Random swims: An evaluation of acoustic telemetry thresholds for reef-shark behavior and residency

doi:10.21203/rs.3.rs-5456674/v1

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Research Article

Random swims: An evaluation of acoustic telemetry thresholds for reef-shark behavior and residency

https://doi.org/10.21203/rs.3.rs-5456674/v1

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published 04 Apr, 2025

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Background

Passive acoustic telemetry (AT) is a method used to quantify residency within an array of receivers, but this technology has limitations for capturing complex behaviors in reef sharks: pulse intervals and detection range drop-offs. This study addressed residency calculation methodologies by examining visitation qualifier functions (thresholds) in commonly used R packages (e.g., VTrack).

Methods

Random walk models simulated the mismatch between detections on acoustic receivers and real shark movement by testing 30-minute, 1-hour, 2-hour and 24-hour visit thresholds to compare gaps between shark detections at different transmitter settings (1- and 5-minute intervals). We also modeled tracks of transient sharks to show how these animals may interact with a passive acoustic receiver differently than resident individuals.

Results

Our results suggested that longer transmitter pulse intervals (1–5 minutes standard for sharks and larger fish) require shorter visit thresholds (< 30 minutes) to reduce variability in residency time. Consequently, using thresholds of less than 2 hours increased the number of counted visits that stemmed from the same events. Similarly, the 5-minute interval also predicted greater elapsed residency times than did the real path. Our directional walks sent transient sharks through a receiver at 0–1 and 1–2 meters per second; under these scenarios, transmitters were unlikely to ping twice (default minimum visit qualifier) if 5-minute pulse intervals were set on their transmitters (16.4%), whereas 1-minute intervals did frequently (84.2%), indicating that a 5-minute interval may misrepresent residency time for transient animals.

Conclusions

Thresholds and detection qualifiers manually set during passive acoustic surveys can bias residency and visitation results, and careful consideration should be applied on the basis of the life history (residential or transient) of the target species.

Acoustic Telemetry

Reef Sharks

VTrack

VEMCO

Shark Tracking

Residency

Random Walks

Reef sharks play pivotal ecological roles in reef systems by maintaining the balance of prey fish communities [1]. Evaluating their movement and residency patterns helps us understand the health and functionality of reefs and connectivity between marine protected areas (MPAs; [2]). However, accurately assessing shark residency at particular locations is challenging to perform on the basis of hourly, daily, weekly, or monthly movement patterns with a small number of deployed receivers [3]. Telemetry allows scientists to observe population dynamics across many different taxa [4] and is primarily utilized to understand migration patterns, home-range, and seasonal residencies, and behavioral states that are difficult to study via other conventional methods like mark and recapture, or baited cameras [5]. Residency is a metric that quantifies site usage as an individual’s presence or absence at a site or across an array of sites [6]. However, acoustic telemetry tags (transmitters) have programmed intervals when sending out signals to a receiver [7], and the effects of pulse intervals on the precision of residency time estimates remain poorly described. Coded tags are commonly set for 1–5 minutes between pulses, a decision primarily meant to maximize battery life, as shorter intervals reduce battery life longevity exponentially, and to avoid signal collisions where multiple tags are deployed in the same study area [7].

Generally, acoustic telemetry (AT) studies include an overall residency index, consisting of daily detections as a proportion of the total track length [1, 8]. Scientists compile the number of visitations at a given site, defined as sharks pinging at least 2 detections of a given individual at the site within a 24-hour period [8], and how much time passed between each visit. The methods used for calculating these metrics vary depending on the study site, duration, scale, focus species, and overall research goals [9, 3, 10]. For long-term residency studies, the use of ‘daily presence or absence’ suffices to evaluate long-term space occupancies in sharks and thereby the simplest approach to determining overall site residency can be accomplished by dividing the number of days on which the individual was detected at a given site by the total track length in days [1]. However, for finer-scale studies of residency or use of a particular site, data resolution from pulse delay and how residency is calculated, may have a significant impact (i.e., hourly movements and diel patterns).

Residency is punctuated by absence periods and their corresponding data gaps. One package that evaluates animal movement and residency patterns, VTrack [9], filters out single detections and calculates residency within the timestamps of specific site visit events instead of generating one overall proportion. The number of visits and the duration per visit can then be analyzed for metrics of habitat utilization [9]. However, especially for studies that use visits numerically as a measure of site preference, determining what qualifies as a new arrival versus a subsequent detection during the same event raises a caveat; sharks that move outside the receiver show up as empty spaces in detection timelines. Depending on the thresholds set to determine new visits, these data gaps may be misinterpreted as missed detections, and transient reef sharks may be missed altogether. The VTrack package uses an {iResidenceThreshold}, which determines how many detections are considered a visit, and an {iTimeThreshold}, which determines how much time between these detections can pass before the event of a separate arrival to a site is considered [9]. These functions are used to optimize event recognition by determining how many minutes must pass between consecutive ping occurrences to constitute a new arrival. Therefore, it is assumed that gaps falling above these thresholds mean that a shark left the area. Their function also enhances site-specific residency data by subsetting individual residency scores for several visitation events (found in the residenceslog table output), which compiles the time from all events. Absence-time thresholds are set at default values of 2 detections per 24 hours but are adjustable to whatever values the scientist considers are appropriate [9]. There is a need to provide guidance on visit thresholds, how adjusting them may affect the validity of residency scores, and how this may alter how we interpret shark movement behaviors.

To evaluate these uncertainties in visit qualification and overall telemetry accuracy, here we (1) quantify how pulse delay affects residency times and visit counts, (2) reveal the main drivers of inaccuracy between acoustic receiver data and actual shark behavior day-to-day, (3) generate new theories for interpreting abacus plot data for singular visits, and (4) provide decision-making guidelines for setting visit thresholds for packages such as VTrack.

Random Walk Model

We developed a two-dimensional constrained random walk model to simulate the movement of an individual shark in a semi enclosed environment. This model operates within a semicircular domain with a radius of 50,000 meters. If the shark reached the study area boundary, it was redirected back toward the center by programming 180⁰ direction changes for brief (1-minute) stretches after contact with any boundary. Our model enforced a vertical boundary at x = 0, where the shark's position was adjusted to remain at or above x = 0, simulating land at negative x values. Each random walk simulation lasted 24 hours, accounting for continuous movement, and with each time step representing one second of real time, the shark moved in a random direction in the two-dimensional grid. Receiver depth was an unnecessary consideration since we assumed that receivers placed by divers along coastlines are able to reach all available depths within their horizontal range. The movement direction was randomly chosen between 0 and 2π radians at each step (providing 360 degrees of direction change), and the velocity was also randomized and sampled from a uniform distribution between 0–1 m per-second swimming speed [11, 12], for the reef–shark swimming velocity). The system included one receiver placed along a linear coastline, with a resulting semicircular detection range, defined by three concentric zones within the semicircle, that provided a probability of missed detections depending on the distance from the center. These probabilities are derived from the literature on detection probability drop-offs in VEMCO receivers [13, 7]: inner zone (pink): 125-meter radius with 100% detection probability. Middle Zone (Orange): Between 125- and 187.5-meter radii with a 75% detection probability. Outer Zone (Red): Between 187.5- and 250-meter radius with 50% detection probability. We assumed that weather conditions did not alter drop-off probabilities throughout the simulation.

Residency Data Extraction

We performed 1,000 random walks under these conditions, counting the number of true and missed detections, the proportion of true detections overall (residency proportion), the visit count, and the amount of time for each visit. Following the generation of each walk, detection points were placed along the paths according to the delay interval and detection drop-offs. Gaps were classified as occurrences of a shark leaving the receiver range and re-entering OR missed detections along the abacus plot, and gap durations averaged the number of seconds for each gap (enabling a distinction between separate visits and an extended visit). Residency for 1-second, 1-minute, and 5-minute intervals was calculated by dividing the total number of detections by the number of possible detections during each trial. We analyzed the “inaccuracy” of the receiver settings by comparing residency scores from the 1-minute and 5-minute intervals to the score of the base scenario (real-time tracking). For example, if the proportion of residency for 1-minute interval data was 0.30 and the 1-second interval was 0.50, the inaccuracy score would equal 0.2; thus, our inaccuracy scores assigned a value to the amount of error between AT data and actual shark detection proportions.

Visit Thresholds and Residency Times

Visit thresholds were chosen to be 30 minutes, 1 hour, 2 hours, and 24 hours to cover the most common settings from previous publications using VTrack [3, 14, 2]. We extracted the presence and absence data for each interval from our random walks. Depending on the experimental thresholds, a consecutive number of false detections constituted a separate visit, absence strings equal to or greater than each threshold of absence was required before counting a new visit. The residency time was marked as the duration of seconds from the first detection to the last detection of each visit string, and all visit durations were compiled to calculate the total residency time. This system allowed us to compare the visit counts and elapsed residency times between each interval, with the actual path in seconds. We set these functions to contain a minimum detection requirement which only counted events of 2 or more detections as separate arrivals and illustrated the differences in visit counts and residency times through modeled correlations and figures. We ran 2-way analysis of variance (ANOVA) for each iteration of the interval and threshold with residency times, Poisson regressions for visit counts (skewed distribution), Tukey’s HSD test for pairwise comparisons of transient visits, and Levenes test for homogeneity of residency proportions.

Directional Nonrandom Walks

We simulated two different types of transient shark encounters (15 minutes long each with 1,000 trials) and compared a highly transient individual with a slower swimming speed and greater turn angle. The fast shark swam at randomized speeds generated every 1 second between 1–2 meters per second, with turn angles randomized every second between pi/144 (near straight line). The slower shark was set between 0–1 m per second with turn angles of pi/36 (directional but with wider turn radius) on the basis of [15], for average shark cruising speeds driven by metabolism. These models operated under the same receiver ranges and boundaries as the prior residency models (Fig. 1).

Compared with the base scenario, both the 1-minute and 5-minute intervals produced different detection proportions. For the 5-minute interval, the ANOVA results indicated a significant effect of residency (DFs = 338, F = 8.866, p = 0.0025) on inaccuracy. The average inaccuracy score for the 1-minute intervals was 0.20 (95% CI: [0.1963, 0.2147]), whereas for the 5-minute intervals, it was 0.20 (95% CI: [0.1970, 0.2155]), resulting in residency being underestimated by both intervals by approximately 20% compared with the actual shark path when the number of detections over time was used as the calculation for residency. Correlations and fitted quadratic models to differentiate the inaccuracy scores of the 5-minute interval settings (residual standard error: 0.06698 on 343 degrees of freedom; multiple R-squared: 0.3721; F-statistic: 101.6 on 2 and 343 DFs, p < 2.2e-16) revealed that nearly 37% of the variance in inaccuracy could be explained by the residency proportion alone. However, with residency as an indicator, inaccuracy increased with respect to margin of error toward higher residency proportions. The inaccuracy with highly residential sharks (50% – 99% residency) produced more variability than sharks with a residency between 0% and 50% (Levenes test p value = 2.2e-16), reducing the predictability of inaccuracy at higher residencies.

Thresholds & Visit Counting

All 3 thresholds were set under 24 hours, and the number of visits by a shark with 5-minute transmitter pulse intervals was overcalculated (p < 0.001 each; see Table 1). Between the 1-minute and 5-minute intervals, visit counts differed for each threshold (p < 0.001 each), indicating that 5-minute intervals generated higher visit counts than 1-minute intervals across the board. Referring to Table 1: for the 30-minute threshold, the mean visit counts are as follows: 1-second interval: 2.68 (SD = 1.39); 1-minute interval: 2.76 (SD = 1.43); and 5-minute interval: 3.25 (SD = 1.66). For the 1-hour threshold, the mean visit counts are as follows: 1-second interval: 2.18 (SD = 0.98); 1-minute interval: 2.23 (SD = 1.01); and 5-minute interval: 2.34 (SD = 1.04). For the 2-hour threshold, the mean visit counts are as follows: 1-second interval: 1.84 (SD = 0.73); 1-minute interval: 1.87 (SD = 0.72); and 5-minute interval: 1.92 (SD = 0.73).

Table 1

The visit counts under different threshold (rows) and detection intervals (columns). Values are mean ± standard deviation.
Threshold	Actual	One-Minute	Five-Minute
Half Hour	2.68 ± 1.39	2.76 ± 1.43	3.25 ± 1.66
One Hour	2.18 ± 0.98	2.23 ± 1.01	2.34 ± 1.04
Two Hours	1.84 ± 0.73	1.87 ± 0.72	1.92 ± 0.73

Elapsed Visit Residency Times

Residency times differed across different thresholds (half hour, one hour, and two hours; p < 0.0001) and interval types (p < 0.001). We discovered that visit time increased with longer set thresholds, particularly for 5-min intervals. The 5-minute intervals produced longer visit times across each threshold, and the variability between all intervals was the lowest under a 30-minute threshold. We found a difference in visit time between the 5-min path and the actual path, which was highest under the 2-hour threshold and lowest under the 30-minute threshold (Levenes test: p = 0.001). Based on Fig. 2, for the half-hour threshold, the mean visit times were as follows: actual: 988.04 minutes (IQR = 702.75); 1-min: 927.35 minutes (IQR = 707.00); and 5-min: 1,011.41 minutes (IQR = 760.00). At the one-hour threshold: actual: 988.44 minutes (IQR = 706.52); 1-min: 927.27 minutes (IQR = 701.50); and 5-min: 1,048.18 minutes (IQR = 735.00). For the two-hour threshold: actual: 1,046.73 minutes (IQR = 696.06); 1-min: 968.35 minutes (IQR = 703.50); and 5-min: 1,191.64 minutes (IQR = 635.00).

Directed random walks: transient sharks

In our directional models, migratory sharks presented shorter lengths of time inside a receiver than did resident sharks did, as predicted, and their abacus plots presented varied detection counts between 1- and 5-minute intervals. We modeled this behavior and found that the success rate of 2-detection minimums disproportionately affected the higher intervals (5 min). Overall, slower sharks spent an average of 6.1 minutes inside the receiver, whereas faster sharks spent 3.1 minutes (p > 2.2e-16).

Significant differences were identified in detection counts among the different shark movement behavior types and intervals (ANOVA, F(3, 3212) = 2780, p < 0.001; Fig. 3). Pairwise comparisons via Tukey's HSD test revealed that fast swimming sharks with 5-minute intervals had significantly fewer detections than those with 1-minute intervals (mean difference = -2.68 detections, 95% CI: -2.87– -2.49, p < 0.001). Slower sharks with 1-minute intervals generated higher detection counts than faster sharks with 1-minute intervals (mean difference = 3.50 detections, 95% CI: 3.31–3.69, p < 0.001). Slower sharks with 1-minute tags also had significantly more detections than those with 5-minute tags (mean difference = 5.38 detections, 95% CI: 5.19–5.57, p = 0.001), suggesting that shorter intervals increased the likelihood of a transient visit being counted.

The probability of a 5-min delay transmitter being detected at least twice with highly transient (fast) individuals was 16.4%, whereas 1-minute tags increased the probability to 85.2%. We confirmed that the interval with a shark moving at 2 meters per second would fail a 2-detection minimum requirement 84% of the time, whereas the 1-minute delay would fail for only 15% of visits under our simulated detection radius. We concluded that sharks may easily pass through a 500-meter diameter receiver without being detected twice; therefore, the tag settings may control whether a visit is counted or not. Transient (faster) sharks spent an average of 2.9 minutes less inside the receiver than slower ones did and, as a result, would have captured fewer site visits with a 2-detection minimum (Fig. 3).

Our results indicate that there is a nonnegligible error in AT data driven by high tag interval settings with low visit thresholds. Evaluations of diel behavior, habitat usage, and site fidelity for reef sharks or otherwise should consider species dispersal and mobility, appropriate technology settings, and the potential for errors in analysis and interpretation. We encourage short-range movement and habitat use studies to consider swim speeds, space use, and receiver ranges before concluding interspecies site usage. A review of more than three hundred acoustic tracking studies revealed that only 48.6% of the studies included results from equipment ranging experiments [16, 7]. Receiver performance (i.e., range drop-offs) can fluctuate with water conditions to cause missed detections [7]. Our study revealed that a single missed detection for a shark could disqualify an entire visit count from the dataset or divide a visit into multiple visits by extending gaps in detections, leading to an underestimate of residency time of 20% and different/missed transient visit counts. These results were also influenced by user-defined ping intervals, showing that long intervals for the conservation of battery life may disproportionately affect the accuracy of residency calculations.

Sharks spending longer periods of time around the perimeter of the receiver may produce weaker resolution in their tracks because of range drop-offs. With this in mind, scientists using the “number of detections over time” as a metric of site fidelity should treat their results as underestimations if some (or all) of their sharks spend considerable time in the outer receiver ranges. Differences in speed, direction changes, and pulse intervals can inflate or deflate the residency characteristics observed in reef sharks. Longer thresholds > 2 hours are estimated to produce less frequent but more accurate visit counts to the real shark path, as shown in our results. In contrast, we found that 30-minute thresholds among different tag intervals tend to create greater similarity in residency time calculations. Therefore, threshold adjustments specific to interval and swim behaviors may prevent residential and transient visit mismatches, exaggerated visit counts, and skewed residency times. Studies of diel behavior using hourly detection data and “number of hours spent at a site” variables [17, 18, 19] are directly affected by these findings. Both the tag interval and visit threshold may have inflated the visit counts, potentially skewing the hours spent at sites. When analyzing hourly residency patterns, these sources of potential error can result in misinterpretations due to these fine-scale miscounts (one extra visit counted earlier than expected could change the entire narrative of a study; see [20] for more on the limitations of diel pattern analysis in AT).

Notable increases in residency time were observed in intervals from the 30-minute threshold to the 2-hour threshold. This pattern is likely attributed to the extended duration of the 2-hour threshold, whereby two detections within the large time window are less likely to constitute a new visit (i.e., fewer visits = greater residency time, as we revealed). However, with a 5-minute interval, the chance of these short detection strings occurring at the necessary intervals to sustain a visit decreases, along with the missed detection probability. As a result, it is likely that shark movements and visits counted with 1-minute interval data would be missed with a 5-minute interval, leading to a decrease in residency time and visit count for that dataset. While certain studies analyze visit count and duration synonymously [1], others contrast them to measure activity and time spent at each site [3]. Our study revealed that visit counts are negatively correlated with total visit time and that higher thresholds increase this effect. To combat this in future studies where visit count is the main response variable, scientists may wish to use higher thresholds (> 2 hours) to mitigate variability with respect to their tag intervals and focus species. Given that our models revealed that lower thresholds (30 minutes) reduced variability in elapsed visit duration, a lower threshold (< 30 minutes) would ultimately improve the comparability of visit durations between tag intervals in studies focusing on elapsed visit time [21, 10].

Transient sharks of two different turn angles and swim speed criteria revealed significant probabilities of failing the two-detection minimum qualifier when passing through the receiver. The faster shark (1–2 m s^− 1 + low turn radius) failed the 2-detection minimum requirement in 84% of the trials for 5-minute intervals, whereas the 1-minute interval resulted in only 15% of visits failing the qualifier for the same shark. This finding suggests that longer tag intervals increase the likelihood of skewing visit counts and residency times for transient sharks. Previous studies have shown that swimming speeds of sharks vary depending on species and water conditions [11]. Since visit counts were higher (3.31 ± 3.69) with slower moving sharks than with faster swimming sharks in our models, we would expect to see a difference in transient versus residential visit proportions between sharks with different cruising speeds.

The accuracy of detection counts can vary on the basis of interval settings. While higher thresholds may be necessary for calculating residential visits and times, transient events must be accounted for under different criteria. We recommend adjusting the minimum detection qualifier on the basis of the tag interval and a species’ average swimming speed to improve the integrity of visit counts. The VTrack package sorts transient visits into their own category [3, 9] however, “transient” may have different definitions based on species swimming behavior, which may result in different transient visit counts from each tag setting. Ultimately, a higher detection minimum when dealing with transient and faster swimming sharks may improve the comparability of cross-study analysis, where different tag settings (from 1–5 minutes) are used and where species of varying swim patterns are under scrutiny.

An analysis of VTrack visitation qualifiers and threshold settings revealed the following takeaways: (1) Different tag delay settings (1- or 5-min) produce different visit counts for the same shark path and threshold. (2) Longer pulse intervals generally inflate the number of visits and their duration; however, at times, this results in fewer visits, as 2 consecutive missed detections cause a missed visit that would have been picked up by the other delay settings. (3) Half-hour thresholds result in more visit counts but lower visit durations along the same path than longer thresholds do (1-hour, 2-hour), since individuals with more visits swam in and out of the receiver range for short bursts of time rather than long periods. (4) The longest thresholds set in VTrack (1- to 2-hour) showed the lowest deviance between the number of site visits of 5-min and 1-min intervals; however, the total duration of visits was more comparable between intervals using 30-minute thresholds. Therefore, electing a higher visit threshold may improve the uniformity of the number of visits among different interval tags, while lower thresholds may create greater residency time evenness between different tag intervals.

Passive Acoustic Telemetry: AT

Marine Protected Areas: MPAs

Analysis of variance: ANOVA

Ethics approval and consent to participate: Not Applicable

Consent for publication: Not Applicable

Availability of data and materials:GitHub Repos: Random-Walks-GF_MW

Competing interests: Not Applicable

Funding: Not Applicable

Authors' contributions:

GF: Directed the study, programmed model codes, authored manuscript drafts.

MW: Revised and coded models in R, guided statistical testing, revised manuscripts

AH: Advised GF as master’s advisor, generated initial ideas for the project, revised manuscripts

Acknowledgments:

We would like to acknowledge Will Vuyk, and Mateo Andres-Vega for help brainstorming ways to test acoustic telemetry analyses, Tyler Hoskins for his assistance and comments on the final manuscripts, Ross Dwyer and Vinay Udyawer for creating their R package VTrack, and helping guide the project, Elisa Bonaccorso for her ideas on range drop-offs, and support for her master’s students, Esteban Guevara and Patricio Salazar for helping outline the ways to analyze theoretically modeled experiments. This research was carried out in part under the auspices of the Cooperative Institute for Marine and Atmospheric Studies (CIMAS), a Cooperative Institute of the University of Miami and the National Oceanic and Atmospheric Administration, cooperative agreement # NA20OAR4320472.

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Random swims: An evaluation of acoustic telemetry thresholds for reef-shark behavior and residency

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Abstract

Background

Methods

Results

Conclusions

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Background

Methods

Random Walk Model

Residency Data Extraction

Visit Thresholds and Residency Times

Directional Nonrandom Walks

Results

Elapsed Visit Residency Times

Directed random walks: transient sharks

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

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