Two pairs of eyes are better than one: Combining individual-based and matrix models for ecological risk assessment of chemicals

Deriving the predicted no effect concentrations of 35 pesticides by the QSAR-SSD method

The widespread use of pesticides results in their frequent detection in water bodies and other environmental media. Pesticide residues may cause certain risks to the environment and human health, and reliable predicted no effect concentrations (PNEC) must be obtained when assessing environmental risks. Species sensitivity distribution (SSD) is an important method for the derivation of chemical PNECs. Construction of the SSD model requires sufficient toxicity data to various species including at least eight families in three phyla, suitable nonlinear fitting functions and assessment factors (AFs) with certain uncertainty. However, most chemicals could not collect sufficient species toxicity data, while some chemicals had sufficient species toxicity data but could not find suitable fitting functions, thus hindering the construction of effective SSD models. To this end, the established QSAR models were applied to predict toxicity of chemicals to specific species to fill in the toxicity data gaps required for SSD and selecting multiple nonlinear functions to optimize the SSD model. Combined with QSAR and SSD methods, a new method of PNEC derivation was developed and successfully applied to the derivation of PNEC for 35 pesticides. Three QSAR models were used to predict the toxicities of six pesticides with few toxicity data. Nine two-parameter nonlinear functions were used to fit the toxicity-cumulative probability data one by one to determine the optimal SSD models. The hazardous concentrations at the cumulative probability of 5% and 10%, i. e, HC₅ and HC₁₀, respectively, were calculated by the optimal SSD model. The assessment factor used to determine the PNEC of the chemical based on the HC₁₀ was derived from the quantitative correlation between HC₁₀ and HC₅ of pesticides found in this study. When the toxicity data are insufficient, it may be more appropriate to calculate the PNECs of chemicals using HC₁₀ than using HC₅.

The Recolonization Concentration Concept: Using Avoidance Assays with Soil Organisms to Predict the Recolonization Potential of Contaminated Sites

In this study the recolonization concentration concept for soil organisms is presented and validated. This concept is based on the empirically deduced avoidance–recolonization hypothesis, which shows a negative correlation between avoidance (AC_x) and recolonization (RC_x) (AC_x = RC_100−x) responses. The concept was validated in a two-step approach composed by (i) individual placement tests, to demonstrate the non-influence of individual placement in a dual chamber avoidance test and (ii) small scale gradient tests to demonstrate that the number of colonizers reaching a soil patch with a certain concentration is independent on their previous exposure to lower concentrations. Overall, data show that avoidance data can be used, when framed under the recolonization concentration concept, to evaluate the recolonization potential of contaminated sites. The recolonization concept is an important theoretical concept that when coupled with spatial modelling tools could be used to tackle the spatial and temporal recovery dynamics of contaminated soil.

A review of bioenergetic modelling for marine mammal populations

Bioenergetic models describe the processes through which animals acquire energy from resources in the environment and allocate it to different life history functions. They capture some of the fundamental mechanisms regulating individuals, populations and ecosystems and have thus been used in a wide variety of theoretical and applied contexts. Here, I review the development of bioenergetic models for marine mammals and their application to management and conservation. For these long-lived, wide-ranging species, bioenergetic approaches were initially used to assess the energy requirements and prey consumption of individuals and populations. Increasingly, models are developed to describe the dynamics of energy intake and allocation and predict how resulting body reserves, vital rates and population dynamics might change as external conditions vary. The building blocks required to develop such models include estimates of intake rate, maintenance costs, growth patterns, energy storage and the dynamics of gestation and lactation, as well as rules for prioritizing allocation. I describe how these components have been parameterized for marine mammals and highlight critical research gaps. Large variation exists among available analytical approaches, reflecting the large range of life histories, management needs and data availability across studies. Flexibility in modelling strategy has supported tailored applications to specific case studies but has resulted in limited generality. Despite the many empirical and theoretical uncertainties that remain, bioenergetic models can be used to predict individual and population responses to environmental change and other anthropogenic impacts, thus providing powerful tools to inform effective management and conservation.

A Review of Key Features and Their Implementation in Unstructured, Structured, and Agent-Based Population Models for Ecological Risk Assessment

Population models can provide valuable tools for ecological risk assessment (ERA). A growing amount of work on model development and documentation is now available to guide modelers and risk assessors to address different ERA questions. However, there remain misconceptions about population models for ERA, and communication between regulators and modelers can still be hindered by a lack of clarity in the underlying formalism, implementation, and complexity of different model types. In particular, there is confusion about differences among types of models and the implications of including or ignoring interactions of organisms with each other and their environment. In this review, we provide an overview of the key features represented in population models of relevance for ERA, which include density dependence, spatial heterogeneity, external drivers, stochasticity, life-history traits, behavior, energetics, and how exposure and effects are integrated in the models. We differentiate 3 broadly defined population model types (unstructured, structured, and agent-based) and explain how they can represent these key features. Depending on the ERA context, some model features will be more important than others, and this can inform model type choice, how features are implemented, and possibly the collection of additional data. We show that nearly all features can be included irrespective of formalization, but some features are more or less easily incorporated in certain model types. We also analyze how the key features have been used in published population models implemented as unstructured, structured, and agent-based models. The overall aim of this review is to increase confidence and understanding by model users and evaluators when considering the potential and adequacy of population models for use in ERA. Integr Environ Assess Manag 2021;17:521-540. © 2020 SETAC.

Quinolones antibiotics in the Baiyangdian Lake, China: Occurrence, distribution, predicted no-effect concentrations (PNECs) and ecological risks by three methods

The occurrence, distribution, and ecological risk of 10 quinolones (QNs) were investigated in the water and sediment samples from Baiyangdian Lake, China. The field samplings were conducted in April (dry season) and August (wet season) 2018, the results showed that QNs was extensively distributed in the Baiyangdian Lake. For the occurrence, Flumequine (FLU) and Ofloxacin (OFL) were the most detected QNs in Baiyangdian Lake. For the temporal variation, the sum concentration of QNs in water and sediment were ranged from 153 ng/L to 3093 ng/L and from 40.1 ng/g to 1475 ng/g in April, while ranged from 3.83 ng/L to 769 ng/L and from 20.3 ng/g to 373 ng/g in August. For the spatial variation, all of QNs exhibited significance difference in concentration at different sampling areas. Furthermore, PNEC plays an important role in ecological risk assessment, thus the PNECs of FLU and OFL were derived by assessment factors (AF), species sensitivity distribution (SSD), and AQUATOX model methods. The results showed that: PNEC_AFs, PNEC_SSDs, and PNEC_AQUATOXs were 18.7 μg/L, 196 μg/L, and 128 μg/L for FLU, respectively; and were 0.021 μg/L, 4.40 μg/L, and 3.00 μg/L for OFL, respectively. The PNECs for FLU and OFL derived by three approaches showed the rank of: PNEC_SSDs > PNEC_AQUATOXs > PNEC_AFs; while the risk quotients (RQs) followed the other rank of: RQ_SSDs < RQ_AQUATOXs < RQ_AFs. The results was indicated that the indirect ecological effects plays an important role in the derived PNECs for QNs, without considering the indirect ecological effects in natural ecosystem can lead to under-protective or over-protective PNECs (RQs) for chemicals. Therefore, AQUATOX model can be applied in deriving PNECs during the ecological risk assessment.

Deriving predicted no-effect concentrations (PNECs) for emerging contaminants in the river Po, Italy, using three approaches: Assessment factor, species sensitivity distribution and AQUATOX ecosystem modelling

Over the past decades, per- and polyfluoroalkyl substances (PFASs) found in environmental matrices worldwide have raised concerns due to their toxicity, ubiquity and persistence. A widespread pollution of groundwater and surface waters caused by PFASs in Northern Italy has been recently discovered, becoming a major environmental issue, also because the exact risk for humans and nature posed by this contamination is unclear. Here, the Po River in Northern Italy was selected as a study area to assess the ecological risk posed by perfluoroalkyl acids (PFAAs), a class of PFASs, considering the noticeable concentration of various PFAAs detected in the Po waters over the past years. Moreover, the Po has a large environmental and socio-economic importance: it is the largest Italian river and drains a densely inhabited, intensely cultivated and heavily industrialized watershed. Predicted no-effect concentrations (PNECs) were derived using two regulated methodologies, assessment factors (AFs) and species sensitivity distribution (SSD), which rely on published ecotoxicological laboratory tests. Results were compared to those of a novel methodology using the mechanistic ecosystem model AQUATOX to compute PNECs in an ecologically-sound manner, i.e. considering physical, chemical, biological and ecological processes in the river. The model was used to quantify how the biomasses of the modelled taxa in the river food web deviated from natural conditions due to varying inputs of the chemicals. PNEC for each chemical was defined as the lowest chemical concentration causing a non-negligible yearly biomass loss for a simulated taxon with respect to a control simulation. The investigated PFAAs were Perfluorooctanoic acid (PFOA) and Perfluorooctanesulfonic acid (PFOS) as long-chained compounds, and Perfluorobutanoic acid (PFBA) and Perfluorobutanesulfonic acid (PFBS) as short-chained homologues. Two emerging contaminants, Linear Alkylbenzene Sulfonate (LAS) and triclosan, were also studied to assess the performance of the three methodologies for chemicals whose ecotoxicology and environmental fate are well-studied. The most precautionary approach was the use of AFs generally followed by SSD and then AQUATOX, except for PFOS, for which AQUATOX yielded a much lower PNEC compared to the other approaches since, unlike the other two methodologies, it explicitly simulates sublethal toxicity and indirect ecological effects. Our findings highlight that neglecting the role of ecological processes when extrapolating from laboratory tests to ecosystems can result in under-protective threshold concentrations for chemicals. Ecosystem models can complement existing laboratory-based methodologies, and the use of multiple methods for deriving PNECs can help to clarify uncertainty in ecological risk estimates.

Capturing ecology in modeling approaches applied to environmental risk assessment of endocrine active chemicals in fish

Endocrine active chemicals (EACs) are widespread in freshwater environments and both laboratory and field based studies have shown reproductive effects in fish at environmentally relevant exposures. Environmental risk assessment (ERA) seeks to protect wildlife populations and prospective assessments rely on extrapolation from individual-level effects established for laboratory fish species to populations of wild fish using arbitrary safety factors. Population susceptibility to chemical effects, however, depends on exposure risk, physiological susceptibility, and population resilience, each of which can differ widely between fish species. Population models have significant potential to address these shortfalls and to include individual variability relating to life-history traits, demographic and density-dependent vital rates, and behaviors which arise from inter-organism and organism-environment interactions. Confidence in population models has recently resulted in the EU Commission stating that results derived from reliable models may be considered when assessing the relevance of adverse effects of EACs at the population level. This review critically assesses the potential risks posed by EACs for fish populations, considers the ecological factors influencing these risks and explores the benefits and challenges of applying population modeling (including individual-based modeling) in ERA for EACs in fish. We conclude that population modeling offers a way forward for incorporating greater environmental relevance in assessing the risks of EACs for fishes and for identifying key risk factors through sensitivity analysis. Individual-based models (IBMs) allow for the incorporation of physiological and behavioral endpoints relevant to EAC exposure effects, thus capturing both direct and indirect population-level effects.

Scientific Opinion addressing the state of the science on risk assessment of plant protection products for in-soil organisms

Following a request from EFSA, the Panel on Plant Protection Products and their Residues developed an opinion on the science behind the risk assessment of plant protection products for in-soil organisms. The current risk assessment scheme is reviewed, taking into account new regulatory frameworks and scientific developments. Proposals are made for specific protection goals for in-soil organisms being key drivers for relevant ecosystem services in agricultural landscapes such as nutrient cycling, soil structure, pest control and biodiversity. Considering the time-scales and biological processes related to the dispersal of the majority of in-soil organisms compared to terrestrial non-target arthropods living above soil, the Panel proposes that in-soil environmental risk assessments are made at in- and off-field scale considering field boundary levels. A new testing strategy which takes into account the relevant exposure routes for in-soil organisms and the potential direct and indirect effects is proposed. In order to address species recovery and long-term impacts of PPPs, the use of population models is also proposed.

Ecological modelling and toxicity data coupled to assess population recovery of marine amphipod Gammarus locusta: Application to disturbance by chronic exposure to aniline

A population agent-based model of marine amphipod Gammarus locusta was designed and implemented as a basis for ecological risk assessment of chemical pollutants impairing life-history traits at the individual level. We further used the model to assess the toxic effects of aniline (a priority hazardous and noxious substance, HNS) on amphipod populations using empirically-built dose-response functions derived from a chronic bioassay that we previously performed with this species. We observed a significant toxicant-induced mortality and adverse effects in reproductive performance (reduction of newborn production) in G. locusta at the individual level. Coupling the population model with the toxicological data from the chronic bioassay allowed the projection of the ecological costs associated with exposure to aniline that might occur in wild populations. Model simulations with different scenarios indicated that even low level prolonged exposure to the HNS aniline can have significant long-term impacts on G. locusta population abundance, until the impacted population returns to undisturbed levels. This approach may be a useful complement in ecotoxicological studies of chemical pollution to transfer individual-collected data to ecological-relevant levels.

Population-level effects in Amphiascus tenuiremis: contrasting matrix- and individual-based population models

Environmental risk assessment (ERA) is generally based on individual-level endpoints, even though protection goals in ERA intend higher biological levels. Population models have the potential to translate individual-level endpoints to population-level responses and range from simple demographic equations to highly complex individual based models (IBMs). The aims of the current study were to develop a matrix model (MM) with the structure and parameterization proposed in the draft OECD guideline "Harpacticoid copepod development and reproduction test with Amphiascus tenuiremis", and an IBM with the same data requirements. Experimental data from lindane exposure from validation studies of the OECD guideline was projected to the population level. Lindane does not only cause effects on survival and reproduction, but also on the time it takes to develop from larvae to adults. The two model approaches were contrasted in terms of their ability to properly project these effects on development. The MM projected smaller effects of the lindane treatments on population growth rate compared to the IBM since in its proposed structure, it did not include the delay in development explicitly. Population-level EC10 for population growth rate in the IBM was at the same level as the most sensitive individual-level endpoint, whereas the EC10 from the MM was not as sensitive. Based on these findings, our conclusion is that the IBM (or an improved MM) should be used for datasets including shifts in development, whereas the simpler MM is sufficient for datasets where only mortality and reproduction are affected, or as a screening tool in lower-tier population-level ERA.