Plasticity in life history traits of a cyprinid fish in an intermittent river

The extreme seasonal environmental variation of intermittent rivers has a profound effect on freshwater fish communities. Yet, few studies have examined the consequences of the seasonal cycles of flooding and drying to fish condition and reproduction in these ecosystems. In this study, we compared the body condition, reproduction and diet of two chub populations from two adjacent sites (a perennial and an intermittent site) on the main stem of a Mediterranean river (Evrotas River, S. Greece). The study was conducted in spring 2017, three months after flow resumption and before the onset of chub reproductive period. Condition (net weight adjusted for length) of fish did not differ significantly between the two sites, despite lower aquatic macroinvertebrate availability at the intermittent site. Fish at the intermittent site compensated for the lower aquatic prey availability by increasing their feeding intensity and by shifting to higher terrestrial prey consumption. In addition, chub liver weight (adjusted for length) and gonadal weight (adjusted for length) were significantly higher at the intermittent site, indicating higher somatic and reproductive investment. These results highlight the resilience of fish populations inhabiting streams with extreme variation in flow, due to natural and/or anthropogenic drought

Plasticity in life history traits of a cyprinid fish in an intermittent river

The extreme seasonal environmental variation of intermittent rivers has a profound effect on freshwater fish communities. Yet, few studies have examined the consequences of the seasonal cycles of flooding and drying to fish condition and reproduction in these ecosystems. In this study, we compared the body condition, reproduction and diet of two chub populations from two adjacent sites (a perennial and an intermittent site) on the main stem of a Mediterranean river (Evrotas River, S. Greece). The study was conducted in spring 2017, three months after flow resumption and before the onset of chub reproductive period. Condition (net weight adjusted for length) of fish did not differ significantly between the two sites, despite lower aquatic macroinvertebrate availability at the intermittent site. Fish at the intermittent site compensated for the lower aquatic prey availability by increasing their feeding intensity and by shifting to higher terrestrial prey consumption. In addition, chub liver weight (adjusted for length) and gonadal weight (adjusted for length) were significantly higher at the intermittent site, indicating higher somatic and reproductive investment. These results highlight the resilience of fish populations inhabiting streams with extreme variation in flow, due to natural and/or anthropogenic drought

Evaluating the performance of habitat models for predicting the environmental flow requirements of benthic macroinvertebrates

<p>Although various methods are currently available for modelling the habitat preferences of aquatic biota, studies comparing the performance of data-driven habitat models are limited. In this study, we assembled a benthic-macroinvertebrate microhabitat-preference dataset and used it to evaluate the predictive accuracy of regression-based univariate Habitat Suitability Curves (HSC), Boosted Regression Trees (BRT), Random Forests (RF), fuzzy-logic-based models using the weighted average (FLWA), maximum membership (FLMM), mean of maximum (FLM) and centroid (FLC) defuzzification algorithms and fuzzy rule-based Bayesian inference (FRB). The results show that the BRT model was the most accurate, closely followed by RF, FRB, FLM and FLMM while the FLC and FLWA algorithms had the lowest performance. However, due to the imbalanced nature of the dataset and in contrast to the fuzzy rule-based algorithms, the HSC, BRT and RF models failed to accurately predict the habitat suitability in low-scored microhabitats. We conclude that, given balanced datasets, BRT and RF can be effectively used in habitat suitability modelling. For imbalanced datasets, a properly adjusted RF model can be applied but when the input dataset is large enough to provide sufficient data-driven IF–THEN rules to train an FRB, FLMM or FLM algorithm, these models will produce the most accurate predictions.</p