Emergy analysis of a silvo-pastoral system, a case study in southern Portugal

The Mediterranean silvo-pastoral system known as Montado, in Portugal, is a complex land use system composed of an open tree stratum in various densities and an herbaceous layer, used for livestock grazing. Livestock also profit from the acorns, and the grazing contributes to avoid shrub encroachment. In the last 20 years, subsidies from the European Union have greatly promoted cattle rearing in this system and the introduction of heavy breeds, at the expense of sheep, goats or the native cattle breeds. The balance of the traditional system is thus threatened, and a precise assessment of the balance between the different components of the system, therefore is highly needed. The goal of this study was to gain a better understanding of a Montado farm system with cattle rearing as the major economic activity by applying the emergy evaluation method to calculate indices of yield, investment, environmental loading and sustainability. By integrating different ecosystem components, the emergy evaluation method allows a comprehensive evaluation of this complex and multifunctional system at the scale of an individual farm. This method provides a set of indices that can help us understand the system and design management strategies that maximize emergy flow in the farm. In this paper, we apply the emergy evaluation method to a Montado farm with cattle rearing, as a way to gain a better understanding of this system at the farm scale. The value for the transformity of veal (2.66E+06 sej J−1) is slightly higher, when compared to other systems producing protein. That means that the investment of nature and man in this product was higher and it requires a premium price on the market. The renewability for Holm Oaks Farm (49 %), lower than for other similar systems, supports the assumption that this is a farm in which, comparing with others, the number of purchased inputs in relation to renewable inputs provided by nature, is higher. The Emergy Investment Ratio is 0.91 for cattle rearing compared to a value of 0.49 for cork and 0.43 for firewood harvesting, making it clear that cattle rearing is a more labor demanding activity comparing with extractive activities as cork and firewood harvesting

“Out of the Can”: A Draft Genome Assembly, Liver Transcriptome, and Nutrigenomics of the European Sardine, Sardina pilchardus

Clupeiformes, such as sardines and herrings, represent an important share of worldwide fisheries. Among those, the European sardine (Sardina pilchardus, Walbaum 1792) exhibits significant commercial relevance. While the last decade showed a steady and sharp decline in capture levels, recent advances in culture husbandry represent promising research avenues. Yet, the complete absence of genomic resources from sardine imposes a severe bottleneck to understand its physiological and ecological requirements. We generated 69 Gbp of paired-end reads using Illumina HiSeq X Ten and assembled a draft genome assembly with an N50 scaffold length of 25,579 bp and BUSCO completeness of 82.1% (Actinopterygii). The estimated size of the genome ranges between 655 and 850 Mb. Additionally, we generated a relatively high-level liver transcriptome. To deliver a proof of principle of the value of this dataset, we established the presence and function of enzymes (Elovl2, Elovl5, and Fads2) that have pivotal roles in the biosynthesis of long chain polyunsaturated fatty acids, essential nutrients particularly abundant in oily fish such as sardines. Our study provides the first sustainableomics datasetexploitation.from a valuable economic marine teleost species, the European sardine, representing an essential resource for their effective conservation, management, and sustainable exploitation. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.Funding: We acknowledge the North Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) that supported this research through the Coral—Sustainable Ocean Exploitation (reference NORTE-01-0145-FEDER-000036). R.R.d.F. thanks the Danish National Research Foundation for its support of the Center for Macroecology, Evolution, and Climate (grant DNRF96). Acknowledgments: Some computational work was performed on the Abel Supercomputing Cluster (Norwegian metacenter for High Performance Computing (NOTUR) and the University of Oslo) operated by the Research Computing Services group at USIT, the University of Oslo IT-department (http://www.hpc.uio.no/). We would like to thank Jette Bornholdt, Amal Al-Chaer and George Pacheco for help with laboratory procedures, and the Bioinformatics Center of the University of Copenhagen for providing laboratory space. This work is part of the CIIMAR-lead initiative Portugal-Fishomics

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio