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Title: Detecting population-environmental interactions with mismatched time series data

Abstract

Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has not been much work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population–environment interactions frommore » coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates.« less

Authors:
 [1];  [2];  [2];  [3]
  1. Univ. of Idaho, Moscow, ID (United States); Univ. of Tennessee, Knoxville, TN (United States)
  2. Univ. of Florida, Gainesville, FL (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1408655
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Ecology
Additional Journal Information:
Journal Volume: 98; Journal Issue: 11; Journal ID: ISSN 0012-9658
Publisher:
Ecological Society of America (ESA)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Ferguson, Jake M., Reichert, Brian E., Fletcher, Robert J., and Jager, Henriëtte I. Detecting population-environmental interactions with mismatched time series data. United States: N. p., 2017. Web. doi:10.1002/ecy.1966.
Ferguson, Jake M., Reichert, Brian E., Fletcher, Robert J., & Jager, Henriëtte I. Detecting population-environmental interactions with mismatched time series data. United States. doi:10.1002/ecy.1966.
Ferguson, Jake M., Reichert, Brian E., Fletcher, Robert J., and Jager, Henriëtte I. Tue . "Detecting population-environmental interactions with mismatched time series data". United States. doi:10.1002/ecy.1966.
@article{osti_1408655,
title = {Detecting population-environmental interactions with mismatched time series data},
author = {Ferguson, Jake M. and Reichert, Brian E. and Fletcher, Robert J. and Jager, Henriëtte I.},
abstractNote = {Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has not been much work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population–environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates.},
doi = {10.1002/ecy.1966},
journal = {Ecology},
issn = {0012-9658},
number = 11,
volume = 98,
place = {United States},
year = {2017},
month = {8}
}