Correcting for size bias in electrofishing removal samples

Ian A. Richter, Henrique Corrêa Giacomini, Derrick Tupper De Kerckhove, Donald A. Jackson and Nicholas Edward Jones

Ecological Modelling, 2022, vol. 467, issue C

Abstract: Correcting for sampling biases and the misleading results they generate is an important but sometimes overlooked aspect of ecological research. Removal sampling, a common method used across survey protocols to estimate abundance, usually assumes constant capture probability across all individuals in a population. However, differences in body size, which are widespread in fish assemblages, are likely to violate this assumption. To correct for potential biases in removal sampling, we used hierarchical Bayesian models that estimate capture probability as a function of body size and common covariates associated with electrofishing of fish in streams. Using a fish-out data set (n= 78) from Alberta, Canada, that includes true abundances, we demonstrate that capture probability increased with body size and the model accurately estimated abundance for the majority of the reaches (83%). Furthermore, model performance improved when site width and conductivity, and their interactions with body size, were included as covariates in a stream fish dataset from Ontario (n= 56). The inclusion of site covariates in the model indicates that these size-based effects on capture probability can be dependent on the environment. The consequence of ignoring size-dependent catch probability is to underestimate biomass when the actual biomass is low and to overestimate biomass when it is high. This removal model allows us to estimate capture probability for multiple pass samples and consequently improves our ability to obtain more reliable abundance estimates. This modeling framework is presented using electrofishing, yet it is flexible enough to be used with a variety of other sampling techniques.

Keywords: Abundance; Capture probability; Populations; Removal sampling; Size bias; Streams (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:467:y:2022:i:c:s0304380022000527

DOI: 10.1016/j.ecolmodel.2022.109929

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