Fishing Effort: fishery patterns from individual actions
Dr. Darren M. Gillis, (dgillis@umanitoba.ca) Biological Sciences,
University Of Manitoba, Winnipeg, MB R3T 2N2
Where to fish is one of the key questions facing fish harvesters, whether
industrial or artisanal. Two different perspectives have been taken by different
fields to examine this question. From an economic perspective, attempts to
monetize the choice of fish harvesters have employed Discrete Choice
methods (e.g. mixed logit). Alternatively, some fisheries biologists have
employed evolution based models of aggregate behaviour, such as the Ideal
Free Distribution and Isodars. In this case, prediction of general vessel
distribution and its implication for the estimation of local abundance are the
main concern. The best approach in any particular circumstance will vary with
the questions asked and the goal of the study.
The isodar equations for the relationship of effort (fi) between two areas (a & b) and the
catch (Ci) from those areas. See listed references for details.
Model performance in predicting the proportion of fishing effort among areas. Both insample and out-of-sample forecasts are shown for the isodar and mixed logit models.
Data from the Browns Bank otter trawl fishery. See references below for details.
Statistical Examination of Logs and VMS
Agent Based Models: Effort, Catch & Abundance
Commercial vessels generate fisheries data through logbooks, landing
records, automated positioning systems, and in some cases independent
observers. When observers are present, detailed activity records are
available. However, often only landing records and logbooks are available,
which provide little information on the time spent moving, searching, or
repositioning gear: aspects of fishing that respond to local abundances. In
the Gulf of St. Lawrence snow crab (Chionoecetes opilio) vessel monitoring
systems (VMS) provide accurate, frequent position records that reflect vessel
speed. Different speeds are characteristic of different actions such as
steaming between destinations (fast), setting traps in new locations
(medium), and retrieving traps (slow). We inferred these unobserved
activities from the VMS records associated with specific trips using hidden
Markov models. The estimated times spent in each activity were treated as
behavioural aspects of effort, similar to the number of traps set and the time
that the traps were in the water (soak time) from the log records.
A GLM modelling catch as a function of HMM inferred movement (setting, steaming) and
gear (soak time, number of traps) in the Gulf of St. Lawrence snow crab fishery using
satellite positioning of vessels (ARGOS VMS) and reported activities (trip logs). The GLM
assumes a gamma distributed response (catch) and an inverse polynomial link function.
For details see the reference below.
1/Catch = b0 + b1∙ q1 + b2∙ q2 + b3∙ 1/q3 + b4 ∙ 1/q4
q1
q2
q3
q4
Of prime interest to fisheries biologists is the question of how individual
variation among vessels and fish harvesters impacts fishery assessments and
population resilience. The first model illustrated below represents a fishery
where catch is taken according to the classic Baranov catch equation by effort
applied by individual vessel with participation thresholds to enter the fishery.
The second model illustrates is the beginning (vessel dynamics) of a spatially
explicit model that will incorporate snow crab distribution, movement, and
population dynamics to examine the impact of variation in vessel characteristics
on the resilience and productivity of this Gulf of St. Lawrence fishery.
The catch-effort relationship in an ABM where each vessel has a probabilistic
threshold, based on the fleet-wide average catch, to participate. On both weekly and
annually aggregated time-scales individual variation generates a non-linear
relationship between catch and effort. * [ Implemented in C, examined using R ]
WEEKLY (within seasons)
ANNUAL (among seasons)
Log10(Catch)
Isodar/IFD vs Discrete Choice Modeling
Ho: slope = 1.00
slope = 1.17
(S.E. = 0.007)
Log10(Effort)
Ho: slope = 1.00
slope = 1.36
(S.E. = 0.02)
Log10(Effort)
A spatially explicit fishery ABM to examine the impact of variation in fleet activity on
crab population dynamics. At this point crab movements and population dynamics
among years are not represented.* [ Implemented in NetLogo]
Gillis, D. M., & van der Lee, A. (2012). Advancing the application of the ideal free
distribution to spatial models of fishing effort: the isodar approach. Canadian Journal of
Fisheries and Aquatic Sciences, 69(10), 1610-1620.
van der Lee, A., Gillis, D. M., & Comeau, P. (2013). Comparative analysis of the spatial
distribution of fishing effort contrasting ecological isodars and discrete choice models.
Canadian Journal of Fisheries and Aquatic Sciences, 71(1), 141-150.
Key Points and Future Directions
Instead of standardizing by catch per unit effort (with number of traps as
nominal effort) we developed a generalized linear model that treated catch per
trip as the response variable. Using AIC, two inferred behavioural covariates
and two variables from the vessel log records were selected in the best
predictive model. Treating all predictors as aspects of fishing effort allows a
more detailed examination of the nature of their relationships to catch.
Charles, C., Gillis, D., & Wade, E. (2014). Using hidden Markov models to infer vessel
activities in the snow crab (Chionoecetes opilio) fixed gear fishery and their application
to catch standardization. Canadian Journal of Fisheries and Aquatic Sciences, 71(12),
1817-1829.
Key Points and Future Directions
* Preliminary results - work under development.
Contact D. Gillis for more details.
Key Points and Developing Questions
• The predictions of each method for the overall distribution of effort
are similar, but the isodar estimates are more precise.
• Fishing effort should be considered as multi-dimensional rather
than based on a single nominal effort value.
• ABMs can provide insight into disproportionalities in the catcheffort relationship. (Implications for CPUE series?)
• The isodar method will be more sensitive to changing distribution
due to the greater precision.
• CPUE could be replace by expected catch per “typical” trip,
defined by the median values of effort related covariates.
• NetLogo provides an effective environment for the development
of spatially explicit, individual based ABMs of fishery systems.
• Discrete choice models are more informative when attempting to
monetize specific aspects of fish harvester behaviour.
• Generalized Additive Models and mixed models treating vessels
as random effects are a natural extension of this work.
• The parameters (including variability) of spatial population
dynamics and individual behaviour must be estimated from
fisheries data. (Use Generalized Linear Mixed Model analyses?)