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Summary statistics for best-fit models of foraging-related response variables for Northern and Southern Resident killer whales 2009-2014
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In cooperative species, human-induced rapid environmental change may threaten cost–benefit tradeoffs of group behavioral strategies that evolved in past environments. Capacity for behavioral flexibility can increase population viability in novel environments. Whether the partitioning of individual responsibilities within social groups is fixed or f...
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Context 1
... constructed separate models with 1) all tag deployments (models a-f, Table 2) and 2) subsets of deployments to explore demographic effects of calf presence (adult females only, model g, Table 2) and living mother presence (adult males only, model h, Table 2). We constructed individual full models with fixed effects of 1) population, sex, and their interaction (models a-f, Table 2), 2) population, presence of calf, and their interaction (for adult females, model g, Table 2), and 3) population, presence of living mother, and their interaction (for adult males, model h, Table 2), and we included offset effects of the log-transformed deployment duration (models a, g, and h) and the square root-transformed cumulative searching time (model b, Table 2) for those models that contained counts as response variables. ...Context 2
... constructed separate models with 1) all tag deployments (models a-f, Table 2) and 2) subsets of deployments to explore demographic effects of calf presence (adult females only, model g, Table 2) and living mother presence (adult males only, model h, Table 2). We constructed individual full models with fixed effects of 1) population, sex, and their interaction (models a-f, Table 2), 2) population, presence of calf, and their interaction (for adult females, model g, Table 2), and 3) population, presence of living mother, and their interaction (for adult males, model h, Table 2), and we included offset effects of the log-transformed deployment duration (models a, g, and h) and the square root-transformed cumulative searching time (model b, Table 2) for those models that contained counts as response variables. ...Context 3
... constructed separate models with 1) all tag deployments (models a-f, Table 2) and 2) subsets of deployments to explore demographic effects of calf presence (adult females only, model g, Table 2) and living mother presence (adult males only, model h, Table 2). We constructed individual full models with fixed effects of 1) population, sex, and their interaction (models a-f, Table 2), 2) population, presence of calf, and their interaction (for adult females, model g, Table 2), and 3) population, presence of living mother, and their interaction (for adult males, model h, Table 2), and we included offset effects of the log-transformed deployment duration (models a, g, and h) and the square root-transformed cumulative searching time (model b, Table 2) for those models that contained counts as response variables. ...Context 4
... constructed separate models with 1) all tag deployments (models a-f, Table 2) and 2) subsets of deployments to explore demographic effects of calf presence (adult females only, model g, Table 2) and living mother presence (adult males only, model h, Table 2). We constructed individual full models with fixed effects of 1) population, sex, and their interaction (models a-f, Table 2), 2) population, presence of calf, and their interaction (for adult females, model g, Table 2), and 3) population, presence of living mother, and their interaction (for adult males, model h, Table 2), and we included offset effects of the log-transformed deployment duration (models a, g, and h) and the square root-transformed cumulative searching time (model b, Table 2) for those models that contained counts as response variables. For beta regression models we transformed values of 0 and 1 following methods of Duoma and Weedon (2019), and we tested for fixed versus variable dispersion by comparing AIC scores for a model with fixed (null) dispersion to those with population, sex, or population:sex as variable dispersion terms (models c and d, Table 2), and retained the model with the lowest AIC score. ...Context 5
... constructed separate models with 1) all tag deployments (models a-f, Table 2) and 2) subsets of deployments to explore demographic effects of calf presence (adult females only, model g, Table 2) and living mother presence (adult males only, model h, Table 2). We constructed individual full models with fixed effects of 1) population, sex, and their interaction (models a-f, Table 2), 2) population, presence of calf, and their interaction (for adult females, model g, Table 2), and 3) population, presence of living mother, and their interaction (for adult males, model h, Table 2), and we included offset effects of the log-transformed deployment duration (models a, g, and h) and the square root-transformed cumulative searching time (model b, Table 2) for those models that contained counts as response variables. For beta regression models we transformed values of 0 and 1 following methods of Duoma and Weedon (2019), and we tested for fixed versus variable dispersion by comparing AIC scores for a model with fixed (null) dispersion to those with population, sex, or population:sex as variable dispersion terms (models c and d, Table 2), and retained the model with the lowest AIC score. ...Context 6
... constructed separate models with 1) all tag deployments (models a-f, Table 2) and 2) subsets of deployments to explore demographic effects of calf presence (adult females only, model g, Table 2) and living mother presence (adult males only, model h, Table 2). We constructed individual full models with fixed effects of 1) population, sex, and their interaction (models a-f, Table 2), 2) population, presence of calf, and their interaction (for adult females, model g, Table 2), and 3) population, presence of living mother, and their interaction (for adult males, model h, Table 2), and we included offset effects of the log-transformed deployment duration (models a, g, and h) and the square root-transformed cumulative searching time (model b, Table 2) for those models that contained counts as response variables. For beta regression models we transformed values of 0 and 1 following methods of Duoma and Weedon (2019), and we tested for fixed versus variable dispersion by comparing AIC scores for a model with fixed (null) dispersion to those with population, sex, or population:sex as variable dispersion terms (models c and d, Table 2), and retained the model with the lowest AIC score. ...Context 7
... constructed separate models with 1) all tag deployments (models a-f, Table 2) and 2) subsets of deployments to explore demographic effects of calf presence (adult females only, model g, Table 2) and living mother presence (adult males only, model h, Table 2). We constructed individual full models with fixed effects of 1) population, sex, and their interaction (models a-f, Table 2), 2) population, presence of calf, and their interaction (for adult females, model g, Table 2), and 3) population, presence of living mother, and their interaction (for adult males, model h, Table 2), and we included offset effects of the log-transformed deployment duration (models a, g, and h) and the square root-transformed cumulative searching time (model b, Table 2) for those models that contained counts as response variables. For beta regression models we transformed values of 0 and 1 following methods of Duoma and Weedon (2019), and we tested for fixed versus variable dispersion by comparing AIC scores for a model with fixed (null) dispersion to those with population, sex, or population:sex as variable dispersion terms (models c and d, Table 2), and retained the model with the lowest AIC score. ...Context 8
... constructed individual full models with fixed effects of 1) population, sex, and their interaction (models a-f, Table 2), 2) population, presence of calf, and their interaction (for adult females, model g, Table 2), and 3) population, presence of living mother, and their interaction (for adult males, model h, Table 2), and we included offset effects of the log-transformed deployment duration (models a, g, and h) and the square root-transformed cumulative searching time (model b, Table 2) for those models that contained counts as response variables. For beta regression models we transformed values of 0 and 1 following methods of Duoma and Weedon (2019), and we tested for fixed versus variable dispersion by comparing AIC scores for a model with fixed (null) dispersion to those with population, sex, or population:sex as variable dispersion terms (models c and d, Table 2), and retained the model with the lowest AIC score. For the beta regression model of the proportion of time spent in prey capture dives, the null dispersion model was optimal. ...Context 9
... the beta regression model of the proportion of time spent traveling or resting, the model with sex as a dispersion term was optimal. Model response variables included total number of prey capture dives within a deployment (negative binomial and Poisson distributions, models a, b, g, and h, Table 2), proportion of deployment time engaged in prey capture dives (beta distribution, model c, Table 2), proportion of deployment time engaged in traveling or resting dives (beta distribution, model d, Table 2), maximum depth of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model e, Table 2), and bathymetry at the location of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model f, Table 2). For the models with count data as the response variable (models a, b, g, and h, Table 2), we explored several candidate models with Poisson and negative binomial distributions, with and without terms for overdispersion and zero inflation, and used AIC model selection to identify the optimal models. ...Context 10
... the beta regression model of the proportion of time spent traveling or resting, the model with sex as a dispersion term was optimal. Model response variables included total number of prey capture dives within a deployment (negative binomial and Poisson distributions, models a, b, g, and h, Table 2), proportion of deployment time engaged in prey capture dives (beta distribution, model c, Table 2), proportion of deployment time engaged in traveling or resting dives (beta distribution, model d, Table 2), maximum depth of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model e, Table 2), and bathymetry at the location of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model f, Table 2). For the models with count data as the response variable (models a, b, g, and h, Table 2), we explored several candidate models with Poisson and negative binomial distributions, with and without terms for overdispersion and zero inflation, and used AIC model selection to identify the optimal models. ...Context 11
... the beta regression model of the proportion of time spent traveling or resting, the model with sex as a dispersion term was optimal. Model response variables included total number of prey capture dives within a deployment (negative binomial and Poisson distributions, models a, b, g, and h, Table 2), proportion of deployment time engaged in prey capture dives (beta distribution, model c, Table 2), proportion of deployment time engaged in traveling or resting dives (beta distribution, model d, Table 2), maximum depth of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model e, Table 2), and bathymetry at the location of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model f, Table 2). For the models with count data as the response variable (models a, b, g, and h, Table 2), we explored several candidate models with Poisson and negative binomial distributions, with and without terms for overdispersion and zero inflation, and used AIC model selection to identify the optimal models. ...Context 12
... the beta regression model of the proportion of time spent traveling or resting, the model with sex as a dispersion term was optimal. Model response variables included total number of prey capture dives within a deployment (negative binomial and Poisson distributions, models a, b, g, and h, Table 2), proportion of deployment time engaged in prey capture dives (beta distribution, model c, Table 2), proportion of deployment time engaged in traveling or resting dives (beta distribution, model d, Table 2), maximum depth of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model e, Table 2), and bathymetry at the location of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model f, Table 2). For the models with count data as the response variable (models a, b, g, and h, Table 2), we explored several candidate models with Poisson and negative binomial distributions, with and without terms for overdispersion and zero inflation, and used AIC model selection to identify the optimal models. ...Context 13
... the beta regression model of the proportion of time spent traveling or resting, the model with sex as a dispersion term was optimal. Model response variables included total number of prey capture dives within a deployment (negative binomial and Poisson distributions, models a, b, g, and h, Table 2), proportion of deployment time engaged in prey capture dives (beta distribution, model c, Table 2), proportion of deployment time engaged in traveling or resting dives (beta distribution, model d, Table 2), maximum depth of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model e, Table 2), and bathymetry at the location of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model f, Table 2). For the models with count data as the response variable (models a, b, g, and h, Table 2), we explored several candidate models with Poisson and negative binomial distributions, with and without terms for overdispersion and zero inflation, and used AIC model selection to identify the optimal models. ...Context 14
... response variables included total number of prey capture dives within a deployment (negative binomial and Poisson distributions, models a, b, g, and h, Table 2), proportion of deployment time engaged in prey capture dives (beta distribution, model c, Table 2), proportion of deployment time engaged in traveling or resting dives (beta distribution, model d, Table 2), maximum depth of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model e, Table 2), and bathymetry at the location of a prey capture dive (Gaussian distribution, log-transformed to meet model assumptions, model f, Table 2). For the models with count data as the response variable (models a, b, g, and h, Table 2), we explored several candidate models with Poisson and negative binomial distributions, with and without terms for overdispersion and zero inflation, and used AIC model selection to identify the optimal models. Additionally, we followed the protocol outlined by Zuur et al. (2009) to identify the optimal random structure, which involves including all reasonable random effects that are potentially important, constructing models with different permutations of these random effects, and using AIC model selection to identify the model with the lowest AIC score. ...Context 15
... ID was defined as the unique tag deployment on an individual, and was used to control for the assumption that dives within deployments should be more similar than dives between deployments. This term allowed us to control for pseudoreplication for all models in which dive was the unit of analysis (models e and f, Table 2). Week-year was defined as the week of the year in which a given tag was deployed. ...Context 16
... the model examining the relationship between the presence of a living mother and the number of prey capture dives by adult males (model h, Table 2), we additionally explored the importance of including a random effect of the categorical age of the tagged male's mother (dead, reproductive, or post-reproductive), but there was no support to include this random effect in the final model. The best models included week-year (all models, Table 2) and deployment ID (models e and f, Table 2) as random effects. ...Context 17
... the model examining the relationship between the presence of a living mother and the number of prey capture dives by adult males (model h, Table 2), we additionally explored the importance of including a random effect of the categorical age of the tagged male's mother (dead, reproductive, or post-reproductive), but there was no support to include this random effect in the final model. The best models included week-year (all models, Table 2) and deployment ID (models e and f, Table 2) as random effects. We used recursive, single term deletion and model comparison of successively simpler models using Likelihood Ratio Tests to determine which fixed effects to omit from the final models. ...Context 18
... the model examining the relationship between the presence of a living mother and the number of prey capture dives by adult males (model h, Table 2), we additionally explored the importance of including a random effect of the categorical age of the tagged male's mother (dead, reproductive, or post-reproductive), but there was no support to include this random effect in the final model. The best models included week-year (all models, Table 2) and deployment ID (models e and f, Table 2) as random effects. We used recursive, single term deletion and model comparison of successively simpler models using Likelihood Ratio Tests to determine which fixed effects to omit from the final models. ...Context 19
... used recursive, single term deletion and model comparison of successively simpler models using Likelihood Ratio Tests to determine which fixed effects to omit from the final models. We used Tukey HSD tests to compare levels of model effects (see Supplementary Information, Table S2). For all tests, α = 0.05. ...Context 20
... trends were not due to differences in effort. Indeed, when controlling for cumulative time spent searching for prey there was a significant interaction between population and sex on the number of prey capture dives (GLMM, z = 2.771, P = 0.0056, Table 2, Supplementary Table S2, presented in Figure 2b as foraging efficiency, prey capture dives per h searching). NRKW females were 257% more efficient than SRKW females and 68% more efficient than NRKW males, while there was an opposite trend that SRKW males were 59% more efficient than SRKW females (observed mean values of prey capture dives per h searching, NRKW F: 12.13, NRKW M: 7.24, SRKW F: 3.40, SRKW M: 5.39). ...Context 21
... spent engaged in prey capture versus traveling and resting dives differed between populations. There was an interaction between population and sex on the proportion of deployment time engaged in dives that resulted in prey capture (GLMM, z = 3.13, P = 0.0017, Table 2, Supplementary Table S2). NRKW females spent 91% and 23% more time engaged in prey capture dives than SRKW females or NRKW males, respectively. ...Context 22
... there was a population effect on the proportion of tag deployment time that a subject spent traveling or resting (GLMM, z = −2.76, P = 0.0058, Table 2, Supplementary Table S2). Across both sexes, NRKW engaged in traveling or resting behavior for 62% more time than SRKW (Figure 2d) (observed mean values of proportion of deployment spent in travel or resting dives, NRKW: 0.463, SRKW: 0.286). ...Context 23
... depth was an important factor contributing to population differences in foraging behavior. There was a significant effect of population on the log of the maximum depth of prey capture dives (LMM, t = 2.1934, P = 0.0342, Table 2, Supplementary Table S2). Average depth of SRKW prey capture was 20% greater than NRKW prey capture depth (Figure 2e) (observed mean values of maximum depth of prey capture dives, NRKW: 90.63, SRKW: 108.54). ...Context 24
... difference was not explained by bathymetry, as the foraging habitats used by the two populations did not differ in depth. Additionally, there was an effect of sex on the log of the foraging habitat depth (LMM, t = 2.3903, P = 0.0219, Table 2, Supplementary Table S2). Across both populations, males tended to make prey capture dives in areas that were 13% deeper than areas in which females foraged (Figure 2f) (observed mean values of habitat depth at location of prey capture dive, F: 171.36, ...Context 25
... = 0.0021; calf: z = −2.714, P = 0.0067, Table 2, Supplementary Table S2). Across both populations, females without calves made more prey capture dives than females with calves. ...Context 26
... females with calves captured more prey than SRKW females with calves, who captured no prey while tags were attached (Figure 3a,b) (observed mean values of prey capture dives per h, NRKW no calf: 4.91, NRKW with calf: 2.71, SRKW no calf: 2.11, SRKW with calf: 0). There was a significant interaction between population and the presence of a living mother on the number of prey capture dives by adult males (GLMM, z = 2.168, P = 0.0302, Table 2, Supplementary Table S2). Accounting for deployment duration, SRKW males with a living mother made 151% more prey capture dives per hour than SRKW males whose mother had died, whereas the opposite was true for NRKW, whereby NRKW whose mother had died made 17% more prey capture dives per hour than NRKW males with a living mother (Figure 3c,d) ...Similar publications
Hoary marmots (Marmota caligata) dig burrows in alpine meadows rich in forage as ready refuge from potential predators. Refuge burrows enable hoary marmots to engage in risk- sensitive foraging when they are away from more secure resting burrows on talus slopes. Grizzly bears (Ursus arctos) commonly excavate refuge burrows while hunting marmots, su...
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... This area may be the highest area of genetic mixing and movement because the caribou congregate here during the wolf breeding season. While other species such as the sympatric piscivorous "resident" and marine mammal eating "Bigg's" killer whale populations show separate genetic ecotypes (Tennessen et al. 2023), the results here indicate the situation is not replicated in this system. The lack of distinct genetic ecotypes of observed wolves along the treeline is suggestive that the boreal/tundra distinction is a spectrum between two different foraging strategies but there are no social (behavioural) or geographic boundaries between these two groups leading to genetic divergence. ...
The goal of the five-year wolf (dìga) management program is to sufficiently reduce wolf (dìga) predation on the Bathurst and Bluenose-East herds to allow for an increase in calf and adult caribou (ekwǫ̀) survival rates to contribute to the stabilization and recovery of both herds. This report summarizes wolf management and monitoring activities undertaken by GNWT and TG during 2023. It provides an update to the previous reports on wolf management activities in Wek’èezhìı during
winter 2020 (Nishi et al. 2020), 2021 (Clark et al. 2021) and 2022 (Wilson et al. 2022) and is intended to fulfill the WRRB’s recommendation (#20-2020) that an “annual report be prepared by GNWT and TG and presented to the Board at a scheduled board meeting to allow for the discussion of adjustments in methodology based on the evidence, beginning fall 2021”.
... The shallow depths used by migrating Chinook are also consistent with the swimming patterns of resident killer whales that appear to make sustained foraging dives between 10-30 m, with very few dives deeper than 100 m [42]. Collectively, these findings suggest that resident killer whales typically search for Chinook in the upper water column (<30 m)-and pursue them to depths exceeding 100 m where captures have typically been recorded in the Salish Sea and North Island Waters [41,43,44]. However, this foraging strategy is unlikely to be as effective in areas where Chinook concentrate and are more vulnerable to capture-such as in the hot-spot areas we studied. ...
... We encountered relatively few if any vessels fishing due to fishery closures-except West of San Juan Island where substantial fishing effort by recreational vessels likely lowered the density of fish present at the time of our survey (1.1 ind.�1000 m −2 ). Most of the fish we detected in all 16 areas were deep and near the bottom, which is consistent with depths where resident killer whales typically capture their prey [41,43,44]. ...
... Average depths of areas we sampled were 128 m in the North Island Waters and 139 m in Salish Sea, which corresponds to maximum reported dive depths associated with fish captures of 91 m for northern resident killer whales, and 109 m for southern resident killer whales [44]. This apparent difference in capture depths of northern and southern resident killer whales may be explained by relative differences in the bathymetries of the two regions, and the propensity of Chinook to seek being near the bottom in locations where they need to minimize their risk of being preyed on by sea lions and killer whales. ...
Differences in the availability of prey may explain the low numbers of southern resident killer whales and the increase in northern resident killer whales in British Columbia and Washington State. However, in-situ data on the availability of their preferred prey (Chinook salmon, Oncorhynchus tshawytscha) in the core feeding areas used by these two populations of fish-eating killer whales have been lacking to test this hypothesis. We used multi-frequency echosounders (38, 70, 120, and 200 kHz) to estimate densities of adult Chinook (age-4+, > 81 cm) within 16 hot-spot feeding areas used by resident killer whales during summer 2020 in the Salish Sea and North Island Waters. We found Chinook were generally concentrated within 50 m from the bottom in the deep waters, and tended to be absent near the surface in the shallow waters (< 50 m). In general, the densities of Chinook we encountered were highest as the fish entered the Salish Sea (from Swiftsure Bank in the south) and Johnstone Strait (from Queen Charlotte Strait to the north)—and declined as fish migrated eastward along the shoreline of Vancouver Island. Median densities of Chinook for all sampled areas combined were 0.4 ind.·1000 m⁻² in northern resident foraging areas, and 0.9 ind.·1000 m⁻² in southern resident killer whale areas (p < 0.05, Mann–Whitney U test). Thus, Chinook salmon were twice as prevalent within the hot-spot feeding areas of southern versus northern resident killer whales. This implies that southern resident killer whales have greater access to Chinook salmon compared to northern residents during summer—and that any food shortage southern residents may be encountering is occurring at other times of year, or elsewhere in their range.
... Foraging behaviour can be especially rare and difficult to identify, but it is often of prime interest in ecology [35,40]. For example, understanding foraging behaviour is vital for the conservation of northern and southern resident killer whales off the coast of British Columbia [16,20,45]. Although both sub-populations have dietary and spatial overlap, northern residents (threatened) have a positive growth trajectory compared to southern residents (endangered) [8,45]. ...
... For example, understanding foraging behaviour is vital for the conservation of northern and southern resident killer whales off the coast of British Columbia [16,20,45]. Although both sub-populations have dietary and spatial overlap, northern residents (threatened) have a positive growth trajectory compared to southern residents (endangered) [8,45]. Studies have shown that various factors contribute to these population trends, including prey availability, anthropogenic pollutants, and vessel disturbances, but the exact causal mechanisms are not fully understood [16,20,26]. ...
... Studies have shown that various factors contribute to these population trends, including prey availability, anthropogenic pollutants, and vessel disturbances, but the exact causal mechanisms are not fully understood [16,20,26]. Each of these factors affects foraging ecology differently, so understanding how often and how successfully these sub-populations hunt may help explain differences in their population trajectories [28,45]. ...
Ecologists often use a hidden Markov model to decode a latent process, such as a sequence of an animal's behaviours, from an observed biologging time series. Modern technological devices such as video recorders and drones now allow researchers to directly observe an animal's behaviour. Using these observations as labels of the latent process can improve a hidden Markov model's accuracy when decoding the latent process. However, many wild animals are observed infrequently. Including such rare labels often has a negligible influence on parameter estimates, which in turn does not meaningfully improve the accuracy of the decoded latent process. We introduce a weighted likelihood approach that increases the relative influence of labelled observations. We use this approach to develop two hidden Markov models to decode the foraging behaviour of killer whales (Orcinus orca) off the coast of British Columbia, Canada. Using cross-validated evaluation metrics, we show that our weighted likelihood approach produces more accurate and understandable decoded latent processes compared to existing methods. Thus, our method effectively leverages sparse labels to enhance researchers' ability to accurately decode hidden processes across various fields.
... In contrast, the fecal samples from the AK pod matched the overall trend of high proportions of Chinook salmon during the Kenai Fjords aggregation and high chum salmon during the eastern Prince William Sound aggregation. On a finer scale, foraging strategies differ among sex-and reproductive classes of southern and northern resident killer whales 27 . While not a focus of this study, these classes are worthy of investigation. ...
Top predators influence ecological communities in part through the prey they consume, which they often track through cycles of seasonal and geographic abundance. Killer whales are top predators in the marine ecosystem. In the North Pacific, they have diverged into three distinct lineages with different diets, of which the fish-eating type is most abundant. In this study, we examine the diet of the southern Alaska resident killer whale population across three major foraging aggregations. We take advantage of two unique sampling methods to reveal strong spatiotemporal patterns in diet from May through September. Chinook, chum, and coho salmon were each dominant in different locations and times, with substantial dietary contributions from Pacific halibut, arrowtooth flounder, and sablefish. The diverse, location-specific, and seasonal nature of the feeding habits of this marine top predator highlights the importance of diet sampling across broad spatiotemporal and population-level scales.
... Biologgers are an essential tool used to understand the behavior of marine mammals. For example, time-depth recorders allow researchers to estimate behavioral states associated with each dive (e.g., foraging, resting, and traveling, Tennessen et al. 2023;McRae et al. 2024). Researchers also use biologging datasets to identify and characterize dive phases, which are important for inferring behavior (e.g., prey capture often occurs in the bottom phase of a foraging dive, Wright et al. 2017;Jensen et al. 2023). ...
... In addition, leaving smaller Chinook salmon implies a reduction in caloric value, which could further affect other marine mammals feeding on similar preys, including SRKW [100,101]. Although one could argue that SRKW might have access to larger fish the same way NRKW do, recent evidence suggests that SRKW might exhibit different foraging strategies than NRWK [102]. When compared to NRKW, SRKW females were found to be less efficient at capturing prey [102]. ...
... Although one could argue that SRKW might have access to larger fish the same way NRKW do, recent evidence suggests that SRKW might exhibit different foraging strategies than NRWK [102]. When compared to NRKW, SRKW females were found to be less efficient at capturing prey [102]. In this context, the size-selective predation by growing populations of more efficient hunters could directly affect the availability of high-quality prey for SRKW. ...
Along the northeast Pacific coast, the salmon-eating southern resident killer whale population (SRKW, Orcinus orca) have been at very low levels since the 1970s. Previous research have suggested that reduction in food availability, especially of Chinook salmon (Oncorhynchus tshawytscha), could be the main limiting factor for the SRKW population. Using the ecosystem modelling platform Ecopath with Ecosim (EwE), this study evaluated if the decline of the Pacific salmon populations between 1979 and 2020 may have been impacted by a combination of factors, including marine mammal predation, fishing activities, and climatic patterns. We found that the total mortality of most Chinook salmon populations has been relatively stable for all mature returning fish despite strong reduction in fishing mortality since the 1990s. This mortality pattern was mainly driven by pinnipeds, with increases in predation between 1979 and 2020 mortality ranging by factors of 1.8 to 8.5 across the different Chinook salmon population groups. The predation mortality on fall-run Chinook salmon smolts originating from the Salish Sea increased 4.6 times from 1979 to 2020, whereas the predation mortality on coho salmon (Oncorhynchus kisutch) smolts increased by a factor of 7.3. The model also revealed that the north Pacific gyre oscillation (NPGO) was the most important large-scale climatic index affecting the stock productivity of Chinook salmon populations from California to northern British Columbia. Overall, the model provided evidence that multiple factors may have affected Chinook salmon populations between 1979 and 2020, and suggested that predation mortality by marine mammals could be an important driver of salmon population declines during that time.
... Orca (Orcinus orca) form clan-specific ecotypes that are socially learned. Across their global range, orcas adapt their predatory behaviours to prey species availability [17,60,61]. However, clan-specific hunting strategies arise through their ability to learn and remember prey distributions, and by developing and sharing the specialised behaviours needed to hunt each prey species [61,62]. ...
... Across their global range, orcas adapt their predatory behaviours to prey species availability [17,60,61]. However, clan-specific hunting strategies arise through their ability to learn and remember prey distributions, and by developing and sharing the specialised behaviours needed to hunt each prey species [61,62]. Orca ecotypes drive food web structures in the northern North Pacific Ocean. ...
Predator-prey ecology and the study of animal cognition and culture have emerged as independent disciplines. Research combining these disciplines suggests that both animal cognition and culture can shape the outcomes of predator-prey interactions and their influence on ecosystems. We review the growing body of work that weaves animal cognition or culture into predator-prey ecology, and argue that both cognition and culture are significant but poorly understood mechanisms mediating how predators structure ecosystems. We present a framework exploring how previous experiences with the predation process creates feedback loops that alter the predation sequence. Cognitive and cultural predator-prey ecology offers ecologists new lenses through which to understand species interactions, their ecological consequences, and novel methods to conserve wildlife in a changing world.
Biologging has been used on a range of wild animals to document spectacular feats of migration and behaviour. We describe the pursuit, capture, and ingestion of an adult Atlantic bluefin tuna (Thunnus thynnus) (175 cm, estimated weight: 81 kg), which was instrumented with a biologging tag, by a predator, most likely an orca (Orcinus orca). The predation event lasted over 19 min, with the tuna exhibiting elevated activity (max acceleration 3.12 g) and a rapid ascent from 126 m at 3.6 m.s− 1 followed by death and handling at the surface. Orca were separately recorded using video tags, capturing and handling tuna cooperatively in a manner consistent with the tuna data. We then present the longest orca accelerometry dataset from the ingested MiniPAT tag, with diel patterns of activity and 77 feeding events. These unique datasets provide insight into the energetic dynamics of two of the ocean’s fastest predators.
Monitoring bycatch in fisheries is essential for effective conservation and fisheries sustainability. False killer whales Pseudorca crassidens in Hawaiian waters are known to interact with both commercial and recreational fisheries, but limited observer coverage across Hawaiian fisheries obscures the ability to assess bycatch. We build upon previous work and assess occurrence of fisheries interactions through photographic evidence of dorsal fin and mouthline injuries for 3 false killer whale populations in Hawai‘i. Photographs of injuries on dorsal fins and mouthlines collected between 1999-2021 were scored for consistency with fishery interactions (‘not consistent’, ‘possibly consistent’, ‘consistent’). For individuals with both dorsal fin and mouthline photos available, the endangered main Hawaiian Islands (MHI) population had the highest rates of injuries consistent with fisheries interactions (28.7% of individuals), followed by the pelagic stock (11.7%), while no individuals from the Northwestern Hawaiian Islands population with both types of photos had fisheries-related injuries. Some individuals from the MHI population were documented with multiple fisheries-related injuries acquired on different occasions, indicating repeated interactions with fisheries. Individuals first began acquiring injuries consistent with fishery interactions at an estimated age of 2 yr. Females were more likely to have fisheries-related dorsal fin injuries than males, but rates of fisheries-related mouthline injuries were similar between the sexes. Injuries consistent with fisheries interactions were acquired throughout the study period, indicating that this is an ongoing issue, not a legacy of past fishery interactions. Our results suggest that efforts to reduce bycatch and begin monitoring of fisheries that overlap the range of the endangered MHI population are needed.
Understanding how the environment mediates an organism's ability to meet basic survival requirements is a fundamental goal of ecology. Vessel noise is a global threat to marine ecosystems and is increasing in intensity and spatiotemporal extent due to growth in shipping coupled with physical changes to ocean soundscapes from ocean warming and acidification. Odontocetes rely on biosonar to forage, yet determining the consequences of vessel noise on foraging has been limited by the challenges of observing underwater foraging outcomes and measuring noise levels received by individuals. To address these challenges, we leveraged a unique acoustic and movement dataset from 25 animal‐borne biologging tags temporarily attached to individuals from two populations of fish‐eating killer whales ( Orcinus orca ) in highly transited coastal waters to (1) test for the effects of vessel noise on foraging behaviors—searching (slow‐click echolocation), pursuit (buzzes), and capture and (2) investigate the mechanism of interference. For every 1 dB increase in maximum noise level, there was a 4% increase in the odds of searching for prey by both sexes, a 58% decrease in the odds of pursuit by females and a 12.5% decrease in the odds of prey capture by both sexes. Moreover, all but one deep (≥75 m) foraging attempt with noise ≥110 dB re 1 μPa (15–45 kHz band; n = 6 dives by n = 4 whales) resulted in failed prey capture. These responses are consistent with an auditory masking mechanism. Our findings demonstrate the effects of vessel noise across multiple phases of odontocete foraging, underscoring the importance of managing anthropogenic inputs into soundscapes to achieve conservation objectives for acoustically sensitive species. While the timescales for recovering depleted prey species may span decades, these findings suggest that complementary actions to reduce ocean noise in the short term offer a critical pathway for recovering odontocete foraging opportunities.
