Elsevier

Ecological Modelling

Volume 221, Issue 22, 10 November 2010, Pages 2687-2698
Ecological Modelling

Behavioral games involving a clever prey avoiding a clever predator: An individual-based model of dusky dolphins and killer whales

https://doi.org/10.1016/j.ecolmodel.2010.07.010Get rights and content

Abstract

Faced with an intermittent but potent threat, animals exhibit behavior that allows them to balance foraging needs and avoid predators and over time, these behaviors can become hard-wired adaptations with both species trying to maximize their own fitness. In systems where both predator and prey share similar sensory modalities and cognitive abilities, such as with marine mammals, the dynamic nature of predator–prey interactions is poorly understood. The costs and benefits of these anti-predator adaptations need to be evaluated and quantified based on the dynamic engagement of predator and prey. Many theoretic models have addressed the complexity of predator–prey relationships, but few have translated into testable mechanistic models. In this study, we developed a spatially-explicit, geo-referenced, individual-based model of a prototypical adult dusky dolphin off Kaikoura, New Zealand facing a more powerful, yet infrequent predator, the killer whale. We were interested in two primary objectives, (1) to capture the varying behavioral game between a clever prey and clever predator based on our current understanding of the Kaikoura system, (2) to compare evolutionary costs vs. benefits (foraging time and number of predator encounters) for an adult non-maternal dusky dolphin at various levels of killer whale-avoidance behaviors and no avoidance rules. We conducted Monte Carlo simulations to address model performance and parametric uncertainty. Mantel tests revealed an 88% correlation (426 × 426 distance matrix, km2) between observed field sightings of dusky dolphins with model generated sightings for non-maternal adult dusky dolphin groups. Simulation results indicated that dusky dolphins incur a 2.7% loss in feeding time by evolving the anti-predator behavior of moving to and from the feeding grounds. Further, each evolutionary strategy we explored resulted in dolphins incurring an additional loss of foraging time. At low killer whale densities (appearing less than once every 3 days), each evolutionary strategy simulated converged towards the evolutionary cost of foraging, that is, the loss in foraging time approached the 2.7% loss experienced by evolving near shore-offshore movement behavior. However, the highest level of killer whale presence resulted in 38% decreases in foraging time. The biological significance of these losses potentially incurred by a dusky dolphin is dependent on various factors from dolphin group foraging behavior and individual energy needs to dolphin prey availability and behavior.

Introduction

Predator–prey interactions are a central theme in ecology and predator–prey models have a long history that can be traced back to the early work of Lotka (1925) and Volterra (1926). The vast majority of these models have focused on the population-level consequences of predators killing prey, most frequently in terms of population dynamics mediated by changes in rates of prey mortality and predator natality (Rosenzweig and Macarthur, 1963). More recently, a growing number of studies have focused on non-lethal aspects of predator–prey interactions, viewing predators and prey as clever, dynamically-interacting individuals who influence each other's behavior (Mitchell, 2002, Lima, 2002, Lima, 1998, Lima, 2002, Luttbeg and Sih, 2004, Brown and Kotler, 2004). The short-term consequences of these non-lethal interactions between predator and prey may include shifts in activity budgets, and local dispersion and movement patterns (Lima and Dill, 1990, Sih and Wooster, 1994, Lima, 1998, Wirsing et al., 2008). The long-term consequences may manifest themselves in terms of changes in lifestyle, and ultimately result in hard-wired evolutionary adaptations (reviewed in Dill, 1987, Sih, 1987, Lima, 1998, Verdolin, 2006). While theoretical models have been proposed to represent the costs and benefits associated with shifts in the behavioral tactics of predator and prey (Lima, 1998, Brown et al., 1999, Brown et al., 2001, Brown and Kotler, 2004), sometimes referred to as the “ecology of fear” (Brown et al., 2001), we are unaware of empirically-based models that simulate the reciprocal behavioral interactions of individual predators and prey within a spatially-explicit context.

Specific representation of the interactive nature of predator–prey interactions is particularly important when both predator and prey share similarly complex sensory modalities and behavioral plasticity. In this regard, predator–prey systems involving marine mammals (Jefferson et al., 1990), in which both predator and prey are equally “clever” (Connor, 2000), provide an especially attractive context for exploring these reciprocal behavioral interactions. In marine mammal systems it is often difficult to test predator influence on prey behavioral ecology due to the inaccessibility and unpredictability of the target species, shorter observation periods, and paucity of predation event observations. Despite limitations, long-term data from both opportunistic and dedicated studies reveal clues to understanding habitat use, movement patterns, and behavior of predator and prey. A majority of marine mammal studies thus far have concentrated on effects of predation, and not on the indirect effects of a predator on prey lifestyle (Wirsing et al., 2007), particularly where both predator and prey are marine mammals sharing similar sensory capabilities.

Srinivasan and Markowitz (2009) provided a review of dusky dolphin (Lagenorhynchus obscurus) predator threats and likely survival strategies in terms of changes in habitat use, social organization (group size, group structure, non-maternal adults vs. maternal group behavior), and movement patterns off Kaikoura, NZ. Like most prey, these anti-predator behaviors include short-term tactics such as fleeing and seeking refuge as well as involve long-term strategies such as spatio-temporal changes in movement and habitat use patterns by social type and predator occurrence (Dill, 1987).

It appears that killer whales (Orcinus orca) present a more potent threat to dusky dolphins than sharks in Kaikoura Canyon (Srinivasan and Markowitz, 2009). It is unclear why these dusky dolphin survival strategies evolved and how effective these strategies are in reducing predation risk without compromising food and social needs. But before we attempt to answer the ultimate question of costs vs. benefits for dusky dolphins adopting various predator avoidance strategies, we need to attain a greater understanding of the behavioral relationship between a clever predator (killer whale) and prey (dusky dolphin), as this is an interactive and feedback driven predator–prey association (Lima, 2002, Mitchell, 2002, Lima, 2002). To achieve this, we first developed a spatially-explicit, individual-based model (IBM) simulating a dusky dolphin avoiding killer whales in a heterogeneous marine habitat near Kaikoura, New Zealand based on our current level of understanding of the system. We then use the model to compare evolutionary costs vs. benefits by focusing principally on two parameters, foraging time and number of killer whale encounters for dusky dolphins adopting short and long-term anti-predator behaviors.

Section snippets

Background information

The marine habitat near Kaikoura, New Zealand (42°30′S, 173°35′E) is characterized by the presence of a vast submarine canyon, which begins about 500 m from shore, is roughly 60 km2 long and 1200 m deep, and has a U-shaped profile (Lewis and Barnes, 1999) (Fig. 1). Dusky dolphins near Kaikoura Canyon occasionally are preyed upon by killer whales (Constantine et al., 1998), which are present in the area from November through May (Dahood et al., 2008), and clearly are fearful of killer whale

Model structure

We developed the model as a grid-based, geo-referenced, stochastic IBM (Grimm and Railsback, 2005), programmed in VB.NET© (Microsoft, 2003). Within the IBM, we defined a habitat class, a dusky dolphin class, and a killer whale class. The habitat class contains 1468 instances, each representing a 1 km × 1 km area of the marine habitat in and around Kaikoura Canyon (Fig. 1). We defined spatial extent of the modeled system based on dusky dolphin surveys (Cipriano, 1992, Markowitz, 2004) and dusky

Model evaluation

We evaluated the model by first verifying that simulated dusky dolphins and killer whales moved and responded to each other in qualitatively appropriate manners with regard to the basic movements and behavioral interactions depicted in Fig. 2, and that there was an appropriate level of killer whale presence in the system under the baseline scenario (i.e., killer whales cruising through the system, on average, once every 3 days). We then had a recognized expert perform a Turing test (Turing, 1950

Simulation of predator–prey games

Given the dynamic nature of killer whale-dusky dolphin interactions is poorly understood, we developed the below experimental design to quantitatively explore the costs (=lost foraging time) and benefits (=reduced predator encounters) of several anti-predator strategies.

Discussion

In a behavioral game between a clever prey and clever predator, our prototypical adult dusky dolphin has two general kinds of anti-predator options: fear-driven or fear-impulse strategies that involve a balancing act between short-term loss in hunger vs. long-term cost of death. At the very basic level, foraging time and encounter rate is determined by predator density in the system or the return rate of the killer whales in the system. Thus, increasing presence of killer whales results in

Acknowledgments

We thank Drs. B. Würsig, J. Packard, and X.B. Wu for their expertise, advice, and support throughout the project. The manuscript was greatly improved by comments by B. Würsig and Leigh Torres. The senior author gratefully acknowledges the support of all the Earthwatch volunteers who helped in data collection efforts. Thanks also to Sierra Deutsch and Jennifer Bennett for research assistance and field support. We thank NIWA for providing valuable bathymetry data. Special thanks to I. Bradshaw,

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    Current address: US Army Engineer Research and Development Center, Vicksburg, MS 39180-6199, USA.

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