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Ecology. 2017 Jun;98(6):1700-1709. doi: 10.1002/ecy.1847. Epub 2017 May 11.

Adult mortality in a low-density tree population using high-resolution remote sensing.

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Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, 02912, USA.
Institute at Brown for Environment and Society, Brown University, Providence, Rhode Island, 02912, USA.
Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, 95064, USA.
Smithsonian Tropical Research Institute, Ancón, 0843-03092, Panamá.


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.


Bayesian state-space; capture-recapture; hierarchical Bayes; population dynamics; remote sensing

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