Bahlai, C. A. and E. F. Zipkin. 2020. The Dynamic Shift Detector: An algorithm to identify changes in parameter values governing populations. PLOS Computational Biology 16:e1007542.

Populations naturally fluctuate in abundance, and the rules governing these fluctuations are a result of both internal (density dependent) and external (environmental) processes. For these reasons, pinpointing when changes in populations occur is difficult. In this study, we develop a novel break-point analysis tool for population time series data. Using a density dependent model to describe a population’s underlying dynamic process, our tool iterates through all possible break point combinations (i.e., abrupt changes in parameter values) and applies information-theoretic decision tools (i.e. Akaike’s Information Criterion corrected for small sample sizes) to determine best fits. Here, we develop the approach, simulate data under a variety of conditions to demonstrate its utility, and apply the tool to two case studies: an invasion of multicolored Asian ladybeetle and declining monarch butterflies. The Dynamic Shift Detector algorithm identified parameter changes that correspond to known environmental change events in both case studies.

DOI: 10.1371/journal.pcbi.1007542

Data URL: https://github.com/cbahlai/dynamic_shift_detector/blob/master/casestudydata/kbs_harmonia94-17.csv

Associated Datatables:

1. Insect Populations via Sticky Traps

Associated Treatment Areas:

• T6 Alfalfa
• T1 Conventional Management
• T2 No-till Management
• T7 Early Successional
• T8 Mown Grassland (never tilled)
• T3 Reduced Input Management
• T4 Biologically Based Management
• T5 Poplar

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