Abstract

Integrated population models combine data from several sources into a single model to allow the simultaneous estimation of demographic parameters and the prediction of population trajectories. They are especially useful when survey data alone are insufficient to estimate precise vital rates and abundance, and to understand mechanisms of population growth and decline. The St. Lawrence Estuary (SLE) beluga population was depleted by intensive hunting over the past century, and had declined to 1000 individuals or less when it was afforded protection in 1979. Despite protective measures, the SLE population has shown no signs of recovery. Low abundance estimates and high calf mortalities observed in recent years have raised concerns as to its current status. An age-structured Bayesian model was used to describe population dynamics by integrating information from two different monitoring programs. The model included information on population size and proportion of young (<2 years-old) obtained from seven photographic aerial surveys flown between 1990 and 2009, and mortalities documented annually by a carcass monitoring program maintained from 1983 to 2012. Results suggest that the population was stable or slightly increasing from the end of the 1960s until the early 2000s when it numbered approximately 1000 belugas. The population then declined to 889 individuals (95%CI 672−1167) in 2012. Although neither dataset on its own could explain this decline, the integrated model was able to shed light on the internal processes involved. Results suggest substantial changes in population dynamics and age structure, moving from a stable period (1984−1998) characterized by a 3-year calving cycle and a population composed of 7.5% newborns and 42% immature individuals, to an unstable state (1999−2012) characterized by a 2-year calving cycle, high newborn mortality and a declining proportion of newborns and immatures (respectively, 6 and 33% in 2012). Independent indices of abundance, population age structure and calf production match model predictions, thus increasing our confidence in its conclusions. The lack of recovery, high adult mortality (6%) and highly variable newborn survival further increase concerns about this population.

Highlights

  • Identifying population declines and their underlying processes is one of the central paradigms in conservation biology (Caughley, 1994)

  • We developed an integrated population models (IPMs) to integrate the best-available information: (1) population size estimated from seven photographic aerial surveys flown between 1990 and 2009, (2) proportion of young obtained from the same surveys, (3) age and sex composition of dead beluga documented by the carcass monitoring program during the 1983−2012 period, (4) prior knowledge of beluga life-history parameters, (5) catch history for the period 1913−1960

  • We described changes in population size and parameters with two linked components: the state and the observation processes (State−space models; De Valpine and Hastings, 2002; Buckland et al, 2004)

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Summary

Introduction

Identifying population declines and their underlying processes is one of the central paradigms in conservation biology (Caughley, 1994). Unprecedented reductions in abundance of numerous species worldwide have hastened efforts to identify population trends and extinction risks (Mace et al, 2008). After heavy exploitation throughout history, many marine mammals appear to have benefitted from a shift from resource exploitation toward wildlife conservation (Magera et al, 2013), with some formerly depleted populations showing remarkable recoveries (Best, 1993; Gerber and Hilborn, 2001; Lotze et al, 2011). Other populations have remained at low abundance levels or have continued to decline despite management measures (Hobbs et al, 2000; Wade et al, 2007). Monitoring of wildlife populations usually relies on accurate assessment of their abundance and trends (Krebs, 1994; Morris and Doak, 2002). Detectability issues during field surveys (e.g. availability and perception biases; Buckland et al, 2001) often lead to highly uncertain abundance estimates

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