Impacts and adaptation to climate change in European economies

This paper evaluates the impacts of climate change to European economies under an increase in global mean temperature at +2 °C and +4 °C. It is based on a summary of conclusions from available studies of how climate change may affect various sectors of the economies in different countries. We apply a macroeconomic general equilibrium model, which integrates impacts of climate change on different activities of the economies. Agents adapt by responding to the changes in market conditions following the climatic changes, thus bringing consistency between economic behaviour and adaptation to climate change. Europe is divided into 85 sub-regions in order to capture climate variability and variations in vulnerabilities within countries. We find that the impacts in the +2 °C are moderate throughout Europe, with positive impacts on GDP in some sub-regions and negative impacts down to 0.1 per cent per year in others. At +4 °C, GDP is negatively affected throughout Europe, and most substantially in the southern parts, where it falls by up to 0.7 per cent per year in some sub-regions. We also find that climate change causes differentiations in wages across Europe, which may cause migration from southern parts of Europe to northern parts, especially to the Nordic countries.     

A bridge over troubled water: A contextual analysis of social vulnerability to climate change in a riverine landscape in south-east Norway

Local communities in the Gudbrandsdalen region in Norway are increasingly exposed to climate-induced hazards such as floods and landslides. A core question is how community members respond to climate change and what factors contribute to more resilient communities. The authors used a contextual approach to analyze data from semi-structured interviews along five dimensions. In Gaustad Muncipality they found that individuals’ motivation to adapt to climate change depended largely on subjective values such as identity, place attachment, cultural values, and social networks among individuals, which means it is crucial that strategic plans for adaptation to climate change at different policy levels are experienced as relevant by community members. While the studied community has experienced heavy floods in river systems and streams, little evidence of adaptation was observed. Instead, they appeared to adopt coping strategies. Landowners may have limited incentives to adapt to climate change due to contraproductive policy measures such as economic compensation for direct losses without requiring improved practices. Effective adaptation to climate change on the local level is likely to require making compensation mechanisms contingent upon landowners showing willingness to change from coping to adaptive practices, as well as a contextualized approach integrating local and scientific forms of knowledge.     

Species-specific patterns of aggregation of wild fish around fish farms

Fish-farming structures are widespread in coastal waters and are highly attractive to wild fish. Several studies have estimated that tons to tens of tons of wild fish aggregate around fish farms. These estimates assumed that the majority of wild fish are concentrated immediately beneath farms, although this assumption has never been explicitly tested. We tested the hypothesis that abundances of wild fish would be greatest immediately beneath farms and progressively diminish with distance at 4 full-scale coastal salmon (Salmo salar) farms in Norway. At each farm, fish were counted with a video-camera system at 5 different distances from the cages (farm = 0 m, 25, 50, 100 and 200 m) throughout the water column on three separate days. Combined across all locations and times, the total abundance of wild fish was 20 times greater at the farm than at the 200 m sampling distance. Saithe (Pollachius virens) dominated assemblages at all 4 farms and were consistently significantly more abundant at the farm than at the 25–200 m distances. This ‘tight aggregation’ around farms corresponds to the reliance of saithe on waste feed when they school near farms. In contrast, patterns of distribution of both cod (Gadus morhua) and poor cod (Trisopterus minutus) varied among farms, with either highest abundances at the farm or a more even distribution of abundance across all 5 distances sampled. No specific pattern of aggregation was evident for the bottom-dwelling haddock (Melanogrammus aeglefinus). Our results suggest that the present 100 m no-fishing zone around salmon farms protects the greatest proportion of farm-aggregated saithe and cod from fishing during the daytime. However, whether this reduces their overall susceptibility to fishing requires further research regarding nighttime distribution and movements.     

Seasonal cycles and long-term trends of plankton in shelf and oceanic habitats of the Norwegian Sea in relation to environmental variables

The plankton abundance data of the Continuous Plankton Recorder (CPR) route from Bergen or Rotterdam to Weather Station Mike (6444^′N, 2E) from 1949 to 1981 were analysed for long-term trends and seasonal production cycles, and were related to environmental data. The data were explored using the canonical correlation analysis and nonparametric techniques like the Nadaraya–Watson regression. While large copepods such as Calanus spp. and Metridia lucens did not show any temporal trends, a sharp decrease in the abundances of smaller copepods and phytoplankton was observed after 1960. The temporal trends were not related to the NAO, but did show a correlation with the wind direction. Seasonal abundance curves showed that production of both phytoplankton and zooplankton taxa started earlier in coastal water compared to Atlantic water. From the 1950s to the 1970s most taxa showed a delay in the start of the seasonal production cycles, indicating a reduction in the length of the productive cycle. This may to some extent explain the reduced abundance of smaller copepods, phytoplankton and other species during the 1960s and 1970s.     

A common garden experiment with larval Northeast Arctic and Norwegian coastal cod cohorts in replicated mesocosms

A replicated mesocosm experiment was carried out to evaluate differential effects of feeding conditions for larval Northeast Arctic (NA) cod and Norwegian coastal (NC) cod. The two populations were (1) reared together with a 6-day older NA cohort (mixed) in high (HC) and low prey concentration (LC; 2000 and 200 prey/L initially), and (2) reared separately in HC treatments (non-mixed) to be able to evaluate both the effect of feeding conditions and possible effects of size interaction within mesocosms. The larvae were fed natural zooplankton, and the two stocks were identified in the mixed mesocosms by otolith marking. NA larvae hatched at a larger size, had higher growth rates, and survived better than NC larvae in both mixed and non-mixed mesocosms in the HC treatment. The second cohort clearly survived better in the non-mixed than in the mixed mesocosms, indicating the presence of an interaction effect before cannibalism could occur. We found a significant higher weight-at-length between NC and NA larvae (<12 mm), which was bigger than the effect difference due to feeding conditions. Furthermore, a positive relation between survival and initial growth within mesocosms was found. We suggest that lower growth at early larval stages was accompanied by lower survival, and suggest that this was further enhanced when larvae interacted with older and larger larvae.     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.