Despite great promise for understanding the impacts and extent of climate change and extreme weather events on aquatic animals, their species, and ecological communities, it is surprising that electronic tagging and tracking tools, like biotelemetry and biologging, have not been extensively used to understand climate change or develop and evaluate potential interventions that may help adapt to its impacts. In this review, we provide an overview of methodologies and study designs that leverage available electronic tracking tools to investigate aspects of climate change and extreme weather events in aquatic ecosystems. Key interventions to protect aquatic life from the impacts of climate change, including habitat restoration, protected areas, conservation translocations, mitigations against interactive effects of climate change, and simulation of future scenarios, can all be greatly facilitated by using electronic tagging and tracking. We anticipate that adopting animal tracking to identify phenotypes, species, or ecosystems that are vulnerable or resilient to climate change will help in applying management interventions such as fisheries management, habitat restoration, invasive species control, or enhancement measures that prevent extinction and strengthen the resilience of communities against the most damaging effects of climate change. Given the scalability and increasing accessibility of animal tracking tools for researchers, tracking individual organisms will hopefully also facilitate research into effective solutions and interventions against the most extreme and acute impacts on species, populations, and ecosystems.

Planktonic lifestyle of polyps in representatives of Margelopsidae are very different from all other species in the hydrozoan clade Aplanulata. Their evolutionary origin and phylogenetic position have been the subject of significant speculation. A recent molecular study based only on COI data placed Margelopsidae as a sister group to all Aplanulata, an unexpected result because margelopsid morphology suggests affiliation with Tubulariidae or Corymorphidae. Here we used multigene analyses, including nuclear (18S rRNA and 28S rRNA) and mitochondrial (16S rRNA and COI) markers of the hydroid stage of the margelopsid species Margelopsis haeckelii and the medusa stage of Margelopsis hartlaubii to resolve their phylogenetic position with respect to other hydrozoans. Our data provide strong evidence that M. haeckelii, the type species of Margelopsis, is a member of the family Corymorphidae. In contrast, M. hartlaubii is sister to Plotocnide borealis, a member of Boreohydridae. These results call into question the validity of the genus Margelopsis and the family Margelopsidae. The systematic position of M. haeckelii is discussed in light of the phylogeny of Corymorphidae.

Abstract Offshore wind farms (OWFs) are expanding rapidly in the North Sea, often creating spatial conflicts with fisheries. Managing such conflicts requires knowledge on the impact of OWFs on the spatial distribution and movement behaviour of fished species. However, such knowledge is still lacking, especially for soft sediment fish such as flatfish, which are vital fisheries resources in the region. Therefore, we used acoustic telemetry to examine the spatial behaviour of European plaice in relation to an OWF and its structures. In a small study area (1.37 km2), we observed high residency for plaice around the turbines and scour protection layer (SPL), which consists of large rocks around the turbine foundation. The fish primarily resided on sandy sediments near the hard substrates, but showed a diurnal pattern of proximity to the turbine, being closer during the day. Considering their trophic ecology, these findings suggest that plaice moves towards the SPL for feeding opportunities on the hard substrate, potentially leading to increased ecological fish production within OWFs. Although most plaice moved away from the OWF in winter, likely towards spawning grounds, many exhibited high site fidelity returning to the study area after the winter migration. OWFs thus offer protection from fishing mortality as “closed” feeding grounds in spring and summer, but not during winter spawning migrations, which may result in spillover effects. These insights should inform local fisheries management in relation to plaice movement within and around OWFs.

The biofouling of submerged surfaces such as ship hulls is often prevented by using anti-fouling components in combination with booster biocides. These booster biocides enter the water column and may affect non-target organisms. Although different negative effects have been associated with the use of booster biocides, their effects on non-target organisms are often unknown. So far, the environmental risks for booster biocides have barely been quantified in the North Sea. In this work, the concentration of five commonly used booster biocides as well as tributyltin has been monitored at five dredged spoil disposal sites in the Belgian part of the North Sea and the harbour and ports of Nieuwpoort, Oostende, and Zeebrugge. Hotspots were discovered where the concentration of one or more booster biocides exceeded the predicted no-effect concentration. Tributyltin has been banned since 2008, but concentrations of 237- to 546-fold of the predicted no-effect concentration were detected in the harbours and ports. Moreover, TBT has been detected in the same order of magnitude in other sea basins, emphasizing the need to monitor the trends and impact of booster biocides and TBT in environmental monitoring programs.

Insight into the detection range of acoustic telemetry systems is crucial for both sampling design and data interpretation. The detection range is highly dependent on the environmental conditions and can consequently be substantially different among aquatic systems. Also within systems, temporal variability can be significant. The number of studies to assess the detection range in different systems has been growing, though there remains a knowledge gap in estuarine habitats. In this study, a 2-month experimental set-up was used to assess the detection range variability and affecting environmental factors of an estuary. Given the expected complex interplay of different factors and the difficulties it entails for interpretation, a structural equation modelling (pSEM) approach is proposed. The detection range of this estuarine study was relatively low and variable (average 50% detectability of 106 m and ranging between 72 and 229 m) compared to studies of riverine and marine systems. The structural equation models revealed a clear, yet complex, tidal pattern in detection range variability which was mainly affected by water speed (via ambient noise and tilt of the receivers), water depth and wind speed. The negative effect of ambient noise and positive effect of water depth became more pronounced at larger distances. Ambient noise was not only affected by water speed, but also by water depth, precipitation, tilt angle and wind speed. Although the tilt was affected by water speed, water depth and wind speed, most of the variability in tilt could be traced back to the receiver locations. Similarly, the receiver locations seemed to explain a considerable portion of the detection range variability. Retrospective power analyses indicated that for most factors only a minor gain in explanatory power was achieved after more than two days of data collecting. Redirecting some of the sampling effort towards more spatially extensive measurements seems to be a relevant manner to improve the insights in the performance of telemetry systems in estuarine environments. Since the low and variable detection range in estuaries can seriously hamper ecological inferences, range tests with sound sampling designs and appropriate modelling techniques are paramount.

Initialized climate model simulations have proven skillful for near-term predictability of the key physical climate variables. By comparison, predictions of biogeochemical fields like ocean carbon flux, are still emerging. Initial studies indicate skillful predictions are possible for lead-times up to six years at global scale for some CMIP6 models. However, unlike core physical variables, biogeochemical variables are not directly initialized in existing decadal preciction systems, and extensive empirical parametrization of ocean-biogeochemistry in Earth System Models introduces a significant source of uncertainty. Here, we propose a new approach for improving the skill of decadal ocean carbon flux predictions using observationally-constrained statistical models, as alternatives to the ocean-biogeochemistry models. We use observations to train multi-linear and neural-network models to predict the ocean carbon flux. To account for observational uncertainties, we train using six different observational estimates of the flux. We then apply these trained statistical models using input predictors from the Canadian Earth System Model (CanESM5) decadal prediction system to produce new decadal predictions. Our hybrid GCM-statistical approach significantly improves prediction skill, relative to the raw CanESM5 hindcast predictions over 1990-2019. Our hybrid-model skill is also larger than that obtained by any available CMIP6 model. Using bias-corrected CanESM5 predictors, we make forecasts for ocean carbon flux over 2020-2029. Both statistical models predict increases in the ocean carbon flux larger than the changes predicted from CanESM5 forecasts. Our work highlights the ability to improve decadal ocean carbon flux predictions by using observationally-trained statistical models together with robust input predictors from GCM-based decadal predictions.

Initialized climate model simulations have proven skillful for near-term predictability of the key physical climate variables. By comparison, predictions of biogeochemical fields like ocean carbon flux, are still emerging. Initial studies indicate skillful predictions are possible for lead-times up to six years at global scale for some CMIP6 models. However, unlike core physical variables, biogeochemical variables are not directly initialized in existing decadal preciction systems, and extensive empirical parametrization of ocean-biogeochemistry in Earth System Models introduces a significant source of uncertainty. Here, we propose a new approach for improving the skill of decadal ocean carbon flux predictions using observationally-constrained statistical models, as alternatives to the ocean-biogeochemistry models. We use observations to train multi-linear and neural-network models to predict the ocean carbon flux. To account for observational uncertainties, we train using six different observational estimates of the flux. We then apply these trained statistical models using input predictors from the Canadian Earth System Model (CanESM5) decadal prediction system to produce new decadal predictions. Our hybrid GCM-statistical approach significantly improves prediction skill, relative to the raw CanESM5 hindcast predictions over 1990-2019. Our hybrid-model skill is also larger than that obtained by any available CMIP6 model. Using bias-corrected CanESM5 predictors, we make forecasts for ocean carbon flux over 2020-2029. Both statistical models predict increases in the ocean carbon flux larger than the changes predicted from CanESM5 forecasts. Our work highlights the ability to improve decadal ocean carbon flux predictions by using observationally-trained statistical models together with robust input predictors from GCM-based decadal predictions.