Long-term dataset for contaminants in fish, mussels, and bird eggs from the Baltic Sea.

Widespread persistent contaminants are a global environmental problem. In the Baltic Sea, wildlife contamination was first noticed in the 1960s, prompting the Swedish Environmental Protection Agency to establish a comprehensive Swedish National Monitoring Programme for Contaminants in Marine Biota (MCoM) in 1978 run by the Swedish Museum of Natural History. Eight species have been analysed, four fish species (Atlantic herring, Atlantic cod, European perch, viviparous eelpout), one bivalve species (blue mussel), and egg from three bird species (common guillemot, common tern, Eurasian oystercatcher). Here, we present a dataset containing MCoM data from its start until 2021. It includes 36 sets of time-series, each analysed for more than 100 contaminants. The longest time-series is for common guillemot and starts in 1968. We describe the structure of MCoM including historic changes to the number of stations, sample treatment, analytical methods, instruments, and laboratories. The MCoM data is available at the Bolin Centre repository and on GitHub through our R package mcomDb. The latter will be updated yearly with new MCoM records.

120-years of ecological monitoring data shows that the risk of overhunting is increased by environmental degradation for an isolated marine mammal population: The Baltic grey seal.

The Baltic Sea is home to a genetically isolated and morphologically distinct grey seal population. This population has been the subject of 120-years of careful documentation, from detailed records of bounty statistics to annual monitoring of health and abundance. It has also been exposed to a range of well-documented stressors, including hunting, pollution and climate change. To investigate the vulnerability of marine mammal populations to multiple stressors, data series relating to the Baltic grey seal population size, hunt and health were compiled, vital demographic rates were estimated, and a detailed population model was constructed. The Baltic grey seal population fell from approximately 90,000 to as few as 3000 individuals during the 1900s as the result of hunting and pollution. Subsequently, the population has recovered to approximately 55,000 individuals. Fertility levels for mature females have increased from 9% in the 1970s to 86% at present. The recovery of the population has led to demands for increased hunting, resulting in a sudden increase in annual quotas from a few hundred to 3550 in 2020. Simultaneously, environmental changes, such as warmer winters and reduced prey availability due to overfishing, are likely impacting fecundity and health. Future population development is projected for a range of hunting and environmental stress scenarios, illustrating how hunting, in combination with environmental degradation, can lead to population collapse. The current combined hunting quotas of all Baltic Nations caused a 10% population decline within three generations in 100% of simulations. To enable continued recovery of the population, combined annual quotas of less than 1900 are needed, although this quota should be re-evaluated annually as monitoring of population size and seal health continues. Sustainable management of long-lived slowly growing species requires an understanding of the drivers of population growth and the repercussions of management decisions over many decades. The case of the Baltic grey seal illustrates how long-term ecological time series are pivotal in establishing historical baselines in population abundance and demography to inform sustainable management.

Per- and polyfluoroalkyl substances (PFAS) in white-tailed sea eagle eggs from Sweden: temporal trends (1969-2021), spatial variations, fluorine mass balance, and suspect screening.

Temporal and spatial trends of 15 per- and polyfluoroalkyl substances (PFAS) were determined in white-tailed sea eagle (WTSE) eggs (Haliaeetus albicilla) from two inland and two coastal regions of Sweden between 1969 and 2021. PFAS concentrations generally increased from ∼1969 to ∼1990s-2010 (depending on target and site) and thereafter plateaued or declined, with perfluorooctane sulfonamide (FOSA) and perfluorooctane sulfonate (PFOS) declining faster than most perfluoroalkyl carboxylic acids (PFCAs). The net result was a shift in the PFAS profile from PFOS-dominant in 1969-2010 to an increased prevalence of PFCAs over the last decade. Further, during the entire period higher PFAS concentrations were generally observed in coastal populations, possibly due to differences in diet and/or proximity to more densely populated areas. Fluorine mass balance determination in pooled samples from three of the regions (2019-2021) indicated that target PFAS accounted for the vast majority (i.e. 81-100%) of extractable organic fluorine (EOF). Nevertheless, high resolution mass-spectrometry-based suspect screening identified 55 suspects (31 at a confidence level [CL] of 1-3 and 24 at a CL of 4-5), of which 43 were substances not included in the targeted analysis. Semi-quantification of CL ≤ 2 suspects increased the identified EOF to >90% in coastal samples. In addition to showing the impact of PFAS regulation and phase-out initiatives, this study demonstrates that most extractable organofluorine in WTSE eggs is made up of known (legacy) PFAS, albeit with low levels of novel substances.

Trend detection with non-detects in long-term monitoring, a mixed model approach.

In long-term monitoring of contaminants in biota, a common approach is to use yearly geometric means of measured concentrations in sampled individuals as a basis for trend analysis. When some or all measurements in a particular year are reported as non-detects, it is unclear how to proceed in calculating the yearly mean. I argue that by casting the problem in terms of a mixed model, non-detects can be accounted for using statistical techniques for censored data. The approach is illustrated using data from the Swedish national monitoring programme for contaminants in biota.

Fish tissue conversion factors for mercury, cadmium, lead and nine per- and polyfluoroalkyl substances for use within contaminant monitoring.

Fish tissue levels have to comply with environmental quality standards (EQSs) within the European Water Framework Directive. However, within monitoring, contaminants are sometimes measured in a different tissue than the tissue for which the environmental (whole fish) or human (fillet (equivalent to muscle tissue)) quality standard is set. Tissue conversion factors (k), describing the relationship between concentrations in different tissues, can be used to obtain a quality standard for the appropriate tissue. Several different approaches have been suggested for the calculation of k. For monitoring purposes, we propose the use of a simple, easy reproducible approach that assumes proportionality between two tissue, or tissue and whole fish, concentrations. This allows for an easy comparison of studies and adoption of ks into independent monitoring programs. Here, we determined ks for three metals (mercury (Hg), lead (Pb), cadmium (Cd)) and nine per- and polyfluoroalkyl substances (PFAS) including perfluorooctanesulfonic acid (PFOS) across six marine and freshwater fish species from Northern European lakes and the Baltic Sea. We found significant species differences for Hg for kmuscle/whole fish, for Cd and Pb for kliver/whole fish and for Cd for kliver/muscle. For perfluoroalkyl carboxylic acids (PFCA), we found a chain length dependence with lowest kliver/muscle at low and high chain lengths (C8, C13) and highest for median chain lengths (C9-C12). Further, there were differences between fish species with kliver/muscle for PFOS almost doubling from eelpout (10.3) to herring (19.2) and increasing up to a factor 4 between eelpout and herring for other PFASs. FOSA had two distinctive groups, herring with a kliver/muscle of 48.7 and a second group with ks of 2.3 to 5.9 for all other fish species. Our results suggest that differences in the tissue somatic index, and contaminant uptake, tissue transfer and metabolism result in the need for species-specific ks within monitoring.

Regional lithium prescription rates and recurrence in bipolar disorder.

BACKGROUND: Lithium is the best documented maintenance treatment in bipolar disorder, but its use varies considerably across and within countries. It is not known whether regional differences in lithium prescription rates translate to differing regional outcomes. AIMS: To estimate associations between county specific lithium prescription rates and county specific recurrence odds of bipolar disorder in Sweden. METHOD: Data from 14,616 patients with bipolar I disorder, bipolar II disorder, or bipolar disorder not otherwise specified were extracted from the Swedish national quality assurance register for bipolar disorders (BipoläR). Lithium prescription frequencies were calculated for 21 counties. Logistic regression analyses were run adjusted for confounders, with any type of recurrence as primary outcome, and incident elated and depressive episodes as secondary outcomes. Subsets of patients with bipolar I, II and not otherwise specified disorder were also analysed separately. RESULTS: Lithium prescription rates for populations with all bipolar subtypes ranged across counties from 37.7 to 84.9% (mean 52.4%). Higher regional prescription rates were significantly associated with lower rate of any type of recurrence. The association was stronger when bipolar I disorder was analysed separately. CONCLUSIONS: The advantages for lithium use long acknowledged for bipolar I disorder are also seen for the rest of the bipolar spectrum. Results suggest that population level outcomes of bipolar disorder could be improved by increasing the number of patients using lithium.

Remobilization of Old Permafrost Carbon to Chukchi Sea Sediments During the End of the Last Deglaciation.

Climate warming is expected to destabilize permafrost carbon (PF-C) by thaw-erosion and deepening of the seasonally thawed active layer and thereby promote PF-C mineralization to CO2 and CH4. A similar PF-C remobilization might have contributed to the increase in atmospheric CO2 during deglacial warming after the last glacial maximum. Using carbon isotopes and terrestrial biomarkers (Δ14C, δ13C, and lignin phenols), this study quantifies deposition of terrestrial carbon originating from permafrost in sediments from the Chukchi Sea (core SWERUS-L2-4-PC1). The sediment core reconstructs remobilization of permafrost carbon during the late Allerød warm period starting at 13,000 cal years before present (BP), the Younger Dryas, and the early Holocene warming until 11,000 cal years BP and compares this period with the late Holocene, from 3,650 years BP until present. Dual-carbon-isotope-based source apportionment demonstrates that Ice Complex Deposit-ice- and carbon-rich permafrost from the late Pleistocene (also referred to as Yedoma)-was the dominant source of organic carbon (66 ± 8%; mean ± standard deviation) to sediments during the end of the deglaciation, with fluxes more than twice as high (8.0 ± 4.6 g·m-2·year-1) as in the late Holocene (3.1 ± 1.0 g·m-2·year-1). These results are consistent with late deglacial PF-C remobilization observed in a Laptev Sea record, yet in contrast with PF-C sources, which at that location were dominated by active layer material from the Lena River watershed. Release of dormant PF-C from erosion of coastal permafrost during the end of the last deglaciation indicates vulnerability of Ice Complex Deposit in response to future warming and sea level changes.

Regionally-varying combustion sources of the January 2013 severe haze events over eastern China.

Thick haze plagued northeastern China in January 2013, strongly affecting both regional climate and human respiratory health. Here, we present dual carbon isotope constrained (Δ(14)C and δ(13)C) source apportionment for combustion-derived black carbon aerosol (BC) for three key hotspot regions (megacities): North China Plain (NCP, Beijing), the Yangtze River Delta (YRD, Shanghai), and the Pearl River Delta (PRD, Guangzhou) for January 2013. BC, here quantified as elemental carbon (EC), is one of the most health-detrimental components of PM2.5 and a strong climate warming agent. The results show that these severe haze events were equally affected (∼ 30%) by biomass combustion in all three regions, whereas the sources of the dominant fossil fuel component was dramatically different between north and south. In the NCP region, coal combustion accounted for 66% (46-74%, 95% C.I.) of the EC, whereas, in the YRD and PRD regions, liquid fossil fuel combustion (e.g., traffic) stood for 46% (18-66%) and 58% (38-68%), respectively. Taken together, these findings suggest the need for a regionally-specific description of BC sources in climate models and regionally-tailored mitigation to combat severe air pollution events in East Asia.

Patchiness and co-existence of indigenous and invasive mussels at small spatial scales: the interaction of facilitation and competition.

Ecological theory predicts that two species with similar requirements will fail to show long-term co-existence in situations where shared resources are limiting, especially at spatial scales that are small relative to the size of the organisms. Two species of intertidal mussels, the indigenous Perna perna and the invasive Mytilus galloprovincialis, form mixed beds on the south coast of South Africa in a situation that has been stable for several generations of these species, even though these populations are often limited by the availability of space. We examined the spatial structure of these species where they co-exist at small spatial scales in the absence of apparent environmental heterogeneity at two sites, testing: whether conspecific aggregation of mussels can occur (using spatial Monte-Carlo tests); the degree of patchiness (using Korcak B patchiness exponent), and whether there was a relationship between percent cover and patchiness. We found that under certain circumstances there is non-random conspecific aggregation, but that in other circumstances there may be random distribution (i.e. the two species are mixed), so that spatial patterns are context-dependent. The relative cover of the species differed between sites, and within each site, the species with higher cover showed low Korcak B values (indicating low patchiness, i.e. the existence of fewer, larger patches), while the less abundant species showed the reverse, i.e. high patchiness. This relationship did not hold for either species within sites. We conclude that co-existence between these mussels is possible, even at small spatial scales because each species is an ecological engineer and, while they have been shown to compete for space, this is preceded by initial facilitation. We suggest that a patchy pattern of co-existence is possible because of a balance between direct (competitive) and indirect (facilitative) interactions.

On observation distributions for state space models of population survey data.

1. State space models are starting to replace more simple time series models in analyses of temporal dynamics of populations that are not perfectly censused. By simultaneously modelling both the dynamics and the observations, consistent estimates of population dynamical parameters may be obtained. For many data sets, the distribution of observation errors is unknown and error models typically chosen in an ad-hoc manner. 2. To investigate the influence of the choice of observation error on inferences, we analyse the dynamics of a replicated time series of red kangaroo surveys using a state space model with linear state dynamics. Surveys were performed through aerial counts and Poisson, overdispersed Poisson, normal and log-normal distributions may all be adequate for modelling observation errors for the data. We fit each of these to the data and compare them using AIC. 3. The state space models were fitted with maximum likelihood methods using a recent importance sampling technique that relies on the Kalman filter. The method relaxes the assumption of Gaussian observation errors required by the basic Kalman filter. Matlab code for fitting linear state space models with Poisson observations is provided. 4. The ability of AIC to identify the correct observation model was investigated in a small simulation study. For the parameter values used in the study, without replicated observations, the correct observation distribution could sometimes be identified but model selection was prone to misclassification. On the other hand, when observations were replicated, the correct distribution could typically be identified. 5. Our results illustrate that inferences may differ markedly depending on the observation distributions used, suggesting that choosing an adequate observation model can be critical. Model selection and simulations show that for the models and parameter values in this study, a suitable observation model can typically be identified if observations are replicated. Model selection and replication of observations, therefore, provide a potential solution when the observation distribution is unknown.

Individual heterogeneity and senescence in silvereyes on Heron Island.

Individual heterogeneity and correlations between life history traits play a fundamental role in life history evolution and population dynamics. Unobserved individual heterogeneity in survival can be a nuisance for estimation of age effects at the individual level by causing bias due to mortality selection. We jointly analyze survival and breeding output from successful breeding attempts in an island population of Silvereyes (Zosterops lateralis chlorocephalus) by fitting models that incorporate age effects and individual heterogeneity via random effects. The number of offspring produced increased with age of parents in their first years of life but then eventually declined with age. A similar pattern was found for the probability of successful breeding. Annual survival declined with age even when individual heterogeneity was not accounted for. The rate of senescence in survival, however, depends on the variance of individual heterogeneity and vice versa; hence, both cannot be simultaneously estimated with precision. Model selection supported individual heterogeneity in breeding performance, but we found no correlation between individual heterogeneity in survival and breeding performance. We argue that individual random effects, unless unambiguously identified, should be treated as statistical nuisance or taken as a starting point in a search for mechanisms rather than given direct biological interpretation.

Stochastic demography and population dynamics in the red kangaroo Macropus rufus.

1. Many organisms inhabit strongly fluctuating environments but their demography and population dynamics are often analysed using deterministic models and elasticity analysis, where elasticity is defined as the proportional change in population growth rate caused by a proportional change in a vital rate. Deterministic analyses may not necessarily be informative because large variation in a vital rate with a small deterministic elasticity may affect the population growth rate more than a small change in a less variable vital rate having high deterministic elasticity. 2. We analyse a stochastic environment model of the red kangaroo (Macropus rufus), a species inhabiting an environment characterized by unpredictable and highly variable rainfall, and calculate the elasticity of the stochastic growth rate with respect to the mean and variability in vital rates. 3. Juvenile survival is the most variable vital rate but a proportional change in the mean adult survival rate has a much stronger effect on the stochastic growth rate. 4. Even if changes in average rainfall have a larger impact on population growth rate, increased variability in rainfall may still be important also in long-lived species. The elasticity with respect to the standard deviation of rainfall is comparable to the mean elasticities of all vital rates but the survival in age class 3 because increased variation in rainfall affects both the mean and variability of vital rates. 5. Red kangaroos are harvested and, under the current rainfall pattern, an annual harvest fraction of c. 20% would yield a stochastic growth rate about unity. However, if average rainfall drops by more than c. 10%, any level of harvesting may be unsustainable, emphasizing the need for integrating climate change predictions in population management and increase our understanding of how environmental stochasticity translates into population growth rate.

Regression analysis and modelling of data acquisition for SELDI-TOF mass spectrometry.

MOTIVATION: Pre-processing of SELDI-TOF mass spectrometry data is currently performed on a largel y ad hoc basis. This makes comparison of results from independent analyses troublesome and does not provide a framework for distinguishing different sources of variation in data. RESULTS: In this article, we consider the task of pooling a large number of single-shot spectra, a task commonly performed automatically by the instrument software. By viewing the underlying statistical problem as one of heteroscedastic linear regression, we provide a framework for introducing robust methods and for dealing with missing data resulting from a limited span of recordable intensity values provided by the instrument. Our framework provides an interpretation of currently used methods as a maximum-likelihood estimator and allows theoretical derivation of its variance. We observe that this variance depends crucially on the total number of ionic species, which can vary considerably between different pooled spectra. This variation in variance can potentially invalidate the results from naive methods of discrimination/classification and we outline appropriate data transformations. Introducing methods from robust statistics did not improve the standard errors of the pooled samples. Imputing missing values however-using the EM algorithm-had a notable effect on the result; for our data, the pooled height of peaks which were frequently truncated increased by up to 30%.

Continuous-index hidden Markov modelling of array CGH copy number data.

MOTIVATION: In recent years, a range of techniques for analysis and segmentation of array comparative genomic hybridization (aCGH) data have been proposed. For array designs in which clones are of unequal lengths, are unevenly spaced or overlap, the discrete-index view typically adopted by such methods may be questionable or improved. RESULTS: We describe a continuous-index hidden Markov model for aCGH data as well as a Monte Carlo EM algorithm to estimate its parameters. It is shown that for a dataset from the BT-474 cell line analysed on 32K BAC tiling microarrays, this model yields considerably better model fit in terms of lag-1 residual autocorrelations compared to a discrete-index HMM, and it is also shown how to use the model for e.g. estimation of change points on the base-pair scale and for estimation of conditional state probabilities across the genome. In addition, the model is applied to the Glioblastoma Multiforme data used in the comparative study by Lai et al. (Lai,W.R. et al. (2005) Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics, 21, 3763-3370.) giving result similar to theirs but with certain features highlighted in the continuous-index setting.