Evaluation of long-term biomarker data from perch (<i>Perca fluviatilis</i>) in the Baltic sea suggests increasing exposure to environmental pollutants

Hanson, Niklas; Förlin, Lars; Larsson, Åke

doi:10.1897/08-259.1

Cited by 32 publications

(24 citation statements)

References 30 publications

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“…This is in agreement with previous studies that have shown that the temporal variation in some biomarker responses in caged juvenile rainbow trout (Oncorhynchus mykiss) in the river Göta älv in western Sweden is higher than the spatial variation [14]. Similar results have been seen for several biomarkers in perch, where runoff from land is likely to contribute to the variation between years in biomarker responses [8,15]. When a potentially impacted site is investigated, it can be argued that it is the variation between reference sites at that time that is of interest.…”

Section: Discussionsupporting

confidence: 92%

“…3). This can be explained by an increasing trend in EROD in Kvädöfjärden since 1988, which has been linked to increasing levels of polycyclic aromatic hydrocarbons (PAHs) in coastal areas and increased runoff from land [8,15]. When Svensksundsviken in 2006 is compared to old data with lower EROD levels, it will appear more polluted.…”

Section: Discussionmentioning

confidence: 99%

“…The number of sampled fish, size (weight and length), age, and water temperature at sampling are shown in Table 1. All reference sites except Missjö are included in the Swedish Environmental Monitoring Program [7][8][9]. All variables were measured in female perch, with the exception of Vtg, which was measured in both female and male perch.…”

Section: Methodsmentioning

confidence: 99%

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Spatial and annual variation to define the normal range of biological endpoints: An example with biomarkers in perch

Hanson

Förlin

Larsson

2010

Environmental Toxicology and Chemistry

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A signal from a biological endpoint can be considered evidence of environmental impact only if it is known that similar signals are unlikely to occur naturally. Using multiple reference sites, the normal range can be estimated. This can be defined as the span that includes 95% of the reference sites. If an investigated site is outside this range, it is interpreted as evidence of impact. The choice of reference sites is thus important for the outcome of the study. Here, biomarker levels in perch (Perca fluviatilis) at a potentially impacted site were compared to different types of reference data. The potentially impacted site was located close to a densely populated area. Four reference sites were located in relatively undisturbed areas on the Swedish Baltic coast. Furthermore, historical data from one of the reference sites were included. The present study showed that multiple reference sites are needed to avoid the risk of interpreting natural variation as impact. The number of reference sites needed depends on the desired level of statistical power. An alternative to multiple reference sites may be to use historical data to estimate the natural variation. However, historical data can include temporal variation due to factors that may not be relevant for the hypothesis that is tested, e.g., climatic variation or changed background levels of pollution.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Spatial and annual variation to define the normal range of biological endpoints: An example with biomarkers in perch

Hanson

Förlin

Larsson

2010

Environmental Toxicology and Chemistry

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“…For the Baltic Sea water column, biomarker knowledge is limited as most studies so far were focused on pollutionrelated compounds (e.g., Beliaeff and Burgeot, 2001;Lehtonen et al, 2006;Hanson et al, 2009). Recently, we reported the water-column distributions and 13 C-content of individual bacteriohopanepolyols (BHPs) and phospholipid fatty acids (PLFA) from the Gotland Deep, located about 150 km SE of the Landsort Deep in the eastern central Baltic Sea.…”

Section: Introductionmentioning

confidence: 99%

Biomarkers in the stratified water column of the Landsort Deep (Baltic Sea)

et al. 2014

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Abstract. The water column of the Landsort Deep, central Baltic Sea, is stratified into an oxic, suboxic, and anoxic zone. This stratification controls the distributions of individual microbial communities and biogeochemical processes. In summer 2011, particulate organic matter was filtered from these zones using an in situ pump. Lipid biomarkers were extracted from the filters to establish water-column profiles of individual hydrocarbons, alcohols, phospholipid fatty acids, and bacteriohopanepolyols (BHPs). As a reference, a cyanobacterial bloom sampled in summer 2012 in the central Baltic Sea Gotland Deep was analyzed for BHPs. The biomarker data from the surface layer of the oxic zone showed major inputs from cyanobacteria, dinoflagellates, and ciliates, while the underlying cold winter water layer was characterized by a low diversity and abundance of organisms, with copepods as a major group. The suboxic zone supported bacterivorous ciliates, type I aerobic methanotrophic bacteria, sulfate-reducing bacteria, and, most likely, methanogenic archaea. In the anoxic zone, sulfate reducers and archaea were the dominating microorganisms as indicated by the presence of distinctive branched fatty acids: archaeol and pentamethylicosane (PMI) derivatives, respectively. Our study of in situ biomarkers in the Landsort Deep thus provided an integrated insight into the distribution of relevant compounds and describes useful tracers to reconstruct stratified water columns in the geological record.

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“…Fish populations can be affected by a number of anthropogenic stressors, including chemical pollution [1,2], eutrophication [3,4], habitat degradation [5,6], and overfishing [7,8].…”

Section: Introductionmentioning

confidence: 99%

Population Modeling of the Fish Species Roach (Rutilus rutilus) to Investigate How Latitudinal Variations in Life History Traits Affect the Sensitivity to Anthropogenic Stress

Hanson

2011

Environ Model Assess

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Anthropogenic stressors, such as pollution and habitat degradation, have affected natural fish populations (and other species) by increased mortality and/or impaired reproduction. Depending on the life history of a population, the sensitivity to a particular stress varies. Demographic population models can be used to evaluate these differences and determine which populations are most sensitive to a specific impact. In the present study, five populations of roach (Rutilus rutilus) with different life histories were modeled and compared for sensitivities to reductions in fecundity or survival. The five populations reflected a north-south gradient with increasing life span, delayed maturity, and reduced fecundity at higher latitudes. Furthermore, three types of population models were used and compared. The population models were an unstructured model, a two-stage model with juveniles and adults and an age-structured model with age-dependent fecundity. The results of the population models showed that populations at higher latitudes are more sensitive to a reduction in survival but less sensitive to a reduction in fecundity. Current methods in ecological risk assessment are based on oversimplifications due to a lack of ecological theory. The use of population models may increase the ecological relevance and help produce better risk assessments. The two-stage model provided results that were similar to the results of the more complex agestructured model in most cases. The unstructured model, however, deviated significantly from the other two models.

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Evaluation of long-term biomarker data from perch (Perca fluviatilis) in the Baltic sea suggests increasing exposure to environmental pollutants

Cited by 32 publications

References 30 publications

Spatial and annual variation to define the normal range of biological endpoints: An example with biomarkers in perch

Spatial and annual variation to define the normal range of biological endpoints: An example with biomarkers in perch

Biomarkers in the stratified water column of the Landsort Deep (Baltic Sea)

Population Modeling of the Fish Species Roach (Rutilus rutilus) to Investigate How Latitudinal Variations in Life History Traits Affect the Sensitivity to Anthropogenic Stress

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