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. 2025 Sep 11;35(6):e70076. doi: 10.1002/eap.70076

Influence of vessel disturbance on Pacific harbour porpoise (Phocoena phocoena vomerina) echolocation

Karina Dracott 1, Chloe V Robinson 1,✉, Lauren Dares 1, Erin Woodley 1, Amy Migneault 1, Caitlin Birdsall 1,2
PMCID: PMC12426586  PMID: 40936331

Abstract

Vessel disturbance is one of many anthropogenic threats that are negatively impacting coastal cetacean populations worldwide. Noise pollution from vessels can cause varying levels of disturbance in cetaceans, depending on several factors such as vessel type and speed. Pacific harbour porpoises (Phocoena phocoena vomerina) are distributed throughout coastal waters of the North Pacific Ocean, with large aggregations observed near the entrance to the Port of Prince Rupert in British Columbia, Canada. This area serves as an important year‐round foraging ground for harbour porpoises. However, it is also one of the fastest growing container ports in North America, with planned increases in activity. Harbour porpoises are highly sensitive to vessel‐related acoustic disturbances, but the effects of vessel activity on their foraging rates remain unclear. In this study, we used a combination of land‐based surveys, passive acoustic monitoring (PAM) devices (C‐PODs and F‐PODs), and automatic identification system (AIS) data to investigate the relationship between vessel activity and harbour porpoise echolocation activity—both foraging and non‐foraging—in the heavily trafficked Chatham Sound, adjacent to the Port. Our results show that an increase in the total number of vessels negatively affected both foraging and non‐foraging echolocation activity, with less echolocation observed in the presence of more ferries and tugs. Similarly, vessels traveling at higher speeds (>6 m/s kn) had a negative effect on echolocation activity. Tugboats and passenger vessels, in particular, had a wider range of effects on all harbour porpoise echolocation activity. Our findings indicate that implementing a vessel slowdown (~5 m/s) along the approach to the Port of Prince Rupert would reduce disturbances to harbour porpoises and likely benefit other coexisting species that rely on quiet oceans for communication and foraging.

Keywords: echolocation activity, foraging ecology, inter‐click intervals, pacific harbour porpoise, passive acoustic monitoring, vessel disturbance

INTRODUCTION

There has been growing concern in recent decades regarding the impact of vessel disturbance (both physical and acoustic) on cetacean species (Putland et al., 2018; Senigaglia et al., 2016; Weilgart, 2007). Acoustic disturbance from underwater noise from seismic exploration, naval sonar operations, marine construction, and vessel activity is problematic for many odontocetes (Hildebrand, 2009; Nabi et al., 2018; Weilgart, 2007). Vessel noise has been shown to result in behavioral responses including avoidance, reduced foraging capabilities, metabolic stress, live strandings, and, in some cases, hearing loss (Dey et al., 2019; Erbe, 2002, 2012; Veirs et al., 2016). Species including bottlenose dolphins (Tursiops truncatus) and short‐finned pilot whales (Globicephala macrorhynchus) have been reported to alter their speed in highly vessel‐trafficked areas, spend more time traveling than socializing or resting in the presence of vessels, and suffer from acoustic communication masking due to vessel noise and sound propagation (Jensen et al., 2009; Marley et al., 2017). Reduction in foraging activity due to noise pollution is of great concern for the long‐term persistence of cetacean populations, particularly for endangered species (Nabi et al., 2018) and species with high metabolic rates (Dey et al., 2019). This is especially the case for the Critically Endangered Southern Resident Killer Whale (Orcinus orca; hereafter SRKW) population, which has been shown to decrease foraging time in the presence of all types of vessels (Holt et al., 2021; Lusseau et al., 2009; Williams et al., 2021, 2024).

Determining the impact of vessel noise on specific cetacean species can be difficult due to the complexity of the threat (Verling et al., 2021; Weilgart, 2007). Several factors contribute to the level of acoustic disturbance from vessels, including their size, type, activity, and speed, as well as environmental parameters such as water depth and bathymetry (Arranz et al., 2021; Erbe, 2002; Leaper, 2019; Lusseau et al., 2009; MacGillivray & de Jong, 2021; Parsons et al., 2021). In addition, these factors can impact cetacean species differently depending on whether they are a low acoustic‐frequency (e.g., large baleen whales), mid acoustic‐frequency (e.g., bottlenose dolphin), or high acoustic‐frequency (e.g., harbour porpoise [Phocoena phocoena]) species (National Marine Fisheries Service, 2018; Southall et al., 2019; Tougaard et al., 2015). These frequency‐specific impacts facilitate the assignment of noise exposure levels, above which adverse effects (behavioral and/or physical injury) are expected (Southall et al., 2019). There are, however, limited empirical data for high‐frequency species, including the harbour porpoise, which is considered one of the species of marine mammal most sensitive to disturbance (Tougaard et al., 2015). Recent studies have provided some suggestions for generalized exposure limits for this species; however, there remain questions around variability in the behavioral response of harbour porpoise to vessel noises occurring below this threshold and the long‐term fitness consequences of this exposure (Bas et al., 2018; Dyndo et al., 2015; Tougaard et al., 2015; Wisniewska et al., 2016). Obtaining additional data on the impact of vessel noise on behavior for vulnerable high‐frequency odontocete species such as the harbour porpoise can facilitate targeted mitigation strategies in highly trafficked areas.

The Port of Prince Rupert is one of the fastest growing container ports in North America and is projected to continue expanding both terminal and shipping operations. This area, also known as southeastern Chatham Sound, overlaps with almost constant Pacific harbour porpoise (Phocoena phocoena vomerina) presence (Dracott et al., 2022). They are classified as “Special Concern” under the Canadian Species at Risk Act (SARA), due to impacts of habitat deterioration and compounding threats such as bycatch, entanglement, and underwater noise. Harbour porpoise estimates range widely for the greater eastern Pacific population, ranging from 1314 individuals (Wright et al., 2021) to 9120 (Williams & Thomas, 2007). These estimates are likely conservative given the nature of the non‐replicated survey efforts. Interestingly, local aggregations of up to 1000 individuals have been estimated during winter months (Dracott et al., 2022), likely due to increased productivity and aggregations of prey in Chatham Sound and surrounding areas caused by tidal mixing (Clarke & Jamieson, 2006; Skov & Thomsen, 2008). Harbour porpoises, being a small cetacean species, have a large body surface to volume ratio and a very high metabolism (Rojano‐Doñate et al., 2018). Consequently, harbour porpoises require an almost constant intake of prey, mainly consisting of small schooling fish such as Pacific herring (Clupea pallasi) and Pacific hake (Merluccius productus; Nichol et al., 2013) to be able to maintain their daily energy requirements (Rojano‐Doñate et al., 2018). Harbour porpoises use high‐frequency echolocation to communicate and hunt (Møhl & Andersen, 1973) and are known to forage across various diel periods worldwide (Carlström, 2005; Dracott et al., 2022). The foraging echolocation of harbour porpoises is distinct; they begin with an “approach phase” and finish with a “terminal buzz,” which is indicative of successful or almost successful prey capture (Bergès et al., 2020; Miller, 2010; Wisniewska et al., 2016). To distinguish foraging acoustic behavior from non‐foraging acoustic behavior, the intervals between clicks (i.e., inter‐click interval [ICI]) can be assessed from acoustic recordings (Au et al., 1999; Koschinski et al., 2008; Wisniewska et al., 2012).

Passive acoustic monitoring (PAM) has been widely used to study the foraging behavior of harbour porpoises (Schaffeld et al., 2016; Todd et al., 2022). PAM has also enabled the collection of data on harbour porpoise behavior in relation to vessel disturbance. Bas et al. (2017) found that harbour porpoises in Turkish waters were less likely to surface and more likely to change their behavioral state when vessels were present, and slow vessels (up to 1.5 m/s) did not evoke the same dramatic change. Similarly, in the United Kingdom, Oakley et al. (2017) recorded strong behavioral reactions of harbour porpoises in response to cargo ships and fast‐moving vessels. In addition, harbour porpoises have shown a decrease in foraging buzzes due to shipping activity in other regions of the Atlantic (Wisniewska et al., 2018). Within British Columbia, noise hotspots have been identified within the Salish Sea (the body of water southeast of Vancouver Island), northeastern Vancouver Island, and Chatham Sound for harbour porpoises (Erbe et al., 2014). Yet, it remains unclear how these perceived acoustic threats are impacting behavioral responses such as foraging rates within these hotspots. There is a need to produce a clearer consensus of how porpoises are impacted within noise hotspot areas such as Chatham Sound, throughout the year, and to specifically identify the influence of factors such as number of vessels, vessel speed, and vessel type on foraging behavior responses (Fisheries and Oceans Canada, 2009; Tougaard et al., 2015).

In this study, we used ICIs from a multiyear harbour porpoise PAM dataset to differentiate between foraging and non‐foraging echolocation activity in relation to vessel activity collected from automatic identification systems (AIS) and land‐based surveys within Chatham Sound.

MATERIALS AND METHODS

Study site and PAM data collection

Our study site consisted of a 2‐km2 region in Chatham Sound, southeast of Digby Island near Prince Rupert in northwestern British Columbia, Canada (see Dracott et al., 2022). Within this site, we used PAM devices (C‐PODs [cetacean‐porpoise detector] and F‐PODs [full waveform porpoise detector]) moored 3.65 m from the sea floor in 18–25 m of water to collect echolocation activity data on harbour porpoise. For this study, one C‐POD was deployed intermittently between July 2016 and October 2018 at the study site (54.2438 N, 130.36183 W) until it was displaced in a storm and could not be recovered. The C‐POD was subsequently replaced with three F‐POD units deployed in rotation at the same location between January 2020 and May 2022 as in the previous paper (Dracott et al., 2022).

PAM data processing

Acoustic files were cropped and adjusted for local time zones. Narrow‐band high‐frequency (NBHF) harbour porpoise echolocation click trains were extracted using the KERNO classifier (Tregenza et al., 2016) and “High” and “Medium” quality trains were retained for analysis as detailed in Dracott et al., 2022. The resulting cleaned data were sorted into foraging‐ and non‐foraging communication categories based on behavior‐associated ICI values (ICI < 10 ms = foraging, ICI of 10–250 ms = non‐foraging; Carlström, 2005; Verfuß et al., 2009). Non‐foraging echolocation was defined as other echolocation used for navigation, prey localization, and potential social communication. We then transformed the F‐POD data (2020–2022) into number of click trains of each category per hour, as well as number of active minutes as detection positive minutes (DPM) of each category per hour. Only the C‐POD data that overlapped with land‐based surveys were used (see Land‐based surveys ), and these were transformed into number of click trains of each category per minute for a more precise analysis. This was possible only for C‐POD data due to the higher precision of vessel presence data collected during 2016–2018 compared to the data from 2020 to 2022.

Vessel presence data

AIS data for the duration of the F‐POD deployment (January 2020–May 2022) in the vicinity of the study area were provided by Ocean Networks Canada (ONC) in AIVDM/AIVDO format. Data were decoded with libais v0.15 (Schwehr, 2023) using the low‐level C++ interface and custom Python code (version 3.8.8) to translate messages into locations and ship information. Resulting AIS tracks were summarized by day to facilitate comparison and analysis with echolocation activity data. Descriptions of fields in the resulting AIS data tracks can be found in Appendix S1: Table S1. Any vessels transmitting AIS messages that passed through a 1‐km radius of the F‐POD were accounted for by hourly presence and categorized by vessel type. Standard AIS vessel data categories were used, including but not limited to cargo, fishing, tug, towing, pilot, pleasure craft, and passenger vessels (e.g., Ferries and cruise ships; Appendix S1: Table S2). This radius was chosen due to knowledge that harbour porpoises react to vessels up to a distance of ~1000 m (Hermannsen et al., 2014; Wisniewska et al., 2018). Vessels transiting through the study area were categorized into speed categories from AIS data (speed computed over successive AIS logs; slow: <2.5 m/s; medium: ≥2.5 and ≤6 m/s; fast: >6 m/s; Appendix S1: Table S2; Leaper, 2019). Most large vessels transiting through the study site are required to use AIS (vessels greater than 500 Gross Register Tonnage [GRT] or 150 GRT and carrying more than 12 passengers), with one notable exception being fishing vessels (Transport Canada, 2005). No vessel AIS data were provided for March 23–25 and August 5–27, 2020; therefore, we excluded PAM data for these time periods to enable analysis of a full AIS dataset.

Land‐based surveys

Between 2016 and 2018, we completed a series of 31 land‐based surveys for harbour porpoise and vessel activity coinciding with C‐POD deployment (Dracott et al., 2022). Vessels were recorded passing through a 1000‐m radius around the C‐POD, allowing us to account for all vessels active in the area, including those without AIS. One aerial survey covering the Prince Rupert area estimated that for every AIS vessel, 1.7 are non‐AIS vessels, which predominantly include fishing and small recreational vessels (Serra‐Sogas et al., 2021). Vessel presence was noted for each minute of the survey and divided by vessel type and activity while in the study area (Appendix S1: Table S2). Vessel types included fishing, tug, ferry, shipping, sailboat, large motor vessels, and small motor vessels. Vessel speed data were not collected.

Statistical analyses

For statistical analysis, we retained a total of 816 days of data from the F‐POD during January 2020 to May 2022 and 31 days for the C‐POD from August 2016 to October 2018. All retained F‐POD data days had matched AIS data from the corresponding time frame (Appendix S1: Table S2). All C‐POD days corresponded with land‐based surveys. Statistical analyses for these data were carried out in R version 4.4.1 (R Core Team, 2024) and R Studio version 2020.09.0 (R Studio Team, 2024).

To investigate whether vessel activity or temporal covariates had an influence on harbour porpoise foraging and non‐foraging echolocation, we used R package mgcv v. 1.8–38 (Wood, 2023) to run generalized additive models (GAM) using the bam() and log link() functions, with negative binomial distribution to account for overdispersion. Prior to running models, we tested for evidence of collinearity in the data using the vif function from the car R package v. 3.0‐12 (Fox et al., 2024) with 5 as a cutoff value (Craney & Surles, 2002). We assessed the response variable for temporal autocorrelation via ACF plots using the acf() function (mgcv package), with 0.2 as the threshold (Dracott et al., 2022). Temporal autocorrelation was detected in our dataset (Appendix S1: Figure S1); therefore, we ran subsequent models with AR1 structure (first‐order autoregressive structure with homogenous variances; Nuuttila et al., 2017). For F‐POD data, we used number of foraging and non‐foraging DPM as the response variables independently. For these data, we ran two models per echolocation type (foraging vs. non‐foraging), totaling four GAMs. Two of the four GAMs tested the effects of year, month, hour, and vessel total, and the remaining two GAMs tested the effects of year, month, hour, and vessel types per echolocation type. For C‐POD data, we used number of foraging and non‐foraging trains as the response variables, respectively. For these data, we ran two models per echolocation type (foraging vs. non‐foraging) to test the effects of vessel activity (transiting, fishing, and working) and vessel types separately. These models were conducted separately due to evidence of collinearity between the total number of vessels and types of vessels. Nonsignificant variables (p > 0.05) were sequentially removed from the model by assessing the resulting p‐values from the Parametric Coefficients and the Approximate Significance of smooth terms for each variable in the model summary. Final model selection was based on a combination of Akaike information criterion (AIC) and the adjusted R 2 values.

To visualize the relationship between foraging and non‐foraging DPM and month per hour, we used the ggplot() function in the R package ggplot2 v.3.3.5 (Wickham et al., 2023). We also plotted diurnal echolocation activity patterns per month, using the mean foraging and non‐foraging DPM across years. For AIS data, we plotted significant vessel variables (determined by the GAM models) against foraging and non‐foraging DPM, as well as month. We used one‐way ANOVAs to test for the relationship between vessel presence and month across all years. Significant relationships were plotted using ggplot2 v.3.3.5.

RESULTS

F‐POD GAM models

Foraging DPM

The final GAM model for foraging DPM with vessel total included the following significant covariates: month (January–December), hour (1 –24), year (2020, 2021, and 2022), and vessel total. This model explained 22.6% of the variation observed (Appendix S1: Table S3), indicating lower predicted foraging DPM with a greater number of vessels present. For foraging DPM and vessel categories, the final model included the following significant covariates: year (2020, 2021, and 2022), month (January–December), hour (1–24), Vessel_Tug, Speed_Med, and Speed_Fast (Figure 1). This model explained 22.2% of the variation observed (Appendix S1: Table S4), with lower predicted foraging DPM with a greater number of tugboats, medium‐speed, and fast‐speed vessels present (Figure 1). Month (p < 0.001) and hour (p < 0.001) were the most important covariates for determining foraging harbour porpoise echolocation activity based on both AIC and adjusted‐R 2 values (Appendix S1: Tables S5 and S6), with a higher predicted foraging DPM between midnight and 5 am and during the month of May (Figure 1).

FIGURE 1.

FIGURE 1

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Predicted foraging minutes for full waveform porpoise detector (F‐POD)‐derived data by variable, including month, hour, vessel type (tugboat and other, and speed category [medium and fast]). The solid blue line represents the predicted values, while the shaded light blue ribbon represents the 95% CIs around these predictions. “Speed_Med” includes vessels transiting between medium: ≥2.5 and ≤6 m/s and “Speed_Fast” includes vessels transiting >6 m/s.

Non‐foraging DPM

The final GAM model for non‐foraging DPM with vessel total included the following significant covariates: month (January–December), hour (1–24), year (2020, 2021, and 2022), and vessel total. This model explained 16.4% of the variation observed (Appendix S1: Table S7), with lower predicted non‐foraging DPM with a greater number of vessels present. For non‐foraging DPM and vessel categories, the final model included the following significant covariates: month (January–December), hour (1–24), year (2020, 2021, and 2022), Vessel_Tug, Vessel_Passenger, Speed_Med, and Speed_Fast (Figure 2). This model explained 15.8% of the variation observed (Appendix S1: Table S8), with lower predicted foraging DPM with a greater number of tugboats, passenger vessels, medium‐speed, and fast‐speed vessels present (Figure 2). Month (p < 0.001) and hour (p < 0.001) were the most important covariates for determining non‐foraging harbour porpoise echolocation activity based on both AIC and adjusted‐R 2 values (Appendix S1: Tables S9 and S10).

FIGURE 2.

FIGURE 2

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Predicted non‐foraging minutes for full waveform porpoise detector (F‐POD)‐derived data by variable, including month, hour, vessel type (tugboat and passenger), and speed category (medium and fast). The solid green line represents the predicted values, while the shaded light green ribbon represents the 95% CIs around these predictions. “Speed_Med” includes vessels transiting between medium: ≥2.5 and ≤6 m/s and “Speed_Fast” includes vessels transiting >6 m/s.

C‐POD GAM models

Foraging and non‐foraging DPM

For C‐POD GAM models, the final GAM model for foraging trains with vessel type and vessel activity status included the following significant covariates: year (2016, 2017, and 2018), and month (January, May–December), hour (1–24; Appendix S1: Tables S11 and S12). These models both explained 30.9% and 30% of the variation observed, respectively, with higher predicted foraging trains during the months of April and October (Appendix S1: Figure S2). For non‐foraging trains, the final GAM model included the same significant covariates observed for the foraging trains GAM model (month and year; Tables 13 and 14). These models explained three 9.7% and 39.2% of the variation observed, respectively, with higher predicted foraging trains during the months of April and September (Appendix S1: Figures S2 and S3). For both foraging and non‐foraging trains, vessel type (p > 0.05) and vessel activity status (p > 0.05) did not appear to be significant covariates. Only 31 days of data were used in this analysis.

Vessel type and activity by month

When comparing both the total vessel number and types of vessels present per month during land‐based surveys, there was no significant relationship between the two across any of the years. For AIS data, when observing the total number of fast vessels per month, there was a significant relationship for 2020 (F 1,10 = 7.14, p < 0.05). There were a greater number of vessels traveling fast throughout the month of March 2020 (Figure 3). The number of tugboats (tugs) determined via AIS data peaked at 1572 for 2020 (average of 131 per month sampled), with a lower number of tugs being present in 2021 and 2022 (Appendix S1: Figure S4; Table S2). Passenger vessels peaked at 194 for 2021 (average of 16 per month sampled), with fewer passenger vessels in 2022 and 2020 respectively (Appendix S1: Table S2). For C‐POD data with land‐based survey vessel data, there were no significant relationships between types of vessels and month for any of the years. For both total foraging and non‐foraging trains, total echolocation activity peaked at ~150 vessels and was at its lowest (<25 trains detected) when there were >450 vessels total (Appendix S1: Figure S5).

FIGURE 3.

FIGURE 3

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Total number of vessels traveling at high speeds (>7 m/s) present within 1 km of our study site per month for 2020–2022. Note that data for 2022 only included January to May, and August 2020 was excluded due to missing automatic identification system (AIS) data.

Variation in foraging and non‐foraging activity

Similar to our initial study (Dracott et al., 2022), we observed annual (Appendix S1: Table S15), monthly, and diel variation in foraging and non‐foraging echolocation activity for harbour porpoise (Figure 4; Appendix S1: Figure S6). There was a peak in both echolocation types for May and a secondary peak during November in 2020 and August in 2021 (Figure 4). There were more DPM per hour for non‐foraging echolocation during the months of January, April, and December compared with foraging echolocation (Figure 4). The highest percentage of trains identified as foraging trains in 2020 and 2021 (the 2 years with year‐round data) occurred during July (50.9%; Appendix S1: Table S13). Conversely, the month with the lowest mean percent of trains identified as foraging trains was January (17.6%, Appendix S1: Table S3). We observed clear nocturnal trends for both foraging and non‐foraging echolocation activity (Appendix S1: Figure S6). For all 3 years there were more non‐foraging DPM compared to foraging DPM, especially during the hours of darkness (Appendix S1: Figure S6). Between 2020 and 2021, the amount of foraging and non‐foraging DPM was similar (Appendix S1: Figure S6).

FIGURE 4.

FIGURE 4

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Monthly variation in foraging detection positive minutes (DPM) per hour (A) and non‐foraging DPM per hour (B) for harbour porpoise between 2020 and 2022 for full waveform porpoise detector (F‐POD) data. Data only available from January to May for 2022 and 21 days in August 2020 were excluded due to missing data. Dotted line depicts the 95% CI around the means.

DISCUSSION

There is an increasing need to understand the species‐specific responses of cetaceans to chronic underwater noise, especially concerning the rapid global growth of marine shipping activity (Kaplan & Solomon, 2016; Tournadre, 2014). In Canada, vessel activity overlaps with recognized important habitat for several SARA‐listed species, including the Pacific harbour porpoise (Breeze et al., 2022). It was previously suggested that high‐frequency odontocetes, such as harbour porpoises, are not at substantial risk of disturbance from large vessel noise due to their limited hearing ability at low frequencies (Kastelein et al., 2002). However, some studies have determined that harbour porpoises respond negatively to medium‐to high‐frequency components of vessel noise, displaying avoidance behaviors such as porpoising, deep diving, and cessation of foraging within range of noise from large vessels (Dyndo et al., 2015; Frankish et al., 2023; Hermannsen et al., 2014; Wisniewska et al., 2018). Similarly, our study found significant decreases in harbour porpoise echolocation with vessel activity, for both foraging and non‐foraging click trains. Conversely, Owen et al. (2024) found that elevated shipping noise and vessel traffic did not influence porpoise occurrence in the Baltic Sea, indicating porpoises continued to use the preferred habitat despite possible impacts on communication, echolocation, and stress levels of individuals.

There are several factors in this study that could influence this decrease in echolocation activity, including but not limited to masking effects from vessel traffic (Clausen et al., 2021) or limitations of the F‐POD (Clausen et al., 2019). There exists a known downward bias in detection of porpoises in minutes when other species or sonars are present (Ivanchikova & Tregenza, 2023). However, this is not a significant factor in this study as the fraction of minutes with either of these biasing factors is less than 0.3%. The pod data were generally quiet (average background ~137 clicks/min) which is below the level at which the POD gets masked (3000 clicks/min). Furthermore, this study does not consider behavioral disturbances that may exist in response to noise sources that occur beyond 1000 m and be clearly audible to harbour porpoise (Wisniewska et al., 2018). For instance, pile driving, which can drive responses for harbour porpoises over 20 km away (Tougaard et al., 2009). Notably, pile driving is actively occurring with expansion of the Port (McCrodan & Hannay, 2014) in close proximity to the study site and would be another important noise source to assess.

Harbour porpoises are known to exhibit seasonal and diel trends in echolocation behavior (Dracott et al., 2022; Nuuttila et al., 2017; Sveegaard et al., 2012; Todd et al., 2009; Weir et al., 2007; Zein et al., 2019). Local prey abundance is thought to influence these observed trends. In our study, interannual fluctuations in Pacific herring have previously been suggested as drivers of harbour porpoise activity patterns in the northeast Pacific (Dracott et al., 2022; Fisheries and Oceans Canada, 2008; Lewandoski & Bishop, 2018). Peaks in foraging click trains between May and October correspond to the presence of nutritionally rich juvenile herring (Fisheries and Oceans Canada, 2008), and increased foraging echolocation during the hours of darkness likely corresponds with diel patterns in prey fish vertical distribution (Godefroid et al., 2019). This also indicates there are potential changes in echolocation behavior between day and night. For non‐foraging echolocation, increased DPM for the months of November and December corresponds with larger aggregations observed over winter months (Dracott et al., 2022), and could possibly indicate social communication (Anderson et al., 2023; Clausen et al., 2011; Sørensen et al., 2018). A previous study showed that social calls occur frequently in wild harbour porpoises, especially between mothers and calves (Sørensen et al., 2018). Harbour porpoises have also been observed hunting collaboratively in groups (Torres Ortiz et al., 2021), which may require some degree of acoustic communication to coordinate successful predation. While we do not yet have evidence of collaborative hunting in this location, we have observed harbour porpoises predating on adult salmonid species (Appendix S1: Figure S7) which can be more than half of the body size of a porpoise in length alone (O'Neill et al., 2014). It is unclear how harbour porpoises consume such large prey (Elliser et al., 2020); however, one study documenting asphyxiation of a harbour porpoise while consuming American shad indicates they attempt to swallow the prey whole (Elliser et al., 2020). While group hunting with prey sharing between individuals occurs with several cetacean species (Fedorowicz et al., 2010; Hoelzel, 1991; Ramos et al., 2021), prey sharing has not been documented to date for harbour porpoises. It is also important to consider that differentiating between foraging and non‐foraging behavior using an ICI <10‐ms threshold has limitations, and there will likely be overlap between these categories.

Presence, type, and speed of vessels are factors that have previously been found to negatively influence both foraging and non‐foraging communication in harbour porpoises (Wisniewska et al., 2018). Vessel type can influence the amount of underwater noise emitted into the marine environment (Hatch et al., 2008; Veirs et al., 2016). Studies from the Salish Sea indicated that cargo and container ships, followed by tugs and passenger vessels, which are mainly propeller‐driven, are the highest contributors to underwater vessel noise (Bassett et al., 2012; MacGillivray & de Jong, 2021; Veirs et al., 2016). We found that the presence of tugs within close range (~1 km) of harbour porpoises significantly decreased both foraging and non‐foraging echolocation behavior. Tugs were the dominant vessel type throughout our study period, and previous studies suggest that tugs likely represent the dominant high‐powered underwater noise in the Pacific Northwest (McCrodan & Hannay, 2014). In addition, passenger vessels, including cruise ships and ferries, were shown to negatively impact non‐foraging echolocation. Passenger vessels are considered an essential service and important transportation link between coastal communities, particularly in remote areas such as the North Coast of BC and Alaska (Bassett et al., 2012). Coastal passenger vessels can contribute varying levels of underwater noise, depending on their propulsion type and speed of travel (McIntyre et al., 2021). Within the Salish Sea alone, passenger ferries are a significant contributor to underwater noise due to a combination of the sheer number of ferries operating in the area, time on the water, vessel configuration, and modes of operation (British Columbia Ferry Services Inc., 2019). In Danish waters, ferries have been shown to significantly impact the behavior of harbour porpoises, with individuals increasing swimming effort and abandoning echolocation altogether when a ferry passed within 7 km of the porpoises (Wisniewska et al., 2018).

Vessel speed in general can greatly influence the amplitude of underwater noise emission, with acoustical footprints effectively reduced with even a 2.5‐m/s decrease in speed (Findlay et al., 2023; Williams et al., 2021). It is therefore unsurprising that vessels traveling at medium (2.5–6 m/s) and higher speeds (>6 m/s) similarly had a significant effect on harbour porpoise echolocation behavior in our study. At higher vessel speeds, increased noise from cavitation can dramatically mask acoustic communication for odontocetes (Jensen et al., 2009), and for harbour porpoises in particular, exposure to vessels traveling at high speeds can cause high‐energy responses including vigorous fluking, interrupted foraging, and even cessation of echolocation (Frankish et al., 2023; Wisniewska et al., 2018). We observed both a decrease in foraging and non‐foraging trains in response to a greater number of vessels traveling at medium and/or fast speeds within our study area. While the porpoises may have reduced clicking or foraging, we did not collect behavioral or locational data on individual animals and, therefore, cannot determine whether their swimming activity increased or if they left the study site. Furthermore, most small vessels traveling at high speeds were not well captured in this study as they are unlikely to have AIS.

CONCLUSIONS

Our study is one of the first to assess multiyear, seasonal disturbance caused by vessels on a free‐living harbour porpoise population. We have determined that vessel speed as well as particular types of vessels (tugs and passenger) have a negative effect on the rate of both foraging‐ and non‐foraging echolocation activity. Despite not visually observing behavioral responses of porpoises to vessel disturbances first‐hand, studies suggest that vessel disturbances are likely to result in short‐term reduced foraging intake (Dyndo et al., 2015; Wisniewska et al., 2018). With projected increase in vessel traffic within Chatham Sound, disturbance for this population will increase, which may have long‐term impacts on fitness for the population due to reduced communication and echolocation success (Tougaard et al., 2015). The extent to which harbour porpoises may become habituated to the presence of vessels is unclear (Dracott et al., 2022; Nabe‐Nielsen et al., 2014); however, some studies indicate limited support for evidence of long‐term habituation (Dyndo et al., 2015; Owen et al., 2024). Moreover, some studies suggest that some individuals have relatively restricted habitat ranges such as in the Salish Sea (Elliser et al., 2025; Elliser & Hall, 2021) and targeted habitat use at the approach to the Port of Prince Rupert may make harbour porpoises vulnerable to localized depletions (Fisheries and Oceans Canada, 2009). Going forward, this study has implications for informing mitigation measures in this highly trafficked area. Reducing the speed of vessels within the 1–5‐km study site could be greatly beneficial to reduce the impact of vessels on harbour porpoise foraging and communication. Voluntary vessel slowdowns (e.g., to 5.5 m/s) in the south coast of BC have been shown to reduce underwater noise within critical habitat for SRKW and resulted in a 22% reduction in potential lost foraging time (Joy et al., 2019; MacGillivray et al., 2019). In addition to reducing underwater noise, vessel slowdowns would also decrease the risk of ship strikes for other cetacean species such as killer whales and humpback whales (Megaptera novaeangliae) within Chatham Sound (Chou et al., 2021; Keen et al., 2023). Reductions in vessel speed also reduce the amount of low‐frequency noise pollution, which in turn will also benefit communication for low‐ and mid‐frequency cetacean species (Hatch et al., 2008; Southall et al., 2019). Lastly, vessel‐based ocean noise could be reduced by investing in technological modifications to vessels such as advanced hull‐propeller designs and electric motors to make vessels quieter and encouraging port‐led incentives and eco‐certifications (Findlay et al., 2023; Wright, 2014).

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Supporting information

Appendix S1.

EAP-35-e70076-s001.pdf (748.1KB, pdf)

ACKNOWLEDGMENTS

We wish to acknowledge that this research took place on the traditional territory of the Nine Allied Tribes of Metlakatla and Lax Kw'alaams who have been stewards of these lands and waters since time immemorial. This project was supported partially by a financial contribution from Fisheries and Oceans Canada/Ce projet fut partiellement appuyé par une contribution financière de Pêches et océans Canada. Deployment and retrieval of the C‐POD and F‐POD was possible thanks to both financial and in‐kind support from the Prince Rupert Port Authority. Thank you to the crew of the Charles Hays and Amwaal for your patience and perseverance throughout this project, to Lance Barrett‐Lennard and Aileen Jeffries for help with the study design, to James Pilkington for advice and assistance with C‐POD deployment logistics, to Julie Merchel and Ashley Bachert for assistance with POD deployment, to Alice Brown‐Dussault for assistance with data analysis, and to the numerous volunteers who conducted land‐based surveys. Thank you to Ocean Networks Canada, the Canadian Hydrographic Service, and the Canadian Coast Guard for supplying important AIS data. Finally, thank you to previous Ocean Wise staff and volunteers for their time and effort collecting survey data. This paper is dedicated to Brent Patriquin, one of the local volunteers who dedicated many hours of his life to appreciating local cetacean species.

Dracott, Karina , Robinson Chloe V., Dares Lauren, Woodley Erin, Migneault Amy, and Birdsall Caitlin. 2025. “Influence of Vessel Disturbance on Pacific Harbour Porpoise (Phocoena Phocoena Vomerina) Echolocation.” Ecological Applications 35(6): e70076. 10.1002/eap.70076

Handling Editor: Roberto Carlucci

DATA AVAILABILITY STATEMENT

Data (Robinson, 2025) are available in Zenodo at https://doi.org/10.5281/zenodo.15700797. Data are also available via the St. Lawrence Global Observatory (Robinson & Ocean Wise Conservation Association, 2025) at https://doi.org/10.26071/60c47bea-d6df-49fa.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1.

EAP-35-e70076-s001.pdf (748.1KB, pdf)

Data Availability Statement

Data (Robinson, 2025) are available in Zenodo at https://doi.org/10.5281/zenodo.15700797. Data are also available via the St. Lawrence Global Observatory (Robinson & Ocean Wise Conservation Association, 2025) at https://doi.org/10.26071/60c47bea-d6df-49fa.

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