The effects of environmental changes on contaminant fate in the ecosystem are poorly understood, even in the otherwise well-studied Baltic Sea. This area is considered one of the most polluted in the world and is currently undergoing rapid shifts related to climate change and eutrophication. In this study, we focus on the effects of an altered productivity base and changes in food web structure on contaminant concentrations in the commercially important Baltic herring, which is also a key-species in the ecosystem. In herring of known size and age, collected within the Swedish National Monitoring Program for Contaminants in Marine Biota during the past two to three decades, retrospective analyses of contaminant concentrations and stable isotopes of carbon and nitrogen including amino acid-specific isotope analyses were performed. Partial least squares regression (PLSR) models were applied to dioxins, PCBs, and mercury time series to examine how biological, ecological, and environmental factors (i.e., age, trophic diversity and position, temperature, salinity, proxies of cyanobacterial blooms and ultimate nutrient sources, abundance of relevant benthic fauna as well as biomass and size structure of the zooplankton community) contribute in explaining contaminant concentrations in herring, beyond atmospheric deposition (the main contaminant input in the Central Baltic basin). Our results emphasize that the contaminant burden in Baltic herring is significantly influenced by factors other than atmospheric deposition. Primarily, changes in herring’s trophic ecology, together with nitrogen-fixing cyanobacterial blooms (supporting both growth biodilution and bloom-induced dilution), were linked to dioxin, PCB, and mercury concentrations in fish. Our results support the need to consider all potential ecological synergies and linkages when managing a rapidly changing system such as the Baltic Sea, in order to minimize noxious blooms without compromising the positive impact on contaminant concentrations in fish.

Cyanobacterial blooms are intensifying worldwide due to eutrophication and climate change, increasing cyanotoxin exposure to aquatic organisms. This study investigated the physiological, biochemical, and behavioural impacts of cyanobacterial blooms on the three-spined stickleback (Gasterosteus aculeatus), a widespread mesopredatory fish. Adult sticklebacks were exposed for two weeks to naturally collected bloom material dominated by toxic Nodularia spumigena, non-toxic Aphanizomenon sp., or a 50:50 mix. We measured toxin accumulation (NOD_eq), hepatic enzymatic activities (ethoxyresorufin-O-deethylase [EROD], glutathione S-transferases [GSTs], glutathione reductase [GR], and catalase [CAT]), and escape swimming performance (centre-of-mass velocity, angular velocity, distance, and duration) in a multiparametric endpoints approach. Sub-lethal toxin levels in muscle tissue ranged from 0.006 to 0.077 µg g⁻¹ d.w. Results showed that fish exposed to toxic-dominated treatments showed significantly elevated EROD activity (up to 200 % increase), moderate increases in GR and GSTs, and reduced CAT activity compared to controls. Notably, distance travelled during escape responses was reduced by ∼50 % in the high-toxicity treatment and showed an inverse correlation with EROD activity, suggesting a trade-off between detoxification effort and swimming performance. Overall, our results demonstrate that EROD is a sensitive biomarker for cyanotoxin exposure in fish under natural bloom conditions. This finding highlights the need to consider natural cyanotoxin effects when interpreting environmental assessments, particularly given the projected increase in bloom frequency and severity under future climate scenarios.

Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R2 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ∼ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 sequential observations (>6 hours). However, we found that diel patterns in fish detection strongly affected the abundance estimates due to vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (r = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.