Adult mortality in a low-density tree population using high-resolution remote sensing
Adult mortality in a low-density tree population using high-resolution remote sensing
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DOI：
10.1002/ecy.1847
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发表时间：
2017-06-01
期刊：
影响因子：
4.8
通讯作者：
Hubbell, Stephen P.
中科院分区：
文献类型：
--
作者：
Kellner, James R.;Hubbell, Stephen P.
我们开发了一个统计框架，以量化死亡率在树冠树木观察使用时间序列从高分辨率遥感。通过定时采集的遥感数据与同步的年开花的树冠树种Handroanthus guayacan，我们在18,883观测尝试巴罗科罗拉多岛，巴拿马（BCI）在11年的时间内，1,006个成年树木2,596独特的检测。有1 057次观测尝试由于云层覆盖或空间覆盖不完整而导致数据缺失。使用卫星数据检测到的来自独立现场样本的123个个体的分数（109个个体，88.6%），我们估计BCI上该物种的成年种群为1，135个个体。我们使用贝叶斯状态空间模型，该模型明确说明了树木检测和缺失观测的概率，以计算0.2%.yr（-1）的年成人死亡率（SE = 0.1，95%CI = 0.06-0.45）。对260棵实地检查的树木的成虫死亡率的独立估计与标准尺度估计值密切匹配（0.33%.yr（-1），SE = 0.16，95%CI = 0.12-0.74）。我们的概念验证研究表明，人们可以远程估计成年死亡率的树冠树种精确存在变量检测和缺失的观测。
We developed a statistical framework to quantify mortality rates in canopy trees observed using time series from high-resolution remote sensing. By timing the acquisition of remote sensing data with synchronous annual flowering in the canopy tree species Handroanthus guayacan, we made 2,596 unique detections of 1,006 individual adult trees within 18,883 observation attempts on Barro Colorado Island, Panama (BCI) during an 11-yr period. There were 1,057 observation attempts that resulted in missing data due to cloud cover or incomplete spatial coverage. Using the fraction of 123 individuals from an independent field sample that were detected by satellite data (109 individuals, 88.6%), we estimate that the adult population for this species on BCI was 1,135 individuals. We used a Bayesian state-space model that explicitly accounted for the probability of tree detection and missing observations to compute an annual adult mortality rate of 0.2%.yr(-1) (SE = 0.1, 95% CI = 0.06-0.45). An independent estimate of the adult mortality rate from 260 field-checked trees closely matched the landscape-scale estimate (0.33%.yr(-1), SE = 0.16, 95% CI = 0.12-0.74). Our proof-of-concept study shows that one can remotely estimate adult mortality rates for canopy tree species precisely in the presence of variable detection and missing observations.