What is marine biodiversity? Towards common concepts and their implications for assessing biodiversity status

Biodiversity' is one of the most common keywords used in environmental sciences, spanning from research to management, nature conservation, and consultancy. Despite this, our understanding of the underlying concepts varies greatly, between and within disciplines as well as among the scientists themselves. Biodiversity can refer to descriptions or assessments of the status and condition of all or selected groups of organisms, from the genetic variability, to the species, populations, communities, and ecosystems. However, a concept of biodiversity also must encompass understanding the interactions and functions on all levels from individuals up to the whole ecosystem, including changes related to natural and anthropogenic environmental pressures. While biodiversity as such is an abstract and relative concept rooted in the spatial domain, it is central to most international, European, and national governance initiatives aimed at protecting the marine environment. These rely on status assessments of biodiversity which typically require numerical targets and specific reference values, to allow comparison in space and/or time, often in association with some external structuring factors such as physical and biogeochemical conditions. Given that our ability to apply and interpret such assessments requires a solid conceptual understanding of marine biodiversity, here we define this and show how the abstract concept can and needs to be interpreted and subsequently applied in biodiversity assessments

A Content-Based Image Retrieval System for Fish Taxonomy

It is estimated that less than ten percent of the world\u27s species have been discovered and described. The main reason for the slow pace of new species description is that the science of taxonomy, as traditionally practiced, can be very laborious: taxonomists have to manually gather and analyze data from large numbers of specimens and identify the smallest subset of external body characters that uniquely diagnoses the new species as distinct from all its known relatives. The pace of data gathering and analysis can be greatly increased by the information technology. In this paper, we propose a content-based image retrieval system for taxonomic research. The system can identify representative body shape characters of known species based on digitized landmarks and provide statistical clues for assisting taxonomists to identify new species or subspecies. The experiments on a taxonomic problem involving species of suckers in the genera Carpiodes demonstrate promising results

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Proceedings Ocean Biodiversity Informatics: International Conference on Marine Biodiversity Data Management, Hamburg, Germany 29 November to 1 December, 2004

The International conference on Marine Biodiversity Data management ‘Ocean Biodiversity Informatics’ was held in Hamburg, Germany, from 29 November to 1 December 2004. Its objective was to offer a forum to marine biological data managers to discuss the state of the field, and to exchange ideas on how to further develop marine biological data systems. Many marine biologists are actively gathering knowledge, as they have been doing for a long time. What is new is that many of these scientists are willing to share their knowledge, including basic data, with others over the Internet. Our challenge now is to try and manage this trend, avoid confusing users with a multitude of contradicting sources of information, and make sure different data systems can be and are effectively integrated

Africa: the FBA connection

Since its inception in the 1930's the Freshwater Biological Association at Windermere, England has been involved in research on African lakes and rivers. Research has included general and multidisciplinary surveys of many lakes including Lake Victoria, Lake Tanganyika, Lake Nyasa (Lake Malawi) and Lake George. The hydrobiology of the River Nile has also been studied. Research into physical and chemical limnology, phytoplankton ecology and primary productivity, invertebrate biology, freshwater fish and fisheries

Assessment of complex microbial assemblages: description of their diversity and characterisation of individual members: Assessment of complex microbial assemblages: description of their diversity and characterisation of individual members

1. Microbial ecology According to Caumette et al. (2015) the term ecology is derived from the Greek words “oikos” (the house and its operation) and “logos” (the word, knowledge or discourse) and can, therefore, be defined as the scientific field engaged in the “knowledge of the laws governing the house”. This, in extension, results in the simple conclusion that microbial ecology represents the study of the relationship between microorganisms, their co-occurring biota and the prevailing environmental conditions (Caumette et al. 2015). The term microbial ecology has been in use since the early 1960s (Caumette et al. 2015) and microbial ecologists have made astonishing discoveries since. Microbial life at extremes such as in the hydrothermal vents (see Dubilier et al. 2008 and references therein) or the abundance of picophytoplankton (Waterbury et al. 1979; Chisholm et al. 1988) in the deep and surface waters of the oceans, respectively, are only a few of many highlights. Nevertheless, a microbial ecologist who, after leaving the field early in their career, now intends to return would hardly recognise again their former scientific field. The main reason for this hypothesis is to be found in the advances made to the methodologies employed in the field. Most of these were developed for biomedical research and were subsequently hijacked, sometimes followed by minor modifications, by microbial ecologists. The Author presents in this thesis scientific findings which, although spanning only a fraction of the era of research into microbial ecology, have been obtained using various modern tools of the trade. These studies were undertaken by the Author during his employment as postdoctoral scientist at Warwick University (UK), as member of staff at Plymouth Marine Laboratory (UK) and as scientist at the TU Bergakademie Freiberg. Although the scientific issues and the environmental habitats investigated by the Author changed due to funding constraints or due to change of work place (i.e. from the marine to the mining environment) the research shared, by and large, a common aim: to further the existing understanding of microbial communities. The methodological approach chosen to achieve this aim employed both isolation followed by the characterisation of microorganisms and culture independent techniques. Both of these strategies utilised again a variety of methods, but techniques in molecular biology represent a common theme. In particular, the polymerase chain reaction (PCR) formed the work horse for much of the research since it has been routinely used for the amplification of a marker gene for strain identification or analysis of the microbial diversity. To achieve this, the amplicons were either directly sequenced by the Sanger approach or analysed via the application of genetic fingerprint techniques or through Sanger sequencing of individual amplicons cloned into a heterologous host. However, the Author did not remain at idle while with these ‘classical’ approaches for the analysis of microbial communities, but utilised the advances made in the development of nucleotide sequence analysis. In particular, the highly parallelised sequencing techniques (e.g. 454 pyrosequencing, Illumina sequencing) offered the chance to obtain both high genetic resolution of the microbial diversity present in a sample and identification of many individuals through sequence comparison with appropriate sequence repositories. Moreover, these next generation sequencing (NGS) techniques also provided a cost-effective opportunity to extent the characterisation of microbial strains to non-clonal cultures and to even complex microbial assemblages (metagenomics). The work involving the high throughput sequencing techniques has been undertaken in collaboration with Dr Jack Gilbert (PML, lateron at Argonne National Laboratory, USA) and, since at Freiberg, with Dr Anja Poehlein (Goettingen University). These colleagues are thanked for their support with sequence data handling and analyses

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

A systematic approach towards the identification and protection of vulnerable marine ecosystems

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Marine Policy 49 (2014):146-154, doi:10.1016/j.marpol.2013.11.017.The United Nations General Assembly in 2006 and 2009 adopted resolutions that call for the identification and protection of vulnerable marine ecosystems (VMEs) from significant adverse impacts of bottom fishing. While general criteria have been produced, there are no guidelines or protocols that elaborate on the process from initial identification through to the protection of VMEs. Here, based upon an expert review of existing practices, a 10-step framework is proposed: 1) Comparatively assess potential VME indicator taxa and habitats in a region; 2) determine VME thresholds; 3) consider areas already known for their ecological importance; 4) compile information on the distributions of likely VME taxa and habitats, as well as related environmental data; 5) develop predictive distribution models for VME indicator taxa and habitats; 6) compile known or likely fishing impacts; 7) produce a predicted VME naturalness distribution (areas of low cumulative impacts); 8) identify areas of higher value to user groups; 9) conduct management strategy evaluations to produce trade-off scenarios; 10) review and re-iterate, until spatial management scenarios are developed that fulfil international obligations and regional conservation and management objectives. To date, regional progress has been piecemeal and incremental. The proposed 10-step framework combines these various experiences into a systematic approach.The New Zealand Ministry of Science and Innovation (now known as the Ministry of Business, Innovation and Employment) provided funding for the worksho