dataesr/scientific-paragraphs-categorization huggingface dataset:
2019a). A poor understanding of the biodiversity that underpins ecosystems and their functioning, hampers sustainable management. Indeed, as much legislative work depends on a validated overview of taxonomic biodiversity  (Araujo and Jong 2015) , biodiversity inventories are essential for the development of monitoring strategies and conservation policies. Moreover, with the growing need to understand natural resources and heritage, biodiversity checklists and databases have become essential tools facilitating communication between taxonomists, naturalist data managers, ecologists, geneticists, museum curators, conservationists, etc. Beyond consolidating taxonomic knowledge, they enable study and management at organismal and ecosystem level, making them essential for national and international conservation  (Lydeard et al.
2004; Régnier et al. 2009 ). As result, there is an increasing demand from policy makers and managers to readily have access to datasets regarding biodiversity  (Gofas et al. 2017). Malacological investigations of fresh and brackish waters are uncommon in West Africa in general and in Bénin in particular. Adanson (1757)  and  Dautzenberg (1912)  investigated the malacological fauna of Senegal and West Africa, respectively, but their works focussed mainly on shells of marine species. The freshwater gastropods of Bénin were studied for the first time by  Germain (1917) , based on collections by Henry Hubert made around the 1910s. The first identification guide of West African molluscs (from Mauritania to Angola) was published in 1950  (Nicklès 1950 ), but focused mainly on marine taxa. Towards the end of the 20 th century, several malacological stud-ies have been undertaken on freshwater and brackish taxa in West Africa and in Bénin  (Sellin et al.
Data compilation All taxa included in this study were cross-checked for their original name combination, synonymies, type locality data, habitats and dubious records against MolluscaBase (accessed at http://www. molluscabase. org during October 2019), and the Worldwide mollusc species Data Base (WMSD accessed at http://www. bagniliggia. it/ during October 2019) when MolluscaBase did not provide the required data. In addition, the conservation status of each species was determined from the IUCN red list (accessed at https://www. iucnredlist. org/ during October 2019). The discussion of the occurrence and conservation status of species whose geographical distribution extends beyond the targeted ecoregions, is mainly limited to the ecoregions covered here. We based our systematic arrangement of subclasses and orders on  Bouchet et al.
Acknowledgements CZK and OHO thank the  Belgian National Focal Point  to the  Global Taxonomy Initiative of the CEBioS programme , financed by the  Belgian cooperation for development (DGD)  for granting the taxonomy capacity building Internships at  RBINS, Brussels. OHO gratefully acknowledges the  International Foundation for Science  (Grant  I2_A_6057-1 ) and the  Rufford Foundation  ( 26531-1 ) for their financial support. BVB was supported by  ANR-JCJC-EVOLINK. We thank the anonymous reviewer for providing helpful comments that improved the manuscript substantially.
The mixture is then incubated for 10 min at room temperature with 100 µL of a 1% ascorbic acid solution prepared in H 2 SO 4 leading to a blue molybdous compound. The optical density values of the complexes are measured by a spectrophotometer at 700 nm. A blank solution of tris-HCl (50 mM, pH 8. 5) undergoes the same treatment. Assays for each phosphate concentration were done in triplicate. melittin or ATP to the suspension. IC 50 values were obtained from the dose-response curve using PRISM ® 5. 04 software. All assays were done in triplicate. In order to determine the type of inhibition exerted by melittin on the enzyme (competitive, non-competitive), enzymatic assays were performed as described in Section 4. 7. with some modifications. Briefly, a first series of assays is performed by incubating the isolated membrane-bound E.
Statistical Analysis Results were expressed as the mean ± standard deviation (SD). Statistical significance between different samples was analyzed using a two-tailed unpaired t-test. This analysis was carried out using GraphPad Prism 5. 04 (GraphPad Software, San Diego, CA, USA). Statistical significance was defined as p < 0. 05. The calculated CI was compared to the reference value (µ = 1) to evaluate if the difference is significant using the Student's t-test: t c = CI -µ s/ √ n (4) where CI, s and n correspond to the CI value, the standard deviation and the number of assays performed, respectively. t c was compared to the theoretical value(t th ) determined from the t-table for n -1 and α = 5%.
Subsequently, at the end of 2010, the MD was recognized as an Intangible Cultural Heritage of Humanity by the United Nations Educational, Scientific and Cultural Organization (UNESCO)  [31]. After this watershed and historical moment, the MDP continued to be developed as has been outlined in Figure  1. In 2011, an international FAO/CIHEAM workshop was organized to assess the sustainability of the MD model in the Mediterranean region, considering its four dimensions and impacts: health and nutrition, environment including biodiversity, economy, and socio-cultural factors. Initially, a comprehensive list of 74 potential indicators was compiled (FAO/CIHEAM, 2012); this was later reduced to 24 indicators, which was deemed more feasible considering the availability of data sources and the methodological approaches developed.
Root (two opposite lateral roots per seedling taken about 2. 5 cm down from the root collar were sampled) and stem tracheids were visualized by perfusing the decapitated samples with 0. 1% toluidine blue and imaged at 90-180x magnification using a digital camera mounted on a widefield zoom stereo microscope (ZM-4TW3-FOR-9M AmScope, USA). Tracheid diameter was measured along four 4 radials rows per sample using an image analysis software (Motic Images version 3. 2, Motic Corporation, China). In addition, the presence or absence of aerenchyma was assessed on 4 lateral roots per sample, which included the two used for tracheid size determination (no aerenchyma was present in the stems). Whole plant hydraulic resistance was calculated as in  Domec et al. (2016)  1/Kplant=1/Kroot+1/Kshoot (1) and resistances of the shoot components (1/Kstem and 1/Kleaf) were calculated from the difference between resistances before and after removal of each leaf.
Field hydraulic and canopy conductance The second, drier site (35°11'N, 76°11'W) located within the lower coastal plain, mixed forest province of North Carolina  (Domec et al. , 2009). This 100-ha mid-rotation (23-year-old) loblolly pine stand (US-NC2 in the Ameriflux database) was established in 1992 and has an understory comprised of other woody species such as sparse red maple and bald cypress trees. Artificial drainage lowers the height of the water table, improving site access and increases productivity, especially during winter months  (Domec et al. , 2015). At both sites, canopy conductance was derived from sapflow measurements and thus comprises the total water vapor transfer conductance from the 'average' stomata of the canopy. Sapflow was measured at breast height using thermal dissipation probes inserted in two floodadapted species (bald cypress and water tupelo) and two others not adapted to flooding (red maple and loblolly pine) (see  Domec et al.
Overall, this study highlights that the structural heterogeneity of unmanaged new forests should enhance their function as stepping stones for insect herbivore diversity in fragmented landscapes. Finally, two tree growth studies complete the special issue with a longer-term perspective upon the dynamics and performance of second-growth forests. Lamonica et al. (2020) develop a spatially explicit growth-neighborhood competition model to investigate how variation in individual tree growth and competition influence the dynamics of spontaneously established forest stands. Applying their model to a tree-ring dataset from several exhaustively sampled Pedunculate oak forest stands in SW France, they observe extensive variation in growth, both within and among stands. Inexistent relationships with the spatial arrangement of trees reflect the individualistic character of the investigated stands.
Moreover, large camelid stocks in Australia, in countries of new camel establishment (Western countries) and those related to the expansion of camel farming, notably in Africa, are not recorded in the international database. In addition, there is no distinction between dromedary and Bactrian data. The present large camelid population in the world is probably more than 40 million and could reach 60 million after 25 years from now if the current demographic trend is maintained.
Introduction The question in connection with the exact number of large camelid heads in the world is often repeatedly asked. In scientific literature, authors usually do not hesitate referring to the official website of FAOSTAT  (Faye and Bonnet 2012). But, in some cases, the number given is not updated, and the reliability of this number never challenged. Moreover, when the same authors give the national statistics on their respective country, the gap between FAO data and national information is not also addressed. In fact, within a country, statistical data also vary according to sources or institutions. Finally, a total confusion occurs, and it is quite difficult to have a clear picture of the current camel population in the world. In the last available FAOSTAT database (year 2018), among the 46 national entities declaring a camel population, only 50% are the official data provided by the national ministries, and the remainder is based on FAO's estimates.
For the most recent data available in 2018, the total number of camels recorded in the world was  35,525,270 (FAOSTAT 2020). In 1961, date of the first available data, 60% of the 38 national entities declared official data. The total camel population at that time was assessed to stand at 12,926,638 heads. The annual growth of world camel population was estimated at 3. 07% but with contrasting national results varying between -1. 95% (Kyrgyzstan, calculated right from independence in 1992) and + 45. 3%  (Oman, calculated from 1961). Globally, 35% of the national entities recorded a negative growth rate (Table  1 ). A mean growth of more than 10-15% cannot be maintained without import of live animals as the natural growth of a camel herd can be estimated as approximately from 5 to 10%  (Bonnet 1996; Adamou 2008 ).
Typology of demographic growth A data table (i, j) including the 38 national entities recorded in 1961 (i) and their annual camel population from 1961 to 2018 (j) was created from the FAO database. To compare growth regardless of the size of the camel population, a new data table of growth index was calculated by using the index 100 in 1961. The annual index was calculated as follows: Index = camel population at the year Y/camel population in 1961 × 100 In order to identify different types of growth between 1961-2018, an automatic cluster analysis (Ascending Hierarchical Classification -AHC) was used  (Everitt et al. 2001). Cluster analysis allowed the grouping of countries with comparable chronicles. Finally, five types of camel population growth were identified with the following patterns: Camel countries with a negative growth (n = 15).
Later, those numbers were considered as overestimated  (Lethbridge et al. 2016). A more realistic estimation of 400,000 wild Australian camels was proposed by scientists from Queensland University (Al-Jassim and Lisle 2016). If Australia has the most important camel population missing in the international FAO database, other countries with a significant number of camels are also missing. For example, Spain is the only European country having a native camel population. This population, though not indigenous, was mainly introduced in the Canary Islands since the fourteenth century  (Wilson and Gutierrez 2015). The present population is around 1200 heads (but according to people, different figures between 800 and 2000 were put forth). Strategically, the camel herd in the Canary Islands is important, as it is the only source of camels able to be imported in other European countries, the sanitary regulations being a barrier for introduction from other camel countries.
New implementations in Southern countries The establishment of commercial or tourism-oriented camel farms is not limited to Western countries. Many introductions have occurred for example in the African safari parks in the southern part of the continent, but also in commercial farms. If in Namibia a small camel herd (less than 100 animals) has been registered in the FAO database since 2013, their presence in this arid country has been attested since 1889, brought from the Canary Islands by German troops  (Wilson 2012 ). In the neighbouring country of Botswana, the Tsabong Camel Park (TCP) in the southern part close to the South African border has a larger camel herd of 370 heads used for tourism  (Seifu et al. 2019 ). In the other countries of the region (South Africa, Zimbabwe, Zambia, Lesotho, Swaziland, Malawi, Mozambique), dromedary camels were introduced by Europeans during the colonial period in the late nineteenth and early twentieth centuries for military and police activities or postal services.
But camel farming is also expanding in the countries formerly associated with the "Sudanese" climate of 500-800 mm annual rainfall. Camel breeding is currently observed in the north of Nigeria, Cameroon, Republique Central Africaine, Uganda and even Tanzania as already mentioned, although only Nigeria among these countries has a declared camel population in the FAO database. In Uganda, for example, where camel production is contributing to overall resilience to droughts  (Asiimwe et al. 2020) , the camel population in 2017 was as high as or more than 40,000 heads  (Wilson 2017).
Official data or estimation? Official data registered in the FAO database are provided by the various national statistics offices of the Ministries of Agriculture or Livestock in these countries. However, in many cases, those data are not supported by an accurate census of the camel stock. Generally, for estimation from a starting data point, a growth percentage is applied systematically which could be readjusted later if a census is achieved as mentioned above in the case of Chad. For example, in Mauritania, the same annual growth (3. 6%) was observed between 1962 and 1967 and later an annual growth of 1. 7% was applied every year from 2009 to 2013. The same growth of 3. 6% was applied regularly in Nigeria. Camels, unlike cattle, are not submitted to mandatory vaccination campaigns. Therefore, censuses are rare, and most of the countries cannot achieve them due to their incurred cost.
com/qataris-ship-home-thousands-camels-stranded-saudi-via-kuwait/). Sometimes, an important gap is observed between official data collected in FAO database and those provided by some national authorities. For example, in Saudi Arabia, Elzaki  Ali et al. (2018)  noted that, based   (2020) , the values are 490,672 heads and 9. 01% annual growth. Such information is repeated in the press where it is stated that "with 1. 4 million camels in country, Saudi Arabia ranks third in the Arab world, behind Somalia and Sudan" (https://gulfnews. com/ world/gulf/saudi/saudi-arabia-pushes-for-internationalforum-on-camels-1. 2007007). In Ethiopia also, the number of camel heads in the country is subject to contradictory statements. For example,  Mirkena et al. (2018) , citing an IGAD (Inter-Governmental Authority for Development) working paper, stated that the camel population in Ethiopia is estimated at 4.
8 million heads while according to FAOSTAT (2020), the camel stock is around 1. 2 million. The gap holding between official FAO data and national sources has been observed for a considerable portion of time. Fig. 16  Changes in camel stock in  USSR (1961 USSR ( -1991) )  and in the sum of new independent states  according to the FAOstat data Fig. 17  Changes in camel stock in  Morocco (1961 Morocco ( -2018) )  and in Sahara during Spanish colonization  (1961) (1962) (1963) (1964) (1965) (1966) (1967) (1968) (1969) (1970) (1971) (1972) (1973) (1974) (1975) (1976)  and after inclusion in Moroccan Kingdom as Saharan provinces  Citing data from the Central Statistics Authority (CSA) in 2007,  Kebede and Gelaye (2010)  stated that the camel population in Ethiopia was 1. 7 million heads while it was 615,000 heads in the FAO database.
Consistency and inconsistency after secession or independence The merger of data from different countries formerly gathered in one unique national entity is a check on their consistency. The example of Ethiopia is once again interesting. After Eritrea seceded in 1992, Ethiopia PDR (People's Democratic Republic) was divided into Ethiopia and Eritrea. However, the sum of camel stocks declared in Eritrea added to the Ethiopian stocks did not correspond to the previously recorded number of heads (Fig. 15 ). Subsequent to the collapse of the Soviet Union in 1991, new independant states (NIS) emerged. Among them, seven declared camel stock (mainly Bactrian) in FAO database. Those countries were Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan and Ukraine in addition to Russia.
Unlike in Ethiopia, the changes in camel stock appeared to be reasonably consistent (Fig. 16 ). The decline observed in the post-independence period is mainly linked to the impact of the privatization process of agricultural farms, especially in Kazakhstan  (Kerven et al. 2006). In 1976, Morocco included the former Spanish Sahara into its national entity. Named "Western Sahara" in the official FAO database, the former Spanish Sahara was declared as "Saharan provinces" by the Moroccan government. Regarding the camel population, a sharp decline was observed from 1977, just after the "green walk", but this sudden decrease was essentially observed in the former Morocco and was not compensated by the Saharan camel stock, which increased slowly at the same time (Fig. 17 ).
Dromedary vs Bactrian The FAOSTAT database did not allow distinguishing between the different species of large camelids. The data regarding camel herds include both dromedary and Bactrian. Some countries have only Bactrian camels, such as Mongolia, China and Russia, or Bactrian camels are significant proportions of their large camelids' population as shown in the case of the other Central Asian Republics. For example, in Kazakhstan, the number of Bactrian camels represents 85% of the total camel stock  (Imamura et al. 2017). Bactrian camels are also present in Iran  (Niasari-Naslaji et al. 2009) , Pakistan  (Isani and Baloch 2000) , India  (Makhdoomi et al. 2013) , Afghanistan  (Tapper 2011) , Turkey  (Dioli 2020)  and Ukraine  (Burger et al. 2019). Some Bactrian camel farms overlap in Western countries, such as Austria or Sweden (Fig.
Even in Western countries where all the livestock is identified, camels are not systematically registered. There is an urgent need to conduct an official count of the camel population worldwide and to distinguish between different camel species in FAO databases (FAOstat) in order to have a clear picture of their current status within the domestic herd. It is particularly important to accurately estimate the place of camel production in the world economy. There is enough evidence to suggest that this place should be strongly growing.
Isothermal titration calorimetry Isothermal titration calorimetry (ITC) experiments were carried out at 293 K and 298 K using a VP-ITC microcalorimeter (GE Healthcare). rBmJHBP was dialyzed against 100 mM sodium phosphate buffer, pH 7. 0, or 100 mM sodium acetate buffer, pH 4. 5. Solutions of JH III were prepared in the same buffer as used for dialysis. The titration sequence included a single 3 µL injection followed by 25 injections of 10 µL of ligand solution, with 210 s intervals between injections. Measurements were performed for 5 µM protein with 70 µM ligand, or 2. 5 µM protein with 35 µM ligand. The OriginLab software (GE Healthcare) was used to fit the raw data using a single bindingsite model. For the analysis the data points obtained after the first 3 µL injection were discarded.
Circular dichroism Near UV CD spectra were recorded between 250 and 320 nm in a JASCO J-815 spectropolarimeter at 20 °C, using a 1 cm path length quartz cell (Hellma). rBmJHBP (25 µM) was dissolved in McIlvaine buffer at pH 3. 5, 4. 5, 5. 5, 6. 5 or 7. 5. Data were averaged over 10 accumulated scans recorded at 1 nm intervals with a 50 nm/min scan speed. The spectra were corrected for buffer contributions and converted to molar ellipticity in deg•cm²•dmol -1.
SHADE TREE CHARACTERIZATION All shade trees within the sampled area were identified according to their species and family. Their characteristics (height (m), circumference (cm), leaf size) and their host status for ALS, brown-eye spot and anthracnose were recorded  (Boshier et al. , 2009; Cerdán et al. , 2012). For all species, we also specified their main usage, and classified them in one or more categories: wood, timber, fruit, shade, N-fixation, native and wild  (Pineda, 2006; Boshier et al. , 2009; Román et al. , 2012; Amores Contreras, 2015; Caceido, 2016). We combined the variables describing each shade tree by performing a multiple factor analysis (MFA) to cluster the shade-tree species within homogeneous groups based on the previously described variables. MFA was performed with R software using the MFA function from the FactoMineR package  (Pagès, 2013).
We took hemispherical pictures to characterize the shade percentage at four different times: November 2016, February 2017, June 2017 and September 2017. Hemispherical pictures were taken above all selected coffee plants with a Nikon Coolpix 4500 equipped with a fisheye converter (FC-E8 0. 21x). These pictures were analyzed using Gap Light Analyzer (GPA-V2) software to assess the shade percentage above each coffee tree  (Frazer et al. , 1999). The annual mean shade percentage was used for the analysis.
The PLS-PM method was specifically chosen for its flexibility to manipulate datasets with numerous variables, and its capacity to represent clearly complex interaction systems. PLS-PM is a blend of two models: a measurement model and a structural model (Fig. 1 ). The measurement model defines the relationships between observed variables and latent variables inside blocks, with each block being represented by a latent variable and built with observed variables (Fig. 1 ). The structural model investigates relationships between latent variables using a linear regression approach. We performed this network analysis with R software using the plspm function from the plspm package  (Sanchez et al. , 2013).
Adding the secondary loss, i. e. dead branches, in 2016 directly affected the number of available berry producing branches in 2017. In future studies, it would also be interesting to integrate coffee growth from previous years to take the biannual resource allocation of coffee plants into account. Another improvement would be to integrate temperature fluctuations and precipitation patterns, which have a marked impact on coffee tree growth and production  (Charbonnier et al. , 2017) , as well as on the degree of ALS incidence and severity  (Avelino et al. , 2007) , but that will require larger datasets.
ACKNOWLEDGEMENTS 21 This research was financially supported by the  STRADIV  project (grant n°  1504-003 ) funded by  Agropolis Fondation. Special thanks to  Marvin Rivera ,  Mariano Suazo  and  Adolfo Cajina , the three farmers who kindly welcomed and guided us in their coffee plantations over the 2year study period. We are grateful to  David Manlay  for English revision. We thank the editors and the anonymous reviewers for the comments we received that helped improving the manuscript.