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(1)Effects of Tour Boats on the Behavior of Bottlenose Dolphins: Using Markov Chains to Model Anthropogenic Impacts DAVID LUSSEAU Department of Zoology, University of Otago, P.O. Box 56, Dunedin, New Zealand, email [email protected]. Abstract: Nature-based tourism activities have been developing over the last decade, but it is still difficult to manage these activities sustainably. This sector is increasingly focusing on whales and dolphins in coastal communities, but the exact effects of these tourism activities are unclear. Markov chain modeling may help researchers assess the effects of tourism activities on the behavioral budget of small cetaceans. Matrix models have been used widely in population ecology to provide successful management guidelines. From June 2000 to August 2001, I collected information on the behavioral state of bottlenose dolphin ( Tursiops spp.) schools from a population residing in Doubtful Sound, Fiordland, New Zealand. In addition, I recorded the occurrence of boat and dolphin interactions. I then calculated the transition probabilities of passing from one behavior to another by using a first-order, time-discrete Markov chain model. Behavioral transitions during which a boatdolphin interaction occurred were compiled in an “impact” chain. All other transitions were tallied in a control chain. I then quantified the effect of boat-dolphin interactions during behavioral transitions by comparing the behavioral transition probabilities of both chains. Socializing and resting behaviors were disrupted by interactions with boats to a level that raises concern. Both the duration of bouts and the total amount of time spent in both these behavioral states were substantially decreased. Dolphins were significantly more likely to be traveling after an interaction with a boat. However, the overall behavioral budget of the population was not significantly affected. Therefore, the bottlenose dolphin population seems to be able to sustain the present level of boat interactions because of its low intensity. More effort is needed to develop prognosis analyses in order to understand how the effect of boat interactions on dolphins changes with variations in intensity.. Efectos de Lanchas de Turismo sobre el Comportamiento de Delfines Tursiops: Utilización de Cadenas de Markov para Modelar Impactos Antropogénicos. Resumen: Durante la última década se han desarrollado actividades turı́sticas basadas en la naturaleza, pero aun hay dificultades para manejar estas actividades de manera sustentable. Este sector se está concentrando en ballenas y delfines en comunidades costeras, pero los efectos precisos de estas actividades no son claros. El modelado con cadenas de Markov puede ayudar a que investigadores evalúen los efectos de actividades turı́sticas sobre el presupuesto conductual de pequeños cetáceos. Los modelos matriciales han sido ampliamente utilizados en la ecologı́a de poblaciones para proporcionar lineamientos exitosos de gestión. De junio 2000 a agosto 2001, recolecté información del estado conductual de grupos de delfines Tursiops de una población en Doubtful Sound, Fiordland, Nueva Zelanda. Adicionalmente, registré la ocurrencia de interacciones lancha/delfı́n. Posteriormente calculé probabilidades de transición de pasar de una conducta a otra mediante un modelo de cadena de Markov de tiempo discreto y primer orden. Las transiciones conductuales en las que ocurrieron interacciones lancha/delfı́n fueron compiladas en una cadena de “impacto.” Todas las otras interacciones fueron registradas en una cadena control. El efecto de las interacciones lancha/delfı́n durante las interacciones fueron cuantificadas comparando las probabilidades de transición conductual de ambas cadenas. Las conductas socializantes y de descanso fueron perturbadas por interacciones con lanchas. Paper submitted February 5, 2002; revised manuscript accepted February 4, 2003.. 1785 Conservation Biology, Pages 1785–1793 Volume 17, No. 6, December 2003.

(2) 1786. Modeling Anthropogenic Impacts on Animal Behavior. Lusseau. a un nivel que es preocupante. Tanto la duración de encuentro como el tiempo total empleado en estos estados conductuales decrecieron sustancialmente. Después de una interacción con una lancha, los delfines tuvieron una probabilidad significativa de viajar. Sin embargo, el presupuesto conductual general de la población no fue afectado significativamente. Por lo tanto, la población de delfines Tursiops parece capaz de mantener el nivel actual de interacciones debido a la baja intensidad de interacciones con lanchas. Se requiere de mayor esfuerzo para desarrollar análisis de pronósticos para poder entender como cambia el efecto de interacciones con lanchas con variaciones en su intensidad.. Introduction Interactions between cetaceans and tour vessels are increasing (Hoyt 2001). The number of tourists involved in cetacean-watching activities has increased from 4 million in 1991 to 9 million in 1998 (Hoyt 2001). Most commonly, this activity is boat based (Hoyt 2001), and the indirect effects (i.e., other than direct collisions) of these activities are still poorly understood. So far, researchers have only described the effects observed and have not been able to assess the biological significance of these impacts (Kruse 1991; Corkeron 1995; Janik & Thompson 1996; Nowacek et al. 2001; Van Parijs & Corkeron 2001). These researchers have observed short-term changes in behavior, but the importance of these changes still needs to be established. Responses observed include direct avoidance of the boat vicinity, increase in dive intervals, increase in speed, and variations in vocalizations (Kruse 1991; Corkeron 1995; Janik & Thompson 1996; Bejder et al. 1999; Nowacek et al. 2001; Van Parijs & Corkeron 2001; Williams et al. 2002). Such responses are difficult to take into consideration in management actions because their relationships to the biology and ecology of cetacean populations are unknown. Changes in behavioral budgets, assessed within discrete categories of behavior (i.e., behavioral states), can provide information on the biological significance of an impact. This parameter has not been explored because of sampling and analysis difficulties (Mann 2000), especially in detecting impacts. Markov chain modeling can circumvent the analytical problems faced to date. The behavior of animals varies over time. The sampling techniques available to researchers enable them to define subcategories of behavior, which we call behavioral states, along the behavior continuum (Altmann 1974). Until recently, most statistical methods used to analyze behavior did not take into consideration its temporally dynamic nature. When assessing the effects of a certain factor on behavior, techniques currently used do not allow unambiguous conclusions about the effects on the time structure of this temporally variable information. Information theory provides a strong basis on which to assess temporal variations in a data set and determine the nature of the effects of factors of interest. The temporal variation in samples of behavioral states obtained by scan sampling from schools of cetaceans (Altmann 1974) can. Conservation Biology Volume 17, No. 6, December 2003. be analyzed with time-discrete Markov chains (TDMC). Markov chains have been widely used in applied sciences and especially in population ecology (Seneta 1966; Caswell 2001; Fujiwara & Caswell 2001). They were first developed by Markov (1906) to demonstrate that independence is not necessary for the law of large numbers. Since then Markov chains have been applied to dependent variables, such as the age structure of a population, to reveal how these relate in time. These chains quantify the dependence of an event on the preceding ones and provide probabilities of transition from one event to another. Because behavioral states are mutually exclusive subcategories arbitrarily defined along the behavior continuum by the observer, they can be defined as mutually dependent, and therefore their sequencing in time can be assessed by means of Markov chains. Stochastic processes such as Markov chains can determine with precision the nature of the effects of different factors on a data set; they are valuable tools for assessment of ecological impact (Tanner et al. 1994; Hill & Caswell 2001). I applied Markov chains to quantify the impact of tourboat interactions on the behavioral state of bottlenose dolphins (Tursiops spp.). My goal was to understand how interactions with tour boats affect the temporal dynamics of behavioral states of dolphins and how these effects consequently alter the behavioral budget of bottlenose dolphins in Doubtful Sound.. Methods Study Area and Population I studied a population of bottlenose dolphins that reside year-round in Doubtful Sound, Fiordland, New Zealand (Williams et al. 1993). This population, composed of 60– 65 individuals, is unusual because it forms a closed community: there is no apparent immigration or emigration (Williams et al. 1993). Doubtful Sound (Fig. 1) is the second largest fjord (83.7 km2 ) of the 14 fjords that comprise Fiordland in New Zealand (Stanton & Pickard 1981). The maximum depth is 434 m, and the fjord rarely reaches depths of <100 m (Stanton & Pickard 1981). Scenic cruises take place year round in this fjord. Even though these tours are not specifically undertaken for watching dolphins, most tour operators interact at least once with.

(3) Lusseau. Modeling Anthropogenic Impacts on Animal Behavior. 1787. Table 1. Definitions of the behavioral states of bottlenose dolphin (Tursiops spp.) schools. State (abbreviation). Definition. Traveling (TR). school moves steadily in a constant direction (faster than the idle speed of the observing vessel), swimming with short, relatively constant dive intervals; group spacing varies Resting (REST) school moves slowly in a constant direction (slower than the idle speed of the observing vessel), swimming with short, relatively constant, synchronous dive intervals; individuals tightly grouped Milling (MI) no net movement; individuals surfacing facing different directions; school often changes direction; dive intervals variable but short; group spacing varies Diving (DIVE) direction of movement varies; group dives synchronously for long intervals; all individuals perform “steep dives” arching their backs at the surface to increase speed of descent; group spacing varies. Diving most likely represents the “feeding” category in other studies (Shane 1990) Socializing (SO) diverse interactive behavioral events observed, such as body contacts, pouncing, genital inspections, and hitting with tail; individuals often change position in the group; group is split into small subgroups spread over a large area; dive intervals vary. dolphin schools during each cruise. Dolphins are a key natural resource for the industry in this location (Lusseau 2002), and its sustainability relies on the welfare of this isolated population.. Sound (average 17 individuals), so following focal groups was more appropriate (Mann 2000). I preferred scan sampling of individuals within the school to sampling of focal groups because of the observer bias inherent to the latter technique (Mann 2000). Observations ended when the weather deteriorated, the focal school was lost, or the day ended; therefore, the end of a sequence of observations was not dependent on the behavior of the focal school.. Data Collection. Observation Platform. I collected behavioral data between June 2000 and August 2001. I conducted systematic surveys of the fjord to detect schools, and I identified individuals in the school with photo-identification. I then sampled the behavioral state of the school on which I was focused every 15 minutes. I scan sampled this focal school (Altmann 1974) to determine the main activity of the school—traveling, resting, milling, socializing, or diving (Table 1). I defined behavioral states to be mutually exclusive and cumulatively inclusive; as a whole they described the entire behavioral budget of the dolphins. These states were similar to the ones used in other studies (Shane 1990; Bearzi et al. 1997). I sampled focal groups to understand the effect of boat interactions at the school level, rather than at the individual level. Dolphins live in large schools in Doubtful. I conducted behavioral observations from a 4.5-m vessel powered with a 50 HP, four-stroke outboard engine. Because of the remoteness of the fjord and its topography, data could only be collected from an observing vessel and not from land. I developed a protocol to eliminate the potential bias of the observing vessel. First, I piloted the observing vessel so as to minimize interactions between dolphins and the vessel. I approached focal schools from the side and back in the same direction as the movement of the school. I matched the speed of the vessel to the speed of the school (idle speed most of the time). I then followed the school at distances ranging from 50 to 200 m. I chose a four-stroke engine for reduced noise emission. This protocol was maintained while the focal school interacted with tour vessels. Therefore, the behavior of. Figure 1. Location of the study area, Fiordland, New Zealand.. Conservation Biology Volume 17, No. 6, December 2003.

(4) 1788. Modeling Anthropogenic Impacts on Animal Behavior. the observing vessel was not changed over the different states of our independent factor. Consequently, any differences observed to depend on the presence of a tour operating vessel was only related to the presence of the tour boat, not the observing vessel. Interactions between Tour Boats and Dolphins A wide range of vessels operates in the fjord, from kayaks (usually in groups of five) to a 15-m catamaran powered by twin diesel engines. Only one vessel operates year round. During the study period, four vessels and one kayak company operated daily tours during the warmer months of summer. In summer there were up to six cruises on the fjord daily. Vessels operating fishing charters also had occasional interactions with dolphins, but these interactions were limited. Because of the remoteness of this fjord, the number of private boats operating was extremely limited, and they rarely interacted with dolphins (7.9% of interactions). If a vessel was within 400 m of the focal school, it was determined to be interacting with the dolphins. I measured distances between tour boats and dolphins with a range finder (Bushnell Corporation, Cody, Kansas) with a precision of 1–5 m. I defined this 400-m limit from a preliminary study, which showed that dolphins tended to break off from their school to ride the front pressure wave from a vessel if the vessel was within 400 m of the school (D.L., unpublished data). The length of boat interactions therefore corresponded to the time boats spent within 400 m of the focal dolphin school. Developing Markov Chains Markov chains quantify the dependence of an event on preceding events (for detailed background information, see Guttorp [1995] and Caswell [2001]). There are several degrees of dependence. If sequencing events are independent, they are described by a zero-order Markov chain. If an event depends only on the immediately preceding one, it fits a first-order Markov chain. If an event depends on the two most preceding events, it is a second-order Markov chain, and so on. This dependence can be affected by any extrinsic factor occurring between events. Therefore, I decided to assess the difference in transition from one event to another, depending on the presence of boat interactions during that sequence. I concentrated only on a first-order Markov chain model to simplify the analytical design and because boat interactions lasted on average less time than a behavioral sampling interval. I developed two two-way contingency tables, preceding behavioral state versus succeeding behavioral state. If no boat interaction occurred between two behavioral samples, I tallied the transition between these two samples in a control table. If a boat interaction occurred between two samples, I tallied the transition in an impact table. Because I had no idea of the extent of the potential. Conservation Biology Volume 17, No. 6, December 2003. Lusseau. impact of a boat interaction, I removed the transition between a sample succeeding an interaction and the following sample. In other words, if a boat interaction occurred between samples 1 and 2, the transition between samples 2 and 3 was discarded. If sample 2 was affected by a boat interaction, then the transition between 2 and 3 could be considered neither impact nor control. I then compared these two tables to detect the effect of boat interactions. Assumptions To analyze these pooled transitions, it is necessary that the probability that a transition will occur remain the same over time. To test for this stability over time, I designed a three-way contingency table (four seasons versus five preceding behaviors versus five succeeding behaviors) for the control chain. If the transition probability was stable over time, season had no effect on the outcome of the transition. To test for the independence of the behavioral transitions from season, I conducted a log-linear analysis of the three-way contingency table. To detect an effect of time on the transitions, I compared the fully saturated model (season × preceding behavior × succeeding behavior) to the model with all two-way interactions. I used a goodness-of-fit test to compare the latter model to the data. The difference in goodness of fit between this model and the saturated model G 2 = G 22way − G 2saturated was also the likelihood ratio test for the effect of season, with degrees of freedom equal to the difference in the degrees of freedom of the two models (Caswell 2001). Additionally, it was necessary to determine whether a first-order relationship existed in these transitions. For a first-order chain to exist, it needs to provide more information than a zero-order chain. I compared the amount of information contained in zero- and first-order chains using a Bayes information criterion (BIC). A BIC estimates the amount of information a model provides and penalizes models for the number of parameters used to explain the data: BIC = 2l(θ̂ | data) − K ln(n),. (1). where l(θ̂ | data) is the value of the maximized loglikelihood over the unknown parameter (θ ), given the model and the data set; K is the number of parameters utilized in the model; and n is the sample size. Therefore, it quantifies the most parsimonious model. It is a consistent estimate of the order of Markov chains (Katz 1981). The higher the BIC of a chain, the more likely it is to exist. A BIC difference of 2log 100 (=9.2) is sufficient to determine the best chain (Guttorp 1995). Markov Chain Modeling and Impact Assessment I constructed a three-way contingency table by merging the impact and control chains—boat presence versus.

(5) Lusseau. Modeling Anthropogenic Impacts on Animal Behavior. preceding behavior versus succeeding behavior. I then applied a log-linear analysis to assess the independence of both preceding and succeeding behaviors from boat presence. I used the difference in goodness of fit between the saturated model and the model considering all twoway interactions to test for the effect of boat presence on the behavioral transitions. Transition probabilities (from preceding to succeeding behavior) were then determined in both control and impact chains by ai j pi j = 5 j=1. ai j. ,. 5 . pi j = 1,. (2). qi = vi ,. of the geometric distribution of pii (Guttorp 1995): tii = with a standard error of SE =. 1 1 − pii. pii × (1 − pii ) , ni. (4). (5). where ni is the number of samples with i as preceding behavior. Once again the average bout length for each state can be compared for both chains.. j=1. where i is the preceding behavior, j is the succeeding behavior (i and j range from 1 to 5 because there are five behavioral states in the repertoire), aij is the number of transitions observed from behavior i to j, and pij is the transition probability from i to j in the Markov chain. Each transition is the proportion of time I observed a succeeding behavior following a preceding behavior (Eq. 2). Therefore, I tested the effect of boat interactions on the behavior-transition probability matrix with a Z test for proportions (Fleiss 1981). Each control transition was compared to its impact counterpart. Both the control and impact Markov chains are irreducible and non-negative: it is possible to go from all behavioral states to all states. Therefore, according to the Perron-Frobenius Theorem (Caswell 2001), there exists a real dominant eigenvalue λ to which correspond real and positive right, w, and left, v, eigenvectors. In addition, the chains follow the ergodic theorem (Caswell 2001). Because of the ergodic nature of the chains, the initial distribution of behavioral state is negligible for the long-term behavior of the chain. It converges toward a stationary behavioral distribution proportional to v. This stationary distribution corresponds to the behavioral budget of the population. It is therefore possible to compare the behavioral budget of the control and impact chains. Differences observed in the budget are inherent to interactions with boats. The stationary distribution can be derived from the left eigenvectors of λ: 5 . 1789. Results During the study period I spent 68 days (534 hours) looking for dolphins and 434 hours following focal schools. I observed 178 boat interactions. Dolphins spent 9.0% of the time I followed them with tour boats. I collected 1297 behavioral transitions, of which 1037 were classified as control and 260 as impact. I collected these behavioral transitions over 166 control sequences and 135 impact sequences. Control sequences lasted 105 minutes on average (median = 75 minutes, SE = 5.9, range = 30–390), and impact sequences averaged 42 minutes (median = 45 minutes, SE = 1.3, range = 30–105). Assumptions Transitions in behavioral states were stable over time (G2 = 53.0, df = 48, p = 0.29). The likelihood-ratio test between the saturated model and the two-way interaction model, which is the goodness of fit of the two-way interaction model, was not significant. First-order transitions in behavioral state provided more information than a zero-order chain (BICfirst−order = −1396.9 and BIC0-order = −1670.3). The BIC difference between the two chain orders was 273.4; therefore, firstorder transitions provided much more information than the sole frequency distribution of the states. Effect of Boat Interactions. qi = 1,. (3). i=1. where i is a behavioral state. I eigenanalyzed the transition matrices with PopTools 2.3, an add-in to Excel developed by the Commonwealth Scientific, Industrial, and Research Organization (CSIRO) (http://www.cse.csiro.au/ cdg/poptools). I tested the differences between the two behavioral budgets with a Z test for proportions (Fleiss 1981) and calculated 95% confidence intervals with the Wilson technique described by Newcombe (1998). Finally, the average bout length of each behavioral state, tii , can be approximated for both chains from the mean. Boat interactions did have an effect on the transitions in behavioral states (G2 = 34.9, df = 16, p = 0.004), but the effect was not homogeneous over all transitions. Boats significantly changed seven transitions (Fig. 2). Four transitions—socializing→diving, socializing→traveling, resting→traveling, and milling→traveling (see Table 1 for definitions of behavior states)—increased as a result of interactions with boats. Three—socializing→socializing, traveling→resting, and resting→resting—showed a decrease, a negative effect. In most cases where an increase in transition probability was detected, traveling was the succeeding behavioral state. Six of these seven transitions (Fig. 2) involved socializing or resting states.. Conservation Biology Volume 17, No. 6, December 2003.

(6) 1790. Modeling Anthropogenic Impacts on Animal Behavior. Lusseau. Figure 2. Effect of boat interactions on transitions in behavioral state of dolphins, based on differences in transition probabilities (pij(impact) − pij(control) ). Therefore, a negative value means that the behavioral transition of the control chain is superior to the one of the impact chain. The graph is composed of five parts, one for each preceding state, separated by vertical lines. In each part, bars correspond to succeeding behavioral states (see legend). The seven transitions with a significant difference (p < 0.05) are marked with a star. Behavioral states are defined in Table 1. The magnitude of the difference in transition probabilities between the two chains is important (Fig. 3). The probability of staying in a socializing state (pso→so ) and the probability of staying in a resting state (prest→rest ) were both decreased by interactions with boats. The probability of staying in a socializing state dropped from 66% to 14%, wheras the probability of staying in a resting state was almost halved. The probability of changing from socializing, milling, and resting behavior to traveling behavior was roughly doubled by boat interactions. No resting bouts were initiated when a boat interacted with dolphins. Finally, the transition probability from socializing to diving drastically increased from 5% to 29%. There was also a difference in the average length of behavior bouts (Fig. 4). The length of social and resting bouts was significantly reduced in the impact chain compared with the control chain (Fig. 4), whereas traveling bouts were longer. Socializing bouts were reduced by 60% and resting bouts by 34%. Traveling bouts increased by 18%. The stationary probability distribution of the chains was also different. In other words, the behavioral budget of dolphins was different when boat interactions occurred (Fig. 5). Overall, dolphins spent more time trav-. Conservation Biology Volume 17, No. 6, December 2003. eling and diving when boats were present than they did socializing and resting. The time spent socializing was significantly reduced by almost half, and the time spent resting was significantly reduced from 11% to 1%. There was no difference between the observed control behavioral budget and the control stationary distribution (G2 = 4.27, df = 4, p = 0.37). The first-order Markov chain model was therefore a good representation of the data.. Discussion The application of transition matrix analyses to the field study of behavior provided more information than standard techniques would have. From the log-linear analysis it was possible to detect an effect of boat interactions on the behavioral transitions. However, it was not possible to relate these significant short-term changes to long-term changes in the behavioral budget and the average length of behavioral bouts. Behavioral states are difficult to sample adequately in the field without observer bias. To minimize this bias, some “sampling sacrifices” are necessary. These limitations render the analysis of the data collected.

(7) Lusseau. Modeling Anthropogenic Impacts on Animal Behavior. 1791. avoidance of boats rather than an actual change in behavioral state. These animals may pass from a surface-active state (Chilvers & Corkeron 2001) to an underwater-active state. The stratified oceanography of the fjord (Gibbs et al. 2001) influences the acoustic propagation of sound. It may therefore be possible for dolphins to “escape” the sounds emitted by boats by avoiding the top freshwater layer of the fjord and remaining in the deep marine layer. Behavior changes following boat interactions affected not only the average duration of socializing and resting bouts, but also the entire behavioral budget of the dolphin community. The decrease in time spent socializing and resting was substantial: socializing bouts were decreased by almost two-thirds and resting bouts by one-third. Boat interactions also reduced the overall amount of time spent socializing and resting. Resting was almost nonexistent as a state in the stationary probability distribution of the impact chain. Sustainability of Tourism in Doubtful Sound. Figure 3. Markov chains representing probabilities of transition in behavioral state: (a) control chain and (b) impact chain. Only transitions affected by boat interactions are represented; values are percentages. Behavioral states are defined in Table 1.. difficult. The technique I describe here palliates these difficulties (such as the estimation of bout length). The transition analyses clarify that tour-boat interactions did affect the behavioral state of bottlenose dolphins in Doubtful Sound. They affected the transition probabilities between behavioral states, dolphins being more likely to travel after boat interactions. They were also less likely to continue socializing or resting after a boat interacted with the focal school. The substantial increase in transition probability from socializing to diving may represent a response mitigating the effect of boat presence on socializing bouts. Bottlenose dolphins in Doubtful Sound display many aerial and surface activities while socializing, such as headbutting, chasing, and jumps of many kinds (D.L., unpublished data). It is possible that dolphins continue socializing when boats interact with them but change the way they socialize by interacting at depth. It is therefore possible that the increase in diving following socializing after boat interactions represents a vertical. It is important to relate these findings to the issue of sustainability of tourism activities in the area. The economic viability of the industry in this area relies on bottlenose dolphins (Lusseau 2002). Can the dolphin population of Doubtful Sound sustain the impact of boat interactions? It is possible to relate the changes in behavioral budget during boat interactions to the cumulative behavioral budget of the population. From the data I collected during this study, I know that dolphins spend 9.0% of daytime with tour boats. Therefore, for 9% of the time they follow a behavioral budget similar to the stationary probability distribution of the impact chain, and for the remaining 91% their behavioral budget is similar to the one of the control chain. Their cumulative diurnal behavioral budget (91% control + 9% impact) did not vary significantly from the control behavioral budget (goodness-of-fit test, G2 adj. = 0.18, df = 4, p = 0.99). However, it is irrelevant to quantify a sustainable level of tourism activities by considering a constant effect of boat interactions with an increase in the abundance of boats. There is a high likelihood that a density-dependent response to the impact is at play, with either a sensitization or a habituation type of temporal response to an increase in the abundance of boat interactions (Terhune 1985; Fowler 1999; Constantine 2001). Moreover, the general horizontal avoidance response, if its frequency of occurrence is increased, may translate into a general avoidance of areas where boat interactions take place. There may also be an energy-budget threshold where the stress caused by boat interactions cannot be energetically counterbalanced by the benefits of the area. The community may then shift to a suboptimal area to escape the energetic costs of boat impact (Gibeau et al. 2002). My retrospective analysis is a first step in the management of tour-boat interactions with dolphins. It is now. Conservation Biology Volume 17, No. 6, December 2003.

(8) 1792. Modeling Anthropogenic Impacts on Animal Behavior. Lusseau. Figure 4. Mean stability in each behavioral state (i.e., average bout length) for each chain. Stability ( y-axis) is given in numbers of samples. Bout length (in minutes) is derived by multiplying the number of samples by 15 minutes (the sampling interval). Error bars are 95% confidence intervals. Behavioral states are defined in Table 1. necessary to concentrate on prospective analyses that will provide a prognosis of thresholds of interactions depending on the population’s behavioral budget and the amount of boat interaction. Further analyses need to be conducted on several populations exposed to different levels of boat interaction to determine the density dependence of the dolphin’s behavioral response to tour-boat impact. It is also necessary to estimate whether the horizontal avoidance detected can indeed lead to area avoidance with an increase in interaction intensity. Again, this. threshold can be detected by replicating this study with different populations exposed to more boat traffic. Once these thresholds are detected, they can be incorporated in a limit-of-acceptable-change management approach. The tourism industry is important to the local community. Dolphins in Doubtful Sound are sensitive to the presence of boats when they are socializing and resting; however, dolphins are also the most attractive to tour boats when they are socializing because of their active surface behavior. This management dilemma can only be. Figure 5. Stationary probability distribution for the control and impact chains. These two probability distributions represent the behavioral budgets of dolphins during an interaction with boats and in control situations. Values are the proportion of time spent in each behavioral state. Error bars are 95% confidence intervals calculated according to Wilson’s technique (Newcombe 1998). Behavioral states are defined in Table 1.. Conservation Biology Volume 17, No. 6, December 2003.

(9) Lusseau. resolved by the precise definition of a limit of acceptable change on the number of boat interactions during a socializing state.. Acknowledgments I thank P. Corkeron, E. Meelis, P. Dixon, and E. Main for their constructive comments, which much improved this manuscript. All the members of the Marine Mammal Research Group of the University of Otago, particularly C. Richter, E. Slooten, S. M. Dawson, O. J. Boisseau, S. M. Lusseau, and D. Clement provided useful comments and ideas on earlier versions of this paper. I thank H. Caswell, who provided many constructive suggestions on this work. This project was funded by the New Zealand Department of Conservation. Additional financial and technical support was provided by the New Zealand Whale and Dolphin Trust, University of Otago, and Fiordland Travel (now named Real Journeys). Literature Cited Altmann, J. 1974. Observational study of behaviour: sampling methods. Behaviour 49:227–267. Bearzi, G., G. 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