Publications

A Marine Climate Impacts Workshop was held from 29 April to 3 May 2012 at the US National Center of Ecological Analysis and Synthesis in Santa Barbara. This workshop was the culmination of a series of six meetings over the past three years, which had brought together 25 experts in climate change ecology, analysis of large datasets, palaeontology, marine ecology and physical oceanography. Aims of these workshops were to produce a global synthesis of climate impacts on marine biota, to identify sensitive habitats and taxa, to inform the current Intergovernmental Panel on Climate Change (IPCC) process, and to strengthen research into ecological impacts of climate change.

The automatic classification of animal sounds presents an enduring challenge in bioacoustics, owing to the diverse statistical properties of sound signals, variations in recording equipment, and prevalent low Signal-to-Noise Ratio (SNR) conditions. Deep learning models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have excelled in human speech recognition but have not been effectively tailored to the intricate nature of animal sounds, which exhibit substantial diversity even within the same domain. We propose an automated classification framework applicable to general animal sound classification. Our approach first optimizes audio features from Mel-frequency cepstral coefficients (MFCC) including feature rearrangement and feature reduction. It then uses the optimized features for the deep learning model, i.e., an attention-based Bidirectional LSTM (Bi-LSTM), to extract deep semantic features for sound classification. We also contribute an animal sound benchmark dataset encompassing oceanic animals and birds1. Extensive experimentation with real-world datasets demonstrates that our approach consistently outperforms baseline methods by over 25% in precision, recall, and accuracy, promising advancements in animal sound classification.

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Combating climate change and achieving the UN Sustainable Development Goals (SDGs) are two important challenges facing humanity. Natural climate solutions (NCSs) can contribute to the achievement of these two commitments but can also generate conflicting trade‐offs. Here, we reviewed the literature and drew on expert knowledge to assess the co‐benefits of and trade‐offs between 150 SDG targets and NCSs within 12 selected ecosystems. We demonstrate that terrestrial, coastal, and marine NCSs enable the attainment of different sets of SDG targets, with low redundancy. Implementing NCSs in various ecosystems would therefore maximize achievement of SDG targets but would also induce trade‐offs, particularly if best practices are not followed. Reliance on NCSs at large scales will require that these trade‐offs be taken into consideration to ensure the simultaneous realization of positive climate outcomes and multiple SDG targets for diverse stakeholders.

Abstract Aim To assess confidence in conclusions about climate‐driven biological change through time, and identify approaches for strengthening confidence scientific conclusions about ecological impacts of climate change. Location Global. Methods We outlined a framework for strengthening confidence in inferences drawn from biological climate impact studies through the systematic integration of prior expectations, long‐term data and quantitative statistical procedures. We then developed a numerical confidence index ( C index ) and used it to evaluate current practices in 208 studies of marine climate impacts comprising 1735 biological time series. Results Confidence scores for inferred climate impacts varied widely from 1 to 16 (very low to high confidence). Approximately 35% of analyses were not associated with clearly stated prior expectations and 65% of analyses did not test putative non‐climate drivers of biological change. Among the highest‐scoring studies, 91% tested prior expectations, 86% formulated expectations for alternative drivers but only 63% statistically tested them. Higher confidence scores observed in studies that did not detect a change or tracked multiple species suggest publication bias favouring impact studies that are consistent with climate change. The number of time series showing climate impacts was a poor predictor of average confidence scores for a given group, reinforcing that vote‐counting methodology is not appropriate for determining overall confidence in inferences. Main conclusions Climate impacts research is expected to attribute biological change to climate change with measurable confidence. Studies with long‐term, high‐resolution data, appropriate statistics and tests of alternative drivers earn higher C index scores, suggesting these should be given greater weight in impact assessments. Together with our proposed framework, the results of our C index analysis indicate how the science of detecting and attributing biological impacts to climate change can be strengthened through the use of evidence‐based prior expectations and thorough statistical analyses, even when data are limited, maximizing the impact of the diverse and growing climate change ecology literature.

Predicted genes corresponding to the four most common enzymes present in photosynthetic organisms: NADH:ubiquinone reductase (H+-translocating), N-acetyl-gamma-glutamyl-phosphate reductase, DNA-directed RNA polymerase and non-specific serine/threonine protein kinase of 174 metagenomes sequenced during the Malaspina 2010 global expedition. From: Alejandra Ortega, Nathan R Geraldi, Intikhab Alam, Allan A Kamau, Silvia G Acinas, Ramiro Logares, Josep M Gasol, Ramon Massana, Dorte Krause-Jensen and Carlos M Duarte. Important contribution of macroalgae to oceanic carbon sequestration. Nature Geoscience.

Giant clams are an important ecological component of coral reefs in the Red Sea, as they enhance the reef’s productivity and provide habitat that can increase diversity. Three species of giant clams, namely Tridacna maxima , T. squamosa , and T. squamosina have been described within the Red Sea. However, due to its scarcity, information about the distribution and ecology of T. squamosina in the Saudi Arabian Red Sea is still lacking. This study used DNA barcoding to confirm the identity of the rare T. squamosina in the Farasan Banks. Six mtCOI fragments (500 bp) of T. squamosina were successfully amplified using the SQUA-primers for the first time. We used our data along with 18 reference sequences (16S) from the online database to assess the genetic diversity and population structure of T. squamosina . Low genetic diversity among the T. squamosina populations inferred from the 16S sequences implies a recent bottleneck for this species, which is supported by their historically higher diversity based on the coalescent-based estimator. Given the small population abundance and limited genetic variation of T. squamosina , it may warrant immediate local protections such as biobanking and fertility preservation programs as well as effective integrated coastal zone management plans.

Additional file 4. Gundog.Tracks() (.R file).