Alfred Wegener Institute for Polar and Marine Research
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Towards ‘the science we need for the ocean we want’: revealing and addressing power relations in knowledge-action co-production practices
Get PDFInternational research programs have endorsed knowledge co-production to enhance the utility and impact of global environmental change research. However, knowledge co-production frequently overlooks the complex interrelations between knowledge and power that permeate transdisciplinary sustainability research (TDSR). We analyse how power relations in transdisciplinary ocean governance projects form research agendas and design of six projects within the Belmont Collaborative Research Action on Ocean Sustainability, representing a broad set of experiences of transdisciplinary marine sustainability research practice spanning a wide set of sustainability issues and geopolitical contexts. We examine how distinctive forms of power shape the ways in which researchers envision socio-environmental change and transdisciplinary work within a spectrum of knowledge-action practices ranging from ‘linear’ to ‘relational’. Our findings highlight the need for a deeper engagement with theories of change throughout the lifespans of transdisciplinary projects. Furthermore, the results point at constraints imposed by existing funding structures and epistemic assumptions on the ability of the projects to adopt relational transdisciplinary co-production research strategies, tailored to specific ocean contexts. We recommend adaptive funding structures which would allow researchers to exercise co-production agility and overcome the tension between the needs for embeddedness and transferability of insights. Finally, we show how the three forms of power interact in the linear and relational models of linking knowledge and action and suggest areas where researchers need to direct their attention when designing and implementing transdisciplinary projects aiming to promote sustainability action in close collaboration with societal actors
Anthropogenic low-frequency sound effects on resting metabolism and energy pathways in two marine benthic crustaceans
Get PDFAnthropogenic sound caused by ship traffic as well as the construction and operation of offshore windfarms have increased exponentially in the last decades. While its impact on marine life is relatively well studied for mammals and fish, the implications of anthropogenic sound on benthic invertebrates are poorly understood. Here, we tested for potential stress responses of common marine invertebrates using two widespread mesograzing crustaceans: the isopod Idotea balthica and the amphipod Gammarus locusta. All experimental animals were gathered from laboratory cultures in the facilities of the Alfred Wegener Institute in Bremerhaven, Germany, in spring 2023. Oxygen consumption rates and the activities of four key mitochondrial enzymes (cytochrome c oxidase, electron transport system complex I and III, citrate synthase and lactate dehydrogenase) were examined under the influence of added low-frequency sound (+ 25 dB SPLRMS re 1 µPa at 90 Hz, above background soundscape) to assess how basal energy demands and supplies were affected. The isopod I. balthica seemed to be robust against added sound exposure over 72 h as neither oxygen consumption rates nor enzyme activities were significantly altered. The amphipod G. locusta, however, displayed significantly lower oxygen consumption rates in response to both short-term (1–4 h; 39% reduction) and longer-term (68–72 h; 35% reduction) added sound exposure, although enzymatic activities were not significantly affected. This study underlines the need to address the potential impact of sound on the energy available for the growth and reproduction of small invertebrates. Overlooked vulnerabilities to noise pollution in key taxa could have far reaching implications for marine food webs, nutrient cycles and ecosystem functioning
Rising Arctic seas and thawing permafrost: uncovering the carbon cycle impact in a thermokarst lagoon system in the outer Mackenzie Delta, Canada
Get PDFClimate warming in the Arctic is directly connected to rising sea levels and increasing erosion of permafrost coasts, leading to inland-migrating coastlines and the transformation of coastal permafrost lakes into thermokarst lagoons. These lagoons represent transitional zones between terrestrial to sub-sea permafrost environments. So far, the effect of the transition on the carbon cycle is fairly unknown. In this study, we conducted long-term anoxic incubation experiments on surface samples from thermokarst lagoons with varying degrees of sea connectivity. We also included terrestrial permafrost and the active layer as endmembers to investigate variations in carbon dioxide (CO2) and methane (CH4) production within lagoon systems and along a land–sea transition transect on Reindeer Island, northeast Mackenzie Delta, Canada. Results show that CH4 production peaks at 4.6 mg CH4 g−1 C in younger, less connected lagoons with high-quality organic matter, leading to up to 18 times higher greenhouse gas (GHG) production (in CO2 equivalents) compared to open lagoons. CO2 production is higher under marine conditions (3.8–5.4 mg CO2 g−1 C) than under brackish conditions (1.7–4.3 mg CO2 g−1 C). Along a land–sea transect, CO2 production increased with increasing marine influence. These findings suggest that the landward migration of the sea, resulting in the inundation of permafrost lowlands and thermokarst lakes, may lead to increased GHG emissions from Arctic coasts in the future
Heatwave duration, intensity and timing as drivers of performance in larvae of a marine invertebrate
Get PDFIn marine ecosystems, crustaceans face an alarming threat from the increasing frequency and intensity of marine heatwaves as their early planktonic stages are particularly temperature sensitive. While the impact of heatwaves on adult crustaceans is well-studied, their effects on larvae remain underexplored. This study focuses on heatwave effects on larvae of the European shore crab, Carcinus maenas. Through a factorial experiment, larvae were exposed to different heatwaves of varying onset timings, durations, and intensities. Survival, development duration, and dry mass decreased under intense heatwaves, with more severe effects observed when heatwaves occurred later in development, highlighting a stage-specific sensitivity to heatwave. We also identified a “region of existence” beyond which larval performance was compromised compared to baseline temperatures. This region defines the heatwave components considered “extreme” for the organism, as well as those inducing neutral or positive effects on performance. Additionally, we distinguished heatwave effects (characterised by their components) from those attributed to the average temperature experienced during the experiments. Our findings demonstrated that larval performance was lower during intense heatwaves compared to the performance expected under a constant average temperature. These findings emphasize the importance of considering heatwave timing relative to the life cycle for predicting marine population responses to climate change
Mapping subsea permafrost around Tuktoyaktuk Island (Northwest Territories, Canada) using electrical resistivity tomography
Get PDFAlong much of the Arctic coast, shoreline retreat and sea level rise combine to inundate permafrost. Once inundated by seawater, permafrost usually begins to degrade. Tuktoyaktuk Island (Beaufort Sea, Northwest Territories, Canada) is an important natural barrier protecting the harbor of Tuktoyaktuk but will likely be breached within the next 2 decades. The state of subsea permafrost and its depth distribution around the island are, however, still largely unknown. We collected marine electrical resistivity tomography (ERT) surveys (vertical electrical soundings) north and south of Tuktoyaktuk Island using a floating cable with 13 electrodes in a quasi-symmetric Wenner–Schlumberger array configuration. We filtered the data with a new approach to eliminate potentially incorrect measurements due to a curved cable and inverted the profiles with a variety of parameterizations to estimate the position of the ice-bearing permafrost table (IBPT) below the seafloor. Our results indicate that north of Tuktoyaktuk Island, where coastal erosion is considerably faster, IBPT depths range from 5 m below sea level (120 m from the shoreline) to around 20 m b.s.l. (up to 800 m from the shoreline). South of the island, the IBPT dips more steeply and lies at 10 m b.s.l. a few meters from the shore, and 200 m from the shore, it is more than 30 m b.s.l. We discuss how marine ERT can be improved by accurately recording electrode positions, although choices made during data inversion are likely a greater source of uncertainty in the determination of the IBPT position. At Tuktoyaktuk Island, IBPT depths below the seafloor increase with distance from the shoreline; comparing the northern and southern sides of the island, the inclination is inversely proportional to coastline retreat rates. On the island’s north side, the historical coastal retreat rate suggests a mean permafrost degradation rate of 5.3 ± 4.0 cm yr−
Answering the key stakeholder questions about the impact of offshore wind farms on marine life using hypothesis testing to inform targeted monitoring
Get PDFAbstract Stakeholders need scientific advice on the environmental impacts of offshore wind (OW) before the facilities are installed. The utility of conventional environmental monitoring methods as a basis for forecasting OW impacts is limited because they do not explain the causes of the observed effects. We propose a multistep approach, based on process-oriented hypothesis testing, targeted monitoring and numerical modeling, to answer key stakeholder questions about planning an OW facility: Q1—Where do we place future OW farms so that impacts on the ecosystem are minimized? Q2—Which species and ecosystem processes will be impacted and to what degree? Q3—Can we mitigate impacts and, if so, how? and Q4—What are the risks of placing an OW facility in one location vs. another? Hypothesis testing can be used to assess impacts of OW facilities on target species-ecological process. This knowledge is transferable and is broadly applicable, a priori, to assess suitable locations for OW (Q1). Hypothesis testing can be combined with monitoring methods to guide targeted monitoring. The knowledge generated can identify the species/habitats at risk (Q2), help selecting/developing mitigation measures (Q3), and be used as input parameters for models to forecast OW impacts at a large spatial scale (Q1; Q4)
Using Texture‐Based Image Segmentation and Machine Learning With High‐Resolution Satellite Imagery to Assess Permafrost Degradation Landforms in the Russian High Arctic
Get PDFAbstract Amplified climate change across the Arctic causes significant permafrost thaw and an increase of permafrost degradation landforms. These landforms range from fine‐scale degrading ice wedge‐polygon‐networks to large‐scale features such as thermo‐erosional gullies and reshape entire landscapes. In particular the expansion of thermo‐erosional gullies could have far‐reaching consequences by restructuring drainage pathways. Our study aims at finding a suitable remote sensing‐based approach for quantifying landscape‐scale permafrost degradation in gully‐dominated Arctic landscapes. We use historical and recent high‐resolution panchromatic satellite imagery allowing multi‐decadal analysis of degradation trajectories. Given that degradation stages are characterized by distinct but subtle textural characteristics in satellite imagery, we tested texture‐based machine learning segmentation methods including Random Forest (RF) using gray level co‐occurrence matrix (GLCM) texture features and deep learning Convolutional Neural Networks (CNNs) using a UNet architecture. For CNN, we tested various framework adjustments. Our results showed that CNN outperforms RF particularly for complex texture‐defined classes. CNN reached a micro mIoU of 0.71 (accuracy 83.2%) compared to 0.61 (accuracy 75.9%) for RF. Well‐developed baydzherakhs, an advanced stage of ice‐wedge‐polygon degradation, were detected with high confidence (recall of 0.78–0.96 for CNN). Data augmentation and the use of GLCM features within CNN enhanced robustness against domain shifts. However, the most efficient way to adapt the trained model for additional sites was achieved through targeted fine‐tuning. In conclusion, CNN segmentation demonstrated satisfying performance in quantifying fuzzy permafrost degradation stages. It can be expanded in space and time and therefore enables studying long‐term permafrost degradation dynamics.
Plain Language Summary Climate change is particularly strong in the Arctic, causing permafrost (permanently frozen ground) to thaw. Permafrost can have high ice contents, whose melting results in localized surface subsidence as the soil collapses into the space previously occupied by the ice. This forms permafrost thaw landforms ranging from meter‐scale melting ice wedge‐polygon networks to features such as erosional gullies extending over hundreds of meters in length. The development of such landforms can reshape landscapes, impact ecosystems, and alter drainage pathways. On high‐resolution satellite imagery, degradation structures can be identified according to their distinct patterns. In our study, we tested machine learning methods to map these structures. To enable long‐term analysis of permafrost thaw, we tested these methods on historical and recent greyscale satellite imagery. The tested methods included pixel‐based classical segmentation (Random Forest) using texture metrics as inputs and deep learning Convolutional Neural Networks (CNNs). Our results showed that CNNs performed best, providing good results in delineating permafrost degradation across large areas. The model can be adapted and improved for other sites by retraining it with a small amount of site‐specific training data. This research is important because it enables understanding how permafrost is changing across the Arctic.
Key Points Image segmentation enables landscape‐scale mapping of permafrost degradation stages based on their texture in panchromatic imagery Convolutional Neural Networks outperform feature‐based Random Forests in identifying subtle target classes with high intra‐class variability Site specific fine‐tuning is an effective way to allow transferring the model to other study site
Late Miocene speleothems show significant warming, temperate vegetation, and wildfires in Arctic Siberia
Get PDFClimate driven northward boreal forest expansion into the tundra biome controlled by permafrost will play a major role in global emissions trajectories. Yet our limited understanding of the interplay between vegetation and permafrost makes predictions of changing boreal forest extent difficult. We analyse fossil pollen, stable carbon isotopes, and lignin and levoglucosan biomarkers from Tortonian speleothems (8.68 ± 0.09 Ma) from the Lena River Delta (N72.27°, E126.94°) in Arctic Siberia to infer palaeotemperature, precipitation, vegetation and fire regimes. The Tortonian provides a potential analogue for near future climate warming under extreme emissions scenarios, with global mean global temperature ca. 4.5°C above modern and atmospheric CO2 concentrations similar to present. We find evidence for a mixed forest regime, capable of maintaining wildfires, in a region currently dominated by tundra. Future transition to a similarly temperate regime would have large-scale impacts on the global carbon cycle
Speleothem evidence for Late Miocene extreme Arctic amplification – an analogue for near-future anthropogenic climate change?
Get PDFAbstract. The Miocene provides an excellent climatic analogue for near-future runaway anthropogenic warming, with atmospheric CO2 concentrations and global average temperatures similar to those projected for the coming century under extreme-emissions scenarios. However, the magnitude of Miocene Arctic warming remains unclear due to the scarcity of reliable proxy data. Here we use stable oxygen isotope and trace element analyses, alongside clumped isotope and fluid inclusion palaeothermometry of speleothems to reconstruct palaeo-environmental conditions near the Siberian Arctic coast during the Tortonian (8.68 ± 0.09 Ma). Stable oxygen isotope records suggest warmer-than-present temperatures. This is supported by temperature estimates based on clumped isotopes and fluid inclusions giving mean annual air temperatures between +6.6 and +11.1 °C, compared with −12.3 °C today. Trace elements records reveal a highly seasonal hydrological environment. Our estimate of > 18 °C of Arctic warming supports the wider consensus of a warmer-than-present Miocene and provides a rare palaeo-analogue for future Arctic amplification under high-emissions scenarios. The reconstructed increase in mean surface temperature far exceeds temperatures projected in fully coupled global climate models, even under extreme-emissions scenarios. Given that climate models have consistently underestimated the extent of recent Arctic amplification, our proxy data suggest Arctic warming may exceed current projections.
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