A Dynamic State Model of Migratory Behavior and Physiology to Assess the Consequences of Environmental Variation and Anthropogenic Disturbance on Marine Vertebrates

Abstract

Integrating behavior and physiology is critical to formulating new hypotheses on the evolution of animal life-history strategies. Migratory capital breeders acquire most of the energy they need to sustain migration, gestation, and lactation before parturition. Therefore, when predicting the impact of environmental variation on such species, a mechanistic understanding of the physiology of their migratory behavior is required. Using baleen whales as a model system, we developed a dynamic state variable model that captures the interplay among behavioral decisions, energy, reproductive needs, and the environment. We applied the framework to blue whales (Balaenoptera musculus) in the eastern North Pacific Ocean and explored the effects of environmental and anthropogenic perturbations on female reproductive success. We demonstrate the emergence of migration to track prey resources, enabling us to quantify the trade-offs among capital breeding, body condition, and metabolic expenses. We predict that periodic climatic oscillations affect reproductive success less than unprecedented environmental changes do. The effect of localized, acute anthropogenic impacts depended on whales' behavioral response to the disturbance; chronic, but weaker, disturbances had little effect on reproductive success. Because we link behavior and vital rates by modeling individuals' energetic budgets, we provide a general framework to investigate the ecology of migration and assess the population consequences of disturbance, while identifying critical knowledge gaps.

Understanding the population consequences of disturbance

“…() examined the relation between foraging activity and energy stores (estimated from changes in buoyancy) of female southern elephant seals ( Mirounga leonina ) over the course of a foraging trip. Other applications have inferred changes in energy stores from models of foraging activity that either treat energy explicitly using a bioenergetic approach (Beltran, Testa, & Burns, ; Christiansen & Lusseau, ; Farmer, Noren, Fougères, Machernis, & Baker, ; McHuron, Costa, Schwarz, & Mangel, ; McHuron, Mangel, Schwarz, & Costa, ; Noren, ; Pirotta, Mangel, et al., ; Villegas‐Amtmann et al., , ) or use an arbitrarily scaled energy metric that represents an underlying motivational state (Nabe‐Nielsen et al., , ; New, Harwood, et al., ; Pirotta, Harwood, et al., ; Pirotta, New, Harwood, & Lusseau, ). Although technologies that can measure the morphometrics of individuals remotely may make it easier to estimate changes in body condition directly (e.g., Christiansen, Dujon, Sprogis, Arnould, & Bejder, ; Miller, Best, Perryman, Baumgartner, & Moore, ), extensive health assessment in cetaceans will probably remain limited to a few closely monitored coastal populations, due to logistical constraints (Wells et al., ).…”

“…In the absence of a direct estimate of calf survival, Christiansen and Lusseau () used the fetal length of minke whales ( Balaenoptera acutorostrata ) as a proxy, and investigated how fetal length was associated with female body condition (Christiansen, Víkingsson, Rasmussen, & Lusseau, ). All other PCoD studies of marine mammals have assumed a simple relationship between various health metrics and vital rates (McHuron, Costa, et al., ; Nabe‐Nielsen et al., , ; Pirotta, Mangel, et al., ; Villegas‐Amtmann et al., , ).…”

“…() and for Eastern North Pacific blue whales ( Balaenoptera musculus ) by Pirotta, Mangel, et al. (), whereas Klaassen, Bauer, Madsen, and Tombre () used stochastic dynamic programming to quantify the effects of disturbance on the survival of Svalbard pink‐footed geese ( Anser brachyrhynchus ).…”

“…The third step is to investigate whether there are empirical data on the relation between behavioral change and individual health. If such information is unavailable, bioenergetic models can be used to examine the potential effects of lost foraging opportunities on an individual's health, as measured by its energy stores (McHuron, Costa, et al., ; Nabe‐Nielsen et al., , ; Pirotta, Mangel, et al., ; Villegas‐Amtmann et al., , ). This allows construction of a full PCoD model for the population (outcome 3 in Figure ), as in New et al.…”

Embracing Complexity and Complexity-Awareness in Marine Megafauna Conservation and Research

“…Finally, a number of recent advances have been made in understanding marine megafauna behavior by addressing the dynamic state space of behavior and by developing big-data approaches that require no "a priori" assumptions about the behaviors of study animals (Beyer et al, 2013). Other examples include the use of Stochastic Dynamic Programming (SDP) and state-dependent behavioral theory to investigate how disturbance affects pinniped pup recruitment (McHuron et al, 2017), a dynamic state model of blue whale migratory behavior and physiology to explore the effects of perturbations on reproductive success (Balaenoptera musculus) (Pirotta et al, 2018), and a study of tagged southern elephant seals (Mirounga leonina) that identifies intrinsic drivers of movement, to describe the migratory and foraging habitats (Rodríguez et al, 2017). State space models have also been used to characterize dynamic movement of sea turtles (Jonsen et al, 2007;Bailey et al, 2008), seabirds (Dean et al, 2013), other marine mammal species (Moore and Barlow, 2011), and sharks (Block et al, 2011).…”