more images which represents different scenarios when some of
the parameters involved in the model are changed: as an example,
changing the composition of the fleet, the location or numbers of ports
and, the implementation of closed areas, like marine parks.
A functional organization diagram
Collation of data on sample fisheries from Western Mediterranean: Data
sets will be requested from all interested countries among those
participating to the COPEMED project.
Data so collected may incorporate spatial and/or numerical constrains
and factors which have an influence of the fishing effort distribution. The
model is tested against observed data and solutions to the equations are
used for producing the image of the fishing effort distribution. The image
obtained is then integrated with other model if and when additional data
are available.
According to the results the model can be refined or adjusted for the best
fitting. The results so obtained can be used to evaluate the best software
that encompass the approach described.
The next step is to write the appropriate computer routine that allows
entering data and obtaining the expected output, including different
Page 26
scenarios. The following schema gives a pictorial representation of
possible flow of the process involved.
Erreur! Liaison incorrecte.
Algorithms
Criteria
Considering criteria as the basis for a decision (in this case a decision on
where to fish or where to move), the model will take advantage of the GIS
capability to incorporate criteria as factors and constrains.
Factors: they are criteria that enhance or detract from the suitability of a
specific alternative for the activity under consideration. It is therefore
measured on a continuous scale. In our model one factor is the distance so
that ideally better areas for fishing are those closest to ports.
Constrains: they serve to limit alternatives under consideration. A good
example of constrains are not trawlable bottom types. Constrains can be
expressed in the form of Boolean map: areas excluded will be coded with a
0 while areas suitable are coded with 1. One interesting consideration is
that in some instances the constraint will be expressed as some
characteristic that the final solution must possess. In our example, one
constraint of this type maybe represented by the total revenue that a single
trip should guarantee.
Equation of the model
The basic equation behind the model, in the case of the GEAM approach
(Caddy & Carocci, 1999, see also Annex 1)) is expressed by mean of the
following:
æ x − v (T −T0 ) ö
−ç
S
è
2
E x = PR ∗ e
where:
Ex is the fishing effort exerted at distance x from port
PR is the relative port fishing power (compared with reference year R)
x is distance from port
v is the velocity with which fish resources are exhausted
S is a dispersion parameter (distance scale)
T is the time since onset of the fishery
T0 is the shift (delay) parameter representing the date of starting the
fishery
The parameter x (distance from port) is calculated as the growing distance
from the considered port(s), in other words the minimum distances
Page 27
measured according to the minimum number of cells that must be
traversed to move from that cell to the nearest source target.
Model output
The model should be able to present two major outputs:
1. an image depicting the actual distribution of the fishing effort or
fishing intensity over a given fishing ground; the image should be in a
format suitable for exchange with other applications, both standard or
created in the present framework. The model should provide the
possibility to access the resulting data either in native format (GIS as
raster images or vector overages) or as "death" images (GIF or TIFF).
2. a series of images depicting different scenarios, following the same
output formats described above.
Software and languages used for operational application
Initial stage
Initially the model has been tested using Idrisi for Windows v2.0 (with
extensive use of Macros) and integrated MS Excel worksheets for
calculations on non-geographic variables.
final stage
It is envisaged that the model can be developed as its minimal
configuration using ArcView extensions or, at its maximal
configuration, as a sub routine of an ad-hoc developed software.
Proposal about the method evolution and/or its integration in a
global approach (briefly described 10-15 lines)
As stated above, the model should take advantage of the integration of
other model proposed on the present framework. It could be very likely
that at the end the present model would constitute a subset or an option to
the global approach, especially in those cases where few parameters or
"layers" are available for the modelling of the fishing effort.
The proposal here presented has been developed using very few casestudies and it is strongly suggested that further tests of the model are
carried on especially in those areas where a good knowledge of the fishing
activities and general geographic characteristics of the fishing ground are
accompanied by poor knowledge of the dynamics of the fishing effort and
its consequences over the fishing resources.
Page 28
Proposal about the general organization of approaches and tools
in the project
As mentioned above, the availability of raw data is of crucial importance
for the success of the project. Particularly, for the method presented in this
report, it is envisaged that two or three case studies, selected in the area of
competence of COPEMED, may be selected to undertake a test phase and
refinements to the global approach.
It is also preferable to involve as much as possible those institutions in
developing countries that are able to carry on and set-up a co-ordinated
approach to the project. In this sense, the availability of data and tools
shown at the meeting in Fuengirola by different participants may
constitute an important incentive for the project. By means of active
exchange of data and processes, the project may have an active role in
developing a co-operation among developing countries.
Regarding the software to be developed, it is still premature to recognize
the feasibility of a full-customized approach. Nevertheless, this type of
approach may well fulfil the important task that is to present to those
involved in the fisheries management a portable and scalable approach to
single problems.
HOW TO APPLY THE GEAM APPROACH USING THE
"PROPORTIONAL EFFORT ALLOCATION" PRINCIPLE
The Digby Bay case study
We refer to the "proportional effort allocation" principle (Caddy, 1975) to
apply the GEAM approach in the case of the distribution of scallop biomass
along the coast and in front of the Digby port. For more detailed information
on the historical and literature on this fishing ground, the reader should refer
to Caddy & Carocci, 1999.
Importing the scanned map
The procedure starts by a scanned image, named SCALLOP.TIF (Figure 4)
of distribution of scallop biomass along the coast and in front of the Digby
port with sampling location and observed biomass values in each location.
The scanned picture has been imported in IDRISI using the TIFIDRIS
command and named SCAL512.IMG.
Page 29
Figure 1: Scanned image of sampling location of scallop biomass
Geo-referencing the image
One general problem when importing is the definition of geographic
parameters in order to geo-reference the image. In our case, we lack any
latitude and longitude reference points. Thus, we have to refer to another
source of data and try to guess the horizontal and vertical extension of the
area covered by the picture. Fortunately, the area covered is large enough
and well recognizable on an atlas or base map so we can easily assess the
extension along the x and y axis of the image and define the resolution of
each pixel of the image by using the DOCUMENT module of IDRISI. In
our specific case, we have established an horizontal (x-axis) dimension of
about 66 km and a vertical (y-axis) dimension of about 112 km, which
results in a resolution of 0.184 km x 0184 km for each pixel of the image.
Digitization of sampling location and scallop biomass values
Once the geo-referencing has been set up, we started digitising the
position of the reported 128 sampling locations and its relative biomass
value. Scallop biomass has been classified in to 5 classes, represented by
five different sized circles, and we have used the following procedure to
store the location and the class of each sampling point.
Using the on-screen digitising feature in IDRISI for point values (Figure 5),
we started digitising the location of sampling with biomass equal 0 and
storing the Feature ID's from 0 to 7 (with automatic step of 1).
Page 30
Figure 2: Idrisi on-screen digitising dialog
The subsequent class has been digitised storing the Feature ID's of each
point from 10 to 79 and so on according to the scheme below.
Table 1 : Scheme used to digitized scallop biomas classes
Classes of biomass
0
0.5 - 24.5 lbs.
25.0 - 49.5 lbs.
50.0 - 99.5 lbs.
>100 lbs.
Feature ID's
1-7
10 - 70
80 - 104
110 - 136
140 - 151
Figure 3 : Scallop biomass distribution image
In this way, one can easily associate to each range of Feature ID's, its value
of biomass. The resulting vector layer is named ABUNDANC.VEC and
then converted to ABUNDANC.IMG image using the POINTRAS module
(Figure 6).
Page 31
Creation of the growing distance image
The next step is to create an IDRISI image of the growing distance from
the Digby port from which the fleet operates. For doing this, and to apply
the COST module in Idrisi, we have first to create the following images:
• an image, defined as friction surface and named BARRIER.IMG, where
fishing ground are identified with value 1 and absolute barriers to
movement of fleet (as coastline, etc.) are identified with value -1 (this
image can be obtained by on-screen digitising the coastline and the
outline border of the Idrisi image SCAL512.IMG)
• an image with the location of the DIGBY port, named DIGBY.IMG
Figure 4: Idrisi dialog box for the creation of the growing distance image
The resulting image, after applying the COST module of Idrisi (Figure 7),
is named DISTDIG (Figure 8).
Figure 5: Image of the growing distance from Digby port
Extract of summary statistics from the image of distances from
Digby port
We can now extract and copy to a table the value, in pixel units, of
distance of each sampling location form the Digby port using the
EXTRACT module of Idrisi.
Page 32
Figure 6: Idrisi dialog box for the EXTRACT module
The feature definition image is ABUNDANC.IMG and the image to be
processed is DISTDIG.IMG (Figure 8). The result is summarized in tabular
format and copied to an Excel spreadsheet to plot the biomass against
distances from the Digby port.
Analyzing data in Excel
The first operation to complete in Excel is to convert from the unit of
distance obtained by the EXTRACT module to real distance. This is easily
achieved once applying the image resolution calculated before to the
values of the growing distance. Once the real distances, in km, are
obtained we can now assign, to each classes of biomass, an average value,
calculated by taking the mean value of each classes (remember that scallop
biomass are represented by 5 classes, as in
Table 1). In Excel we then applied the FREQUENCY formula to plot each
classes of biomass against predefined classes of distances form the Digby
port. This allows calculating, by simple aggregation, the total amount of
biomass founded in each interval of classes of distance. Table 2
summarizes the result obtained.
Page 33
Table 2 : Scallop biomass for each classes of distance from Digby port
Distances (km)
3
6
9
12
15
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
Mean total biomass (lbs.)
0
0
0
24
49
135
209
284
134
384
508
846
837
835
523
335
60
187
60
36
24
Application of the GEAM approach
The next step is to apply the GEAM model in order to found the best
fitting of calculated fishing effort with observed data. Once the GEAM
formula (Caddy & Carocci, 1999) has been inserted in Excel, data from
observed biomass against calculated fishing effort with distance are
plotted seeking for the minimal of the square of the sum of differences
between the two data set, using the SOLVER routine in Excel. This routine
allows searching for the best fitting. With Solver, you can find an optimal
value for a formula in one cell- called the target cell- on a worksheet.
Solver works with a group of cells that are related, either directly or
indirectly, to the formula in the target cell. Solver adjusts the values in the
changing cells you specify- called the adjustable cells- to produce the
result you specify from the target cell formula. You can apply constraints
to restrict the values Solver can use in the model, and the constraints can
refer to other cells that affect the target cell formula. Let's see an example:
Page 34
Square of
the sum of
the
differences
Parameter for computed effort in the GEAM approach
15
km
2
year
2
km/year
21
Target for SOLVER
3.66E-29
Distances Total biomass mean Standardized Computed
observed
effort
biomass
3
0
-0.90
0.004
6
0
-0.90
0.011
9
0
-0.90
0.024
12
24
-0.81
0.050
15
49
-0.73
0.095
18
135
-0.43
0.169
21
209
-0.18
0.277
24
284
0.08
0.418
27
134
-0.44
0.584
30
384
0.43
0.752
33
508
0.85
0.895
36
846
2.02
0.982
39
837
1.98
0.996
42
835
1.98
0.931
45
523
0.90
0.804
48
335
0.26
0.641
51
60
-0.69
0.472
54
187
-0.25
0.321
57
60
-0.69
0.201
60
36
-0.77
0.116
63
24
-0.81
0.062
Standardized
computed effort
S
T0
v
T
Values obtained
from EXTRACT
module in IDRISI
Values calculated
from the GEAM
approach formula
Differences
from
observed vs.
computed
-1.16
-1.14
-1.10
-1.03
-0.90
-0.70
-0.40
0.00
0.46
0.93
1.33
1.57
1.61
1.43
1.07
0.62
0.15
-0.28
-0.61
-0.84
-1.00
Differences of
standardized
-2.60E-01
-2.43E-01
-2.06E-01
-2.17E-01
-1.75E-01
-2.66E-01
-2.20E-01
-8.33E-02
8.95E-01
5.03E-01
4.74E-01
-4.46E-01
-3.78E-01
-5.50E-01
1.70E-01
3.62E-01
8.37E-01
-2.25E-02
8.15E-02
-7.19E-02
-1.82E-01
6.05E-15
Sum of the
differences
Page 35
Once the computed values have been obtained, the resulting plot can
be drawn as shown in Figure 10.
1.2
0.8
0.4
0.0
3
9
15
21
27
33
39
45
51
57
63
distance from Digby (km)
Figure 7 : Plotted data of mean scallop biomass (solid line) and the
calculated trajectory fishing effort (dotted line) with distance from
Digby port, resulting from the application of the GEAM approach
References
Caddy, J.F. and Carocci, F. 1999. The spatial allocation of fishing intensity
by port-based inshore fleets: a GIS application. Ices Journal of Marine
Sciences, 56: pp 388-403
Caddy, J.F. 1975. Spatial model for an exploited shellfish population, and
its application to the Georges Bank scallop fishery. J. Fish. Res. Bd.
Canada, 32(8): pp 1305-1328
Page 36
Annex 2
Survey data statistics for spatial fishing
effort analysis
Artisanal Fisheries Activities Areas
Laurent DRAPEAU
I.
OBJECTIVE :
Effort localisation for artisanal (industrial) fisheries is one key element for
understanding spatial distribution as well as temporal distribution of
fishermen and interaction between different fleet segments. Modelling the
effort distribution is a promising approach, observations and statistics is
another complementary one. The method presented here aims at exploration
of effort through extensive use of statistics surveys. These surveys describe
accessible areas and exploited areas. Based on exploited areas description
surveys (bottom types, bathymetry, accessibility…), gears characteristics
(engine, sailing tools…), fishing grounds (GPS), landings sites, an open sea
partition is determined.
1. Input and Output DATA
Identification of fishing grounds is a complex task. Fishermen have an
implicit knowledge of their fishing grounds. To define accurately these areas
surveys are required as well as digitised information’s related to the
environment. Two steps can be defined in the method approach :
identification and creation on fishing grounds, and use of these fishing
grounds characteristics (either spatial or attributes ones) to display and
spatialised the efforts and from a general point of view the activity.
Input
Technical characteristics of the unit gear : What gear is used : name, kind of
boat, engine characteristics, sailing tools on board, conservation mode, tanks
capacity for a trip. These elements are of main interest to defined what
“accessible Areas “(Activity radius definition).
Fishing grounds characteristics from landings sites : From surveys we very
often use the name of the fishing grounds. These fishing grounds can be
observed and defined spatially from GPS positions. In that case for each
landings sites and specific gear we need positions to create the areas. When
Page 37
no positions are available, we can estimate the areas using other sources of
information’s from surveys. On shore references (cap, estuaries, geographical
references) will defined a complete set of positioning. Distance from the coast
to reach the fishing grounds, geographical extend of it, and/or multivariate
description of it. Range of the bathymetry within the areas, bottom types,
administrative zoning, species areas distributions.
Physical obstacles : The use of information’s like presence of sand, rocks,
depths, presence of waves, can be restrictive information’s useful to define an
activity area
For industrial fisheries the data can be collected from :
a) boat registry (landings sites and authorised zones are defined)
b) trip records (statistical strata, geographical strata)
c) Log books
d) Onboard observers
Very often, data collected by observers have a very high resolution (spatial
and temporal), with geographical references (starting and ending positions)
which give an opportunity to efficiently display activity zones then according
to catches exploited areas.
For artisanal fisheries the data can be collected from :
a) Census and surveys (catches, efforts, geographical areas)
For these fisheries, landing sites surveys giving fishing zones names and
associated catches give the same opportunity.
Output
Authorised zoning : The open space partitioning is based on interpretation of
regulation texts which defined accessibility rules within the economic
exclusive zones.
Page 38
Accessible zones : Areas where the fleet segment can potentially practise its
fishing activity. These zones can then be analysed respectfully to the
regulations to define effective fishing areas. The restrictions that can be
applied are for instances : (shallow areas for big boats, rough sea area for
small ones, accessibility costs..)
Activity zones : The activity zone is defined within the accessible zone where
the boats developed actually their fishing trawls. They are defined through
constraints on bottom types for instances. An activity area doesn’t take in
considerations the seasonally distributions of the resource (target specie)
Exploited zones : If abundance’s indexes, species occurrences, high or low
variability areas are available they can be very useful to defined exploited
areas according to the segment fleet. We can defined their exploited zone as
the intersection of the activity area and the abundance area for the target
species.
Page 39
Functional organization diagram
When the data are not available at a compatible scale with the question, GIS
functionality’s give an opportunity to use a statistical approach (multivariate
approach) using the either the spatial information’s and the attributes
information’s related to geographical information’s as mentioned above to
generate geographical entities. The main steps of the organisation can be
summed up that way :
1) Collection of statistical surveys
-definition of fleet potential total effort by landing site (extrapolation by
inventories)
2) Collection of environmental data
-bathymetry and bottom types
3) Extraction of information related to the activities areas
-landing site(s) of reference
-fleet typology
-fleet characteristics
-target species
4) Creation of complementary coverage’s
-distance to the landing sites
-cost to the landing sites according to time (speed), petrol consumption’s
-species distributions maps
5) Multivariate analysis
-Extensive use of GIS functionality’s to overlay, collected information’s
-Use of the statistical surveys analysis to queries the resulting images
-creation of a resulting image giving the associated fishing’s grounds
6) Spatial distribution of effort
-application of the potential fishing areas to the total effort
Environment
variability
RESSOURCE
EXPLOITATION
Density maps
Fleets
Spatial and temporal
variability
Potential catches
Radius
accessibility
Catches
maps
Efforts
maps
Accessibility function
Accessible potential catches
Profitable function
Profitable exploitation
Optimality function
Limitation confinement
Page 40
Algorithms
No specific algorithm is applicable as the methodology is related to available
information in database. Nevertheless here is a summary of the approach that
was used to defined, use and explore different kind of geographical objects of
interest for fisheries. Software’s and languages used for operational
application.
The first stage of the project used Idrisi and ArcInfo GIS for digitising
problems, topological restrictions and coverage’s construction. ArcView 2.0
was at this stage used to prevent the use of ArcPlot (ArcInfo module) in order
to display and interactively querying the data. ArcView 3.2 greatly improved
the functionality’s and it is reasonable to think that with Spatial Analyst
(commercial extension) and freeware extensions available from the users
community (Xtools, GeoTools…) it will be possible to overcome the different
difficulties faced during a GIS implementation (from digitalisation to queries).
Proposal about the method evolution and or its integration in a global
approach
The present approach is mainly based on an extensive use of database
available on fisheries. It relies on this availability and an expert knowledge of
the fleet comportment. Starting from this status the GIS tools can be very
effective to display and then analyse spatial and temporal comportment. The
method can be improved by adjunction of spatial hypothesis like the ones
present in the methodological group. For poor data set, the definition of
accessible areas, and exploited areas can be based on these assumptions.
Proposal about the general organization of approaches and tools in the project
By the time the methodology is explained it is not feasible to defined
accurately its position in relation with the different approaches. Nevertheless
it can already be set that the statistical approach requires a rather rich data set.
However “richness” of a dataset has to be defined. Here we make the
assumption that a rich dataset is a data set where multivariate information are
available at reasonable spatial and temporal resolutions (data collected by an
observatory for instance). This approach is available at different level of
richness of the dataset starting from raw descriptions of the fleet to accurate
levels. Nevertheless, it appears that for poor dataset this approach can benefit
from the modelling approaches present in the network. If no information
about geographical references are collected in surveys, or if no derivatives
geographical information layers like the ones previously quoted are available
from the study area then applying a friction distance or a GEAM model from
landings sites can be useful to replace missing information on grounds. On
another hand, when accurate dataset are available, this approach can be very
useful to test accuracy of these models.
Page 41
Annex 3
L’application “localisation de l’activité des flottilles et des moyens de
production”, et les concepts associés
Marc Taconet, FAO – Rome (Italie) Alexis Bensch, FAO – Alicante
(Espagne) Y. Kazaoui, INRH – Casablanca (Maroc), R. Sarr, CRODT –
Dakar (Sénégal)
Un décideur applique une méthode de gestion relevant du concept de géoaménagement lorsqu’il peut maîtriser les facteurs susceptibles de contrôler la
distribution spatiale des flottilles et leur accès à la ressource. Au préalable, il
convient:
- d’identifier les critères déterminants de la distribution géographique de
l’exploitation (tonnage, puissance, mode de conservation, technique de
pêche, …);
- de maîtriser l’impact de ces critères sur la distribution géographique de la
l’exploitation.
Ces concepts de zones de géo-aménagement sont introduits grâce à un
modèle conceptuel simplifié mettant en évidence les relations entre les
attributs navires susceptibles de déterminer la localisation de l’exploitation, et
les zones géographiques correspondantes. Aux différents niveaux de
connaissance envisageables sur les paramètres d’exploitation, correspondent
des types distincts de zones de géo-aménagement, relevant de divers niveaux
de résolution spatio-temporelle.
Figure 1 : Modèle conceptuel de données : entités géographiques
homologues
Page 42
Figure 2 : Concepts géographiques liés à l’aménagement des pêcheries :
illustration en pêche artisanale
Par étapes successives, on peut déterminer les zones suivantes :
- La zone potentiellement accessible
d’un type de bateau à partir d’un port
d’attache, selon ses caractéris-tiques
techniques (puissance, autonomie,…).
- La zone d’activité, à l’intérieur de la
zone accessible, qui correspond aux
limitations imposées par l’engin de
pêche utilisé, et la pratique de pêche
mise en oeuvre.
- La zone d’exploitation (ou pêcherie) qui correspond à l’intersection entre
la zone d’acivité et l’aire de distribution
de l’espèce ciblée.
Figure 3 : Zones accessibles à partir du port
Page 43
Figure 4 : Pêche industrielle : zones d’activité relatives aux contraintes
imposées par l’armement
Figure 5 : Localisation des pêcheries : 2 approches pour une même entité
La détermination d’une pêcherie peut
être abordée de 2 manières (figure 5) :
- Par une approche déductive (modélisation spatiale basée sur une
connaissance experte), en prenant en
compte
successivement
la
zone
d’accessibilité, la zone d’activité, et
l’aire de distribution de l’espèce ciblée
(voir figure 2, ou figures 3+4+5), ce qui
fournit
une
zone
d’exploitation
théorique.
- Par une approche inductive (statistique), en compilant les données terrain
provenant de la flottille concernée
(localisation des traits de chalut), pour
déterminer la zone d’exploitation
effective.
Ces concepts et les méthodes de modélisation correspondants ont été intégrés
dans l’application “Localisation de l’activité des flottilles et des moyens de
production)”, développée dans le cadre du projet SIG Afrique de l’Ouest pour
Page 44
structurer, sélectionner et analyser les données flottille. Le système de gestion
de base de données Foxpro, et les logiciels SIG ArcInfo et ArcView ont été
utilisés pour développer l’application.
Figure 6 : Structure générale de l’application
L’interface utilisateur “Flottille” utilise par ailleurs le concept de groupement
d’exploitation pour regrouper, sur la base d’une combinaison de paramètres
d’exploitation partagés en commun, tous les navires, ou toutes les marées, ou
toutes les opérations de pêche d’un jeu de données. Le modèle de données
permet de stocker aussi bien :
- des données de recensement de navires que des données d’observateurs
embarqués (différents niveaux de connaissance) ;
- des données par bateau individuels ou par flottille (différents niveaux
d’agrégation).
Page 45
Niveaux de connaissance et d’agrégation du modèle de données
Exemple jeu de données
Réference
Recensement pêche
artisanale
a terre : port d’attache
en mer : zone autorisée
zone accessible
Suivi des débarquements
a terre : site de
débarquement
en mer : strate statistique
zones d’activité
Observateurs embarqués
en mer : opération de
pêche
carré statistique
site de pêche
géographique
Unité d’exploitation
Excursion
Unité d’observation
de l’activité de pêche
1 ou plusieurs
Le modèle de données permet de stocker à un Meta niveau une description de
la structure de chaque jeu de données, ce qui permet à l’utilisateur d’être
assisté lors de sa requête. Les étapes de traitement d'un jeu de données sont
les suivantes :
- Partition : suivant l'information contenue dans le jeu de données,
l'utilisateur est autorisé à segmenter la flottille selon des critères dont il
doit définir les classes (qualitatives ou quantitatives). Les partitions
possibles sont présentés par types.
- Calcul des statistiques : à chaque type de partition correspond une liste
possible de statistiques calculables.
L'utilisateur choisit ensuite la résolution temporelle avec laquelle la statistique
sera calculée pour les groupements d'exploitation issus de la combinaison des
critères de partitionnement. A chaque jeu de données est associé une
résolution temporelle minimale.
Une partition du jeu de données est ainsi élaborée à partir des groupements
définis dans le cadre d’une même requête. L’application Foxpro calcule
ensuite une statistique (nombre de navires, capture, effort de pêche,…) pour
chaque groupement d’exploitation, selon une résolution temporelle cohérente
Page 46
avec les paramètres sélectionnés. Le fichier résultant de ce calcul peut enfin
être couplé sous ArcView aux objets géographiques pertinents stockés dans la
base de données (ports, strates statistiques, traits de chalut,…), afin de
représenter et analyser la distribution géographique de cette statistique (voir
Figure 7).
L’application offre enfin des fonctionnalités d’archivage des groupements
d’exploitation réalisés, et des cartes correspondantes élaborées. D’autres
cartes élaborées par modélisation spatiale à partir d’une connaissance experte
peuvent également être archivées et directement interfacées aux tableaux
statistiques pertinents.
Figure 7 : Flux d’informations dans l’application
Page 47
Annex 4 Analyse territoriale de la réglementation des pêches
Gildas Le Corre – IFREMER - Sète
Dans l'aménagement des pêches, la réglementation est un des outils de régulation
des pêcheries et parmi les modalités utilisées pour gérer l’exploitation des
ressources, les réglementations à composante territoriale représentent une partie
importante de l’arsenal juridique. En utilisant les capacité des SIG pour l’intégration
de données géo-référencées et l’analyse spatiale, on peut étudier les représentations
territoriales des systèmes réglementaires et analyser la projection spatiale des droits
et interdictions appliqués aux différents usages.
Le système réglementaire maritime est généralement considéré comme complexe et
deux causes principales sont identifiées :
- le milieu maritime est un milieu ouvert et les nombreux textes réglementaires
définissent des zones ou des limites d'application sur une multiplicité de points
d’ancrages.
- le résultat d'une combinaison des textes juridiques produits à plusieurs
niveaux de décision et spécifiquement orientés vers différents domaines
d'applications tend à constituer un ensemble hétérogène.
L’approche présentée ici correspond à un projet avec analyse de l’existant,
développement méthodologique, conceptuel et applicatif pour produire un outil
opérationnel. Le projet est conduit en trois étapes.
1. Formalisme et gestion des données réglementaires
La base des textes réglementaires est constituée en fonction de l’objectif : il peut
s’agir d’intégrer et de représenter un ensemble focalisé sur un secteur géographique
défini ou focalisé sur une activité principale.
L’information spatiale est extraite d’un texte réglementaire en caractérisant la source
(le texte complet ou un paragraphe spécifique), la définition géographique de la
réglementation, l’activité ou les activités concernées et la modalité de la contrainte
(autorisation, interdiction, dérogation, exclusion, …). En information complémentaire,
on caractérise la période ou la durée d’application.
Une difficulté est la recherche d’exhaustivité dans la collecte des textes
réglementaires ; c’est un travail bibliographique qui nécessite généralement
d’explorer de nombreuses références et de nombreux renvois entre textes. La
seconde difficulté majeure est la formalisation du référentiel géographique sur lequel
s’appuie la réglementation. Cette étape doit être réalisée avec un minimum
d’interprétation et doit pouvoir être considérée comme objective par l’ensemble des
utilisateurs ultérieurs.
A partir des textes et de l’extraction d’information qui a été réalisée, il est possible de
construire les objets géographiques de référence sur lesquels s’appuient les
différentes réglementations. Cette base de données est construite simultanément
Atelier SIG COPEMED – Malaga 15-19/12/1998
1
sous forme d’arc et de polygones, chaque objet « primitive » étant unique. Le thème
géographique correspondant à une réglementation est ensuite construit avec une
composition de ces primitives.
Certaines réglementations ne font appel qu’à une seule primitive, comme par
exemple la réglementation qui interdit la pratique des arts traînants à l’intérieur de la
zone des 3 miles. L’arc 3 miles est construit à partir du texte de référence qui donne
la définition générique de cette limite. Ce thème réglementaire intègre donc
explicitement la limite des 3 miles et implicitement le trait de côte et la limite des eaux
territoriales vers le large et avec les pays riverains pour délimiter deux secteurs
réglementés par ce texte ; une zone côtière interdite pour l’activité des arts traînants
et une zone plus au large autorisant ces pratiques.
Cette partie de l’application est réalisée sous ArcInfo, en exploitant les fonctions de
route et de régions système. La constitution de cette base est progressive et
nécessite donc une gestion des historiques sur les introductions et les modifications
réalisées. En option, on peut conserver un lien vers le document réglementaire, s’il
est disponible dans un format texte informatique.
La base de données d’objets réglementaires géographique ainsi réalisée peut être
exploitée avec ArcInfo. L’architecture de l’application est également conçue pour
pouvoir être exploitée par ArcView. La base ArcInfo fonctionne alors comme un
serveur qui met à disposition une série de thèmes ArcView correspondant chacun à
une réglementation. Chaque utilisateur peut alors constituer une composition de
réglementation qui constitue le système réglementaire qu’il veut étudier.
2. Représentation de systèmes de réglementations
Il existe deux voies principales pour représenter l’organisation territoriale d’un
système réglementaire en format cartographique :
- le système est représenté sous forme d’une décomposition en réglementations
élémentaires. Ce mode de représentation permet une manipulation interactive
avec affichage sélectif de certains thèmes et modification de leurs légendes. Par
cette manipulation, on recherche les similarités, les complémentarités ou les
oppositions entre réglementations. L’ordre de superposition appliquée à la liste
des réglementations permet également de restituer la notion de priorité dans
leurs applications. Par exemple, une dérogation s’applique après la
réglementation générale en proposant une ou plusieurs exceptions ; le thème
dérogation sera donc positionné « au-dessus » du thème de réglementation
générale.
- le système est représenté sous forme synthétique qui agrège l’ensemble des
réglementations élémentaires. Cette approche permet de représenter des
secteurs qui possèdent la même combinaison de contraintes : la zone étudiée est
subdivisée en territoires homogènes au sens de la réglementation. Selon les cas
d’étude, ces territoires ont des tailles et des formes très variables. La prise en
compte d’une nouvelle réglementation dans le système étudié implique le recalcul
de la vue synthétique.
2
3. Diagnostic et évolution de systèmes de réglementation
Une partie du diagnostic sur un système réglementaire est réalisé dans sa phase
descriptive par l’étape de représentation des données. Pour la phase d’analyse,
plusieurs outils ont été développés spécifiquement pour permettre la recherche et le
calcul automatique de territoires réglementaires.
L’outil « sonde géographique » permet d’interroger le système constitué pour
identifier la combinaison locale des contraintes. L’interrogation est réalisée par un
pointeur ponctuel. Les résultats sont :
- une table attributaire contenant l’ensemble des réglementations définies en ce
point,
- le territoire géographique qui correspond exactement à la combinaison des
contraintes identifiée localement.
L’outil « sonde activité » permet de définir à priori une combinaison réglementaire par
les usages et les contraintes et d’obtenir en retour les secteurs qui correspondent à
cette combinaison.
L’exploitation simultanée de ces deux outils permet une exploration du système
étudié. Il est en particulier possible d’étudier le comportement territorial du système
réglementaire étudié en suivant son évolution temporelle et ses modifications après
introductions ou disparitions successives d’éléments de réglementation. Il est
également possible d’injecter ou de substituer une réglementation hypothétique et de
tester par simulation son impact sur l’organisation globale du système réglementaire.
L’exemple 3.1. illustre la constitution d’une combinaison d’activités et des contraintes
associées avec l’outil « sonde activité »:
- en premier choix, on sélectionne un thème parmi la liste des réglementations
qui ont été intégrées dans une vue qui correspond à une étude de cas,
- en second choix, on sélectionne une contrainte associée à un usage,
Lorsque cette sélection est réalisée, on ajoute ce critère à la table qui contient
l’ensemble des critères et constitue le sous-système réglementaire que l’on veut
étudier. Quand toutes les réglementations dont on veut représenter la combinaison
spatiale sont inclues dans cette table, la validation lance le calcul de l’aire
correspondant à ce sous-système réglementaire.
L’exemple 3.2. illustre la réponse de la « sonde géographique ». Avec le curseur, un
point a été sélectionné sur la carte. Le résultat textuel est l’affichage d’une table
attributaire qui identifie toutes les réglementations définies à la verticale de ce point
parmi l’ensemble des réglementations intégrées par l’utilisateur dans cette vue. A ce
point, chaque réglementation est définie par une contrainte appliquée à un usage.
Le résultat cartographique est la représentation de l’ensemble du secteur caractérisé
par le même sous-système réglementaire.
Avec un point de vue spécifique à une activité, on peut caractériser la réglementation
sur l’activité de pêche des petits pélagiques par :
- les « petits senneurs » (taille inférieure a 12 mètres) au-delà de l’isobathe des
10 mètres,
- les « grands senneurs » (taille supérieure à 12 mètres) au-delà de l’isobathe
des 20 mètres.
3
Avec un point de vue sur le sous-système senneurs petits pélagiques, on peut
rechercher quelle est la partition du territoire dans le cas d’une compétition très forte
qui exclurait de fait les « petits senneurs » de tous secteurs dans lesquels les
« grands senneurs » ont réglementairement accès. On peut inclure des critères
complémentaires pour prendre en compte des interactions négatives, comme dans
cet exemple avec les chalutiers et positives avec les dragues à coquillages.
On constate d’abord que le territoire résultant est très réduit par rapport à celui
obtenu du point de vue spécifique « petit senneur » (inférieur à 10% en surface). On
observe également que le territoire définit ici est n’est simple qu’en apparence ;
- il correspond principalement, à l’espace compris entre l’isobathe 10 mètres et
l’isobathe 20 mètres, mais pas exactement,
- il existe des interactions locales entre ces réglementations, qui sont capable
des générer des contours relativement « accidentés » pour ce territoire.
A partir de ce sous-système réglementaire, on peut effectuer une nouvelle analyse
sans intégrer la réglementation sur l’activité de chalutage et on constate que le
résultat n’est que marginalement modifié. Par cette approche, il est ainsi possible
d’évaluer l’indépendance ou la « sur-réglementation » de certains systèmes de
gestion des pêches.
Dans un usage plus opérationnel que dans cet exemple de présentation, l’intérêt de
cet outil est sa capacité à formaliser l’analyse et à la généralisation multi-critères
pour réaliser l’étude de sous-systèmes réglementaires complexes.
4
1.1. Construction Topologique
" … 3 miles …"
"… secteur entre …"
Primitive
Région
Catalogue
d'arcs
Catalogue
de
polygones
5
1.2. Architecture Application
SERVEUR ARCINFO
CLIENT ARCVIEW
Base de Données Réglementation
Textes - Objets Géographiques
Etude A
Etude B
7
2. Représentation d’un Système Réglementaire
Décomposition
Combinaison
8
3.1. Diagnostic de Système Réglementaire
9
3.2. Diagnostic de Système Réglementaire
10

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