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      Distribution and Numbers of Pygmies in Central African Forests

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          Abstract

          Pygmy populations occupy a vast territory extending west-to-east along the central African belt from the Congo Basin to Lake Victoria. However, their numbers and actual distribution is not known precisely. Here, we undertake this task by using locational data and population sizes for an unprecedented number of known Pygmy camps and settlements ( n = 654) in five of the nine countries where currently distributed. With these data we develop spatial distribution models based on the favourability function, which distinguish areas with favourable environmental conditions from those less suitable for Pygmy presence. Highly favourable areas were significantly explained by presence of tropical forests, and by lower human pressure variables. For documented Pygmy settlements, we use the relationship between observed population sizes and predicted favourability values to estimate the total Pygmy population throughout Central Africa. We estimate that around 920,000 Pygmies (over 60% in DRC) is possible within favourable forest areas in Central Africa. We argue that fragmentation of the existing Pygmy populations, alongside pressure from extractive industries and sometimes conflict with conservation areas, endanger their future. There is an urgent need to inform policies that can mitigate against future external threats to these indigenous peoples’ culture and lifestyles.

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          Most cited references54

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

            Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
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              A review of methods for the assessment of prediction errors in conservation presence/absence models

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                6 January 2016
                2016
                : 11
                : 1
                : e0144499
                Affiliations
                [1 ]Grupo de Biogeografía, Diversidad y Conservación, Departamento de Biología Animal, Universidad de Málaga, Facultad de Ciencias, Málaga, Spain
                [2 ]Division of Biology and Conservation Ecology, School of Science and the Environment, Manchester Metropolitan University, Manchester, United Kingdom
                [3 ]Center for International Forestry Research (CIFOR), CIFOR Headquarters, Bogor, Indonesia
                [4 ]Department of Anthropology, University College London, London, United Kingdom
                [5 ]Department of Anthropology, Washington State University, Vancouver, Washington, United States of America
                [6 ]Global Conservation Program, Wildlife Conservation Society, Bronx, New York, United States of America
                [7 ]Dipartimento di Scienze, Università Roma Tre, Rome, Italy
                [8 ]Asociación Zerca y Lejos, Madrid, Spain
                [9 ]46 Elm Row, Edinburgh, United Kingdom
                [10 ]Faculty of International Studies, Tenri University, Tenri City, Nara, Japan
                [11 ]Chameleon Strategy, London, United Kingdom
                [12 ]46 Yoshida-Shimoadachi, Sakyo, Kyoto, Japan
                [13 ]Faculty of Education, Yamaguchi University, Yoshida, Yamaguchi-shi Yamaguchi, Japan
                [14 ]Graduate School of Asian and African Area Studies, Kyoto University, Shimoadachi-cho, Yoshida, Sakyo-ku, Kyoto, Japan
                [15 ]COOPI-Cooperazione Internazionale ONG Onlus, Milano–I, Italy
                [16 ]Department of Geography, University of Florida, Gainesville, Florida, United States of America
                [17 ]Institut Santé et Société, Université du Québec à Montréal, Montréal, Québec, Canada
                [18 ]DDL Lab. CNRS—Université Lumière Lyon 2, Lyon, France
                [19 ]Bioclimate, Research and Development, Edinburgh, United Kingdom
                [20 ]Department of Anthropology, Durham University, Durham, United Kingdom
                [21 ]Faculty of Humanity and Environment, Hosei University, Fujimi, Chiyoda-ku, Tokyo, Japan
                [22 ]Consultative Group on International Agricultural Research (CGIAR), CIFOR Headquarters, Jalan CIFOR, Situ Gede, Bogor, Indonesia
                University of Florence, ITALY
                Author notes

                Competing Interests: There is no competing interest. The authors declare no competing financial interests. Funding received has been for the execution of the work reported in this paper. None of the authors are members of any organisation that dictate or influence the results or discussions in this paper.

                Conceived and designed the experiments: JEF JO RN. Performed the experiments: TB GMC MF FG BH SH JH MI KK JK JL NM AM BN AN DOE PP MR EGJS MT HY. Analyzed the data: JO MAF JEF RR JMV. Wrote the paper: JO JEF.

                ‡ These authors are co-first authors on this work.

                Article
                PONE-D-15-31350
                10.1371/journal.pone.0144499
                4711706
                26735953
                4c6b6f27-d8dd-4056-aff1-8268b55f0aef
                © 2016 Olivero et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 17 July 2015
                : 19 November 2015
                Page count
                Figures: 4, Tables: 1, Pages: 16
                Funding
                This work was supported by the United States Agency for International Development (USAID), Department for International Development (DFID) and the Leverhulme Trust (grant RP2011-R-045).
                Categories
                Research Article
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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