Abstract:Habitat use of juvenile southern flounder Paralichthys lethostigma was examined within a shallow estuarine seascape during June and July 2011 using acoustic telemetry. Fine-scale movement and habitat use of P. lethostigma was investigated with an acoustic positioning system placed in a seascape that varied in habitat type, physicochemical conditions and bathymetry. The use of different habitat types was examined with Euclidean distance-based analyses, and generalized additive models were used to determine the … Show more
“…Our results confirm the usefulness of this technique in improving the quality of the positioning [7]. In many studies, the HPE threshold is set between 10 and 20 [16,17,23,24] but this choice is seldom discussed or objectively assessed. In the environmental conditions experienced in this study, we observed a large variability in the number of calculated positions and the mean positioning error for HPE filters between 10 and 20.…”
Section: Discussionsupporting
confidence: 81%
“…The VPS can estimate fine-scale positional information on multiple tagged animals simultaneously over a large area [9]. This system is now widely used in fish behavior studies in marine and freshwater environments [7,[14][15][16][17][18][19][20]. However, its performance has not yet been extensively documented (but see [7]).…”
Background: Recent improvements in fixed acoustic monitoring receivers allow the tracking of individual aquatic animals over long periods of time with regular fine-scale positions. The VEMCO Positioning System (VPS) is now widely used, but various methodological issues remain to be clarified. The aim of this study was to analyze the spatial distribution of the probability of location and the positioning error over the entire surface of a hydropower reservoir, prior to analyzing fish behavior. Findings: Filtering the data set by the horizontal position error (HPE) significantly reduced the positioning error. Retaining only the positions with an HPE less than 15 retained 79% of VPS positions and decreased the positioning error by 33% (mean = 3.3 m, SD = 3.3 m). A higher probability of location was observed inside than outside the receiver array (44% and 36%, respectively). Moreover, the positioning error significantly differed inside (n = 243, mean = 2.4 m, SD = 2.1 m) and outside (n = 253, mean = 4.2 m, SD = 4.0 m) the receiver array (P < 0.001). Finally, the lowest positioning errors were detected in the area with the highest receiver density. Conclusions: The VPS measures fish positioning in a reservoir, under suitable conditions, with satisfactory accuracy. We showed that the probability of location and the positioning error differed spatially in accordance with previous results in other conditions. Consequently, these analyses are recommended as a prerequisite to further spatial analyses using VPS-derived data.
“…Our results confirm the usefulness of this technique in improving the quality of the positioning [7]. In many studies, the HPE threshold is set between 10 and 20 [16,17,23,24] but this choice is seldom discussed or objectively assessed. In the environmental conditions experienced in this study, we observed a large variability in the number of calculated positions and the mean positioning error for HPE filters between 10 and 20.…”
Section: Discussionsupporting
confidence: 81%
“…The VPS can estimate fine-scale positional information on multiple tagged animals simultaneously over a large area [9]. This system is now widely used in fish behavior studies in marine and freshwater environments [7,[14][15][16][17][18][19][20]. However, its performance has not yet been extensively documented (but see [7]).…”
Background: Recent improvements in fixed acoustic monitoring receivers allow the tracking of individual aquatic animals over long periods of time with regular fine-scale positions. The VEMCO Positioning System (VPS) is now widely used, but various methodological issues remain to be clarified. The aim of this study was to analyze the spatial distribution of the probability of location and the positioning error over the entire surface of a hydropower reservoir, prior to analyzing fish behavior. Findings: Filtering the data set by the horizontal position error (HPE) significantly reduced the positioning error. Retaining only the positions with an HPE less than 15 retained 79% of VPS positions and decreased the positioning error by 33% (mean = 3.3 m, SD = 3.3 m). A higher probability of location was observed inside than outside the receiver array (44% and 36%, respectively). Moreover, the positioning error significantly differed inside (n = 243, mean = 2.4 m, SD = 2.1 m) and outside (n = 253, mean = 4.2 m, SD = 4.0 m) the receiver array (P < 0.001). Finally, the lowest positioning errors were detected in the area with the highest receiver density. Conclusions: The VPS measures fish positioning in a reservoir, under suitable conditions, with satisfactory accuracy. We showed that the probability of location and the positioning error differed spatially in accordance with previous results in other conditions. Consequently, these analyses are recommended as a prerequisite to further spatial analyses using VPS-derived data.
“…With the VPS system, little effort has been made to defend filtering cutoffs beyond reference to prior use [13], and ambiguous filter criteria are at risk of inadvertently becoming acceptable practice through the accumulation of use. In our case, adoption of an ambiguous filter based on a previous study (HPE 10 to 20 [14][15][16]), would have been less useful than the carefully evaluated filter cutoff of 8 and indefensible (Table 2). Telemetry technology represents a very different tool than typical scientific instruments as its design is rarely consistent, is difficult to standardize, and does not generate data points with fixed accuracy and precision.…”
Section: Discussionmentioning
confidence: 68%
“…Studies either used a conservatively high HPE cutoff to remove the major problematic positions but retain most data (e.g., for an HPE number of 20, 83% retention [14]), or employed a lower HPE cutoff for the study and sacrificed large amounts of data (e.g., for an HPE number of 10, 58% retention [15]). In other cases, HPE was used but the level of data reduction was not reported (for an HPE number of 15 [12]), no filtering occurred and the HPE estimated for synchtags was assumed to characterize the precision of all animal positions in the array [10], or the authors referenced an example of the accuracy attained from a similarly configured array used in an unrelated study [16].…”
Section: Filtering Spatial Data With Hpementioning
Background: Telemetry systems that estimate animal positions with hyperbolic positioning algorithms also provide a technology-specific estimate of position precision (e.g., horizontal position error (HPE) for the VEMCO positioning system). Position precision estimates (e.g., dilution of precision for a global positioning system (GPS)) have been used extensively to identify and remove positions with unacceptable measurement error in studies of terrestrial and surfacing aquatic animals such as turtles and seals. Few underwater acoustic telemetry studies report using position precision estimates to filter data in accordance with explicit data quality objectives because the relationship between the precision estimate and measurement error is not understood or not evaluated. A four-step filtering approach which incorporates data-filtering principles developed for GPS tracking of terrestrial animals is demonstrated. HPE was evaluated for its effectiveness to remove uncertain fish positions acquired from a new underwater fine-scale passive acoustic monitoring system.
“…We assumed that fish of this size would be able to handle the tag burden, as researchers in other studies have tagged Winter Flounder Pseudopleuronectes americanus smaller than 19 cm (Fairchild et al 2009) and other Paralichthys spp. at similar sizes compared to the halibut in our study (Furey et al 2013). Surgical implantation of tags in other species of flatfish appears to have had no effects on feeding and activity behavior (Moser et al 2005).…”
Section: Fish Movements As An Estuary Restoration Metricmentioning
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.