Download - FishReg

Nephrops in
Kattegat/Skagerrak
SDU & FOI
(University of Southern Denmark &
Institute of Food and Resource
Economics)
Presentation
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•
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•
•
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Data sources
Collected data
Challenges
Suggestions/Discussion
Hypotheses
Questions
Data sources
1. DFAD-database
–
–
–
Based on log-book data
Contains individual fishing vessel data & catches
Used for general/descriptive section
2. FOI account statistics
−
−
−
Sample of commercial Danish fishing vessels
Account statistics i.e. business economic data
Used in socioeconomic section
Data sources (continued)
3. Danish Directorate of Fisheries
− Log-book-, inspection- and violation
databases
− Due to confidentiality reasons data is
anonymised
− Used for section on control & enforcement
Collected data
− Unique opportunity to have access to
Danish control/enforcement data
− Danish Directorate of Fisheries very
cooperative and supportive
− Time consuming to establish contact and
access to control-data causing a delay in
the upload of data
Collected data (cont.)
− Status: Data is collected and will be
uploaded ASAP
− Enforcement data is sensitive and can
only be uploaded on an aggregated level
Challenges
1) Since enforcement data is anonymised
and economic data does not include all
vessels the three data sources have not
been linked → Average numbers and
possible loss of individual characteristics
driving “violation behaviour” (Defining
groups fishermen could be divided in)
Challenges (cont.)
2) Difficulties in how to define enforcement effort.
We have:
−
Total number of different types of inspections 20052006 (boardings- and dock-side inspections,
administrative- and paper “inspections”)
−
−
Number of inspections at individual fishing vessel level
Hours at sea for inspection vessels (for total Danish
sea area) → Average inspections in
Kattegat/Skagerrak can be calculated
Challenges (cont.)
3) Only average cost data of boarding and
dock-side inspections → By this no idea
of the shape of the cost curve (i.e.
increasing/decreasing marginal costs)
and different assumptions/scenarios may
be needed)
Challenges (cont.)
4)
5)
Violations leading to a sanction are only included and
not ongoing cases or violations without a sanction.
May cause too low a number of violations and
consequently violation-fraction
No biological data. TACs are based on LPUE
(observed and experiments), estimated fishing effort
and TV-monitoring.
However, Nephrop stocks appear stable (1991-) and
we are considering using a length-based model in the
description of this.
Challenges (cont.)
5) (cont.)
NEPHROPS in Kattegat/Skagerrak
300000
250000
200000
NEP
150000
100000
50000
year
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
0
1991
biomass in tons
350000
Challenges (cont.)
5) (cont.)
COD in Kattegat
biomass in tons
14000
12000
10000
8000
COD
6000
4000
2000
0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
year
Challenges (cont.)
6) How to take discards of undersized
Nephrops into account
7) Observed control efforts are already
prioritized/focused on areas where
violations have severe ecological
consequences → random sample is not
obtained (characteristics of violators are
missing. See slide 6)
Challenges (cont.)
8) Difficult to separate violations in fish species
i.e. to identify “Nephrops-violations” from
“Cod-violations” if both have been landed.
9) Danish Directorate of Fisheries: “There seems
no clear and unambiguous relation between
the number of inspections and the detected
violations” (!) (Due to changes in the control
strategy? Change in behaviour of fishermen?)
Challenges (cont.)
9) (cont.)
6000
450
400
5000
Inspections
350
4000
300
250
3000
200
2000
150
100
1000
50
0
0
2003
2004
year
2005
Inspections
Violations
Suggestions/Discussion
a) To derive effort (in COBECOS software
effort is restricted to btw. 0.001 and
0.999)
1. Effort = #inspections per fishing vessel
Max # inspections per fish. Vessel
2. Effort = #inspections per fishing vessel
Total # inspections
Suggestions/Discussion (cont.)
b)
→
P(inspection) = #inspections for individual vessel
#fishing trips (measured as #landings)
P(violation) = #violations for individual vessel
#inspections for individual vessel
P(fine) =
#sanctions for individual vessel = 1
#violations for individual vessel
P(detection) = P(insp.)*P(violation)*P(sanction) =
#sanctions for individual vessel
#fishing trips for individual vessel
Suggestions/Discussion (cont.)
b)
(cont.) P(detection) depends on the number of fishing
trips → more fishing trips = less chance of detection.
(Is this in accordance with the theoretical model?)
Actual- and expected probability of being sanctioned
may vary a great deal
Different probabilities for first-time and previous
violators? (Previous violators are – to a certain extent
– more often inspected)
Hypotheses
I.
II.
Previous violators face a higher P(inspection)
– given their P(violation) - P(detection) is also
higher → but P(violation) can change over
time
Increased control efforts increases
P(detection) – given the P(violation).
Questions
1)
2)
3)
4)
5)
A number of violations are “unintended” due
to complicated regulation. These violations
are “irrational”, how do we deal with that?
Calculation of P(detection)?
Expected vs. observed probabilities.
Calculation?
Identification of previous violators?
Hypothesis I-II? (Previous slide)
Questions (cont.)
6)
7)
Distribution of supporting enforcement
activities (e.g. pooling of data) on
different types of enforcement effort?
(Suggestion: ABC-costing?)
Can COBECOS software handle more
species?
Thank for your interest as well as valuable
comments, suggestions and input.