dataesr/scientific-paragraphs-categorization huggingface dataset:
This likelihood-based approach allows the estimation of region-dependent rates of speciation, extinction, and range evolution from a phylogeny, using a model that combines features of constant-rates birth-death models with a three-state Markov model  44. We coded each species according to its distribution using three distinct states: Wallacea, outside Wallacea or widespread. Life history data was obtained from the taxonomic literature and museum records. We constructed the likelihood functions representing 12 different evolutionary models for the relationship between geographical distributions and diversification rate. To support the Wallacean species-pump hypothesis, the best model should demonstrate a higher diversification rate for Wallacea. We tested the hypothesis that diversification was rapid in the early stage of the group as expected under the ecological opportunities provided by the emergence of regional islands.
Acknowledgements We are grateful to  Donald Lafontaine  for providing biological material from birdwings intercepted at Canadian borders. We also want to thank  Björn Stelbrink ,  Nick Matzke  and  Bernard Platevoet  for earlier comments on the study. Financial support was provided by a  Marie Curie International Outgoing Fellowship  (project  627684   BIOMME ) to FLC, an  NSERC   Discovery Grant  to FAHS, and the  Department 'Santé des Plantes et Environnement ' of the  Institut National de la Recherche Agronomique (INRA, SPE)  to GJK.
The Bamboung protected area (Senegal, West Africa) has been implemented in 2003. However, little is known about its recovering effects on fish assemblage functional diversity. To address this issue, a morphology-based functional grouping was adopted, and results were compared to prior classifications. Functional richness relative to each guild and species abundance was used to define a functional redundancy index. Morphometric measurements were done in the field on freshly killed specimens and completed by digital image processing using the Image-J software in laboratory. Data relative to biomass and abundance were obtained from the IRD (Research Institute for Development) / Adaptive Response of Fish Assemblages to the Environmental Pressure (RAP) Unit-Research database. Hierarchical clustering, analysis using the ward method, was applied to the matrix of morphological traits to discriminate fish assemblages into four functional guilds: zoobenthos feeders, algivorous / detritivorous fish, benthopelagic fish, and demersal and ichthyophagous fish.
Fish Sampling Quantitative data relative to fish species abundance and biomass was collected from a database provided by the Research Institute for Development (IRD, France) in the "Narou Heuleuk" Project, funded by the French Global Environmental Fund. Experimental fishing for this project was initiated in 2003, prior to the effective fishing ban. Since then, three additional monitoring campaigns were conducted each year. Fishes were randomly sampled using a purse seine net of 0. 005 km², each site was fished once per trip without targeting shoals. The net was carried by eight fishermen using a motorized boat. For quantitative estimation, all collected material was transferred to a research catamaran serving as a mobile research laboratory. The weight and the number of individuals of each species were recorded after each haul.
Fishes were then dissected: the first branchial arch and the digestive tract were removed and preserved in 10 % alcohol solution for length measurements. All digital images were processed using the free online-access Image-J software. Morphometric distances were measured and coded as follows: standard length (SL); maximum body depth (MBD); body depth on the pectoral fin insertion (BDPF), horizontal eye diameter (HED); caudal fin depth (CFD); caudal fin surface (CFS); pectoral fin surface (PFS) pectoral fin length (PFL); pectoral fin position (PFP); head depth (HD); mouth position (MP); eye position (EP); caudal peduncle depth (CPD). In the laboratory, maximum gill raker length (GRL) and digestive tract length (DTL) were respectively measured using a microscope fitted with a micrometer and a metal ruler (precision = 1 mm).
Data Manipulation and Analysis A preliminary analysis of the data showed that the biomass values had a high variability, potentially related to the differential success in hauling and to seasonal patterns in fish assemblage dynamic. Thus, the patterns of biomass evolution were examined in an annual scale to reduce biases introduced by hauling success variability. Fish species were successively classified according to their morpho-functional guild, their trophic status and their bioecological category. For trophic and bioecological groups, all species recorded in the creek were considered, while for the new guilds the analysis was restricted to species for which, morphological data were available. The omnivorous group was low represented in terms of species richness and biomass proportion and has been excluded from the analysis.
Morpho-Functional Groups Composition The primary aim of this study was to develop a new functional classification of fish assemblages. The secondary aim was to assess the functional impact of the reserve throughout a multiple-scale functional guilds, allowing the reduction of biases related to unstable environment conditions and transience in trophic relationships. The study has been impaired by some limitations in our sampling efforts. Less than 50 species for which morphometric data had been recorded, were classified into our new functional guilds, while all the 72 species described in the system were included in the analysis based on the two previous classifications. This is due to the very low occurrence in the reserve of some marine species in particular, which are not captured during our sampling campaigns.
Acknowledgements The  Bamboung Protected Area project has been initially  funded by the  French Fund for Global Environment. I'm grateful to the "Département Soutien et Formation" of the Research Institute for Development (IRD, France)  for funding my doctoral research fellowship. Internship and data sharing were the fruit of the collaboration between the precedent Institute and the  Aquatic Living Resources Unit Research of the Institute of Fisheries and Aquaculture (IUPA)  and the  Department of Animal Biology of Dakar University (UCAD). Researches were performed in the IRD / RAP (Adaptive Response of fish assemblages to the environment Pressure)  Unit Research program  and laboratories in Bel-Air (Senegal). Finally, I'm thinking the crew on board (fishermen and the technical staff) in the Diassanga catamaran for monitoring campaigns and sample collection.
The domains share 33% sequence identity (figure  2a ) and as can be seen from the alignment, the K. lactis longin domain is more compact than that found in S. cerevisiae. The K. lactis longin domain (aa 58-226) crystallized in the P4 2 2 1 2 space group with four molecules in the asymmetric unit (AU). Phases for structure solution were obtained from a SAD experiment on a single gold derivative crystal prepared by soaking the native crystals overnight in 1 mM KAu(CN) 2. The final structure was determined at 2. 14 A resolution by molecular replacement of a higher resolution native dataset. Several loops were missing in the electron density, between b1-b2, b2-a1 and a1-b3, in addition to nine residues at the N-terminus and 13 residues at the C-terminus, most likely owing to their inherent flexibility.
The four molecules seen in the crystal structure do not represent a biologically relevant assembly, as an analytical ultracentrifugation experiment indicated that the protein was monomeric in solution (electronic supplementary material, figure  S1b ). Interestingly, a DALI search  [26]  of the Lst4 longin domain gave as the top hit human synbindin (Protein Data Bank (PDB) ID: 2zmv), which has an insertion within the longin domain in the same position as the first insertion in Fnip1/2 (b2-a1). This insertion in synbindin folds into an atypical PDZ domain  [27] , which indicates it is not unprecedented for the longin domains to be split with other functional regions inserted in them. There are at present, however, no experimental longin domain structures that contain insertions within the second Fnip1/2 insertion region, b4-b5.
Sizeexclusion chromatography was performed on a Superdex 75 16/60 column with a final buffer of 20 mM Tris pH 8. 0, 500 mM NaCl, 2. 5 mM TCEP. Following this, the protein was concentrated to 12 mg ml 21 (K. lactis) using VivaSpin 20 10 kDa MWCO centrifugal concentrators (Sartorius) for crystallization. Analytical ultracentrifugation experiments were performed on a Beckman-Coulter XL-1 ultracentrifuge. Samples rsob. royalsocietypublishing. org Open Biol. 5: 150174 were spun at 40 000 rcf at 208C, and 300 absorbance scans of the protein sample were taken. The data were analysed and fitted using SEDFIT  [37].
Crystallization of the K. lactis Lst4 longin domain The L4 L domain from K. lactis was crystallized in 4 ml hanging drops in a 1 : 1 ratio with 100 mM Bis -Tris, pH 5. 8, 3. 1 M NaCl, 1. 1% butan-1-ol; crystals appeared after 48 h and grew to full size within one week. Crystals were cryoprotected in reservoir solution containing 4 M NaCl, before freezing. Data were collected on native crystals at diamond light source (DLS) beamline I02. Gold derivative crystals were prepared by soaking the native crystals in reservoir solution containing 1 mM KAu(CN) 2 overnight. Crystals were backsoaked in reservoir solution for 2 h before cryoprotection and freezing. Derivative data collection was performed at DLS on beamline I03.
Determining the K. lactis Lst4 longin domain structure Phases were obtained for the K. lactis L4L structure using a single gold derivative crystal, from which a single wavelength anomalous diffraction (SAD) dataset at a wavelength of 1. 04 A ẘas collected. The resulting diffraction images were autoprocessed with XDS  [38]  and scaled with AIMLESS (CCP4). [39]. The calculated Matthews coefficient  [40]  of 2. 09 indicated that the crystal solvent content was 41. 1% and there were four molecules in the AU. Using the AutoSol wizard of PHENIX  [41] , 15 Au atoms were located and the figure of merit after density modification was 0. 41. The resulting map was submitted to the AutoBuild wizard of PHENIX, which built a large proportion of the four chains. At this stage chain C was deemed to be the most complete, so this was manually refined in COOT  [42] , and the single chain was used as a probe for molecular replacement using the higher resolution native data set at 2.
As for the N-starved cells, they were washed twice with N-starvation media and were grown for 1 h. For rapamycin treatment, cells were grown up in SC þ 2% glucose-URA and treated with rapamycin 200 ng ml 21 for 1 h. Live cells were imaged by confocal microscopy (AOBS SP2; Leica) at room temperature (63Â NA 1. 4 objective) using LCS software (Leica) for acquisition. Images were handled in PHOTOSHOP (Adobe), and equivalent adjustments were applied rsob. royalsocietypublishing. org Open Biol. 5: 150174 to all images. All yeast strains are listed in the electronic supplementary material, table  S3.
All elution fractions were snap frozen in liquid nitrogen and stored at 2808C. AUC was performed as above, but owing to the lower protein concentration available (0. 15 mg ml 21 ), interference data were collected and used for SEDFIT analysis of the sedimentation velocity in order to calculate the predicted molecular weight. SEC-MALS analysis was performed to determine the experimental molecular weight of the Lst7/ Lst4 complex as previously described  [46, 47]. The complex (50 mg in 50 ml) was run on a Tosoh TSKgel SuperSW2000 4. 6 Â 300 mm column equilibrated in PBS at a flow rate of 0. 35 ml min 21 using a Shimadzu LC-20AD isocratic HPLC coupled to a Dawn Heleos MALS detector and an Optilab T-rEX refractive index detector (Wyatt Technology). The molecular weight was determined using ASTRA 5 software (Wyatt Technologies).
Acknowledgements This work was supported by  ANR  (project  BLAN07-1_190839   SPOs Preligands) , the  Ministère de l'Enseignement Supérieur et de la Recherche ,  CNRS ,  ECM  and  AMU. We thank  Christophe Chendo  and  Dr. Valérie Monnier  for mass spectrometry analyses ( Spectropole, Fédération des Sciences Chimiques de Marseille ),  Dr A. Tenaglia  is gratefully acknowledged for helpful discussions.
While explicitly addressed in the field of extreme event statistics  [36] , the determination of the start of the tail is an issue that has been ignored in the evaluation of the Lévy walk hypothesis in movement ecology. Power-tailed behaviour has typically been tested for a tail starting at the minimum value of the dataset or using histogram-based criteria that are very sensitive to the binning. To address this issue, we provide a pragmatic definition of the tail of the move length distribution. Applying this methodology to the movements of two species of GPS-tracked seabirds and fishermen, we (1) illustrate the wide applicability of the proposed method on a variety of tracking data and (2) reveal inter-individual and interspecific variability in the spatial strategies selected by predators foraging on a common prey field.
The log-likelihood of a sample of move lengths l = (l 1 ,l 2 ,. l n ) is given by  [39] : L ðl; k; sÞ ¼ Y n i¼1 f k;s ðl i Þ ¼ Ànlnk þ ln P n i¼1 1 þ kl i s À 1 k À1 ! ! ðEq3Þ The analytical maximization of the log-likelihood for GPD is not possible; hence numerical optimizations are carried out. In the present work, the maximization of the log-likelihood functions is performed with the Matlab toolbox « Statistics », that proceeds using a gradient descent initialized with the method of moments. From 200 simulations, carried out for values of k from 0. 1 to 1. 25, we analyse the estimation bias on model parameters that may emerge, as real foragers' datasets (1) cannot exhibit infinite move length values and (  2 ) are samples of finite size. The effect of the finite move length values was explored with a sample of size 1000 and by setting maximum values of the move lengths between the 90 th and the 99.
By coupling model estimation and the GOF measure in this way, we guarantee a consistent and adaptive definition of the tail, ensuring the statistical consistency of the fitted model. As case studies, we consider two datasets collected in the Peruvian coastal upwelling ecosystem on anchovy (Engraulis ringens) main predators: seabirds and the purse seine fishery. In such pelagic ecosystems, water masses and fish schools are constantly moving  [42]  so that precise prey localization is unpredictable regardless of the predator, human or animal  [8]. Foraging movements for all predators aim at the same goal: dealing with uncertainty in prey localization and maximizing encounters. Yet, they may adopt distinct forms due to differences in the cognitive, locomotory and motivational states of each predator.
Synthetic metrics like the scale and shape parameters from the BGPD may be very useful for quantifying and comparing alternative spatial strategies. The first dataset consisted of the movements deployed during four foraging trips by two seabird species, boobies (Sula variegata) and guanay cormorants (Phalacrocorax bougainvilli) from Isla Pescadores, a colony located off Lima, Peru (11°47'12" South, 77°14'25" West), where the two species breed in sympatry (authorizations for the fieldwork were delivered by SER-NANP, Peru; no animals were handled directly for this study as data were collected from previous works). Their positions were recorded each second with miniaturized GPS [see  43  for protocol details; raw data available in Supporting Information S1, S2, S3 and S4 Dataset]. The original positions were resampled into moves (see Supporting Information S1 Protocol), where moves are defined as elementary behavioural events  [1].
We fitted a BGPD, and alternative classical models for comparison, to the distribution of the move lengths observed for each foraging trip, each of them gathering more than 500 moves. The second dataset consisted of the movements deployed by three industrial anchovy purse seiners, as monitored by the Peruvian Vessel Monitoring System (VMS  [13, 8] ; between 4 and 18°South and 72 and 82°West). VMS provides approximately one position per hour for each vessel. The original positions of fishing trips are re-sampled into moves (  [1, 13, 8] ; see the electronic supplementary material]. Since fishing trips in this fishery last on average 24h and VMS sampling is about 1 per hour, each trip is described by ~24 positions, producing less than 20 moves. Since model fits based on samples with less than 200 moves should not be considered, we fit a BGPD (and alternative models for comparison) to the yearly distributions of the move lengths of each vessel, considering that a given fisherman should exhibit some consistency in its spatial behaviour over time.
Fishing vessels The fishing trips from the three anchovy purse seiners are presented in Fig 5. Bertrand et al. [8] , based on graphical estimation methods, concluded that the movements of the three vessels were well described by Lévy walks, vessel a being the most diffusive (m ¼ 1:43) and vessel c the least diffusive (m ¼ 2:43). Edwards  [23]  on the same dataset concluded that although bounded Lévy was the most plausible model for vessels a and b, and exponential for vessel c, no model actually passed the G-GOF test. The conclusion was that these datasets did not conform to any candidate models. Table  2  compares for each vessel the fitting of the exponential, power, Generalized Pareto and Bounded Generalized Pareto distributions to the move length distributions. In the three cases, BGPD presented the minimum AIC, regardless of the rule chosen for defining the start of the tail of the move length distribution (i.
Acknowledgments We thank  A. Bertrand  and  A. Chaigneau  for their comments on the manuscript and  A. Johansen  and  B. H. Victor  for their advice on English editing. We thank  E. Seier  for valuable discussions on goodness-of-fit tests. This work is a contribution to the cooperation agreement between the  Instituto del Mar del Peru (IMARPE)  and the  Institut de Recherche pour le Développement (IRD). It was supported by and is a contribution to the  ANR  project  TOPINEME  and the  International Joint Laboratory DISCOH.
ACKNOWLEDGMENTS This work was funded by the  Swedish VR  Grant No. D621-2013-4309  and by the  European Commission  through the  EPiGRAM project  (Grant agreement No. 610598. epigramproject. eu). This work used resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at  PDC. S. M. was partially supported by the  Space Hazards Induced near Earth by Large, Dynamic Storms (SHIELDS)  project, funded by the  U. S. Department of Energy  through the  Los Alamos National Laboratory Directed Research and Development program.
Since Fe is actively taken up into phytoplankton and transferred throughout the food web, including removal by particle settling and remineralisation in deep waters, the assessment of its availability is quite complex and cannot be judged from dFe levels in surface waters alone  (Breitbarth et al. , 2010). Advances in chemical oceanographic techniques for trace elements through the GEOTRACES program  (SCOR Working Group, 2007)  now allow the measurement of Fe associated with different phases (dissolved and particulate), internal biological recycling and Fe export from surface waters. The results from earlier iron biogeochemical budgets for FeCycle-I  (Boyd et al. , 2005; Frew et al. , 2006) , KEOPS-1  (Blain et al. , 2007; Chever et al. , 2010) , CROZEX (CROZet natural iron bloom and EXport experiment;  Planquette et al.
Sampling All trace metal sampling and analytical procedures followed recommended protocols in the cookbook 2  published by the international program GEOTRACES  (Bishop et al. , 2012; Cutter and Bruland, 2012; Planquette and Sherrell, 2012). All methods have been successfully used previously by this team during the KEOPS-1  (Blain et al. , 2008b)  and SAZ-Sense projects  (Bowie et al. , 2009). Subtle differences in methods employed during the earlier KEOPS-1 and SAZ-Sense projects are described in those papers and/or later in this manuscript.
2 µm filtered seawater for 1 min  (Hudson and Morel, 1989; Tang and Morel, 2006). The filters were placed into plastic vials and 10 mL of the scintillation cocktail "Filtercount" (Perkin Elmer) added. Vials were agitated for 24 h before the radioactivity on filters was counted with the Tricarb ® scintillation counter (precision < 10 %). Controls were obtained with 300 mL of microwave-sterilised seawater (750 W for 5 min) incubated and treated the same way. Subsamples for enumeration by flow cytometry were collected from each bottle just before the filtration step. Cells were fixed in glutaraldehyde (1 %) and kept frozen (-80 • C) until processing and analysis. Data were corrected by blank subtraction and Fe uptake rates normalised to the concentration of Fe in each incubation (in situ dFe and 55 Fe added).
, 2005). Here, we follow a similar approach to that used by  Bowie et al. (2009)  for the SAZ-Sense study south of Tasmania (Australia) at our three study sites. Since all parameters in our iron budget calculations were only measured at stations R-2, A3-2 and E-5, discussion will focus on these stations. Data for stations A3-1, E-1 and E-3 are given to provide a context for spatial and temporal changes in the Fe pools and fluxes during KEOPS-2, and they are collated in Table  1.
Error bounds are provided where available. Due to logistical constraints resulting in missing data at some stations, we will focus on R-2, A3-2 and E-5 in the discussion. Data for stations A3-1, E-1 and E-3 are given to provide a context for spatial and temporal changes in the pools and fluxes during KEOPS-2. The mixed layer depths were calculated on the density plane to allow for heave (internal tides driven by topography) and other localised events. 2 Due to logistical reasons there was no TMR cast for dFe at station E-1. 3 Due to ISP failure, there were no mixed layer samples for pFe at station E-3. 4 The P-trap was lost at R-2. We therefore estimated the pFe export flux using the 234 Th flux in suspended particles at 200 m (449 ± 203 dpm m -2 d -1 ; from Table  1  in  Planchon et al. , 2014)  and a mean Fe / Th ratio collected in the upper 200 m above the trap (2.
FeCycle-II had complex biogeochemical dynamics due to a storm event and subsequent deep-water mixing (during sediment trap deployment at their station A3), splitting the study into two phases ("eddy centre" and "eddy periphery"). To aid interpretation of Fe / C export data in the context of iron fertilisation, only data from the pseudo-Lagrangian phase 1 (i. e. deployments A1 and A2 during bloom development and export) from that study are included in this plot  (Ellwood et al. , 2014). ing is consistent with other observations at both the plateau and plume sites which were Fe replete in early spring but somewhat surprising for the HNLC reference site R-2. This may partly be a result of an overestimate of the atmospheric supply used in calculations presented here from literature data. Another explanation is that the parameters used in our "short-term" iron budget calculations are decoupled in time (e.
Acknowledgements. We thank the captain  B. Lassiette , officers and crew of RV  Marion Dufresne ,  Pierre Sangiardi  ( Institut Paul-Émile Victor ) and the  Institut National des Sciences de l'Univers for voyage logistics  and their support of the science, and voyage leader  Bernard Quéguiner  ( Institut Méditerranéen d'Océanologie ) and chief scientist  Stéphane Blain  ( LOMIC, Université Pierre et Marie Curie ) for leading the KEOPS-2 expedition. We acknowledge  Thomas Rodemann  ( UTAS ) for CHN analysis in the  Central Science Laboratory , together with members of the  ISP  and P-trap teams for support at sea. Michael Ellwood  ( Australian National University ) kindly loaned the  TMR  for the project. Access to ICP-MS was provided through  Australian Research Council LIEF funds  ( LE0989539 ).