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Journal articles on the topic 'Species sensitivity distributions (SSD)'

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Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Oginah, S. A., L. Posthuma, M. Hauschild, and P. Fantke. "Splitting species sensitivity distributions (SSD) to improve accuracy of ecotoxicity results." Toxicology Letters 350 (September 2021): S179—S180. http://dx.doi.org/10.1016/s0378-4274(21)00666-4.

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2

Kefford, Ben J., Dayanthi Nugegoda, Leon Metzeling, and Elizabeth J. Fields. "Validating species sensitivity distributions using salinity tolerance of riverine macroinvertebrates in the southern Murray–Darling Basin (Victoria, Australia)." Canadian Journal of Fisheries and Aquatic Sciences 63, no. 8 (August 1, 2006): 1865–77. http://dx.doi.org/10.1139/f06-080.

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Species sensitivity distributions (SSDs) are commonly used in risk assessment and in setting water quality guidelines, yet their predictions have not been validated against loss of species with increasing pollutant concentrations in nature. We used a rapid toxicity testing method to determine the acute salinity tolerance (72 h LC50 values (concentration of salinity lethal to 50% of individuals)) of 110 macroinvertebrate taxa from the southern Murray–Darling Basin in central Victoria, Australia, and construct an SSD. This SSD was compared with loss of riverine macro invertebrates species from increasing salinity in Victoria. Macroinvertebrate species richness per individual sample, when salinity was <9.9 mS·cm–1, was invariant of salinity. However, when species richness was calculated across multiple samples above about 0.3–0.5 mS·cm–1, it declined with increasing salinity. This decline was predicted from the SSD after application of a variable safety factor calculated from an exponential or quadratic equation. Our findings confirm that SSDs can predict the loss of freshwater macroinvertebrate species from increases in salinity. This suggests that SSDs may be useful more generally for other aquatic organisms, other stressors, and toxicants.

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3

Iwasaki, Yuichi, and Kiyan Sorgog. "Estimating species sensitivity distributions on the basis of readily obtainable descriptors and toxicity data for three species of algae, crustaceans, and fish." PeerJ 9 (March 3, 2021): e10981. http://dx.doi.org/10.7717/peerj.10981.

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Estimation of species sensitivity distributions (SSDs) is a crucial approach to predicting ecological risks and water quality benchmarks, but the amount of data required to implement this approach is a serious constraint on the application of SSDs to chemicals for which there are few or no toxicity data. The development of statistical models to directly estimate the mean and standard deviation (SD) of the logarithms of log-normally distributed SSDs has recently been proposed to overcome this problem. To predict these two parameters, we developed multiple linear regression models that included, in addition to readily obtainable descriptors, the mean and SD of the logarithms of the concentrations that are acutely toxic to one algal, one crustacean, and one fish species, as predictors. We hypothesized that use of the three species’ mean and SD would improve the accuracy of the predicted means and SDs of the logarithms of the SSDs. We derived SSDs for 60 chemicals based on quality-assured acute toxicity data. Forty-five of the chemicals were used for model fitting, and 15 for external validation. Our results supported previous findings that models developed on the basis of only descriptors such as log KOW had limited ability to predict the mean and SD of SSD (e.g., r2 = 0.62 and 0.49, respectively). Inclusion of the three species’ mean and SD, in addition to the descriptors, in the models markedly improved the predictions of the means and SDs of SSDs (e.g., r2 = 0.96 and 0.75, respectively). We conclude that use of the three species’ mean and SD is promising for more accurately estimating an SSD and thus the hazardous concentration for 5% of species in cases where limited ecotoxicity data are available.

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4

Charles, Sandrine, Dan Wu, and Virginie Ducrot. "How to account for the uncertainty from standard toxicity tests in species sensitivity distributions: An example in non-target plants." PLOS ONE 16, no. 1 (January 7, 2021): e0245071. http://dx.doi.org/10.1371/journal.pone.0245071.

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This research proposes new perspectives accounting for the uncertainty on 50% effective rates (ER50) as interval input for species sensitivity distribution (SSD) analyses and evaluating how to include this uncertainty may influence the 5% Hazard Rate (HR5) estimation. We explored various endpoints (survival, emergence, shoot-dry-weight) for non-target plants from seven standard greenhouse studies that used different experimental approaches (vegetative vigour vs. seedling emergence) and applied seven herbicides at different growth stages. Firstly, for each endpoint of each study, a three-parameter log-logistic model was fitted to experimental toxicity test data for each species under a Bayesian framework to get a posterior probability distribution for ER50. Then, in order to account for the uncertainty on the ER50, we explored two censoring criteria to automatically censor ER50 taking the ER50 probability distribution and the range of tested rates into account. Secondly, based on dose-response fitting results and censoring criteria, we considered input ER50 values for SSD analyses in three ways (only point estimates chosen as ER50 medians, interval-censored ER50 based on their 95% credible interval and censored ER50 according to one of the two criteria), by fitting a log-normal distribution under a frequentist framework to get the three corresponding HR5 estimates. We observed that SSD fitted reasonably well when there were at least six distinct intervals for the ER50 values. By comparing the three SSD curves and the three HR5 estimates, we shed new light on the fact that both propagating the uncertainty from the ER50 estimates and including censored data into SSD analyses often leads to smaller point estimates of HR5, which is more conservative in a risk assessment context. In addition, we recommend not to focus solely on the point estimate of the HR5, but also to look at the precision of this estimate as depicted by its 95% confidence interval.

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5

Park, Jinhee, and Sang Don Kim. "Derivation of Predicted No Effect Concentrations (PNECs) for Heavy Metals in Freshwater Organisms in Korea Using Species Sensitivity Distributions (SSDs)." Minerals 10, no. 8 (August 6, 2020): 697. http://dx.doi.org/10.3390/min10080697.

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Natural and artificial heavy metal exposure to the environment requires finding thresholds to protect aquatic ecosystems from the toxicity of heavy metals. The threshold is commonly called a predicted no effect concentration (PNEC) and is thought to protect most organisms in an ecosystem from a chemical. PNEC is derived by applying a large assessment factor (AF) to the toxicity value of the most sensitive organism to a chemical or by developing a species sensitivity distribution (SSD), which is a cumulative distribution function with many toxicity data for a chemical of diverse organisms. This study developed SSDs and derived PNECs using toxicity data of organisms living in Korea for four heavy metals: copper (Cd), cadmium (Cu), lead (Pb), and zinc (Zn). Five distribution models were considered with log-transformed toxicity data, and their fitness and uncertainty were investigated. As a result, the normal distribution and Gumbel distribution fit the data well. In contrast, the Weibull distribution poorly accounted for the data at the lower tails for all of the heavy metals. The hazardous concentration for 5% of species (HC5) derived from the most suitable model for each heavy metal was calculated to be the preferred PNEC by AF 2 or AF 3. PNECs, obtained through a suitable SSD model with resident species and reasonable AF, will help protect freshwater organisms in Korea from heavy metals.

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6

Mano, Beatriz, Fátima Jesus, Fernando J. M. Gonçalves, Sónia P. M. Ventura, and Joana Luísa Pereira. "Applicability of heuristic rules defining structure–ecotoxicity relationships of ionic liquids: an integrative assessment using species sensitivity distributions (SSD)." Green Chemistry 22, no. 18 (2020): 6176–86. http://dx.doi.org/10.1039/d0gc02486d.

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7

Liu, Yuxia, Qixing Zhou, Yi Wang, Siwen Cheng, and Weiduo Hao. "Deriving Soil Quality Criteria of Chromium Based on Species Sensitivity Distribution Methodology." Toxics 9, no. 3 (March 16, 2021): 58. http://dx.doi.org/10.3390/toxics9030058.

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Chromium (Cr) is one of the most severe heavy metal contaminants in soil, and it seriously threatens ecosystems and human health through the food chain. It is fundamental to collect toxicity data of Cr before developing soil quality criteria/standards in order to efficiently prevent health risks. In this work, the short-term toxic effects of Cr(VI) and Cr(III) on the root growth of eleven terrestrial plants were investigated. The corresponding fifth percentile hazardous concentrations (HC5) by the best fitting species sensitivity distribution (SSD) curves based on the tenth percentile effect concentrations (EC10) were determined to be 0.60 and 4.51 mg/kg for Cr (VI) and Cr (III), respectively. Compared to the screening level values worldwide, the HC5 values in this study were higher for Cr(VI) and lower for Cr(III) to some extent. The results provide useful toxicity data for deriving national or local soil quality criteria for trivalent and hexavalent Cr.

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8

Mu, Yunsong, Fengchang Wu, Cheng Chen, Yuedan Liu, Xiaoli Zhao, Haiqing Liao, and John P. Giesy. "Predicting criteria continuous concentrations of 34 metals or metalloids by use of quantitative ion character-activity relationships–species sensitivity distributions (QICAR–SSD) model." Environmental Pollution 188 (May 2014): 50–55. http://dx.doi.org/10.1016/j.envpol.2014.01.011.

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9

Liu, Wen-Xiu, Wei He, Ning Qin, Xiang-Zhen Kong, Qi-Shuang He, Hui-Ling Ouyang, Bin Yang, et al. "Residues, Distributions, Sources, and Ecological Risks of OCPs in the Water from Lake Chaohu, China." Scientific World Journal 2012 (2012): 1–16. http://dx.doi.org/10.1100/2012/897697.

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The levels of 18 organochlorine pesticides (OCPs) in the water from Lake Chaohu were measured by a solid phase extraction-gas chromatography-mass spectrometer detector. The spatial and temporal distribution, possible sources, and potential ecological risks of the OCPs were analyzed. The annual mean concentration for the OCPs in Lake Chaohu was 6.99 ng/L. Aldrin, HCHs, and DDTs accounted for large proportions of the OCPs. The spatial pollution followed the order of Central Lakes > Western Lakes > Eastern Lakes and water area. The sources of the HCHs were mainly from the historical usage of lindane. DDTs were degraded under aerobic conditions, and the main sources were from the use of technical DDTs. The ecological risks of 5 OCPs were assessed by the species sensitivity distribution (SSD) method in the order of heptachlor > γ-HCH > p,p′-DDT > aldrin > endrin. The combining risks of all sampling sites were MS > JC > ZM > TX, and those of different species were crustaceans > fish > insects and spiders. Overall, the ecological risks of OCP contaminants on aquatic animals were very low.

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10

Xu, Fu-Liu, Yi-Long Li, Yin Wang, Wei He, Xiang-Zhen Kong, Ning Qin, Wen-Xiu Liu, Wen-Jing Wu, and Sven Erik Jorgensen. "Key issues for the development and application of the species sensitivity distribution (SSD) model for ecological risk assessment." Ecological Indicators 54 (July 2015): 227–37. http://dx.doi.org/10.1016/j.ecolind.2015.02.001.

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11

Jeong, Buyun, Jinsung An, and Kyoungphile Nam. "Time series analysis for determining ecologically acceptable Cu concentration from species sensitivity distribution with biotic ligand models in soil pore water." Environmental Engineering Research 26, no. 2 (April 3, 2020): 200021–0. http://dx.doi.org/10.4491/eer.2020.021.

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A site-specific, ecologically acceptable concentration of Cu in soil pore water was determined with four trophic levels of soil-residing organisms. Specifically, soil pore water was periodically collected from a site contaminated with heavy metals using in-situ samplers. Dissolved Cu concentration, Ca2+, Mg2+, Na+, K+, Cl-, SO42-, NO3-, dissolved organic carbon, pH, and temperature were analyzed to derive a half-maximal effective concentration of Cu (EC50[Cu]T) using a biotic ligand model (BLM). The BLM parameters, such as binding constants (logKXBL) and the fraction of biotic ligand sites occupied by Cu ions (f), were adapted from previous studies. The EC50{Cu2+,} values were used to construct a species sensitivity distribution (SSD) curve from which the hazardous concentration, protecting 95% of the soil-residing organisms (HC5), was determined. Using ten BLM-based acceptable concentrations of Cu obtained by combining BLM and SSD, time series analysis was conducted with the fixed monitoring benchmark method to obtain maximum Cu concentration as an endpoint exhibiting no-adverse-effect which was found to be 0.084 mg/L of Cu in soil pore water at the test site. This study provides a systematic tool for determining an ecologically acceptable concentration of Cu in the soil by incorporating soil pore water chemistry and time series analysis.

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12

Jung, Jae-Woong, Jae Soon Kang, Jinsoo Choi, and June-Woo Park. "A Novel Approach to Derive the Predicted No-Effect Concentration (PNEC) of Benzophenone-3 (BP-3) Using the Species Sensitivity Distribution (SSD) Method: Suggestion of a New PNEC Value for BP-3." International Journal of Environmental Research and Public Health 18, no. 7 (March 31, 2021): 3650. http://dx.doi.org/10.3390/ijerph18073650.

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The necessity for the aquatic ecological risk assessment for benzophenone-3 (BP-3) is increasing due to its high toxic potential and high detection frequency in freshwater. The initial step in the ecological risk assessment is to determine predicted no-effect concentration (PNEC). This study derived PNEC of BP-3 in freshwater using a species sensitivity distribution (SSD) approach, whilst existing PNECs are derived using assessment factor (AF) approaches. A total of eight chronic toxicity values, obtained by toxicity testing and a literature survey, covering four taxonomic classes (fish, crustaceans, algae, and cyanobacteria) were used for PNEC derivation. Therefore, the quantity and quality of the toxicity data met the minimum requirements for PNEC derivation using an SSD approach. The PNEC derived in this study (73.3 μg/L) was far higher than the environmental concentration detected in freshwater (up to 10.4 μg/L) as well as existing PNECs (0.67~1.8 μg/L), mainly due to the difference in the PNEC derivation methodology (i.e., AF vs. SSD approach). Since the SSD approach is regarded as more reliable than the AF approach, we recommend applying the PNEC value derived in this study for the aquatic ecological risk assessment of BP-3, as the use of the existing PNEC values seems to unnecessarily overestimate the potential ecological risk of BP-3 in freshwater.

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13

Bandeira, Felipe Ogliari, Paulo Roger Lopes Alves, Thuanne Braúlio Hennig, Juliane Brancalione, Diego José Nogueira, and William Gerson Matias. "Chronic effects of clothianidin to non-target soil invertebrates: Ecological risk assessment using the species sensitivity distribution (SSD) approach." Journal of Hazardous Materials 419 (October 2021): 126491. http://dx.doi.org/10.1016/j.jhazmat.2021.126491.

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14

Kefford, Ben J., Carolyn G. Palmer, and Dayanthi Nugegoda. "Relative salinity tolerance of freshwater macroinvertebrates from the south-east Eastern Cape, South Africa compared with the Barwon Catchment, Victoria, Australia." Marine and Freshwater Research 56, no. 2 (2005): 163. http://dx.doi.org/10.1071/mf04098.

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Salinity is rising in many southern African and Australian rivers with unknown effects on aquatic organisms. The extent of spatial variation, at any scale, in salt tolerances of aquatic organisms is unknown, so whether data from one location is applicable elsewhere is also unknown. The acute tolerances (72-h median lethal concentration (LC50)) to sea salt of 49 macroinvertebrate taxa from the south-east Eastern Cape (SEEC), South Africa were compared with those of 57 species from the Barwon Catchment, Victoria, Australia. The mean LC50 values from both locations were similar (Barwon: 31 and SEEC: 32 mS cm−1) and less abundant (rare) taxa tended to be more tolerant than more abundant (common) taxa. There was, however, a greater range of LC50 values (5.5–76 mS cm−1) in the Barwon Catchment than in the SEEC (11–47 mS cm−1). The species sensitivity distribution (SSD) for SEEC taxa was bimodal whereas the Barwon Catchment’s SSD had a single peak. With few exceptions, members of an order had similar tolerances in both locations. The differences in SSD between locations were related to crustacean, odonate and non-arthropod relative richness. Although it is not ideal to extrapolate SSDs from one location to another, it may be reasonable to assume similar salinity tolerances among related taxa.

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15

Botha, Tarryn L., Tanyn E. James, and Victor Wepener. "Comparative Aquatic Toxicity of Gold Nanoparticles and Ionic Gold Using a Species Sensitivity Distribution Approach." Journal of Nanomaterials 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/986902.

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Gold nanoparticles (nAu) are used in drug delivery systems allowing for targeted cellular distribution. The effects of increased use and release of nanoparticles into the environment are not well known. A species sensitivity distribution (SSD) allows for the ecotoxicological hazard assessment of a chemical based on single species toxicity tests. Aquatic toxicity needs to be related to particle characterization in order to understand the effects. The behaviour of nAu in the medium changed as the concentration increased. The toxic potential of ionic gold and nAu was expressed as a hazardous concentration where 5% of species will be harmed (HC5). The HC5 for nAu was much higher (42.78 mg/L) compared to the ionic gold (2.44 mg/L). The differences between the hazard potentials of nAu and ionic gold were attributed to the nAu not releasing any Au ions into solution during the exposures and following an aggregation theory response. Exposures to ionic gold on the other hand followed a clear dose dependent response based on the concentration of the ionic metal. Although SSDs present an indication of the relative hazard potential of nanoparticles, the true worth can only be achieved once other nanoparticle characteristics and their behavior in the environment are also considered.

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16

Guan, Bo, Lei Guo, Mark Gibson, and Zhengyan Li. "The derivation of water quality criteria for bisphenol A for the protection of marine species in China." Water Quality Research Journal 53, no. 3 (April 25, 2018): 156–65. http://dx.doi.org/10.2166/wqrj.2018.035.

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Abstract Bisphenol A (BPA) is an environmental estrogen that occurs widely in the aquatic environment and causes feminization to various species, including fishes and gastropods. This study aims to develop the water quality criteria for BPA in the marine environment using the species sensitivity distribution (SSD) methodology from a scientific basis. Both acute and chronic toxicity data tested with saltwater species resident to China were collected. Additional tests were conducted to supplement toxicity data with local saltwater biota, including mollusk (Ruditapes philippinarum) and fish species (Scophthalmus maximus and Pagrosomus major). Based on SSD modelling, the criterion maximum concentration of BPA was estimated to be 273 μg/L. The criterion continuous concentration (CCC) for reproductive and non-reproductive effects was calculated to be 0.46 μg/L and 4.90 μg/L, respectively. Based on the derived criteria, the acute risk of BPA in coastal waters of China was determined to be negligible with RQs (risk quotients) of <0.01. The chronic risk was however much higher with RQs of up to 0.4 and 4.3 based on non-reproductive and reproductive CCC, respectively. The ecological risk assessment for BPA based on reproductive CCC can, therefore, better protect the safety of marine species.

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17

Qie, Yu, Cheng Chen, Fei Guo, Yunsong Mu, Fuhong Sun, Hao Wang, Ying Wang, et al. "Predicting criteria continuous concentrations of metals or metalloids for protecting marine life by use of quantitative ion characteristic–activity relationships–species sensitivity distributions (QICAR-SSD)." Marine Pollution Bulletin 124, no. 2 (November 2017): 639–44. http://dx.doi.org/10.1016/j.marpolbul.2017.02.055.

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18

Gao, Pei, Lei Guo, Zhengyan Li, and Mark Gibson. "The derivation of water quality criteria for nonylphenol considering its endocrine disrupting features." Water Quality Research Journal 50, no. 3 (May 6, 2015): 268–78. http://dx.doi.org/10.2166/wqrjc.2015.032.

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Nonylphenol (NP) is an endocrine disruptor and causes feminization in various organisms. This study aims to determine the water quality criteria for NP in China based on species sensitivity distribution (SSD) models considering both reproductive and traditional toxicity effects. A total of 17 chronic values with reproductive endpoints and 14 chronic values with traditional endpoints tested with aquatic species resident in China were found in published literature, among which six values were from marine species. As chronic toxicity data for marine species were limited, the acute-to-chronic toxicity ratio methodology was employed to extrapolate from acute-to-chronic toxicity values. The SSD models were then built with a whole set of chronic toxicity values for NP. Based on model simulation, the chronic water quality criterion in fresh water was calculated as 1.37 μg/L and 4.29 μg/L for reproductive endpoints and traditional endpoints, respectively. The criterion in seawater was derived as 1.68 μg/L for traditional endpoints. Although these criteria were derived by a third-party organization not affiliated with the Chinese authority for criteria development, they were obtained from a scientific point of view and can be used to evaluate water quality and ecological risks of nonylphenol in various water bodies.

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19

Bogart, Sarah J., Ali Azizishirazi, and Greg G. Pyle. "Challenges and future prospects for developing Ca and Mg water quality guidelines: a meta-analysis." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1764 (December 3, 2018): 20180364. http://dx.doi.org/10.1098/rstb.2018.0364.

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Anthropogenic activities have the potential to increase water hardness (Ca + Mg) in receiving waters to toxic concentrations, and thus, water quality guidelines (WQG) for Ca and Mg are warranted. However, Ca can modify Mg toxicity in Ca-poor water and additional interactions with other major ions (Na + , K + , HCO 3 − /CO 3 2− , SO 4 2− and Cl − ) may occur, potentially obscuring the water hardness–effect relationship. In a meta-analysis of toxicological studies, we: (i) evaluate the performance of three WQG derivation methods, and (ii) determine the influence of several variables (acute/chronic data, anions, Ca:Mg ratios, non-geographically relevant species) on the models. We find that the most sensitive species- or species sensitivity distribution (SSD)-based WQG derivation methods greatly overestimate water hardness toxicity, particularly if non-resident species are included. Broad-scale implementation of most sensitive species- or SSD-based WQG is impractical because water hardness varies beyond and within the regional scale. Anion type does not affect water hardness toxicity across species, but the Ca : Mg ratio is toxicologically relevant, underscoring the importance of considering ion ratios when developing major ion WQG. Although data supporting formal water hardness WQG are unavailable, we suggest using a two-component background condition approach that supports simultaneous management of water hardness and Ca : Mg ratio, and WQG that are applicable beyond the regional scale. This article is part of the theme issue ‘Salt in freshwaters: causes, ecological consequences and future prospects’.

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20

He, Wei, Xiangzhen Kong, Ning Qin, Qishuang He, WenXiu Liu, Zelin Bai, Yin Wang, and Fuliu Xu. "Combining species sensitivity distribution (SSD) model and thermodynamic index (exergy) for system-level ecological risk assessment of contaminates in aquatic ecosystems." Environment International 133 (December 2019): 105275. http://dx.doi.org/10.1016/j.envint.2019.105275.

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21

Reinecke, Koot, Werner Schoeman, and Sophié Reinecke. "Cellular biomarker responses of limpets (Mollusca) as measure of sensitivity to cadmiumcontamination." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 27, no. 2 (September 16, 2008): 123–42. http://dx.doi.org/10.4102/satnt.v27i2.86.

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Due to the availability and chemical nature of some heavy metals, sub-lethal toxicant levels may persist in the ocean waters and may cause physiological problems and toxicity in invertebrates and other marine organisms. Although studies of metal concentrations in False Bay showed relatively low mean concentrations of Cd, invertebrates such as molluscs, crustaceans and many other groups are able to accumulate high levels of heavy metals in their tissues and still survive in the heaviest polluted areas. They can accumulate numerous pollutants from natural waters in quantities that are many orders of magnitude higher than background levels. Bioaccumulation ofcadmium in intertidal species could cause stress which may be measurable at the cellular level. A variety of limpet species that may serve as suitable ecotoxicological monitoring species occur in abundance on rocky shores along the South African coastline. The aim of this study was to obtain sensitivity data which could contribute to the selection of a suitable monitoring species and the eventual establishment of a species sensitivity distribution model (SSD) with a biomarker responseas endpoint. The limpets Cymbula oculus, Scutellastra longicosta, Cymbula granatina and Scutellastragranularis as well as water samples were collected at two localities in False Bay, South Africa. Analysis of water and biological samples were done by atomic absorption spectrometry. Exposures were done to three different sublethal concentrations of cadmium in the laboratory in static flow tanks over three days. There was a moderate increase in cadmium body concentrations over time. Results obtained at three exposure concentrations showed no significant differences in metal concentrations between the different C. oculus samples. Significant differences were obtained between the control and the exposure groups for each exposure time except between the control and the 1mg/L CdCl2 exposure group after 24 and 72 hours of exposure. Cd body concentrations(soft tissue) varied between 4.56 and 21.41µg/g (wet mass).Mean Cd concentrations in soft tissue of S. longicosta was considerably lower (varying between 1.18 and 19.58 µg/g Cd ) than in the tissues of C. oculus. The control group differed significantly from the 0.8 and 1 mg/L CdCl2 exposures after 48 and 72 hours. Mean Cd body concentrations in S. granular is were the highest of all exposed species, reaching a level of 148 µg/g Cd at the highest exposure concentration and differed significantly from the means of the other samples of the 0.8 mg/L CdCl2 exposure group after 72 hours and from the 1 mg/L CdCl2 group after 24 hours. Significant differences were also obtained between theCd body concentrations of C. granatina for the three exposure concentrations and three exposure times. Lysosomal membrane integrity was determined for both exposed and control animals, using the neutral red retention assay. Three of the four species showed a significant decrease in retention times with an increase in Cd concentration. Inter-species differences in sensitivity to environmentally relevant cadmium concentrations were reflected in the biomarker responses. Based on reduction of NRR times, the order of relative sensitivity to cadmium was S. granularis >C. oculus> S. longicosta.> C.granatina.

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MIURA, Patrícia Tidori, Claudio Martín JONSSON, Sonia Claudia do Nascimento de QUEIROZ, Edsandra Campos CHAGAS, Francisco Célio Maia CHAVES, and Felix Guillermo Reyes REYES. "Ecological risk assessment of Piper aduncum essential oil in non-target organisms." Acta Amazonica 51, no. 1 (March 2021): 71–78. http://dx.doi.org/10.1590/1809-4392202002691.

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ABSTRACT One possible alternative to chemotherapeutic agents in the treatment and prevention of diseases in fish farms is the use of Piper aduncum essential oil. However, ecotoxicological data are required to ensure its proper use and to prevent adverse effects on non-target organisms. These data are relevant since this essential oil is described as having insecticidal, molluscicidal and cytotoxic activitiy that may be associated with its chemical composition. Thus, the aim of this study was to evaluate the ecotoxicity of P. aduncum essential oil to five test organisms using the species sensitivity distribution (SSD) statistical approach. The chemical composition of the essential oil was characterized by means of gas chromatography coupled to mass spectrometry (GC-MS) and gas chromatography-flame ionization detection (GC-FID) for identification and quantitation purposes, respectively. The main component (75.5%) of the essential oil was dillapiole. The hazardous concentration for 5% of biological species (HC5) was calculated to determine the 95% protection level, resulting in a value of 0.47 mg L-1 (with a confidence interval of 0.028 - 1.19 mg L-1. ). A concentration range related to the level of protection for aquatic communities (the predicted no-effect concentration, PNEC) was determined through the application of safety factors to the HC5 value. The ecotoxicity parameters showed that P. aduncum essential oil can be used safely in water bodies at a concentration equal to or below 0.09 mg L-1.

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23

Hiki, Kyoshiro, and Yuichi Iwasaki. "Can We Reasonably Predict Chronic Species Sensitivity Distributions from Acute Species Sensitivity Distributions?" Environmental Science & Technology 54, no. 20 (September 14, 2020): 13131–36. http://dx.doi.org/10.1021/acs.est.0c03108.

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24

Fox, David R., and Elise Billoir. "Time-dependent species sensitivity distributions." Environmental Toxicology and Chemistry 32, no. 2 (December 28, 2012): 378–83. http://dx.doi.org/10.1002/etc.2063.

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25

Garner, Kendra L., Sangwon Suh, Hunter S. Lenihan, and Arturo A. Keller. "Species Sensitivity Distributions for Engineered Nanomaterials." Environmental Science & Technology 49, no. 9 (April 24, 2015): 5753–59. http://dx.doi.org/10.1021/acs.est.5b00081.

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26

Wheeler, J. R., E. P. M. Grist, K. M. Y. Leung, D. Morritt, and M. Crane. "Species sensitivity distributions: data and model choice." Marine Pollution Bulletin 45, no. 1-12 (September 2002): 192–202. http://dx.doi.org/10.1016/s0025-326x(01)00327-7.

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27

Azevedo, Ligia B., An M. De Schryver, A. Jan Hendriks, and Mark A. J. Huijbregts. "Calcifying Species Sensitivity Distributions for Ocean Acidification." Environmental Science & Technology 49, no. 3 (January 13, 2015): 1495–500. http://dx.doi.org/10.1021/es505485m.

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Forbes, Valery E., and Peter Calow. "Species Sensitivity Distributions Revisited: A Critical Appraisal." Human and Ecological Risk Assessment: An International Journal 8, no. 3 (July 2002): 473–92. http://dx.doi.org/10.1080/10807030290879781.

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van Goethem, T. M. W. J., L. B. Azevedo, R. van Zelm, F. Hayes, M. R. Ashmore, and M. A. J. Huijbregts. "Plant Species Sensitivity Distributions for ozone exposure." Environmental Pollution 178 (July 2013): 1–6. http://dx.doi.org/10.1016/j.envpol.2013.02.023.

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Moore, Dwayne RJ, Colleen D. Priest, Nika Galic, Richard A. Brain, and Sara I. Rodney. "Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions." Integrated Environmental Assessment and Management 16, no. 1 (January 2020): 53–65. http://dx.doi.org/10.1002/ieam.4207.

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D’Andrea, María, and Julie Brodeur. "shinyssd v1.0: Species Sensitivity Distributions for Ecotoxicological Risk Assessment." Journal of Open Source Software 4, no. 37 (May 29, 2019): 785. http://dx.doi.org/10.21105/joss.00785.

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Thorley, Joe, and Carl Schwarz. "ssdtools: An R package to fit Species Sensitivity Distributions." Journal of Open Source Software 3, no. 31 (November 29, 2018): 1082. http://dx.doi.org/10.21105/joss.01082.

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McDaniel, Margaret, and Terry W. Snell. "Probability distributions of toxicant sensitivity for freshwater rotifer species." Environmental Toxicology 14, no. 3 (July 1999): 361–66. http://dx.doi.org/10.1002/(sici)1522-7278(199907)14:3<361::aid-tox10>3.0.co;2-i.

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Wheeler, James R., Kenneth M. Y. Leung, David Morritt, Neal Sorokin, Howard Rogers, Robin Toy, Martin Holt, Paul Whitehouse, and Mark Crane. "Freshwater to saltwater toxicity extrapolation using species sensitivity distributions." Environmental Toxicology and Chemistry 21, no. 11 (November 2002): 2459–67. http://dx.doi.org/10.1002/etc.5620211127.

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Wang, Xiaonan, Zhenguang Yan, Zhengtao Liu, Cong Zhang, Weili Wang, and Handong Li. "Comparison of species sensitivity distributions for species from China and the USA." Environmental Science and Pollution Research 21, no. 1 (September 8, 2013): 168–76. http://dx.doi.org/10.1007/s11356-013-2110-2.

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Kwak, Jin Il, Jongmin Moon, Dokyung Kim, Rongxue Cui, and Youn-Joo An. "Species Sensitivity Distributions for Nonylphenol to Estimate Soil Hazardous Concentration." Environmental Science & Technology 51, no. 23 (November 16, 2017): 13957–66. http://dx.doi.org/10.1021/acs.est.7b04433.

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Adam, Nathalie, Claudia Schmitt, Luc De Bruyn, Dries Knapen, and Ronny Blust. "Aquatic acute species sensitivity distributions of ZnO and CuO nanoparticles." Science of The Total Environment 526 (September 2015): 233–42. http://dx.doi.org/10.1016/j.scitotenv.2015.04.064.

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Leung, Kenneth M. Y., John S. Gray, W. K. Li, Gilbert C. S. Lui, Yuan Wang, and Paul K. S. Lam. "Deriving Sediment Quality Guidelines from Field-Based Species Sensitivity Distributions." Environmental Science & Technology 39, no. 14 (July 2005): 5148–56. http://dx.doi.org/10.1021/es050450x.

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Coffey, D. Brooke, Susan Cormier, and John Harwood. "Using Field-Based Species Sensitivity Distributions to Infer Multiple Causes." Human and Ecological Risk Assessment: An International Journal 20, no. 2 (November 25, 2013): 402–32. http://dx.doi.org/10.1080/10807039.2013.767071.

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Awkerman, Jill A., Sandy Raimondo, Crystal R. Jackson, and Mace G. Barron. "Augmenting aquatic species sensitivity distributions with interspecies toxicity estimation models." Environmental Toxicology and Chemistry 33, no. 3 (January 24, 2014): 688–95. http://dx.doi.org/10.1002/etc.2456.

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Smetanová, S., L. Bláha, M. Liess, R. B. Schäfer, and M. A. Beketov. "Do predictions from Species Sensitivity Distributions match with field data?" Environmental Pollution 189 (June 2014): 126–33. http://dx.doi.org/10.1016/j.envpol.2014.03.002.

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Dalgarno, Seb. "shinyssdtools: A web application for fitting Species Sensitivity Distributions (SSDs)." Journal of Open Source Software 6, no. 57 (January 25, 2021): 2848. http://dx.doi.org/10.21105/joss.02848.

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Giddings, Jeffrey M., Gertie Arts, and Udo Hommen. "The relative sensitivity of macrophyte and algal species to herbicides and fungicides: An analysis using species sensitivity distributions." Integrated Environmental Assessment and Management 9, no. 2 (February 15, 2013): 308–18. http://dx.doi.org/10.1002/ieam.1387.

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Maltby, Lorraine, Naomi Blake, Theo C. M. Brock, and Paul J. Van den Brink. "INSECTICIDE SPECIES SENSITIVITY DISTRIBUTIONS: IMPORTANCE OF TEST SPECIES SELECTION AND RELEVANCE TO AQUATIC ECOSYSTEMS." Environmental Toxicology and Chemistry 24, no. 2 (2005): 379. http://dx.doi.org/10.1897/04-025r.1.

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Beaudouin, Rémy, and Alexandre R. R. Péry. "COMPARISON OF SPECIES SENSITIVITY DISTRIBUTIONS BASED ON POPULATION OR INDIVIDUAL ENDPOINTS." Environmental Toxicology and Chemistry 32, no. 5 (March 19, 2013): 1173–77. http://dx.doi.org/10.1002/etc.2148.

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Duboudin, Cédric, Philippe Ciffroy, and Hélène Magaud. "EFFECTS OF DATA MANIPULATION AND STATISTICAL METHODS ON SPECIES SENSITIVITY DISTRIBUTIONS." Environmental Toxicology and Chemistry 23, no. 2 (2004): 489. http://dx.doi.org/10.1897/03-159.

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Monti, Gianna S., Peter Filzmoser, and Roland C. Deutsch. "A Robust Approach to Risk Assessment Based on Species Sensitivity Distributions." Risk Analysis 38, no. 10 (May 3, 2018): 2073–86. http://dx.doi.org/10.1111/risa.13009.

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Boeckman, Chad J., and Raymond Layton. "Use of species sensitivity distributions to characterize hazard for insecticidal traits." Journal of Invertebrate Pathology 142 (January 2017): 68–70. http://dx.doi.org/10.1016/j.jip.2016.08.006.

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Barron, Mace G., Crystal R. Jackson, and Jill A. Awkerman. "Evaluation of in silico development of aquatic toxicity species sensitivity distributions." Aquatic Toxicology 116-117 (July 2012): 1–7. http://dx.doi.org/10.1016/j.aquatox.2012.02.006.

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Migliorati, Sonia, Gianna Serafina Monti, and Marco Vighi. "Ecological hazard assessment via species sensitivity distributions: The non‐exchangeability issue." Biometrical Journal 63, no. 4 (January 25, 2021): 875–92. http://dx.doi.org/10.1002/bimj.201900404.

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