When prey is patchily distributed, predators are expected to spend more time searching for food in proximity of recent prey captures before searching in other areas. This behavior, known as area-restricted search, results in predators remaining localized in areas where prey had been detected previously because of the higher probability of encountering additional prey. However, few studies have tested these predictions on marine species because of the difficulties of observing feeding behavior. In this study, we utilized passive acoustic detections of echolocating dolphins to identify foraging behavior. C-PODs (click train detectors) were deployed for two years with an acoustic recorder attached to the same mooring during the second year. The time series of feeding buzzes, indicative of foraging behavior, revealed that both bottlenose (Tursiops truncatus) and common dolphins (Delphinus delphis) were more likely to stay in the area longer when foraging activity was high at the beginning of the encounter. The probability of foraging was also higher following previous foraging activity. This suggests that dolphins were feeding on spatially patchy prey and previous foraging experience influenced their movement behavior. This is consistent with the predictions of area-restricted search behavior, a nonrandom foraging strategy.
Ocean noise varies spatially and temporally and is driven by natural and anthropogenic processes. Increased ambient noise levels can cause signal masking and communication impairment, affecting fitness and recruitment success. However, the effects of increasing ambient noise levels on marine species, such as marine mammals that primarily rely on sound for communication, are not well understood. We investigated the effects of concurrent ambient noise levels on social whistle calls produced by bottlenose dolphins (Tursiops truncatus) in the western North Atlantic. Elevated ambient noise levels were mainly caused by ship noise. Increases in ship noise, both within and below the dolphins' call bandwidth, resulted in higher dolphin whistle frequencies and a reduction in whistle contour complexity, an acoustic feature associated with individual identification. Consequently, the noise-induced simplification of dolphin whistles may reduce the information content in these acoustic signals and decrease effective communication, parent–offspring proximity or group cohesion.
Passive acoustic monitoring (PAM) is a widely used technique for studying the distribution and habitat use of cetaceans. The C-POD, an acoustic sensor with an onboard automated click detector, has been deployed in diverse acoustic environments, but studies verifying its offshore detection rates and factors affecting detection probability are scarce. To empirically evaluate the performance of C-PODs in detecting bottlenose dolphins (), C-PODs were deployed alongside archival acoustic recorders 12-30 km offshore in the Northwest Atlantic Ocean. The C-POD and acoustic recordings, post-processed using PAMGUARD software, were compared for a period of 6852 h. C-POD false positive rates were very low (mean 0.003%), and positive hourly detection accuracy was very high (mean 99.6%). Analysis of the acoustic environment and dolphin click characteristics revealed that true positive detections by C-PODs were significantly more likely to occur when PAMGUARD detected more clicks and there was increased high frequency noise (>20 kHz), likely from distant or unclassified clicks. C-PODs were found to be reliable indicators of dolphin presence at hourly or greater time scales. These results support the application of C-PODs in PAM studies that aim to investigate patterns of dolphin occurrence, such as those related to offshore windfarms.
Bottlenose dolphins (Tursiops truncatus) are migratory marine mammals that live in both open-ocean and coastal habitats. Although widely studied, little is known about their occurrence patterns in the highly urbanized estuary of the Chesapeake Bay, USA. The goal of this study was to establish the spatial and temporal distribution of bottlenose dolphins throughout this large estuarine system and use statistical modeling techniques to determine how their distribution relates to environmental factors. Three years (April-October 2017–2019) of dolphin sighting reports from a citizen-science database, Chesapeake DolphinWatch, were analyzed. The dolphins had a distinct temporal pattern, most commonly sighted during summer months, peaking in July. This pattern of observed occurrence was confirmed with systematic, passive acoustic detections of dolphin echolocation clicks from local hydrophones. Using spatially-exclusive Generalized Additive Models (GAM), dolphin presence was found to be significantly correlated to spring tidal phase, warm water temperature (24–30°C), and salinities ranging from 6–22 PPT. We were also able to use these GAMs to predict dolphin occurrence in the Bay. These predictions were statistically correlated to the actual number of dolphin sighting reported to Chesapeake DolphinWatch during that time. These models for dolphin presence can be implemented as a predictive tool for species occurrence and inform management of this protected species within the Chesapeake Bay.
Passive acoustic monitoring (PAM) offers opportunities to collect data on the occurrence of vocal species for long periods of time, at multiple locations, and under a range of environmental conditions. Some species emit individually distinctive calls, including bottlenose dolphins (Tursiops truncatus) that produce signature whistles. Our study used PAM to determine the seasonal occurrence of bottlenose dolphins and utilized individually specific signature whistles to (1) track individuals spatially and temporally, (2) assess site fidelity off Maryland (MD), USA, (3) estimate the minimum abundance of dolphins in the study area, and (4) develop a dynamic habitat‐based relative abundance model applicable as a real‐time dolphin relative abundance prediction tool. Acoustic recorders were deployed at two sites offshore of Ocean City, MD, and at one site in the upper Chesapeake Bay, MD. Acoustic recordings from 2016 to 2018 were analyzed for signature whistles, and re‐occurrences of individual whistles were identified using a combination of machine learning and manual verification. A habitat‐based density model was created using the number of signature whistles combined with environmental conditions. A total of 1518 unique signature whistles were identified offshore of Maryland and in the upper Chesapeake Bay. There were 184 re‐occurrences of 142 whistles, with a mean of 135 d between re‐occurrences (range = 1–681 d). These repeated detections of the same individuals occurred most frequently at the site near Ocean City, MD, indicating the highest site fidelity. Re‐occurrences were recorded among all three sites, indicating movement of dolphins between the Chesapeake Bay and off the Atlantic coast of Maryland. The weekly number of individual dolphins detected off the Atlantic coast was significantly related to two environmental variables: sea surface temperature and chlorophyll a concentration. This habitat model could be used to predict relative dolphin abundance offshore of Maryland and inform management within the region, including in relation to offshore wind energy development and other stakeholders.
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