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 been little 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 pop

SUBMITTER: Ferguson JM 

PROVIDER: S-EPMC5704982 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature