dataesr/scientific-paragraphs-categorization huggingface dataset:
10 on a standard rich medium (data not shown). Chemical Characterization of the bcbot3Δ and bcbot4Δ Mutants. The KO bcbot3Δ and bcbot4Δ mutants, which had been derived from the B05. 10 strain, were grown on a solid malt agar medium. The metabolites were recovered in ethyl acetate and purified by column chromatography on silica gel. The products were finally purified by HPLC to yield the metabolites shown in Figure  4. The results indicated that none of the mutants produced botrydial (1), confirming that these mutations were involved in botrydial biosynthesis. The chromatographic study, characterization, and quantification of the metabolites biosynthesized by the bcbot3Δ mutant showed, in addition to small amounts of botcinin A (6), high levels, (35 mg/L), of the biosynthetic intermediate 4β-acetoxy-15α-hydroxyprobotryane (2d), Figure  2 , which was identified by extensive NMR studies and comparison with authentic samples.
The transformation medium was a minimal medium supplemented with saccharose (MMV; 20 g/L glucose, 200 g/L saccharose, 2 g/L NaNO 3 , 1 g/L KH 2 PO 4 , 500 mg/L MgSO 4 •7H 2 O, 500 mg/L KCl, 10 mg/L FeSO 4 •7H 2 O, 15 g/L agar). The bcbot3 and bcbot4 mutants were selected with nourseothricin fungicide (Werner Bioagent, Germany; 70 μg/mL). Standard Molecular Methods: Gene Deletion of the Cytochrome P450-Encoding Genes BcBOT3 and BcBOT4. The B. cinerea genes BcBOT3 and BcBOT4 (GenBank AY277723. 2) were deleted by homologous recombination. The 5′ genomic noncoding regions of BcBOT3 and BcBOT4 (882 bp and 1442 bp, respectively) were amplified by PCR. In the same way, we obtained the 3′ flanking sequences of BcBOT3 and BcBOT4 (1027 bp and 866 bp, respectively) and the nourseothricin resistance gene (nat).
Data collection All measurements (GEIs, body masses of mothers and pups) were taken daily between 08:00 h am and 11:00 h am. The day when pups were found was considered as the day of parturition, referred to as day 0 of lactation. To minimize disturbances, all measurements were suspended on the day of parturition. Females had ad libitum access to food and water throughout the experiment at all different ambient temperature conditions. Daily food intake (in g) was continuously monitored except during the mating period. To this end, the animals were provisioned with a fixed amount of pelleted food during lactation. Fresh supply was provided and weighed in the morning if necessary. The remaining pellets from the previous day were weighed and the difference (consumption) was noted. All animals received the same diet during the experiment (commercial hamster diet, Ssniff, Germany).
Mother's gross energy intake (GEI) was then calculated by multiplying the daily intake of pelleted hamster food in g/day by its energy content of 16. 5 kJ/g (data provided by Ssniff, Germany). Furthermore, we added the energy content of the pups presumably consumed by the mother. For calculation, we assumed an energy content of golden hamster pups of 2. 41 kcal/g, as it had been suggested for juvenile laboratory mice of the same age class  [31]. In absence of the possibility to measure the energy content of dead hamster pups and in view of the many similarities in juvenile development in rodents in general, we considered this approach both reasonable and justifiable. Note that between around postnatal days 11 to 13, juvenile golden hamsters usually start picking up solid food themselves (pers.
Statistical data analysis and sample sizes All statistical analyses were performed using R, version 3. 2. 0 [65]. We applied multifactorial linear mixed effects models, LMMs and generalized linear mixed effects models GLMMs for Poisson distributed data by using the R package lme4  [66]. Models always included mother identity as a random factor, as several litters (up to 4) were born from the same mothers. That is, the model structure accounted for repeated measurements of consecutive litters per female. Repeated reproductive events of individual females were assigned to different temperature conditions in the majority of cases (86%) to reduce the chance that single mothers might bias the results by high numbers of repeated measurements within a particular treatment group. We also included parity (i.
Acknowledgements We are grateful to  Violet Berger  and  Zsuzsanna Zsabo  for their help and assistance with data collection as well as to the student helpers  Rene Haider ,  Anna Mokry ,  Maria Thaler  and  Marilies Scheinost. We thank  Felix Knauer  for fruitful discussion and constructive criticism, and we thank our colleagues  Hanno Gerritsmann  and  Christiana Winding  for veterinary assistance. We would like to thank the thoughtful comments of two anonymous reviewers on earlier drafts, which greatly improved the manuscript.
In this study, we used data from climate stations and terrain models to produce improved, high resolution estimates of temperature over a large domain (Sweden; 50 m grained grids over 450 000 km²). For a dataset of 78 alpine species, we then asked: 1) do ordinary SDMs (based on the Worldclim climate dataset, grain-size 1 km) over-or underestimate current and future suitable habitats at species' range limits, compared to fine-grain SDMs (50 m) that account for topographic heterogeneity of the landscape? 2) Do ordinary SDMs over-or underestimate local extinction of alpine species due to climate change? 3) What proportion of potential microrefugia (defined here as stable, suitable habitats across time periods) are missed by using coarse-grain climate predictors (1 km) vs fine-grain climate predictors (50 m) and by using fine-grain climate predictors that ignore topographic climate-forcing factors other than elevation vs fine-grain climate predictors that account for a comprehensive list of such topographic forcing factors?.
Relative elevation is a proxy for cool air pooling, and topography wetness index a proxy for relative soil moisture (see Material and methods for detailed calculations). period in 2011 (dataset from previous study,  Vercauteren et al. 2013 , Meineri et al. 2015). This landscape is highly diverse in terms of topography and is therefore appropriate for testing topoclimate grids (see Supplementary material Appendix A1. 2 for topographic ranges encompassed within the logger validation data). The loggers were all set into old-growth forest ( 80 yr old), so the forest canopy likely affected recorded temperatures. However, the sites were selected to be as similar as possible in terms of canopy cover, forest age, and species composition, while varying in many other aspects relevant for microclimate, such as altitude, aspect, and topographic position.
Thus the canopy effect within the logger validation data should not severely affect the differences between validation score. Furthermore, any strong effect of forest canopy would decrease rather than increase both absolute validation scores and relative differences between downscaling approaches, making the validation more conservative. Further information about logger sheltering, calibration, and accuracy are described in  Vercauteren et al. (2013). For the two validation datasets and the four climate datasets, linear models were used to assess the correlation between predictions and observations for each of the fifteen temperature variables. Root mean square errors (RMSE) were also estimated to assess the overall error of the models. Daily calculations of growing degree day (GDD) were done before estimating RMSE, to obtain values of comparable scale to the other climatic variables.
Species data We selected the 78 vascular alpine plants (Supplementary material Appendix A1. 4) using two criteria. First, we discarded generalist species growing in alpine areas; and second, we kept only species that had at least 50 presence records with a spatial resolution of at least 50 m, within the open access citizen science database Artportalen (< www. artportalen. se/ >; last accessed January 2015). This database gathers Swedish species data from museums and field observations of both researchers and naturalists. For each species, we extracted 3000 random absences within Sweden, which corresponds to the ratio and density advised in  Barbet-Massin et al. (2012)  for regression techniques.
Validation The fifteen temperature variables investigated were also extracted from the Worldclim dataset (global climate interpolations accounting for elevation, 1000 m resolution;  Hijmans et al. 2005)  to test whether our downscaled temperature estimates better predict local-scale temperature than one of the most commonly used climate datasets. The temperature grids from the three different downscaling approaches, as well as from the Worldclim data, were validated on an independent regional network of temperature loggers (hereafter logger validation data), as well as on an independent national network of climate stations (hereafter SMHI validation data; Fig. 1 ). The climate stations used in the SMHI validation data consist of 20% of the original pool of Swedish climate stations. This subset of climate station data was excluded before calibrating the models and is therefore independent (see Supplementary material Appendix A1.
2 for topographic ranges encompassed). The logger validation data were used to assess the models' ability to predict local temperature variations, while the SMHI validation dataset was used to assess their ability to predict large-scale temperature variations. The logger validation data were collected by 57 Maxim 1922L iButton loggers recording temperature at a height of 1 m inside forests (note that this dataset did not cover alpine regions). These loggers were placed in the Swedish province Ångermanland (latitude 62. 5-63. 3°, longitude 16. 8-18. 9°, decimal degrees, Fig. 1 ), central Sweden, for a one-year time et al. 2013). The temperature and precipitation anomalies between 1990-2010 and 2070-2100 were estimated for the SDMs' projection region based on the RCA4 regional climate change model described by  Sjökvist et al.
Analysis of modelled species distributions To assess whether ordinary SDMs under-or over-estimate current and future area of suitable habitat compared to fine-scale SDMs, for each species we compared the suitable habitat predicted by the Worldclim SDMs (ordinary) to the modelled distributions obtained from the fine-scale ElevOnly and TopoClim SDMs using linear mixed effects models (LMM). In these LMMs, current and future predicted areas of suitable habitat for each species were used as response variables, and a categorical variable describing the climate dataset used for each SDM (Worldclim, ElevOnly, or TopoClim) was used as a fixed explanatory variable. Subsequently, we used generalized linear mixed effect models (GLMM) for binomial data to assess whether ordinary SDMs under-or over-predicted local extinction rates.
In the GLMMs, a dummy variable describing whether each species is predicted to become extinct within 2071-2100 was used as a response variable and a categorical variable describing the climate dataset used to fit the SDMs was used as an explanatory variable. Species was used as a random factor in both the LMM and GLMM models to nest the analyses within species  (Franklin et al. 2013 ). All models were followed by a Tukey post-hoc test for multiple comparisons between groups. Finally, we assessed the proportion of potential microrefugia that was captured in the TopoClim SDMs, but not in the Worldclim SDMs (ordinary SDMs) or in the ElevOnly SDMs. We compared the proportion of habitat predicted suitable at both current and future time periods (using stable suitable habitat as a proxy for microrefugia) according to the TopoClim SDMs, but not predicted as such according to the other type of SDMs (e.
g. stable habitat missed,  Franklin et al. 2013). Note that this definition of microrefugia does not imply that climate remains stable between current and future time periods, but simply that the habitat remains climatically suitable. All statistical analyses were carried out in R. The R script used to extract the temperature grids is provided in Supplementary material Appendix A2, and the one used to model and project the SDMs is provided in Supplementary material Appendix A3. Data available from the Dryad Digital Repository: < http://dx. doi. org/10. 5061/dryad. 78r91 >  (Meineri and Hylander 2016). These variables are representative of plant physiological requirements  (Prentice et al. 1992 , Austin 2007 , Austin and Van Niel 2011). We could not obtain good estimates of MaxTemp and therefore excluded this variable from the SDMs.
GDD and MinTemp were derived from the ElevOnly and TopoClim climate grids at a 50 m resolution for the ElevOnly and TopoClim SDMs, respectively, and from the Worldclim dataset at a 1000 m resolution for the Worldclim SDMs. Before being included in any of the models, the sum of annual solar radiation was aggregated to a 1000 m spatial resolution, so that it would not influence the comparison between models. Water balance was calculated as the difference between annual precipitation and annual potential evapotranspiration, as further described in Supplementary material Appendix A1. 5.
Modelling Species distributions were modelled using the two regression methods, generalized linear model (GLM), generalized additive model (GAM), and two classification methods, boosted regression tree (GBM, also known as generalized boosted model), and random forest models (see  Elith et al. 2006 , Evans et al. 2011  for a technical decription of these models). For each species and climate dataset, the output from all four methods was averaged to constitute an ensemble consensus model (BIOMOD2,  Thuiller et al. 2009 ). The detailed procedure for model fitting is described in Supplementary material Appendix A1. 6 for details. For each species and climate dataset, the SDMs were validated on an external subsample consisting of 20% of the original species data (including both presence and absence) to assess overall model performances.
Species distribution models The accuracies of the SDMs were high, regardless of the climate data used to fit models (mean AUC  0. 9 and mean AUC  0. 8 in the overall and highland validation datasets, respectively). In the highlands, model quality was only slightly higher within the downscaled ElevOnly and TopoClim SDMs (mean AUC  0. 81 for both downscaled SDMs) than within the Worldclim data SDMs (mean AUC  0. 80). For the current time period, the Worldclim SDMs predicted on average 16% more area of suitable habitat than did the ElevOnly SDMs (p  0. 001), and 11% more area of suitable habitat than did the TopoClim SDMs (p  0. 001). For future projections, the ElevOnly SDMs predicted less area of suitable habitat than the Worldclim SDMs (severe scenario RCP8. 5; -29%, p  0. 024), whereas the TopoClim SDMs predicted no significant differences in suitable habitat compared to the Worldlim SDMs.
Topoclimate grids The variation in local temperature, based on logger data in a central Swedish landscape (Fig. 1 ), was best predicted by the TopoClim temperature grid (Table  2 ; average R²  0. 57, RMSE  1. 72°C, Fig. 2a, b ). This was true for every temperature variable modelled except MaxTemp. The ElevOnly grids (Table  2 ) had an intermediate accuracy (R²  0. 46, RMSE  2. 03°C) compared to the TopoClim and Wordclim grids (R²  0. 37, RMSE  2. 56°C) (Fig. 2a, b ). The TopoSol grids (Table  2 ) were the least accurate, and performed relatively poorly with regard to this logger validation dataset (R²  0. 22, Fig. 2a ), although they exhibited a lower overall error rate (RMSE  2. 28°C) than the Worldclim grids (Fig. 2b ). The variation in minimum temperature recorded by the loggers in central Sweden was poorly explained within all datasets, except in the TopoClim grid (logger validation R²  0.
56, RMSE  3. 97°C), which explained at least twice as much variation in MinTemp as any other dataset. The Worldclim data did not explain any of the local variation in MinTemp (R²  0. 04, RMSE  4. 17°C) recorded by the loggers. The TopoSol grid of MaxTemp (R²  0. 46, RMSE  1. 66°C) outperformed the ElevOnly (R²  0. 26, RMSE  1. 49°C), TopoClim (R²  0. 27, RMSE  1. 46°C), and Worldclim (R²  0. 00, RMSE  3. 01°C) grids in terms of explained variance. The performance differences between the four types of temperature grids were similar when tested on the countrywide validation datasets (SMHI validation, Fig. 2c, d , Supplementary material Appendix A1. 8). All four temperature datasets provided accurate national scale estimations of temperature. R² values ranged between 0. 91 and 0. 94, except for MaxTemp, which had consistently lower R2-values.
Discussion We demonstrate a simple way to derive fine-grain, comprehensive topoclimate grids covering large domains. These grids can improve the capacity to identify and locate microrefugia for species threatened by climate change. In contrast to previous studies suggesting that a large network of temperature loggers is required to produce finegrain topoclimate grids  (Ashcroft 2010 , Dobrowski 2011 , Hannah et al. 2014) , we exclusively used data from climate stations. Despite the two-meter-high, open, flat-ground setting of climate stations, enough variation in well-known topographic climate-forcing factors was found to remain among stations to produce meaningful downscaled estimates of several temperature variables. Using our downscaled grids to produce SDMs for a large set of alpine plants, we demonstrate that improving the spatial resolution of climate estimates by accounting for the effects of local topography results in less pessimistic predictions of climate change impacts  (Randin et al.
Grain-size issue The Worldclim SDMs, based on 1 km grain-size climate data, predicted more area of suitable habitat at the current time period than did the 50 m grain-size ElevOnly and TopoClim SDMs. This is in line with previous studies arguing that coarse grain SDMs overestimate the thermal tolerance of mountain plants and thus overestimate the area of suitable habitat  (Trivedi et al. 2008 , Franklin et al. 2013). Coarse-grain climate data are often more representative of the lower altitudes than the higher altitudes encountered within each pixel. This is because 1) the likelihood of sampling the most extreme topographic settings decreases with increasing grain-size  (Randin et al. 2009 ) and 2) the available area often decreases toward higher altitude (except in plateau landscape)  (Elsen and Tingley 2015).
Fine-grain modelling of temperature The downscaling approach 'TopoClim' produced temperature estimations of better quality than those produced by the ElevOnly approach or by the Worldclim data  (Hijmans et al. 2005). This result was significant for both the local-and large-scale variations in temperature. Furthermore, different combinations of variables were retained in the TopoClim models after stepwise selection, but all retained relationships make sense in light of previous research. For example, low relative elevation and high relative soil moisture ('valley bottoms') were associated with low winter temperatures, and short distance to sea was associated with high winter temperatures. Similarly, short distance to the sea and high elevation were associated with cooler summer temperatures (Supplementary material Appendix A1.
8, reviewed by  Dobrowski 2011). We noted that substantial variation in temperature remains unexplained within our TopoClim grids in the logger validation data (about 40%). Nonetheless, the variation in temperature explained by our models is relatively high, given that the effect of canopy cover is not accounted for. Furthermore, the quality of the Topoclim grids was higher for almost all temperature variables compared to the other approaches. Thus our study demonstrates that the effects of several of the most influential topographic variables can be detected and quantified in measurements from climate stations. Despite promising validation scores, using climate stations to produce fine-scale climate grids has limitations. For example, variation in solar radiation could not be accurately accounted for.
Acknowledgements -This research was funded by the  Strategic Research Program EkoKlim at Stockholm Univ. and by a  FORMAS project grant   2014-530  [to KH]. We thank  Mick Ashcroft  for valuable comments and suggestions on the paper. Data accessibility: weather station data can be accessed from the  Swedish Meteorological and Hydrological Inst. (SMHI, < http://opendatacatalog. smhi. se/explore>; last accessed January 2015). The 50 m terrain model can be accessed from the Swedish national mapping authority (Lantmateriet, < www. lantmateriet. se>; last accessed January 2015). Species data are available from the Artportalen website (< www. artportalen. se>; last accessed January 2015).
Phlebotomine sand flies are haematophagous dipterans of primary medical importance. They represent the only proven vectors of leishmaniasis worldwide and are involved in the transmission of various other pathogens. Studying the ecology of sand flies is crucial to understand the epidemiology of leishmaniasis and further control this disease. A major limitation in this regard is that traditional morphological-based methods for sand fly species identifications are time-consuming and require taxonomic expertise. DNA metabarcoding holds great promise in overcoming this issue by allowing the identification of multiple species from a single bulk sample. Here, we assessed the reliability of a short insect metabarcode located in the mitochondrial 16S rRNA for the identification of Neotropical sand flies, and constructed a reference database for 40 species found in French Guiana.
So far, two techniques have been mainly used for molecular identifications of sand flies: the polymerase chain reaction-restriction fragment-length polymorphism (PCR-RFLP;  Aransay et al. 1999; Terayama et al. 2008; Latrofa et al. 2012; Tiwary et al. 2012; Minter et al. 2013)  and DNA barcoding  (Kr€ uger et al. 2011; Kumar et al. 2012; Contreras Guti errez et al. 2014; Al-Ajmi et al. 2015; Maia et al. 2015; Nzelu et al. 2015; Romero-Ricardo et al. 2016). DNA barcoding consists in the sequencing of a short standard DNA marker for the taxonomic assignment of a sample by looking for the closest match in a reference database  (Hebert et al. 2003a ). In the last decade, it has emerged as the main alternative for species identification in a wide range of taxa, including medically important arthropods  (Ondrejicka et al.
1 Carl Roth GmbH). The morphological and morphometrical data were obtained with the DM5500B LeicaTM microscope associated with the calibration software LEICA APPLI- CATION SUITE 3. 8. 0. Species identifications were performed using the taxonomic guide of  Young & Duncan (1994) , which includes all the references for the descriptions of sand fly species found in French Guiana and Trinidad and Tobago, except for L. depaquiti described later  (Gantier et al. 2006). Specimens were then stored as vouchers. A complete checklist of the sand fly species found in French Guiana is available in the Supporting information. DNA was extracted using the DNeasy Blood and Tissue kit (Qiagen, Valencia, CA, USA) and amplified using the Ins16S_1 primer pair  (Clarke et al. 2014 ; Ins16S_1-F. 5 0 -TRRGACGAGAAGACCCTATA-3 0 ; Ins16S_1-R.
2007). Tags were eight base pairs long with at least five differences between them to minimize ambiguities in the downstream analyses. PCR products were pooled and sent to the GeT-PlaGe core facilities of GenoToul (Toulouse, France) for paired-end sequencing on a MiSeq platform (Illumina, San Diego, CA, USA). Prior to sequencing, DNA was purified using the HighPrep PCR system (MAGBIO GENOMICS, Gaithersburg, MD, USA) and used for library construction with the Illumina NEXTflex PCR-Free DNA sequencing kit following the supplier's instructions (Bioo Scientific corp. , Austin, TX, USA). Sequencing data was preprocessed and stored on the NG6 platform  (Mariette et al. 2012) , and all downstream bioinformatics analyses were performed on computing facilities of the GenoToul bioinformatics platform (Toulouse, France).