Effects of sampling effort and data sources on precision of common carp and bigmouth buffalo population estimates

IF 2.2 2区 农林科学 Q2 FISHERIES
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引用次数: 0

Abstract

Biologists often manage abundant invasive Common Carp (Cyprinus carpio) populations for suppression below population density or biomass thresholds via commercial harvest that also removes large numbers of native Bigmouth Buffalo (Ictiobus cyprinellus). Thus, biologists need population estimates to monitor abundance but often avoid them due to the time and effort required to produce population estimates and pervading estimate imprecision. While population abundance of these fishes can be challenging to estimate, commercial fishers harvest large numbers of fish that could improve population estimates with less effort from biologists. Yet, how population estimates from fishery-dependent and fishery-independent data sources compare is unknown. We compared the precision of Schnabel fishery-independent (electrofishing) and Lincoln-Petersen fishery-dependent (commercial harvest) population estimates for Common Carp and Bigmouth Buffalo in seven northwest Iowa lakes. We then assessed how sampling effort and environmental factors affected the number of fish recaptured by electrofishing and how the number of fish marked and recaptured affected estimate variability. Lincoln-Petersen estimates were more precise and provided similar abundance estimates compared with Schnabel estimates for Bigmouth Buffalo, while both models provided comparable population estimates and precision for Common Carp. Common Carp electrofishing recaptures were positively related to electrofishing effort, number of fish tagged, lake size, and water temperature. Bigmouth Buffalo recaptures were positively related to number of fish tagged and negatively related to lake area and water temperature. Coefficient of variation for fishery-dependent estimates declined with number of fish tagged and recaptured; 750 Common Carp and 150 Bigmouth Buffalo needed to be marked at large to obtain precision of CV ≤ 0.25. Our results suggest Lincoln-Petersen estimates with fishery-dependent data represent an improvement over fishery-independent estimates for Bigmouth Buffalo while both techniques provided comparable estimates for Common Carp. Incorporating harvest data into population estimates can reduce sampling effort while improving or maintaining precision.

取样工作和数据来源对鲤鱼和大口水牛种群估计精度的影响
生物学家经常通过商业捕捞来管理大量入侵的鲤鱼(Cyprinus carpio)种群,将其抑制在种群密度或生物量阈值以下,同时也会清除大量本地大口水牛(Ictiobus cyprinellus)。因此,生物学家需要进行种群估计来监测丰度,但由于进行种群估计所需的时间和精力以及普遍存在的估计不精确性,生物学家往往避免进行种群估计。虽然对这些鱼类的种群丰度进行估算具有挑战性,但商业渔民捕获的大量鱼类可以减少生物学家的工作量,从而改善种群估算。然而,依赖渔业和不依赖渔业的数据来源的种群估计值如何比较尚不清楚。我们比较了爱荷华州西北部七个湖泊中施纳贝尔(电鱼)和林肯-彼得森(Lincoln-Petersen)渔业依赖性(商业捕捞)对鲤鱼和大口水牛种群数量估计的精确度。然后,我们评估了取样工作和环境因素如何影响电鱼重新捕获的鱼的数量,以及标记和重新捕获的鱼的数量如何影响估计值的变化。林肯-彼得森估算法与施纳贝尔估算法相比,对大嘴水牛的估算更为精确,并提供了相似的丰度估算,而这两种模型对鲤鱼的种群估算和精确度都相当。鲤鱼电鱼重捕率与电鱼强度、标记鱼数量、湖泊大小和水温呈正相关。大口水牛的重捕率与标记鱼的数量呈正相关,而与湖泊面积和水温呈负相关。依赖渔业的估计变异系数随着标记和重新捕获的鱼的数量而下降;需要标记750条鲤鱼和150条大嘴水牛才能获得CV≤0.25的精度。我们的结果表明,对于大口水牛,依赖渔业数据的林肯-彼得森估计值比依赖渔业数据的估计值要高,而对于鲤鱼,两种技术提供的估计值相当。将捕捞数据纳入种群估计可以减少取样工作量,同时提高或保持精度。
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来源期刊
Fisheries Research
Fisheries Research 农林科学-渔业
CiteScore
4.50
自引率
16.70%
发文量
294
审稿时长
15 weeks
期刊介绍: This journal provides an international forum for the publication of papers in the areas of fisheries science, fishing technology, fisheries management and relevant socio-economics. The scope covers fisheries in salt, brackish and freshwater systems, and all aspects of associated ecology, environmental aspects of fisheries, and economics. Both theoretical and practical papers are acceptable, including laboratory and field experimental studies relevant to fisheries. Papers on the conservation of exploitable living resources are welcome. Review and Viewpoint articles are also published. As the specified areas inevitably impinge on and interrelate with each other, the approach of the journal is multidisciplinary, and authors are encouraged to emphasise the relevance of their own work to that of other disciplines. The journal is intended for fisheries scientists, biological oceanographers, gear technologists, economists, managers, administrators, policy makers and legislators.
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