An improved method for estimating individual growth variability in fish, and the correlation between von Bertalanffy growth parameters
By G. Pilling, G. Kirkwood, Stephen G. Walker. 2002
161 citations recorded by the source corpus.
Subfields: Aquatic Science, Global and Planetary Change, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/bbaa307819ffcb1380ea54bb21dad6585bdc385e
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In Aquatic Science
In Global and Planetary Change
In Nature and Landscape Conservation
Citations in the corpus
- A statistical framework to explore ontogenetic growth variation among individuals and populations: a marine fish example (2015, background): “…generally has a populationaverage and time-invariant focus, it can readily be expanded to include individual-level effects (e.g., Sainsbury 1980, Pilling et al. 2002, Eveson et al. 2007) and used to explore environmental and temporal trends if timeseries data are available (e.g., Rowling and…”
- Admitting ageing error when fitting growth curves: an example using the von Bertalanffy growth function with random effects (2007, background): “For example, individual variation in VBGF growth parameters (Sainsbury 1980; Wang and Thomas 1995; Pilling et al. 2002), seasonal variation in growth (Pitcher and MacDonald 1973; Cloern and Nichols 1978; Appledoorn 1982), autocorrelated error (Venugopalan and Prajneshu 1997), and stochastic environm”
- Nonlinear models for repeated measurement data: An overview and update (2003, background): “Additional applications are found in the literature in fisheries science (Pilling, Kirkwood, and Walker 2002) and plant and soil sciences (Schabenberger and Pierce 2001).”
- Otolith science entering the 21st century (2005, methodology): “vidual growth variability (Pilling et al. 2002) should provide an improved ability to extract information from data, some-”
- Some explorations of the life history ratios to describe length composition, spawning-per-recruit, and the spawning potential ratio (2015, background): “However, it must be emphasized to distinguish between the correlation of the estimated parameters and the real biological correlation of the population parameters (Pilling et al., 2002).”