AimThe role of biotic interactions in influencing species distributions at macroscales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro-scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models.Location Finland, northern Europe.Methods Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four-fold cross-validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot. ResultsAt 10-km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross-validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10-km resolution, although these improvements were less apparent at 40-km resolution for both land cover and biotic interactions.Main conclusions Predictions of species distributions at macro-scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species-climate impact models.

Abstract. We investigated dissolved methane distributions along a 6 km transect crossing active seep sites at 40 m water depth in the central North Sea. These investigations were done under conditions of thermal stratification in summer (July 2013) and homogenous water column in winter (January 2014). Dissolved methane accumulated below the seasonal thermocline in summer with a median concentration of 390 nM, whereas during winter, methane concentrations were typically much lower (median concentration of 22 nM). High-resolution methane analysis using an underwater mass-spectrometer confirmed our summer results and was used to document prevailing stratification over the tidal cycle. We contrast estimates of methane oxidation rates (from 0.1 to 4.0 nM day−1) using the traditional approach scaled to methane concentrations with microbial turnover time values and suggest that the scaling to concentration may obscure the ecosystem microbial activity when comparing systems with different methane concentrations. Our measured and averaged rate constants (k') were on the order of 0.01 day−1, equivalent to a turnover time of 100 days, even when summer stratification led to enhanced methane concentrations in the bottom water. Consistent with these observations, we could not detect known methanotrophs and pmoA genes in water samples collected during both seasons. Estimated methane fluxes indicate that horizontal transport is the dominant process dispersing the methane plume. During periods of high wind speed (winter), more methane is lost to the atmosphere than oxidized in the water. Microbial oxidation seems of minor importance throughout the year.

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A framework for the automatic detection of natural oil slicks and estimation of their associated oil seeps using synthetic aperture radar (SAR) images is presented, and the methodology used has been explained in detail. The designed detection system is the first automatic oil seep estimation system known to exist. The system detects oil slicks in individual SAR images and estimates their origins on the sea surface. Spatial clustering of temporally recurrent slick origins is conducted in order to estimate the locations of the associated oil seeps on the sea floor. The system is implemented in the programming language Python and a direct rule-based approach is employed for the classification unit. A data set of 178 images of the Black Sea acquired by ENVISAT's Advanced Synthetic Aperture Radar was used to test the algorithm. In this paper, the methodology used to design the algorithm and the automatically estimated oil seep locations are reported. The efficiency of the system with respect to manual detection is discussed.

A framework for the automatic detection of natural oil seeps using Synthetic Aperture Radar (SAR) images, implemented in Python, is presented. Dark objects are detected using morphological thresholding. For each object, features are computed, which are used to classify the object as either a natural oil slick or a look-alike. The classification scheme has been implemented using a rule-based approach. The slick origins are detected and clustered together spatially, in order to detect the seep origin. A dataset of 122 images from ENVISAT's Advanced Synthetic Aperture Radar (ASAR) were used to test the algorithm. In this paper, only preliminary results are reported.