Modelling and quantitative methods in fisheries

By M. Haddon. 2001

1,181 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Nature and Landscape Conservation, Oceanography

View Paper: https://www.semanticscholar.org/paper/bd9af4ec8c43aaaa961cfa484f7fb51b22c6f92d

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In Global and Planetary Change

In Nature and Landscape Conservation

In Oceanography

Citations in the corpus

Admitting ageing error when fitting growth curves: an example using the von Bertalanffy growth function with random effects (2007, background): “Estimates of growth drive size- and age-structured stock assessment models (Quinn and Deriso 1999), scale yield calculations (Beverton and Holt 1957; Gulland 1983; Haddon 2001), and are related to life history traits such as natural mortality (M) and age or length at maturity (Charnov 1993; Jensen 1”
Stock assessment in inland fisheries: a foundation for sustainable use and conservation (2016, neutral): “Edwards et al. (2012) provide a more technical yet concise overview of many of these methods and full details and guidance for use can be found e.g., in Hilborn and Walters (1992), Hoggarth et al. (2006) and Haddon (2011).”
Habitat Use and Stranding Risk of Juvenile Chinook Salmon on a Seasonal Floodplain (2005, methodology): “A randomization t-test with 1,000 iterations (Haddon 2001) was used to compare salmon density on the east and west sides of the floodplain.”
A re-examination of the age and growth of sand tiger sharks, Carcharias taurus, in the western North Atlantic: the importance of ageing protocols and use of multiple back-calculation techniques (2006, methodology): “A likelihood ratio test was used to determine whether differences between female and male growth parameters were significant or if a single set of growth parameters better described the data (Kimura 1980; Quinn and Deriso 1999; Haddon 2001) (SAS Institute Inc.”
Response of non-native lake trout (Salvelinus namaycush) to 15 years of harvest in Yellowstone Lake, Yellowstone National Park (2011, methodology): “Methods followed Haddon (2001) except agespecific fishery selectivity was estimated using a double logistic function (Linton et al. 2007).”
Inter-oceanic analysis of demographic variation in a widely distributed Indo-Pacific coral reef fish (2008, methodology): “The rVBGF model was fitted through the observed data of each sample, and the best-fit model describing each dataset was determined by minimizing the negative log of the likelihood given a probability density function with a Poisson distribution (Kimura 1980, Haddon 2001).”
Climate‐driven range extension of a sea urchin: inferring future trends by analysis of recent population dynamics (2009, methodology): “…because growth patterns were consistent in NE and SE Tasmania (growth trajectories between sites were compared using likelihood ratio tests, after Haddon, 2001), the generation of age frequencies with this method provided an unbiased and precise tool for broad-scale examination of C. rodgersii…”
The effects of size-selective fisheries on the stock dynamics of and sperm limitation in sex-changing fish (2004, background): “1156 High Street Santa Cruz, California 95064 Fisheries models are generally used numerous protogynous species are com to predict the impact of fishing on mercially important, namely red porgy stock dynamics and yield (Quinn and (Pagrus pagrus), gag grouper (MycDeriso, 1999; Haddon, 2001).”
Valuing water quality changes using a bioeconomic model of a coastal recreational fishery (2006, background): “, [13]), though this convention has been challenged by Harley et al.”
Applied Linear Regression (2004, background): “11.2 (Date file: lakemary) In fisheries studies, the most commonly used mean function for expected length of a fish at a given age is the von Bertalanffy function (Bertalanffy, 1938; Haddon and Haddon, 2010), given by E”
Time series analysis and forecasting techniques applied on loliginid and ommastrephid landings in Greek waters (2006, methodology): “Time series analysis techniques (ARIMA models), artificial neural networks (ANNs) and Bayesian dynamic models were used to forecast annual loliginid and ommastrephid landings recorded from the most important fishing ports in the Northern Aegean Sea (1984–1999).”
Environmental variability and fisheries: what can models do? (2008, methodology): “The present situation of the world fisheries is the result of the interplay of many different factors, of which economic forces, political interests, a lack of control and enforcement of fishery regulations, but also great natural variations of fish stock sizes and productivities and resulting poor ”
Sex-change rules, stock dynamics, and the performance of spawning-per-recruit measures in protogynous stocks (2005, background): “…survival, and reproduction (e.g., Metcalfe et al., 1989; Snyder and all affect the dynamics of a population Dingle, 1990; Schultz and Warner, and its response to fishing and man- 1991; Wainwright et al., 1991; Mitagement (Quinn and Deriso, 1999; telbach et al., 1992; Nishibori and Haddon, 2001).”
Life history of the cownose ray, Rhinoptera bonasus, in the northern Gulf of Mexico, with comments on geographic variability in life history traits (2005, methodology): “We also used a likelihood ratio test implemented using the solver addin in Excel (Haddon 2001) to determine whether growth models differed between sexes (Kimura 1980, Cerrato 1990).”
Integrating genetic data into management of marine resources: how can we do it better? (2008, background): “Several texts (Hilborn and Walters 1992; Quinn and Deriso 1999; Haddon 2001; Walters and Martell 2004) describe the plethora of methods used for fisheries stock assessment, which differ with respect to data requirements, complexity and the quantities they are designed to estimate.”