Incubation recess behaviors influence nest survival of Wild Turkeys

In ground nesting upland birds, reproductive activities contribute to elevated predation risk, so females presumably use multiple strategies to ensure nest success. Identification of drivers reducing predation risk have primarily focused on evaluating vegetative conditions at nest sites, but behavioral decisions manifested through movements during incubation may be additional drivers of nest survival. However, our understanding of how movements during incubation impact nest survival is limited for most ground nesting birds.  Using GPS data collected from female Eastern Wild Turkeys (n = 206), we evaluated nest survival as it relates to movement behaviors during incubation, including recess frequency, distance traveled during recesses, and habitat selection during recess movements. We identified 9,361 movements off nests and 6,529 recess events based on approximately 62,065 hours of incubation data, and estimated mean nest attentiveness of 84.0%.  Numbers of recesses taken daily were variable across females (range: 1‒7). Nest survival modeling indicated that  increased cumulative distance moved during recesses each day was the primary driver of positive daily nest survival. Our results suggest behavioral decisions are influencing trade-offs between nest survival and adult female survival during incubation to reduce predation risk, specifically through adjustments to distances traveled during recesses.

Methods
We monitored live‒dead status daily during the reproductive season using handheld Yagi antennas and R4000 (Advanced Telemetry Systems, Inc., Isanti, MN) or Biotracker receivers (Biotrack Ltd., Wareham, Dorset, UK). Live-dead status was determined via GPS‒VHF transmitter mortality signals scheduled to activate if stationary for 24 hours. We downloaded GPS locations ≥ 1 per week via a VHF/UHF handheld command unit receiver (Biotrack Ltd., Wareham, Dorset, UK). We viewed GPS locations and determined incubation when female locations became concentrated around a single point for 1‒2 days (Collier and Chamberlain 2011, Conley et al. 2015, Yeldell et al. 2017, Wood et al. 2018). Nesting females were not disturbed or flushed from nest sites during monitoring, but instead were live‒dead checked daily via VHF from a distance of > 20 m.Our nest monitoring data produced a ragged telemetry dataset (Rotella et al. 2004), and we used the nest survival approach outlined by (Dinsmore et al. 2002) to evaluate influences of incubation recess movements on daily nest survival.  The ragged telemetry approach serves as a general model for known fate data in program MARK (White and Burnham 1999) when loss date may not be known exactly and is flexible for integrating time‒dependent individual covariates (Rotella et al. 2004, Collier et al. 2009). For each nesting female, we created an encounter history for the entire incubation period and scaled each nesting event (k = 1) to the same start point, as evaluating temporal variation in nest survival was not our objective (Dinsmore et al. 2002).  We recorded the last day each nest was known to be alive (l) and the final date that the female incubated (m) based on our VHF and GPS data (Conley et al. 2015, Conley et al. 2016, Yeldell et al. 2017) and assigned each nest a fate of 0 = survived or 1 = failed.  We followed the approach of Collier et al. (2009), and developed time dependent covariates for both the daily frequency and distance of recess movements, and time‒dependent covariates for the cumulative values of daily frequency and distance of recess movements.  We developed a set of candidate models which we used to evaluate time‒specific variation in wild turkey behaviors to better understand how variation in behavioral decisions during incubation drive nest survival.  Underlying our work was the hypothesis that behavioral changes, manifested via the movement ecology of wild turkeys during nesting, would impact nest success.  Our initial expectation was that, generally, increased movements would increase the level of attention on the landscape, which would thus increase nest failure.  As such, we included models evaluating fully time‒dependent covariates for daily frequency of recess movements and daily distance of recess movements, as well as cumulative frequency and distance of recess movements (Franklin 2001, Collier et al. 2009).  We also developed time-specific trend models for cumulative frequency of recesses and distance of recess movements, which assumed that the effect of each covariate did not vary by day and was thus constant over time (Franklin 2001). We used an information‒theoretic approach (Burnham and Burnham 2002) to rank candidate models and assess model strength (based on ΔAICc) using the standard from Burnham and Anderson (2002), and estimated daily nest survival for the best fitting candidate model given the data.

对于地面筑巢的高地鸟类而言，繁殖活动会提升被捕食的风险，因此雌性个体大概率会采用多种策略以保障筑巢成功。以往对降低被捕食风险驱动因素的识别，主要集中于评估巢址的植被条件，而孵卵期间通过移动体现的行为决策，或许是影响巢存活的额外驱动因素。然而，针对大多数地面筑巢鸟类，我们对孵卵期移动如何影响巢存活的认知仍较为有限。

本研究利用从206只雌性东部野火鸡（Eastern Wild Turkeys）收集的GPS数据，评估了与孵卵期移动行为相关的巢存活情况，涉及离巢频次、离巢移动距离以及离巢移动过程中的生境选择。基于约62065小时的孵卵数据，我们共识别出9361次离巢行为与6529次离巢事件，并估算出平均巢照料率为84.0%。不同雌性个体的每日离巢次数存在差异，范围为1至7次。巢存活模型结果显示，每日离巢累计移动距离的增加，是提升每日巢存活的主要驱动因素。本研究结果表明，行为决策会通过调整离巢移动距离，在孵卵期的巢存活与雌性成体存活之间形成权衡，以此降低被捕食风险。

## 方法

繁殖季期间，我们采用手持式八木天线（Yagi antennas）与R4000型接收机（Advanced Telemetry Systems公司，美国明尼苏达州伊桑蒂市）或Biotracker接收机（Biotrack有限公司，英国多塞特郡韦勒姆市），每日监测个体的存活/死亡状态。存活/死亡状态通过GPS-VHF发射机的死亡信号判定：若个体静止时长达到24小时，该信号将自动触发。我们通过VHF/UHF手持式指令单元接收机（Biotrack有限公司，英国多塞特郡韦勒姆市），每周至少下载1次GPS定位数据。我们通过查看GPS定位数据，当雌性个体的定位点连续1至2天集中于同一区域时，判定其进入孵卵期（Collier & Chamberlain, 2011; Conley et al., 2015; Yeldell et al., 2017; Wood et al., 2018）。监测期间，我们不会惊扰筑巢雌性或将其从巢址驱离，而是通过VHF在20米以外的距离每日开展存活/死亡状态检查。

本研究的巢监测数据属于不规则遥测数据集（Rotella et al., 2004），我们采用Dinsmore等人（2002）提出的巢存活分析方法，评估孵卵期离巢移动对每日巢存活的影响。当无法精准获知个体丢失日期时，不规则遥测方法可作为MARK软件（White & Burnham, 1999）中已知结局数据的通用模型，且可灵活整合时间依赖的个体协变量（Rotella et al., 2004; Collier et al., 2009）。针对每只筑巢雌性，我们为整个孵卵期创建了遭遇历史，并将每一次筑巢事件（k=1）统一至相同的起始点——因为本研究的目标并非评估巢存活的时间变异（Dinsmore et al., 2002）。基于VHF与GPS数据，我们记录了每个巢被确认仍存活的最后日期（l）与雌性个体终止孵卵的最终日期（m），并为每个巢赋予结局赋值：0代表存活，1代表失败（Conley et al., 2015, 2016; Yeldell et al., 2017）。

我们遵循Collier等人（2009）的方法，为每日离巢频次、每日离巢距离构建了时间依赖协变量，并为每日离巢频次与距离的累计值构建了时间依赖协变量。我们构建了一组候选模型，用于评估野火鸡行为的时间特异性变异，以深入理解孵卵期行为决策的变异如何影响巢存活。本研究的核心假设为：筑巢期间野火鸡移动生态学所体现的行为变化，会对筑巢成功率产生影响。我们最初的预期为：通常而言，移动距离增加会提升个体对周边环境的关注度，进而提升巢失败概率。因此，我们纳入了评估完全时间依赖协变量的模型，涵盖每日离巢频次、每日离巢距离以及离巢频次与距离的累计值（Franklin, 2001; Collier et al., 2009）。我们还构建了离巢累计频次与累计离巢距离的时间特异性趋势模型，该模型假设每个协变量的效应不随日期变化，即随时间保持恒定（Franklin, 2001）。我们采用信息论方法（Burnham & Burnham, 2002）对候选模型进行排序，并依据Burnham与Anderson（2002）的标准，基于ΔAICc值评估模型效能，同时基于最优拟合候选模型估算每日巢存活概率。