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1 September 2009 Population Growth and Demography of Common Loons in the Northern United States
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Abstract
We used recent developments in theoretical population ecology to construct basic models of common loon (Gavia immer) demography and population dynamics. We parameterized these models using existing survival estimates and data from long-term monitoring of loon productivity and abundance. Our models include deterministic, 2-stage, density-independent matrix models, yielding population growth-rate estimates (λ) of 0.99 and 1.01 for intensively studied populations in our Wisconsin, USA, and New Hampshire, USA, study areas, respectively. Perturbation analysis of these models indicated that estimated growth rate is extremely sensitive to adult survival, as expected for this long-lived species. Also, we examined 20 years of count data for the 2 areas and evaluated support for a set of count-based models of population growth. We detected no temporal trend in Wisconsin, which would be consistent with fluctuation around an average equilibrium state but could also result from data limitations. For New Hampshire, the model set included varying formulations of density dependence and partitioning of stochasticity that were enabled by the annual sampling resolution. The best model for New Hampshire included density regulation of population growth and, along with the demographic analyses for both areas, provided insight into the possible importance of breeding habitat availability and the abundance of nonbreeding adults. Based on these results, we recommend that conservation organizations include nonbreeder abundance in common loon monitoring efforts and that additional emphasis be placed on identifying and managing human influences on adult loon survival.
Jason S. Grear, Michael W. Meyer, John H. Cooley, Anne Kuhn, Walter H. Piper, Matthew G. Mitro, Harry S. Vogel, Kate M. Taylor, Kevin P. Kenow, Stacy M. Craig and Diane E. Nacci "Population Growth and Demography of Common Loons in the Northern United States," Journal of Wildlife Management 73(7), (1 September 2009). https://doi.org/10.2193/2008-093
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