Data from: Studying phenology by flexible modeling of seasonal detectability peaks

1.Many animals and plant species have advanced spring phenology in response to climate warming. The majority of avian phenological studies are based on arrival dates. Consequently knowledge on bird phenology is mainly based on migratory species. In addition, arrival dates of migratory birds may be substantially affected by en-route climate conditions; thus failing to provide good indicators for spring phenology on the breeding grounds. Correlating arrival dates with other phenological data or with environmental covariates may be meaningless in these cases. 2.We propose the date of highest singing activity, quantified by detection probability, as a powerful proxy for breeding phenology that is applicable to migratory and sedentary bird species alike. In contrast to arrival dates, breeding phenology is mainly (non-migrants) or at least partially (migrants) influenced by conditions experienced within the breeding area. 3.We developed a new method for flexible estimation of peak detectability date in spring by combining multi-season site-occupancy with semi-parametric regression modeling (thin-plate splines). We applied our approach to opportunistic observations of 27 bird species (mostly passerines) in Switzerland. 4.We found substantial differences among species in the date of spring peak detectability: late February to mid-April in sedentary and short-distance migratory species and mid-April to late May in long-distance migrants. Among 10 species with data for >9 years, five showed a trend in detectability peaks towards an earlier spring phenology by nine to 17 days within 10 years. The mean shift over all species was ~3.5 days per 10 years. 5.Our approach is widely applicable, especially for temporally and spatially large-scale data from monitoring or citizen-science programs. Besides using the detectability peak as measure of phenology, the estimated seasonal pattern in detectability can help designing monitoring programs for improved efficiency. Our approach may be applied to any species with pronounced acoustic displays or other behavioral traits strongly influencing detectability during the breeding period. We believe that it can contribute substantially to unraveling how species and communities respond to environmental change.

1. 许多动植物物种已因气候变暖而呈现春季物候提前的现象。当前多数鸟类物候学研究均以鸟类到达繁殖地的日期为研究对象，因此我们对鸟类物候的认知主要局限于迁徙物种。此外，迁徙鸟类的到达日期极易受迁徙途中气候条件的显著影响，故而无法作为繁殖地春季物候的可靠指示指标。在此类情境下，将到达日期与其他物候数据或环境协变量进行关联分析往往并无意义。
2. 本研究提出以鸣唱活动峰值日期（通过探测概率量化）作为繁殖物候的有效替代指标，该指标可同时适用于迁徙鸟类与留居鸟类。与到达日期不同，繁殖物候主要（留居鸟类）或至少部分（迁徙鸟类）受繁殖区域内的环境条件影响。
3. 我们结合多季节位点占用（multi-season site-occupancy）模型与半参数回归建模（薄板样条，thin-plate splines），开发了一种可灵活估算春季探测概率峰值日期的新方法。我们将该方法应用于瑞士境内27种鸟类（多数为雀形目鸟类）的偶然观测数据。
4. 研究发现不同物种的春季探测峰值日期存在显著差异：留居鸟类与短距离迁徙鸟类的峰值日期为2月下旬至4月中旬，长距离迁徙鸟类则为4月中旬至5月下旬。在数据时长超过9年的10个物种中，有5个物种的探测峰值日期呈现出提前趋势——10年内提前了9至17天。所有物种的平均物候提前速率约为每10年3.5天。
5. 本方法具备广泛适用性，尤其适用于来自监测项目或公民科学（citizen-science）项目的大时空尺度数据。除了将探测峰值作为物候衡量指标外，估算得到的探测季节性动态还可用于优化监测方案的设计，提升监测效率。该方法可应用于所有在繁殖期具备显著声学展示行为或其他能强烈影响探测率的行为特征的物种。我们认为该方法将有助于深入解析物种与群落如何响应环境变化。