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Title: Calculation of narrower confidence intervals for tree mortality rates when we know nothing but the location of the death/survival events

Abstract

1. Many ecological applications, like the study of mortality rates, require the estimation of proportions and confidence intervals for them. The traditional way of doing this applies the binomial distribution, which describes the outcome of a series of Bernoulli trials. This distribution assumes that observations are independent and the probability of success is the same for all the individual observations. Both assumptions are obviously false in many cases. 2. I show how to apply bootstrap and the Poisson binomial distribution (a generalization of the binomial distribution) to the estimation of proportions. Any information at the individual level would result in better (narrower) confidence intervals around the estimation of proportions. As a case study, I applied this method to the calculation of mortality rates in a forest plot of tropical trees in Lambir Hills National Park, Malaysia. 3. I calculated central estimates and 95% confidence intervals for species-level mortality rates for 1,007 tree species. I used a very simple model of spatial dependence in survival to estimate individual-level risk of mortality. The results obtained by accounting for heterogeneity in individual-level risk of mortality were comparable to those obtained with the binomial distribution in terms of central estimates, but the precision increasedmore » in virtually all cases, with an average reduction in the width of the confidence interval of ~20%. 4. Spatial information allows the estimation of individual-level probabilities of survival, and this increases the precision in the estimates of mortality rates. The general method described here, with modifications, could be applied to reduce uncertainty in the estimation of proportions related to any spatially structured phenomenon with two possible outcomes. More sophisticated approaches can yield better estimates of individual-level mortality and thus narrower confidence intervals.« less

Authors:
ORCiD logo [1]
  1. Ecology and Evolutionary Biology University of Michigan Ann Arbor MI USA, ForestGEO Smithsonian Tropical Research Institute Washington DC USA
Publication Date:
Research Org.:
Univ. of Michigan, Ann Arbor, MI (United States).
Sponsoring Org.:
USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division
OSTI Identifier:
1562159
Alternate Identifier(s):
OSTI ID: 1562160; OSTI ID: 1623547
Grant/Contract Number:  
FG02-00ER41132
Resource Type:
Published Article
Journal Name:
Ecology and Evolution
Additional Journal Information:
Journal Name: Ecology and Evolution Journal Volume: 9 Journal Issue: 17; Journal ID: ISSN 2045-7758
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United Kingdom
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Environmental Sciences & Ecology; Evolutionary Biology; Bernoulli trial; binomial distribution; confidence intervals; demography; ForestGEO; Lambir Hills National Park; Poisson binomial distribution; spatial aggregation; tropical forest dynamics

Citation Formats

Arellano, Gabriel. Calculation of narrower confidence intervals for tree mortality rates when we know nothing but the location of the death/survival events. United Kingdom: N. p., 2019. Web. doi:10.1002/ece3.5495.
Arellano, Gabriel. Calculation of narrower confidence intervals for tree mortality rates when we know nothing but the location of the death/survival events. United Kingdom. doi:https://doi.org/10.1002/ece3.5495
Arellano, Gabriel. Tue . "Calculation of narrower confidence intervals for tree mortality rates when we know nothing but the location of the death/survival events". United Kingdom. doi:https://doi.org/10.1002/ece3.5495.
@article{osti_1562159,
title = {Calculation of narrower confidence intervals for tree mortality rates when we know nothing but the location of the death/survival events},
author = {Arellano, Gabriel},
abstractNote = {1. Many ecological applications, like the study of mortality rates, require the estimation of proportions and confidence intervals for them. The traditional way of doing this applies the binomial distribution, which describes the outcome of a series of Bernoulli trials. This distribution assumes that observations are independent and the probability of success is the same for all the individual observations. Both assumptions are obviously false in many cases. 2. I show how to apply bootstrap and the Poisson binomial distribution (a generalization of the binomial distribution) to the estimation of proportions. Any information at the individual level would result in better (narrower) confidence intervals around the estimation of proportions. As a case study, I applied this method to the calculation of mortality rates in a forest plot of tropical trees in Lambir Hills National Park, Malaysia. 3. I calculated central estimates and 95% confidence intervals for species-level mortality rates for 1,007 tree species. I used a very simple model of spatial dependence in survival to estimate individual-level risk of mortality. The results obtained by accounting for heterogeneity in individual-level risk of mortality were comparable to those obtained with the binomial distribution in terms of central estimates, but the precision increased in virtually all cases, with an average reduction in the width of the confidence interval of ~20%. 4. Spatial information allows the estimation of individual-level probabilities of survival, and this increases the precision in the estimates of mortality rates. The general method described here, with modifications, could be applied to reduce uncertainty in the estimation of proportions related to any spatially structured phenomenon with two possible outcomes. More sophisticated approaches can yield better estimates of individual-level mortality and thus narrower confidence intervals.},
doi = {10.1002/ece3.5495},
journal = {Ecology and Evolution},
number = 17,
volume = 9,
place = {United Kingdom},
year = {2019},
month = {7}
}

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DOI: https://doi.org/10.1002/ece3.5495

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