Larocque Sarah M, Bzonek Paul A, Brownscombe Jacob W, Martin Gillian K, Brooks Jill L, Boston Christine M, Doka Susan E, Cooke Steven J, Midwood Jonathan D
Fisheries and Oceans Canada, Great Lakes Laboratory for Fisheries and Aquatic Science, Burlington, Ontario, Canada.
Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, Ottawa, Ontario, Canada.
J Fish Biol. 2025 May;106(5):1601-1618. doi: 10.1111/jfb.15899. Epub 2024 Aug 20.
Conservation decisions surrounding which fish habitats managers choose to protect and restore are informed by fish habitat models. As acoustic telemetry has allowed for improvements in our ability to directly measure fish positions year-round, so too have there been opportunities to refine and apply fish habitat models. In an area with considerable anthropogenic disturbance, Hamilton Harbour in the Laurentian Great Lakes, we used telemetry-based fish habitat models to identify key habitat variables, compare habitat associations among seasons, and spatially identify the presence distribution of six fish species. Using environmental data and telemetry-based presence-absence from 2016 to 2022, random forest models were developed for each species across seasons. Habitat variables with the highest relative importance across species included fetch, water depth, and percentage cover of submerged aquatic vegetation. The presence probability of each species was spatially predicted for each season within Hamilton Harbour. Generally, species showed a spatial range expansion with greater presence probability in the fall and winter to include parts of the harbor further offshore, and a range contraction in the spring and summer toward the nearshore, sheltered areas, with summer having the most limited habitat availability. Greater habitat suitability was predicted in western Hamilton Harbour for the majority of species, whereas the east end was less suitable and may benefit from habitat restoration. These types of fish habitat models are highly flexible and can be used with a variety of data, not just telemetry, and should be considered as an additional tool for fish habitat and fisheries managers alike.
围绕鱼类栖息地管理者选择保护和恢复哪些栖息地的保护决策,是由鱼类栖息地模型提供信息的。随着声学遥测技术提高了我们全年直接测量鱼类位置的能力,完善和应用鱼类栖息地模型也有了机会。在劳伦琴五大湖中有大量人为干扰的汉密尔顿港,我们使用基于遥测的鱼类栖息地模型来识别关键栖息地变量、比较不同季节的栖息地关联,并在空间上确定六种鱼类的分布情况。利用2016年至2022年的环境数据和基于遥测的有无数据,为每个物种在不同季节建立了随机森林模型。在所有物种中相对重要性最高的栖息地变量包括风区、水深和沉水水生植被的覆盖百分比。在汉密尔顿港内,对每个季节每种鱼类的出现概率进行了空间预测。一般来说,各物种在空间上呈现范围扩展,秋季和冬季出现概率更高,范围包括港口更远的近海部分;春季和夏季则向近岸、受庇护区域收缩,夏季的栖息地可用性最为有限。预测汉密尔顿港西部对大多数物种来说栖息地适宜性更高,而东端则不太适宜,可能需要进行栖息地恢复。这类鱼类栖息地模型具有高度灵活性,可与多种数据一起使用,而不仅仅是遥测数据,应被视为鱼类栖息地和渔业管理者的一种额外工具。