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Energy and environmental stability are positively correlated with species richness along broad-scale spatial gradients in terrestrial ecosystems, so their relative importance in generating and preserving diversity cannot be readily disentangled. This study seeks to exploit the negative correlation between energy and stability along the oceanic depth gradient to better understand their relative contribution in shaping broadscale biodiversity patterns. We develop a conceptual framework by simulating speciation and extinction along energy and stability gradients to generate expected patterns of biodiversity for a suite of complementary phylogenetic diversity metrics. Using a time-calibrated molecular phylogeny for New Zealand marine ray-finned fishes and a replicated community ecological sampling design, we then modelled these metrics along large-scale depth and latitude gradients. Our results indicate that energy-rich shallow waters may be an engine of diversity for percomorphs, but also suggest that recent speciation occurs in ancient fish lineages in the deep sea, hence questioning the role of energy as a key driver of speciation. Despite potentially facing high extinction early in their evolution, ancient phylogenetic lineages specialized for the deep-sea were likely preserved by environmental stability during the Cenozoic. Furthermore, intermediate depths might be a “museum” (or zone of overlap) for distinct lineages that occur predominantly in either shallow or deep-sea waters. These intermediate depths (500-900m) may form a “phylogenetic diversity bank”, perhaps providing a refuge during ancient (Mesozoic) extreme anoxic events affecting the deep sea and more recent (Pliocene-Pleistocene) climatic events occurring in shallow ecosystems. Finally, the phylogenetic structures observed in fish communities at intermediate depths suggest other processes might restrict the co-occurrence of closely related species. Overall, by combining a conceptual framework with models of empirical phylogenetic diversity patterns, our study paves the way for understanding the determinants of biodiversity across the largest habitat on earth.

In Eastern Boundary Upwelling Systems nutrient-rich waters are transported to the ocean surface, fuelling high photoautotrophic primary production. Subsequent heterotrophic decomposition of the produced biomass increases the oxygen-depletion at intermediate water depths, which can result in the formation of oxygen minimum zones (OMZ). OMZs can sporadically accumulate hydrogen sulfide (H2S), which is toxic to most multicellular organisms and has been implicated in massive fish kills. During a cruise to the OMZ off Peru in January 2009 we found a sulfidic plume in continental shelf waters, covering an area >5500 km(2), which contained ∼2.2×10(4) tons of H2S. This was the first time that H2S was measured in the Peruvian OMZ and with ∼440 km(3) the largest plume ever reported for oceanic waters. We assessed the phylogenetic and functional diversity of the inhabiting microbial community by high-throughput sequencing of DNA and RNA, while its metabolic activity was determined with rate measurements of carbon fixation and nitrogen transformation processes. The waters were dominated by several distinct γ-, δ- and ε-proteobacterial taxa associated with either sulfur oxidation or sulfate reduction. Our results suggest that these chemolithoautotrophic bacteria utilized several oxidants (oxygen, nitrate, nitrite, nitric oxide and nitrous oxide) to detoxify the sulfidic waters well below the oxic surface. The chemolithoautotrophic activity at our sampling site led to high rates of dark carbon fixation. Assuming that these chemolithoautotrophic rates were maintained throughout the sulfidic waters, they could be representing as much as ∼30% of the photoautotrophic carbon fixation. Postulated changes such as eutrophication and global warming, which lead to an expansion and intensification of OMZs, might also increase the frequency of sulfidic waters. We suggest that the chemolithoautotrophically fixed carbon may be involved in a negative feedback loop that could fuel further sulfate reduction and potentially stabilize the sulfidic OMZ waters.

Abstract North Pacific ocean desert (NPOD) refers to the subtropical North Pacific Ocean of low chlorophyll-a (Chl-a) concentrations, as the largest ocean desert globally. Studies have suggested a development of NPOD over recent decades based on limited evidences from in-field measurements and yet elusive mechanism. In this study, we characterize intensity, area and position of the NPOD from year 1998 to 2018, and investigate its control by the coherent climate processes, based on an available, longest satellite observations of Chl-a concentration. Our results suggested that NPOD oligotrophication and expansion processes were correlated with warming upper oceans in most part of the NPOD, except for the SW NPOD area where the Chl-a variations were linked with regional change in sea surface heights. Moreover, based on our analysis, insignificant shift but only NW-SE variability of the NPOD mean position was likely controlled by the Pacific decadal oscillation processes.

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.

Sea turtles are an iconic group of marine megafauna that have been exposed to multiple anthropogenic threats across their different life stages, especially in the past decades. This has resulted in population declines, and consequently many sea turtle populations are now classified as threatened or endangered globally. Although some populations of sea turtles worldwide are showing early signs of recovery, many still face fundamental threats. This is problematic since sea turtles have important ecological roles. To encourage informed conservation planning and direct future research, we surveyed experts to identify the key contemporary threats (climate change, direct take, fisheries, pollution, disease, predation, and coastal and marine development) faced by sea turtles. Using the survey results and current literature, we also outline knowledge gaps in our understanding of the impact of these threats and how targeted future research, often involving emerging technologies, could close those gaps.

Oceanic production of calcium carbonate is conventionally attributed to marine plankton (coccolithophores and foraminifera). Here we report that marine fish produce precipitated carbonates within their intestines and excrete these at high rates. When combined with estimates of global fish biomass, this suggests that marine fish contribute 3 to 15% of total oceanic carbonate production. Fish carbonates have a higher magnesium content and solubility than traditional sources, yielding faster dissolution with depth. This may explain up to a quarter of the increase in titratable alkalinity within 1000 meters of the ocean surface, a controversial phenomenon that has puzzled oceanographers for decades. We also predict that fish carbonate production may rise in response to future environmental changes in carbon dioxide, and thus become an increasingly important component of the inorganic carbon cycle.

Abstract Litter breakdown in the streambed is an important pathway in organic carbon cycling and energy transfer in the biosphere that is mediated by a wide range of streambed organisms. However, most research on litter breakdown to date has focused on a small fraction of the taxa that drive it (e.g. microbial vs. macroinvertebrate‐mediated breakdown) and has been limited to the benthic zone (BZ). Despite the importance of the hyporheic zone (HZ) as a bioreactor, little is known about what, or who, mediates litter breakdown in this compartment and whether breakdown rates differ between the BZ and HZ. Here, we explore the relationship between litter breakdown and the variation in community structure of benthic and hyporheic communities by deploying two standardized bioassays (cotton strips and two types of commercially available tea bags) in 30 UK streams that encompass a range of environmental conditions. Then, we modelled these assays as a response of the streambed compartment and the biological features of the streambed assemblage (Prokaryota, Protozoa and Eumetazoa invertebrates) to understand the generality and efficiency of litter processing across communities. Litter breakdown was much faster in the BZ compared with the HZ (around 5 times higher for cotton strips and 1.5 times faster for the tea leaves). However, differences in litter breakdown between the BZ and the HZ were mediated by the biological features of the benthos and the hyporheos. Biomass of all the studied biotic groups, α‐diversity of Eumetazoa invertebrates and metabolic diversity of Prokaryota were important predictors that were positively related to breakdown coefficients demonstrating their importance in the functioning of the streambed ecosystem. Our study uses a novel multimetric bioassay that is able to disentangle the contribution by Prokaryota, Protozoa and Eumetazoa invertebrates to litter breakdown. In doing so, our study reveals new insights into how organic matter decomposition is partitioned across biota and streambed compartments.