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1. 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. 2. 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. 3. 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. 4. 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. 5. 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. 6. 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. 7. Sustainable management of long-lived slowly growing species requires an understanding of the drivers of population growth and the repercuss

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.

Swedish National Monitoring program on harbour porpoises

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 monitori

Within the National Monitoring of Contaminants in Biota, Stockholm University has analysed chlorinated pesticides and PCBs (CLC), brominated compounds (BFR) and perfluoroalkyl substances (PFAS) since the start of the time period (the earliest samples from the end of 1960s). However, from 2021 and 2022 the analyses will instead be performed by the Swedish Food Agency (CLC, BFR) and Swedish University of Agricultural Sciences (PFAS). Therefore, there is a need for comparisons between the laboratories in order to investigate if conversion factors will be needed for any of the substances. Thus, the aim of this report was to compare concentrations of CLC, BFR and PFAS analysed at different laboratories.Samples from fish (marine and freshwater), blue mussels and guillemot eggs were analysed at the different laboratories. Thereafter concentrations were compared using t-tests and conversion factors were calculated.Results for CLCs and BFRs showed, with the exception of BDE-100 and BDE-154, that differences were small between Stockholm University and Swedish Food Agency.We therefore recommend that no conversions should be used for the CLCs or BFRs, but suggest a re-evaluation of BDE-100 and -154 at a later stage.For most PFASs too many data points were below LOQ for reliable conversion factors to be calculated. Differences were seen between Stockholm University and Swedish University of Agricultural Sciences for l-PFOS, b-PFOS, and PFOS (sum of l-PFOS and b-PFOS) when considering all samples. However, the samples were run in two batches at Stockholm University (separated time-wise, but also from two different environments – freshwater and marine). When splitting the data into these two batches, only one showed a significant statistical difference between the labs. Due to the few number of samples, the possible batch effect and the relative small difference between labs (l-PFOS: 23% for all samples combined), it is not recommended to apply conversion factors.In this report, w

For small cetaceans bycatch in fishing gear is one of the largest threats towards their conservation. In order to effectively manage this threat, an estimation of the maximum number of animals that can be killed by anthropogenic activities each year is required. Such calculations rely on species or population specific information on population dynamics, and information on the current state of the population relative to carrying capacity. Here, we calculate the mortality limit for the Belt Sea population of harbour porpoises (Phocoena phocoena) based on a modified potential biological removal (mPBR) method. We estimate that for the population to reach the conservation objective set by ASCOBANS (the population should reach 80% of carrying capacity (assumed here to be 50,000 animals), with the assumption that this is achieved within 100 years with an 80% probability) a maximum of 29 animals can be removed annually. This removal rate applies a recover factor (Fr) of 0.1 that accounts for many uncertainties in population dynamics, abundance estimates, and the current removal rate and depletion level of the population. However, even using the highest possible Fr value (1.0) (which is not recommended for populations with unknown status and does not allow the population to reach the conservation objective), the mPBR limit of the population is 292, which is still significantly lower than the current estimated levels of bycatch in this population (~700 a year). Therefore, urgent action is required in order to ensure that this population is able to reach the conservation objective and achieve good environmental status under a range of European legislation.

The Baltic Proper harbor porpoise (Phocoena phocoena) is currently listed as critically endangered (CR), with the Static Acoustic Monitoring of the Baltic Sea Harbor Porpoise (SAMBAH) project concluding that only ~500 individuals remain. This population has a distribution that spans the waters of nine countries, making regular abundance estimates and management action challenging. Given the continued decline of other depleted porpoises, namely the vaquita (Phocoena sinus), the question is often raised about whether management action would even have a positive impact, or whether it is too late for population recovery. When abundance estimates are sparse over time, monitoring programs at key sites are likely to serve as the best indication of population trends, and may provide an early indication of changes at the population level. We compared passive acoustic monitoring data from 12 stations that were utilized both in the SAMBAH project (2011–2013) and as a part of the Swedish National Monitoring Program (2017–2020) to determine trends in detection rates. There was a 29% increase in mean daily detection rate during May–October (over the breeding season) between the two study periods. At the three stations with the highest number of detections, log linear regression revealed a yearly increase of 2.4% between 2011 and 2019 (−4.4–9.6, 95% CI). This may be indicative of the beginnings of population recovery, or simply an indication that the decline has stalled. The rate of increase is still well below what is likely to be possible for porpoise populations, and unlikely to buffer against any potential increase in pressures in the future. We therefore call for urgent management action to remove threats and protect this CR population, the only resident cetacean in the Baltic region, in order to give it the best chance of recovery.

Continuous Integration is the practice of building and testing software every time a code change is merged into its entire codebase. At the merge, the source code is compiled, dependencies are resolved, and test cases are executed. Detecting a fault at an early stage implies that fewer resources need to be spent to find the fault since fewer merges need to be checked for errors. In this work, we analyze a dataset that comes from a Ericsson Continuous Integration flow that executes test cases daily. We create models to efficiently classify log events of interest in logs from failing test cases. For all models, each word in the log events is exchanged with the corresponding word embedding. The embeddings come from the FastText Continuous Bag of Words and Skip-gram models that use character n-grams for each word. For Linear Regression, Random Forest, XGBoost model, Support Vector Machine, and Multi-layer Perceptron, the word embeddings of the words of the log event is merged by weighting the words with the corresponding frequency-inverse document frequency from the dataset. The best performance was achieved with XGBoost, with a mean F1-score of 0:932 and a standard deviation of 0:034 when evaluating 100 3-fold cross-validations with different seeds. The LSTM model, which takes sequential input, got a mean F1-score of 0:896 and a standard deviation of 0:061. These results demonstrate the suitability of our approach to facilitating log analysis and defects detection tasks, reducing time and effort from developers., Kontinuerlig integration är när man bygger och testar mjukvara varje gång en kodändring är sammanslagen med kodbasen. När sammanslagningen utförs så är källkoden kompilerad, beroenden är lösa, och testfall är exekverade. Upptäckten av en fel tidigt betyder att mindre resurser behöver läggas på att hitta felet, eftersom färre kodsammanslagningar behöver analyseras. I denna studie analyserar vi ett dataset som kommer från ett kontinuerligt integrations flöde hos Ericsson som utför testfall dagligen. Vi skapar en model som effektivt klassificerar loghändelser av intresse i loggar i loggar från fallerande test fall. Gemensamt för alla modeller är att varje ord är utbytt mot motsvarande ordinbäddningar som kommer från FastTexts Continuous Bag of Words och Skip-Gram modeller som använder n-grams av tecken för varje ord. För linjär regression, Random Forest, XGBoost, Support Vector Machine och Multi-Layer Perceptron modellerna så är ordinbäddningarna för orden i varje log meddelande sammanslagna genom att vikta dem med motsvarande frequency-inverse document frequency värde. Det bästa resultatet uppnåddes av XGBoost, med ett medelvärde på F1-score på 0.932 och en standardavvikelse på 0.034 när vi evaluerar 100 st 3-fold korsvalideringar med olika frön. LSTM modellen, som tar ordinbinbäddningarna i en sekventiell ordning, fick ett medelvärde på F1-score på 0.896 och en standardavvikelse på 0.061. Dessa resultat visar lämpligheten i vårt tillvägagångssätt för att underlätta logganalys, vilket reducerar den tid och fokus som utvecklare och utövare behöver lägga på att på logganalys.

Relatively little is known about the amount of time that lapses between the photosynthetic fixation of carbon by vascular land plants and its incorporation into the marine sedimentary record, yet the dynamics of terrestrial carbon sequestration have important implications for the carbon cycle. Vascular plant carbon may encounter multiple potential intermediate storage pools and transport trajectories, and the age of vascular plant carbon accumulating in marine sediments will reflect these different pre-depositional histories. Here, we examine down-core C-14 profiles of higher plant leaf wax-derived fatty acids isolated from high fidelity sedimentary sequences spanning the so-called bomb-spike, and encompassing a ca. 60-degree latitudinal gradient from tropical (Cariaco Basin), temperate (Saanich Inlet), and polar (Mackenzie Delta) watersheds to constrain integrated vascular plant carbon storage/transport times (residence times). Using a modeling framework, we find that, in addition to a young (conditionally defined as < 50 y) carbon pool, an old pool of compounds comprises 49 to 78 % of the fractional contribution of organic carbon (OC) and exhibits variable ages reflective of the environmental setting. For the Mackenzie Delta sediments, we find a mean age of the old pool of 28 ky (+/- 9.4, standard deviation), indicating extensive pre-aging in permafrost soils, whereas the old pools in Saanich Inlet and Cariaco Basin sediments are younger, 7.9 (+/- 5.0) and 2.4 (+/- 0.50) to 3.2 (+/- 0.54) ky, respectively, indicating less protracted storage in terrestrial reservoirs. The young pool showed clear annual contributions for Saanich Inlet and Mackenzie Delta sediments (comprising 24% and 16% of this pool, respectively), likely reflecting episodic transport of OC from steep hillside slopes surrounding Saanich Inlet and annual spring flood deposition in the Mackenzie Delta, respectively. Contributions of 5-10 year old OC to the Cariaco Basin show a short delay of OC in