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Exotic birds show lags in population growth

Authors

  • Kevin Aagaard,

    Corresponding author
    1. Graduate Program in Ecology & Evolution, Department of Ecology Evolution & Natural Resources, Rutgers University, New Brunswick, NJ, USA
    • Correspondence: Kevin Aagaard, Graduate Program in Ecology & Evolution, Department of Ecology Evolution & Natural Resources, Rutgers University, New Brunswick, NJ 08901, USA.

      E-mail: aagaard@scarletmail.rutgers.edu

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  • Julie Lockwood

    1. Graduate Program in Ecology & Evolution, Department of Ecology Evolution & Natural Resources, Rutgers University, New Brunswick, NJ, USA
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Abstract

Aim

A key aspect of the ecology and management of biological invasions is the prevalence and duration of lag phases in population growth. Here, we explore the occurrence of lag phases in exotic bird populations using the Audubon Christmas Bird Count database.

Location

Hawaiian Island archipelago.

Methods

We expand on the use of piecewise model fitting techniques to detect lags in exotic bird populations on Hawaii. We searched for explanations as to the occurrence of these lags using five possible mechanisms (body size, niche breadth, propagule pressure, length of record and lag phase growth rate).

Results

We found evidence of lag phases for 14 of 17 species we evaluated (range: 10–38 years, mean using observed data = 16 ± 12), and we discovered very rapid growth to maximum abundance following the end of the lag phase (mean using observed data = 8 ± 6 years). We found no evidence for any association between the possible mechanisms influencing the occurrence and duration of the lag phases.

Main conclusions

Our results are the first to rigorously quantify lags in exotic animal populations; most existing evidence comes from plants. We show that lags are as common in birds as in plants, although we provide preliminary evidence that the duration of lags in birds is shorter than in plants. We highlight the need for continued efforts to elucidate lag phase occurrence and duration in biological invasions, and we demonstrate the expanded utility of piecewise model fitting approaches to quantify these lags using count data.

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