The visual fields of common guillemots Uria aalge and Atlantic puffins Fratercula arctica: foraging, vigilance and collision vulnerability

Significant differences in avian visual fields are found between closely related species that differ in their foraging technique. We report marked differences in the visual fields of two auk species. In air, Common Guillemots Uria aalge have relatively narrow binocular fields typical of those found in non-passerine predatory birds. Atlantic Puffins Fratercula arctica have much broader binocular fields similar to those that have hitherto been recorded in passerines and in a penguin. In water, visual fields narrow considerably and binocularity in the direction of the bill is probably abolished in both auk species. Although perceptual challenges associated with foraging are similar in both species during the breeding season, when they are piscivorous, Puffins (but not Guillemots) face more exacting perceptual challenges when foraging at other times, when they take a high proportion of small invertebrate prey. Capturing this prey probably requires more accurate, visually guided bill placement and we argue that this is met by the Puffin's broader binocular field, which is retained upon immersion; its upward orientation may enable prey to be seen in silhouette. These visual field configurations have potentially important consequences that render these birds vulnerable to collision with human artefacts underwater, but not in air. They also have consequences for vigilance behaviour

Report on evaluation framework for holistic management – summary of the concept, requirements and management implications

Due to its unique characteristics with substantial drainage area and limited water exchange with the North Sea, considerable salinity gradient, permanent stratification as well as a combination of numerous, strong anthropogenic and climatic pressures the Baltic Sea environment is under constant stress. Considering the intensity of exploitation and complexity of pressures of natural and anthropogenic origin as well as their cumulative effects, the Baltic Sea biodiversity has to be investigated and managed in a holistic and integrated way. Even if some of those pressures cannot be successfully managed especially in a short-term perspective, it is absolutely crucial to consider their impact in suggested management actions. The approach needs to be adaptive to handle relevant spatial and temporal scales as well as foreseen and unforeseen changes. The goal of the BIO-C3 project was to move from the current “single driver/threat” approach to a science based, comprehensive, and integrated approach. In this report we are presenting data, monitoring, and knowledge requirements as well as assessment and analytical tools that are needed to establish a management evaluation framework for an adaptive, integrated management of biodiversity in the Baltic Sea. A comprehensive analysis of various aspects of drivers and pressures on marine biodiversity has been provided and discussed including the assessment of the cumulative effects of multiple human pressures as their perceived impacts on the Baltic ecosystem are in most cases a combination of different human induced pressures and an isolated analysis of the impact(s) of single pressures remain extremely challenging or impossible. The recently suggested risk management process to entrench the cumulative effect assessments adequately handles the associated uncertainty and streamlines the uptake of scientific outcomes into the science-policy interface..

On the front line: integrated habitat mapping for olive ridley sea turtles in the southeast Atlantic

notes:types: JOURThis study demonstrates that it is imperative that marine conservation policy recognizes the spatial extent of highly migratory species with expansive ranges. It also highlights that deficiencies exist in current knowledge of bycatch, both in gear specificity and in catch per unit effort. With integration of vessel monitoring system (VMS) data and those on fisheries catch, knowledge and understanding of bycatch may be improved, and this will ultimately facilitate development of appropriate management strategies and long-term sustainability of fisheries and their supporting ecosystems

Food webs under changing biodiversity - Top-down control

Report on effects of changing predation pressure on benthic and pelagic specie

Assessing the capability of Sentinel-2 time-series to estimate soil organic carbon and clay content at local scale in croplands

The use of remote sensing data methods is affordable for the mapping of soil properties of the plowed layer over croplands. Carried out in the framework of the ongoing STEROPES project of the European Joint H2020 Program SOIL, this work is focused on the feasibility of Sentinel-2 based approaches for the high resolution mapping of topsoil clay and organic carbon (SOC) contents at the within-farm or within-field scales, for cropland sites of contrasted climates and soil types across the Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series and several spectral indices (NDVI, NBR2, BSI, S2WI), were developed and compared for i) pure bare soil condition (maxBSI), ii) driest soil condition (minS2WI), iii) average bare soil condition (Median) and iv) dry soil conditions excluding extreme reflectance values (R90). Three spectral modeling approaches, using the Sentinel-2 bands of the output temporal mosaics as covariates, were tested and compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude and latitude as covariates (QRFxy); (iii) a hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation of the soil properties. We tested pairs of mosaic and spectral approaches on ten sites in Turkiye, Italy, Lithuania, and USA where soil samples were collected and SOC and clay content were measured in the lab. The average RPIQ of the best performances among the test sites was 2.50 both for SOC (RMSE = 0.15%) and clay (RMSE = 3.3%). Both accuracy level and uncertainty were mainly influenced by site characteristics of cloud frequency, soil types and management. Generally, the models including a spatial component (QRFxy and LMEM) were the best performing, while the best spatial mosaicking approaches mostly were Median and R90. The most frequent optimal combination of mosaicking and model type was Median or R90 and QRFxy for SOC, and R90 and LMEM for clay estimation