Environmental DNA Shaping A New Era of Ecotoxicological Research

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Environmental DNA Shaping A New Era of Ecotoxicological Research Xiaowei Zhang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06631 • Publication Date (Web): 22 Apr 2019 Downloaded from http://pubs.acs.org on April 23, 2019

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Environmental Science & Technology

Environmental DNA Shaping A New Era of Ecotoxicological Research

Xiaowei Zhang*

State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China

Correspondence: Xiaowei Zhang, PhD, Prof. School of Environment, Nanjing University 163 Xianlin Avenue, Nanjing, 210023, China Tel: (86)-25-89680623 E-mail address: [email protected] [email protected]

ACS Paragon Plus Environment


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(All photos and images were taken by this author using Microsoft PowerPoint software)


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Abstract:

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Aquatic ecosystems, such as rivers and lakes, are exposed to multiple stressors from

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anthropogenic activity and changes in climate, which have resulted in a general decrease in

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biodiversity, alteration of community structures, and can ultimately result in reduction of

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resources provided by natural ecosystems. Adverse outcomes caused by pollutants to

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ecosystems are determined not only by toxic properties, but also ecological contexts of

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ecosystems, including indigenous biodiversity and community composition. It is therefore

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important to identify key factors, such as diversity of species and traits that determine the

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vulnerability of structures and functions of ecosystems in response to toxic substances.

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Detection and quantification of biodiversity and its activities using environmental DNA

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(eDNA) is arguably one of the most important technical advances in ecology in recent years.

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A huge opportunity has appeared to allow more relevant approaches for assessments of risks

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posed to ecosystems by toxic substances. eDNA approaches provide effective and efficient

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tools to evaluate the effects of chemical pollutants on 1) the occurrences and population of

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wildlife, 2) communities, and 3) the function of ecosystem in the field. Here a conceptual

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framework of adverse outcome pathways to relate molecular initiating events to apical

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ecosystem-level responses is proposed to connecting laboratory-based prediction to

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observations under field conditions. Particularly, future research opportunity on effects on

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biodiversity, community structure, and ecosystem function by toxic substances will be

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discussed.



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1. Challenges in Ecotoxicology

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Accelerating production of synthetic chemicals and their releases to the environment at

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a global scale has caused concern on their long-term adverse consequences and the

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sustainability of natural ecosystems1, 2. Environmental pollutants (such as endocrine disrupting

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chemicals, or EDCs) can cause adverse effects on individuals of wildlife, decline of population

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and biodiversity, and then affect services provided by ecosystems. Simultaneously, aquatic

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ecosystems are under global pressure from non-chemical stressors, such as degradation of

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habitat, altered flows, elevated nutrients, invasive species, new pathogens, and climate change2.

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A major challenge in ecotoxicology is how to distinguish between chemical and non-chemical

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stressors from the causes of degraded ecosystems. This limitation is largely due to the lack of

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effective and efficient tools to evaluate the effects of chemical pollutants on 1) the occurrences

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and population of wildlife, 2) communities, and 3) the function of ecosystem in the field.

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First, neglecting monitoring of wildlife has long been recognized as the key weakness

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of current ecotoxicological research strategies3. Historically, studies of toxicity have focused

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on a few species or even sub-organism responses that were then used to try to predict

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ecosystem-level responses4. For example, to protect local stocks of fishes, various countries

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encourage the use of their local fish species, such as fathead minnow (Pimephales promelas)5,

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Japanese medaka (Oryzias latipes)6, and Chinese rare minnow (Gobiocypris rarus)7 for

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ecotoxicological tests. It is rare to assess effects of chemicals in the field by monitoring the

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occurrences and population of these fish species. It is doubtful, however, where this strategy

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really helps to preserve local biodiversity. Although monitoring macroinvertebrate species

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(some taxonomic groups from Arthropoda, Annelida, Mollusca) has been routinely carried out

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in Canada and European countries, in many countries, especially in developing countries, it is

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still expensive and impractical to monitor aquatic wildlife in the field by conventional

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morphology-based approaches.


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Second, impacts of chemicals on biodiversity and community structure is also key,

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missing information needed for assessment of exposures8, 9. Exposure to pollutants can lead to

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declines of biodiversity and changes of community structures, which together result in

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decreases or changes in goods and service provided by ecosystem (Figure 1). For the past half

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century, empirical selections of algae (primary producer), crustaceans (secondary producer;

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consumer), and fish (secondary producer; predator) as key targets (receptors) for toxicity test

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of toxicants has been successfully applied for protection of aquatic communities10. Some of

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these model species have been widely used in toxicity testing, ecological risk assessment and

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environmental criteria derivation. However, such a reductionist approach is challenged by

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ignoring variations in local biodiversity and cross-species toxicity sensitivity. Furthermore, it

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has been shown that “indirect” mechanisms of chemical pollutants, such as trophic

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interactions11, parasite-host,12 invasive species13 and behaviors14, might modulate adverse

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effects on ecosystems. Both theoretical modeling and field observation show that not only

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species richness, but also the traits of species are important for the ecosystem service

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However, without adequate analyses of effects of environmental pollutants on ecosystem

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biodiversity and community structure, it will be difficult to assess effects of individual

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chemicals on complex ecological systems on regional scales.

15, 16.

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Finally, effects on fundamental ecological processes controlling functions of

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ecosystems has previously been neglected in ecological risk assessment. It has been

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increasingly agreed by scientists and regulators that ecological risk assessment of chemicals

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should be based on magnitudes of effects on ecosystem services (ES), though there remain a

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number of significant challenges17. Ecosystem services generally describe ecosystem attributes

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and process that support the well-being of human populations or ecological functions upon

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which they rely, while ecosystem function refers specifically to fundamental ecological

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processes, such as primary and secondary productivity, natural biogeochemical cycling and



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decomposition of detritus18. Most ecosystem functions evaluation methods are based on one-

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time measurement on physical, chemical and biological attributes, providing only snapshots of

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the ecosystem status. To date, effective and robust approach to measure actual functions or

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services remains one of the most desired goals in ecological risk assessment.

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Adverse effects of pollutants on functions of ecosystems can be evaluated by

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experimental systems, such as sediment19 and stream microcosms or mesocosms20,

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addition to direct toxic effects on individual species, indirect effects of chemicals on

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community structure22, population19, 22 and functions (such as biomass) 11, 13 can be assessed.

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Community ecology models (e.g., food web modeling) have been shown to be powerful in

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assessing ecologically significant adverse effects in aquatic ecosystems23. Despite nonrandom

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selection of small fractions of biodiversity and limited functional endpoints, such experimental

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approaches are not common in ecotoxicological studies (ecological risk assessment) of

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chemicals. A major challenge of ecotoxicology remains: how to define no-observed-effect

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concentrations (NOEC) to protect of all biodiversity from toxic chemicals, when we know

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relatively little about the flora and fauna are exposed24.

In

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Advances in Environmental DNA (eDNA) technologies

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Detection and quantification of biodiversity and its activities using environmental DNA

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(eDNA) is arguably one of the most important technical advances in ecology in recent years.

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The field of molecular ecology has recently witnessed surges in research activities related to

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development and application of eDNA-based technologies25. eDNA is the DNA directly

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extracted from environmental samples, including soil, sediment, water or air, without

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enrichment, which is now routinely being used as non-invasive means to detect individual

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species and communities in aquatic ecosystems. eDNA technologies provide a full spectrum

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utility for assessing adverse effects by environmental stressors at different levels of biological


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organization in ecosystems (Figure 2). Species-specific eDNA methods target particular

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species by detecting specific DNA fragments, by use of polymerase chain reaction (PCR)

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assays. Occurrences of species can be indicated by a regular PCR, while biomass and

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abundances of targeted organisms can be determined by quantitative PCR (qPCR) or digital

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PCR (dPCR), which can be linked to the populations26. Though there are still some technical

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issues, such as PCR primer bias and DNA degradation affected by environmental conditions

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27,

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eDNA would undoubtedly reduce some of concerns28, 29.

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results of ongoing research and standardization of methods, spatial-temporal modeling of

Community-based (or semi-targeted) eDNA approaches can be powerful in evaluating

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the taxonomic profiles and functions of ecosystems (Figure 2)9,

18.

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metabarcoding has emerged as a molecular tool for identifying a large proportion of the

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biological communities in environments, from micro-organisms to macro-organisms

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eDNA metabarcoding provides taxonomic identification of multiple species from an eDNA

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sample based on DNA barcoding of sequences generated from high-throughput sequencing.

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Although much work remains to be done to develop this approach as a quantitative method,

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global ongoing efforts are devoted to develop reliable biomonitoring pipeline for community

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assessment, particularly for ecological status assessment 18, 27, 28, 32.

In particular eDNA

27, 30, 31.

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Functional and metabolic diversities of species within naturally occurring communities

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can be profiled by metagenomics or metatranscriptomics from the eDNA (or eRNA).

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Specifically, metatranscriptomics is the aggregated, multispecies equivalents of individual

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organism transcriptomics. Functional profiling of microbial communities can be predicted by

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mapping a subset of abundant marker genes (i.e., 16SrRNA) or by directly measurement of

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meta-transcriptomes. Computational approaches have been developed to predict functional

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compositions of metagenomes by use of marker genes that can be identified in reference

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genome databases33. Furthermore, metatranscriptomics allows for a more direct assessment of



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ecosystem function, generally defined as the fundamental ecological processes (e.g., nitrogen

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cycling34) that occur in aquatic ecosystems18, 35.

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Prospects of applying eDNA technologies in ecotoxicology research and risk

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assessment

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1. eDNA monitoring of the presence and population of wildlife under pollutant exposure.

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One of the most attractive capacities of eDNA for applied ecotoxicologists is rapid and efficient

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methods to monitor occurrences of populations of wildlife in the field. With sustained

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scientific efforts to support eDNA biomonitoring and development of standardized protocols,

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more validated eDNA assays are available for detecting freshwater fish, amphibian,

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crustaceans and zooplanktons in the field27, 30. However, results of several studies have shown

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that persistence of eDNA in aquatic ecosystems is affected by abiotic and spatial conditions.

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Results of recent studies have shown that rates of decay of eDNA can be simulated by use of a

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simple, pseudo-first-order decay model and that distributions and abundances of target species

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across a river network can be estimated by use of observed eDNA, that were corrected for rates

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of degradation and transport predicted from hydro-geomorphological characteristics of the

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network.

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Almost all of the ecotoxicity test species can now be monitored by eDNA on their

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occurrence pattern and population in a local ecosystem, which can be compared to predictions

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from laboratory tests and modeling (Figure 3). Most of the current practices applied in

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assessment of hazards and risks are based on laboratory ecotoxicity studies, while the

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hypothesis that laboratory ecotoxicity studies can always identify sensitive species and key

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endpoints in a timely manner is questionable3. When prospective risk assessment by laboratory

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test and population modeling indicate that populations of a specific species might be at risk, an

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eDNA biomonitoring can be triggered. Recent development of quantitative adverse outcome


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pathway (qAOP) frameworks are expected to provide quantitative predictions on wildlife

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population by combining in vitro testing and computational models

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species-specific demography, spatio-temporal variations (such as seasonally varying river flow

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and temperature, fishing pressure, and inter-specific competition) can also be incorporated in

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ecotoxicological model to predict fish population altered by chemical pollutant in a river

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network37. By combing with eDNA biomonitoring, these prospective risk assessment models

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could significantly improve our understanding on adverse ecological consequence by pollutant

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exposure. Alternatively, forensic ecotoxicology could be applied to areas where declines have

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been suggested based on traditional methods of monitoring, based on visual assessment of

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taxonomy, based on morphometry of can also be demonstrated by use of eDNA biomonitoring.

36.

In addition to the

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2. Assessing effects of pollutants on biodiversity and community structures. The eDNA

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approach has been demonstrated to provide direct evidence for community-level effects of

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toxic substances under both laboratory conditions38, 39 and field-based mesocosms40(Figure 3).

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A conceptual framework has been proposed to characterize community-level effects of

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pollutants, including phylogenetic diversity, community structure and function by use of eDNA

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approaches (Figure 4). First, status and trends in biodiversity and community composition

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across the Tree of Life, including bacteria, protist, algae, fungi and metazoan, under pollutant

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exposure can be analyzed via nondestructive eDNA sampling and analysis. In sediments spiked

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with copper in field-based microcosms,40unprecedented capacity for resolving the taxonomic

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composition of the exposed community by eDNA metabarcoding approach were demonstrated

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by effective monitoring of 939 prokaryotic taxa and 878 eukaryotic taxa across the Tree of

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Life. Secondly, eDNA analyses of a controlled mesocosm (with various concentrations/doses

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of chemical) can delineate complex response patterns of sensitive, neutral and opportunistic

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species groups. Effects of copper exposure on the population density (relative abundance),



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biodiversity (unique taxon number), community structure, and bio-interactions of metazoans,

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protists, algae, fungi, and bacteria were evaluated by use of dose-response modeling40. Dose-

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dependent modeling of relative abundances of each taxa can not only screen for more sensitive

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taxa in a wide range of biodiversity, but also calculate the concentration that leads to 50%

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maximum effects (changes of relative abundance) (EC50). The ecological threshold for to

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protect biodiversity (e.g., HC50) from effects of pollutants, can be derived by the species

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sensitivity distribution (SSD) model (Figure 4)

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were positively or negatively selected by pollutant exposure ) detected by use of

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metabarcoding approach can be used to investigate the ecotoxicological mechanisms of altered

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ecosystem function (e.g. algal biomass, primary production.) 39.

38, 40.

Finally, novel bioindicators (taxa that the

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Adverse ecological outcomes caused by pollutants depend, not only on toxic potencies,

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but also on the ecological context of the ecosystem, including biodiversity, community

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composition, and diversity of traits. It is important to identify key factors that determine

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vulnerability of ecosystem function to pollutant exposure by integrating both theoretical and

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field-based approach. Such practices would provide potential management recommendations

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to reduce potential effects of chemicals to nearby ecosystem. Recently we have developed an

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meta-barcoding approach to profile the composition of zooplankton community in the

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watershed of Tai Lake (Ch: Taihu) and presented a novel, rapid, species sensitivity distribution

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(SSD) approach based on operational taxonomic units (OTUs) to derive water quality criteria

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(WQC) of NH341. Comparison of operational taxonomic units (OTU) results and traditional

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taxonomic species identification showed a nice validation of the metabarcoding approach30.

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Field-based derivation of the threshold for effects of a toxic substance, total ammonia nitrogen

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(TAN), by metabarcoding approach provided a more sensitive value than that derived by use

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of the SSD based on standard laboratory toxicity data, which demonstrated the advantage of

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analysis based on the site-specific entire assemblage and includes effects of accessory factors.


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Such approach can be expanded to be used to monitor for effects on other groups of organisms

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by different kinds of environment stressors. Threshold Indicator Taxa ANalysis (TITAN) is an

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excellent example that biomonitoring data on stream macroinvertebrate community can be

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used to derive taxa-specific and community-level thresholds for urbanization stressors in the

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field42. In future, metabarcoding of macroinvertebrates can increase the efficiency of stream

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macroinvertebrate community biomonitoring43, which might be applied together with TITAN

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method in support of stream management applications.

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3. Assessing effects of exposure to pollutants on community function. Fundamental

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ecological processes mediated by microorganisms can be evaluated directly by use of

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metagenomics. By calculating relative read abundances of functional genes and metabolic

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pathways in a community affected by pollutants, metatranscriptome offers an approach for

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direct analysis of ecosystem functions in mesocosm34 or in field35. Furthermore, monitoring

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of the metatranscriptome can provide solutions of functional profiling for specific communities

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(such as protist, or zooplanktons) with appropriate sampling and enrichment methods. Few

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studies have used this approach to profile the community function of macro-organisms, since

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most of the macro-organism communities have not been characterized at functional level. The

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diversity of body size and the variation of morphological characteristics of macro-organisms

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make the automation task of functional quantification daunting. However, the continuously

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growing trait database of macro-organism species coupling with the taxonomic identification

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by metabarcoding will make a semi-automatic functional analysis possible.

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Alteration in functions of ecosystems can be predicted by changes of biodiversity and

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community composition using ecological theory modeling18. Biodiversity-ecosystem

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functioning (BEF) framework allows simulation of biodiversity-mediated effect on ecosystem

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function23. In addition to species richness, community trophic structure, and variation within



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and covariation among responses and effect traits of species are also critical to predict the

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change of ecosystem function15. Under the recently developed Community Assembly and the

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Functioning of Ecosystems (CAFE) approach,14 both species richness and changes of

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community composition are integrated to predict alteration at ecosystem function using the

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Price equation. These theoretical models provide promising tools to predict the toxic substance

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induced effects on ecosystem function by taking advantage of community characterization

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(biodiversity and community composition) by eDNA approaches, such as metabarcoding.

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Networks-based approach can be used in functional assessment of pollutant exposure

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in both laboratory microcosm and in field, by the integration of taxonomic resolution and

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breadth of metabarcoding data, and database of ecological interactions. An ecological network

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is a representation of the biotic interactions (trophic or mutualistic) in an ecosystem, which

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characterize critical processes and services (such as pollination, clean water and fisheries)18, 44.

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Metrics of ecological networks, such as trophic level and linkage density, are often directly

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linked to ecosystem function. Database of ecological interactions (e.g. food web) can be rapidly

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expanded using the text mining technology44. A recent example is the text-mining pipeline a

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program, General Architecture for Text Engineering (GATE) system, which has combined

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rule-based and machine-learning modules in development of ecological network for freshwater

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macroinvertebrates43.

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Challenges and opportunities

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Several challenges remain before eDNA methods can be readily applied in ecological

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assessment and risk assessment of chemical pollutants. First, accurate and standardized eDNA

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protocols (PCR or metabarcoding) independent of target species or environment should be

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established. Many government agencies sponsored eDNA research program are currently

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optimizing various conditions (such as sample volume and numbers, filter type and pore size


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etc.) to achieve a set detection probability for targeted species or community composition28.

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Second, the integrity and reliability of existing genetic databases of biodiversity are key factors

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determining dependability of eDNA measurement. Recent research from my lab on freshwater

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zooplankton showed that development of regional species barcode databases can significantly

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improve identification of field-collected species by use of metabarcoding45. Third we are still

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at the very beginning to understand the fundamental biodiversity structure in aquatic ecosystem,

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such as riverine network systems46. Future studies will further take advantage of eDNA

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approaches to uncover broad biodiversity and link it to ecosystem function, which could

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revolutionize our understanding of environmental protection from anthropogenic pollution.

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By combining reliable monitoring of communities by use of eDNA wither in barcoding

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for specific species or metabarcoding for multiple operational taxonomic units, biodiversity-

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ecosystem function modeling, a conceptual framework of ecological adverse outcome

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pathways (eAOP) can expand the current chemical AOP concept to community and ecosystem

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(Figure 5). Such a framework can also guide studies of to understand mechanisms of toxic

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action and assessments of accessory factors and non-chemical stressors. Understand of eAOPs,

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for adverse outcomes can be expanded from the chemical regulatory endpoints (such as

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mortality, development and reproduction) to those endpoints important to land and water

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management at biodiversity and ecosystem function. In the future, field-based evaluation and

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diagnosis of environmental effects can be facilitated by biodiversity and traits-based

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monitoring combing with effect-based method and chemical analysis 47, 48. Furthermore, future

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ecotoxicological studies will encourage the combination of field-based microcosm and eDNA

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approaches, which can evaluate the response of indigenous species that cannot be reared or

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tested in laboratory, and make more environmentally relevant assessment of adverse effects at

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community and ecosystem levels.

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Acknowledgements. I thank the funding support by the Fundamental Research Funds for the

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Central Universities.

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Biography and Author Picture

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Dr. Xiaowei Zhang is a molecular (eco)toxicologist and the Director of the Research Institute

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on Environmental Safety of Chemicals, Nanjing University. He obtained his BSc from Nanjing

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University in 2000, and MSc from the City University of Hong Kong in 2003 and PhD from

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Michigan State University in 2008. His research interests include mechanistic understanding

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of the adverse ecological and health effects by pollutants, and development of novel approaches

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for ecological risk assessment. Dr. Zhang has published 150 papers and serves as an editor for

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Environmental Toxicology and Chemistry. In 2016, he was awarded the title of Young

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Changjiang Scholar, Ministry of Education.

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References: 1. Schwarzenbach, R. P.; Escher, B. I.; Fenner, K.; Hofstetter, T. B.; Johnson, C. A.; von Gunten, U.; Wehrli, B., The challenge of micropollutants in aquatic systems. Science 2006, 313 (5790), 1072-7. 2. Vorosmarty, C. J.; McIntyre, P. B.; Gessner, M. O.; Dudgeon, D.; Prusevich, A.; Green, P.; Glidden, S.; Bunn, S. E.; Sullivan, C. A.; Liermann, C. R.; Davies, P. M., Global threats to human water security and river biodiversity. Nature 2010, 467 (7315), 555-61. 3. Johnson, A. C.; Sumpter, J. P., Are we going about chemical risk assessment for the aquatic environment the wrong way? Environ. Toxicol. Chem. 2016, 35 (7), 1609-1616. 4. Forbes, V. E.; Calow, P.; Sibly, R. M., The extrapolation problem and how population modeling can help. Environ. Toxicol. Chem. 2008, 27 (10), 1987-94. 5. Miller, D. H.; Ankley, G. T., Modeling impacts on populations: fathead minnow (Pimephales promelas) exposure to the endocrine disruptor 17beta-trenbolone as a case study. Ecotoxicol. Environ. Saf. 2004, 59 (1), 1-9. 6. Zhang, X.; Hecker, M.; Tompsett, A. R.; Park, J. W.; Jones, P. D.; Newsted, J.; Au, D.; Kong, R.; Wu, R. S.; Giesy, J. P., Responses of the medaka HPG axis PCR array and reproduction to prochloraz and ketoconazole. Environ. Sci. Technol. 2008, 42 (17), 6762-9. 7. Ma, T.; Wang, Z.; Gong, S., Comparative sensitivity in Chinese rare minnow (Gobiocypris rarus) and Japanese Medaka (Oryzias latipes) exposed to ethinylestradiol. J Environ. Sci. Health. A. Tox. Hazard. Subst. Environ. Eng. 2007, 42 (7), 889-94. 8. Ramade, F., Assessment of damage to ecosystems: a major issue in ecotoxicological research. Qual. Assur. 1997, 5 (3), 199-220. 9. Zhang, X.; Xia, P.; Wang, P.; Yang, J.; Baird, D. J., Omics Advances in Ecotoxicology. Environ. Sci. Technol. 2018, 52 (7), 3842-3851. 10. Calow, P.; Forbes, V. E., Peer Reviewed: Does Ecotoxicology Inform Ecological Risk Assessment? Environ. Sci. Technol. 2003, 37 (7), 146A-151A. 11. Halstead, N. T.; McMahon, T. A.; Johnson, S. A.; Raffel, T. R.; Romansic, J. M.; Crumrine, P. W.; Rohr, J. R., Community ecology theory predicts the effects of agrochemical mixtures on aquatic biodiversity and ecosystem properties. Ecol. Lett. 2014, 17 (8), 932-41. 12. Halstead, N. T.; Hoover, C. M.; Arakala, A.; Civitello, D. J.; De Leo, G. A.; Gambhir, M.; Johnson, S. A.; Jouanard, N.; Loerns, K. A.; McMahon, T. A.; Ndione, R. A.; Nguyen, K.; Raffel, T. R.; Remais, J. V.; Riveau, G.; Sokolow, S. H.; Rohr, J. R., Agrochemicals increase risk of human schistosomiasis by supporting higher densities of intermediate hosts. Nat. Commun. 2018, 9 (1), 837. 13. Rodrigues, A. C. M.; Machado, A. L.; Bordalo, M. D.; Saro, L.; Simao, F. C. P.; Rocha, R. J. M.; Golovko, O.; Zlabek, V.; Barata, C.; Soares, A.; Pestana, J. L. T., Invasive Species Mediate Insecticide Effects on Community and Ecosystem Functioning. Environ. Sci. Technol. 2018, 52 (8), 4889-4900. 14. Bannar-Martin, K. H.; Kremer, C. T.; Ernest, S. K. M.; Leibold, M. A.; Auge, H.; Chase, J.; Declerck, S. A. J.; Eisenhauer, N.; Harpole, S.; Hillebrand, H.; Isbell, F.; Koffel, T.; Larsen, S.; Narwani, A.; Petermann, J. S.; Roscher, C.; Cabral, J. S.; Supp, S. R., Integrating community assembly and biodiversity to better understand ecosystem function: the Community Assembly and the Functioning of Ecosystems (CAFE) approach. Ecol. Lett. 2018, 21 (2), 167-180. 15. Heilpern, S. A.; Weeks, B. C.; Naeem, S., Predicting ecosystem vulnerability to biodiversity loss from community composition. Ecology 2018, 99 (5), 1099-1107. 16. Cernansky, R., The biodiversity revolution. Nature 2017, 546 (7656), 22-24.



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17. Maltby, L.; van den Brink, P. J.; Faber, J. H.; Marshall, S., Advantages and challenges associated with implementing an ecosystem services approach to ecological risk assessment for chemicals. Sci. Total. Environ. 2018, 621, 1342-1351. 18. Joel F. Gibson, E. D. S., Donald J. Baird, C. Max Finlayson, Xiaowei Zhang, Mehrdad Hajibabaei, Wetland Ecogenomics –The Next Generation of Wetland Biodiversity and Functional Assessment. Wetl. Sci. Prac. 2015, 27, 6. 19. Jeppe, K. J.; Yang, J. H.; Long, S. M.; Carew, M. E.; Zhang, X. W.; Pettigrove, V.; Hoffmann, A. A., Detecting copper toxicity in sediments: from the subindividual level to the population level. J. Appl. Ecol. 2017, 54 (5), 1331-1342. 20. King, R. S.; Brain, R. A.; Back, J. A.; Becker, C.; Wright, M. V.; Djomte, V. T.; Scott, W. C.; Virgil, S. R.; Brooks, B. W.; Hosmer, A. J.; Chambliss, C. K., Effects of pulsed atrazine exposures on autotrophic community structure, biomass, and production in field-based stream mesocosms. Environ. Toxicol. Chem. 2016, 35 (3), 660-75. 21. Van Straalen, N. M., Ecotoxicology becomes stress ecology. Environ. Sci. Technol. 2003, 37 (17), 324A-330A. 22. McMahon, T. A.; Halstead, N. T.; Johnson, S.; Raffel, T. R.; Romansic, J. M.; Crumrine, P. W.; Rohr, J. R., Fungicide-induced declines of freshwater biodiversity modify ecosystem functions and services. Ecol. Lett. 2012, 15 (7), 714-22. 23. De Laender, F.; Rohr, J. R.; Ashauer, R.; Baird, D. J.; Berger, U.; Eisenhauer, N.; Grimm, V.; Hommen, U.; Maltby, L.; Melian, C. J.; Pomati, F.; Roessink, I.; Radchuk, V.; Van den Brink, P. J., Reintroducing Environmental Change Drivers in Biodiversity-Ecosystem Functioning Research. Trends. Ecol. Evol. 2016, 31 (12), 905-915. 24. Hermens, J. L.; Ankley, G. T.; Sumpter, J. P., Ecotoxicology--a multidisciplinary, problem-driven science. Environ. Sci. Technol. 2004, 38 (23), 446A-447A. 25. Seymour, M., Rapid progression and future of environmental DNA research. Commun. Biol. 2019, 2, 80. 26. Carraro, L.; Hartikainen, H.; Jokela, J.; Bertuzzo, E.; Rinaldo, A., Estimating species distribution and abundance in river networks using environmental DNA. Proc. Natl. Acad. Sci. USA 2018, 115 (46), 11724-11729. 27. Deiner, K.; Bik, H. M.; Machler, E.; Seymour, M.; Lacoursiere-Roussel, A.; Altermatt, F.; Creer, S.; Bista, I.; Lodge, D. M.; de Vere, N.; Pfrender, M. E.; Bernatchez, L., Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 2017, 26 (21), 5872-5895. 28. Hering, D.; Borja, A.; Jones, J. I.; Pont, D.; Boets, P.; Bouchez, A.; Bruce, K.; Drakare, S.; Hanfling, B.; Kahlert, M.; Leese, F.; Meissner, K.; Mergen, P.; Reyjol, Y.; Segurado, P.; Vogler, A.; Kelly, M., Implementation options for DNA-based identification into ecological status assessment under the European Water Framework Directive. Water research 2018, 138, 192-205. 29. Fonseca, V. G., “Pitfalls in relative abundance estimation using eDNA metabarcoding”. Mol. Ecol. Res.2018, 18 (5), 923-926. 30. Yang, J.; Zhang, X.; Xie, Y.; Song, C.; Zhang, Y.; Yu, H.; Burton, G. A., Zooplankton Community Profiling in a Eutrophic Freshwater Ecosystem-Lake Tai Basin by DNA Metabarcoding. Sci. Rep. 2017, 7 (1), 1773. 31. Xie, Y.; Floehr, T.; Zhang, X.; Xiao, H.; Yang, J.; Xia, P.; Burton, G. A., Jr.; Hollert, H., In situ microbiota distinguished primary anthropogenic stressor in freshwater sediments. Environ. Pollut. 2018, 239, 189-197. 32. Bohan, D. A.; Vacher, C.; Tamaddoni-Nezhad, A.; Raybould, A.; Dumbrell, A. J.; Woodward, G., Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks. Trends. Ecol. Evol. 2017, 32 (7), 477-487.


Page 16 of 25

Page 17 of 25

389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438


33. Langille, M. G. I.; Zaneveld, J.; Caporaso, J. G.; McDonald, D.; Knights, D.; Reyes, J. A.; Clemente, J. C.; Burkepile, D. E.; Thurber, R. L. V.; Knight, R.; Beiko, R. G.; Huttenhower, C., Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 2013, 31 (9), 814-821. 34. Zheng, Y.; Hou, L.; Liu, M.; Newell, S. E.; Yin, G.; Yu, C.; Zhang, H.; Li, X.; Gao, D.; Gao, J.; Wang, R.; Liu, C., Effects of silver nanoparticles on nitrification and associated nitrous oxide production in aquatic environments. Sci. Adv. 2017, 3 (8), e1603229. 35. Grossmann, L.; Beisser, D.; Bock, C.; Chatzinotas, A.; Jensen, M.; Preisfeld, A.; Psenner, R.; Rahmann, S.; Wodniok, S.; Boenigk, J., Trade-off between taxon diversity and functional diversity in European lake ecosystems. Mol. Ecol. 2016, 25 (23), 5876-5888. 36. Conolly, R. B.; Ankley, G. T.; Cheng, W.; Mayo, M. L.; Miller, D. H.; Perkins, E. J.; Villeneuve, D. L.; Watanabe, K. H., Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology. Environ. Sci. Technol. 2017, 51 (8), 4661-4672. 37. Forbes, V. E.; Railsback, S.; Accolla, C.; Birnir, B.; Bruins, R. J. F.; Ducrot, V.; Galic, N.; Garber, K.; Harvey, B. C.; Jager, H. I.; Kanarek, A.; Pastorok, R.; Rebarber, R.; Thorbek, P.; Salice, C. J., Predicting impacts of chemicals from organisms to ecosystem service delivery: A case study of endocrine disruptor effects on trout. Sci. Total. Environ. 2019, 649, 949-959. 38. Yang, J.; Xie, Y.; Jeppe, K.; Long, S.; Pettigrove, V.; Zhang, X., Sensitive community responses of microbiota to copper in sediment toxicity test. Environ. Toxicol. Chem. 2018, 37 (2), 599-608. 39. Corcoll, N.; Yang, J.; Backhaus, T.; Zhang, X.; Eriksson, K. M., Copper Affects Composition and Functioning of Microbial Communities in Marine Biofilms at Environmentally Relevant Concentrations. Front. Microbiol. 2018, 9, 3248 40. Yang, J.; Jeppe, K. J.; Pettigrove, V. J.; Zhang, X., eDNA metabarcoding supporting community assessment of environmental stressor in a field-based sediment microcosm study. Environ. Sci. Technol. 2018.52(24),14469-14479. 41. Yang, J.; Zhang, X.; Xie, Y.; Song, C.; Sun, J.; Zhang, Y.; Giesy, J. P.; Yu, H., Ecogenomics of Zooplankton Community Reveals Ecological Threshold of Ammonia Nitrogen. Environ. Sci. Technol. 2017. 51(5),3057-3064. 42. King, R. S.; Baker, M. E.; Kazyak, P. F.; Weller, D. E., How novel is too novel? Stream community thresholds at exceptionally low levels of catchment urbanization. Ecol. Appl. 2011, 21 (5), 1659-78. 43. Zacchaeus G. Compson, W. A. M., Colin J. Curry, Dominique Gravel, Alex Bush, Christopher J.O. Bakerk, Mohammad Sadnan Al Manirk, Alexandre Riazanovk, Mehrdad Hajibabaei, Shadi Shokralla, Joel F. Gibson, Sonja Stefani, Michael T.G. Wright, Donald J. Baird, Linking DNA Metabarcoding and Text Mining to Create Network-Based Biomonitoring Tools: A Case Study on Boreal Wetland Macroinvertebrate Communities. In Advances in Ecological Research, Elsevier Ltd: 2018; Vol. 59, pp 33-74. 44. Gray, C.; Baird, D. J.; Baumgartner, S.; Jacob, U.; Jenkins, G. B.; O'Gorman, E. J.; Lu, X.; Ma, A.; Pocock, M. J.; Schuwirth, N.; Thompson, M.; Woodward, G., FORUM: Ecological networks: the missing links in biomonitoring science. J. Appl. Ecol. 2014, 51 (5), 1444-1449. 45. Yang, J. H.; Zhang, X. W.; Zhang, W. W.; Sun, J. Y.; Xie, Y. W.; Zhang, Y. M.; Burton, G. A.; Yu, H. X., Indigenous species barcode database improves the identification of zooplankton. Plos One 2017, 12 (10). 46. Altermatt, F., Diversity in riverine metacommunities: a network perspective. Aquat. Ecol. 2013, 47 (3), 365-377. 47. Brack, W.; Escher, B. I.; Muller, E.; Schmitt-Jansen, M.; Schulze, T.; Slobodnik, J.; Hollert, H., Towards a holistic and solution-oriented monitoring of chemical status of European



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water bodies: how to support the EU strategy for a non-toxic environment? Environ. Sci. Eur. 2018, 30 (1), 33. 48. Leung, K. M., Joining the dots between omics and environmental management. Integr. Environ. Assess. Manag. 2018, 14 (2), 169-173.


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Figure Legends

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Figure 1. Ecological pathways of toxic substances. The middle triangle shows interactions of

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toxic stressor, functional keystone species, biodiversity and ecosystem service. Ecosystem

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service of aquatic ecosystem can be degraded by toxic stressors directly (decreasing quality)

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or via “indirect” mechanisms (population decline, abnormal behavior of functional species via

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toxicity, or alteration of biodiversity and community structure). Toxic substances, such as

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metals, organic chemicals and hazardous materials, can directly induce toxicity on functional

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species, and alter the biodiversity directly or indirectly. In the field, alterations at the level of

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biodiversity and community structure often lead to irreversible changes of ecosystem and

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adverse outcomes of ecosystem service. The three board arrows indicate approaches to address

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the pollution issue of toxic substance. Prediction of ecological adverse outcome by toxic

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stressor are to be extended from testing at the level of certain species, to biodiversity and

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community level. Monitoring of biodiversity and community structure in the field can not only

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be used to assess the health status of aquatic ecosystem, but also to characterize its functionality.

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Evaluation of ecosystem services in this context can be used to diagnose the causation of

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adverse outcomes.

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Figure 2. Environmental DNA technologies provide powerful utilities to assess ecological

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adverse effects by stressors at different levels of biological organization.

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Figure 3. Building up the connection between laboratory test and field biomonitoring by eDNA

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approaches in ecotoxicological study. The white box indicates the conventional morphology-

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based approach; the orange box indicates the computational approaches; the green box

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indicates the eDNA based approaches.

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Figure 4. A conceptual framework to characterize community effects (phylogenetic diversity,

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community structure and function) of chemical stressor by use of eDNA approach.



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Characterization of the concentration dependent response by stressor can used to estimate its

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environmental threshold or criteria in risk assessment.

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Figure 5. Ecological adverse outcome pathways (eAOP) expands the current chemical AOP

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concept (blue zone) to other stressors (yellow zone) and to community and ecosystem (green

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zone). MIE: molecular initiating event; KE-c: key event at cellular level; KE-t: key event at

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tissue or organ level; AOR: adverse outcome at regulatory endpoints; KE-i: key event at

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individual level; AOB: adverse outcome at biodiversity level (including keystone species);

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AOE: adverse outcome at ecosystem function level.

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Figure 2

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Figure 3

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Environmental DNA Shaping A New Era of Ecotoxicological Research

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