dataesr/scientific-paragraphs-categorization huggingface dataset:
The Role of the Number of Phylogenies on Site Scores In our dataset the number of phylogenies with species occurring at a site ranged between 5 and 16 (mean and median = 11). So, the fi rst point that we investigated was the role of the number of phylogenies in site's scores. This showed that over 75 % of the site's ranking with Ws sum was explained by the number of phylogenies with species in the site (Regression model: Sum Ws = -2. 13 + 0. 555 number of phylogenies; F = 41. 75; DF = 14; p = 0. 000; R 2 = 0. 75). With Ws ranks the infl uence of the number of phylogenies is a bit smaller but still important (Regression model: Ws ranks = -1. 03 + 0. 259 number of phylogenies; F = 26. 75; DF = 14; p = 0. 000; R 2 = 0. 66). Based on it, we decided to standardize by dividing total Ws sum or total Ws ranks in the site by the number of phylogeny occurring in it.
Phylogenetic data are increasing and the modern computational prioritization tools are better able to account for such data even at broad scales and for large numbers of species. These developments facilitate the inclusion of phylogenetic diversity into conservation planning. We hope that it will become a routine part of spatial conservation prioritization procedures, and that the message will also better reach the broader public through active communication. Acknowledgements A. A. was funded by the Academy of Finland (grant #250126 to A. A. ). LZ was funded by the ANR-BiodivERsA project CONNECT (ANR-11-EBID-002), as part of the ERA-Net BiodivERsA 2010 call. A part of the computations presented here were performed using the CIMENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the Rhône-Alpes region (GRANT CPER07_13 CIRA: http://www.
Information related to geographical distribution and evolutionary distinctiveness were considered in setting conservation goals for each species. Thus, we have prioritized the most relictual species, because they are phylogenetically rare, and the species of more restricted distribution, because restricted distribution ranges are associated with higher vulnerability to extinction in cases where habitat destruction pop up simultaneously in several points of the landscape. This study contributes to the proposed priority areas already published for the Cerrado through the inclusion of relevant evolutionary information and the use a more refi ned and complete database.
Data Used and Pre-processing Planning Unit s Planning units (PUs) are subdivisions of the study area into small spatially explicit units. Among many possible ways of obtaining PUs, we used a hydrosheds arrangement built from SRTM (Shuttle Radar Topography Mission; Hydrosheds , http://hydrosheds. cr. usgs. gov/index. php ). This is the same database used by Brazilian government to set priority areas for conservation in Cerrado and Pantanal Biomes  (MMA 2012, unpublished data). The use of sub basins as PUs has many advantages over other arrangements such as grids or hexagons: fi rstly, they have natural and biogeographically meaningful limits; secondly, they allow an hierarchical structure of basins within basins, which is very useful to switch scales and adjust data and results to different needs.
ibama. gov. br/monitorabiomas/cerrado/index. htm ), and excluded any PU having no remnants of natural vegetation. Conservation Cost The cost for each PU was obtained from WWF  (Soares et al. 2012 ). The database was built by the Conservation Science Team based on potential future deforestation, using Land Change Modeler module of Idrisi Selva. Distance to roads, to cities, to infrastructure and to previously deforested areas were included as driver to changes in land cover from 2002 to 2010, and then applied to 2010 natural vegetation map to predict which areas are more likely to be deforested in the next 10 years. Focal Species Eighty-two out of 209 amphibian species known to occur in the Cerrado  (Valdujo et al. 2012 )  were selected as focal species. The criteria were based on endemism, range size (both obtained from  Valdujo et al.
The differences in results should be linked to the fact that (1) our study was based on a more complete database (see  Valdujo et al. 2012 ) , (2) we used modeled distribution maps based on topographic and climatic species requirements and (3) we included evolutionary characteristics. The selection of areas along the elevation gradient, including both lowlands and river valleys as uplands and mountains, is related to the fact that endemic species have different habitat requirements  (Valdujo et al. 2012 ). The São Francisco River basin has the highest species richness , certainly due to the high richness of endemic and restricted range species in the Espinhaço complex (see review in  Leite et al. 2008 ). Other high elevation areas where endemic species have high richness are the Guimarães, Canasta and Central Brazil uplands  (Valdujo et al.
Material and Methods We used the most detailed primary-data species-level phylogenies available for the three major mammalian groups containing aquatic species Cetacea  (May-Collado and Agnarsson 2006 ; May-Collado et al. 2007 ) , Carnivora  (Agnarsson et al. 2010 ) , and Afrotheria  (Kuntner et al. 2009  ). The conservation status for 127 aquatic mammals was obtained from the IUCN Red List of Threatened Species database  (2010. 4-2013. 2 ) and transformed to probability estimates of extinction risk using two of the methods discussed in  Mooers et al. ( 2008 )  "pessimistic" and "IUCN50". The "pessimistic" method is an arbitrary transformation that designates a sizable probability of extinction to every category. So, even for the 'least concern' species has a probability of 0. 2, which is much higher than in the IUCN 50 scenario (see Table  1  )  (Mooers et al.
2007  ) also implemented in the TUATARA module and gathered information on species richness. To identify conservation priority areas (CPAs) we used distribution range maps from the IUCN spatial database ( 2013 ) as a baseline to produce species richness , ED, EDGE and HEDGE maps, under both IUCN extinction probabilities transformation methods, pessimistic and IUCN50. The IUCN spatial database depict species' range distribution as polygons based on the extent of occurrence, which is defi ned as the area contained within a minimum convex hull around species' observations or records. This convex hull or polygon is further improved by including areas known to be suitable or by removing unsuitable or unoccupied areas based on expert knowledge. For each species the distribution range was converted to a grid system with cells of 5′ × 5′ (approximately 10 × 10 km at the Equator line).
The same procedure was repeated to assign the estimated values of ED, EDGE and HEDGE to the grid cells of each species' occurrence, and maps were calculated by overlying individual grids. For example, the species richness map represents the sum of all presence grids. Under this spatial framework CPAs represent areas with the highest scores due to a high number of species regardless of ED, EDGE, and HEDGE scores or to a few species with high probability scores. To understand how well these patterns of aquatic mammal conservation priorities are already included in existing MPAs, we used information from the World Database on Protected Areas website ( http://protectedplanet. net/ ). We calculated the percentage of each species range within all designated MPAs of the world and to preserve areal relationships we fi rst re-projected both the species ranges and the MPAs, to an equal area using Behrmann projection.
Island Biogeography Current global databases often lack the spatial and ecological granularity necessary to conduct such a large-scale analysis, without requiring great effort in obtaining and polishing the data. However, one way that we can at least test this proposed conservation prioritisation method is by examining islands, which we do here on mammals. In this chapter, we use λ M in combination with the current prioritization scheme of EDGE for two purposes. First, we investigate whether phylogenetic diversity correlates with characteristics of islands. We expect, based on the principles of island theory that predict lower immigration and emigration rates, that with increasing remoteness and decreasing size, species could accumulate evolutionary history. Second, we prioritise important islands containing an over proportional amount of evolutionary distinct species, indicating a potentially increased risk of living on small remote islands, requiring special attention.
Digital Distribution Maps of the IUCN Red List of Threatened Species We began with the datasets of terrestrial mammal species as defi ned by the IUCN Red List database  (IUCN 2013  ). Then we focused on terrestrial mammal species living only on islands, and excluded all species that did not have distributions confi ned to islands only. We defi ned islands as landmasses smaller than Greenland (2,130,800 km 2 ), with New Guinea (785,753 km 2 ) as the largest island. IUCN's terrestrial mammal spatial data had 1728 unique species identifi ed as residing on an island. When we intersected this with the GSHHS shoreline data, which fulfi lled our defi nition for island, there were 1501 species. Finally, we restricted this to obligate islanders only, i. e. species not found on any continental mainland, and had 389 species with island-only distributions.
Data Analysis After fi nding those islands where both GSHHS and IUCN datasets intersected, we calculated the relative λ M of every patch within a species' distribution and scaled their values from 0 to 1. 0, with the highest value indicating the island/patch that contributed most to the overall long-term persistence (see Fig. 2  ). We also designated any species with only one island/patch in their distribution automatically with a λ M score of 1 (see Fig. 1b  ), because of its signifi cant importance for that species. We then took these scores and for each, multiplied by the species' ED score. To.
2009 ). It is still a puzzle to be teased apart, how the interaction of intrinsic factors, e. g. innate biological susceptibility, and extrinsic factors, i. e. those mostly due to human impact, affect the outcome that ultimately leads to extinction  (Bennett and Owens 1997 ). Already there are numerous efforts underway to stave off the extinction of island species, such as the previously mentioned Threatened Island Biodiversity (TIB) database ( http://tib. islandconservation. org/ ), whose primary focus is on eradicating threatening non-natives. The high levels of endemic richness already warrant special conservation protection  (Kier et al. 2009  ). Species on continents can experience island effects, e. g. mountains or islands within lakes, which would still make island conservation studies, such as this, applicable to them.
Species Distribution To map species distributions we compiled a database of 5386 species-locality records. This database combines fi eldwork data from the authors and records from more than 60 museums and private collections, with the most signifi cant contributions (>200 records each) from the Natural History Museum, London (BMNH), the Museo de Historia Natural, Universidad Mayor de San Marcos, Lima (MUSM), the McGuire Center, Florida Museum of Natural History, Gainesville (FLMNH), the United States National Museum, Washington D. C. (USNM), the American Museum of Natural History, New York (AMNH), and sight records  (Willmott & Hall, unpublished data). Despite ithomiines being one of the best collected Neotropical butterfl y groups, representation is still patchy at fi ne scales because many regions are yet to be sampled.
For example, using a dataset of 50 clades (López-Osorio and Miranda-Esquivel 2010 ), found that species richness and evolutionary distinctiveness of several groups of vertebrates and some groups of insects and plants are high in the southern upper Amazon. But, unlike the genera studied here, they also found very high diversity in the Guianas (see also Miranda-Esquivel chapter " Support in Area Prioritization Using Phylogenetic Information "). Similarly,  Amori et al. 2013  noted that rodent diversity peaks in the upper Amazon, but also found diversity hotspots in the Guianas and Atlantic forest. Primates similarly show increasing diversity from east to west (Da  Silva et al. 2005b ) , as well as birds  (Haffer 1990  ), non-volant mammals  (Costa et al. 2000 )  and plants  (Ter Steege et al.
Overall, these results broadly refl ect the extent to which each genus numerically dominates butterfl y communities in each region. Oleria are abundant members of Amazon forest communities and tend to be co-mimetic, leading to low vulnerability in these regions. Ithomia are abundant in the southwestern Amazon and Atlantic forests and in Nicaragua-Costa Rica, where they show low vulnerability, while Napeogenes , most of whose species are rare everywhere, proves also to be vulnerable everywhere. This analysis is a preliminary exploration of the possibility of using more sophisticated measures related to the ecology of Neotropical butterfl ies. As such, it suffers from several problems. Our dataset includes about 100 species out of ca. 380 ithomiines species, many of which are involved in the mimicry rings considered here but ignored in our vulnerability index.
These prospects are magnifi ed by the remarkable facilities for obtaining entire or large parts of genomes or other molecular sequences of any kind of organisms, and by the sheer magnitude of biological (gene sequences, trait databases, species occurrences, red list s) and environmental data (climate layers for past, present and future interpolated to very fi ne spatial scale s; land-use layers, spatial data indicating particular important risks such as fi res, fl oods, and so on) now available in the public domain. All these allow for rapid estimation of the phylogenetic relationships for a large number of organisms in association with potential distribution and threats for species and lineages. In addition, under the stimulus of modern phylogenetic and molecular methods, systematics is going through a signifi cant transformation that will certainly infl uence biodiversity conservation  (Mace et al.
2002 ; Wilson 2003 ; Faith et al. 2010  ; Lean and MacLaurin chapter " The Value of Phylogenetic Diversity "). Obviously, this new framework enhanced by molecular biology and metagenomics will maintain biological signifi cance and usefulness as long as molecular proxies will remain related to species concepts, taxon names and classifi cations linking to the wider biological knowledge  (Mace 2004 ; German National Academy of Sciences Leopoldina 2014 ). The peril to invest only in an isolated and blind molecular database was already keenly emphasized by many taxonomists at the occasion of the rise of the barcoding initiative (e. g. ,  Will et al. 2005 ). Building the network between names, biological knowledge and molecular data is from far the biggest challenge of present-day systematics and other sciences of diversity , much beyond the molecular technical tour de force  (Grandcolas et al.
Acknowledgements Acknowledgements Thanks to  Daniel Faith ,  Philippe Grandcolas ,  James Justus ,  Arne Mooers , Acknowledgements We are grateful for the assistance of  Ananias Escalante  for access tree image from Pacheco et al. 2011. Phoenix group members (evolves. massey. ac. nz) provided illuminating discussion. The manuscript was improved by comments from two reviewers and the editors. The authors were assisted by the  Marsden Fund  (Grant no. 12-MAU-008 ), administered by the  Royal Society of New Zealand. Darwin C (1859) On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life, 1st edn. John Murray ,  London Daugherty CH , Cree A,  Hay JM , Thompson MB (1990) Neglected taxonomy and continuing extinction of tuatara ( Sphenodon ).
Acknowledgements The authors thank  Roseli Pellens ,  Samuel Scheiner  and an anonymous reviewer for helpful comments and suggestions. This work was supported by the  Taiwan National Science Council  under Contracts  100-2118-M007-006  and  103-2628-M007-007  for AC. CC is sup-Acknowledgments The authors thank  Tung Lam Nguyen  for developing PDA web service. The authors also thank  Roseli Pellens  and  Philippe Grandcolas  for inviting us to write this chapter and three anonymous reviewers for constructive comments. This work was supported by  University of Vienna  ( Initiativkolleg I059-N)  to O. C. and the  Austrian Science Fund -FWF  ( I760-B17 ) to B. Q. M. and A. v. H. Acknowledgements I would like to thank  Frederick Matsen  ( Fred Hutchinson Cancer Research Center ),  Daniel Faith  ( Australian Museum ),  Peter Wilson  ( Macquarie University ),  Anne Chao  ( National Tsing Hua University ) and  Robert Colwell  ( University of Connecticut ) for their input regarding the rarefaction of PD and what it all might mean.
Daniel Miranda-Esquivel  ( Universidad Industrial de Santander ) and  Pedro Cardoso  ( University of Helsinki ) provided helpful feedback on an earlier version of this chapter. This research was supported by the  Australian Research Council  ( DP0665761  and  DP1095200 ). Acknowledgements This project was funded by the  French Agency of Research , through the grant  ANR Bioneocal  to PG. We thank the authors of the phylogenies and in particular  Ulf Swenson  and  Marianne Espeland , for giving access to their databases of species occurrences, and  Hervé Jourdan  ( IRD Nouméa ) for helping RP with the delimitation of the study areas. We also thank  David Nipperess  and  Romain Nattier  for their comments that helped to improve this manuscript. Acknowledgements We thank  Roseli Pellens  for inviting us to write this chapter and for her review and comments.
Anni Arponen ,  Annemarie Ohler  and an anonymous reviewer for their comments and suggestions. Mario Barroso Ramos-Neto  and  Mariana da Silva Soares (Landscape Ecology Lab of WWF-Brasil)  provided the planning units and cost layers for the Cerrado , built in a joint initiative with the  Ministry of Environment. Hussam Zaher (MZUSP) ,  Marcos Carvalho  (ZUFMT),  Rogério Bastos  (ZUFG),  Felipe Toledo  (ZUEC),  Célio Haddad  (CFBH),  Luciana Nascimento  ( MCN PUC-MG ),  Denise Rossa-Feres (DZSJRP) ,  Franco Leandro de Souza (UFMS) ,  José P. Pombal Junior  (MNRJ),  Ulisses Caramaschi  (MNRJ) and  Ulisses Galatti  (MPEG) gently allowed us to examine museum specimens under their care. Mariana Gomes  helped organizing the database. This study was funded  FAPESP  (fellowship  2007/51956-6  and grant  06/58011-4 ),  CAPES ,  CNPq ,  FAP-DF, Conservation International  ( CP-FY 08/017  and  CP-FY 08/018 ),  Neotropical Grassland Conservancy , and  Fundação O Boticario de Proteção à Natureza  ( 0747_20071  and  0798_20082 ).
Acknowledgements We would like to thank  Bart Kranstauber  for discussions, and  Laura J. May-Collado ,  Roseli Pellens , and an anonymous reviewer for helpful feedback. Acknowledgements AVLF acknowledges support from the  FAPESP  ( BIOTA-FAPESP 2011/50225-3 ), from the  Brazilian Research Council-CNPq  (fellowship  302585/2011-7 , and " SISBIOTA-Brasil/CNPq  grant  563332/2010-7 ), and from the collaborative grant " Dimensions US-Biota-São Paulo: Assembly and evolution of the Amazon biota  and its environment: an integrated approach",  US NSF ,  NASA , and  FAPESP  ( 2012/50260-6 ), and from the  National Science Foundation  ( DEB-1256742 ). KRW acknowledges support from the  Leverhulme Trust , the  National Geographic Society , the  Darwin Initiative , the  National Science Foundation  ( DEB-0639861 ,  DEB-0103746 ), and the  Museo Ecuatoriano de Ciencias Naturales and Ministerio del Ambiente in Quito , for support in museum and fi eld work.
Acknowledgments Anaëlle Soulebeau is funded by a doctoral grant from  Doctoral School ED227, Ministère de la Recherche , at the  Muséum National d'Histoire Naturelle (Paris, France ). The laboratory work was supported by the  ANR -funded project  EVORANGE  ( ANR-09-PEXT-011 ) and agreement No. 2005/67  between Genoscope and the  MNHN  under the project " Macrophylogeny of life " directed by  Guillaume Lecointre. We thank  Nicolas Chazot  and  Benoit Lefeuvre  for their help with the R script and GIS analysis, and  Philippe Grandcolas  and  Sven Buerki  for comments that helped to improve this manuscript. Acknowledgements We thank  Agathe Haevermans  for kindly providing the drawings for the two fi gures of this chapter.
Island Studies Islands represent less than 5 % of the earth's land area , harbour 80 % of known species extinctions since 1500  (Ricketts et al. 2005 ) , and make up 39 % of today's IUCN Critically Endangered species (TIB 2012 ). Endangered island species, such as those targeted and listed in the Threatened Island Biodiversity (TIB) database, are currently of major concern due to invasive species. However, we can still examine the effects of isolation and area from an island point of view. On a global scale , this method aims to show which islands or species are most important for conservation, based on the spatial properties of the islands and the phylogenetic rarity of the species themselves. Islands are a natural laboratory for evolutionary specialization and adaptation, because such an environment greatly shapes the select set of species living there in such isolation  (Losos and Ricklefs 2009 ).
Data analysis Differential survival between mirror and nude carps was tested within each pond with a chi-square test comparing the number of survivors from each type, which was supposed to be equal in case of absence of differential survival. This was true both for the pregrowing phase, where the segregation of the N allele was supposed to produce an equal amount of nude and mirror phenotypes, and for the grow-out phase where equal initial numbers of nude and mirror juveniles were seeded in each pond. Body weight differences were tested within pond with a Student test, using individual body weight as the experimental unit. Global comparison of means between ponds for body weight, biomass or biomass gain were done with a paired Student test or a Wilcoxon signed-rank test, using the pond mean value of a given genotype as the experimental unit.
Acknowledgements This work was performed in the  Madapisci consortium of project FSP Parrur , financed by the  French Embassy in Madagascar. Additional support was received from  Agence Franc aise du Développement and Région Aquitaine. APDRA Pisciculture Paysanne  is thanked for active participation and logistic support, from the headquarters ( Marc Oswald ,  Fabien Cousseau ) and on the field trials ( Bertrand Pajon ,  Rija Anriamarolaza ,  Feno Zaka Rajaonarivelo ,  Solofo Rasolofoniaina ). Rojomalala Razafimandimby  and  Monique Ravakarivelo  are thanked for active help in the field work and data production.
The LOAC has an integration time of 10 s, with a pumping flow of about 2 L min -1. Even in low polluted ambient air at ground ("background conditions"), typical counting measurements available in the literature have shown that concentrations are greater than 1 particle cm -3 for size classes smaller than 0. 5 µm (e. g. Ketzel et al. , 2004) , which corresponds to 2000/6 = more than 300 particles during the 10 s LOAC integration time. For particles in the 0. 5-1 µm size classes, concentrations are greater than 0. 1 particle cm -3 , giving more than 30 particles. Thus, 2 min of measurements will provide good statistics for the LOAC data analysis. For all the cross-comparison exercises presented below, the measurements were integrated from 2 to 15 min. For the 2 min integration time, the number of particles given above must be multiplied by 12, giving at least 3000 for the three first size classes and 300 for the other ones.