Physical habitat is crucial for structuring local fish assemblages. Understanding habitat structure is important for fish management and conservation. Herein, we assessed whether sonar‐derived substrate data could explain spatial variation in species‐catch rates. Using a side‐scan sonar, we mapped the substrates of two non‐wadeable rivers in Illinois, USA and conducted standardized fish surveys at 40 sites over a 3‐year period. We used four fish species from lentic and lotic guilds, with each guild represented by a large piscivore and small insectivore. For each of the 40 sites, we characterized substrate composition at five spatial scales (0.1, 0.5, 1, 2, and 5 km) and used linear regression to explain site variations in species abundance or biomass. We hypothesized that larger spatial scales would better explain the catch rates of large species, and that biomass would be better explained than abundance. The proportion of variance in fish‐catch rates, explained by substrate composition, varied greatly (0.02 ≤ adjR² ≤ 0.74, mean adjR² = 0.38) with respect to species, spatial scales, and predictors used. However, we did not observe a consistent relationship between body size and the most relevant scale. Species biomass was more closely related to substrate composition than was species abundance and the best models selected based on AICc reached an average adjR² of 0.49 (0.25–0.74) across the four species, compared with that of 0.43 (0.20–0.64) obtained via the best abundance‐based models. We conclude that substrate data obtained using side‐scan sonar are useful for improving our understanding of river fish ecology.

中文翻译：

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侧面扫描声纳分类基质与鱼捕获率在多个空间尺度上的关系

物理栖息地对于构建当地鱼类群至关重要。了解栖息地的结构对于鱼类的管理和保护很重要。在此，我们评估了声纳来源的底物数据是否可以解释物种捕获率的空间变化。使用侧面扫描声纳，我们绘制了美国伊利诺伊州两条不可灌溉河流的底物，并在三年内在40个地点进行了标准化鱼类调查。我们使用了来自lentic和lotic公会的四种鱼类，每种公会都以大型食肉动物和小型食虫动物为代表。对于40个站点​​中的每个站点，我们在五个空间尺度（0.1、0.5、1、2和5 km）上表征了底物组成，并使用线性回归来解释物种丰度或生物量中的站点变化。我们假设更大的空间尺度可以更好地解释大型物种的捕获率，生物质比丰度更好。鱼类捕获率方差的比例由底物成分解释，差异很大（0.02≤adj[R ²  ≤0.74，平均形容词- [R ² = 0.38）相对于所使用的物种，空间尺度，和预测。但是，我们没有观察到体重与最相关的体重秤之间的一致关系。与物种丰富度相比，物种生物量与底物组成更紧密相关，并且基于AICc选择的最佳模型在四种物种中均达到0.49（0.25–0.74）的平均ad R R ²，而获得的平均值为0.43（0.20–0.64）通过基于最佳丰度的模型。我们得出的结论是，使用侧扫声纳获得的底物数据有助于增进我们对河鱼生态学的理解。