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RESEARCH ARTICLE

Improvement on species sensitivity distribution methodsfor deriving site-specific water quality criteria

Yeyao Wang & Lingsong Zhang & Fansheng Meng &

Yuexi Zhou & Xiaowei Jin & John P. Giesy & Fang Liu

Received: 25 May 2014 /Accepted: 27 October 2014 /Published online: 13 November 2014# Springer-Verlag Berlin Heidelberg 2014

Abstract Species sensitivity distribution (SSD) is the mostcommon method used to derive water quality criteria, butthere are still issues to be resolved. Here, issues associatedwith application of SSD methods, including species selection,plotting position, and cutoff point setting, are addressed. Apreliminary improvement to the SSD approach based on post-stratified sampling theory is proposed. In the improved meth-od, selection of species is based on biota of a specific basin,and the whole species in the specific ecosystem are consid-ered. After selecting species to be included and calculating thecumulative probability, a newmethod to set the critical thresh-old for protection of ecosystem-level structure and function isproposed. The alternative method was applied in a case studyin which a water quality criterion (WQC) was derived forammonia in the Songhua River (SHR), China.

Keywords SSD .Water quality criteria . Plotting position .

Threshold . Stratified sampling . Site-specific . Asia .

Ammonia . Nitrogen . Toxicity . Statistics

Introduction

Widespread occurrence of toxic substances caused by activi-ties of humans can adversely affect aquatic organisms(Groombridge and Jenkins 2002). Research on differences insensitivities to toxicants among species has become a focusand shared concern of environmental scientists and managers.In order to optimize the level of protection at an acceptablelevel, some countries with more developed economies havewell-established systems to estimate the maximum limitwhich can be accepted by the ecosystem (Jin et al. 2014). Ofwhich, the species sensitivity distribution (SSD) method iscurrently the most commonly used method and has beenadopted by USEPA (1985), ANZECC & ARMCANZ(2000), RIVM (2007), and CCME (2007) as the officialmethod to derive water quality criteria (WQC) for protectionof the structure and function of ecosystems.

The SSD method to derive WQC originated almost simul-taneously in Europe and in the USA (USEPA 1985; Kooijman1987). The theoretical basis of SSD is that it is possible todescribe the variability and range of sensitivities among indi-vidual taxa with a statistical or empirical distribution function(Posthuma et al. 2002). In short, the basic assumption of theSSD concept is that: (1) relative sensitivities of a set of speciescan be described by some distribution such as the triangular,normal, or logistic distribution; (2) the data on sensitivities ofindividual species to toxicants that is used to construct SSDare seen as a random sample from the entire population ofpossible sensitivities and are used to estimate descriptiveparameters of the SSD; and (3) when a certain portion ofspecies are protected, the ecosystem is also protected. Basedon these assumptions, toxicity data are ranked and then astatistical distribution fitted. Hazardous concentrations (HCs)can be estimated, which are protective of a given proportion ofthe species present within a specified community (Posthumaet al. 2002; van Straalen and Denneman 1989). Generally, the

Responsible editor: Thomas Braunbeck

Electronic supplementary material The online version of this article(doi:10.1007/s11356-014-3783-x) contains supplementary material,which is available to authorized users.

Y. Wang : L. Zhang (*) : F. Meng :Y. ZhouState Key Laboratory of Environmental Criteria and RiskAssessment, Chinese Research Academy of EnvironmentalSciences, Beijing 100012, Chinae-mail: [email protected]

Y. Wang :X. Jin : F. LiuChina National Environmental Monitoring Center, Beijing 100012,China

J. P. GiesyDepartment of Veterinary Biomedical Sciences and ToxicologyCentre, University of Saskatchewan, Saskatoon, Saskatchewan,Canada

Environ Sci Pollut Res (2015) 22:5271–5282DOI 10.1007/s11356-014-3783-x

http://dx.doi.org/10.1007/s11356-014-3783-x

SSD method has proven to be a useful approach to predict theentire communities (Schroer et al. 2004; Maltby et al. 2005),but there are still some issues relative to the application ofSSDs (Forbes and Calow 2002). These include, among others,the selection of species to be included, methods to deriveplotting positions, and what to select as the assessment end-point, such as the concentration to affect 5 % of species(HC5). There are polemics particularly around the sufficiencyof the SSD approach to protect ecosystem-level structure andfunction (Forbes and Calow 2002).

Sensitivity (tolerances) of individual species is not arandom phenomenon, and it will not change regardless ofthe methods used to describe it. Hence, it was deemeddesirable to develop a method to select species more ran-domly than has been done previously and have a greaterlikelihood of accurately describing the entire range of sen-sitivities within a community or organisms in a particularecosystem. To do this, it was necessary to develop a statis-tical method to estimate the distribution of sensitivities ofall species in an ecosystem (Forbes and Calow 2002).According to statistical theory, the actual composition ofthe sample should be randomly selected (Rice 2011), whichis also consistent with one of the basic assumptions of SSDmethods. Hence, every member of the population will beselected with uniform probability.

It has also been suggested that the data set should bestatistically and ecologically representative of the community(Forbes and Calow 2002; Wagner and Løkke 1991). It isimpossible to know the total number of species in a commu-nity and nearly impossible to know which species are thecritical species, which, if eliminated, would result in majorchanges in the structure and/or function of a community. Rarespecies are treated with the same weight as abundant species(Posthuma et al. 2002). However, by technical means and costconstraints, species for which toxicological data is availablehave generally been selected based on availability or ease ofmaintenance in culture rather than by random sampling.Otherwise, some researchers have given priority to somespecified taxa, trophic levels, and species based on theirexperience because they think the specified members arerepresentative in some respects. In some situation, this strate-gy might be effective, especially when there are relatively fewtoxicity data. It can promote the toxicity test covering differenttrophic levels and having greater taxonomic differences, butthis solution violates the random principle and cannot ensurethat there is no bias in estimates.

The dispute about methods of determining plotting positionduring probabilistic assessments has long been debated(Langbein 1960; Benson 1962; Jordaan 2005; Makkonen2006). The most commonly used methods are the Weibulland Hazen methods, which have been adopted by USEPA(1985) and ANZECC & ARMCANZ (2000), RIVM (2007)as well as CCME (2007) (Eqs. 1 and 2).

Weibull method:

p ¼ i

nþ 1ð1Þ

Hazen method:

p ¼ i−0:5n

ð2Þ

where p is the cumulative probability, i is the rank of thesample, and n is the sample size.

Theoretically, there is no difference between plots based onthese two methods when the sample size is infinitely large. Infact, data for many toxicants is lacking, especially for speciesendemic to developing countries. Thus, there will be a signif-icant difference between them which will make the results ofcalculation different. It is difficult to explain which is morereasonable for the developer and stakeholder.

In order to eliminate effects of the “tail” of the SSD and geta more exact criterion value, Van Straalen and Denneman(1989) have introduced the concept of a “cutoff point” p intothe calculation of criteria. According to their concept, thechoice of a cutoff point can be chosen by the manager, andthe corresponding concentration could be calculated which iscalled the HCp. Consequently, this method has become theofficial method to derive environmental quality criteria usedby some countries (VROM 1989; ANZECC & ARMCANZ2000; CCME 2007). However, in practical application, themost commonly used cutoff point value is the 5th centile,which indicates the concentration less than which fewer than5 % of species would be affected. Until now, there were noclear reasons why a value of 5 % should be chosen. Thepractice was, to a large extent, arbitrary (Okkerman et al.1993; Versteeg et al. 1999). In this case, theoretically, 5 %species would be affected. In fact, the choice of the HC5seems to have followed the convention of statistics in whicha type I error (α) of 5 % is accepted (Posthuma et al. 2002).Otherwise, with toxicity testing of more and more species,more and more toxicity data are used in the criteria calcula-tion. Thus, the calculation process becomes an interpolationfrom extrapolation, and the most sensitive species with sensi-tivities less than the HC5 would be expected to be affected.USEPA’s newest WQC for ammonia (USEPA 2013) is anexcellent example of this situation. Compared with the 1999WQC document (USEPA 1999), the more recently availabletoxicity data were used to calculate the WQC, and the acuteand chronic criteria value decreased from 24 to 17 and from4.5 to 1.9 mg total ammonia nitrogen (TAN)/L, respectively.The 1999 WQC was based primarily on effects on early lifestages of fishes, whereas the 2013WQC is based on effects onmore sensitive invertebrate genera, including unionid mussels,of which, the most sensitive species are Lasmigona subviridis

5272 Environ Sci Pollut Res (2015) 22:5271–5282

and Venustaconcha ellipsiformis (SMAV=23.41 and23.12 mg TAN/L respectively). Development of WQC wasbased on an implicit assumption that 5 % of species could beallowed to be adversely affected and still maintain integrity ofan ecosystem. In fact, the 5th centile was chosen becausewhen the distribution of sensitivities in single species testsunder laboratory conditions, where individuals were exposedto the maximum and continuous concentration, it was equiv-alent to the threshold concentrations less than which no ad-verse effects were observed in multi-species tests(mesocosms) (Giesy et al. 1999). The aim of this study wasto develop an improved solution based on sample theory andto improve upon the traditional SSD method. The improvedsolution is expected to be more reasonable and convincingthat it is protective of structures and functions of ecosystems.

Improvement on traditional SSD method

Species selection and SSD curve construction

The relationship between sensitivity and/or tolerance of aspecies to a toxicant and its natural history such as feedingguild, morphology, and physiological traits is a concern ofmany eco-toxicologists (Forbes and Calow 2002).Researchers have tried to describe sensitivities of genericspecies with specific sets of characteristics so that they couldpredict sensitivities of species for which no information onsensitivity to a particular toxicant existed (Slooff 1983; Vaalet al. 1997; Zhang et al. 2010; Wang et al. 2014; Zhang et al.2014). These researchers systematically reported variabilityamong sensitivities of species to toxicants, and Baird and Vanden Brink (2007) proposed a method to predict the sensitivityof a species to specific toxicants by use of their unique andsimilar traits. It has been determined that the method offerssome promise as a mechanistic alternative to the otherwiseempirical approach to selection of species included in an SSD.Species that feed on similar foods and have similar physiol-ogies will possibly have similar exposures and responses totoxicants. In the classification system known as “biotaxy,”aquatic organisms are divided into different taxa accordingto biological variances and phylogenetic relationships.

In statistics, the target population should be defined beforea sampling process is begun. Thus, in assessing the potentialeffects of contaminants at the community level of organiza-tion, all aquatic species in a specific aquatic ecosystem weredefined as the target population, but in practice, it is difficultand unreasonable to establish a global aquatic ecosystem scaleWQC. Thus, aquatic ecosystems are usually divided intodifferent subsystems for management convenience and “ba-sin” is the mostly used scale because of significant differencein biota. Thus, establishment of a basin scale WQC is neces-sary because of the difference of species to be protected. In

this assessment, the basin was used as the appropriate scale toderive a WQC, and all aquatic species in a specific basin canbe identified from a combination of reviews of the literatureand surveys in the field and used to develop the target popu-lation to be protected and/or for sampling.

Based on the discussion above, an assumption was madethat the species in the same taxonmight have relatively similarsensitivities to specific toxicants. Thus, taxa can be used asstratification variables. Toxicity can be defined as the randomselection and result from corresponding strata rather than fromall the species. Based on this assumption, all the screenedtoxicity-tested species could be sorted by use of biotaxy intodifferent strata. Thus, the post-stratified sample method(Daniel 2011) has been advanced for calculating the cumula-tive probability in which a weighting coefficient could be usedin order to prevent an overrepresentation (bias) of some strata(taxonomic groups). The mechanism of the improvement canbe represented by a function.

When the species number in a specific basin is N, it can bedivided into lmutually exclusive, homogeneous strata accord-ing to biotaxy, and the species number in each stratum is Ni(i=1,2,…,l). After retrieving and screening, the number ofscreened toxicity-tested species is n, and it also can be sortedinto different strata, and the sample number of each stratum isni (i=1,2,…,l). Thus, the sampling fraction of each stratumcan be expressed (Eq. 3).

f i ¼ niNi

i ¼ 1; 2;…; lð Þ ð3Þ

Equation (4) was used to present the sample set.

X ¼X 1

X 2

⋅⋅⋅X l

8>><>>:

9>>=>>; ¼

s11 s12 … s1n1s21 s22 … s2n2… … … …sl1 sl2 … slnl

8>><>>:

9>>=>>; ð4Þ

in which X stands for the sample set of target population andXi(i=1,2,⋅⋅⋅,l) stands for the sample set of each stratum (Ni).Xi(i=1,2,⋅⋅⋅,l)∈X and sini i ¼ 1; 2; ⋅⋅⋅; lð Þ stand for the sample.

Under ideal conditions, the sampling fraction of each stra-tum would be the same, but in actuality, it is difficult to obtainresults of toxicity tests for species in all strata, especiallychronic toxicity (Christensen et al. 2003; Jager et al. 2007;Wu et al. 2013). This limitation results in the number ofelements (screened toxicity test species) sorted into each stra-tum disproportionally to their representation in the communityand even missing in some strata. Thus, here, discussions ofcalculation of cumulative probability in three situations,which are selected to reduce bias, are presented.

Environ Sci Pollut Res (2015) 22:5271–5282 5273

(1) Under ideal conditions, the screened toxicity test speciescover all strata and the sampling fraction of each stratumis the same.

Toxicity values are ranked and assigned a sequence(Eq. 5).

Ri ¼ 1; 2…;Xi¼1

l

ni

!ð5Þ

Thus, cumulative probability can be described (Eq. 6).

Pi ¼ RiXni; i ¼ 1; 2;…;

Xni ð6Þ

(2) When the screened toxicity test species cover all strata,the sampling fraction of each stratum is different. Then,the mean value of each stratum was calculated (Eq. 7),which represents

X i− ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

si1 � si2 �…� sinlnlp

; j ¼ ni; l ¼ 1; 2;…; l;ð Þð7Þ

the mean sensitivity of each stratum respectively. Then,toxicity values are ranked and assigned a sequenceRi={1,2,…l}, and a weighted process is introduced intothe cumulative probability calculation (Eq. 8).

Pi ¼ Ri

l�

Xi¼1

l

N i

Nð8Þ

(3) When the screened toxicity test species does not cover allstrata, the samples can be represented (Eq. 9).

X ¼

X 1

X 2…X k

X kþ1ð Þ…X l

8>>>>><>>>>>:

9>>>>>=>>>>>;

¼

s11 s12 … s1is21 s22 … s2 j…sl1∅…

…sl2

……

…slm

∅

8>>>>>><>>>>>>:

9>>>>>>=>>>>>>;ð9Þ

in which Xi(i=1,2, ⋅ ⋅⋅,k) stands for the sample of in-volved strata respectively, and Xi(i=k+1,k+2,⋅⋅⋅,l) arean empty set.

Thus, sensitivities of strata not included cannot beestimated, and this becomes an uncertainty factor forcumulative probability calculation. Under this condition,a suggestion to adopt a “conservative method” was

followed assuming that the not included strata containedmore sensitive taxa.

When the number of species in the strata not includedare Nj(j=k+1,k+2,⋅⋅⋅,l), respectively; the total numberof species in the strata not considered can be represented(Eq. 10).

Xj¼kþ1

l

N j ð10Þ

The geometric mean value of each stratum (Eq. 7)represents the sensitivity of the corresponding stratum.Toxicity values are then ranked and assigned a sequencenumber Ri(i=1,2, ⋅ ⋅⋅,k), and weighted values are thenplotted as the cumulative probability (Eq. 11).

Pi ¼ Ri

k þ 1�

Xi¼1

k

N i þXj¼kþ1

l

N j

!

Nð11Þ

Threshold value for use in regulatory decisions

The threshold of 5 % is somewhat arbitrary and not supportedby any actual detailed analyses other than the fact that it isoften equivalent to the NOAEL frommulti-species tests wheredata is available, generally for pesticides (Giesy et al. 1999).Thus, there is no guarantee that this level of protection, basedon the results of toxicity tests on individual species that areconducted under laboratory conditions, would protect func-tion of a community. The importance of biodiversity forfunctions of ecosystems has been demonstrated, and loss ofbiodiversity can impair capacities of communities and ecosys-tems to provide the “ecosystem services” such as providingfood, process organic matter, including contaminants, andrecover from perturbations (Hooper et al. 2005; France andDuffy 2006; Tilman et al. 2006; Worm et al. 2006).Ecosystems, while made up of both the physical environmentand a range of individual species, have transcendent, emergentproperties that are greater than the sum of their parts (Giesyand Odum 1980). Thus, it is difficult to predict what effectseliminating one of more species would have on the overallfunctions of ecosystems. Therefore, theoretically, from thepoint of view of conservation of biodiversity, all speciesshould be protected. At least, criteria should be less than thethreshold for effects on the most sensitive species. Generally,the number of species in a specific basin is knowable throughhistoric data retrieving and even field investigation. In thisassessment, the number of species in a specific basin (N)therefore has a proportion of each taxon of 1

N . Thus, the

threshold for effects should be 1N or some value less than 1

N

rather than 5%. Thus, theoretically, all species in the basin can


be considered in setting the threshold for effects or the pro-tectiveWQC. One limitation of using a probabilistic approachis that there is no 0 or 100 % on the probabilistic scale. That isthere is no concentration that is less than the value that couldadversely affect a theoretical species. Similarly, there is noconcentration less than which no species would be affected.Similarly, there is no concentration that is 100 % safe. Thislimitation leads to a semantic issue between assessors of riskand managers. This is particularly true for communicationwith the lay public that wants a completely safe environmentwith no risk of adverse effects on people or wildlife. For thisreason, the concept of resolution becomes paramount. Forinstance, if N was 100, then theoretically, each species wouldrepresent 1/100 or 1 % of the total number of species and theresolution of the assessment would be 1 %. That is, if thethreshold for effect was set to the concentration equivalent tothe effect concentration for the most sensitive species, therewould be a 1 % chance that a species would be affected, but itis unknown what the probability for affecting the function ofthe ecosystem would be.

Technique flow of the improved SSD methods for derivingWQC

The flow chart of the steps used to calculate the improved SSDfor use in deriving WQC is given (Fig. 1). The first step is toretrieve the number of species in a specific basin and collectall of the toxicity data for all of the relevant species. If there aresome unknown species in the basin, there is no basis forderiving WQC to protect the missing species because boththe traditional SSD method and the improved method need toselect some of the species onwhich to conduct toxicity tests,so

to some extent, the number of species is restricted to all theknown species in the basin. Different biological classificationsshould consider the range in sensitivities among species. Inthis way, the total universe of species to consider might bereduced. That is, some species with certain physiologicalcharacteristics might be “exempted” from consideration. Forexample, toxicity of ammonia to aquatic plants can usually beignored, so only the aquatic animal was considered in derivingammoniaWQC to aquatic life (USEPA 1999), but, due to theirsensitivities, when the SSD approach is used to derive a WQCfor a metal, aquatic plants and microorganisms should beconsidered. Another issue when describing the species (N) inan environment is that some species might have disappearedfrom the basin because of environmental pollution or otheractivities of humans. In order to achieve restoration of com-munities of aquatic organisms, all the species that couldtheoretically occur should be considered even if they disap-peared from the water system in recent years.

The steps in the proposed analysis include the following:(1) determining the number of species (N) in a receiving waterto be protected, including possible indirect effects such aseffects on food items; (2) determining for species-identifiedtoxicity data that exists or can be derived; (3) selecting anappropriate model to calculate cumulative probability; (4)constructing the plot position of toxicity data versus accumu-lating probability and fit the SSD curve; and (5) using 1/N asthe cutoff point to obtain the WQC.

Until now, identification of all of the species in an ecosys-tem has been impossible, especially for the smaller organisms.However, now, the use of ecosystem-wide genomics offers thepotential to do exactly that. The authors are currently devel-oping and applying a combination of genomic and informaticsthat will allow for the identification and enumeration of all thespecies in a particular ecosystem.

Case study of ammonia for the Songhua River, China

Biota of the Songhua River

The Songhua River (SHR) is in the Northeast China and flows1434 km from the Changbai Mountains through Jilin andHeilongjiang provinces. The river drains 557,000 mi2

(1,440,000 km2) of land and has an annual discharge of2460 m3/s (87,000 Cu ft/s). Aquatic life is abundant in thislarge river, and it supplies a number of ecosystem services, inparticular food products for consumption by humans.Therefore, it has been decided, for social and economic rea-sons, that it is important to produce these valued assessmentendpoints.

Recently, because of environmental pollution, overfishing,and effects on habitat, such as erosion and the associated

Fig. 1 Flow chart for development of improved SSD for use in derivingWQC


Table 1 Known aquatic life list native to the Songhua River

Phylum Number Class Number Order Number Family Number

Chordata 115 Actinopterygii 103 Gasterosteiformes 2 Gasterosteidae 2

Esociformes 1 Esocidae 1

Salmoniformes 17 Salmonidae 11

Osmeridae 3

Thymallidae 2

Salangidae 1

Cypriniformes 66 Cyprinidae 60

Cobitidae 6

Perciformes 7 Belontiidae 1

Channidae 1

Serranidae 1

Percidae 1

Gobiidae 1

Eleotridae 2

Siluriformes 6 Siluridae 2

Bagridae 4

Gadiformes 1 Lotidae 1

Scorpaeniformes 1 Cottidae 1

Acipenseriformes 2 Acipenseridae 2

Cephalaspidemorphi 3 Petromyzontiformes 3 Petromyzontidae 3

Amphibia 9 Caudata 3 Hynobiidae 3

Anura 6 Bufonidae 1

Microhylidae 1

Discoglossidae 1

Ranidae 2

Hylidae 1

Arthropoda 243 Malacostraca 6 Decapoda 6 Cambaridae 1

Atyoidae 1

Palaemonidae 4

Maxillopoda 36 Cyclopoida 20 Cyclopidae 20

Harpacticoida 5 Canthocamptidae 5

Calanoida 11 Diaptomidae 8

Temoridae 2

Centropagidae 1

Branchiopoda 94 Cladocera 94 Leptodoridae 1

Macrothricidae 6

Polyphemidae 1

Moinidae 4

Chydoridae 28

Sididae 6

Bosminidae 4

Daphnidae 44

Insecta 107 Diptera 17 Culicidae 17

Odonata 49 Comphidae 11

Reronareyidae 8

Libellulidae 21

Agriidae 2

Corduliidate 7

Trichoptera 28 Polycentropidae 3


siltation and turbidity, the numbers of individuals of somevalued species have decreased, and some have been extir-pated from the SHR and adjacent water bodies. In order torestore the valued ecological services of the SHR, all of thevalues species both currently present and those that werepresent historically should be considered. All availableliterature since 1960 was collected, and a list of aquaticorganisms was compiled. There were 423 species identifiedto occur in the SHR. These belonged to 4 phyla, whichincluded 115 chordates, 248 arthropods, 26 Molluska and34 Annelida (Table 1). Relative proportions of species indifferent taxa are significantly different; Arthropoda,Chordata, Annelida, and Mollusca were determined to be58.6, 27.2, 8.0, and 6.2 %, respectively. According to theassumption in this study, if each species was given the sameweight in cumulative probability calculation, there may bea significant bias.

Toxicity dada retrieving and screening

Toxicity data were retrieved from the ECOTOX database andother literature. For better comparison and consistency, toxicitydata were further selected based on the following criteria: (1)Toxicity test should follow ASTM or other certificated stan-dards; (2) same test endpoints, LC50, should be used; (3) thetoxicity data used in the paper were standardized to pH=8 and25 °C using the method in reference (USEPA 1999); and (4) ifmore than one toxicity study was available for the same specieswith different endpoints, theminimumvaluewas used. If severaltoxicity tests were available for the same species and endpoint,the geometric mean of these values was used (Table 2).

After retrieving and screening, there were nine speciesnative to the SHR for which data on acute toxicity of ammoniawas available, and three species native to the SHR for whichdata on chronic toxicity of ammonia was available, so only the

Table 1 (continued)

Phylum Number Class Number Order Number Family Number

Hydropsychidae 6

Rhyacophilidae 5

Molannidae 1

Leptoceridae 2

Limnephilidae 1

Phryganeidae 10

Ephemeroptera 13 Baetidae 2

Ephemerellidae 11

Mollusca 26 Gastropoda 14 Mesogastropoda 5 Viviparidae 3

Bithyniidae 1

Melaniidae 1

Sorbeoconcha 1 Pleuroceridae 1

Pulmonata 8 Lymnaeidae 7

Planorbidae 1

Bivalvia 12 Veneroida 1 Corbiculidae 1

Eulamellibranchia 11 Margaritanidae 3

Unionodae 7

Sphaeriidae 1

Annelida 23 Clitellata 2 Tubificida 2 Tubificidae 2

Hirudinea 21 Rhynchobdellida 11 Glossiphoniidae 11

Arhynchobdellida 7 Haemopidae 2

Salifidae 1

Erpobdellidae 3

Hirudinidae 1

Branchiobdellida 3 Branchiobdellidae 3

Rotifera 16 Rotifera 16 Monogononta 16 Brachionida 10

Asplancchnidae 1

Trichocercidae 3

Synchaetidae 2

Total 423


Table 2 Compiled, screened acute toxicity data of ammonia to aquatic life (pH=8 and 25 °C)

Phylum Class Order Family (n) FMAV(mg TAN/L)

Species SMAV(mg TAN N/L)

Vertebrata Actinopterygii Acipenseriformes Acipenseridae(2)

19.48 Acipenser sinensis 10.4

Acipenser brevirostrum 36.49

Salmoniformes Salmonidae(11)

23.9 Salmo trutta 23.75

Salmo.salar 42.66

Oncorhynchus gorbuschaa 42.07

Oncorhynchus mykissa 19.3

Oncorhynchus kisutch 20.27

Oncorhynchus aguabonita 26.1

Oncorhynchus clarki 18.37

Oncorhynchus tshawytscha 19.18

Prosopium williamsoni 12.09

Salvelinus fontinalis 36.39

Salvelinus namaycush 37.1

Cypriniformes Cyprinidae(9)

25.69 Notemigonus crysoleucas 14.67

Cyprinus carpioa 24.74

Hybognathus amarus 16.9

Cyprinella whipplei 18.83

Cyprinella spiloptera 19.51

Cyprinella lutrensis 45.65

Campostoma anomalum 26.97

Pimephales promelas 37.07

Gobiocypris rarus 47.07

Perciformes Percidae(3)

21.81 Sander vitreus 27.52

Etheostoma spectabile 17.97

Etheostoma nigrum 16.64

Siluriformes Cottidae(2)

41.4 Ictalurus punctatus 33.14

Cottus bairdi 51.72

Gasterosteiformes Gasterosteidae(1)

65.53 Gasterosteus aculeatusa 65.53

Amphibia Anura Ranidae (1) 22.43 Rana pipiens 22.43

Hylidae (2) 16.66 Pacific regilla 19.49

Pacific crucifer 14.24

Arthropoda Malacostraca Decapoda Cambaridae(4)

50.22 Procambarus clarkii 21.23

Pacifastacus leniusculus 56.49

Orconectes nais 46.73

Orconectes immunis 328.3

Branchiopoda Cladocera Daphnidae(5)

21.07 Daphnia pulicaria 15.23

Daphnia magnaa 24.25

Simocephalus vetulusa 21.98

Ceriodaphnia dubia 20.64

Ceriodaphnia acanthina 23.73

Chydoridae (1) 25.01 Chydorus sphaericusa 25.01

Ephemeroptera Baetidae (2) 37.92 Callibaetis sp. 25.64

Callibaetis skokinus 56.09

Ephemerellidae(1)

68.05 Dorycera grandis 68.05

Trichoptera Limnephilidae(1)

153 Philarctus guaeris 153

Mollusca Lamellibranchia Eulamellibranchia Unionidae (12) 7.4 Lasmigona subviridus 3.54


acute criteria of ammonia was studied in this paper. Thecumulative probability was calculated according to Eq. 11due to not all strata (families) were represented. Other re-searchers have discussed utilization of non-endemic speciesto derive WQC (Maltby et al. 2005; Davies et al. 1994; Dyeret al. 1997; Hose and Van den Brink 2004; Jin et al. 2011), butcurrently, due to the paucity of toxicity data, there is no clearconclusion about the accuracy of this approach. In the assess-ment, the results of which are presented here; toxicity data fornon-endemic species were used to calculate mean toxicityvalues (MTV) (Eq. 7). When no toxicity information wasavailable for endemic species, based on the assumptions givenabove (Species selection and SSD curve construction). Forexample, there are seven species of Unionidae that occurredin the SHR, but there were no toxicity data for any of thesespecies of clam, so the mean toxicity value of 12 non-endemicspecies in the family Unionidae were used (Table 3).

Construction of the SSD and Derivation of the HCp

In the classical taxonomic classification system, species areclassified into seven taxonomic categories which includekingdom, phylum, class, order, family, genus, and species.The endemic species of the SHR were classified into 4 phyla,11 classes, 34 orders, and 91 families (Table 1). According tostratification sampling theory, within-stratum differencesshould be minimized, and between-strata differences shouldbe maximized. After comparison, the within-stratum differ-ences were great when the stratum was divided into phylum,

class, or order, and there would have been too many strata ifdivided by genus, so the stratum was divided by family andfamily mean toxicity values (FMTV) which was calculated as

Table 2 (continued)

Phylum Class Order Family (n) FMAV(mg TAN/L)

Species SMAV(mg TAN N/L)

Villosa iris 5.04

Lampsilis abrupta 2.19

Lampsilis siliquoidea 5.65

Lampsilis fasciola 6.21

Lampsilis higginsii 6.25

Lampsilis cardium 7.69

Lampsilis rafinesqueana 11.65

Epioblasma capsaeformis 6.04

Utterbackia imbecillis 7.16

Actinonaias pectorosa 12.22

Pyganodon grandis 21.76

Corbiculidae(1)

6.02 Corbicula flumineaa 6.02

Pulmonata Lymnaeidae(1)

13.63 Lymnaea stagnalisa 13.63

Planorbidae (1) 32.54 Helisoma trivolvis 32.54

Annelida Oligochaeta Haplotaxida Tubidicidae (2) 29.52 Limnodrilus hoffmeisteri 26.17

Tubifex tubifex 33.3

a Screened native species to SHR

Table 3 Results of ranked FMAVand cumulative probability

Ri Taxa (family) Ni ∑Ni FMAV(mg TAN/L)

Pi

1 Untested family 238 238 –

2 Corbiculidae 1 239 6.02 0.0565

3 Unionodae 7 246 7.4 0.0872

4 Lymnaeidae 7 253 13.63 0.1196

5 Hylidae 1 254 16.66 0.1501

6 Acipenseridae 2 256 19.48 0.1816

7 Daphnidae 44 300 21.07 0.2482

8 Percidae 1 301 21.81 0.2846

9 Ranidae 2 303 22.43 0.3223

10 Salmonidae 11 314 23.9 0.3712

11 Chydoridae 28 342 25.01 0.4447

12 Cyprinidae 60 402 25.69 0.5702

13 Tubificidae 2 404 29.52 0.6208

14 Planorbidae 1 405 32.54 0.6702

15 Baetidae 2 407 37.92 0.7216

16 Cottidae 1 408 41.4 0.7716

17 Cambaridae 1 409 50.22 0.8219

18 Gasterosteidae 2 411 65.53 0.8745

19 Ephemerellidae 11 422 68.05 0.9478

20 Limnephilidae 1 423 153 1.0000


the geometric mean of the SMAVs available for the familywere ranked, then the cumulative probability (Species selec-tion and SSD curve construction) (Table 3). The data werethen fit to the logistic cumulative distribution function by useof standard regression techniques for the cumulative distribu-tion function of the logistic distribution (Eq. 12; Table 3 andFig. 2).

F ¼ a

1þ xx0

� �b ð12Þ

where F is the proportion of species affected, x is ln(concentration) of FMAV (mg/L), and a, b, x0 are parametersto be determined.

There were differences among the methods used to fit thecumulative probability distribution (Table 4). The results of thefirst two methods resulted in WQC that were greater than thetoxicity value of the most sensitive species which, in this case,was 6.02 mg TAN/L. Thus, in some extent, we can say that itwould not provide comprehensive protection to the most knownsensitive species. When the “improved” method was applied,the result obtained was 5.09 mg TAN/L. The result was adoptedas it provides more comprehensive protection to aquatic life.

The USEPA method uses genus mean toxicity values(GMTVs) instead of species mean toxicity values (SMTVs)to calculate HC5 which aim to reduce the bias introduced by

excessive species in some taxon, but the minimum toxicityvalue may be covered up by the average processing; thus, thefinal HC5 may be higher than the minimum toxicity valueespecially when the number of GMTVs is more than 19, andthe same problem also exists in the traditional SSDmethod. Inorder to overcome the problem, a safety factor may be intro-duced, but the safety factor was chosen by expertise of thecriteria developer, and it was difficult to explain the relation-ship between SSD curve and ecosystem.

In 2013, the US EPA revised its WQC for ammonia toreplace the value previously recommended in 1999. The acuteand chronic criteria value decreased from 24 to 17 and from4.5 to 1.9 mg TAN/L (pH=8, T&=20°C), respectively(USEPA 2013; USEPA 1999). People believe the 1999 criteriawhich, based on salmonid fish and bluegill sunfish early lifestage toxicity information, can provide comprehensive protec-tion until some more sensitive species, including unionidmussels and gill-breathing snails, were founded recently. Itwas a good example to explain the importance of speciesselection which used to derive HC5.

Until now, species toxicity data are very few relative to thetotal number (N) of species in specific ecosystem; perhaps,there are still some more sensitive species that are unclear tous, so the result of statistical extrapolation cannot avoid beingquestioned or criticized when we cannot guarantee that spe-cies selection was random. Therefore, the weighted process-ing in this study based on taxa in a basin may avoid thissituation. The only difficulty is probably there are still someunknown species in a basin which are not involved in calcu-lation. However, from another angle, neither the traditionalmethods nor the improved method can provided accurate andreliable protection to the species that we do not know that existat all. The only thing that we can do is use a conservativeestimation method. The results of the analyses suggest that themethod is feasible and that it delivers output which is (thus)related via input data choice to the ecosystems of interest.

Conclusions

The distribution of relative sensitivities of aquatic organismsis an objective natural law, which is fundamental and will not

Table 4 Comparisons of results of various methods to fit toxicological data into the SSD

Method Distribution model Cutoff point Calculated result(mg TAN/L)

95 % Toxicity data used

USEPA method Triangular 0.05 7.54 – Screened native species

Traditional SSD method Log-logistic 0.05 7.16 Screened native species

0.05 7.64 All screened species

Improved method logistic 0.0024 5.09 3.18∼6.61 All screened species

Fig. 2 Species sensitivity distribution of acute toxicity data for ammonia


change with additional knowledge. Currently, due to factorssuch as costs, including time, and a lack of methods to cultureand exposure of some endemic species, especially those thatare threatened or endangered, they cannot be collected fromthe wild for use in toxicity testing. Therefore, it will always bedifficult to have complete knowledge of the range of sensitiv-ities of organisms and, thus, not possible to have completelyaccurate predictions of thresholds that will protect all species.However, based on sampling theory, it should be possible toidentify a threshold that is likely to be protected of all of theidentified species of concern. Also, sampling method, samplesize, and data analysis are important for the derivation of theSSD and for conclusions based on them. Here, a method wasintroduced that allows construction of a SSD curve that canmore accurately represent site-specific distributions of sensi-tivities of aquatic organisms and better protect structure andfunctions of ecosystems. In principle, this method has a self-adjustment feature. The cumulative probability was calculatedbased on site-specific biota which can avoid the bias ofoverrepresentation. Furthermore, the threshold was set byconsidering all of the species in the specific ecosystem toprovide better ecosystem protection. The FMAV for non-endemic species were also used in WQC that is derived as asupplement when data for all endemic species were not avail-able. While more effort is needed to increase the power andprecision of this approach, it can provide more representativesite-specific WQC.

Acknowledgments This research was financially supported by theNational Science and Technology Major Project (2014ZX07502-002),National Natural Science Foundation of China (21307165), and SpecialFund for Environmental Scientific Research in the Public Interest(201309008). Prof. Giesy was supported by the program of 2012 “HighLevel Foreign Experts” (#GDW20123200120) funded by the State Ad-ministration of Foreign Experts Affairs, the P.R. of China to NanjingUniversity, and the Einstein Professor Program of the Chinese Academyof Sciences. He was also supported by the Canada Research Chairprogram, a Visiting Distinguished Professorship in the Department ofBiology and Chemistry, and State Key Laboratory in Marine Pollution,City University of Hong Kong.

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Table 1 Acute toxicity of ammonia to aquatic life used in the paper

Phylum Class Order Family Species

SMAV

(mg TAN/L at

pH=8 and 25°C)

LC50

(mg TAN/L at

pH=8 and 25°C)

Reference

Mollusca Lamellibranchia

Pulmonata Planorbidae Helisoma trivolvis 32.54

30.87 Arthur et al. 1987


Lymnaeidae Lymnaea stagnalis 13.63 13.63 Williams et al. 1986

Eulamellibranchia Unionidae

Actinonaias pectorosa 12.22 11.66 Keller 2000

12.81 Keller 2000

Epioblasma capsaeformis 6.037 6.712 Ingersoll 2004

5.43 Wang et al. 2007

Lampsilis abrupta 2.191 2.191 Wang et al. 2007

Lampsilis cardium 7.689 7.689 Newton and Bartsch 2007

Lampsilis fasciola 6.207

8.714 Ingersoll 2004

3.893 Mummert et al. 2003

7.049 Wang et al. 2007

Lampsilis higginsii 6.249 5.692 Newton and Bartsch 2007

6.86 Newton and Bartsch 2007

Lampsilis rafinesqueana 11.65 12.95 Ingersoll 2004

10.48 Wang et al. 2007

Lampsilis siliquoidea 5.646 8.789 Wang et al. 2008

Lasmigona subviridus 3.539 3.36 Black 2001

Pyganodon grandis 21.76 25.13 Scheller 1997

Utterbackia imbecillis 7.164 9.104 Wade et al. 1992

7.134 Black 2001

8.003 Black 2001

15.46 Black 2001

2.755 Black 2001

6.287 Keller 2000

6.422 Keller 2000

7.76 Keller 2000

Villosa iris 5.036

2.343 Mummert et al. 2003

6.002 Wang et al. 2007

3.533 Ingersoll 2004

7.07 Scheller 1997

7.81 Scheller 1997

2.858 Wang et al. 2007

10.48 Wang et al. 2007

Planorbidae Musculium transversum 13.74




Corbiculidae Corbicula fluminea 6.018 9.996 Belanger et al. 1991

3.623 Belanger et al. 1991

Vertebrata Actinopterygii Gasterosteiformes Gasterosteidae Gasterosteus aculeatus 65.53

50.4 Hazel et al. 1971

155.4 Hazel et al. 1971

61.46 Hazel et al. 1971

60.78 Hazel et al. 1971

33.25 Hazel et al. 1971

48.76 Hazel et al. 1971

109.4 Hazel et al. 1971

Salmoniformes Salmonidae

Oncorhynchus aguabonita 26.1 26.1 Thurston and Russo 1981

Oncorhynchus clarki 18.37

21.76 Thurston et al 1978




10.07 Thurston et al 1981a




Oncorhynchus gorbuscha 42.07 38.33 Rice and Bailey 1980

46.18 Rice and Bailey 1980

Oncorhynchus kisutch 20.27

14.02 Buckley 1978

19.1 Robinson-Wilson and Seim 1975





22 Robinson-Wilson and Seim 1975


Oncorhynchus mykiss 19.3






31.97 Broderius and Smith Jr. 1979

14.99 Calamari et al. 1977

25.17 DeGraeve et al. 1980

31.76 Reinbold and Pescitelli 1982a






12.57 Thurston and Russo 1983































11.01 Thurston et al. 1981a









20.35 Thurston et al. 1981b





27.18 Thurston et al. 1981c






49.5 Wicks and Randall 2002

7.347 Wicks et al. 2002

46.97 Wicks et al. 2002

Oncorhynchus tshawytscha 19.18

25.98 Servizi and Gordon 1990

14.5 Thurston and Meyn 1984



Prosopium williamsoni 12.09




Salmo salar 42.66

20.45 Knoph 1992

22.27 Knoph 1992

45.42 Knoph 1992

52.12 Knoph 1992

61.56 Knoph 1992

75.67 Knoph 1992

88.86 Knoph 1992

89.79 Knoph 1992

28.95 Knoph 1992

36.24 Knoph 1992

38.98 Knoph 1992

41.97 Knoph 1992

62.1 Knoph 1992

69.49 Knoph 1992

54.8 Knoph 1992

57.41 Knoph 1992

23.67 Soderberg and Meade 1992




Salmo trutta 23.75




Salvelinus fontinalis 36.39 34.86 Thurston and Meyn 1984

38 Thurston and Meyn 1984

Salvelinus namaycush 37.1





Cypriniformes Cyprinidae Campostoma anomalum 26.97 26.97 Swigert and Spacie 1983

Cyprinella lutrensis 45.65 43.43 Hazel et al. 1979

47.99 Hazel et al. 1979

Cyprinella spiloptera 19.51

16.85 Rosage et al. 1979

21.67 Rosage et al. 1979

20.34 Swigert and Spacie 1983

Cyprinella whipplei 18.83 18.83 Swigert and Spacie 1983

Cyprinus carpio 24.74

31.18 Hasan and MacIntosh 1986

29.48 Hasan and MacIntosh 1986

16.48 Rao et al. 1975

Hybognathus amarus 16.9 16.9 Buhl 2002

Notemigonus crysoleucas 14.67 14.67 Swigert and Spacie 1983

Pimephales promelas 37.07














32.86 Mayes et al. 1986

23.97 Nimmo et al. 1989

10.74 Nimmo et al. 1989

12.96 Nimmo et al. 1989

22.23 Nimmo et al. 1989

30.1 Nimmo et al. 1989

16.96 Nimmo et al. 1989

24.12 Nimmo et al. 1989

25.93 Nimmo et al. 1989

18.77 Nimmo et al. 1989





36.67 Sparks 1975









43.55 Thurston et al. 1983



Catostomidae

Campostoma anomalum 26.97 26.97 Arthur et al. 1987

Catostomus commersoni 36.68




21.61 Nimmo et al. 1989

13.1 Nimmo et al. 1989

41.11 Reinbold and Pescitelli 1982b

38.73 Reinbold and Pescitelli 1982b


Catostomus platyrhynchus 31.7




Chasmistes brevirostris 16.15 11.42 Saiki et al. 1999

22.85 Saiki et al. 1999

Deltistes luxatus 13.19 16.81 Saiki et al. 1999

10.35 Saiki et al. 1999

Perciformes Percidae

Etheostoma nigrum 16.64

23.97 Nimmo et al. 1989

24.61 Nimmo et al. 1989

10.18 Nimmo et al. 1989

13.87 Nimmo et al. 1989

16.28 Nimmo et al. 1989

15.63 Nimmo et al. 1989

Etheostoma spectabile 17.97 19.49 Hazel et al. 1979

16.56 Hazel et al. 1979

Sander vitreus 27.25 20.29 Reinbold and Pescitelli 1982c




24.07 Mayes et al. 1986

Siluriformes

Cottus bairdi 51.72 51.72 Thurston and Russo 1981

Ictalurus punctatus 33.14




23.19 Colt and Tchobanoglous 1978








15.09 Diamond et al. 1993

29.57 Reinbold and Pescitelli 1982d

29.35 Reinbold and Pescitelli 1982d

51.72 Roseboom and Richey 1977

38.36 Roseboom and Richey 1977

64.58 Sparks 1975


32.34 West 1985

49.38 West 1985

Acipenseriformes Acipenseridae Acipenser brevirostrum 36.49 36.49 Fontenot et al. 1998

Amphibia Anura

Ranidae Rana pipiens 22.43 31.04 Diamond et al. 1993


Hylidae

Pseudacris crucifer 14.24 17.78 Diamond et al. 1993


Pseudacris regilla 19.49

7.77 Schuytema and Nebeker 1999





Annelida Oligochaeta Haplotaxida Tubidicidae Limnodrilus hoffmeisteri 26.17 26.17 Williams et al. 1986

Tubifex tubifex 33.3 33.3 Stammer 1953

Arthropoda

Insecta

Ephemeroptera

Baetidae Callibaetis skokianus 56.09



Callibaetis sp. 25.64 25.64 Thurston et al. 1984

Ephemerellidae Drunella grandis 68.05




Trichoptera Limnephilidae Philarctus quaeris 153 158.7 Arthur et al. 1987


Malacostraca Decapoda Cambaridae

Orconectes immunis 238.4 210.3 Arthur et al. 1987


Orconectes nais 46.73 46.73 Evans 1979

Pacifastacus leniusculus 56.49 56.49 Harris et al. 2001

Procambarus clarkii 21.23 17.22 Diamond et al. 1993


Branchiopoda Cladocera

Chydoridae Chydorus sphaericus 25.01 25.01 Dekker et al. 2006

Daphnidae

Ceriodaphnia acanthina 23.73 23.73 Mount 1982

Ceriodaphnia dubia 20.64

17.61 Andersen and Buckley 1998

21.71 Andersen and Buckley 1998

19.88 Bailey et al. 2001

24.01 Bailey et al. 2001

26.23 Black 2001

51.45 Black 2001

59.83 Black 2001

18.01 Cowgill and Milazzo 1991

15.06 Manning et al. 1996

23.52 Nimmo et al. 1989

5.494 Nimmo et al. 1989

18.38 Sarda 1994

18.45 Sarda 1994

14.52 Scheller 1997

Daphnia magna 24.25

45.66 Gersich and Hopkins 1986

5.792 Gulyas and Fleit 1990

30.38 Parkhurst et al. 1979,1981

64.46 Reinbold and Pescitelli 1982c

37.28 Russo et al. 1985

13.8 Russo et al. 1985

16.32 Russo et al. 1985

12.46 Russo et al. 1985

10.75 Russo et al. 1985

35.06 Russo et al. 1985

36.4 Russo et al. 1985

38.88 Russo et al. 1985

34.77 Russo et al. 1985

Daphnia pulicaria 15.23 15.23 DeGraeve et al. 1980

Simocephalus vetulus 21.98

29 Arthur et al. 1987


24.15 Mount 1982

18.9 Mount 1982

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Table 2 List of fish in Songhua River water system

Phylum Class Order Family Genus Species

Chordata Actinopterygii

Gasterosteiformes Gasterosteidae Gasterosteus G. aculeatus

Pungitius P. sinensis

Esociformes Esocidae Esox E. reicherti

Salmoniformes

Salmonidae

Coregonus C. ussurinsis

C. chadary

Oncorhynchus

O. keta

O masou

O Gorbuscha

Salvelinus S. malma

S. pluvius

Oncorhynchus O. mykiss

Brachynuystax B. lenok

Hucho H. taimen

H. ishikawai

Osmeridae

Hypomesus H. olidus

Osmerus O. mordax

Hypomesus H. transpacificus

nipponesis

Thymallidae Thymallus T. arcticus

T. arcticus grubei

Salangidae Protosalanx P. hyalocranius

Cypriniformes Cyprinidae

Abbottina A. rivularis

A. liaoningensis

Parabramis P. pekinensis

Culter

C. mongolicus

C. alburnus

C. dabryidabryi

C. oxycephalus

C. dabryi

shinkainensis

Ctenopharyngodon C. idellus

Squaliobarbus S. curriculus

Megalobrama M. amblycephalus

M. skolkovil

Elopichthys E. bambusa

Xenocypris X. micro1epis

X. argentea

Phoxinus P. perenurus

P. lagowskii


P. czkanowskii

P. lagowskii

P. phoxinus

phoxinus

Gnathopogon G. mantschuricus

Hemibarbus H. labeo

H. maculatus

Carassius C. auratus gibelio

Hemiculter

H. leucisculus

H. bleekeri lucidus

H. bleekeri

H. bieekerib

Gobio

G. soldatovi

G. cynocephalus

G. tenuicorpus

G. macrocephalus

G. lingyuanensis

Cyprinus C. carpio

haematopterus

Hypophthalmichthys H. molitrix

Zacco Z. platypus

Opsariichthys O. bidens

Pseudorasbora P. parva

Pseudaspius P. leptocephalus

Rutilus R. rutilus lacustris

Rhodeus R. sericeus

R. fangi

Ladislavia L. taczanowskj

Mylopharyngodon M. piceus

Gobiobotinae G. pappenheimi

Sarcocheilichthys S. lacustris

Sarcocheilichthys S. czerskii

Tribolodon T. brandti

T. hakonsis

Saurogobio S. dabryi

Paraleucogobio P. strigatus

Rostrogobio R. amurensis

Aphyocypris A. chinensis

Leucisus L. waleckii

Squalidus S. argentatus

S. chankaensis

Aristichthys A. nobilis


Acheilognathus A. chankaensis

Cultrichthys C. erythropterus

C. compressocorpus

Cobitidae

Nemachilichthys N. nudus

Lefua L. costata

Paramisgurnus P. dabryanus

Cobitis C. lutheri

Misgurnus M. mohoity

Parabotia P. fasciata

Perciformes

Belontiidae Macropodus M. chinensis

Channidae Channa C. argus

Percidae Lucioperca L. lucioperca

Serranidae Siniperca S. chuatsi

Eleotridae Perccottus P. glehni

Eleotridae Hypseleotris H. swinhonis

Gobiidae Rhinogobius R. nagoyae

Siluriformes

Bagridae

Pelteobagrus P. nitidus

P. fulvidraco

Pseudobagrus P. ussuriensis

Leiocassis L. argentivittatus

Siluridae Silurus S. soldatovi

S. asotus

Gadiformes Lotidae Lota L. lota

Acipenseriformes Acipenseridae Huso H. dauricus

Acipenser A. schrenckii

Scorpaeniformes Cottidae Mesocottus M. haitej

Cephalaspidemorphi Petromyzontiformes Petromyzontidae Lethenteron

L. raissncri

L. japonica

L. morii

Table 3 List of chordata in Songhua River water system


Chordata Amphibia

Caudata Hynobiidae

Hynobius H. leechii

Salamandrella S. keyserlingii

Onychodactylus O. fischeri

anura

Bufonidae Bufo B. raddei

Microhylidae Kaloula K. borealis

Discoglossidae Bombina B.orientalis

Ranidae Rana R. chensinensis

R. emeljanovi

Hylidae Hyla H. japonica

Table 4 List of arthropoda in Songhua River water system


Arthropoda

Malacostraca Decapoda

Cambaridae Cambaroides C. dauricus

Atyoidae Neocaridina N. heteropoda heteropoda

Palaemonidae

Exopalaemon E. modestus

Macrobrachium M. nipponense

Palaemonetes P. sinensis

P. sinensis

Maxillopoda

Cyclopoida Cyclopidae

Acanthocyclops

A. vernalis

A. viridis

A. bicuspidatus

A. bisetosus

Macrocyclops M. albidus

Cyclops C. strenuus

Paracyclops P. fimbriatus

P. affinis

Ectocyclops E. phaleratus

Thermocyclops T. dybowskii

T. kawamurai

Microcyclops

M. rubellus

M. longiramus

M. robustus

M. javanus

M. inchoatus

M. bicolor

Eucyclops

E. serrulatus

E. macruroides

E. macruroides

Harpacticoida Canthocamptidae

Canthocamptus C. carinatus

Bryocamptus B. vejdovskyi

Attheyella

A. crassa

A. dogieli

A. amurensis

Calanoida Diaptomidae

Neutrodiaptomus N.pachypoditus

N. genogibbosus

Tropodiaptomus T. oryzanus

Mongolodiaptomus M.birulai

Neodiaptomus N. schmackeri

Acanthodiaptomus A. pacificus

Sinodiaptomus S. chaffanjoni

S. sarsi


Temoridae Epischura E. chankensis

Heterocope H. appendiculata

Centropagidae Boeckella B. orientalis

Branchiopoda Cladocera

Leptodoridae Leptodora L. Kindti

Macrothricidae

Ilyocryptus I. sordidus

Lathonura L. rectirostris

Macrothrix

M. rosea

M. hirsuticornis

M. laticornis

M. triserialis

Polyphemidae Polyphemus P. pediculus

Moinidae Moina

M. rectirostris

M. micrura

M. macrocopa

M. chankensis

Chydoridae

Acroperus A. harpae

A. angustatus

Leydigia L. acanthocercoides

Graptoleberis G. testudinaria

Alona

A. karua

A. intermedia

A. quadrangularis

A. affinis

A. diaphana

A. rectangula

A. guttata

A. costata

Alonella A. excisa

Disparalona D. rostrata

Oxyurella O. tenuicaudis

Dunhevedia D. crassa

Pleuroxus

P. striatus

P. trigonellus

P. aduncus

P. hamulatus

Chydorus

C. sphaericus

C. ovalis

C. gibbus

C. barroisi

Pseudochydorus P. globosus

Peracantha P. truncata

Eurycercus E. lamellatus


C. rectirostris

Sididae

Diaphanosoma

D. brachyurum

D. chankensis

D. leuchtenbergianum

D. sarsi

Latonopsis L. australis

Sida S. crystallina

Bosminidae Bosmina

B. longirostris

B. coregoni

B. fatalis

B. deilersi

Daphnidae

Scapholeberis S. mucronata

S. kingi

Simocephalus

S. serrulatus

S. vetulus

S. vetuloides

S. exspinosus

S. serrulatus

Ceriodaphnia

C. quadrangula

C. hamata

C. laticaudata

Daphnia

D. carinata

D. pulex

D. obtusa

D. longispina

D. hyalina

D. cristata

D. magna

Insecta Odonata

Comphidae

Anisogomphus A. maacki

Davidius D. lunatus

Gomphidia G. confluens

Nihonogomphus N. ruptus

Ophiogomphus O. obscurus

Shaogomphus S. postocularis

S. schmidti

Siebordius S. albardae

Sinictingogomphus S. clavatus

Trigomphus T. citimus

T. succumben

Coenagriidae

Cercion C. plagiosum

Coenagrion C. bifurcatum

C. lanceolatum


Enallagma E. deserti

Erythromma E. najas

Ischnura

I. asiatica

I. elegans

I. senegalensis

Libellulidae

Deielia D. phaon

Leucorrhinia L. dubia orientalis

L. ijimai

Libellula L. basilinea

L. guadrimaculata

Lyriothemis L. pachygaster

Orthetrum

O. melania

O. albistylum

O. neglectum

Pantala P. flavescens

Sympetrum

S. croceolum

S. danae

S. depressiusculum

S. eroticum ardens

S. flaveolum

S. imitens

S. infscatum

S. pedmontanum

S. risi

S. striolatum imitoides

S. uniforme

Agriidae Caloptery C. x atrata

C. x virgo

Corduliidate

Cordulia C. aenea amuresis

Epitheca E. bimaculata

Epophthalmia E. elegans

Macromia

M. a amphigena

M. beijingensis

M. daimoji

M. manchurica

Trichoptera

Polycentropidae

Plectrocnemia P. kusnezovi

Plectrocnemia P. wui

Neureclipsis N. mandjuricus

Hydropsychidae

Hydropsyche H. nevae

Hydropsychodes H. tokunagai

Hydroptila H. chinensis

H. ornithocephala


H. introspinata

Stactobiella S. biramosa

Rhyacophilidae Rhyacophila

R. Hokkaidensis

R. Retracia

R. Narvae

R. Lata

R. Yamanakensis

Molannidae Molanna M. falcata

Lepto ceridae Setodes S. argentata

Oecetis O. nigropunctata

Limnephilidae Limnophilus L. amurensis

Phryganeidae

Agrypnia

A. colorata

A. czerskyi

A. picta

Oligotricha O. lapponica

Phryganea

P. sinensis

P. japonica

P. bipunctata

Sembis S. melaleuca

S. phalaenoides

Eubasilissa E. avalokhita

Ephemeroptera

Baetidae Cloeon C. dipterum

Baetis B. sp.

Ephemerellidae

Drunella

D. aculea

D. cryptomoria

D. lepnevae

D. solida

D. triacantha

D. trispina zeoensis

Cincticostella C. tshernovae

C. castanea

Ephemerella

E. keijoensis

E. auricilli

E. notofascia

Diptera Culicidae Aedes

A. alektorovi

A. antuensis

A. cataphylla

A. chemulpoensis

A. cinereus

A. communis

A. cyprius

A. diantaeus


A. esoensis

A. excrucians

A. flavescens

A. flavopictus

A. galloisi

A. hatorii

A. implicatus

A. koreicoides

A. koreicus

A. leucomelas

A. lineatopennis aureus

A. mercurator

A. nipponicus

A. pullatus

A. punctor

A. sasai

A. sibiricus

A. sticticus

Anopheles

A. koreicus

A. messeae

A. sineroides

A. yatsushiroensis

Armigeres A. subalbatus

Culex

C. hayashii

C. jacksoni

C. modestus

C. orientalis

C. pipiens pallens

C. rubensis

C. sinensis

C. whitmorei

Culiseta

C. alaskaensis

C. bergrothi

C. nipponica

Toxorhynchites T. christophi

Tripteroides T. bambusa

Table 5 List of Mollusca in Songhua River water system


Mollusca Gastropoda Mesogastropoda Viviparidae Cipangopaludina C. cahayensis


C. chinensis

Viviparus V. chui

Bithyniidae Parafossarulus P. striatulus

Sorbeoconcha Pleuroceridae Semisulcospira S. cancellata

S. amurensis

Pulmonata Lymnaeidae

Lymnaea L. stagnalis

Radix

R. plicatula

R. ovata

R. lagotis

R. auricularia

Galba Golba pervia

G. truncatula

Planorbidae Polypylis P. hemisphaerula

Bivalvia

Veneroida Corbiculidae Corbicula C. fluminea

Eulamellibranchia

Margaritanidae

Margaritiana M. dahurica

Unio U. douglasiae

Lanceolaria L. grayana

Unionodae Anodonta

A. woodiana

A. woodiana elliptica

A. Welliptica

A. euscaphys

A. arcaeformis

A.arcaeformisflavotincta

Table 6 List of annelida in Songhua River water system


Annelida

Clitellata Tubificida Tubificidae Spirosperma S. nikolskyi

Limnodrilus L.hoffmeisteri

Hirudinea

Rhynchobdellida Glossiphoniidae

Hemiclepsis H. marginata

Glossiphonia

G. complanata

G. heteroclita

G. lata

G. multipapillata

G. weberi

G. complanata

G. lata

Batracobdella B. paludosa

Helobdella H. nuda

H. marginata

Arhynchobdellida Haemopidae Whitmania W. laevis

W. pigra


Salifidae Barbronia B. weberi

Erpobdellidae

Dina D. lineata

Erpobdella E. octoculata

E. testacea

Hirudinidae Hirudo H. nipponia

Branchiobdellidae Branchiobdella

B. orientalis

B. kobayashii

B. macroperistomium

Table 7 List of rotifera in Songhua River water system


Rotifera Rotaria Monogononta

Brachionida

Brachionus

B. angularia

B. budapestiensis

B. calyciflorus

B. capsuliflorus

B. forficula

Argonotholca A. foliacea

Kellicottia K. longispina

Euchlanis E. dilatata

Keratella K. quadrala

K. valga

Asplancchnidae Asplanchna A. priodonala

Trichocercidae Trichocerca

T. bicristata

T. capucina

T. elongata

Synchaetidae Polyarthra P. euryptera

P. trigla

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