- Research article
- Open Access
Multivariate evaluation of the effectiveness of treatment efficacy of cypermethrin against sea lice (Lepeophtheirus salmonis) in Atlantic salmon (Salmo salar)
- Daniel F Jimenez1Email author,
- Crawford W Revie†2,
- Simon P Hardy1,
- Peder A Jansen1 and
- George Gettinby†3
© Jimenez et al.; licensee BioMed Central Ltd. 2013
Received: 1 April 2013
Accepted: 16 December 2013
Published: 20 December 2013
The sea louse Lepeophtheirus salmonis is the most important ectoparasite of farmed Atlantic salmon (Salmo salar) in Norwegian aquaculture. Control of sea lice is primarily dependent on the use of delousing chemotherapeutants, which are both expensive and toxic to other wildlife. The method most commonly used for monitoring treatment effectiveness relies on measuring the percentage reduction in the mobile stages of Lepeophtheirus salmonis only. However, this does not account for changes in the other sea lice stages and may result in misleading or incomplete interpretation regarding the effectiveness of treatment. With the aim of improving the evaluation of delousing treatments, we explored multivariate analyses of bath treatments using the topical pyrethroid, cypermethrin, in salmon pens at five Norwegian production sites.
Conventional univariate analysis indicated reductions of over 90% in mobile stages at all sites. In contrast, multivariate analyses indicated differing treatment effectiveness between sites (p-value < 0.01) based on changes in the proportion and abundance of the chalimus and PAAM (pre-adult and adult males) stages. Low water temperatures and shortened intervals between sampling after treatment may account for the differences in the composition of chalimus and PAAM stage groups following treatment. Using multivariate analysis, such factors could be separated from those which were attributable to inadequate treatment or chemotherapeutant failure.
Multivariate analyses for evaluation of treatment effectiveness against multiple life cycle stages of L. salmonis yield additional information beyond that derivable from univariate methods. This can aid in the identification of causes of apparent treatment failure in salmon aquaculture.
Lepeophtheirus salmonis is a major ectoparasite pathogen of farmed Atlantic salmon (Salmo salar) in the Northern Hemisphere. Sea lice infestations have a detrimental effect on health, production and market value of fish. The ecological impact on intensive salmon aquaculture has been of concern since published reports first suggested a link between the decline of wild salmon stocks and the presence of sea lice on salmon farms [1–6]. The Norwegian Food and Safety Authority (NSFA) have implemented precautionary measures to reduce the impact of sea lice from salmon production sites. These measures mandate the delousing or harvesting of production sites once sea lice infestations surpass the threshold set by NSFA [7, 8]. While novel intervention strategies are being explored, the successful control of sea lice infestations in production sites is currently heavily dependent on effective delousing using chemotherapeutants.
Management systems for production of salmon in Norway have rapidly grown in the last decade, largely through intensification of fish production through the use of large pens and automation of feeding. Such operations however may magnify certain problems associated with the application of bath treatments. For example, the effectiveness of topical bath administrations is dependent on accurate dosage and a rapid and uniform distribution of the chemotherapeutant in the water column . Inadequate medicinal exposure will result in incomplete treatment. The unintended effects of inadequate treatments include the repeated usage of chemotherapeutants over the production cycle and a risk that chemical resistance will emerge in sea lice populations .
Evaluation of field treatment effectiveness should identify those treatments that do not achieve the expected effect. Whilst reported treatment failures have been identified in Norway using alternative methods such as bioassays and probit modelling , current methods monitor the average reduction in mobiles (pre-adult and adult sea lice) following treatment. This simplified analysis risks overlooking changes that may cause treatment failure such as reduced sensitivity to chemotherapeutants.
Multivariate analyses enable the concurrent evaluation of effectiveness of treatment against all sea lice life cycle stages. Delousing effectiveness is multifactorial, being dependent on the interaction between biotic factors, such as life cycle stage, gender and phase in the molting cycle, as well as abiotic factors such as water temperatures and salinity. Consequently, a more robust interpretation of treatment effect can be made by incorporating data from sea lice life cycle stages. This type of approach is used in ecological studies to evaluate the effect or alterations that environmental stressors or toxicants produce in the composition of species community [12, 13].
Our aim was to develop improved methods for the evaluation of treatment effectiveness in the field. Evaluation of treatment effectiveness against stages of salmon lice using multivariate methods provides a complete assessment of the overall effect of the drug and allows detailed comparisons between treatments. Specifically, it reveals those life cycle stages of L. salmonis that after treatment were characteristic of particular sites and hence which had potential use as indicators of treatment failure. Multivariate analysis also reveals other aspects that can be of interest when evaluating drug effects such as the changes in composition of L. salmonis stage groups following treatment.
Presence and abundance of sea lice by stage group
Estimates and 95% CI of treatment effectiveness against all mobile stages of L. salmonis at five sites located in Western Norway according to the methods described in the text
Interval of days after treatment
99.20 - 99.99
99.48 - 100.00
97.00 - 99.34
88.36 - 96.57
90.70 - 97.84
Changes in the composition of sea lice stages with treatment
Average and standard deviation (SD) of within-site distances, calculated from the matrix of Bray-Curtis distances both before and after treatment
Permutational multivariate analyses for the abundances of L. salmonis stage groups at five sites
Type of statistic
Value of statistic
Value of statistic
Frequency, abundance and indicator value (%) for the L. salmonis stage groups at five sites before and after treatment
A) Before treatment
B) After treatment
At one site (site D) cypermethrin treatment was less effective against all stage groups. Analysis at pen level for site D showed that treatment against PAAM and adult females was effective for all pens (above 90%) except for one where the percentage reduction for PAAM was 63% (95% CI: 10–87) and for adult females was 82% (95% CI: 50–96).
Synthetic pyrethroids are widely used against sea lice in Norwegian aquaculture. Three synthetic pyrethroids are available for lice treatments, cypermethrin (Excis®) deltamethrin (Alpha Max®) and high-cis cypermethrin (Betamax®). These compounds have similar characteristics (administration and distribution), mechanisms of action, and therapeutic efficacies. Synthetic pyrethroids are insecticides that act by preventing closure of voltage-gated sodium channels resulting in abnormal hyper excitability, spastic paralysis and death. Synthetic pyrethroids are highly efficacious against PAAM and adult females  but reportedly less efficacious against chalimus stages [15, 16].
Current analytical methods for evaluating treatment effectiveness calculate the percentage reduction in the mobile stages only. This method has low sensitivity when sampling is limited and is unlikely to fully reflect the treatment outcome  since calculation of treatment effectiveness is based on average abundance for only one stage group (PAAM or all mobiles). It is recognised that univariate analyses fail to control for experimental error and do not take account of the covariance structure in the data . We performed multivariate analyses to combine the information from all group stages and determine the stage groups that best indicate (changes in magnitude or direction of) treatment effectiveness. This assessment of the community structure differs from the more statistical approaches to clustering adopted in previously published research [19, 20].
In this study, we examined the effectiveness of cypermethrin treatments conducted in 33 pens at five different sites. Treatment with cypermethrin resulted in a larger than 90% reduction of all mobiles, which would conventionally be taken as an indication that treatment was effective at all sites. However, high treatment effectiveness against chalimus was observed only at one site, which suggests that chalimus was the only stage group that contributed to the significant differences in abundance observed between sites after treatment. Absolute numbers of chalimus were low which makes it difficult to evaluate treatment effectiveness. In addition these smaller stages are more difficult to enumerate accurately when sampling live fish in a production environment .
We had expected effective treatment to homogenize the initially heterogeneous sea lice populations, yet multivariate analysis and ordination analysis revealed that there existed a latent representation of sea lice stage composition that indicated increased heterogeneity following treatment. In particular chalimus and to a lesser extent PAAM were identified as stage groups accounting for the phenomenon observed.
Several site factors could offer explanations as to how differences in estimated treatment effectiveness arise. One factor may be water temperature. Low water temperatures can significantly delay the development of chalimus stages . This is suggested by the limited reduction of chalimus in low temperatures at sites B and C, despite the significant reduction in PAAM. It has been shown that time to chalimus mortality following application of cypermethrin is increased with low temperatures [23, 24]. The reduction in chalimus observed at site C was 37% (mean abundance was 0.31 [95% CI: 0.21-0.39]) 10 days after treatment; however, 20 days after treatment this reduction had increased to 90% (mean abundance at site C was 0.02 [95% CI: 0.00-0.06], while no increase in PAAM was observed), consistent with a delayed molting due to low temperature.
A further contributory factor may be the effect of cypermethrin on the development of the chalimus since cypermethrin delays metamorphosis within the chalimus stages . The combination of temperature and developmental delay in chalimus will differentially affect the stage group composition and argues for the analysis of all stage groups when determining treatment effectiveness for this type of topical intervention [15, 16]. Since univariate analysis utilizes only changes in PAAM or mobile populations the assessment of treatment effectiveness will be incomplete and potentially misleading.
The time interval between treatment and sampling was not identical across sites. The size of treatment effect will be influenced by the time interval between treatment and sampling. It may therefore be advisable to standardize the time at which treatment effectiveness is observed.
Using univariate methods, cypermethrin treatment was highly effective against PAAM stages, as defined by a reduction in the lice numbers equal or larger than 90%. Multivariate analyses questions this apparently satisfactory outcome. Effectiveness following treatment may not be as simple as a numerical reduction in a particular lice stage but may for example be a change in the distribution of population numbers across several lice cycle stages following treatment, an alteration of the sex ratio of the adults or an unforeseen delay in stage development. Multivariate methods yield data that may help more accurately define treatment effectiveness and we recommend the adoption of such methods in order to study empirical data from ‘best practice’ treatment studies.
Overall, cypermethrin treatment was effective at all production sites but differences were found in the composition of sea lice stages between sites after treatment. As treatment effectively reduced the PAAM and adult females this suggests that abiotic factors may account for differences in sea lice composition after treatment between sites. This aspect cannot be addressed from our data due to a limited sample size of five farms. Identification of these factors merits further investigation.
The efficacy of outcome from topical treatment with synthetic pyrethroid is multifactorial, and not solely dependent on achieving adequate levels in the water column. With the emergence of chemical resistance there is a pressing need for comprehensive interpretation of collected data such as that offered by the multivariate approach. We have used multivariate methods to evaluate the effectiveness of cypermethrin treatment against sea lice. Multivariate methods, unlike the currently adopted univariate methods which focus on a single stage group of L. salmonis, provide an improved measure of the treatment effectiveness against all parasitic stage groups. Multivariate analyses could be extended to evaluate treatment against other ectoparasites of veterinary and medical importance.
The study included five Atlantic salmon (Salmo salar) sites (A, B, C, D and E) located in the Sogn and Fjordane county in the western region of Norway. Average seaway distances between sites were below 50 km, except for site E which was around 70 km from the nearest site. All bath treatments were conducted with full tarpaulin enclosures  using Betamax Vet® (Novartis Aqua Norge, Oslo, Norway) in accordance with manufacturer’s recommendations. Bath treatments were performed in all pens at each production site between November 2011 and February of 2012. The number of pens (p) per site varied from five to nine; for site A (p = 6), B (p = 7), C (p = 9), D (p = 6) and E (p = 5).
All treatments were completed within five days at a given site. Pens were treated consecutively at a rate of one or two pen treatments per day. Water temperatures (based on monthly average values at the time of treatment) were similar in three sites (C, D and E) ranging between 6.8 - 8.2°C. The coldest and warmest temperatures were recorded at site B (5.4°C) and site A (10.5°C), respectively. Delousing treatments in Norway are mandatory when lice levels exceed the thresholds provided in Norwegian regulations. As all pens on a site must be treated there is no opportunity to leave some pens untreated to act as controls, as would be possible in a clinical trial.
Sampling was performed before and after treatment at weekly or biweekly intervals. Pen samplings were conducted from ten days prior to, and up to approximately 50 days following treatment. All pens (n = 33) except one at site D (where fish were slaughtered) were sampled before and between 3 and 16 days after treatment; only two sites and around half of the pens were sampled after day 23. The total numbers of fish sampled were 455 before and 412 following treatment, respectively. Sample size per pen at each sampling point ranged from ten to 24 fish, with most groups comprising ten fish (62%). Each sample included a count of mobile Caligus elongatus and counts of L. salmonis for three lice cycle stages: chalimus, PAAM (pre-adult and adult males) and adult females. In this communication, we did not analyse data associated with C. elongatus as infestation with this species was only detected at one site before treatment and at very low levels. Counts of sea lice are routinely conducted by farmers and the results reported to authorities as mandated by Norwegian regulations . Fish are sampled from each pen with a dip pen net, anesthetized for examination and returned to the pen after recovering from anaesthesia .
Statistical analyses. Summary statistics before and after treatment
Arithmetic mean and median abundance (number of lice per fish) and prevalence (number of fish with lice) values were calculated for each stage group of L. salmonis to characterize the level of infestation at each site. For the analysis, we aggregated the count values for all pens within a site. Estimates and 95% confidence intervals were calculated using the adjusted bootstrap percentile (bias-corrected and accelerated, BCa) method  in the boot package in R [28, 29]. We generated 1,000 bootstrap samples from the original data set. ANOVA was used to identify differences in the mean counts of L. salmonis between sites after treatment. When the effect of site was significant, we used a Tukey’s Honestly Significant Difference (HSD) to determine which sites differed from each other. All statistical analyses were carried out in Version 2.15.1 of R .
Calculation of treatment effectiveness
Traditionally, treatment effectiveness is calculated as percentage reduction in PAAM or in all mobile stages (pre-adult and adult sea lice) . We calculated treatment effectiveness based on counts taken approximately two weeks after treatment (between days 10 and 20 after treatment). Pre- post treatment comparisons are widely used to detect treatment effects and reflect efficacy. In particular in sea lice trials control pens are often unavailable and any observed lice reduction following treatment can be safely attributed to the treatment which has previously been widely demonstrated to be effective to obtain marketing authorisation. We calculated 95% confidence intervals using the quasi-Poisson method as this has been previously shown to be effective for this purpose [31, 32].
The composition of L. salmonis stage groups was studied using Bray-Curtis distances in combination with non-metric multidimensional scaling (NMDS). These procedures are well suited for arthropod community analysis [33, 34] since they avoid assumptions of linear relationships and are less susceptible to bias introduced by large numbers of zero counts in the data . Bray-Curtis distances were calculated on untransformed data with the R package Vegan . Guidelines for the interpretation of NMDS plots have been provided by Dufrêne . Briefly, objects that are closer together within the NMDS plot are more similar (i.e. in terms of stage group composition) than those further apart. The stress value is used as a measure of goodness of fit between the original data (matrix of distances) and the ordered position of objects in the two dimensional space (NMDS configuration). Small stress values indicate a solution with good fit. In particular stress values below 0.1 indicate a good configuration, while values greater than 0.2 indicate a poor fit . We did not calculate correlations between community dissimilarities and ordination distances, as this can be misleading when using a non-linear method (NMDS). Ordination using principal coordinate analysis produced similar results to those obtained with NMDS (data not shown).
In addition to ANOVA, we used three non-parametric procedures to statistically examine differences in the composition of L. salmonis between sites in response to treatment. Non-parametric procedures are preferred for data with skew distributions such as parasitic infestations. These procedures included the multiple response permutation procedure (MRPP), the analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (Adonis). All these permutation procedures compared the ranks of distances between groups (farm sites) with the ranks of distances within groups. The site factor was tested in 1,000 permutations of residuals under the null hypothesis. To avoid finding falsely significant results, we performed an inferential statistical procedure similar to Levene’s test. This procedure is based on a permutation-based test of multivariate homogeneity of group dispersions (variance in the sites) . Results from inferential testing indicated that the within-group dispersion was not significantly different between sites before and after treatment (data not shown) .
The MRPP tests the relationship of entities in the multidimensional space by comparing the weighted mean of within-site distances to the within-site means from randomly assigned sites. A significant p-value (<0.05) indicates that differences detected between sites are greater than would be expected from random assignment to sites. It also provides a measure of the magnitude of differences between group means (A); computed as A = 1- (δ/mδ), where the observed delta (δ) describes the weighted mean within-site distance, and the expected delta (mδ) is computed as the mean delta for all possible partitions of the data. For example, when the composition of sea lice at pens within-sites are identical, then δ = 0 and A = 1. The value of A becomes smaller as the level of agreement is increasingly reduced from than that expected by chance. The advantage of the MRPP statistic is that it is robust to unequal variance, non-normally distributed data and unbalance designs .
Analysis of similarities (ANOSIM)  is similar in concept to MRPP but uses a different test statistic. The result is summarized in the R statistic which indicates the magnitude of difference between group means. The R statistic ranges from 0 (no separation) to 1 (high separation). R values >0.75 are indicative of high separation, R >0.5 as separated but overlapping and R <0.25 as barely separable.
The permutation multivariate analysis of variance PERMANOVA (Adonis) is a permutation-based version of the multivariate analysis of variance . Similar to the other permutation tests, it uses distances between sites to partition variance. Significance testing is carried out using F-tests derived from permutations of the raw data.
Indicator species analysis
The concept of indicator species has been previously used in the fields of marine ecology [42–44]. For this Dufrêne and Legrende  proposed a flexible and asymmetrical approach to identify indicator species. This method combines the relative abundance (specificity) with the frequency (fidelity) of species (or stage groups) at a site and finds the stage groups that are significantly concentrated at a site or group of sites. Stage groups with significant indicator species values provide some measure of the characteristic of a site and can be used to monitor changes.
A stage group may be considered characteristic of a site if it has an IndVal value greater than 25% and a p-value < 0.1, as discussed in . The significance level was increased to decrease the Type II error that is commonly found as a result of low power resulting from the permutation test with a low number of replicates (pens). The p-value for a Monte Carlo test (1,000 permutations) evaluates the statistical significance of the IndVal.
This manuscript is dedicated to the knowledge and memory of Peter Andreas Heuch for his passion for science, his zeal for understanding marine biology and his contribution to this study. This research is a part of a multi-disciplinary project (Topilouse) to improve the application of bath treatments against salmon lice. This research was founded by the Norwegian Research Council, the Fisheries and Aquaculture Research Fund, and our industry partners Marine Harvest, Salmar Farming, Pharmaq, Novartis, Rantex, Storvik Aqua and the Well Boat Owners Association. In particular, we wish to acknowledge Rune Olsen, Ritchie Gordon and Marine Harvest for providing the data used in this study.
- Bjørn PA, Finstad B, Kristoffersen R: Salmon lice infection of wild sea trout and Arctic char in marine and freshwaters: the effects of salmon farms. Aquac Res. 2001, 32: 947-962. 10.1046/j.1365-2109.2001.00627.x.View ArticleGoogle Scholar
- Bjørn PA, Finstad B: Salmon lice, Lepeophtheirus salmonis (Krøyer), infestation in sympatric populations of Arctic char, Salvelinus alpinus (L.), and sea trout, Salmo trutta (L.), in areas near and distant from salmon farms. ICES J Mar Sci. 2002, 59: 131-139. 10.1006/jmsc.2001.1143.View ArticleGoogle Scholar
- Heuch PA, Bjorn PA, Finstad B, Holst JC, Asplin L, Nilsen F: A review of the Norwegian ‘National action plan against salmon lice on salmonids’: the effect on wild salmonids. Aquaculture. 2005, 250: 535-535. 10.1016/j.aquaculture.2005.10.003.View ArticleGoogle Scholar
- Krkošek M, Ford JS, Morton A, Lele S, Myers RA, Lewis MA: Declining wild salmon populations in relation to parasites from farm salmon. Science. 2007, 318: 1772-1775. 10.1126/science.1148744.PubMedView ArticleGoogle Scholar
- Costello MJ: How sea lice from salmon farms may cause wild salmonid declines in Europe and North America and be a threat to fishes elsewhere. Proc R Soc B. 2009, 276: 3385-3394. 10.1098/rspb.2009.0771.PubMedPubMed CentralView ArticleGoogle Scholar
- Krkošek M, Revie CW, Gargan PG, Skilbrei OT, Finstad B, Todd CD: Impact of parasites on salmon recruitment in the Northeast Atlantic Ocean. Proc Biol Sci. 2013, 280: 2012-2359.Google Scholar
- Ministry of Fisheries and Coastal Affairs: Sea lice regulations for the control of sea lice in aquaculture. Guidelines for aquaculture. 2009, Oslo, Norway, (in Norwegian). [http://www.lovdata.no/cgi-wift/wiftldles?doc=/app/gratis/www/docroot/for/sf/fi/fi-20090818-1095.html&emne=luseforskrift*&]Google Scholar
- Norwegian Food Safety A: Regulatory guidelines for aquaculture. 2010, Oslo, Norway, (In Norwegian). [http://www.fom-as.no/files/Fagarkiv_rapporter/Avlusning/Lovverk/Veileder_til_FOR_200_54627a__2_.pdf]Google Scholar
- Treasurer JW, Grant A, Davis PJ: Physical constraints of bath treatments of Atlantic Salmon (Salmo salar) with a sea lice burden (Copepoda: Caligidae). Contrib Zool. 2000, 69: 1-14.Google Scholar
- Bloland PB, Ettling M: Making malaria treatment policy in the face of drug resistance. Ann Trop Med Parasitol. 1999, 93: 5-23.PubMedView ArticleGoogle Scholar
- Sevatdal S, Copley L, Wallace C, Jackson D, Horsberg TE: Monitoring of the sensitivity of sea lice (Lepeophtheirus salmonis) to pyrethroids in Norway, Ireland and Scotland using bioassays and probit modelling. Aquaculture. 2005, 244: 19-27. 10.1016/j.aquaculture.2004.11.009.View ArticleGoogle Scholar
- Clarke KR: Non-parametric multivariate analyses of changes in community structure. Aust J Ecol. 1993, 18: 117-143. 10.1111/j.1442-9993.1993.tb00438.x.View ArticleGoogle Scholar
- Ramette A: Multivariate analyses in microbial ecology. FEMS Microbiol Ecol. 2007, 62: 142-160. 10.1111/j.1574-6941.2007.00375.x.PubMedPubMed CentralView ArticleGoogle Scholar
- Hart JL, Thacker JR, Braidwood JC, Fraser NR, Matthews JE: Novel cypermethrin formulation for the control of sea lice on salmon (Salmo salar). Vet Rec. 1997, 140: 179-181. 10.1136/vr.140.7.179.PubMedView ArticleGoogle Scholar
- Willis KJ: Toxicity of the aquaculture pesticide cypermethrin to planktonic marine copepods. Aquac. Res. 2004, 35: 263-270. 10.1111/j.1365-2109.2004.01008.x.View ArticleGoogle Scholar
- Fraser NR: Effect of cypermethrin formulated as GPRD01 on chalimus stages of sea lice infecting Atlantic salmon (Salmo salar) at a sea water temperature of 7.5C. Grampian Pharmaceuticals Ltd. 1985, Lancashire, UK: Grampian, Research DivisionGoogle Scholar
- Jimenez DF, Heuch PA, Revie CW, Gettinby G: Confidence in assessing the effectiveness of bath treatments for the control of sea lice on Norwegian salmon farms. Aquaculture. 2012, 344: 58-65.View ArticleGoogle Scholar
- Timm NH: Applied multivariate analysis. New York: Springer-Verlag 2002.Google Scholar
- Revie CW, Hollinger E, Gettinby G, Lees F, Heuch PA: Clustering of parasites within cages on Scottish and Norwegian salmon farms: alternative sampling strategies illustrated using simulation. Prev Vet Med. 2007, 81: 135-147. 10.1016/j.prevetmed.2007.04.004.PubMedView ArticleGoogle Scholar
- Revie CW, Gettinby G, Treasurer JW, Wallace C: Evaluating the effect of clustering when monitoring the abundance of sea lice populations on farmed Atlantic salmon. J Fish Biol. 2005, 66: 773-783. 10.1111/j.0022-1112.2005.00642.x.View ArticleGoogle Scholar
- Heuch PA, Gettinby G, Revie CW: Counting sea lice on Atlantic salmon farms: Empirical and theoretical observations. Aquaculture. 2011, 320: 149-153. 10.1016/j.aquaculture.2011.05.002.View ArticleGoogle Scholar
- Johnson SC, Albright LJ: The developmental stages of Lepeophtheirus-salmonis (Kroyer, 1837) (Copepoda, Caligidae). Can J Zool. 1991, 69: 929-950. 10.1139/z91-138.View ArticleGoogle Scholar
- Johannessen A: Early stages of Lepeophtheirus salmonis (Copepoda, Caligidae). Sarsia. 1977, 63: 169-176.Google Scholar
- Johnson SC: A comparison of development and growth rates of Lepeophtheirus salmonis (Copepoda: Caligidae) on naive Atlantic (Salmo salar) and chinook (Oncorhynchus tshawytscha) salmon. Pathogens of wild and farmed fish: sea lice. Edited by: Boxshall GA, Defaye D. Chichester, UK: Ellis Harwood, 1993, 68-80.Google Scholar
- Braidwood JC, Jayne LC: Control of sea lice in fish. Patent 5770621. Leyland, UK: Grampian Pharmaceuticals Limited 1998.Google Scholar
- Burka JF, Hammell KL, Horsberg TE, Johnson GR, Rainnie DJ, Speare DJ: Drugs in salmonid aquaculture – a review. J vet Pharmacol Therap. 1997, 20: 333-349. 10.1046/j.1365-2885.1997.00094.x.View ArticleGoogle Scholar
- Efron B: Better bootstrap confidence intervals. J Am Statist Assoc. 1987, 82: 171-185. 10.1080/01621459.1987.10478410.View ArticleGoogle Scholar
- Davison AC, Hinkley DV: Bootstrap methods and their applications. Cambridge: Cambridge University Press 1997.View ArticleGoogle Scholar
- Ripley BD, Canty A: R package version 1.2-27. boot: Bootstrap R (S-Plus) functions. http://cran.r-project.org/web/packages/boot/index.html.
- Development Core Team R: R: A language and environment for statistical computing. R Foundation for statistical computing. Vienna, Austria: RFS computing 2011.Google Scholar
- Venables WN, Ripley BD: Profile-likelihood CI, based on the assumption of a quasipoisson distribution. Modern Applied Statistics. New York: Springer 2002.View ArticleGoogle Scholar
- Zeileis A, Kleiber C, Jackman S: Regression models for count data in R. J Stat Softw. 2008, 27: 1-25.Google Scholar
- Gower JC, Legendre P: Metric and Euclidian properties of dissimilarity coefficients. J Classif. 1986, 3: 5-48. 10.1007/BF01896809.View ArticleGoogle Scholar
- Anderson M: A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001, 26: 32-46.Google Scholar
- Beals EW: Bray-Curtis Ordination: An effective strategy for analysis of multivariate ecological data. Advances in Ecological Research. Edited by: MacFadyen A, Ford ED. London, UK: Academic Press, 1984, 1-55.Google Scholar
- Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Henry M, Stevens H, Wagner H: vegan: Community Ecology Package. R package version 2.0-8. 2013, [http://CRAN.R-project.org/package=vegan]Google Scholar
- Dufrêne M, Legendre P: Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr. 1997, 67: 345-366.Google Scholar
- Kruskal J: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika. 1964, 29: 1-27. 10.1007/BF02289565.View ArticleGoogle Scholar
- Anderson MJ, Ellingsen KE, McArdle BH: Multivariate dispersion as a measure of beta diversity. Ecol Lett. 2006, 9: 683-693. 10.1111/j.1461-0248.2006.00926.x.PubMedView ArticleGoogle Scholar
- Legendre P, Legendre L: Numerical ecology. 1998, Amsterdam: Elsevier Science BVGoogle Scholar
- McArdle BH, Anderson MJ: Fitting multivariate models to community data. A comment on distance-based redundancy analysis. Ecology. 2001, 82: 290-297. 10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2.View ArticleGoogle Scholar
- Mouillot D, Culioli JM, Do Chi T: Indicator species analysis as a test of non-random distribution of species in the context of marine protected areas. Environ Conserv. 2002, 29: 385-390.View ArticleGoogle Scholar
- Sosa-López A, Mouillot D, Do Chi T, Ramos-Miranda J: Ecological indicators based on fish biomass distribution along trophic levels: an application to the Terminos coastal lagoon, Mexico. ICES J Mar Sci. 2005, 62: 453-458. 10.1016/j.icesjms.2004.12.004.View ArticleGoogle Scholar
- Claudet J, Pelletier D, Jouvenel J-Y, Bachet F, Galzin R: Assessing the effects of marine protected area (MPA) on a reef fish assemblage in a northwestern Mediterranean marine reserve: Identifying community-based indicators. Biol Conserv. 2006, 130: 349-369. 10.1016/j.biocon.2005.12.030.View ArticleGoogle 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/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.