1. 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. Isodar/IFD vs Discrete Choice Modeling Key Points and Future Directions The predictions of each method for the overall distribution of effort are similar, but the isodar estimates are more precise. The isodar method will be more sensitive to changing distribution due to the greater precision. Discrete choice models are more informative when attempting to monetize specific aspects of fish harvester behaviour. 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. Statistical Examination of Logs and VMS Fishing effort should be considered as multi-dimensional rather than based on a single nominal effort value. CPUE could be replace by expected catch per “typical” trip, defined by the median values of effort related covariates. Generalized Additive Models and mixed models treating vessels as random effects are a natural extension of this work. 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. Agent Based Models: Effort, Catch & Abundance ABMs can provide insight into disproportionalities in the catch- effort relationship. (Implications for CPUE series?) NetLogo provides an effective environment for the development of spatially explicit, individual based ABMs of fishery systems. The parameters (including variability) of spatial population dynamics and individual behaviour must be estimated from fisheries data. (Use Generalized Linear Mixed Model analyses?) 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. Model performance in predicting the proportion of fishing effort among areas. Both in- sample 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. 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. Key Points and Future Directions 1/Catch =  0 +  1 ∙  1 +  2 ∙  2 +  3 ∙ 1/  3 +  4 ∙ 1/  4 11 22 33 44 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. 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. * Preliminary results - work under development. Contact D. Gillis for more details. 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] Key Points and Developing Questions 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. 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 ] slope = 1.17 (S.E. = 0.007) slope = 1.36 (S.E. = 0.02) WEEKLY (within seasons) ANNUAL (among seasons) Log 10 (Effort) Log 10 (Catch) Ho: slope = 1.00