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  • Journal article
    Hudson LN, Newbold T, Contu S, Hill SL, Lysenko I, De Palma A, Phillips HR, Alhusseini TI, Bedford FE, Bennett DJ, Booth H, Burton VJ, Chng CW, Choimes A, Correia DL, Day J, Echeverría-Londoño S, Emerson SR, Gao D, Garon M, Harrison ML, Ingram DJ, Jung M, Kemp V, Kirkpatrick L, Martin CD, Pan Y, Pask-Hale GD, Pynegar EL, Robinson AN, Sanchez-Ortiz K, Senior RA, Simmons BI, White HJ, Zhang H, Aben J, Abrahamczyk S, Adum GB, Aguilar-Barquero V, Aizen MA, Albertos B, Alcala EL, Del Mar Alguacil M, Alignier A, Ancrenaz M, Andersen AN, Arbeláez-Cortés E, Armbrecht I, Arroyo-Rodríguez V, Aumann T, Axmacher JC, Azhar B, Azpiroz AB, Baeten L, Bakayoko A, Báldi A, Banks JE, Baral SK, Barlow J, Barratt BI, Barrico L, Bartolommei P, Barton DM, Basset Y, Batáry P, Bates AJ, Baur B, Bayne EM, Beja P, Benedick S, Berg Å, Bernard H, Berry NJ, Bhatt D, Bicknell JE, Bihn JH, Blake RJ, Bobo KS, Bóçon R, Boekhout T, Böhning-Gaese K, Bonham KJ, Borges PA, Borges SH, Boutin C, Bouyer J, Bragagnolo C, Brandt JS, Brearley FQ, Brito I, Bros V, Brunet J, Buczkowski G, Buddle CM, Bugter R, Buscardo E, Buse J, Cabra-García J, Cáceres NC, Cagle NL, Calviño-Cancela M, Cameron SA, Cancello EM, Caparrós R, Cardoso P, Carpenter D, Carrijo TF, Carvalho AL, Cassano CR, Castro H, Castro-Luna AA, Rolando CB, Cerezo A, Chapman KA, Chauvat M, Christensen M, Clarke FM, Cleary DF, Colombo G, Connop SP, Craig MD, Cruz-López L, Cunningham SA, D'Aniello B, D'Cruze N, da Silva PG, Dallimer M, Danquah E, Darvill B, Dauber J, Davis AL, Dawson J, de Sassi C, de Thoisy B, Deheuvels O, Dejean A, Devineau JL, Diekötter T, Dolia JV, Domínguez E, Dominguez-Haydar Y, Dorn S, Draper I, Dreber N, Dumont B, Dures SG, Dynesius M, Edenius L, Eggleton P, Eigenbrod F, Elek Z, Entling MH, Esler KJ, de Lima RF, Faruk A, Farwig N, Fayle TM, Felicioli A, Felton AM, Fensham RJ, Fernandez IC, Ferreira CC, Ficetola GF, Fiera C, Filgueiras BK, Fırıncıoğlu HK, Flaspohler D, Floren A, Fonte SJ, Fournier A, Fowler RE, Franzén M, Fraseret al., 2016,

    The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    , Ecology and Evolution, Vol: 7, Pages: 145-188, ISSN: 2045-7758

    The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

  • Journal article
    De Palma A, Purvis A, 2016,

    Predicting bee community responses to land-use changes: effects of geographic and taxonomic biases

    , Scientific Reports, Vol: 6, ISSN: 2045-2322

    Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.

  • Journal article
    Barraclough TG, Humphreys AM, 2015,

    The evolutionary reality of species and higher taxa in plants: a survey of post-modern opinion and evidence

    , NEW PHYTOLOGIST, Vol: 207, Pages: 291-296, ISSN: 0028-646X
  • Journal article
    Milner-Gulland EJ, Sainsbury K, Burgess N, Howe C, Sabuni F, Puis E, Killenga Ret al., 2015,

    Exploring stakeholder perceptions of conservation outcomes from alternative income generating activities in Tanzanian villages adjacent to Eastern Arc mountain forests.

    , Biological Conservation, Vol: 191, Pages: 20-28, ISSN: 0006-3207
  • Journal article
    Hui T-YJ, Burt A, 2015,

    Estimating effective population size from temporally spaced samples with a novel, efficient maximum-likelihood algorithm

    , Genetics, Vol: 200, Pages: 285-293, ISSN: 1943-2631

    The effective population size Embedded Image is a key parameter in population genetics and evolutionary biology, as it quantifies the expected distribution of changes in allele frequency due to genetic drift. Several methods of estimating Embedded Image have been described, the most direct of which uses allele frequencies measured at two or more time points. A new likelihood-based estimator Embedded Image for contemporary effective population size using temporal data is developed in this article. The existing likelihood methods are computationally intensive and unable to handle the case when the underlying Embedded Image is large. This article tries to work around this problem by using a hidden Markov algorithm and applying continuous approximations to allele frequencies and transition probabilities. Extensive simulations are run to evaluate the performance of the proposed estimator Embedded Image, and the results show that it is more accurate and has lower variance than previous methods. The new estimator also reduces the computational time by at least 1000-fold and relaxes the upper bound of Embedded Image to several million, hence allowing the estimation of larger Embedded Image. Finally, we demonstrate how this algorithm can cope with nonconstant Embedded Image scenarios and be used as a likelihood-ratio test to test for the equality of Embedded Image throughout the sampling horizon. An R package “NB” is now available for download to implement the method described in this article.

  • Journal article
    Fiegna F, Moreno-Letelier A, Bell T, Barraclough TGet al., 2015,

    Evolution of species interactions determines microbial community productivity in new environments

    , ISME JOURNAL, Vol: 9, Pages: 1235-1245, ISSN: 1751-7362
  • Journal article
    Samani P, Low-Decarie E, McKelvey K, Bell T, Burt A, Koufopanou V, Landry CR, Bell Get al., 2015,

    Metabolic variation in natural populations of wild yeast

    , ECOLOGY AND EVOLUTION, Vol: 5, Pages: 722-732, ISSN: 2045-7758
  • Journal article
    Fujisawa T, Vogler AP, Barraclough TG, 2015,

    Ecology has contrasting effects on genetic variation within species versus rates of molecular evolution across species in water beetles

    , PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 282, ISSN: 0962-8452
  • Journal article
    Igea J, Bogarin D, Papadopulos AST, Savolainen Vet al., 2015,

    A comparative analysis of island floras challenges taxonomy-based biogeographical models of speciation

    , Evolution, Vol: 69, Pages: 482-491, ISSN: 0014-3820

    Speciation on islands, and particularly the divergence of species in situ, has long been debated. Here, we present one of the first, complete assessments of the geographic modes of speciation for the flora of a small oceanic island. Cocos Island (Costa Rica) is pristine; it is located 550 km off the Pacific coast of Central America. It harbors 189 native plant species, 33 of which are endemic. Using phylogenetic data from insular and mainland congeneric species, we show that all of the endemic species are derived from independent colonization events rather than in situ speciation. This is in sharp contrast to the results of a study carried out in a comparable system, Lord Howe Island (Australia), where as much as 8.2% of the plant species were the product of sympatric speciation. Differences in physiography and age between the islands may be responsible for the contrasting patterns of speciation observed. Importantly, comparing phylogenetic assessments of the modes of speciation with taxonomy-based measures shows that widely used island biogeography approaches overestimate rates of in situ speciation.

  • Journal article
    Neafsey DE, Waterhouse RM, Abai MR, Aganezov SS, Alekseyev MA, Allen JE, Amon J, Arca B, Arensburger P, Artemov G, Assour LA, Basseri H, Berlin A, Birren BW, Blandin SA, Brockman AI, Burkot TR, Burt A, Chan CS, Chauve C, Chiu JC, Christensen M, Costantini C, Davidson VLM, Deligianni E, Dottorini T, Dritsou V, Gabriel SB, Guelbeogo WM, Hall AB, Han MV, Hlaing T, Hughes DST, Jenkins AM, Jiang X, Jungreis I, Kakani EG, Kamali M, Kemppainen P, Kennedy RC, Kirmitzoglou IK, Koekemoer LL, Laban N, Langridge N, Lawniczak MKN, Lirakis M, Lobo NF, Lowy E, MacCallum RM, Mao C, Maslen G, Mbogo C, McCarthy J, Michel K, Mitchell SN, Moore W, Murphy KA, Naumenko AN, Nolan T, Novoa EM, O Loughlin S, Oringanje C, Oshaghi MA, Pakpour N, Papathanos PA, Peery AN, Povelones M, Prakash A, Price DP, Rajaraman A, Reimer LJ, Rinker DC, Rokas A, Russell TL, Sagnon NF, Sharakhova MV, Shea T, Simao FA, Simard F, Slotman MA, Somboon P, Stegniy V, Struchiner CJ, Thomas GWC, Tojo M, Topalis P, Tubio JMC, Unger MF, Vontas J, Walton C, Wilding CS, Willis JH, Wu Y-C, Yan G, Zdobnov EM, Zhou X, Catteruccia F, Christophides GK, Collins FH, Cornman RS, Crisanti A, Donnelly MJ, Emrich SJ, Fontaine MC, Gelbart W, Hahn MW, Hansen IA, Howell PI, Kafatos FC, Kellis M, Lawson D, Louis C, Luckhart S, Muskavitch MAT, Ribeiro JM, Riehle MA, Sharakhov IV, Tu Z, Zwiebel LJ, Besansky NJet al., 2015,

    Highly evolvable malaria vectors: The genomes of 16 Anopheles mosquitoes

    , Science, Vol: 347

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

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