Looming-Eye buoys do not reduce seabird depredation in poundnets (supporting dataset)

Materials and methodsThe experiment was carried out over a period of 46 days (2021-04-26 to 2021-06-11) at two pound net sites (’test’ and ’control’) spaced 1200 meters apart. The number of birds by species was counted at each site between 4 and 8 times a day on 8 selected dates in the time interval 07:30 to 11:40 with a total of 80 observations. Each count was made by one of three different observers, and a net was never observed by more than one observer on the same day, which precludes estimation of any observer effects. The looming eyes device was introduced at the test site between day 5 and 6 while the control site remained unchanged.While all species of bird were counted, we focus on cormorant and gulls in this analysis, since these are the ones attracted by the opportunity to feed on garfish in the pound nets. More precisely, the following species/groups were considered: great cormorant, greater black-backed gull, herring gull, lesser black-backed gull, other small gulls, terns, and unidentified gulls. Three analyses were performed using different groupings of the bird counts as response variable:1. All (counts of great cormorants and all gulls).2. Cormorants only3. Gulls onlyFor each of these, a GAM model was used to test the effectiveness of the device:log(μi) = f1 (timei) + f2 (deployTimei) I(Loomingi) + f3(timeOfDayi)where f1, f2, and f3 are Duchon splines with first derivative penalization. First derivative penalization implies that splines go toward constant values beyond the data range as opposed to second derivative penalization, where trends are extrapolated. The overall effect of time is described by f1, and f2 describes the deviation from that overall pattern at the treatment site as a function of time since the looming eyes were deployed. This is accomplished by multiplying f2 by and indicator function I(Loomingi), which is zero when there was no looming eyes (i.e. either the control net or before looming eyes were introduced at the treatment net site), and one if the looming eyes device was present. Smoothness selection was carried out with the maximum likelihood (ML) method [1]. The distribution of the response variable is assumed to be either Poisson or negative binomial. Model selection is based on the Akaike information criterion (AIC).References[1] Simon N. Wood. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1):3–36, 2011.

材料与方法本实验在46天内（2021年4月26日至2021年6月11日）于两个相距1200米的净捕地点（测试点和对照点）进行。在8个选定的日期（07:30至11:40）内，每天对每个地点的鸟类种类数量进行4至8次计数，共计80次观察。每次计数均由三位不同的观察者之一完成，且在同一天内，同一张网不会被超过一位观察者观察，从而排除了观察者效应的估算。在第5天至第6天之间，测试点引入了逼视眼设备，而对照点保持不变。尽管对所有鸟类种类进行了计数，但在本分析中，我们专注于鸬鹚和海鸥，因为这些鸟类被机会在围网中捕食鲈鱼所吸引。更确切地说，考虑了以下物种/群体：大鸬鹚、大黑背鸥、鲅鸥、小黑背鸥、其他小型鸥、海燕和未识别鸥。使用不同的鸟类计数分组作为响应变量，共进行了三次分析：1. 所有鸟类（包括大鸬鹚和所有鸥类）。2. 仅包括鸬鹚。3. 仅包括鸥类。对于每一项，使用广义加性模型（GAM）来检验设备的有效性：

(log(mu_i) = f_1(time_i) + f_2(deployTime_i) imes I(Looming_i) + f_3(timeOfDay_i))

其中，(f_1)、(f_2)和(f_3)是带有一阶导数惩罚的杜尚样条。一阶导数惩罚意味着样条在数据范围之外趋向于常数，而二阶导数惩罚则是外推趋势。时间效应的总描述由(f_1)给出，(f_2)描述了自逼视眼部署以来，治疗点整体模式的偏差。这是通过将(f_2)与指示函数(I(Looming_i))相乘来实现的，当没有逼视眼时（即对照网或治疗网点引入逼视眼之前），(I(Looming_i))为零；如果逼视眼设备存在，则为一。平滑度选择采用最大似然（ML）方法进行。响应变量的分布假设为泊松分布或负二项分布。模型选择基于赤池信息量准则（AIC）。

参考文献[1]：Simon N. Wood. 快速稳定的限制性最大似然和边际似然估计半参数广义线性模型。皇家统计学会会刊：B系列（统计方法论），73(1)：3-36，2011年。