Impact of sampling efficiency on the performance of data-driven fish habitat models

Mouton, Ans; Dillen, Alain; Neucker, Tom Van den; Buysse, David; Stevens, Maarten; Coeck, Johan

doi:10.1016/j.ecolmodel.2012.03.003

Cited by 9 publications

(7 citation statements)

References 41 publications

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“…By contrast, trout cod capture probabilities were relatively stable (0.07 to 0.12). Although morphologically similar (apart from differences in adult size), these two species occupy different habitats in riverine systems (Koehn 2009); therefore, differential habitat use is more likely to be the principal determinant of capture probability rather than differences in species morphology (see Mouton et al 2012). Mesa and Schreck (1989) found that cutthroat trout (Onchorhynchus clarkii) hid in more complex habitats after electrofishing.…”

Section: Discussionmentioning

confidence: 97%

“…Capture probability varies across several important environmental, biological, and methodological gradients in large lowland river systems (Bayley and Austen 2002;Speas et al 2004) and is specifically related to habitat use by the target species (Mouton et al 2012). It is therefore important to estimate the degree to which capture probability varies under specific sampling and environmental conditions so that the statistical robustness of population estimates can be assessed and, where possible, corrected accordingly as a function of the calibrated gear methodology (Bayley and Austen 2002).…”

Section: Discussionmentioning

confidence: 99%

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Efficiency of electrofishing in turbid lowland rivers: implications for measuring temporal change in fish populations

Lyon

Bird

Nicol

et al. 2014

Can. J. Fish. Aquat. Sci.

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To quantify how electrofishing capture probability varies over time and across physiochemical and disturbance gradients in a turbid lowland river, we tagged between 68 and 95 fish·year −1 with radio transmitters and up to 424 fish·year −1 with external and passive integrated transponder (PIT) tags. We surveyed the site noninvasively using radiotelemetry to determine which of the radio-tagged fish were present (effectively closing the radio-tagged population to emigration) and then electrofished to estimate the proportion of available fish that were captured based on both this and standard mark-recapture methods. We replicated the electrofishing surveys three times over a minimum of 12 days each year, for 7 years. Electrofishing capture probability varied between 0.020 and 0.310 over the 7 years and between four different large-bodied species (Murray cod (Maccullochella peelii), trout cod (Maccullochella macquariensis), golden perch (Macquaria ambigua ambigua), and silver perch (Bidyanus bidyanus)). River turbidity associated with increased river discharge negatively influenced capture probability. Increasing fish length increased detection of fish up to 500 mm for Murray cod, after which capture probability decreased. Variation in capture probability in large lowland rivers results in additional uncertainty when estimating population size or relative abundance. Research and monitoring programs using fish as an indicator should incorporate strategies to lessen potential error that might result from changes in capture probabilities.Résumé : Afin de quantifier les variations de la probabilité de prise à la pêche électrique dans le temps et le long de gradients physicochimiques et de perturbation dans une rivière turbide de basse terre, nous avons doté de 68 à 95 poissons·année -1 de radioémetteurs et jusqu'à 424 poissons·année -1 d'étiquettes externes et de transpondeurs passifs intégrés (PIT). Nous avons sondé le site de manière non intrusive par radiotélémétrie afin de déterminer lesquels des poissons radioétiquetés étaient présents (excluant du fait l'émigration pour la population radioétiquetée), puis effectué une pêche électrique pour estimer la proportion de poissons disponibles capturés selon cette méthode et des méthodes de marquage-recapture normales. Nous avons répété les levés par pêche électrique trois fois sur au moins 12 jours chaque année, pendant sept ans. La probabilité de capture par pêche électrique variait dans une fourchette de 0,020 à 0,310 sur les sept ans et pour quatre espèces de gros poissons (la morue de Murray (Maccullochella peelii), la perche Macquarie (Maccullochella macquariensis), la perche dorée (Macquaria ambigua ambigua) et la perche argentée (Bidyanus bidyanus)). La turbidité de la rivière associée à un débit accru avait une incidence négative sur la probabilité de capture. Plus la longueur des poissons était grande, plus la détection était élevée pour les poissons allant jusqu'à 500 mm en ce qui concerne la morue de Murray; au-delà de cette longueur, la probabilité diminuait. Le...

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Efficiency of electrofishing in turbid lowland rivers: implications for measuring temporal change in fish populations

Lyon

Bird

Nicol

et al. 2014

Can. J. Fish. Aquat. Sci.

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“…The development of habitat selection models, in combination with hydraulic models and integrated into water management software, has contributed to understand the variations in habitat suitability (often for fish and macroinvertebrates) as a function of flow alteration (e.g., Conallin, Boegh, & Jensen, 2010;Garbe & Beeyers, 2017;Hayes, Hughes, & Kelly, 2007;Lamouroux, Mérigoux, Dolédec, & Snelder, 2013;Poff et al, 2010;Rosenfeld, 2017;Tomsic, Granata, Murphy, & Livchak, 2007). Probably due to the strong variations in local hydraulics within rivers and their strong dependence on discharge, many fish habitat selection models were developed at the microhabitat scale (e.g., Booker & Graynoth, 2013;Lamouroux, Capra, Pouilly, & Souchon, 1999;Mouton et al, 2012), which represent the immediate and daily habitat occupied by fish (Odum, 1953), or at the mesohabitat scale (e.g., Booker & Graynoth, 2013;Gosselin, Maddock, & Petts, 2012;Vezza, Parasiewicz, Calles, Spairani, & Comoglio, 2014), which represent the functional habitat for fish activities (Kemp et al, 1999). Thus, microhabitat and mesohabitat models allow studying local processes involved in fish ecology, considering fish sensitivity to local hydraulic variations.…”

Section: Introductionmentioning

confidence: 99%

“…Among the abundant literature on habitat selection, many statistical models have been used to study microhabitat selection (Ahmadi-Nedushan et al, 2006;Conallin et al, 2010). This includes the simple comparison of microhabitat densities across habitat categories (e.g., habitat suitability curves of Lamouroux et al, 1999;Mouton et al, 2012), generalized linear models (GLMs, e.g., Labonne et al, 2003;Jowett & Davey, 2007), fuzzy-models (e.g., Muñoz-Mas, Martinez-Capel, Schneider, & Mouton, 2012) that compute a weighted average of different models, and more recent machinelearning techniques such as random forests (e.g., Shiroyama & Yoshimura, 2016;Vezza et al, 2014) or neural networks (e.g., Fukuda, 2011;Muñoz-Mas, Fukuda, Portoles, & Martinez-Capel,-2018) that are complex non-parametric classification methods (Guisan & Zimmermann, 2000).…”

Section: Introductionmentioning

confidence: 99%

Predictive models of fish microhabitat selection in multiple sites accounting for abundance overdispersion

Plichard

Forcellini

Coarer

et al. 2020

River Research & Apps

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Microhabitat selection models are frequently used in rivers to evaluate anthropogenic effects on aquatic organisms. Fish models are generally developed from few rivers, with debatable statistical treatments for coping with overdispersed abundance distributions. Analyses of data from multiple rivers are needed to test their transferability and increase their relevance for stakeholders. Using 3,528 microhabitats sampled in nine French rivers during 129 surveys, we developed models for 35 specific size classes of 22 fish species. We used mixed‐effects generalized linear models (accounting for multiple surveys), involving B‐spline transformations (accounting for nonlinear responses) and assuming a negative binomial distribution (accounting for abundance overdispersion). We compared models of increasing complexity: no selection (M1), an “average” selection similar in all surveys (M2), two models with different selection across surveys (M3–M4). Of 132 univariate cases (specific size classes by habitat), 63% indicated selection for depth, 71% for velocity, 45% for substratum size and 13% for substratum heterogeneity. A total of 50 models were retained, involving 26/35 specific size classes. Model fits indicated low explained deviance (R2MF < 0.19) and higher rank correlations (ρ < 0.69) between observed and modelled values. However, Bayesian posterior predictive checks validated these results since excellent fits would generate R2MF lower than 0.59 and ρ lower than 0.78. We found high transferability among rivers and dates, because (a) M2 was the most appropriate in 26/50 cases; (b) the R2MF and ρ values by M2 was, respectively, 72% and 75% of that explained by the complex M4 and (c) independent river cross‐validations showed good transferability. Bivariate models for selected specific size classes improved univariate model fits (ρ from 0.30 to 0.38). Overall, using a nonlinear mixed‐effect approach, our results confirmed the relevance of “average” models based on several rivers for developing helpful e‐flow tools. Finally, our modelling approach opens opportunities for integrating additional effects as the spatial distribution of competitors.

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“…Hence, microhabitats showing absence of fish might actually have been occupied by individuals that were simply missed or fish that only occupy this habitat at night. This potential bias could lead to weaker relationship in the logistic regression when analysing absence/presence vs physical characteristics (Mouton et al 2012). Singled-pass electrofishing can also lead to biases on quantity measurement, especially when there is a low density of fish as it is the case of juvenile Arctic char (Hanks et al 2018;Hedger et al 2018).…”

Section: R a F T Limitation Of Methodology And Hypothesismentioning

confidence: 99%

Summer stream habitat preferences of Nunavik anadromous Arctic char (Salvelinus alpinus) fry and parr

Dubos

St‐Hilaire

Bergeron

2022

Can. J. Fish. Aquat. Sci.

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Arctic char is a fish species known to occupy diverse habitats within the Arctic region. However, summer habitat use during the juvenile stage of the anadromous form is largely unknown. The present study aims to characterize fry and parr summer habitat preferences. Surveys were conducted by electrofishing, associated with physical habitat characterization on several rivers of the Ungava Bay, Nunavik, Canada. At the microhabitat and station scales, fry showed significant habitat preferences for shallow water and slow velocity. At the mesohabitat scale, fry showed a significant habitat selectivity for riffles. This habitat selectivity implies that habitat models can be built to evaluate the potential of habitat suitability for Arctic char fry. However, no significant habitat selectivity was found for parr. Parr size was nonetheless positively correlated with velocity, which was found to be a limitative factor for juvenile habitat use. This first attempt at modeling juvenile anadromous Arctic char habitat in rivers emphasizes the importance of selecting an appropriate spatial scale and reiterates the fact that parr showed relatively high plasticity in stream habitat selection.

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Impact of sampling efficiency on the performance of data-driven fish habitat models

Cited by 9 publications

References 41 publications

Efficiency of electrofishing in turbid lowland rivers: implications for measuring temporal change in fish populations

Efficiency of electrofishing in turbid lowland rivers: implications for measuring temporal change in fish populations

Predictive models of fish microhabitat selection in multiple sites accounting for abundance overdispersion

Summer stream habitat preferences of Nunavik anadromous Arctic char (Salvelinus alpinus) fry and parr

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