On the Estimation of Detection Probabilities for Sampling Stream-Dwelling Fishes.

Peterson, James T.

doi:10.2172/783958

Cited by 1 publication

(2 citation statements)

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“…We considered a Poisson model for fish dispersion, which seems acceptable in view of the results of the posterior predictive checks, not issuing warnings with high numbers of under-and over-estimates. The model assumes that the distribution of the fish of a given size class in proximity of a sampled site is not spatially structured (Peterson, 1999). While riverine salmonid do not show gregarious behavior, due to strong intraspecific competition, salmonid distribution can be patchy as a consequence of a spatial structure of the physical habitat.…”

Section: Ecological Results and Discussion Of Main Assumptionsmentioning

confidence: 99%

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Advantages and insights from a hierarchical Bayesian growth and dynamics model based on salmonid electrofishing removal data

Leunda

Boithias

Ardaíz

et al. 2019

Ecological Modelling

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Growth is a fundamental ecological process of stream-dwelling salmonids which is strongly interrelated to critical life history events (emergence, mortality, sexual maturity, smolting, spawning). The ability to accurately model growth becomes critical when making population predictions over large temporal (multi-decadal) and spatial (meso) scales, e.g., investigating the effect of global change. Body length collection by removal sampling is a widely-used practice for monitoring fish populations over such large scales. Such data can be efficiently integrated into a Hierarchical Bayesian Model (HBM) and lead to interesting findings on fish dynamics. We illustrate this approach by presenting an integrated HBM of brown trout (Salmo trutta) growth, population dynamics, and removal sampling data collection processes using large temporal and spatial scales data (20 years; 48 sites placed along a 100 km latitudinal gradient). Growth and population dynamics are modelled by ordinary differential equations with parameters bound together in a hierarchical structure. The observation process is modelled with a combination of a Poisson error, a binomial error, and a mixture of Gaussian distributions. Absolute fit is measured using posterior predictive checks, which results indicate that our model fits the data well. Results indicate that growth rate is positively correlated to catchment area. This result corroborates those of other studies (laboratory, exploratory) that identified factors besides water temperature that are related to daily ration and have a significant effect on stream-dwelling salmonid growth at a large scale. Our study also illustrates the value of integrated HBM and

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Section: Ecological Results and Discussion Of Main Assumptionsmentioning

confidence: 99%

“…where A (m 2 ) is the area which is sampled by EFRS. The Poisson distribution models stochasticity of fish presence and assumes that the distributions of individuals for a given size class are independent of one another and are not spatially structured, e.g., via physical habitat characteristics (Peterson, 1999).…”

Section: Modelling Observationsmentioning

confidence: 99%