Ecological risk assessments are hampered by limited availability of ecotoxicity data. The present study aimed to explore the possibility of deriving species sensitivity distribution (SSD) parameters for nontested compounds, based on simple physicochemical characteristics, known SSDs for data‐rich compounds, and a quantitative structure–activity relationship (QSAR)‐type approach. The median toxicity of a data‐poor chemical for species assemblages significantly varies with values of the physicochemical descriptors, especially when based on high‐quality SSD data (from either acute median effect concentrations or chronic no‐observed‐effect concentrations). Beyond exploratory uses, we discuss how the precision of QSAR‐based SSDs can be improved to construct models that accurately predict the SSD parameters of data‐poor chemicals. The current models show that the concept of QSAR‐based SSDs supports screening‐level evaluations of the potential ecotoxicity of compounds for which data are lacking. Environ Toxicol Chem 2019;38:2764–2770. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC

Stable isotopes are often used to provide an indication of the trophic level (TL) of species. TLs may be derived by using food-web-specific enrichment factors in combination with a representative baseline species. It is challenging to sample stable isotopes for all species, regions and seasons in Arctic ecosystems, e.g. because of practical constraints. Species-specific TLs derived from a single region may be used as a proxy for TLs for the Arctic as a whole. However, its suitability is hampered by incomplete knowledge on the variation in TLs. We quantified variation in TLs of Arctic species by collating data on stable isotopes across the Arctic, including corresponding fractionation factors and baseline species. These were used to generate TL distributions for species in both pelagic and benthic food webs for four Arctic areas, which were then used to determine intra-sample, intra-study, intra-region and inter-region variation in TLs. Considerable variation in TLs of species between areas was observed. This is likely due to differences in parameter choice in estimating TLs (e.g. choice of baseline species) and seasonal, temporal and spatial influences. TLs between regions were higher than the variance observed within regions, studies or samples. This implies that TLs derived within one region may not be suitable as a proxy for the Arctic as a whole. The TL distributions derived in this study may be useful in bioaccumulation and climate change studies, as these provide insight in the variability of trophic levels of Arctic species.

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The occurrence of persistent organic pollutants (POPs) in the Arctic has been of constant concern, as these chemicals cause reproductive effects and mortality in organisms. The Arctic acts as a chemical sink, which makes this system an interesting case for bioaccumulation studies. However, as conducting empirical studies for all Arctic species and POPs individually is unfeasible, in silico methods have been developed. Existing bioaccumulation models are predominately validated for temperate food chains, and do not account for a large variation in trophic levels. This study applies Monte Carlo simulations to account for variability in trophic ecology on Svalbard when predicting bioaccumulation of POPs using the optimal modeling for ecotoxicological applications (OMEGA) bioaccumulation model. Trophic magnification factors (TMFs) were calculated accordingly. Comparing our model results with monitored POP residues in biota revealed that, on average, all predictions fell within a factor 6 of the monitored POP residues in biota. Trophic variability did not affect model performance tremendously, with up to a 25% variability in performance metrics. To our knowledge, we were the first to include trophic variability in predicting biomagnification in Arctic ecosystems using a mechanistic biomagnification model. However, considerable amounts of data are required to quantify the implications of trophic variability on biomagnification of POPs in Arctic food webs.

Abstract. Environmental pollution is an important driver of biodiversity loss. Yet, to date, the effects of chemical exposure on wildlife populations have been quantified for only a few species, mainly due to a lack of appropriate laboratory data to quantify chemical impacts on vital rates. In this study, we developed a method to quantify the effects of toxicant exposure on wildlife population persistence based on field monitoring data. We established field-based vital-rate-response functions for toxicants, using quantile regression to correct for the influences of confounding factors on the vital rates observed, and combined the response curves with population viability modelling. We then applied the method to quantify the impact of DDE on three bird species: the White-tailed Eagle, Bald Eagle, and Osprey. Population viability was expressed via five population extinction vulnerability metrics: population growth rate (r 1 ), critical patch size (CPS), minimum viable population size (MVP), probability of population extirpation (PE), and median time to population extirpation (MTE). We found that past DDE exposure concentrations increased population extirpation vulnerabilities of all three bird species. For example, at DDE concentrations of 25 mg/kg wet mass of egg (the maximum historic exposure concentration reported in literature for the Osprey), r 1 became small (White-tailed Eagle and Osprey) or close to zero (Bald Eagle), the CPS increased up to almost the size of Connecticut (White-tailed Eagle and Osprey) or West Virginia (Bald Eagle), the MVP increased up to approximately 90 (White-tailed Eagle and Osprey) or 180 breeding pairs (Bald Eagle), the PE increased up to almost certain extirpation (Bald Eagle) or only slightly elevated levels (White-tailed Eagle and Osprey) and the MTE became within decades (Bald Eagle) or remained longer than a millennium (White-tailed Eagle and Osprey). Our study provides a method to derive species-specific field-based response curves of toxicant exposure, which can be used to assess population extinction vulnerabilities and obtain critical levels of toxicant exposure based on maximum permissible effect levels. This may help conservation managers to better design appropriate habitat restoration and population recovery measures, such as reducing toxicant levels, increasing the area of suitable habitat or reintroducing individuals.