dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2000; Parsons et al. , 2011; Sievanen et al. , 2012). Additionally we found it difficult to identify datasets that matched well with the concerns of our informants. We hypothesized at the beginning of the project that the indicators and diagnostics of change that would arise from ethnographic inquiries would parallel the observations of ecologists and that they could be compared. But this is not always the case. In rural and protected areas in France for example, we thought À at the beginning of the project and according to the existing data À that changes of seasonality and of climate would be of great importance to local actors and that birds would be relevant indicators of such changes. We hypothesized that bird observations were crucial for local farmers to adapt their agricultural practices.
There is comparatively little ecological data available on these types of species. These new observations on pests opened a new range of potential research projects to be conducted by ecologists in collaboration with social scientists in the near future. The early phases of the project focused on questions of interdisciplinarity, and the role of the natural scientists in PIAF. After the first year of fieldwork, we had to re-design the participation of ecologists in the collaborative work. Because both social and natural scientists were brought into the research program at the same time and all helped design the research questions and methodology, this challenge was embraced collectively and we succeeded in making fundamental new contributions to the direction of the research. During the last PIAF atelier in December 2015 we primarily discussed the data collected during the first fieldwork phase and on how to analyze these data and put into practice the comparison and the interdisciplinary work.
We discussed the limitations of our first interdisciplinary objectives, the available useful ecological data and the way social scientists and ecologists could both participate in the analysis of some sociological data. This led to new interdisciplinary challenges within our program and new roles for the ecologists. Within the PIAF program there is no direct investigation of issues based on ecological methodologies, so we chose to see how ecology and ecologists could help in the analysis of ethnographic data from an interdisciplinary point of view. PIAF ecologists will use their analytical tools and knowledge to provide quantitative analyses of the ethnographic data collected, notably within the freelists. The objectives are not to answer questions about the effects of climate change on species, for example, but to find equivalences among relevant species/indicators between countries for local actors: do these indicators have similar characteristics, common patterns to be tested at other study sites of the program À providing at the same time new possibilities for comparison at our gradient and international scales.
The Tier 2 PMIP4 sensitivity experiments will separate the influence of individual forcings (GHG and ice sheets) on the LGM climate. Thus, the PMIP4-CMIP6 Tier 1 LGM experiment, and the associated Tier 2 sensitivity experiments, will help to understand the response to multiple forcings, the sensitivity to individual forcings, and how the responses to individual features and forcings combine to produce the full LGM response. 2. Can models represent the reconstructed climatic and environmental changes for the LGM? Model evaluation based on LGM climate or environmental reconstructions has been an ongoing activity since the beginning of PMIP  (Braconnot et al. , 2012; Harrison et al. , 2014 Harrison et al. , , 2015;; Annan and Hargreaves, 2015). Model-data comparisons have been performed at data sites and this has helped identify discrepancies in the LGM experimental set-up (e.
However, in PMIP4, given improvements in the climate models themselves, the inclusion of additional boundary conditions (dust, vegetation) and updates to pre-existing boundary conditions (e. g. ice sheets, river routing, GHGs) in line with latest knowledge, the simulations of regional climate should be more realistic. In addition, models now explicitly represent processes or climate system components such as marine biogeochemistry, oxygen and carbon isotopes, dust emission and transport, and vegetation dynamics, making it possible to make direct comparisons with environmental records and reducing the uncertainties re-sulting from the interpretation of these records in terms of climate signals in model-data comparisons. An important aspect of the data-model comparisons will be to determine whether there is sufficient data to characterize and quantify differences in regional climates resulting from the uncertainties in the imposed boundary conditions (i.
Changes in vegetation and dust, which produce changes in regional climate, also need to be taken into account when regional reconstructions (such as over the tropical oceans) are used to constrain climate sensitivity  (Hopcroft and Valdes, 2015a). By increasing the number of simulations available, including important regional forcings, and focusing on uncertainties in these forcings, the LGM PMIP4-CMIP6 experiments will provide a much better data set to re-examine climate sensitivity.
Sensitivity to vegetation and dust Experiments designed to test the sensitivity of the LGM climate to vegetation and dust, run with model versions or set- LGM simulation with interactive vegetation, with no corresponding DECK simulation, then this is also considered a PMIP4 sensitivity run, which should be named lgm_v2 (2 for PMIP4 only). Simulations with or without changes in dust are already included in the PMIP4-CMIP6 protocol, so the sensitivity to dust can be analysed through these simulations. However, if a modelling group runs an LGM experiment with interactive dust but with no corresponding DECK simulation, this simulation would be a PMIP4 sensitivity experiment, named lgm_d2n, with n varying according to the data used to set the emission regions (see  Sect. 4. 11). Sensitivity experiments with vegetation and dust different from the PMIP4-CMIP6 simulations should be named lgm_vm_dn, with m = 1 or 2 and n defined according to the definitions above.
(2016). The implementation of the LGM ice sheets will vary from one model to the other. Here, we give the main implementation steps that have been followed for the IPSL climate Step 0 Step 1 Figure 3. Summary of steps to be followed for the definition of the basic surface types for the atmosphere and ocean boundaries. model (Fig. 3 ). The details of the implementation may differ for other models, but the same steps should be followed and documented. Step 0: Computing the land fraction (sftlf), land-ice fraction (sftgif), and orography (diff_orog) from the ice-sheet reconstruction data sets. The PMIP3 reconstruction files include information about the land fraction (sftlf for surface type land fraction), landice fraction (sftgif for surface type glacier fraction), and difference in orography (diff_orog) that needs to be applied to the piControl orography in order to obtain an lgm orography.
This information, in particular sftlf, is not directly available in the GLAC-1D reconstruction and is incomplete in the ICE-6G_C reconstruction (e. g. the Caspian Sea, above the present-day sea level, is missing). The variables available for each reconstruction are listed in Table  2. They can be found on the PMIP4 website (http://pmip4. lsce. ipsl. fr), as provided by the authors of the reconstructions. In particular, the variable names and the resolution have not been modified. In the present step 0, we describe how we compute sftlf, sftgif, and orog from the GLAC-1D and ICE-6G_C data. The IPSL model requires orog at 1/6 • resolution for its gravity wave drag parameterization, which is why we compute diff_orog at this high resolution. The procedure is as follows ("Pre-pare_LGM_BC_files. py" Python script provided on the PMIP4 website): the input variables listed in Table  1  are read in; these include the land fraction for ICE-6G_C but not for GLAC-1D; for GLAC-1D, the land-sea mask for the present and for the LGM are defined as where topography is positive; small holes (usually one to two isolated grid boxes) in the land-ice fraction are filled, using the "bi-nary_fill_holes" function of the python scipy/ndimage package (for ICE-6G_C, 155 points are filled in, to be compared to the total number of land-ice grid points, with is initially 423 610; for GLAC_1D, 62 points are filled in; the total number of points fully covered by land ice is 23 348); the land fraction is updated to include the land-ice fraction; this land fraction includes unrealistic isolated continental points which are well below sea level (we have considered a threshold of -500 m.
The second option is used for the IPSL model, and the corresponding program is provided in the Supplement (program "bathy_lgm. py"). The results are shown in Fig. 5  for the NEMO model used in the IPSL coupled model. Figure  5a  shows the changes for global ocean extent, with ocean points disappearing at the location of the LGM ice sheets (e. g. Hudson Bay, Baltic Sea, Barents-Kara seas) and where the present ocean is shallow enough to be sensitive to the LGM sea-level drop (e. g. Bering Strait, Sunda and Sahul shelves, north of Siberia, off the Patagonian eastern coast). Figure  5b  shows the global changes in ocean bathymetry, which for this example have been prescribed at the average sea-level drop for the ICE-6G_C reconstruction. Figure  5c, d, e     area. We have ensured that the imposed change in bathymetry matches the reconstructed one for the Greenland-Iceland Rise and Iceland-Faeroe Rise.
g. with well-stratified glacial ocean states), to minimize spin-up duration. Such ocean states, such as described in  Werner et al. (2016) , which provide 3-D fields of sea temperature, salinity, and associated stable water isotopes on a regular 1 • × 1 • grid, are available on the PMIP4 website and from the PMIP2 and PMIP3 databases. The model should be spun up until equilibrium. In previous PMIP protocols (in particular http://pmip2. lsce. ipsl. fr) the simulations were considered at equilibrium when the trend in globally averaged SST was less than 0. 05 • C/century, the Atlantic Meridional Overturning Circulation (AMOC) was stable and, for models including representations of the carbon cycle or dynamic vegetation, the requirement was that the carbon uptake or release by the biosphere is less than 0.
Output The data should be formatted so as to comply with the CMIP6 standards (to be documented in the GMD CMIP6 special issue; cf. Eyring et al. , 2016)  and PMIP4 data request  (Kageyama et al. , 2016 ) so that analyses including other PMIP and CMIP6 simulations can be performed easily. The current list of variables is given in the Supplement but is still subject to potential changes following adjustments of the full CMIP6 list. The PMIP4 data request can be found on the PMIP4 website (https://pmip4. lsce. ipsl. fr/doku. php/ database:pmip4request#the_pmip4_request).
Analyses and outlook The LGM experiment is a major investment by climate modelling groups, but provides a demanding test of model reliability under extreme and well-documented conditions. Indeed, our experience is that several groups have found model errors while setting up their LGM climate simulations, in particular in the coupling between the atmosphere and the ocean and in the global freshwater budget. The PMIP4-CMIP6 simulations, along with PMIP4 sensitivity experiments and previous PMIP2 and PMIP3 experiments, will create an unprecedented data set about the LGM climate state. With a larger number of simulations, and a better sampling of the forcing uncertainties, we should be able to reach more robust conclusions about, for example, the ability of state-of-the-art climate models to represent a climate very different from the pre-industrial or present climates: benchmarking these simulations will provide a measure of how well models simulate large climate changes, comparable in magnitude to changes expected over the 21st century.
Although there are data sets documenting environmental conditions and climate at the LGM, the planned PMIP4-CMIP6 analyses would benefit from the improvement and geographic expansion of these data sets. In addition, there is scope for the creation of new data sets, particularly data sets that can be used to evaluate aspects of the more complex Earth system models that are being run in PMIP4-CMIP6; the relationships between climate or environmental changes at far away locations, or between different features of the climate system: for instance, as alluded to in the introduction, we expect the atmospheric and ocean circulations in the North Atlantic area to be sensitive to the ice-sheet height; the PMIP4-CMIP6 experimental design allows for multi-model studies on this topic; at large scales, the polar amplification and landsea contrasts that have been studied with PMIP2 and PMIP3 experiments could be altered with the PMIP4 more complex simulations including vegetation or/and dust changes; and the potential constraint from the LGM (in particular via the LGM tropical SSTs) on climate sensitivity.
The Tier 2 sensitivity experiments will allow the quantification of the role of individual forcings and feedbacks in climate. This is an essential step in understanding the LGM climate, but also in characterizing and understanding common and/or contrasting features of the most recent past warming (between the LGM and the present) and the predicted future warming. These are a few examples of possible analyses of the PMIP4-CMIP6 lgm simulations. The analysis of the PMIP4-CMIP6 and PMIP4 sensitivity experiments also relates to other CMIP6 projects and we hope these data will also be analysed by experts from other CMIP6 MIPs. For instance, the understanding of the impacts of the LGM climate forcings and the role of radiative feedbacks is related to CFMIP  (Webb et al. , 2017)  and RFMIP  (Pincus et al.
, 2016). The PMIP4 single forcing experiments can be used in view of the CFMIP experiments testing the impact of uniform lowering of SSTs or CO 2 decrease (in AMIP configuration) and the connection to climate sensitivity for CO 2 increase should be made easier to analyse with these experiments. In terms of diagnostics that can be used to analyse the role of each component of the climate models in setting up the LGM climate, we also expect new studies based on diagnostics developed by the CMIP6 MIPs on the ocean (OMIP,  Griffies et al. , 2016; Orr et al. , 2017) , land surface and snow (LS3MIP,  van den Hurk et al. , 2016) , aerosols (AerChemMIP,  Collins et al. , 2017) , sea ice (SIMIP,  Notz et al. , 2016) , and ice sheets (IS-MIP6,  Nowicki et al. , 2016). It is therefore important to keep the relevant output for these analyses, and the PMIP4 data request has been built based on the lists for these other MIPs.
Acknowledgements. Masa Kageyama  and  Qiong Zhang  acknowledge funding from  French-Swedish  project  GIWA. Sandy P. Harrison  acknowledges funding from the  European Research Council  for "GC2. 0:Unlocking the past for a clearer future". Ruza F. Ivanovic  is funded by a  NERC   Independent Research Fellowship  (no. NE/K008536/1 ). Fabrice Lambert acknowledges support from  CONICYT  projects  15110009 ,  1151427 ,  ACT1410 , and  NC120066. Bette L. Otto-Bliesner ,  Esther C. Brady , and  Robert A. Tomas  acknowledge the funding by the  U. S. National Science Foundation of the National Center for Atmospheric Research. Peter O. Hopcroft  is funded by  UK NERC  ( NE/I010912/1  and  NE/P002536/1 ). Edited by: James Annan Reviewed by: two anonymous referees.
Code and data availability. All the forcing data sets, their references, and their code can be found on the PMIP4 website (https: //pmip4. lsce. ipsl. fr/doku. php/exp_design:lgm, PMIP4 repository, 2017). The forcings will also be added to the ESGF Input4MIPS repository (https://esgf-node. llnl. gov/projects/input4mips/, with details provided in the "input4MIPs summary" link). Acknowledging CMIP6 according to the instructions given in Eyring et al. (2016), PAGES, and WCRP, which endorse PMIP, as well as the modelling groups which have contributed to the CMIP6 and PMIP4 effort, will be greatly appreciated. The Supplement related to this article is available online at https://doi. org/10. 5194/gmd-10-4035-2017supplement.
645), and 1% (k 1-α =2. 326), using the formulas given in the  ISO11929-3, (2000). The reason of using an older version of this standard is that some labs still have this version in their quality system and accreditation. The important issue was that all laboratories used the same definition. The nuclear decay data were obtained from Monographie BIPM-5 and the DDEP (Decay Data Evaluation Project) website (http://www. nucleide. org/DDEP_WG/ DDEPdata_by_Z. htm) and from the NNDC web site (http://www. nndc. bnl. gov) for 42 Ar and 42 K. At Canfranc the JANIS NEA and ENDF/8-VII. 1 libraries were used to obtain nuclear decay data.
Acknowledgements The "ITPA TG-D JEX DIAG-5: Field test of an activation probe" was supported by the  Diagnostics Topical Group  ( TG-D) of International Tokamak Physics Activity (ITPA ). This work was supported by the EUFRAT open access work package of the  European Commission's Joint Research Centre EURATOM  programme under Horizon2020 (EUFRAT.
The original electronics have been replaced with custom-made boards to handle the higher power consumption and operate off 28 VDC, the primary power supply in aircrafts. The whole system is controlled by a credit-card-sized ARM computer running a tailor-made embedded Linux operating system. It features a touchscreen, a TSI-like serial port protocol, and digital pulse output. With these modifications the saturator heating and condenser cooling are decoupled. In addition, the critical orifice and external heavy pump are replaced by a laminar flowmeter and a miniature rotary vane pump. The user may set the temperature of the saturator, condenser and optics as well as the flow rate independently from one another. The B3010 was operated at saturator temperature 55 • C, optics head 56 • C and condenser 11 • C.
4, 1. 8, 2. 4 and 4. 4 nm. The concentration at each size was controlled by adding a dilution flow of compressed and filtered air downstream of the DMA. Simultaneous data were collected for the 3777; however, the dilution flow was again enough to change the aerosol-to-sheath flow ratio of the 3777 due to a small change in the inlet pressure, and therefore the 3777 data of this experiment are not presented. However, assuming that any possible undercounting at high concentration originates from particle coincidence in the optics, the concentration calibration of the 3777 should be practically the same as of the 3772 CPC when the dilution of 0. 15/1 is taken into account. 3 Results.
Choice of coral species Five coral species, chosen among the most common scleractinian coral genera from the Pacific: Pocillopora (P. damicornis type β sensu  Schmidt-Roach et al. , 2014  Genbank references KY110998-KY111024), Acropora (A. cytherea and A. pulchra), Porites (P. rus) and Pavona (P. cactus), were collected from a fringing reef with a depth 0. 5-2. 0 m off Moorea island in French Polynesia (17 • 30 9S, 149 • 50 9W) (Fig. 1 ). These five coral species display different biological traits, and were among corals characterized as having varying resistance during severe local bleaching events in 2002 and 2007  (Penin, Vidal-Dupiol & Adjeroud, 2012). Acropora is considered as the ''sentinel'' coral genus, described as having high sensitivity to environmental stressors (e. g. ,  McClanahan et al.
µL -1 for field sample analysis. The following run protocol was performed: 1 cycle of pre-incubation of 10 min at 95 • C; 40 cycles of amplification: 30 s at 95 • C, 1 min at 60 • C or 64 • C for Symbiodinium and coral host respectively, and 1 min at 72 • C; and a final step, for melting temperature curve analysis, of 1 min at 95 • C, 30 s at 60 • C and 30 s at 95 • C. Each sample was analysed twice on the same plate, as one technical replicate, and averaged when the variation between both Ct values was not exceeding 1 (if not, samples were re-processed until Ct ≤ 1). An interplate calibrator (i. e. , positive control with known concentrations and Ct values: mixture of DNA from Symbiodinium clades A-F), tested in triplicate (one technical replicate), was added to each plate to calibrate Ct values (performed manually on the MxPro software to set the fluorescent threshold to a fixed Ct value) among different plates of coral DNA samples.
Statistical analysis All statistical analyses were performed using R version 3. 3. 1 software  (R Core Team, 2013). For each Symbiodinium clade, positively quantified in coral DNA, the symbiont/host ratio (i. e. , S/H ratio) were log + 1 transformed for further analyses. Slopes, intercepts, and the Pearson correlation coefficient (R 2 ) were evaluated by pairwise comparisons with Student's t -tests using R package RVAideMemoire version 0. 9-27  (Hervé, 2013). Discriminant analysis of principal components (DAPC) on S/H ratios, available for the 5 coral species, was performed in R package ade4 version 1. 7-4  (Dray & Dufour, 2007)  in order to characterize their preferential endosymbiotic assemblages and densities. Therefore, the discrimination represented by ellipses was applied through the coral species as factor.
ACKNOWLEDGEMENTS We thank  B Espiau  and  M Vairaa  from  CRIOBE  and  J Fievet  (in charge of qPCR platform utilization) from  IFREMER from Taravao  for their advices and assistance in laboratory. We are also grateful to  MA Coffroth  and her laboratory (BURR; http://www. nsm. buffalo. edu/Bio/burr/)for providing us cultured Symbiodinium strains. We also thank  C Bonneville  who performed analyses of Symbiodinium diversity in the water column during her master internship.
Although sponges are important components of benthic ecosystems of the Caribbean Sea, their diversity remained poorly investigated in the Lesser Antilles. By organizing a training course in Martinique, we wanted both to promote taxonomy and to provide a first inventory of the sponge diversity on this island. The course was like a naturalist expedition, with a field laboratory and a classroom nearby. Early-career scientists and environmental managers were trained in sponge taxonomy. We gathered unpublished data and conducted an inventory at 13 coastal sites. We explored only shallow water habitats (0-30 m), such as mangroves, reefs or rocky bottoms and underwater caves. According to this study, the sponge fauna of Martinique is currently represented by a minimum of 191 species, 134 of which we could assign species names.
However, the Eastern Caribbean, the Lesser Antilles, and, primarily, the French islands remained poorly investigated  [29]. According to the World Porifera Database, about 242 sponge species occurred in the Eastern Caribbean  [30]. In the French Antilles, Alcolado & Busutil  [31]  recently published an inventory of 111 sponge species from Guadeloupe Island, thereby supplementing the few records made by Toffart  [32]  on epibiont fauna of mangrove roots. Nonetheless, it is noteworthy that only 21 species have been reported from Martinique and almost none from the other French islands (e. g. Saint Martin, Saint Barthelemy). We can only explain this impressive lack of knowledge in the French Antilles, compared to the Eastern Caribbean area, by the absence of sponge specialists among biologists involved in studies of these islands.