17,557 research outputs found

    Biologically informed ecological niche models for an example pelagic, highly mobile species

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    Background: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative modelling techniques have provided some information, but few studies have explored model development for non-breeding pelagic seabirds. Here, I present a first phase in developing robust niche models for highly mobile species as a baseline for further development.Methodology: Using observational data from a 12-year time period, 217 unique model parameterisations across three correlative modelling algorithms (boosted regression trees, Maxent and minimum volume ellipsoids) were tested in a time-averaged approach for their ability to recreate the at-sea distribution of non-breeding Wandering Albatrosses (Diomedea exulans) to provide a baseline for further development.Principle Findings/Results: Overall, minimum volume ellipsoids outperformed both boosted regression trees and Maxent. However, whilst the latter two algorithms generally overfit the data, minimum volume ellipsoids tended to underfit the data. Conclusions: The results of this exercise suggest a necessary evolution in how correlative modelling for highly mobile species such as pelagic seabirds should be approached. These insights are crucial for understanding seabird–environment interactions at macroscales, which can facilitate the ability to address population declines and inform effective marine conservation policy in the wake of rapid global change

    Biodiversity and Biocollections: Problem of Correspondence

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    This text is an English translation of those several sections of the original paper in Russian, where collection-related issues are considered. The full citation of the original paper is as following: Pavlinov I.Ya. 2016. [Bioraznoobrazie i biokollektsii: problema sootvetstvia]. In: Pavlinov I.Ya. (comp.). Aspects of Biodiversity. Archives of Zoological Museum of Lomonosov Moscow State University, Vol. 54, Pр. 733–786. Orientation of biology, as a natural science, on the study and explanation of the similarities and differences between organisms led in the second half of the 20th century to the recognition of a specifi c subject area of biological explorations, viz. biodiversity (BD). One of the important general scientifi c prerequisites for this shift was understanding that (at the level of ontology) the structured diversity of the living nature is its fundamental property equivocal to subjecting of some of its manifestations to certain laws. At the level of epistemology, this led to acknowledging that the “diversifi cationary” approach to description of the living beings is as justifi able as the before dominated “unifi cationary” one. This general trend has led to a signifi cant increase in the attention to BD. From a pragmatic perspective, its leitmotif was conservation of BD as a renewable resource, while from a scientifi c perspective the leitmotif was studying it was studying BD as a specifi c natural phenomenon. These two points of view are united by recognition of the need for scientific substantiation of BD conservation strategy, which implies the need for a detailed study of BD itself. At the level of ontology, one of the key problems in the study of BD (leaving aside the question of its genesis) is determination of its structure, which is interpreted as a manifestation of the structure of the Earth’s biota itself. With this, it is acknowledged that the subject area of empirical explorations is not the BD as a whole ( “Umgebung”) but its particular manifestations (“Umwelts”). It is proposed herewith to recognized, within the latter: fragments of BD (especially taxa and ecosystems), hierarchical levels of BD (primarily within- and interorganismal ones), and aspects of BD (before all taxonomic and meronomic ones). Attention is drawn to a new interpretation of bioinformatics as a discipline that studies the information support of BD explorations. An important fraction of this support are biocollections. The scientifi c value of collections means that they make it possible both empirical inferring and testing (verification) of the knowledge about BD. This makes biocollections, in their epistemological status, equivalent to experiments, and so makes studies of BD quite scientific. It is emphasized that the natural objects (naturalia), which are permanently kept in collections, contain primary (objective) information about BD, while information retrieved somehow from them is a secondary (subjective) one. Collection, as an information resource, serves as a research sample in the studies of BD. Collection pool, as the totality of all collection materials kept in repositories according to certain standards, can be treated as a general sample, and every single collection as a local sample. The main characteristic of collection-as-sample is its representativeness; so the basic strategy of development of the collection pool is to maximize its representativeness as a means to ensure correspondence of structure of biocollection pool to that of BD itself. The most fundamental characteristic of collection, as an information resource, is its scientific signifi cance. The following three main groups of more particular characteristics are distinguished: — the “proper” characteristics of every collection are its meaningfulness, informativeness, reliability, adequacy, documenting, systematicity, volume, structure, uniqueness, stability, lability; — the “external” characteristics of collection are resolution, usability, ethic constituent; — the “service” characteristics of collection are its museofication, storage system security, inclusion in metastructure, cost. In the contemporary world, development of the biocollection pool, as a specific resource for BD research, requires considerable organizational efforts, including work on their “information support” aimed at demonstrating the necessity of existence of the biocollections

    The application of predictive modelling for determining bio-environmental factors affecting the distribution of blackflies (Diptera: Simuliidae) in the Gilgel Gibe watershed in Southwest Ethiopia

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    Blackflies are important macroinvertebrate groups from a public health as well as ecological point of view. Determining the biological and environmental factors favouring or inhibiting the existence of blackflies could facilitate biomonitoring of rivers as well as control of disease vectors. The combined use of different predictive modelling techniques is known to improve identification of presence/absence and abundance of taxa in a given habitat. This approach enables better identification of the suitable habitat conditions or environmental constraints of a given taxon. Simuliidae larvae are important biological indicators as they are abundant in tropical aquatic ecosystems. Some of the blackfly groups are also important disease vectors in poor tropical countries. Our investigations aim to establish a combination of models able to identify the environmental factors and macroinvertebrate organisms that are favourable or inhibiting blackfly larvae existence in aquatic ecosystems. The models developed using macroinvertebrate predictors showed better performance than those based on environmental predictors. The identified environmental and macroinvertebrate parameters can be used to determine the distribution of blackflies, which in turn can help control river blindness in endemic tropical places. Through a combination of modelling techniques, a reliable method has been developed that explains environmental and biological relationships with the target organism, and, thus, can serve as a decision support tool for ecological management strategies

    Evolutionary biology for the 21st century

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    New theoretical and conceptual frameworks are required for evolutionary biology to capitalize on the wealth of data now becoming available from the study of genomes, phenotypes, and organisms - including humans - in their natural environments.Molecular and Cellular BiologyOrganismic and Evolutionary Biolog

    The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

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    Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version
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