State‐dependent behavioural theory for assessing the fitness consequences of anthropogenic disturbance on capital and income breeders

Abstract

Summary Anthropogenic disturbance is of increasing concern for wildlife populations, necessitating the development of models that link behavioural changes at the individual level with biologically meaningful changes at the population level. We developed a general framework for estimating the fitness consequences of disturbance that affects foraging behaviour using state‐dependent behavioural theory implemented by Stochastic Dynamic Programming (SDP). We illustrate this framework using generalized examples of pinnipeds, a group of marine carnivores that include both capital‐ and income‐breeding species. We examined how disturbance affected pup recruitment separately for each reproductive strategy, and the impact of foraging decisions and parameter values on model results. The effect of disturbance on pup recruitment was greater for income than capital breeders for all disturbance scenarios. Disturbance had negligible effects on pup recruitment when it occurred within less frequented foraging patches, but moderate to large effects when it occurred within an important foraging patch. Model results were sensitive to values of patch productivity (the energy gained from successful foraging), the probability of disturbance and individual behavioural choices in the face of disturbance. State‐dependent behavioural theory implemented by SDP is a powerful tool for investigating when behavioural changes in response to disturbance may be meaningful at the population level. This approach allows us to incorporate many factors that are known to influence the behavioural and physiological responses of animals to anthropogenic disturbance, and places disturbance within the context of a temporally and spatially variable environment. The general framework we have developed can be used to estimate the consequences of anthropogenic disturbance across a broad range of species.

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., , ).…”

“…The ability of this approach to forecast population‐level effects of disturbance was demonstrated for pinnipeds by McHuron, Costa, 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 ).…”

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).…”

East or west: the energetic cost of being a gray whale and the consequence of losing energy to disturbance

“…Stochastic dynamic programming (SDP) will aid in estimating how far gray whales deviate from optimal foraging strategies in the face of disturbance (Mangel & Clark 1988, Clark & Mangel 2000. The next step in SDP disturbance modeling will require additional information on the prey and predator landscapes (Schwarz et al 2016, McHuron et al 2017. Together, these research efforts would help establish the most effective mitigation strategy.…”