Open Literature Review Summary

Chemical Name: Atrazine

CAS No: 1912-24-9

PC Code: 080803

ECOTOX Record Number and Citation: Andrus, J.M., D. Winter, M. Scan lan, S. Sullivan, W. Bollman, J.B. Waggoner, A.J. Hosmer, R.A. Brain. 2015. Spatial and temporal variation of algal assemblages in six Midwest agricultural streams having varying levels of atrazine and other physiochemical attr ibutes. Science of the Total Environment 505: 65-89.

Date of Review: February 23, 2016

- This research article describes the seasonal and environmental factors that were found to be potentially influencing changes in aquatic plant comm unities that have recorded atrazine exposures.

- The study sites were selected from a fe w of the AEEMP monitoring program and continued with watersheds sampled in the authors 2013 publication (Andrus et al. 2013; IA-03, IL-10, MO-05) while adding th ree additional sites from the program (IL17, MO-07N, HO-05). Community sampling site s were selected at one location at the point closest to the AEEMP monitoring program data collection location.

Table 1 : Peak and 60-day maximum runn ing average concentrations ( μ g/L) of measured atrazine concentrations reported in the AEEMP monitoring program (EPA calculated averages). Shaded cells indicate exceedances of the CELOC (60-day maximum average = 3.4 μ g/L)

Year IA-03 IL-10 IL-17 MO-05 MO-07N OH-05 Peak 60- day Avg Peak 60- day Avg Peak 60- day Avg Peak 60- day Avg Peak 60- day Avg Peak 60- day Avg 2010 50.6 10.5 5.0 0.4 36.7 5.7 84.2 12.9 92.5 20.2 11.0 2.0 2011 39.8 4.9 11.5 1.4 228.2 17.2 70.5 6.2 90.9 14.6 30.6 1.4 2012 17.8 2.0 5.4 0.7 39.4 6.3 14.0 4.0 41.1 11.9 14.8 1.9

- The study collected data relevant to assessing the diversity and community composition, as well as abiotic factors that may contribute to changes in the community.

- The study adequately sampled for abiotic environmental factors.

- The algal community sampling was primarily done in a robust manner given the study objectives, with the following concerns:o There was only one replicate included within each site per collection, as opposed to the Andrus etal 2013 study which included 3 samples per collection. It is unclear if the variablity across sites chosen for the 2011 collection year would be consistent with the 2012 collection year and across the newly added sites. The impact of this reduction in samples is unknown.

o Different volumes of water were taken to evaluate phytoplankton samples, thus it is unclear how changes in the phyt oplankton diversity, AFDM, chl-a, and community composition may be impacted by the different sample sizes.

o Periphyton collection using periphytomet ers submerged at a depth of 5 cm may not reflect changes in the stream bed periphyton communities.

o It was not reported how often or in which localities the periphytometers were found above the water surface (dried out), damaged, or incorrectly positioned. The authors briefly mention the sue of the reserved periphytometer in those but it is not clear in the report how often the reserved periphytometers were relied upon or if they showed different communities from the primary periphytometers at the same site locations.

o It is unclear how selection of periphyton samples for chl-a, AFDM and taxonomic identification was made to avoid potential sampling bias.

o Chl-a levels for phytoplankton were below detection limits. The report indicates that instream optical chlorophyll probes were relied upon for two streams, but these were not identified, and impacts on comparisons between the glass fiber filters and the optical probes were not provided to evaluate potential sampling method differences.

o Different water volumes were used to evaluate the taxonomic phytoplankton composition of the non-diatom (10 ml subsample) and diatoms (250 ml subsample) components. Implications of this difference are unknown.

Author Conclusions

- Abundance

o Periphytic communities were dominated by diatoms

 Cyanophyta, green algae and eugleno phyta generally increased over the sampling period.

 Diatoms remained dominant throughout the sampling period

o Phytoplankton

 Diatoms were dominant in the spring

 Cyanophyta increased in May and June,

 Green algae and euglenophyta gained dominance in the summer

o Significant differences in univariate diversity and abundance metrics, as well as diatom indices and periphyton and phytoplankton assemblages between sites and temporally within sites

 Most influential gradient in the CCAs was the stream site.

 Nutrient availability, pH, ion and mineral content, hydrology, temperature, land cover, geology and habitat qualtiy contributed to thevariation associated with the stream sites and accounted for large proportions of the assemblage structure in the CCAs

 Year of sampling explained a large proportion of the assemblage structure in the CCAs, and was attrib uted to the environmental factors described above.

 In this study, variables that exhibited substantial seasonal variation included nitrogen, phosphorus, TSS, depth, flow, water temperature, wind speed and atrazine.

o The average algal taxa richness and evenness values for sites monitored in this study were generally closely in line wi th those from the NAWQA dataset from the same ecoregion.

 diatom taxa richness was much greater than NDA taxa richness in both periphyton and phytoplankton (approximately 2–3 times higher).

 Periphytic and planktonic assemblages taken from AEMP sites typically had similar or slightly greater taxa richness than the corresponding NAWQA sites.

 At IL-17, MO-05, and MO-07N, periph yton taxa richness was substantially higher than mean values for NAWQA sites in the same ecoregions (no comparisons were available for phytoplankton assemblages at these sites).

 However, planktonic diatom assemblages at the IL-10 and OH-05 sites had much higher richness and evenness than comparable NAWQA sites.

 Algal community evenness at the site s monitored in this study was very similar to those at the NAWQA sites, again with the exception of planktonic diatom assemblages at the IL-10 and OH-05 sites, which, although similar to other evenness valu es in this study, were substantially more even than communities at NAWQA sites.

 These data suggest that assemblages from the sites monitored in this study are reasonably typical of local algal communities, although the number of NAWQA samples in each ecoregion varied and therefore the data may be representative of streams in each ecoregion to differing degrees.

- Seasonality

o The algal communities are changing with the seasons, and these changes are similar across the study sites

o Stream flow, NO2+NO3, total phosphorus, solar radiation, pH, wind speed, alkalinity and hardness, and atrazine concentration showed substantial vector projection in the direction of the chronology CA plots for all sites.

o Attribute primary changes in communit y changes to seasonality and flow.

- Atrazine Effects

o the influence of the days since ≥ 30 μ g/L atrazine variable was higher in models of both algal communities than of the days since ≥ 10 μ g/L (the set of events ≥ 10 μ g/L also including events ≥ 30 μ g/L), and both were of greater influence thanthe daily composite atrazine concentrat ion (periphyton: 0.67% and 0.56% vs. 0.33%; phytoplankton: 0.84% and 0.63% vs.0.54%). That the ≥ 30 μ g/L threshold is more influential than the ≥ 10 μ g/L threshold indicates that the set of pulses ≥ 10 μ g/L but <30 ppb are considerably less influential to the community structure. Additionally, the collective inertia explained by the entire group of atrazine variables was low (periphyton: 1.47%; phytoplankton: 2.16%) and was the least of all variable groups [similar results for TSS and nutrients]

o The two variable groups explaining the most unique inertia in both periphyton and phytoplankton assemblages were water chemistry and hydroclimate (periphyton: 5.28% and 6.01%; phytoplankton: 7.46% and 6.19%)

o As a group, the atrazine variables explain the least unique inertia. This finding is consistent both with the first year of data in this study, which suggested that other environmental variables have a greater effect on community structure relative to atrazine (Andrus et al., 2013), and with other studies that found that seasonal or other environmental changes had a larger role in structuring algal community composition than did concentrations of atrazine

o the algal communities at the sites monitored in this study may be more tolerant in general to anthropogenic inputs. It could therefore be expected that diatom dominated communities such as those in this study would be less sensitive overall to atrazine.

o The mean Pollution Index (where Most Pollution Sensitive species have a score of 1 and Most Pollution Tolerant species ha ve a score of 3) at our sites reflect an overall moderate to high pollution toleranc e (IA-03: 1.83; IL-10: 2.15; IL-17: 1.98; MO-05: 1.79; MO-07 N: 1.89; OH-05: 2.08 ). We would therefore expect atrazine concentrations to have less of an obs ervable influence on community structure at the monitored sites as all sites had communities generally tolerant to anthropogenic disturbance.

o Both the periphyton and phytoplankton models suggest that community shifts abate with greater time elapsed since an atrazine perturbation.

o Models of algal univariate metrics and assemblages show several consistent patterns.

 the inclusion of parameters with seas onal dynamics in models reflects algal community succession and known seasonal abundance patterns.

 Geographic variables are also pr evalent in all models, and imply landscape-scale controls on algal co mmunities. Both scour effects from stochastic events and differences in hydrology between 2011 and 2012 are visible in these models. Variation partitioning of these models show that, for diversity and abundance metrics, the amount of variation uniquely explained by individual variable groups was generally of the order shared variation > water chemistry > geography > hydroclimate > atrazine.

 For CCA models of periphyton assemblages, this order was: shared variation > hydroclimate > water ch emistry > geography > atrazine For phytoplankton assemblage the order was: shared variation > water chemistry > hydroclimate > geography > atrazine.

 Collectively, atrazine variables uniquely accounted for no more than 2.2% of inertia in assemblage models.

 The uniquely explained variance or inertia of the days since ≥ 30 ppb atrazine variable in assemblage mode ls was larger than that of other atrazine variables

x This analysis is consistent with the pulsatile behavior of atrazine exposure, where atrazine variables associated with peaks (e.g. days since ≥ 30 ppb)were of considerably greater consequence to algal community structure than variables representing daily or longer term exposure trends (daily composite and site average atrazine concentrations), implyi ng a transitory nature (i.e. recovery of some form) of community changes potentially associated with atrazine

 In all models, substantial variation or inertia was shared between variable groups, suggesting that considerable ambiguity exists in determining the effects of environment on algal communities in these systems.

Description of Use in Document (QUAL, QUAN, INV): QUAL

Rationale for Use: The study contributes to the unders tanding of aquatic plant community composition and seasonal dynamics within stream s exposed to atrazine and other agricultural and anthropogenic stressors.

Limitations: The primary limitation in this study is the lack of a set of reference streams which have had limited or no atrazine exposure. The st reams have peak values that reach toxic levels for individual species based on la boratory testing, and 60-day average concentrations for IA-03, IL-17, MO-05, and MO-07 exceed the 2015 CELO C of 3 ug/L. However, these exceedances of the CELOC represent a 50% chance of an effect on the communities, so not seeing a strong and direct atrazine influence in the study results is not unanticipated, especially given the prior exposure history and anthropogenic impacts expected in these streams.

Primary Reviewer: Frank T. Farruggia Ph.D., EPA/OCSPP/OPP/EFED/ERB3

Digitally signed by FRANK FARRUGGIA DN: c=US, o=U.S. Government, ou=USEPA, ou=Staff, cn=FRANK FARRUGGIA, dnQualifier=0000048865 Date: 2016.03.24 08:59:55 -04'00'