y and Ecology 343 (2007) 138–147www.elsevier.com/locate/jembe

Journal of Experimental Marine Biolog

Evaluation of low impact pressure waves as a quantitativesampling method for small fish in shallow water

Martin Snickars a,⁎, Alfred Sandström b, Antti Lappalainen c, Johanna Mattila a

a Husö Biological Station, Department of Biology, Åbo Akademi University, Akademigatan 1, FI-20500 Turku, Finlandb Swedish Board of Fisheries, Institutes of Coastal and Freshwater Research, Stångholmsvägen 2, SE-17893 Drottningholm, Sweden

c Finnish Game and Fisheries Research Institute Viikinkaari 4, FI-00791 Helsinki, Finland

Received 8 June 2006; received in revised form 2 November 2006; accepted 13 December 2006

Abstract

Low impact pressure waves (LIMP) generated by small (b1 g) underwater ignition capsules have been used to qualitatively andsemi-quantitatively, as catch per unit effort, sample young-of-the-year (YOY) and juvenile fish in shallow (0–3 m) waters. In orderto evaluate this method on small fish, four categories, 0+ and 1+ year Eurasian perch, 1+ year roach and 0+ to 1+ year pike, weresubjected to the LIMP at distances of 0.5–5 m from the detonation point. At a distance b1 m all specimens were stunned and eitherfloated to the surface or sank to the bottom, whereas almost all were unaffected at distances N3 m. The efficiency radius of theLIMP varied between 1.5 and 3.0 m, and the effective area was 7–28 m2 depending on habitat and species. The average effectivearea for the two extensively studied categories (0+ perch and 1+ roach) was 17.9 m2. Both depth and vegetation affected theeffective area. The smallest area was obtained at b1 m depth with vegetation present, and the largest at 1–2 m depths with novegetation present. Within each fish category, the method sampled fish independently of length suggesting that a size/year-class issampled equally. The proportion of affected fish that sank was large and species-dependent. In addition to the netting of surfacedfish, collecting fish from the bottom is recommended for achieving an accurate quantitative estimate. The tested parameters of theLIMP, Pmax and Impulse, declined as a function of distance following a negative exponential growth model with a baseline. Thesimilar-shaped responses among the fish categories suggest that the method works at depths b3 m also in heterogeneousenvironments, and that the method can be used to quantitatively estimate abundance of small fish with swim bladders. A model forquantitative abundance estimates based on the evaluation is presented.© 2007 Elsevier B.V. All rights reserved.

Keywords: Esox lucius L.; Fish sampling; Method evaluation; Perca fluviatilis L.; Rutilus rutilus (L.); Swim bladder

1. Introduction

Precise information on abundance and presence/absence is necessary in many type of studies on fishassemblages. For example, it is important to get accurateand unbiased information on the early life-stages of fish,

⁎ Corresponding author. Tel.: +358 2 215 3426; fax: +358 2 215 3428.E-mail address: masnicka@abo.fi (M. Snickars).

0022-0981/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.jembe.2006.12.008

since recruitment often is a key mechanism in fishproduction. Abundance studies of small fish in shallowbrackish or marine waters have traditionally beenconducted with a variety of active sampling methodssuch as beach seines, small trawls and drop-samplers, aswell as visual surveys (Mankki and Vauras, 1974;Thorman and Wiederholm, 1983; Aneer et al., 1992;Brosse et al., 2001). These methods are, however, lesssuitable in several types of habitats. Particularly shallow

139M. Snickars et al. / Journal of Experimental Marine Biology and Ecology 343 (2007) 138–147

areas with heterogeneous topography, dense vegetation,large rocks and poor visual conditions are difficult tosample. Such environments are normally importanthabitats for small fish (Crowder and Cooper, 1982;Werner and Hall, 1988; Eklöv and Hamrin, 1989), anduse of traditional sampling techniques may severely biasabundance estimates (Gray and Bell, 1986). Also, therelatively large area sampled with several traditionalmethods makes it difficult to determine the relationshipsof microhabitat properties and abundance. Electro-fishing, a widely used point-abundance samplingmethod in freshwater is less efficient in saline due tothe high conductivity (Zalewski and Cowx, 1990).

To get estimates on abundance of small fish in highlyvariable shallow brackish or marine habitats, Scandina-vian fisheries managers and scientists have been using apoint abundance sampling design employing smallunderwater detonations (e.g. Karås and Neuman,1981; Nilsson et al., 2005; Sandström et al., 2005).Sampling with low impact pressure waves (LIMP)allows sampling of small and young fishes with swimbladders (Goertner et al., 1994). An overpressure wavefrom an underwater detonation starts to proceed equallyin all directions, later reflecting from bottom andsurface, and the effect diminishes exponentially withdistance to the detonation point (Karlsson et al., 2004).The specific physical behaviour of a wave in waterallows catch probability estimates for small fish to becalculated as a function of the horizontal distance fromthe pressure wave source. So far, the method has beenused semi-quantitatively as catch-per-unit-effort CPUE,because the efficiency of the method has not beenproperly evaluated.

The aim of this study was to test the possibility to usethe LIMP quantitatively as a fish sampling method bymeasuring the strength and the effective radius of thepressure wave at different depths and habitats. Twoseparate experimental evaluations were conducted.Firstly, the effective radius of the LIMP was tested indifferent habitats. Secondly, the strength of the pressure

Table 1The four fish categories used were 0+ perch, Perca fluviatilis L., 1+ roach,

Treatment 0+perch

1+roach

1+perch

0–1+pike

b1 m, veg. 5 5 – –b1 m, no veg. 4 5 3 41–2 m, veg. 4 4 – –1–2 m, no veg. 4 4 1 10

The number of trials, total coverage (%; mean±S.D.; minimum–maximum)pectinatus L., and total depth in centimetres (cm; mean±S.D.; minimum–ma1–2 m = between 1 and 2 m depth, veg. = vegetation present, no veg. = veg

wave and how it transmits in typical shallow habitatswas measured empirically.

2. Materials and methods

2.1. Effective radius of the LIMP

The experiment was conducted at the Husö Biolog-ical Station, Åland, SW Finland. A controlled experi-ment was conducted using four fish categories; 0+perch, Perca fluviatilis L. (LT±S.D., min–max, 38.9±5.6, 25–65 mm), 1+ roach, Rutilus rutilus (L.) (59.4±5.2, 43–88 mm), 1+ perch (81.8±9.2, 70–116 mm), and0+ to 1+ pike, Esox lucius L. (153±50.5, 86–255 mm),all of which were caught with a beach seine in the HusöBay some days prior to the experiment start. The fisheswere kept separately in flow-through tanks. Theexperiments were conducted in accordance with theethical guidelines for animal experiments at the ÅboAkademi University, Finland. Experiment 1 was con-ducted during 20–31 July 2004.

The experiment was designed to test the effects ofdepth and vegetation on the effective radius of the LIMP.Two depth categories, b1 m and 1–2 m depth,respectively, were combined with two vegetation catego-ries, presence of vegetation and absence of vegetation(Table 1). The extensively studied categories, 0+ perchand 1+ roach, were subjected to four treatments, and 1+perch and 0–1+ pike to two due to a low number ofspecimens. High standing macrophytes as Potamogetonpectinatus L. with a few small patches of Ceratophyllumdemersum L. and Myriophyllum spicatum L. dominatedthe treatments with presence of vegetation. The totalcoverage and height of the dominating P. pectinatus wasmeasured by assigning five randomly selected 0.25 m2

frames at the experimental site. The other vegetationspecies occurred so sparsely that they hardly could affectthe performance of the LIMP. In treatments with novegetation, all vegetation was manually harvested fromthe experimental site before the experimental runs. The

Rutilus rutilus (L.), 1+ perch, and 0–1+ pike, Esox lucius L.

P. pectinatus Total depth, cm

Total cov, % Height, cm

60±33; 30–100 40±18; 18–58 82±12; 65–10582±12; 65–105

69±25, 40–90 65±10; 55–75 136±10; 120–150136±10; 120–150

and height (cm; mean±S.D.; minimum–maximum) of Potamogetonximum). Abbreviations for the treatments: b1 m=less than 1 m depth,etation absent.

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bottom at the experimental site was muddy with 6%carbon content.

The fishes were exposed to the LIMP in cages madeof 1.0 mm thick soft knot-less net with a mesh-size of5.0 mm. The diameter of the cages was 0.25 m and theheight was 0.5 m. Ten experimental cages were attachedto a rectangular 2⁎5 m floating wooden framework atevery 0.5 m from the fixed detonation point at the shortside end, i.e. at distances of 0.5–5.0 m. The top of eachcage was at the water surface level. The cages atdistances of 1.0–4.5 m were placed alternately along thetwo long sides of the fleet so that all cages were fullyexposed to the pressure wave and no shading-effect ofneighbouring cages occurred. The cages at the distanceof 0.5 and 5.0 m, respectively, were placed along the twoopposite short side ends of the fleet so that the cage at0.5 m was placed directly behind and the cage at thedistance of 5.0 m in front of the detonation point.

The test assumed an even distribution of fish. Tenspecimens of 0+ perch and 1+ roach, respectively, weretested simultaneously, i.e. 20 specimens per cage at eachof the ten distances. Ten specimens and one specimen of1+ perch and 0–1+ pike, respectively, were testedseparately. The pike were tested at distances 1, 2, 3 and5 m. A detonation-capsule (0.94 g RDX/Pentyl-capsule,Dyno Nobel NONEL®) was ignited at a fixed point at adepth of 0.3 m when the water depth was b1 m,respectively at 0.5 m when the water depth was 1–2 m.When initiated with a NONEL® DynoStart 2, a lowenergy signal (2000 m/s) propagates through a hollow,three-layer plastic shock tube (7.8 m long), coated on theinnermost wall with a reactive explosive compound thatdelivers the firing impulse to the detonation-capsule thatdetonates. Two minutes after a detonation, the fisheswere observed and divided into three status groups, twoaffected groups and one unaffected group. Specimensfloating on the surface (affected), laying on the bottomof the cage (affected) or still swimming (unaffected)were in situ counted at each distance. The length (to thenearest mm) of specimens of each category belonging toeach of these three status groups was measured. Theproportion of all affected specimens (pooled floating,laying on the bottom) in each treatment was comparedwith a field data set from Sweden. In the field studyspecimens of perch and roach were sampled with thesame method and either netted if floating or counted bysnorkeling if laying on the bottom. The field samplingwas conducted in a Swedish archipelago area with thesame depth interval and vegetation composition (A.Sandström et al., unpublished, Swedish Board ofFisheries) as in our experiment. In order to evaluatethe post-treatment condition of the experimental fishes,

the condition of the specimens was monitored for90 min after each trial. Fishes from all treatments werecarefully put in separate aquaria according to theirrespective status group, i.e. a total of 30 possible status–distance combinations were monitored. Every 10 minany change in the status of a specimen was noted.

2.2. Experiment 2. Pressure wave measurements

In order to measure the strength of the LIMP, as wellas the possible effect of the cage, the strength of thepressure wave was measured inside and directly outsideeach experimental cage with an Accura 100 usingsimultaneously two turmalin-sensors with a Kistler5001-amplifier. The equipment measures instantaneouschanges of pressure in the water at a reading rate of100,000/s. The strength of the pressure wave wasmeasured in the same habitats as in Experiment 1 usingthe same experimental setup. The sensors were fixed at0.5 m depth. The sensor depth was tested by comparingthe 0.5 m sensor-depth with a sensor-depth of 0.5 mabove the sediment surface in two treatments (b1 m and1–2 m, with vegetation absent, respectively). Theperformance of the LIMP was also tested in a densereed belt (Phragmites australis (Cav.) Steud., with anaverage total coverage of 70–80%. The total depth was0.9 m, and the sensor depth 0.5 m. One detonation wasignited per treatment. Experiment 2 was conducted 4–5October 2004.

2.3. Data analysis

In respective treatment of Experiment 1, the distancewhere N95% of the total number of affected specimenswas obtained (hereafter referred to as the N95%distance) was calculated for each fish category. TheN95% distance was calculated by cumulatively addingthe number of affected specimens at all distances,starting at 0.5 m from the detonation point, and dividingthat number with the total number of affected specimensuntil reaching the desired percentage level. At that level,the corresponding distance was assigned as the N95%distance in metres. This distance was chosen becausepreliminary inspections of the curves revealed that theaffected specimens of all fish categories showed asimilar response to the method, and it was motivated toget an estimate on the maximum effective radius of themethod. A fixed distance enabled a definition of theeffective radius of the LIMP in all treatments. TheN95% distances were used to calculate the sphericaleffective area (radius2⁎π) within which the LIMPsamples N95% of the total number affected fish.

Fig. 1. Mean number (N)±s.e of affected specimens as a function of distance from the detonation point (distance, m). a) 0+ perch, Perca fluviatilis L.,b) 1+ roach, Rutilus rutilus (L.), c) 1+ perch, P. fluviatilis and d) 0–1+ pike, Esox lucius L. Filled triangles represent 1–2 m depth with vegetationabsent, open triangles represent 1–2 m with vegetation present, filled rectangles represent b1 m depth with vegetation absent, and open rectanglesrepresent b1 m with vegetation present. Note the different measure of Y-axis for 0–1+ pike.

141M. Snickars et al. / Journal of Experimental Marine Biology and Ecology 343 (2007) 138–147

Model curves were fitted to the observed values ofthe fish response and the LIMP parameters, respectively.The shape of the fish response curve was analysed byfitting curves to the observed fish response data set as afunction of the distance from the detonation point using

Table 2The distance in meters where N95% of the total number of affected specipercentage±s.e. affected specimens, mean percentage±s.e. affected specimenthe LIMP where N95% of the total number affected specimens at the distanc

Category Treatment N95% distance (m) Mean % affected of t

0+ perch b1 m no veg. 2.5 96.7±1.20+ perch b1 m veg. 2.0 97.6±1.50+ perch 1–2 m no veg. 2.5 95.3±1.60+ perch 1–2 m veg. 2.5 99.3±0.71+ perch b1 m no veg. 1.5 97.3±2.71+ perch 1–2 m no veg. 2.0 1001+ roach b1 m no veg. 2.5 97.8±1.61+ roach b1 m veg. 2.0 1001+ roach 1–2 m no veg. 2.5 1001+ roach 1–2 m veg. 2.5 98.9±1.10–1+ pike b1 m no veg. 3.0 1000–1+ pike 1–2 m no veg. 3.0 91.3

Abbreviations are the same as in Table 1.

a non-linear regression with Levenberg–Marquardtalgorithm. The algorithm minimizes the sum of squaresof the differences between the fitted and experimentaldata and calculates the set of parameters, the coefficientof determination (r2) and the residual sum of squares

mens were attained (N95% distance), the corresponding actual means of total number tested specimens, and the calculated effective area ofe

otal affected Mean % affected of total tested Effective area (m2)

77.5±5.6 19.677.5±6.2 12.678.0±7.2 19.672.0±7.9 19.670.0±15.3 7.180.0±16.8 12.675.5±7.2 19.674.5±8.1 12.690.5±3.4 19.676.5±7.8 19.660.0 28.360.0 28.3

Table 3Final parameters, a, b, and c of the sigmoid Gompertz model fitted to the fish response data of the treatments

Category Treatment a (fixed) b c r2 RSS

0+ perch b1 m no veg. 10 −3.667 −1.394 0.920 50.3700+ perch b1 m veg. 10 −5.290 −2.630 0.968 27.8600+ perch 1–2 m no veg. 10 −5.060 −2.050 0.908 71.3700+ perch 1–2 m veg. 10 −5.786 −2.482 0.939 47.6001+ perch b1 m no veg. 10 −17.160 −9.940 0.979 10.0001+ perch 1–2 m no veg. 10 −27.940 −19.190 0.956 11.0001+ roach b1 m no veg. 10 −5.112 −2.081 0.953 34.0601+ roach b1 m veg. 10 −8.420 −4.520 0.987 12.2101+ roach 1–2 m no veg. 10 −10.770 −3.970 0.975 22.0501+ roach 1–2 m veg. 10 −9.300 −3.930 0.930 58.0300–1+ pike b1 m no veg. 1 −1.739 −0.550 0.920 0.0190–1+ pike 1–2 m no veg. 1 −1.602 −0.404 0.830 0.030

Abbreviations: r2 = the coefficient of determination, and RSS = the residual sum of squares. Abbreviations are the same as in Table 1.

Table 4A comparison of the total number of perch, Perca fluviatilis L. (0+perch in both comparisons), and roach, Rutilus rutilus (L.)(experimental: 1+ and field survey: 0+ roach, respectively) floatingon the surface and laying on the bottom ( percentage of the totalnumber of fish) in the experiment and in the field (N=159 samples inthe field) 2 min after a 0.94 g detonation

Species Experiment Field survey

Surface Bottom Surface Bottom

Perch 15 (2.4) 613 (97.6) 14 (13.5) 90 (86.5)Roach 373 (58.3) 267 (41.7) 8 (66.7) 4 (33.3)

142 M. Snickars et al. / Journal of Experimental Marine Biology and Ecology 343 (2007) 138–147

(RSS). The models were fitted using the XLfit 4software (demo version) for Excel Windows. Asigmoidal, declining Gompertz model was found tobest fit the fish response data:

y ¼ a expð−exp½b−c⁎x�Þ ð1Þ

where y=N specimens, x=distance in metre, a= initialnumber of specimens (fixed to ten), b=a dimensionlessparameter, and c=the rate of change in number ofaffected specimens at a point of the curve. Difference inlength between affected and unaffected specimens wastested using ANOVA with untransformed data forrespective fish category, as ANOVA is consideredrobust against non-normality (Underwood, 1981). Thechange in status in post-treatment condition wascalculated as the proportional difference of numbers ofspecimens between initial (0 min) and end of theobservation period (90 min), where positive valuesindicate an increase in number, and a negative value adecrease in number from initial values.

Two parameters of the LIMP that have been reportedto affect fish with swim bladders were extracted fromthe data (Karlsson et al., 2004, and references therein).The front pressure (overpressure) of the wave, Pmax (inmilli-Pascal, mPa) was calculated as the differencebetween the maximum value (magnitude) of the firstpulse and the zero (initial) value of the pressure wave atevery ten distances tested. The Impulse was calculatedas the mean pressure (Mpa)⁎duration (in milliseconds),i.e. the time until the pressure reaches initial values ofthe total pressure wave. A negative exponential growthmodel with a baseline was fitted to these two parametersas a function of distance from the detonation point:

y ¼ a expðb⁎xÞ þ c ð2Þ

where y=Pmax and Impulse, respectively, x=distance inmetre, a=the radius of attained values of Pmax andImpulse, respectively, b=slope factor, and c=the finalminimum value of Pmax and Impulse, respectively. Theeffect of the cage on the performance of the LIMP wastested with ANOVA General Linear model (GLM,Repeated measures) of between subject cage effects forPmax and Impulse values, respectively, over the tendistances. Untransformed data of both Pmax and Impulsewas used as the assumptions of the GLM were met.

3. Results

3.1. Experiment 1

At a distance b1.0 m almost all specimens wereaffected by the LIMP, i.e. laying on the bottom orfloating on the surface. Depth and vegetation bothaffected the result. At a depth of b1 m with vegetationpresent, the number of affected specimens decreasedfaster over the distance compared with the othertreatments (Fig. 1; Tables 2 and 3). The response curvesof the fishes were normally steeper and decreased fasterat depths b1 m than at 1–2 m depths. The N95%

Fig. 2. The percentage of change in the status of affected (blackrectangles, N=380) (floating and laying on bottom; pooled data) andunaffected (white rectangles, N=190) specimens during the post-treatment observation. A value N0 indicates an increase, and a valueb0 a decrease in number from values of 0 min.

143M. Snickars et al. / Journal of Experimental Marine Biology and Ecology 343 (2007) 138–147

distance was achieved at distances of 1.5–3.0 m. Theextensively studied 1+ roach and 0+ perch had identicalN95% distances, whereas 1+ perch had the shortestdistances. Normally 60–90% of the total number oftested specimens was affected, and the values seemedfairly consistent across treatments and fish categories.However, the 0–1+ pike may show incompatible valuesdue to the low power, i.e. only one specimen tested pertreatment in comparison to ten specimens for the othertreatments. The effective area, i.e. where the methodsamples N95% of the total number affected specimens,

Fig. 3. Pmax and Impulse (mPa⁎ms) values, upper and lower panels, respectivtreatments with vegetation absent, right panel: treatments with vegetation prdepth, and triangles represent 1–2 m depth with the sensor 0.5 m above the

of the LIMP varied between 7.1 and 28.3 m2 for allcategories, 12.6–19.6 m2 for the categories except 0–1+pike, and 17.9±1.1 m2 for the extensively studied 0+perch and 1+ roach. From the distance of 3.0 m andupwards almost all fish were unaffected. Based on theresults in Table 2, fish abundance per m2 can be derivedusing the model:

y ¼ ð½x=a�=bÞ=c ð3Þ

where y=fish/m2, x=number of affected fish, a=pro-portion affected fish of total affected fish at N95% metermark, b=proportion affected fish of total tested atN95% meter mark, and c=calculated effective area. Thefish response curves all showed similar asymptoticshapes with an initial high number of affected specimensup to about 1.0–1.5 m, where after a steady decreasewith variable steepness and a final levelling off innumber of affected specimens took place. The fit of thesigmoid Gompertz model was good for all treatments,and the three model parameters were able to explain thefish response curve adequately. The r2 was in generalN0.90 and varied between 0.83 and 0.99. The values ofRSS corresponded with the observed fit (Table 3). 1+perch had the steepest response curve slopes, and thehighest negative values of parameters a and b. 0+ perchand 0–1+ pike both showed relatively gentle slopes. Theresponse curves of 1+ roach showed the highest

ely, as a function of distance for the experimental treatments. Left panel:esent. Circles represent b1 m depth, open rectangles represent 1–2 msediment surface.

Table 5The final parameters (a, b and c) of the Exponential Growth Modelwith Baseline and the r2 and RSS values obtained for the decrease instrength of Pmax (Mpa)

Treatment a b c r2 RSS

b1 m, no veg., cage 5.195 −0.861 0.374 0.992 0.084b1 m, no veg., no cage 6.877 −1.134 0.477 0.992 0.1151–2 m, no veg., cage 9.216 −1.437 0.594 0.995 0.0871–2 m, no veg., no cagea 6.740 −1.240 0.595 0.989 0.1301–2 m, no veg., no cage 3.352 −0.424 −0.099 0.975 0.098b1 m, veg., cage 9.401 −1.302 0.522 0.992 0.173b1 m, veg., no cage 7.687 −1.386 0.535 0.980 0.2451–2 m veg., cage 4.675 −0.778 0.382 0.981 0.1791–2 m veg., no cage 7.932 −1.339 0.585 0.976 0.3841–2 m veg., no cagea 2.663 −1.110 0.691 0.830 0.406Reed, no cage 57.725 −5.528 0.097 0.999 0.011

a = sensor 0.5 above sediment surface. Cage and no cage = treatmentswith and without experimental cages, respectively. Reed = Phragmitesaustralis (Cav.) Steud. For additional treatment abbreviations seeTable 1.

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variability with both depth and vegetation as factorsresponsible for the variation, and with b1 m depth withvegetation present and 1–2 m depth with vegetationabsent, respectively as opposite “extremes”.

The LIMP affected the specimens within each fishcategory independently of length. Mean length±s.e.(mm) of affected 0+ perch was 38.7 ± 0.3, andunaffected 39.1 ± 0.2 mm (ANOVA, p = 0.28).Corresponding values for 1+ perch were 81.6±1.0 and81.9±0.6 (ANOVA, p=0.77), and 59.4±0.2 and 59.5±0.2for affected respective unaffected 1+ roach (ANOVA,p=0.55). The comparison between the experimentaldata and the field data on specimens floating on thesurface and laying on the bottom showed relatively

Table 6The final parameters (a, b and c) of the Exponential Growth Modelwith Baseline and the r2 and RSS values obtained for the decrease instrength of Impulse (Pmax

⁎duration in ms)

Treatment a b c r2 RSS

b1 m, no veg., cage 0.191 −0.892 0.005 0.998 b0.001b1 m, no veg., no cage 0.196 −0.989 0.006 0.996 b0.0011–2 m, no veg., cage 0.415 −1.678 0.018 0.985 b0.0011–2 m, no veg., no cagea 0.303 −1.406 0.017 0.991 b0.0011–2 m, no veg., no cage 0.150 −0.819 0.018 0.963 b0.001b1 m, veg., cage 0.350 −1.526 0.014 0.995 b0.001b1 m, veg., no cage 0.347 −1.374 0.013 0.994 b0.0011–2 m veg., cage 0.167 −0.836 0.007 0.990 b0.0011–2 m veg, no cage 0.200 −0.904 0.009 0.998 b0.0011–2 m veg., no cagea 0.085 −1.121 0.022 0.890 b0.001Reed, no cage 0.300 −2.557 −0.009 0.930 b0.001a = sensor 0.5 above sediment surface. Cage and no cage = treatmentswith and without experimental cages, respectively. Reed = Phragmitesaustralis (Cav.) Steud. For additional treatment abbreviations seeTable 1.

good consistency in the proportions for both perchand roach (Table 4). A clearly larger proportion of theroach floated compared with perch.

Overall, the change between the two status categorieswas low during the 90 min of post-treatment observa-tions. Floating or sunken specimens also stayedaffected, whereas the number of unaffected specimenswas stable (Fig. 2).

3.2. Experiment 2

The parameters of the LIMP, Pmax and Impulse,respectively, decreased exponentially with the distance tothe detonation point. The Exponential Growth Modelwith Baseline model was well fitted to the values of theparameters, and the r2 for the treatments were in generalN0·9 with considerably low RSS values (Fig. 3; Tables 5and 6). The most pronounced differences among thetreatments were observed at distances b1.5 m from thedetonation point. At distances N1.5 m, no majordifferences were detected. Both in the vegetation presentand the vegetation absent treatments, Pmax and Impulseshowed highest values at the 1–2 m depth treatment at adistance of 1.5–2.5 m, reflecting the higher number ofaffected specimens in the 1–2 m treatment at the samedistances in Experiment 1. The pattern was lesspronounced in comparisons for the effect of vegetation.The effect of sensor depth was tested in two treatments,the 1–2m depth with vegetation present respective 1–2mdepth vegetation absent, where both Pmax and Impulseshowed a more moderate slope of the curve with lowervalues than in the other treatments at distances b1 m fromthe detonation point. In the reed belt treatment the LIMPhad an incommensurably fast decline of both Pmax andImpulse, with low values already at 1 m distance from thedetonation point (Fig. 4; Tables 5 and 6).

The experimental cages had no effect on theperformance of the LIMP. Mean Pmax±s.e for cage-

Fig. 4. Values of Pmax (triangles) and Impulse (rectangles) as functionsof distance in the reed (Phragmites australis (Cav.) Steud.) belt.

145M. Snickars et al. / Journal of Experimental Marine Biology and Ecology 343 (2007) 138–147

treatments was 1.42±0.04, and for no cage-treatments1.37±0.04 (repeated measure ANOVA, p=0.39). Thecorresponding values for Impulse were 0.0043±0.003and 0.0044±0.003 for cage and no cage-treatments,respectively (repeated measure ANOVA, p=0.85).

4. Discussion

The experimental evaluation of the LIMP suggeststhat by using the method with the presented model incombination with the results of Table 2, it is possible toquantitatively estimate abundance of small fish withswim bladders, e.g. juveniles of large families such asPercidae and Cyprinidae, in both open and vegetatedshallow waters. Also, as having qualities to meet therequirements of point-abundance sampling, the methodenables a precise description of the relationship betweenfish and common habitat parameters, such as depth andvegetation coverage, which is crucial for e.g. accuratehabitat mapping. The evaluation was able to establishgood estimates on the efficiency of the method to catchsmall fish (TL 25–225 mm). The method samples fish inthe tested length interval accurately within a radius ofb3 m. The effective area was calculated to c. 13–19 m2,depending on habitat and fish. Habitat heterogeneity, i.e.depth and vegetation both affected the radius, but nevermore than 0.5 m within a fish category. The evaluationindicated a size-dependency, which is in line with earlierfindings that show decreased vulnerability with in-creased body size (Karlsson et al., 2004 and referencestherein). A larger body size probably reduces thesensitivity to the pressure wave. Pressure waves mainlyaffect gas-filled cavities such as the swim bladder. Alarger and heavier body probably is less sensitive to thepressure wave due to a more rigid body structure thatcan withstand the change in pressure better than a smallbody. A crucial point in the size-dependency of themethod is that within fish category there were noindications of size selectivity, which suggests that themethod sample specimens equally well within an agegroup. Hence, the two extensively studied fish catego-ries, 0+ perch and 1+ roach, were close to equallyaffected, which indicates that identically sized small fishwith swim bladder can be caught with equal efficiency.

The fishes responded to the LIMP with an initial highnumber of specimens affected after which the responsequickly decreased to a low level. The relatively goodconsistency in the proportions of fish floating and layingon the bottom between the experimental data and datacollected in the field indicates that the evaluationcorresponds well with field situations. The significantproportion of fish laying on the bottom gives that the

collection of sampled specimens should be conductedeither by netting floating fishes or preferable incombination with collecting fishes laying on the bottome.g. by snorkeling or scuba-diving, even if it isaccompanied with an increased effort. In this way itcan be assured that all affected specimens are collected,and any differences among species´ likelihood to sink orfloat are accounted for.

The test failed to show a consistent response of pike,which probably was due to low test power. However, itcannot be ruled out that pikemay have a comparably largeresponse area to the method, which to a degree can besupported from field observations using the method (A.Sandström et al., unpublished, Swedish Board ofFisheries). Nevertheless, the results suggest that estimat-ing pike abundance quantitatively should be conductedconservatively. The test performed in a dense reed, P.australis, belt showed a very rapid decline of testparameters, and the values were reaching zero already ata distance of c. 1 m. The method should therefore be usedwith extra caution in dense vegetation that in its structurecontains a large portion of gas, such as reed. Gas buffersthe energy of a progressing pressure wave (Moser, 1999),and therefore the effective radius is drastically shortened.The physical behaviour of a pressure wave propagating inthe water is universal and affected by differences in thedensity of the water, e.g. due to salinity and temperaturegradients (Karlsson et al., 2004 and references therein).Over the short distance covered by the method, it isunlikely that such gradients occur under normal condi-tions. The evaluation was conducted in a habitat, whichcan be classified as muddy and soft. Therefore, reductionof reflection from the bottom is greater compared with ahard substrate type, such as sand, gravel, stones andboulders. These attenuate less the reflection of thepressure wave (Karlsson et al., 2004 and referencestherein), which probably increases the efficiency of thepressure wave over distance. Hence, an overestimation ofthe efficiency is not likely in the present evaluation.Considering the low initial pressure value of the LIMPand the presented narrow interval of the efficiency,differences in the efficiency due to substrate type seemmarginal as the attenuation of the horizontal wave is likelymore important. Obstacles in the water column situatedbetween the fish and the detonation are likely to have amore pronounced role on the efficiency of the method.Vegetation, especially if gas-filled, was shown to sharplyreduce the propagation of the pressure wave comparedwith a situation where no such vegetation was present inthe water column. Consequently, in addition to substratetype, it seems important to control and monitor thepresence, coverage and type of vegetation as well as other

146 M. Snickars et al. / Journal of Experimental Marine Biology and Ecology 343 (2007) 138–147

potential obstacles in thewater column. Under poor visualconditions, e.g. in turbid waters, monitoring of vegetationand fish laying on the bottom may be difficult on a visualbasis. Under such circumstances, alternative methodscould be used, especially considering that heterogeneoushabitats are less common in turbid waters with normallyreduced vegetation coverage.

The post-treatment observations indicate that themethod samples small fish consistently meaning here alow change of the initial status after a sample. A low rateimplies that the method samples fish efficiently withinthe defined area, whereas fish outside the area are notaffected and do not show behavioural changes. It ispossible that an even longer period of observation wouldhave revealed changes in the status that was notobserved in this study. However, it can also be arguedthat any behavioural changes would have been detectedeven within the considerable long observation period.Our finding that unaffected individuals stay unaffectedis supported by the study of Sverdrup et al. (1994). Theymonitored Atlantic salmon (Salmo salar) in laboratoryand the response to explosions and found no immediatemortality to the pressure wave or during the seven daysof observation.

Consequently, the results support the method from anethical perspective, considering the low risk of escape ofaffected specimens on one hand and the normal behaviourof unaffected specimens after the sample on the other hand.The relatively small sampling area of themethod comparedwith several other methods, e.g. seining, should normallyresult in a comparably lownumber of collected fishwithoutloosing the accuracy of the abundance estimate. Thisfacilitates an increased number of samples per unit effort,and should enhance the statistical power of the study. Also,the method is lenient to the environment, e.g. submergedvegetation compared with some alternative methods(Aneer et al., 1992). Considering the above, the methodseems environmentally and ethically sound with lowimpact on the environment (inclusive bottom substrate,water, vegetation and invertebrate fauna) it is attempted tomonitor; only a small area is affected, which also reducesthe risk of catching and affecting excessive numbers of fish.

To conclude, it is suggested that the results of thisstudy and the model presented could be extrapolated toother juvenile and/or small fishes with swim bladders toquantitatively estimate abundance in shallow heteroge-neous habitats.

Acknowledgments

We thank Husö Biological Station for excellentworking conditions. Thanks to Bertil Sandberg at the

Swedish Defence Research Agency for the expertise andhandling of the Accura 100 equipment. We are indebtedto Malin Lönnroth, Stefan Sandström, Matias Scheinin,and Pia Åkerblom for their help during the experiment,and to two anonymous referees for their valuablecomments. The study was funded by a grant fromERDF, Interreg IIIA to MS and by the Swedish Board ofFisheries. [RH]

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