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Estimating Reproductive Parameters of a Newly Discovered Weather Loach Population
Estimating Reproductive Parameters of a Newly Discovered Weather Loach Population
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Estimating Reproductive Parameters of a Newly Discovered Weather Loach Population
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Estimating Reproductive Parameters of a Newly Discovered Weather Loach Population
Estimating Reproductive Parameters of a Newly Discovered Weather Loach Population
Journal Article

Estimating Reproductive Parameters of a Newly Discovered Weather Loach Population

2025
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Overview
ABSTRACT Aquatic invasive species have negative impacts on native biodiversity and pose a significant threat to overall ecosystem health. Successfully established non‐native species possess life history traits that are advantageous for colonization and expansion into novel environments. The reproductive traits and strategies of fish are often good predictors of invasion success. Thus, understanding reproductive dynamics of non‐native species in their introduced environments is an important component for predicting expansion and effectively managing invasive populations. The Weather Loach Misgurnus anguillicaudatus is a recently discovered introduced species in Georgia, USA, and little is known about its life history attributes where it is not native. Thus, the objectives of this study were to: (1) estimate mean batch fecundity of female Weather Loach; (2) determine timing and periodicity of spawning; and (3) evaluate whether the gonadosomatic index (GSI) is a reliable indicator of reproductive status in this species. Based on observed peaks in eggs larger than 500 µm, we identified the presumed spawning season for Weather Loach to be occurring from April through August. The highest average fecundity observed was during July (10,539 eggs) and the lowest average fecundity observed was during April (3083 eggs). The GSI was a strong predictor of fecundity and tracked the number of mature eggs present in each month of the year. Our estimates of batch fecundity and determination of the annual spawning season can help managers better understand reproductive dynamics and develop predictive population models aimed at evaluating management activities.