Influence of environment and fleet dynamics on catch rates of eastern Scotian Shelf cod through the early 1980s

Gillis, Darren M.; Frank, Kenneth T.

doi:10.1006/jmsc.2000.0990

Cited by 17 publications

(13 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, it is important to stress that red grouper, the main target of LL and DLL, is one of the most affected resources in the region (Burgos and Defeo 2004). Hence, the shifting behavior of this fleet between target species could be influenced by fluctuations in resource abundance of the main targets and market conditions, as already reported by several authors for other fisheries (Béné and Tewfik 2001;Gillis and Frank 2001).…”

Section: Métiers and Fishing Strategiesmentioning

confidence: 87%

“…Fishers have historically been flexible and adaptive, partly because they are constantly in situations where they have to adjust to different conditions, such as the weather, changes in fish prices, and access to resources (Bockstael and Opaluch 1983;McKelvey 1983;Gillis and Frank 2001;Stergiou et al 2003;Wiyono et al 2006;Daw 2008). Under these conditions, they can uphold their fishing activity by either maintaining or increasing fishing effort, shifting target species, or searching for new fishing grounds (Hilborn and Walters 1992;Thunberg et al 1995;Béné and Tewfik 2001;.…”

mentioning

confidence: 97%

See 1 more Smart Citation

Dynamics of Fishing Gear and Spatial Allocation of Fishing Effort in a Multispecies Fleet

Monroy¹,

Salas

Bello‐Pineda

2010

N American J Fish Manag

View full text Add to dashboard Cite

Optimization of fishing effort allocation and efficient deployment of fishing gear are necessary for successful management of multispecies fisheries. Campeche Bank, located in the southwestern Gulf of Mexico, provides an opportunity to understand fishing effort and fishing gear dynamics through the analysis of the fishing operations of a semi‐industrial fleet and obtain insights into how to identify management units for these types of fisheries. We analyzed the operation of this fleet over 4 years by splitting it into groups and then fully describing the behavior of each one. Our analysis was based on two data sets, including records from logbooks (8,390 fishing trips) and interviews with skippers from fishing vessels at the landing ports (882 trips). We used multivariate statistical techniques (SIMPER, hierarchical cluster analysis, and nonmetric multidimensional scaling analysis) to define targeted species and define the groups (métiers). Geographical information systems tools helped us to identify the spatial distribution of fishing effort by gear on an annual and monthly basis. We identified four different groups that resulted from the combination of fishing gear, target species, and fishing zones. Shifting behavior between gears and target species was a fishing strategy developed by several vessels, and technological interactions were evident between two gear types that employ longlines. Spatial allocation of fishing effort indicated a preference for certain fishing zones by vessels that operated two of the four gears. This pattern was consistent throughout the years studied; however, monthly differences did exist. The results provide information to identify management units in multispecies fisheries through the identification of métiers and the understanding of fishing strategies.

show abstract

Section: Métiers and Fishing Strategiesmentioning

confidence: 87%

mentioning

confidence: 97%

Dynamics of Fishing Gear and Spatial Allocation of Fishing Effort in a Multispecies Fleet

Monroy¹,

Salas

Bello‐Pineda

2010

N American J Fish Manag

View full text Add to dashboard Cite

show abstract

“…Technical considerations may determine potential fishing areas, for example, depth limits for free-diving or smooth ground for trawling (Bene and Tewfik, 2001;Rijnsdorp et al, 1998), while technical innovations may allow more accurate navigation or monitoring of trends in catch data (Eales and Wilen, 1986). Social factors affecting effort distribution may include the activities of other fishers leading to interference competition (Gillis and Frank, 2001) and the existence of rules and institutions. Rules may be formal, like the establishment of MPAs, or informal territorial arrangements established, enforced and monitored by fishers themselves (de Castro and Begossi, 1995;Schlager and Ostrom, 1992).…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of effort by artisanal fishers: Exploring economic factors affecting the lobster fisheries of the Corn Islands, Nicaragua

Daw

2008

Fisheries Research

View full text Add to dashboard Cite

1Spatial distribution of fishing effort is increasingly recognised as an important consideration for fisheries 2 management, as it can affect trends in catch rates, and be incorporated into planning of spatial management tools 3 like marine protected areas (MPAs). One-hundred and ninety-eight household questionnaires provided a coarse 4 indication of effort distribution of artisanal lobster fishers around the Corn Islands, and 32 semi-structured 5 interviews with skippers were used to map individual fishing sites and describe the operating costs and revenues 6 of typical dive and trap-fishing operations. Artisanal fisheries had ranges of up to 50 km, and had moved 7 significantly offshore within the previous 10 years. At the scale of a 5 x 5 minute lat/long grid, trap fishing effort 8 was highly aggregated (dispersion coefficient = 3.5), while diving had a regular dispersion (d.c. = 0.1). 9Descriptions of catch composition at each site showed a clear spatial pattern in the distribution of two locally-10 recognised types of lobster potentially indicating local stock structures. Economic information was summarised 11 into balance sheets for typical fishers and suggested that fuel accounted for about 52 and 37% of the operating 12 costs of dive and trap fishing captains respectively. Qualitative questions highlighted trap theft, adoption of 13 geographical positioning system (GPS) technology and fuel costs as major factors affecting spatial behaviour. 14 The costs and benefits of using more distant grounds were examined by testing for relationships between stated 15 typical catch rates and distance of 90 trap-fishing grounds; and between fuel expenditure and catches in 291 16 daily records of the activities of 3 divers. Maximum catch rates stated by trap fishers were significantly higher at 17 more distant sites and daily catches by the divers had a positive relationship with fuel expenditure, which 18 suggested that increasing fuel expenditure to target more distant sites would lead to higher gross revenue as well 19 as higher net revenues, after considering variable operating costs. Thus there appears to be an economic 20 incentive to extend the range of the fishery. However, fishers may not perceive these positive trends in catch rate 21 with distance due to catch variability, and costs other than fuel complicate the trade-off between catch and 22 distance. 23

show abstract

“…The IFD has previously been considered in fisheries research having been applied to exploited species, their prey, and their harvesters and has been demonstrated to be a good first approximation of the spatial distribution of vessels in fisheries containing multiple fishers moving between distinct foraging sites (Gillis 2003). Empirical results suggest that the spatial distribution of fishing vessels follows the IFD (Gillis et al 1993), despite differing costs between foraging regions (Gillis and Frank 2001) or other IFD assumption violations (Voges et al 2005); though, significant deviations have been found (Abernethy et al 2007).…”

Section: Introductionmentioning

confidence: 77%

“…Under its simple assumptions of ideal knowledge and no cost, the IFD predicts profit rates to equalize among spatially distinct patches within a fishery (Gordon 1954;Hilborn and Ledbetter 1979;Gillis 2003). These assumptions, however, particularly that of no costs, have been found to be violated for many reasons including differing levels of risk (Hilborn and Ledbetter 1979), weather patterns (Gillis and Frank 2001), or distance from port (Gillis et al 1993;Swain and Wade 2003). Violations such as these lead to fundamentally different expected distributions of effort, which were not readily included in earlier IFD-based fisheries analyses.…”

Section: Discussionmentioning

confidence: 97%

Comparative analysis of the spatial distribution of fishing effort contrasting ecological isodars and discrete choice models

LeeA.

ComeauP.

2014

Can. J. Fish. Aquat. Sci.

View full text Add to dashboard Cite

The spatial dynamics of catch and effort data are often overlooked in fisheries research despite its well-documented utility in understanding the distribution and abundance of fish. We apply a recently developed fisheries isodar model to an otter trawl groundfish fishery on the Scotian Shelf and compare its predictive performance with a more traditional discrete choice model random utility model. Isodars represent the expected distribution of foragers between two habitats when fitness is equal and can be a representation of the ideal free distribution. Here, fitness was defined with relative catch rates, cost differentials, and interference effects between habitats. Random utility models were constructed as mixed logit models to give the expected probability of fishing in a particular area based on a collection of predictors. The predictions of the isodar models consistently outperformed the mixed logit for both in-sample and out-of-sample forecasts and the isodar was determined to be the preferred model based on its increased accuracy and simplicity. The isodar model can provide a statistically powerful and easily implemented tool in effort studies, especially in situations of aggregated or limited data, which can inform conservation and management decisions.Résumé : Il est courant, dans la recherche sur les pêches, de ne pas tenir compte de la dynamique spatiale des données sur les prises et l'effort, malgré l'utilité bien documentée de cette dernière pour la compréhension de la répartition et de l'abondance des poissons. Nous appliquons un nouveau modèle des pêches reposant sur l'approche d'isodars à une pêche aux poissons de fond au chalut à panneaux sur le plateau néo-écossais et comparons sa performance prédictive à un modèle d'utilité aléatoire de choix discret. Les isodars représentent la répartition attendue des individus en quête de nourriture entre deux habitats pour des aptitudes égales et peuvent refléter la répartition libre idéale. Dans le cas présent, l'aptitude est définie sur la base des taux de prises relatifs, des différences de coûts et des effets d'interférence entre habitats. Des modèles d'utilité aléatoire ont été élaborés en tant que modèles logit mixte pour obtenir la probabilité de pêche attendue dans une zone donnée en fonction d'un ensemble de variables explicatives. Les prédictions des modèles d'isodars sont uniformément meilleures que celles du modèle logit mixte pour les prévisions portant tant sur l'échantillon de référence que sur d'autres répartitions et, étant donné son exactitude et sa simplicité accrues, il est établi que le modèle d'isodars est le modèle à privilégier. Le modèle d'isodars peut constituer un outil statistiquement puissant et facile à utiliser dans les études sur l'effort pour éclairer les décisions de conservation et de gestion, particulièrement dans des cas où les données sont cumulatives ou limitées. [Traduit par la Rédaction]

show abstract

Influence of environment and fleet dynamics on catch rates of eastern Scotian Shelf cod through the early 1980s

Cited by 17 publications

References 23 publications

Dynamics of Fishing Gear and Spatial Allocation of Fishing Effort in a Multispecies Fleet

Dynamics of Fishing Gear and Spatial Allocation of Fishing Effort in a Multispecies Fleet

Spatial distribution of effort by artisanal fishers: Exploring economic factors affecting the lobster fisheries of the Corn Islands, Nicaragua

Comparative analysis of the spatial distribution of fishing effort contrasting ecological isodars and discrete choice models

Contact Info

Product

Resources

About