- Open Access
Performance of an ultrasonic telemetry positioning system under varied environmental conditions
© Steel et al.; licensee BioMed Central Ltd. 2014
Received: 14 June 2014
Accepted: 1 September 2014
Published: 1 October 2014
Advances in ultrasonic telemetry, including the ability to accurately position a transmitter within an array of hydrophone receivers, have led to increased opportunities to investigate a broad spectrum of ecological questions in aquatic systems. The quality and efficiency of positioning a transmitter relies upon factors controlled by the researcher (for example, geometry of the receiver array) as well as environmental conditions (for example, water quality or environmental noise). While the physics of sound wave propagation are well understood, the high amount of environmental variability in and among aquatic habitats makes it difficult to predict exactly how any given ultrasonic signal will behave. To evaluate variability in system performance across different receiver arrays in diverse environments we present positional records for fixed-location tags recorded with a popular positioning array, the VEMCO Positioning System (VPS). Using these records we evaluate the relationships between system performance, measured as both horizontal positioning error and positioning efficiency, and user-controlled and environmental variables. We used generalized linear mixed models to assess performance at a coastal site, a site in a freshwater tidal estuary, and a riverine site.
The positioning errors were similar across sites, with median errors ranging from 1.6 to 3.3 m. In contrast, there was large variation in positioning efficiency across sites, with poor positioning efficiency in the coastal habitat (7%), possibly due to high levels of bioacoustic noise, and moderate efficiency in the river (21%) and estuary habitats (27%). Our statistical models indicate that array geometry was consistently the most important predictor of positioning performance. Environmental noise and water movement also emerged as additional predictors of performance at several sites.
The results provide insight into VPS performance capabilities and emphasize the importance of testing array geometries. Additionally, water quality parameters should be monitored and receiver mooring designs should be carefully considered before embarking upon a telemetry study. We hope this work will guide future researchers in creating more effective designs for positioning arrays, and facilitate the collection of high quality information about movement and behavior patterns of aquatic organisms.
Animal movement is fundamental to much of ecology and animal biology . Organisms must move to find food, avoid predators, encounter mates, and respond to the environment. Thus knowing the fine-scale movement of targeted animals is an important aspect of understanding their habitat use and ecology. Ultrasonic telemetry allows for the monitoring of movement in aquatic environments, which can otherwise be difficult to observe [2, 3]. Approaches can be varied, ranging from simple detections of animal presence at stationary receivers (for example, [4–6]) to detailed recording of animal positions and three-dimensional acceleration [7–9]. Telemetry systems can collect fine-scale positions of transmitters when receivers are deployed in an array, and the differences in the arrival time of a single transmission at multiple receivers are used to calculate its position [10–14]. Recent development of precision timing mechanisms and autonomous receivers with long battery lives have allowed for the deployment of flexible, long-term positioning arrays for research in varied aquatic ecosystems [15, 16].
Predictor variables considered in GLMMs, by site
Viewing angle (°)
Viewing angle (°)
Viewing angle (°)
Wave height (m)
River stage (ft)
Dominant wave period (s)
Water temperature (°C)
Water temperature (°C)
Average wave period (s)
Water temperature (°C)
Direction of flowa
Mean wave directiona (°)
The positional data were recorded over approximately equal time spans at each site (49 to 60 days), but the number of fixed transmitters and the quantity of recorded positions varied by site and by transmitters within sites (Figure 2). Over the 60-day study period for the coastal site, 4,266 positions were recorded from six V9-2H transmitters with 450 to 500 s random delays (11.9 positions recorded per transmitter-day). At the estuarine site a subset of 40 days were selected for analysis at eight even intervals within the 340 days the array was in place (see methods for further explanation of sampling constraints). Over the reduced study period, 38,498 positions were recorded from 10 V13-1L transmitters with 600 to 800 s random delays and three V13-1L transmitters with 170 to 310 s random delays (averaging 74.0 positions recorded per transmitter-day). Over the 49-day study period for the riverine array, 7,735 positions were recorded from five V13-1H transmitters and one V16-1H transmitter, all with 600 to 800 s random delays (averaging 26.3 positions recorded per transmitter-day).
The diel period did not have an effect on positioning errors, but at some sites it did strongly influence positioning efficiency. At the coastal site, there was higher efficiency during the day (Mann-Whitney U test: P <0.0001; Figure 5) and at the estuarine site we saw the opposite pattern with higher efficiency at night (Mann-Whitney U test: P <0.0001; Figure 6). There was no significant difference in efficiency by diel period at the riverine site.
Parameters included in final GLMMs for both performance metrics at each site, with random intercept terms for transmitter and Julian day
VA + diel + mean wave period + wtemp
VA + diel + mean wtemp
VA + wtemp + EC + absQ
VA + diel + mean wtemp + mean Q
VA + river stage
VA + mean river stage + mean turb + mean wtemp
In addition to containing parameter estimates for array geometry, most final GLMMs also included variables describing water movement (that is, wave action, discharge, or river stage; Table 2). At the coastal site, the model of HPEm contained a parameter indicating a positive relationship between error and mean wave period (Figure 8). At the estuarine site, the final model for HPEm included a parameter for the absolute magnitude of discharge (selected to account for bidirectional tidal flows), but the overall effect size for this variable was small (Figure 8). At the riverine site, river stage, used as a proxy for water movement, showed a positive relationship with error in the final HPEm model. Notably, it was the only parameter other than array geometry in the model (Figure 8). For the final models of positioning efficiency, no parameter for water movement was included at the coastal site. However, at both the estuarine and riverine sites, water movement (measured as mean discharge and mean river stage, respectively) had a negative relationship with positioning efficiency, suggesting that when water movement was high transmitter positioning was more infrequent. The size of this effect was greater at the riverine site (Figure 9).
Three of the six final GLMMs included a two-level categorical variable for diel period, considered in model selection as a proxy for daily cycles of environmental noise. Consistent with the preliminary examination of variables, diel period was not particularly important for models of HPEm. It was only included in the final HPEm model for the coastal array, indicating that when all other variables were controlled for, positioning errors were slightly smaller during the night (Table 2). Diel period was retained in models of positioning efficiency at both the coastal and estuarine sites (Table 2). Model results confirm that more positions were recorded during the day in the coastal array, and that more positions were recorded at night in the estuarine array.
Aside from geometry, water movement, and diel period, the other parameters included in the final models represented water quality metrics such as temperature, electrical conductivity, or turbidity. Water temperature showed positive relationships with HPEm at both the coastal and estuarine sites, and electrical conductivity showed a very slight positive relationship with HPEm at the estuarine site (Figure 8). Water temperature showed a positive relationship with positioning efficiency at the coastal site, but a negative relationship with efficiency at both the estuarine and riverine sites. Turbidity was only included in the model for positioning efficiency at the riverine site, and was modeled as a negative relationship (Figure 9).
Among the three arrays compared here, the median error in positions ranged from 1.6 to 3.3 m. These errors were calculated based on all recorded data, without any position filtering as is frequently done before fine-scale analysis of animal movements [19, 25]. This error range is smaller than the unfiltered mean error of 5 m (SD = 7.8 m) reported by Roy et al.  for a VPS deployed in a large hydropower reservoir. It is similar to the unfiltered mean error estimate of 2.6 m (SD = 2.3 m) reported by Espinoza et al.  for an estuary site, and larger than the filtered mean error range of 1.0 to 1.8 m reported by Andrews et al.  from a VPS placed in a protected ocean environment.
Transmitters present in array during study period
Fixed transmitter delay (s)
Estimated total transmissions emitted/day
Positioning efficiency of fixed transmitters
GLMM results indicated that the geometry of receivers in a positioning array was the most consistent factor affecting performance. For all analyses addressing HPEm, we saw that when viewing angles became more acute (that is, tags were farther from the internal area of the array) the error increased, which is in agreement with previous work on the spatial distributions of error in a positioning array [12, 18, 19]. However, when examining the positioning efficiency, two of the three models suggested that more acute angles resulted in increased efficiency. This result is surprising, and may be due to greater concentrations of transmitter signals closer to the center of the arrays, resulting in more transmitter collisions and reduced positioning efficiency as the viewing angle increases. Alternatively, the pattern may be spurious, resulting from unmeasured attributes that varied by specific transmitter location. These attributes could include elements such as localized vegetation, hydrodynamics, or softer substrates that may increase signal absorption. Because these factors vary in space but are are unrelated to the array geometry, they could lead to confounding results.
Five of the six final models included a metric for water movement, suggesting this is a common feature affecting positioning performance. Depending upon the habitat type, water movement was measured as wave behavior, tidal influence and flow, or river stage, all of which have the ability to move moored receivers and introduce error into recorded transmitter positions. This source of error is likely to be most extreme for non-rigid mooring systems. All the receivers tested here were attached to stainless steel cable, anchored using bottom weights and floated into a vertical position with small buoys. This mooring design allowed the receivers to move slightly within the water column. Among the three arrays tested, this movement was likely the most pronounced in the riverine array where the model predicted that a 0.5 m (1.7 ft) rise in stage, and accompanying increase in water velocity, should result in an additional 1.8 m of positioning error. Therefore careful attention should be paid to mooring designs in research areas that are likely to have high or variable currents. While the error due to receiver movement may be reduced through the use of more rigid mount designs, without a self-righting capability these designs pose an additional risk of the mounts becoming unintentionally fixed in a horizontal position. In addition to contributing to receiver movement, extremely strong flows such as those commonly encountered in riverine systems may also create an ‘acoustic wall’ along the region of fastest water movement. A subjective assessment of the transmitter detections in the riverine system indicated that very few detections were recorded across the thalweg, and this may have contributed to the low positioning efficiency.
The statistical models also suggest an interesting relationship between positioning performance and water temperature. In the models of HPEm, the coastal and estuarine sites show increased positioning error as water temperatures increased. We believe this is not due to effects of warmer water but rather be due to the use of mean water temperature estimates in the postprocessing positioning equations. User-defined parameters of temperature and salinity at the study site are used to estimate the speed of sound in the triangulation equations, and larger errors in these estimates lead to larger positioning errors. When we examined the difference between the actual water temperatures and the average temperatures assumed in the postprocessing positioning equations, we did in fact find larger differences at higher water temperatures, which may explain the results from the statistical models. Likewise, the model for HPEm at the estuarine site also included a positive relationship between positioning error and conductivity. A comparison of the measured variation in salinity and the average values used in the positioning equations suggest the same mechanism may have led to the positive conductivity relationship indicated in the model. For long study periods in habitats with variable temperature and/or salinity, system performance could be improved by estimating the speed of sound from water quality parameters measured more frequently than those used in these case studies.
At all three sites there was reasonable evidence for declining performance due to environmental noise. In the coastal array we saw decreased positioning efficiency at night, which has been noted in other marine applications of ultrasonic telemetry as well [4, 21]. Nocturnal organisms such as snapping shrimp can create sharp, loud noises that are difficult to discriminate from transmitter pulses  and can raise the variable noise threshold of receivers, making the transmission more difficult to detect (D. Webber, personal communication). The diel period was also an important factor at the estuarine array, but it showed the opposite relationship. The study site is a popular area for recreational fishing and boating, and it is likely that the increased motorized boating during daylight hours contributes high levels of background noise. It is also possible that sound waves from recreational sonar units (fish-finders) impede system performance during daytime periods (D. Webber, personal communication). Finally, at the riverine site we saw a strong relationship between river stage and both performance metrics. As discussed above, we expect the hydrodynamics at the site contributed to positioning errors through receiver movement. But we also noted that higher flows were related to reduced positioning efficiency, possibly because flow increases mobilized additional bedload. The collision of gravels and cobbles along the substrate can be clearly heard via mobile receivers, and it is plausible that high bedload movement can cause enough ambient noise to interfere with the reception of ultrasonic transmitters at greater discharges. In addition, turbulent flow patterns can entrain air into the water column, which can absorb an ultrasonic transmission and lead to reduced positioning efficiency .
Based on the results of this analysis, it is clear that researchers planning to use an ultrasonic positioning system should carefully evaluate the appropriateness of such a system for each research question and environment . Time should be invested in testing various receiver geometries, as this consistently appears as the most important predictor of performance. Careful consideration of methods to reduce receiver movement may also increase system performance. When research will be conducted at a site with variable temperature and salinity, water quality parameters should be monitored and utilized in postprocessing to reduce errors in the calculated positions. Finally, researchers should be prepared for moderate to high rates of data loss due to environmental noise. Even for the estuarine array, which performed best among those evaluated, on average only 28% of the expected transmitter pulses were converted into positions; the values for the coastal and riverine sites were even lower. Finally, while the environmental conditions examined here are common to many aquatic systems, this analysis did not include environments with thermoclines, nor environments affected by surface ice. It is important to note that each individual study area will present its own unique set of variables that researchers should consider on a case-by-case basis.
The ability to observe animal movement and habitat use in aquatic ecosystems can provide critical insight into basic ecological questions. Innovations in ultrasonic positioning systems have expanded our ability to conduct important research. Through sharing information on the performance abilities of these systems under real environmental challenges, we hope that future researchers will be able to design more effective positioning arrays and collect high quality information about the movement and behavior patterns of focal species.
Details of array deployments
Pink abalone (Haliotis corrugata)
Largemouth bass (Micropterus salmoides)
Green sturgeon (Acipenser medirostris)
24 September 2009 –27 November 2009
2 September 2009 –9 August 2010
10 April 2012 –12 June 2012
Study length (days)
Mean rec. - rec. distance (m)
Fixed transmitters (n)
Mean trans. - rec. distance (m)
Internal array area (hectares)
12 - 14
4 - 5
1 - 12
0 - 0.5
Water temperature (°C)
13 - 22
8 - 25
6 - 17
Boulders, bedrock, and claystone
Mud and silt
Gravel and cobble
The estuarine site was in a shallow tidal lagoon known as Mildred Island (37.975°, -121.530°) in the freshwater portion of the San Francisco Estuary, near the town of Stockton, California. Before 1983, this area was a deeply subsided agricultural island. The protective levees broke in 1983 and the flooded land was not reclaimed . The resulting lake-like habitat is approximately 1.5 km wide and 3 km long, surrounded by overgrown levees with varying amounts of rock revetment. It has a fairly uniform depth of 4 to 5 m, soft substrates, a maximum tidal range of 1.2 m, and damped currents driven by both tidal and freshwater inputs . Water temperatures generally range from 8° to 25°C over the course of the year. The study site was located in the SW corner of the flooded island, bounded on the northern and southern sides by levee revetment lined with beds of emergent California tule (Scirpus californicus). The shallow subtidal habitats of the northern shoreline were dominated by sand with sparse patches of submerged aquatic vegetation (SAV) while the subtidal habitats along the south-western shoreline were dominated by mud with dense beds of SAV. One of the two primary levee breaches in Mildred Island is located at the SE corner of the study site, which resulted in stronger currents along the eastern edge of the array. There is generally a moderate amount of boat traffic in the area from sport fishing and pleasure boats.
The riverine site was in the Sacramento River south of the town of Red Bluff, California. The study site was located just below the confluence of Antelope Creek (40.082°, -122.116°) at a deep hole known to be a spawning aggregation site for the focal species, green sturgeon (Acipenser medirostris). The river is approximately 120 m wide in the reach where the VPS was deployed, and has a variable depth profile reaching 11.5 m at the deepest point, with mean depths around 4.5 m. Under regulation from Shasta dam, the mean discharge is 330 m3 s-1 (1946 to 2009) . During the study period cross-sectional surveys of the site recorded a mean discharge of 342 m3 s-1 and mean water velocities ranging from 0.425 m s-1 to 0.689 m s-1, with complex hydrodynamics and local velocities reaching more than 1.5 m s-1 (Thomas et al. unpublished observations). Water temperatures generally range from 6°C to 17°C over the course of the year. Most of the substrate in this reach is gravel and cobble, with areas of sand and areas of scoured bedrock. Both banks of the river are used for agriculture, with orchards on the east bank and grazing lands on the west.
Positioning array design
The initial design of each VPS positioning array was based on in-situ range tests to inform the optimal distances between receivers, and upon constraints due to geography of the local habitat and behavior of the study species. Each array used a variable number of VR2W receivers to detect transmitters operating at a frequency of 69 kHz, with an output power in the range of 147 to 156 dB re 1 μPa @ 1 m. Transmitters were programmed to emit a coded signal, and code bursts were sent at a set random interval (for example, 30 to 90 s) to reduce the chance that two transmitters would repeatedly collide with one another. Receivers were suspended on a stainless-steel cable approximately 1 to 2 m above the substrate and held vertically with foam floats. Time-synchronizing transmitters were moored at fixed locations within the study array, either co-located with receivers or placed at independent locations (Figure 2). These fixed transmitters were primarily intended to allow synchronization of the internal clocks of the independent VR2W receivers, but they also provided an opportunity to assess the performance of the array. The location of all receivers and fixed-location transmitters were recorded at each deployment using differentially corrected GPS technology. See Espinoza et al.  for additional details on deployment and data processing for a VPS.
Two metrics were selected to compare the performance of each positioning array under varying environmental conditions: the horizontal positioning errors and the positioning efficiency. Each of these was estimated for fixed-location transmitters located within arrays. Horizontal positioning errors were calculated as the distance in meters between a VPS positioned location and the GPS location recorded for that transmitter. At the riverine site, strong currents resulted in one unexpected transmitter movement that required a new location to be derived from the VPS positioning data during postprocessing. This derived position was then used as the reference location for calculating subsequent horizontal positioning errors. Positioning efficiency was normalized for different transmitter programming by dividing the number of positions the VPS recorded by the expected number of transmissions over 12 h intervals.
Based on the intrinsic constraints of the VPS system and variation in aquatic acoustic conditions, we selected a set of variables to assess for relationships with system performance (Table 1). We considered the geometry of the array as an important user-defined factor for performance based on previous research [12, 16, 17, 19]. The spatial configuration of fixed transmitters and receivers was summarized with a ‘viewing angle’, defined by Berge et al.  and described above. Viewing angles were calculated using a geographic information system (ArcMap 10.1, ESRI Corporation, Redlands, CA, USA).
Environmental variables were obtained from publically available long-term monitoring programs (for example, Wave Rider Buoys and USGS monitoring stations). While this is not as preferable as direct measurements at a site, these sources do provide good estimates of the relative change in continuous water quality and weather parameters, as confirmed with temperature loggers installed at the estuarine site. To evaluate potential diel patterns in environmental noise we also included a two-level categorical variable. Transmitter detections between 19:00 and 7:00 were considered ‘night’ detections and those between 7:00 and 19:00 were considered ‘day’ detections.
At the coastal study site we used environmental data from the NOAA National Buoy Data Center  recorded at a buoy located approximately 11.9 km NW of the study site in water 200 m deep (Station 46231, Scripps Institute of Oceanography @ 32.748°, -117.370°). We used measurements summarized over 20 min sampling periods for water temperature (°C), average and dominant wave period (s), mean wave direction (degrees true), and significant wave height (m). The distribution of giant and understory kelps relative to tags and receivers was not measured during the study and these may have influenced performance. In particular, giant kelp has the greatest potential to block transmissions with gas bladders continuously positioned from the bottom to top of the water column. However, care was taken at the time of placement by divers on SCUBA to not place tags and receivers immediately next to algae to prevent entanglement.
At the estuarine study site we used environmental data from the USGS National Water Information System  recorded at a gauge located approximately 3.5 km downstream of the study site on the Middle San Joaquin River, near Holt, California (USGS gauge 11312685 @ 32.530°, -117.431°). We used measurements taken at 15 min intervals for water temperature (°C), electrical conductivity (μS/cm), and discharge (cfs) which was modeled in two parts: absolute discharge and a categorical variable indicating ebb or flood tide. Temperature measurements closely matched values collected by on-site data loggers. We excluded available variables that were tightly correlated with other selected metrics (river stage and velocity) as well as those that vary at small spatial scales within the estuary (chlorophyll and water turbidity) because we did not feel the measurements at the gauge would appropriately reflect conditions at the study site. In addition to variables from the USGS gauge, every 6 weeks we sampled the density of submerged aquatic vegetation at the estuarine site along set transects using a thatching rake. These measurements were converted into an index of overall macrophyte density to be included in our models.
At the riverine study site we used environmental data accessed from California Department of Water Resources’ California Data Exchange Center , recorded at a gauge located approximately 11.5 rkm upstream of the study site on the Sacramento River, near the Red Bluff Diversion Dam (USBR gauge @ 40.1544°, -122.2020°). We used hourly measurements of water temperature (°C), river stage (ft), and water turbidity (ntu). We excluded available measurements for dissolved oxygen because we did not feel the measurements at the gauge would reliably reflect conditions at the study site.
To assess performance of the VPS arrays, we constructed a general linear mixed model for each performance metric at each site. All potential explanatory variables were evaluated for collinearity within each model. Where there were high variance inflation factors, variables were selected for removal based upon the bivariate relationships between those variables in question and the performance metric. The variables with the strongest relationships were retained for the subsequent model selection process. In addition, because of the periodic nature of the sampling for macrophytes at the estuarine site, we reduced the full dataset of system performance to only 5 days surrounding each of the eight sampling periods. The resulting dataset included 40 days, evenly spaced throughout the deployment period. This created a sample size that was more comparable to the 60- and 49-day datasets analyzed from the coastal and riverine sites, respectively.
There were many positions with small horizontal positioning errors and few with large errors. Thus, to reduce the heterogeneity of variance in the model residuals, the measured horizontal positioning error (HPEm) was transformed with a ln(Y i + 1) transformation. The new response variable was modelled using a Gaussian linear mixed model. Based on the data structure we tested both transmitter ID and Julian date as random effects by evaluating all combinations of random effects using AIC values, following the procedure recommended by Zuur et al. . To model positioning efficiency, we used a binomial general linear mixed model with a logit link function as appropriate for a proportional response variable. Both transmitter ID and Julian date were tested for inclusion as random effects. Because the explanatory variables were continuous and approximately normally distributed, they were summarized with mean values calculated over the same 12 h periods used to compute positioning efficiency. For the estuarine site we also considered a random intercept and slope for the macrophyte index, letting the slope vary by transmitter because macrophyte growth was the only variable which displayed substantial spatial variation. To select the final suite of fixed effects for each model, we used backwards step-wise selection based on AIC values and log-likelihood ratio tests . Statistical analyses were performed in R , and mixed models were fit using the lme4 package .
The authors would like to thank the numerous people who provided vital field support, including Michael Thomas, Phillip Sandstrom, Gabriel Singer, Dennis Cocherell, Eric Chapman, Ryan Battleson, and Cynthia Catton. Many thanks to Dale Webber, Kevin A. Hovel, John L. Butler, Steven G. Morgan, and Megan Wyman who provided intellectual and/or financial support. Thanks also to two anonymous referees who provided constructive feedback. The coastal ocean study was funded by the NOAA Species of Concern Program and the Joint Doctoral Program in Ecology at San Diego State University and University of California, Davis. The estuarine study was funded by the Interagency Ecological Program, the University of California, Davis, and the National Science Foundation under Grant No. 0841297. The riverine study was funded by the US Bureau of Reclamation.
- Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, Smouse PE: A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci U S A 2008, 105: 19052–19059. 10.1073/pnas.0800375105PubMed CentralPubMedView ArticleGoogle Scholar
- Lucas MC, Baras E: Methods for stuyding spatial behavoir of freshwater fishes in the natural environment. Fish Fish 2000, 1: 283–316. 10.1046/j.1467-2979.2000.00028.xView ArticleGoogle Scholar
- Cooke SJ, Midwood JD, Thiem JD, Klimley AP, Lucas MC, Thorstad EB, Eiler J, Holbrook C, Ebner BC: Tracking animals in freshwater with electronic tags: past, present and future. Anim Biotelem 2013, 1: 1–5. 10.1186/2050-3385-1-1View ArticleGoogle Scholar
- Heupel MR, Semmens JM, Hobday AJ: Automated acoustic tracking of aquatic animals: scales, design, and deployment of listening station arrays. Mar Fresh Re 2006, 57: 1–13. 10.1071/MF05091View ArticleGoogle Scholar
- Klimley AP, Voegeli F, Beavers SC, Le Boeuf BJ: Automated listening stations for tagged marine fishes. Mar Technol Soc J 1998, 32: 94–101.Google Scholar
- Klimley AP, Holloway C: Homing synchronicity and schooling fidelity by yellowfin tuna. Mar Biol 1999, 133: 307–317. 10.1007/s002270050469View ArticleGoogle Scholar
- Cooke SJ, Hinch SG, Wikelski M, Andrews RD, Kuchel LJ, Wolcott TG, Butler PJ: Biotelemetry: a mechanistic approach to ecology. Trends Ecol Evol 2004, 19: 334–343. 10.1016/j.tree.2004.04.003PubMedView ArticleGoogle Scholar
- Klimley AP, Le Boeuf BJ, Cantara KM, Richert JE, Davis SF, Van Sommeran S, Kelly JT: The hunting strategy of white sharks ( carcharodon carcharias ) near a seal colony. Mar Biol 2001, 138: 617–636. 10.1007/s002270000489View ArticleGoogle Scholar
- Whitney NM, Pratt HL Jr, Pratt TC, Carrier JC: Identifying shark mating behavior using three-dimensional acceleration loggers. Endang Species Res 2010, 10: 71–82.View ArticleGoogle Scholar
- Clark DS, Green JM: Activity and movement patterns of juvenile Atlantic cod, gadus morhua , in conception Bay, Newfoundland, as determined by sonic telemetry. Can J Zoolog 1990, 68: 1434–1442. 10.1139/z90-214View ArticleGoogle Scholar
- O’Dor RK, Andrade Y, Webber DM, Sauer WHH, Roberts MJ, Smale MJ, Voegeli FM: Applications and performance of radio-acoustic positioning and telemetry (RAPT) systems. Hydrobiologia 1998, 371: 1–8.View ArticleGoogle Scholar
- Klimley A, Le Boeuf B, Cantara K, Richert J, Davis S, Van Sommeran S: Radio acoustic positioning as a tool for studying site-specific behavior of the white shark and other large marine species. Mar Biol 2001, 138: 429–446. 10.1007/s002270000394View ArticleGoogle Scholar
- Voegeli FA, Smale MJ, Webber DM, Andrade Y, O’Dor RK: Ultrasonic telemetry tracking and automated monitoring technology for sharks. Environ Biol Fish 2001, 60: 267–281. 10.1023/A:1007682304720View ArticleGoogle Scholar
- Watkins WA, Schevill WE: Sound source location by arrival-times on a non-rigid three-dimensional hydrophone array. Deep-Sea Res 1972, 19: 691–706.Google Scholar
- Andrews KS, Tolimieri N, Williams GD, Samhouri JF, Harvey CJ, Levin PS: Comparison of fine-scale acoustic monitoring systems using home range size of a demersal fish. Mar Biol 2011, 158: 2377–2387. 10.1007/s00227-011-1724-5View ArticleGoogle Scholar
- Espinoza M, Farrugia TJ, Webber DM, Smith F, Lowe CG: Testing a new acoustic telemetry technique to quantify long-term, fine-scale movements of aquatic animals. Fish Res 2011, 108: 364–371. 10.1016/j.fishres.2011.01.011View ArticleGoogle Scholar
- Smith GW, Urquhart GG, Maclennan DN, Sarno B: A comparison of theoretical estimates of the errors associated with ultrasonic tracking using a fixed hydrophone array and field measurements. Hydrobiologia 1998, 372: 9–17.View ArticleGoogle Scholar
- Berge J, Capra H, Pella H, Steig T, Ovidio M, Bultel E, Lamouroux N: Probability of detection and positioning error of a hydro acoustic telemetry system in a fast-flowing river: intrinsic and environmental determinants. Fish Res 2012, 125: 1–13.View ArticleGoogle Scholar
- Roy R, Beguin J, Argillier C, Tissot L, Smith F, Smedbol S, De-Oliveira E: Testing the VEMCO positioning system: spatial distribution of the probability of location and the positioning error in a reservoir. Anim Biotelem 2014, 2: 1–6. 10.1186/2050-3385-2-1View ArticleGoogle Scholar
- Nelson DR: Telemetering techniques for the study of free-ranging sharks. In Sensory Biology of Sharks, Skates, and Rays. Edited by: Hodgson ES, Mathewson RF. Arlington, VA: Office of Naval Research, Dept of the Navy; 1978:419–482.Google Scholar
- Payne NL, Gillanders BM, Webber DM, Semmens JM: Interpreting diel activity patterns from acoustic telemetry: the need for controls. Mar Ecol Prog Ser 2010, 410: 295–301.View ArticleGoogle Scholar
- Fish MP: Biological sources of sustained ambient sea noise. In Marine bio-Acoustics. Edited by: Tavolga WN. New York, NY: Pergamon Press; 1964:175–194.Google Scholar
- Coates JH, Hovel KA, Butler JL, Klimley AP, Morgan SG: Movement and home range of pink abalone haliotis corrugata : implications for restoration and population recovery. Mar Ecol Prog Ser 2013, 486: 189–201.View ArticleGoogle Scholar
- Guisan A, Zimmermann NE: Predictive habitat distribution models in ecology. Ecol Model 2000, 135: 147–186. 10.1016/S0304-3800(00)00354-9View ArticleGoogle Scholar
- Meckley TD, Holbrook CM, Wagner CM, Binder TR: An approach for filtering hyperbolically positioned underwater acoustic telemetry data with position precision estimates. Anim Biotelem 2014, 2: 7. 10.1186/2050-3385-2-7View ArticleGoogle Scholar
- Simpfendorfer CA, Heupel MR, Collins AB: Variation in the performance of acoustic receivers and its implications on positioning algorithms in a riverine setting. Can J Fish Aquat Sci 2008, 65: 482–492. 10.1139/f07-180View ArticleGoogle Scholar
- Jackson GA: Currents in the high drag environment of a coastal kelp stand off California. Cont Shelf Res 1997, 17: 1913–1928. 10.1016/S0278-4343(97)00054-XView ArticleGoogle Scholar
- Dayton PK, Tegner MJ, Edwards PB, Riser KL: Sliding baselines, ghosts, and reduced expectations in kelp forest communities. Ecol Appl 1998, 8: 309–322. 10.1890/1051-0761(1998)008[0309:SBGARE]2.0.CO;2View ArticleGoogle Scholar
- Lopez CB, Cloern JE, Schraga TS, Little AJ, Lucas LV, Thompson JK, Burau JR: Ecological values of shallow-water habitats: implications for the restoration of disturbed ecosystems. Ecosystems 2006, 9: 422–440. 10.1007/s10021-005-0113-7View ArticleGoogle Scholar
- Monsen NE, Cloern JE, Lucas LV, Monismith SG: A comment on the use of flushing time, residence time, and Age as transport time scales. Limnol Oceanogr 2002, 47: 1545–1553. 10.4319/lo.2002.47.5.1545View ArticleGoogle Scholar
- Report W-D: 11389500 Sacramento River at Colusa, CA. US Geological Survey: Department of the Interior; 2009. [http://wdr.water.usgs.gov/wy2013/pdfs/11389500.2013.pdf]Google Scholar
- NOAA national buoy data center [http://www.ndbc.noaa.gov]
- USGS national water information system [http://waterdata.usgs.gov/nwis]
- California department of water resources’ California data exchange center [http://cdec.water.ca.gov]
- Zuur AF, Ieno EN, Walker N, Saveliev AA, Smith GM: Mixed Effects Models and Extensions in Ecology With R. New York, NY: Springer; 2009.View ArticleGoogle Scholar
- R core team: R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2013. [http://www.R-project.org]Google Scholar
- Bates D, Maechler M, Bolker B, Walker S: lme4: linear mixed-effects models using eigen and S4. 2013.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.