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AbstractThe Paris Conference of Parties (COP21) agreement renewed momentum for action against climate change, creating the space for solutions for conservation of the ocean addressing two of its largest threats: climate change and ocean acidification (CCOA). Recent arguments that ocean policies disregard a mature conservation research field and that protected areas cannot address climate change may be oversimplistic at this time when dynamic solutions for the management of changing oceans are needed. We propose a novel approach, based on spatial meta‐analysis of climate impact models, to improve the positioning of marine protected areas to limit CCOA impacts. We do this by estimating the vulnerability of ocean ecosystems to CCOA in a spatially explicit manner and then co‐mapping human activities such as the placement of renewable energy developments and the distribution of marine protected areas. We test this approach in the NE Atlantic considering also how CCOA impacts the base of the food web which supports protected species, an aspect often neglected in conservation studies. We found that, in this case, current regional conservation plans protect areas with low ecosystem‐level vulnerability to CCOA, but disregard how species may redistribute to new, suitable and productive habitats. Under current plans, these areas remain open to commercial extraction and other uses. Here, and worldwide, ocean conservation strategies under CCOA must recognize the long‐term importance of these habitat refuges, and studies such as this one are needed to identify them. Protecting these areas creates adaptive, climate‐ready and ecosystem‐level policy options for conservation, suitable for changing oceans.

Chronic, low intensity herbivory by invertebrates, termed background herbivory, has been understudied in tundra, yet its impacts are likely to increase in a warmer Arctic. The magnitude of these changes is however hard to predict as we know little about the drivers of current levels of invertebrate herbivory in tundra. We assessed the intensity of invertebrate herbivory on a common tundra plant, the dwarf birch (Betula glandulosa-nana complex), and investigated its relationship to latitude and climate across the tundra biome. Leaf damage by defoliating, mining and gall-forming invertebrates was measured in samples collected from 192 sites at 56 locations. Our results indicate that invertebrate herbivory is nearly ubiquitous across the tundra biome but occurs at low intensity. On average, invertebrates damaged 11.2% of the leaves and removed 1.4% of total leaf area. The damage was mainly caused by external leaf feeders, and most damaged leaves were only slightly affected (12% leaf area lost). Foliar damage was consistently positively correlated with mid-summer (July) temperature and, to a lesser extent, precipitation in the year of data collection, irrespective of latitude. Our models predict that, on average, foliar losses to invertebrates on dwarf birch are likely to increase by 6--7% over the current levels with a 1 textdegreeC increase in summer temperatures. Our results show that invertebrate herbivory on dwarf birch is small in magnitude but given its prevalence and dependence on climatic variables, background invertebrate herbivory should be included in predictions of climate change impacts on tundra ecosystems.

The earth is warming at an alarming rate, especially in the Arctic, where a marked decline in sea ice cover may have far-ranging consequences for endemic species. Little auks, endemic Arctic seabirds, are key bioindicators as they forage in the marginal ice zone and feed preferentially on lipid-rich Arctic copepods and ice-associated amphipods sensitive to the consequences of global warming. We tested how little auks cope with an ice-free foraging environment during the breeding season. To this end, we took advantage of natural variation in sea ice concentration along the east coast of Greenland. We compared foraging and diving behaviour, chick diet and growth and adult body condition between two years, in the presence versus nearby absence of sea ice in the vicinity of their breeding site. Moreover, we sampled zooplankton at sea when sea ice was absent to evaluate prey location and little auk dietary preferences. Little auks foraged in the same areas both years, irrespective of sea ice presence/concentration, and targeted the shelf break and the continental shelf. We confirmed that breeding little auks showed a clear preference for larger copepod species to feed their chick, but caught smaller copepods and nearly no ice-associated amphipod when sea ice was absent. Nevertheless, these dietary changes had no impact on chick growth and adult body condition. Our findings demonstrate the importance of bathymetry for profitable little auk foraging, whatever the sea-ice conditions. Our investigations, along with recent studies, also confirm more flexibility than previously predicted for this key species in a warming Arctic.

A “Well-to-Propeller” Life Cycle Assessment of maritime transport was performed with a European geographical focus. Four typical types of vessels with specific operational profiles were assessed: a container vessel and a tanker (both with 2-stroke engines), a passenger roll-on/roll-off (Ro-Pax) and a cruise vessel (both with 4-stroke engines). All main engines were dual fuel operated with Heavy Fuel Oil (HFO) or Liquefied Natural Gas (LNG). Alternative onshore and offshore fuel supply chains were considered. Primary energy use and greenhouse gas emissions were assessed. Raw material extraction was found to be the most impactful life cycle stage (~90% of total energy use). Regarding greenhouse gases, liquefaction was the key issue. When transitioning from HFO to LNG, the systems were mainly influenced by a reduction in cargo capacity due to bunkering requirements and methane slip, which depends on the fuel supply chain (onshore has 64% more slip than offshore) and the engine type (4-stroke engines have 20% more slip than 2-stroke engines). The combination of alternative fuel supply chains and specific operational profiles allowed for a complete system assessment. The results demonstrated that multiple opposing drivers affect the environmental performance of maritime transport, a useful insight towards establishing emission abatement strategies.

A stochastic age-structured population model was developed to explore biologically favourable levels of effort and closing periods within the sardine pelagic fishery in the eastern Mediterranean Sea. Results suggested that the developed age-structured model captured the observed biomass fluctuations and catches reasonably well and represents the first comprehensive investigation of alternative management strategies for eastern Mediterranean sardine fishery that include stochasticity. The present study provided direct evidence for the importance of the correct timing of the temporal fishing ban. Significant benefits were found both in terms of biomass and catch from a corrective shift in the fishing closed period. The current findings suggested that protecting the younger age groups from fishing in the period October-December, by shifting the ban period earlier than December may profit, biologically, the stock and economically the fishing sector. Progressive reductions in fishing mortality/effort also yield significant positive biological and fishery benefits in the short term.

Les méthodes de réduction d'échelle statistiques sont des outils de post-traitement populaires qui sont largement utilisés dans de nombreux secteurs pour adapter les résultats biaisés à résolution grossière des simulations climatiques mondiales à l'échelle régionale à locale généralement requise par les utilisateurs. Ils vont de méthodes simples et pragmatiques de correction de biais (BC), qui ajustent directement les sorties du modèle d'intérêt (par exemple les précipitations) en fonction des observations locales disponibles, à des méthodes plus complexes de pronostic parfait (PP), qui dérivent indirectement des prédictions locales (par exemple les précipitations) à partir de variables appropriées du modèle à grande échelle de l'air supérieur (prédicteurs). Les méthodes de réduction d'échelle statistiques ont été largement utilisées et évaluées de manière critique dans les applications du changement climatique ; cependant, leurs avantages et leurs limites dans les prévisions saisonnières ne sont pas encore bien compris. En particulier, un problème clé dans ce contexte est de savoir s'ils servent à améliorer la qualité/compétence prévisionnelle des résultats des modèles bruts au-delà de l'ajustement de leurs biais systématiques. Dans cet article, nous analysons ce problème en appliquant deux méthodes BC et deux méthodes PP à la pointe de la technologie pour réduire les précipitations à partir d'un rétroprojecteur saisonnier multimodèle dans une région tropicale difficile, les Philippines. Pour évaluer correctement la valeur ajoutée potentielle au-delà de la réduction des biais du modèle, nous considérons deux scores de validation qui ne sont pas sensibles aux changements de la moyenne (catégories de corrélation et de fiabilité). Nos résultats montrent que, alors que les méthodes BC maintiennent ou aggravent la compétence des prévisions du modèle brut, les méthodes PP peuvent apporter une amélioration significative des compétences (aggravation) dans les cas où les variables prédictives à grande échelle considérées sont meilleures (pires) prédites par le modèle que les précipitations. Par exemple, les méthodes PP augmentent (diminuent) la fiabilité du modèle dans près de 40 % des stations considérées en été boréal (automne). Par conséquent, le choix d'une approche pratique de réduction d'échelle (BC ou PP) dépend de la région et de la saison. Los métodos de reducción de escala estadística son herramientas populares de posprocesamiento que se utilizan ampliamente en muchos sectores para adaptar los resultados sesgados de resolución gruesa de las simulaciones climáticas globales a la escala regional a local que generalmente requieren los usuarios. Van desde métodos de corrección de sesgo (BC) simples y pragmáticos, que ajustan directamente los resultados del modelo de interés (por ejemplo, precipitación) de acuerdo con las observaciones locales disponibles, hasta métodos de pronóstico perfecto (PP) más complejos, que derivan indirectamente predicciones locales (por ejemplo, precipitación) de variables apropiadas del modelo a gran escala del aire superior (predictores). Los métodos estadísticos de reducción de escala se han utilizado ampliamente y se han evaluado críticamente en aplicaciones de cambio climático; sin embargo, sus ventajas y limitaciones en el pronóstico estacional aún no se comprenden bien. En particular, un problema clave en este contexto es si sirven para mejorar la calidad/habilidad de pronóstico de los resultados del modelo bruto más allá del ajuste de sus sesgos sistemáticos. En este documento analizamos este problema aplicando dos métodos BC y dos PP de última generación para reducir la precipitación de un retroceso estacional multimodelo en una región tropical desafiante, Filipinas. Para evaluar adecuadamente el valor añadido potencial más allá de la reducción de los sesgos del modelo, consideramos dos puntuaciones de validación que no son sensibles a los cambios en la media (categorías de correlación y fiabilidad). Nuestros resultados muestran que, mientras que los métodos BC mantienen o empeoran la habilidad de los pronósticos del modelo en bruto, los métodos PP pueden producir una mejora significativa de la habilidad (empeoramiento) en los casos en que las variables predictoras a gran escala consideradas son mejores (peores) predichas por el modelo que la precipitación. Por ejemplo, se encuentra que los métodos PP aumentan (disminuyen) la confiabilidad del modelo en casi el 40% de las estaciones consideradas en el verano boreal (otoño). Por lo tanto, la elección de un enfoque de reducción de escala conveniente (ya sea BC o PP) depende de la región y la temporada. Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season. طرق تقليص النطاق الإحصائي هي أدوات شائعة لما بعد المعالجة تستخدم على نطاق واسع في العديد من القطاعات لتكييف المخرجات المتحيزة ذات الدقة الخشنة من محاكاة المناخ العالمي إلى النطاق الإقليمي إلى المحلي المطلوب عادةً من قبل المستخدمين. وهي تتراوح من طرق بسيطة وعملية لتصحيح التحيز (BC)، والتي تعدل بشكل مباشر مخرجات النموذج محل الاهتمام (مثل هطول الأمطار) وفقًا للملاحظات المحلية المتاحة، إلى طرق التكهن المثالي (PP) الأكثر تعقيدًا، والتي تستمد بشكل غير مباشر التنبؤات المحلية (مثل هطول الأمطار) من متغيرات النموذج المناسبة واسعة النطاق في الهواء العلوي (التنبؤات). تم استخدام طرق تقليص النطاق الإحصائي على نطاق واسع وتقييمها بشكل نقدي في تطبيقات تغير المناخ ؛ ومع ذلك، فإن مزاياها وقيودها في التنبؤ الموسمي ليست مفهومة جيدًا بعد. على وجه الخصوص، تتمثل المشكلة الرئيسية في هذا السياق في ما إذا كانت تعمل على تحسين جودة/مهارة التنبؤ لمخرجات النموذج الخام بما يتجاوز تعديل تحيزاتها المنهجية. في هذه الورقة، نقوم بتحليل هذه المشكلة من خلال تطبيق طريقتين حديثتين قبل الميلاد وطريقتين للبولي بروبلين لتقليل هطول الأمطار من توقعات موسمية متعددة النماذج في منطقة استوائية صعبة، الفلبين. لتقييم القيمة المضافة المحتملة بشكل صحيح بما يتجاوز الحد من تحيزات النموذج، نأخذ في الاعتبار درجتي التحقق غير الحساستين للتغيرات في المتوسط (فئتي الارتباط والموثوقية). تظهر نتائجنا أنه في حين أن طرق BC تحافظ على مهارة تنبؤات النموذج الخام أو تزيدها سوءًا، فإن طرق PP يمكن أن تسفر عن تحسن كبير في المهارات (تدهور) في الحالات التي تكون فيها المتغيرات التنبؤية واسعة النطاق التي يعتبرها النموذج أفضل (أسوأ) من هطول الأمطار. على سبيل المثال، تم العثور على طرق PP لزيادة (تقليل) موثوقية النموذج في ما يقرب من 40 ٪ من المحطات التي يتم النظر فيها في الصيف الشمالي (الخريف). لذلك، يعتمد اختيار نهج تصغير النطاق المناسب (إما BC أو PP) على المنطقة والموسم.