3 Presentations

• 
  02:00 PM - 02:30 PM

  Vigilance in mixed-species groups

  • Frank Van Langevelde, presenter, Wageningen University

  Mixed-species groups are usually explained by foraging advantages and reduced predation risk. The optimal level of vigilance of individuals in mixed-species groups depends partly on the vigilance levels of both conspecifics and heterospecifics. However, the benefits and costs do not need to be evenly distributed between the species. We modelled the evolutionary stable strategy (ESS) for the optimal level of vigilance of an individual in a mixed-species group. We depict the vigilance game in a mixed-species group with two species using their respective ESS level of vigilance as function of the average vigilance level of the other species.

• 
  02:30 PM - 03:00 PM

  A predator-prey model for bald eagles and colonial seabirds in the Pacific Northwest of North America

  • Eric T. Funasaki, presenter, Sul Ross State University
  • Shandelle M. Henson, Andrews University
  • James L. Hayward, Andrews University
  • Robert A. Desharnais, California State University LA

  Bald eagle populations in North America rebounded in the latter part of the twentieth century. An unintended consequence may be a negative impact on seabirds. We fit a Lotka-Volterra-type predator-prey model to eagle and colonial seabird data collected most years between 1980 and 2016. The model fit the data with generalized R2 = 0.82, supporting the hypothesis that the seabird dynamics were due largely to eagle population dynamics. Point estimates of the model parameters indicated approach to stable coexistence. Within the 95% confidence intervals for the parameters, however, 11.0% of bootstrapped parameter vectors predicted seabird colony extinction.

• 
  03:00 PM - 03:30 PM

  Performance of a state-space multispecies model: what are the consequences of ignoring predation and process errors in stock assessments?

  • Vanessa Trijoulet, presenter, DTU
  • Gavin Fay, SMAST
  • Timothy J. Miller, NOAA

  Little is known on how ignoring predation and process errors in fish assessment can affect the perception of the stocks. We developed a multispecies model that simulated data with observation and process errors. Four estimation models that differed by accounting or not for predation or process errors were fitted to the data and model bias was calculated. Ignoring predation had the largest impact on stock perception, and resulted in large bias in parameters and outputs. Ignoring process errors showed limited bias. If predation is large, assuming a constant mortality over time and/or age could have large consequences on tactical advice.