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Noise (mV) at 69 kHz calculated at all three VR2Tx receivers for each hour of the day. Solid dots represent the mean hourly value and error bars are ± standard deviation. Shading indicates nocturnal hours between 1930 and 06:00

Noise (mV) at 69 kHz calculated at all three VR2Tx receivers for each hour of the day. Solid dots represent the mean hourly value and error bars are ± standard deviation. Shading indicates nocturnal hours between 1930 and 06:00

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Background Acoustic telemetry has been used with great success to quantify the movements of marine fishes in open habitats, however research has begun to focus on patterns of movement and habitat usage within more structurally complex habitats. To date, there has been no detailed assessment of the performance of acoustic telemetry within seagrass,...

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... Numerous environmental factors are linked to patterns of scattering and absorption, thereby modifying this distance across space and time. Characteristics, such as bottom substrate, bathymetry, and various water column properties (e.g., stratification, bubbles, and turbidity), can disrupt transmission through scattering and absorption, as well as via the refraction and reflection of sound signals [6,7,[9][10][11]. Ambient noise in the study environment, caused by either environmental (e.g., wind, waves, rain), biological (e.g., animal noises), or anthropogenic sources (e.g., boats or mechanical noises) can also mask or disrupt the transmission of acoustic signals [4,9,[12][13][14]. ...
... Characteristics, such as bottom substrate, bathymetry, and various water column properties (e.g., stratification, bubbles, and turbidity), can disrupt transmission through scattering and absorption, as well as via the refraction and reflection of sound signals [6,7,[9][10][11]. Ambient noise in the study environment, caused by either environmental (e.g., wind, waves, rain), biological (e.g., animal noises), or anthropogenic sources (e.g., boats or mechanical noises) can also mask or disrupt the transmission of acoustic signals [4,9,[12][13][14]. In some cases, features of mooring design and receiver attachment, including position of the receiver in the water column, receiver tilt, and noise produced by the mooring itself, can also play a major role in receiver performance [15]. ...
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Range tests play a critical role in designing acoustic telemetry studies, guiding equipment configuration, deployment techniques, and the analysis of animal movement data. These studies often strive to capture the effects of environmental variation on detection efficiency over time but are frequently limited in spatial and temporal scale. This could lead to disparities between test results and the circumstances encountered during animal tracking studies. In this study, we evaluated detection range and efficiency at two distinct spatial and temporal scales in a dynamic intertidal ecosystem. Two range tests were conducted, the first being a small-scale study using 6 receivers deployed over 1 month. Using modern acoustic receivers with built-in transmitters and environmental sensors, we then conducted a large-scale range test with 22 receiver stations over a full year to approximate the area and duration of a typical animal movement study. Differences in detection range between the two studies occurred as a result of environmental variation and tag power output, with midpoint ranges estimated as 123 m (small scale, low power), 149 m (small scale, high power) and 311 m (large scale, very high power). At both scales, wind speed emerged as the most influential factor explaining temporal variation in predicted detection efficiency. However, this effect was modulated by wind direction which varied as a result of land sheltering and fetch between the two study scales. At the small scale, detection efficiency decreased with winds from the south and east, while at the large scale, northern and westerly winds were most detrimental. Water temperature had a positive effect on predicted detection efficiency at both scales, while relative water level was positive at the small scale and negative at the large scale. Additional factors, including precipitation and Topographic Position Index, were found to influence detection efficiency at a large scale. Moreover, sensors associated with receivers in the larger array revealed the significant influences of receiver tilt and ambient noise. These discrepancies in the outcomes of the two studies underscore the critical role of scale in range test design and emphasize the need for long-term, in situ range testing at relevant spatial scales.
... The presence of vegetation also negatively affects receiver performance by blocking and attenuating signals, resulting in low detection probability and small detection range. For example, Swadling et al. (2020) compared the detection efficiency of acoustic receivers between transmitters positioned above and among a seagrass canopy and found a significant reduction in detection range when the transmitter sat among the canopy (from 90 to 45 m). Similarly, Weinz et al. (2021) conducted range testing of acoustic receivers in seasonally submerged vegetated habitats in a freshwater system and reported substantial differences in detection range (from nearly 200 m when vegetation coverage was low to less than 10 m when the coverage was high). ...
... Knowledge of the relationship between mangrove systems and the animals that use them is critical to predict how coastal communities may respond to the loss of mangrove systems. For acoustic telemetry, however, mangrove systems are challenging environments to operate in mainly due to the shallow water depth, which changes rapidly with tidal movements, physical complexity from submerged trunks and roots (e.g., Swadling et al., 2020), and turbidity (e.g., Cooke et al., 2013). As a result, there have been no long-term acoustic telemetry studies conducted within mangrove habitats (i.e., inside mangrove forests) (Kanno et al., 2023). ...
... In other nearshore vegetated ecosystems, there are some studies that may provide insights to guide studies in mangroves. Recently, several studies tested the detection performance of active and passive acoustic telemetry in wetland environments with submerged aquatic vegetation in lakes and found that acoustic transmitters can be used in vegetated shallow water environments (Swadling et al., 2020;Thiemer et al., 2022;Weinz et al., 2021). Matley et al. (2022) conducted passive acoustic monitoring for yellow perch Perca flavescens in vegetated lake habitats with the consideration of range test results. ...
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... This is a common limitation of acoustic tracking owing to increased biological noise at night on reefs (How & de Lestang, 2012;Huveneers et al., 2016;Stocks et al., 2014;Swadling et al., 2020) and ...
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Rock hind (Epinephelus adscensionis) and spotted moray (Gymnothorax moringa) are ubiquitous mesopredators that co-occur in the nearshore waters of Ascension Island in the South Atlantic Ocean, where they have significant cultural and subsistence value, but management of their non-commercial take is limited. This isolated volcanic system is home to high biomass and low species diversity, which poses two key questions: How can two mesopredators that perform similar ecological roles coexist? And if these two species are so ecologically similar, can they be managed using the same approach? Here, we combined acoustic telemetry, stomach content analysis, and stable isotope analysis to (i) explore space use and diet choices within and between these two species and (ii) to assess appropriate species-specific management options. Although rock hind had high residency and small calculated home ranges (0.0001–0.3114 km2), spotted moray exhibited shorter periods of residency (<3 months) before exiting the array. Vertical space use differed significantly across the 20-month tracking period, with individual differences in vertical space observed for both species. A hierarchical generalized additive model using 12-h averaged depth data identified that rock hind occurred lower in the water column than spotted moray, with both species occupying moderately deeper depths at night versus day (+1.6% relative depth). Spotted moray depth was also significantly predicted by lunar illumination. Aggregating samples by species and tissue type, Bayesian ecological niche modeling identified a 53.14%–54.15% and 78.02%–97.08% probability of niche overlap from fin clip and white muscle, respectively, whereas limited stomach content data indicated a preference for piscivorous prey. Variability in niche breadth between years suggests these species may exploit a range of prey items over time. These findings indicate that although these two species perform a similar ecological role by feeding on prey occupying the same trophic levels, subtle differences in movement behaviors between them suggest a one-rule-fits-all management approach is not likely the most effective option.
... First, the Brownian motion variance parameter (σ 2 m ) estimates the variance in a fish's position between two points using a maximum likelihood approach. Second, the location error was also required, and this was set as the predicted receiver detection range of 85 m based on previous range testing (Swadling et al. 2020). Home-range estimates derived from BBMMs were calculated as 50% (core home range) and 95% (home range extent) utilisation distributions. ...
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... Here again, known tag deployments in fixed locations can help quantify changes in detection range. Lastly, positioning studies requiring multiple detections of a single tag transmission can include detection variability in study design, helping to inform the number and placement of receivers to optimize coverage for intended testing [40][41][42]. These study types would all be impacted and biased if detection efficiency is non-constant among receivers, and recognizing the existence and causes of this bias can help inform various aspects of study design and interpretation [14,43]. ...
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Abstract Background Acoustic telemetry is a commonly used technology to monitor animal occupancy and infer movement in aquatic environments. The information that acoustic telemetry provides is vital for spatial planning and management decisions concerning aquatic and coastal environments by characterizing behaviors and habitats such as spawning aggregations, migrations, corridors, and nurseries, among others. However, performance of acoustic telemetry equipment and resulting detection ranges and efficiencies can vary as a function of environmental conditions, leading to potentially biased interpretations of telemetry data. Here, we characterize variation in detection performance using an acoustic telemetry receiver array deployed in Wellfleet Harbor, Massachusetts, USA from 2015 to 2017. The array was designed to study benthic invertebrate movements and provided an in situ opportunity to identify factors driving variation in detection probability. Results The near-shore location proximate to environmental monitoring allowed for a detailed examination of factors influencing detection efficiency in a range-testing experiment. Detection ranges varied from
... Tags were programmed to emit a unique signal (69 kHz) with high power output (151 dB re 1 µPa at 1 m) at random intervals between 150 and 210 s (180 s nominal). High power output was chosen in an attempt to overcome potential signal attenuation owing to burial of crabs [38,46,59] and the presence of seagrass within the study site [60]. Random signal transmission times were employed to minimize potential signal overlap (i.e., codecollision) which can block detection. ...
... Measurement error within our array was generally low (median = 1.26 m) and consistent with inherent GPS error (2-3 m; [17]), which was used to define the 'known' positions of receivers and reference tags. Additionally, some error may have been due to the presence of structurally complex habitats such as seagrass, mangroves and oyster farming infrastructure, which can lead to issues with signal attenuation [60], refraction, or reflection (sometimes refered to as 'multipath'; [105]). Measurement error can increase the overlap between state-dependent distributions (e.g., similar step lengths) which may lead to difficulty differentiating states [106]. ...
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Background Effective fisheries management of mobile species relies on robust knowledge of animal behaviour and habitat-use. Indices of behaviour can be useful for interpreting catch-per-unit-effort data which acts as a proxy for relative abundance. Information about habitat-use can inform stocking release strategies or the design of marine protected areas. The Giant Mud Crab (Scylla serrata; Family: Portunidae) is a swimming estuarine crab that supports significant fisheries harvest throughout the Indo-West Pacific, but little is known about the fine-scale movement and behaviour of this species. Methods We tagged 18 adult Giant Mud Crab with accelerometer-equipped acoustic tags to track their fine-scale movement using a hyperbolic positioning system, alongside high temporal resolution environmental data (e.g., water temperature), in a temperate south-east Australian estuary. A hidden Markov model was used to classify movement (i.e., step length, turning angle) and acceleration data into discrete behaviours, while also considering the possibility of individual variation in behavioural dynamics. We then investigated the influence of environmental covariates on these behaviours based on previously published observations. Results We fitted a model with two well-distinguished behavioural states describing periods of inactivity and foraging, and found no evidence of individual variation in behavioural dynamics. Inactive periods were most common (79% of time), and foraging was most likely during low, incoming tides; while inactivity was more likely as the high tide receded. Model selection removed time (hour) of day and water temperature (°C) as covariates, suggesting that they do not influence Giant Mud Crab behavioural dynamics at the temporal scale investigated. Conclusions Our study is the first to quantitatively link fine-scale movement and behaviour of Giant Mud Crab to environmental variation. Our results suggest Giant Mud Crab are a predominantly sessile species, and support their status as an opportunistic scavenger. We demonstrate a relationship between the tidal cycle and foraging that is likely to minimize predation risk while maximizing energetic efficiency. These results may explain why tidal covariates influence catch rates in swimming crabs, and provide a foundation for standardisation and interpretation of catch-per-unit-effort data—a commonly used metric in fisheries science.
... Identifying key considerations and associated factors that potentially influence the performance of VPS is a proactive step to ensure data yield is sufficient to meet the study objectives and reduce positioning error, which in turn governs data analysis choices. These factors include the characteristics of the system studied, including water type (freshwater, saltwater and brackish), habitat type (Guzzo et al. 2018, Becker et al. 2020, Swadling et al. 2020), depth (Brownscombe et al. 2017, Taylor et al. 2017b, bathy metric complexity (Andrews et al. 2011) and in-water structures (Bašić et al. 2019, Barilotti et al. 2020, van der Knaap et al. 2021. Secondly, the ecology of the animal studied and their associated behavioural strategies (e.g. ...
... However, basic visualisations of ambient noise levels recorded by VR2 and HR generation receivers can be insightful in identifying periods of suboptimal system performance during post-study assessment. For example, Swadling et al. (2020) identified how environmental conditions, including environmental noise, changed throughout the day and used generalised additive mixed models (GAMMs) to assess their relative role in the detection probability of fixed tags. Best practice is currently identifying periods of high ambient noise, poor sync tag performance, and excluding these data during data filtering, or at the very least reporting when these conditions occur. ...
Article
Fine-scale tracking technology has advanced our understanding of aquatic animal behaviour by deriving near-continuous movements of animals ranging in size from small invertebrates to large predatory fish. Commercial fine-scale positioning systems, such as the VEMCO Positioning System, can pinpoint an animal’s location within metres of its true position. While methodological applications of commonly used presence-absence acoustic telemetry have identified factors that can limit array performance, the factors that influence position yield and accuracy and introduce error in fine-scale positioning systems have yet to be synthesised. Evidenced through a systematic review of the literature, we highlight key considerations and potential pitfalls faced when designing and conducting a fine-scale tracking study. Key factors impacting data acquisition are grouped under 4 key categories linked to the study system, species studied, and logistical and technological constraints. Thereafter, in line with these categories, we provide a framework that can be used prior to, during, and post-study to identify sources of error and data loss to optimize system design and acquired results. We provide details on user assessment tools that include a pre-study trial period using fixed tags to assess array geometry and data yield, an in situ checkpoint data download, and a post-study assessment of fixed transmitter performance. We highlight the utility of this framework and integrated assessment tools by presenting a real-world case study that ultimately was compromised. We anticipate that this framework can be used to standardize reporting of essential steps and checks that will generate comparable data for future synthesis, which will further advance fine-scale tracking approaches.
... Considering that the transmission detected or not detected was the binary response variable, a logistic regression model was fitted to the data to predict the detection probability at different distances (i.e., the appropriate receiver spacing) by using the "glm" function in the R version 4.2.0 (Kessel et al., 2014;Swadling et al., 2020). The model significance was examined by the Hosmer and Lemeshow goodnessof-fit test, and p > 0.05 means it provides a satisfactory fit to the data (Hosmer and Lemeshow, 2000). ...
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Due to there being a lack of suitable approaches for evaluating the effectiveness of artificial reefs, two experiments were designed to examine the feasibility of acoustic telemetry, a rapidly developing method for tracking aquatic animals. The first experiment was conducted to understand the deployment procedures of an acoustic telemetry system and determine the appropriate deployment of receivers’ spacing, while the second experiment was conducted to quantify the site fidelity and habitat use of 11 reef fish in the Fangchenggang artificial reef area in the northern South China Sea, China. The results indicated that the logistic regression model was an effective way to balance the detection probability at different distances between the range test transmitter and receiver, with above 50% detection probability within 240 m and 80% detection probability within 110 m. The residency index, as a quantification of site fidelity, was 0.85 ± 0.24. The 100% minimum convex polygon, 95% kernel utilization distribution, and 50% kernel utilization distribution, which are the indicators of habitat use, were 34,522.94 ± 35,548.95, 1,467.52 ± 1,619.05, and 236.01 ± 294.59 m², respectively. High site fidelity and the small spatial scale of habitat use for reef fish demonstrated that artificial reefs were an effective man-made structure to attract fish. Overall, this study supports the feasibility of the acoustic telemetry system, indicating that it provides a good approach for quantifying the associations between artificial reefs and fish.
... Within Jervis Bay, New South Wales Department of Primary Industries installed an acoustic receiver array (n = 27 receivers; Innovasea VR2-W receivers) to monitor the movements and residency of a number of fish species on rocky reefs within the bay and across the bay entrance. The receivers were estimated to have a detection range of ~250 m (50% probability of detection; Swadling et al. 2020). Additional receivers were deployed around the Australian coastline (n = 112 between Jervis Bay and Tasmania), as a part of the Integrated Marine Observing System (IMOS) animal-tracking database, a national collaborative research initiative of various universities, government and private research institutions. ...
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Context Understanding migratory species’ habitat selection is complicated by variation in movement strategies. Stable-isotope analysis provides a powerful tool to investigate such variation. Aims We used acoustic telemetry and stable-isotope analysis to better understand the movement strategies of Port Jackson sharks. Methods We compared the δ13C and δ15N values of fin tissue from acoustically tracked individuals that undertook three distinct movement strategies. Hierarchical cluster analysis was then used to cluster movement strategies of a larger sample of sharks on the basis of δ13C and δ15N values. Key results Tracked individuals that remained in Jervis Bay were enriched in 13C, compared with those that migrated south after the breeding season. Individuals were assigned to six clusters and δ13C and δ15N values indicated that migrating males and females may utilise different geographical areas or niches during the non-breeding season. Conclusions By using stable isotope analysis and acoustic telemetry, we identified distinct groups of Port Jackson sharks with similar broad-scale movement strategies. Implications These variable movement strategies may lead to different reproductive fitness advantages on an individual and population level, having implications for the broader ecosystem, given the important role mesopredators play in southern reef marine ecosystems.
... While not addressed within this study, some of the fish sampled here may be residents of the meadow [46], and thus may be more affected by seagrass density than transient species. Detecting fish where vegetation is particularly dense may also require more detailed methods than remote cameras [84][85][86]. This sampling method mostly detected the supra-canopy fishes associated with the meadow whereas methods such as visual census may be more appropriate for sampling within-canopy species [86]. ...
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Habitat complexity plays a critical role in shaping biotic assemblages and ecosystem processes. While the impacts of large differences in habitat complexity are often well understood, we know less about how subtle differences in structure affect key ecosystem functions or properties such as biodiversity and biomass. The late-successional seagrass Posidonia australis creates vital habitat for diverse fauna in temperate Australia. Long-term human impacts have led to the decline of P. australis in some estuaries of eastern Australia, where it is now classified as an endangered ecological community. We examined the influence of P. australis structural complexity at small (seagrass density) and large (meadow fragmentation) spatial scales on fish and epifauna communities, predation and sediment erosion. Fine-scale spatially balanced sampling was evenly distributed across a suite of environmental covariates within six estuaries in eastern Australia using the Generalised Random Tessellation Structures approach. We found reduced erosion in areas with higher P. australis density, greater abundance of fish in more fragmented areas and higher fish richness in vegetated areas further from patch edges. The abundance of epifauna and fish, and fish species richness were higher in areas with lower seagrass density (seagrass density did not correlate with distance to patch edge). These findings can inform seagrass restoration efforts by identifying meadow characteristics that influence ecological functions and processes.