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      Global patterns in functional rarity of marine fish

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          Abstract

          Rare species, which represent a large fraction of the taxa in ecological assemblages, account for much of the biological diversity on Earth. These species make substantial contributions to ecosystem functioning, and are targets of conservation policy. Here we adopt an integrated approach, combining information on the rarity of species trait combinations, and their spatial restrictedness, to quantify the biogeography of rare fish (a taxon with almost 13,000 species) in the world’s oceans. We find concentrations of rarity, in excess of what is predicted by a null expectation, near the coasts and at higher latitudes. We also observe mismatches between these rarity hotspots and marine protected areas. This pattern is repeated for both major groupings of fish, the Actinopterygii (bony fish) and Elasmobranchii (sharks, skates and rays). These results uncover global patterns of rarity that were not apparent from earlier work, and highlight the importance of using metrics that incorporate information on functional traits in the conservation and management of global marine fishes.

          Abstract

          Rare species are crucial for biological diversity and ecosystem functioning. Here, the authors combine taxonomic and functional diversity data to quantify rarity across marine fish species, identifying mismatches between rarity hotspots and protected areas.

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          Most cited references46

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          MissForest--non-parametric missing value imputation for mixed-type data.

          Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch
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            Fishing down marine food webs

            The mean trophic level of the species groups reported in Food and Agricultural Organization global fisheries statistics declined from 1950 to 1994. This reflects a gradual transition in landings from long-lived, high trophic level, piscivorous bottom fish toward short-lived, low trophic level invertebrates and planktivorous pelagic fish. This effect, also found to be occurring in inland fisheries, is most pronounced in the Northern Hemisphere. Fishing down food webs (that is, at lower trophic levels) leads at first to increasing catches, then to a phase transition associated with stagnating or declining catches. These results indicate that present exploitation patterns are unsustainable.
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              Incorporating plant functional diversity effects in ecosystem service assessments.

              Global environmental change affects the sustained provision of a wide set of ecosystem services. Although the delivery of ecosystem services is strongly affected by abiotic drivers and direct land use effects, it is also modulated by the functional diversity of biological communities (the value, range, and relative abundance of functional traits in a given ecosystem). The focus of this article is on integrating the different possible mechanisms by which functional diversity affects ecosystem properties that are directly relevant to ecosystem services. We propose a systematic way for progressing in understanding how land cover change affects these ecosystem properties through functional diversity modifications. Models on links between ecosystem properties and the local mean, range, and distribution of plant trait values are numerous, but they have been scattered in the literature, with varying degrees of empirical support and varying functional diversity components analyzed. Here we articulate these different components in a single conceptual and methodological framework that allows testing them in combination. We illustrate our approach with examples from the literature and apply the proposed framework to a grassland system in the central French Alps in which functional diversity, by responding to land use change, alters the provision of ecosystem services important to local stakeholders. We claim that our framework contributes to opening a new area of research at the interface of land change science and fundamental ecology.
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                Author and article information

                Contributors
                its5@st-andrews.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                15 February 2022
                15 February 2022
                2022
                : 13
                : 877
                Affiliations
                GRID grid.11914.3c, ISNI 0000 0001 0721 1626, Centre for Biological Diversity, School of Biology, , University of St Andrews, ; St Andrews, Scotland UK
                Author information
                http://orcid.org/0000-0003-4478-8103
                http://orcid.org/0000-0001-9687-0593
                http://orcid.org/0000-0002-0036-2795
                Article
                28488
                10.1038/s41467-022-28488-1
                8847455
                35169123
                b8471e68-9126-49ce-aa98-6e765ce65ed5
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 March 2021
                : 28 January 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000275, Leverhulme Trust;
                Award ID: RPG-2019–402
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                biodiversity,biogeography,community ecology,conservation biology,macroecology
                Uncategorized
                biodiversity, biogeography, community ecology, conservation biology, macroecology

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