Abstract Measuring fishing effort is one important element for effective management of recreational fisheries. Traditional intensive angler intercept survey methods collect many observations on a few water bodies per year to produce highly accurate estimates of fishing effort. However, scaling up this approach to understand landscapes with many systems, such as lake districts, is problematic. In these situations, spatially extensive sampling might be preferable to the traditional intensive sampling method. Here we validate a model-based approach that uses a smaller number of observations collected using multiple methods from many fishing sites to estimate total fishing effort across a fisheries landscape. We distributed on-site and aerial observations of fishing effort across 44 lakes in Vilas County, Wisconsin and then used generalized linear mixed models (GLMMs) to account for seasonal and daily trends as well as lake-specific differences in mean fishing effort. Estimates of total summer fishing effort predicted by GLMMs were on average within 11 % of those produced by traditional mean expansion. These estimates required less sampling effort per lake and can be produced for many more lakes per year. In spite of the higher uncertainty associated with model-based estimates from fewer observations, the improvements associated with the addition of only three aerial observations per lake highlighted the potential for improved precision with relatively few additional observations. Thus, the combination of GLMMs and extensive data collection from multiple sources could be used to estimate fishing effort in regions where intensive data collection for all fishing sites is infeasible, such as lake-rich landscapes. By using these methods of extensive data collection and model-based analysis, managers can produce frequently updated assessments of system states, which are important in developing proactive and dynamic management policies.

中文翻译：

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估算整个景观的捕捞努力量：一种使用模型集成多个数据源的空间扩展方法

摘要 衡量捕捞努力量是有效管理休闲渔业的重要因素之一。传统的密集垂钓者拦截调查方法每年收集几个水体的许多观察结果，以产生对捕捞努力量的高度准确的估计。然而，扩大这种方法以了解具有许多系统（例如湖区）的景观是有问题的。在这些情况下，空间广泛的采样可能比传统的密集采样方法更可取。在这里，我们验证了一种基于模型的方法，该方法使用从许多捕鱼地点使用多种方法收集的较少数量的观察结果来估计整个渔业景观的总捕捞努力量。我们在维拉斯县的 44 个湖泊中分发了捕捞作业的现场和空中观察结果，威斯康星州，然后使用广义线性混合模型 (GLMM) 来解释季节性和日常趋势以及平均捕捞努力量的特定湖泊差异。GLMM 预测的夏季总捕捞努力量估计值平均在传统平均扩张产生的估计值的 11% 以内。这些估计对每个湖泊所需的采样工作较少，并且每年可以为更多的湖泊进行估算。尽管与基于模型的较少观测的估计相关的不确定性较高，但与每个湖泊仅增加三个空中观测相关的改进突出了通过相对较少的附加观测提高精度的潜力。因此，GLMM 与来自多个来源的广泛数据收集相结合，可用于估算无法对所有捕捞地点进行密集数据收集的地区的捕捞努力量，例如湖泊丰富的景观。通过使用这些广泛的数据收集和基于模型的分析方法，管理人员可以对系统状态进行频繁更新的评估，这对于制定主动和动态的管理策略很重要。