Dataset Information

Movement-assisted localization from acoustic telemetry data.

ABSTRACT: Background:Acoustic telemetry technologies are being increasingly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at receivers (i.e., "localization"). Multiple processes provide information for localization estimation including detection/non-detection data at receivers, information on signal rate, and an underlying movement model describing how individuals move and utilize space. Frequently, however, localization methods only integrate a subset of these processes and do not utilize the full spatial encounter history information available from receiver arrays. Methods:In this paper we draw analogies between the challenges of acoustic telemetry localization and newly developed methods of spatial capture-recapture (SCR). We develop a framework for localization that integrates explicit sub-models for movement, signal (or cue) rate, and detection probability, based on acoustic telemetry spatial encounter history data. This method, which we call movement-assisted localization, makes efficient use of the full encounter history data available from acoustic receiver arrays, provides localizations with fewer than three detections, and even allows for predictions to be made of the position of an individual when it was not detected at all. We demonstrate these concepts by developing generalizable Bayesian formulations of the SCR movement-assisted localization model to address study-specific challenges common in acoustic telemetry studies. Results:Simulation studies show that movement-assisted localization models improve point-wise RMSE of localization estimates by >50% and greatly increased the precision of estimated trajectories compared to localization using only the detection history of a given signal. Additionally, integrating a signal rate sub-model reduced biases in the estimation of movement, signal rate, and detection parameters observed in independent localization models. Conclusions:Movement-assisted localization provides a flexible framework to maximize the use of acoustic telemetry data. Conceptualizing localization within an SCR framework allows extensions to a variety of data collection protocols, improves the efficiency of studies interested in movement, resource selection, and space-use, and provides a unifying framework for modeling acoustic data.

SUBMITTER: Hostetter NJ

PROVIDER: S-EPMC7327795 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Movement-assisted localization from acoustic telemetry data.

Hostetter Nathan J NJ Royle J Andrew JA

Movement ecology 20200630

<h4>Background</h4>Acoustic telemetry technologies are being increasingly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at receivers (i.e., "localization"). Multiple processes provide information for l ...[more]

PMID: 32617163

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Project description:A wide array of technologies are available for gaining insight into the movement of wild aquatic animals. Although acoustic telemetry can lack the fine-scale spatial resolution of some satellite tracking technologies, the substantially longer battery life can yield important long-term data on individual behavior and movement for low per-unit cost. Typically, however, receiver arrays are designed to maximize spatial coverage at the cost of positional accuracy leading to potentially longer detection gaps as individuals move out of range between monitored locations. This is particularly true when these technologies are deployed to monitor species in hard-to-access locations.Here, we develop a novel approach to analyzing acoustic telemetry data, using the timing and duration of gaps between animal detections to infer different behaviors. Using the durations between detections at the same and different receiver locations (i.e., detection gaps), we classify behaviors into "restricted" or potential wider "out-of-range" movements synonymous with longer distance dispersal. We apply this method to investigate spatial and temporal segregation of inferred movement patterns in two sympatric species of reef shark within a large, remote, marine protected area (MPA). Response variables were generated using network analysis, and drivers of these movements were identified using generalized linear mixed models and multimodel inference.Species, diel period, and season were significant predictors of "out-of-range" movements. Silvertip sharks were overall more likely to undertake "out-of-range" movements, compared with gray reef sharks, indicating spatial segregation, and corroborating previous stable isotope work between these two species. High individual variability in "out-of-range" movements in both species was also identified.We present a novel gap analysis of telemetry data to help infer differential movement and space use patterns where acoustic coverage is imperfect and other tracking methods are impractical at scale. In remote locations, inference may be the best available tool and this approach shows that acoustic telemetry gap analysis can be used for comparative studies in fish ecology, or combined with other research techniques to better understand functional mechanisms driving behavior.

Project description:Background:California horn sharks (Heterodontus francisci) are nocturnally active, non-obligate ram ventilating sharks in rocky reef habitats that play an important ecological role in regulating invertebrate communities. We predicted horn sharks would use an area restricted search (ARS) movement strategy to locate dense resource patches while minimizing energetic costs of travel and nighttime activity. As ectotherms, we predicted environmental temperature would play a significant role in driving movement and activity patterns. Methods:Continuous active acoustic tracking methods and acceleration data loggers were used to quantify the diel fine-scale spatial movements and activity patterns of horn sharks. First passage time was used to identify the scale and locations of patches indicative of ARS. Activity was assessed using overall dynamic body acceleration (ODBA) as a proxy for energy expenditure. Behavior within a patch was characterized into three activity patterns: resting, episodic burst activity, and moderate, consistent activity. Results:After resting in daytime shelters, individuals travelled to multiple reefs throughout the night, traversing through depths of 2-112?m and temperatures of 10.0-23.8?°C. All sharks exhibited area restricted search patch use and arrived at their first patch approximately 3.4?±?2.2?h (mean?±?SD) after sunset. Sharks exhibited moderate, consistent activity in 54% of the patches used, episodic burst activity in 33%, and few (13%) were identified as resting at night. ODBA peaked while sharks were swimming through relatively deeper (~?30?m), colder channels when traversing from one patch to the next. There was no consistent pattern between ODBA and temperature. Conclusions:We provide one of the largest fine-scale, high-resolution paired data sets for an elasmobranch movement ecology study. Horn sharks exhibited ARS movement patterns for various activity patterns. Individuals likely travel to reefs known to have profitable and predictable patches, potentially tolerating less suitable environmental temperatures. We demonstrate how gathering high-resolution information on the movement decisions of a community resident enhances knowledge of community structure and overall ecosystem function.

Project description:The recent miniaturization of acoustic tracking devices has allowed fishery managers and scientists to collect spatial and temporal data for sustainable fishery management. The spatial and temporal dimensions of fish behavior (movement and/or vertical migrations) are particularly relevant for rockfishes (Sebastes spp.) because most rockfish species are long-lived and have high site fidelity, increasing their vulnerability to overexploitation. In this study, we describe the short-term (with a tracking period of up to 46 d) spatial behavior, as determined by acoustic tracking, of the black rockfish Sebastes schlegelii, a species subject to overexploitation in the Yellow Sea of China. The average residence index (the ratio of detected days to the total period from release to the last detection) in the study area was 0.92 ± 0.13, and most of the tagged fish were detected by only one region of the acoustic receiver array, suggesting relatively high site fidelity to the study area. Acoustic tracking also suggested that this species is more frequently detected during the day than at night in our study area. However, the diel detection periodicity (24 h) was only evident for certain periods of the tracking time, as revealed by a continuous wavelet transform. The habitat selection index of tagged S. schlegelii suggested that S. schlegelii preferred natural reefs, mixed sand/artificial reef bottoms and mixed bottoms of boulder, cobble, gravel and artificial reefs. The preference of this species for the artificial reefs that were recently deployed in the study area suggests that artificial seascapes may be effective management tools to attract individuals. The vertical movement of tagged S. schlegelii was mostly characterized by bottom dwelling behavior, and there was high individual variability in the vertical migration pattern. Our results have important implications for S. schlegelii catchability, the implementation of marine protected areas, and the identification of key species habitats, and our study provides novel information for future studies on the sustainability of this important marine resource in eastern China.

Dataset Information

Movement-assisted localization from acoustic telemetry data.

Publications

Movement-assisted localization from acoustic telemetry data.

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