Evaluation of mechanical removal rates for rehabilitating over-crowded largemouth bass Micropterus salmoides populations in Alabama small impoundments
Type of DegreeMaster's Thesis
School of Fisheries, Aquaculture, and Aquatic Sciences
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Largemouth bass Micropterus salmoides populations in small impoundments exhibit poor growth and body condition under high population densities typically found in unmanaged ponds. The effectiveness of largemouth bass harvest at reducing these conditions and restoring overcrowded populations back to desirable population size structures is unclear. We evaluated mechanical removal rates for rehabilitating over-crowded largemouth bass population in Alabama small impoundments. Via boat electrofishing, we removed 0-83% of largemouth bass populations under 356 mm over two years at eleven Auburn University Fisheries Research Unit ponds and evaluated changes in largemouth bass condition, size structure, growth and recruitment. Significant positive relationships were identified for 𝑙𝑜𝑔𝑒 differences in largemouth bass PSD-Q, PSD-P, mean relative weights of 254-356 mm largemouth bass, and CPUE of >356 mm largemouth bass as a function of proportion biomass removed from 2019-2021. However, the magnitude of these associations was small and driven primarily by reductions at unharvested control ponds rather than strong responses at treatment ponds. A significant negative relationship was also identified for 𝑙𝑜𝑔𝑒 differences in largemouth bass mean GSI. No significant relationships were identified for 𝑙𝑜𝑔𝑒 differences in growth or recruitment as a function of proportion biomass removed from 2019-2021. The removal effort necessary to achieve a mechanical removal rate of 0.5 (50% reduction) ranged from 4-9 complete shoreline circuits as largemouth bass catchability declined with removal effort. Despite significant relationships for some variables, two years of mechanical removals via boat electrofishing did not appear to substantially alter largemouth bass populations in these small impoundments. Rather the removal process maintained the current state of largemouth bass growth, condition, and population size structure in treated ponds compared to control ponds. Given the substantial amount of time, effort, and funding required to conduct intensive largemouth bass removals, my results offer clarity on the practicality and value of mechanical removals via boat electrofishing as a management tool for overcrowded largemouth bass ponds. Closed-population mark-recapture methods for estimating population size are widely used in fisheries applications. A key assumption of these methods is equal capture probability for every individual of the population; however, this assumption is often violated resulting in biased estimates of population size. Closed-population mark-recapture models often generate abundance estimates from electrofishing capture data. Varying behavioral responses induced by electrofishing likely lead to changes in capture probabilities and consequently biased abundance estimates. More complex closed population mark-recapture models are available in programs such as RMark to allow for multi-phase mark-recapture study designs to be robust to variation in capture probabilities. More clarity on the limitations of detecting differences in capture and recapture probabilities using programs like RMark are essential to understanding how to more strategically design closed population mark-recapture studies to calculate better abundance estimates. The objectives of this work were to show the limitations of detecting a recapture effect when using a mark-recapture/removal study design, investigate the effect of recapture behavior on bias in abundance estimates, and evaluate how our findings correlate with model selection of 29 mark-recapture/removal datasets. I used RMark to conduct Monte Carlo simulations of closed population capture-recapture models for ranges of variability in capture and recapture probabilities, population size, initial capture probability, and the number of removal events. Population size, initial capture probability, and variability in capture probability had the strongest influence on detecting a recapture effect and, therein, selecting for a model with recapture effect as the best fit model to the data. Ultimately, the best scenarios for high mean percent model selection for a recapture effect contained high population size, no variability in capture probability, and high initial capture probability. Population size and variability in capture probability had the strongest influence on mean proportional error in derived abundance estimates of models with a recapture effect relative to the true population size. I found very little bias in abundance estimates of models with a recapture effect relative to the true population size for nearly all scenarios except those with low population size, a low number of removal events, and a low initial capture probability. Lastly, derived abundance estimates of models without a recapture effect overestimated true population size in all scenarios except those with both a high initial capture probability and a high number of removal events. Overall, I found that the best fit models for all 29 data sets correlated well with the results of my simulations. The results of this study provide important insight into the dangers of assuming equal capture probability and using simple closed population capture-recapture models that are not robust to potential differences in capture and recapture probabilities. My results suggest that marking fish can reduce bias in abundance estimates if differences in capture and recapture probabilities can be tested for by using a more complex model.