dataesr/scientific-paragraphs-categorization huggingface dataset:
The heat flux data are computed from the ERA-interim meteorological analyses and details of the calculations are given in Kuttippurath et al. 25. The EEASC data are obtained via NASA auto-mailer system. The solar flux (SF) data are from ftp://ftp. ngdc. noaa. gov/STP/SOLARDATA/SOLARRADIO/FLUX/PentictonAdjusted/monthly/, the quasi-biennial oscillation (QBO) data are from http://www. geo. fu-berlin. de/met/ag/strat/produkte/qbo/, and the aerosol optical thickness data are from http://data. giss. nasa. gov/modelforce/strataer/, the AAO data are from http://www. ncdc. noaa. gov/teleconnections/, the total column ozone data of TOMS are from http://toms. gsfc. nasa. gov/ozone/ OMI data are from http://avdc. gsfc. nasa. gov/pub/data/satellite/Aura/OMI/V03/L2OVP/ OMTO3/, and MSR data are from http://www.
Acknowledgements The ozonesonde data are publicly available from NDACC (http://www. ndacc. org)and WOUDC (http://www. woudc. org). We are particularly grateful to the many researchers involved in the ozonesonde datasets used in this study. We also thank all the proxy, TOMS, OMI and MSR data providers, and making them available publicly on the respective websites. A part of PJN's contribution to this study was during her tenure at CESS and she thanks then CESS director N. P. Kurian  and  V. Nandakumar  for their support. A part of this study was performed at  LATMOS/CNRS Paris. Therefore, that affiliation is also given. JK acknowledges his gratitude to  Cathy Boonne  (ETHER/IPSL),  Alain Hauchcorne  ( LATMOS/CNRS ) and the director of  LATMOS/CNRS Philippe Kekhut  for their help and support during his tenure at  CNRS/LATMOS.
With many sophisticated methods available for estimating migration, ecologists face the difficult decision of choosing for their specific line of work. Here we test and compare several methods, performing sanity and robustness tests, applying to large-scale data and discussing the results and interpretation. Five methods were selected to compare for their ability to estimate migration from spatially implicit and semi-explicit simulations based on three large-scale field datasets from South America (Guyana, Suriname, French Guiana and Ecuador). Space was incorporated semi-explicitly by.
We show that migration should be considered more an approximation of the resemblance between communities and the summed regional species pool. Application of migration estimates to simulate field datasets did show reasonably good fits and indicated consistent differences between sets in comparison with earlier studies. We conclude that estimates of migration using these methods are more an approximation of the homogenization among local communities over time rather than a direct measurement of migration and hence have a direct relationship with beta diversity. As betadiversity is the result of many (non)-neutral processes, we have to admit that migration as estimated in a spatial explicit world encompasses not only direct migration but is an ecological aggregate of these processes. The parameter m of neutral models then appears more as an emerging property revealed by neutral theory instead of being an effective mechanistic parameter and spatially implicit models should be rejected as an approximation of forest dynamics.
Hence, the latter are more often used when estimating migration, even though field data comes from a spatially explicit reality. In this study, we therefore extend the comparison of estimation methods toward the practical ability of these methods to estimate migration from simulated datasets based on both spatially implicit and spatially semi-explicit models. We focus on five different parameter estimation methods: (1) a sampling formula by  Etienne (2005) , (2) the Inference method by  Jabot, Etienne, and Chave (2008) , (3) the G st statistic adopted from population genetics by  Munoz, Couteron, and Ramesh (2008) , (4) the two-stage sampling formula by  Etienne (2009b) , which is an extension on the two-stage estimation method by  Munoz, Couteron, Ramesh, and Etienne (2007) , and (5) a method by  Chisholm and Lichstein (2009)  based on plot geometry and absolute dispersal distances.
| Comparison of model parameter estimation Each parameter estimation method, as described above, was used to generate an estimation of migration for a number of situations using spatially implicit, (semi-)explicit simulated, and field datasets. Results were compared from the simulated datasets in terms of their ability to accurately describe migration as parameterized to construct the datasets. After using the simulated datasets, we turned to the actual field data, having multiple local communities assumed to be a sample from the larger metacommunity for which migration was also estimated using the same estimation methods. Etienne's (2005)  sampling formula and the Inference method of  Jabot et al. (2008)  were both tested using the TeTame freeware version 2. 1 http://chave. upstlse. fr/projects/tetame.
htm. Etienne's (2009b)  two-stage sampling method was tested using the PARI/GP environment ("PARI/GP version 2. 4. 3", 2008). Chisholm & Lichsteins's method was tested using  MATLAB (2004) , and the G st statistic was computed using the package untb  (Hankin, 2007)  in the R environment  (R Core Team, 2014). Other R packages used were Quantreg, Vegan, Labdsv, and FasianOptions  (Koenker, 2013; Oksanen et al. , 2013; Roberts, 2013; Wuertz et al. , 2013). All R scripts used are available upon request from main author.
We created three different metacommunities: two for the simulated spatially implicit datasets and one for the spatially semiexplicit dataset. Because of the observed difference between the Guianas and Ecuador in terms of diversity and composition  (ter Steege et al. , 2013, fig S10)  and the regions being separated by a large geographical distance, we created two different metacommunities for the spatially implicit simulations related to these two regions rather than one large metacommunity. They are hereafter referred to as MC-high and MC-low, respectively (metacommunity high and low diversity). ter  Steege et al. (2013)  estimated mean tree densities for all species per degree grid cell and by fitting the mean rank abundance curve to these figures to construct  191, 600, 511 individuals and 4, 582 species)  and  611, 001, 426 and 6, 834) , for details on both see the Supporting information.
For the simulated spatially explicit dataset, a separate metacommunity was constructed using the same methods based on the Reserva Ducke forest, with 5. 5 million trees and a Fisher's α of 272  (ter Steege et al. , in press) , hereafter referred to as MC_spatial. The logseries for each community was constructed starting from the left tail (the most dominant species). The fixed parameters alpha and x were first calculated from the number of individuals (N) and species (S), after which the maximum dominance according to Fisher's logseries for all species is calculated, which is then given the first rank. For each subsequent rank, the predicted number of species is then calculated until all species are given a rank and all individuals are distributed.
| Spatially implicitly simulated data For the spatially implicit datasets, we used the exact same sampling procedure as proposed by Hubbell in the original UNTB. Each time step, one individual dies, which is replaced by an individual having an ancestor either in the local community (with probability 1 -m) or from the metacommunity (with probability m). The identity of the recruit is then only dependent on its relative abundance in the respective community. Datasets based on GS and FG (67 and 63 plots) were sampled from the MC-low assuming they share the same metacommunity and the dataset based on EC (72 plots) from MC-high. Sampling of the local communities was repeated for a range of migration parameters (see Table  S1 ). For details on the number of time steps used see the Appendix S2. After the construction of the simulated datasets, migration was estimated using the above-mentioned estimation methods.
| Spatially semi-explicitly simulated data Spatially semi-explicit simulations were carried out by modeling a lattice of 20 × 20 plots, each with 500 individuals. We assume no spatial explicit arrangement of individuals within a plot. Taking a random sample from the metacommunity creates the forest at time t 0. Each time step (t +1 ) one individual from each plot to be replaced was chosen at random from the MC_spatial metacommunity, and this was repeated for 10,000 time steps. Recruitment was generated from either of three sources: (1) migration from adjacent plots (m. adj), (2) migration from the MC_spatial metacommu- nity (m. meta), or (3) local recruitment (1-(m. adj + m. meta) = 1 -m). According to studies on long-distance dispersal of seeds (LDD), the majority of seeds (>99%) often fall within ca.
| Species composition of field data Three different sets of field data from the Amazon Tree Diversity Network  (ter Steege et al. , 2013)  were used for analysis. Two sets belong to the Guiana Shield: Guyana/Suriname combined and French Guiana, and the third set contains data from forests in Ecuador. Hereafter, they are referred to as GS, FG, and EC, respectively. All three sets are completely independent and nonoverlapping  (Pos et al. , 2014). Datasets are composed of 63-72 one-hectare plots with all trees ≥10 cm DBH inventoried. Species names of all datasets were standardized with the W3 Tropicos database within each dataset, using TNRS  (Boyle et al. , 2013).
| Comparing parameter estimation methods: spatially implicit and explicit Sanity checks on each method showed that the Inference method and G st statistic were able to approximate the complete range of migration parameters based on each different field dataset accurately. Etienne's one-stage sampling method showed larger deviations. The two-stage sampling by Etienne was only used for the spatially implicit dataset based on EC due to extreme long computation time (see details in the Appendix S1) but also generated accurate estimations. Average difference between given and estimated migration was. 08,. 007,. 02, and. 004 for the G st statistic, Inference method, Etienne's one-stage sampling, and Etienne's two-stage sampling, respectively (see Table  1  for a summary and Table  S1  for details).
As both methods generate a single migration value, they were only able to estimate the joint migration probability. As example, in one of the simulated sets, the parameters were set such that 1% of replacements were drawn from the eight cells surrounding the cell in which an individual died and 20% of replacements are drawn from the metacommunity surrounding these adjacent cells (m. adj of. 01 and m. meta of. 20, dataset 3). Both the G st statistic and the Inference method estimated a migration probability of. 21, indicating that these probabilities are additive in the estimation, and it is still unknown whether migration is from close by or far away. Estimation of the joint migration probability was only accurate when migration from the metacommunity was higher than from the adjacent plots.
| Parameter estimation from field data The G st statistic, Inference, and Etienne's two-stage sampling formula were used to estimate migration from the three field datasets. Calculation of migration using the corrected plot geometry method was based on the following parameters: edge length of plot (w) 100 m for all three sets (as each plot is 1 ha) and mean absolute dispersal distances in the ranges 15-25 m for GS, 25-35 m for FG as it has more pioneer species in comparison with the first, and 40-50 m for EC as it is relatively comparable to the BCI plot in Panama having rich soils sustaining rapid dynamics (i. e. , fast growth). This yielded migration parameters of. 237 with a confidence interval (CI) of. 182-. 293,. 344 (CI. 293-. 396), and. 489 (CI. 444-. 533) for GS, FG, and EC, respectively (see also Table  3 ).
| Comparing parameter estimation from field and simulated datasets We implemented all migration parameters from the spatially implicit simulations in the spatially implicit model and compared the results of the relative abundance distribution (RAD), number of species and singletons (i. e. , species with one individual), and Fisher's alpha generated from the simulations to the actual field data  (Figures S5, S6 and Table 4). As example, for GS (having 1,042 morphospecies and 210 singletons), the simulated dataset based on the MC_low metacommunity using a migration of 0. 046 (G st statistic estimation from field data) yielded a total number of 826 species belonging T A B L E 1 Summary of Table  S1 , with the mean difference between given and estimated migration (Δm), using spatially implicit simulations.
Here, we show that although the estimation methods we compared were able to correctly estimate migration from models of which they were derived, they fail to do so for models in which there is a spatially explicit relationship. For the spatially implicit simulations, the Inference method  (Jabot et al. , 2008)  and G st statistic  (Munoz et al. , 2008)  yielded comparable results and were able to estimate migration very precisely (Table  1  and S1 ). The two-stage sampling method by Etienne was only used for the spatially implicit datasets based on EC due to long computation time, but showed comparable results. The only exception was the onestage estimation method by  Etienne (2005) , which in particular for higher probabilities of migration showed a larger deviation (see also Figure  1 ).
| Reinterpreting estimation of migration from field data We showed that estimates of migration from all three regions differed markedly (see Table  4 ). Although there were small differences between estimations when using different methods, relative differences between each dataset within one method were comparable. Guyana and Suriname showed the lowest migration probabilities, followed by French Guiana and finally Ecuador. Knowing that these estimates of migration are actually ecological aggregates, what differences in these forests can we attribute to these differences in migration probability? The relationship between community dynamics and alphadiversity was already shown for forests within Guyana (ter Steege  & Hammond, 2001). Ter  Steege et al. (2003)  furthermore showed that on average, Western Amazonian forests are 150 individuals/ha denser than Eastern Amazonian forests and also have a higher alphadiversity.
, in preparation). A different interesting question is regarding the influence of species richness and the ratio between species richness of the metacommunity and the local communities. Here, we focused on tropical forest systems as we have access to large-scale datasets to test these models. But asking similar questions across multiple scales of diversity would most likely yield even more questions on the importance of regional diversity and the size of the species pool, which may prove a significant challenge. Our main conclusion here is that spatially implicit models mimic the real world in a very good way simply because they make us of an aggregated ecological parameter, incorporating not only dispersal but everything determining the connection between a regional species pool and a local species pool.
, as described in more detail in ter Steege et al. (2013). The EC dataset has 72 plots of 1 ha, yielding 34,544 individuals and 2,021 morphospecies. The GS and FG datasets are composed of 67 and 63 one-hectare plots, respectively. In GS, 37,446 individual trees were distributed among 1,042 morphospecies, and FG had 35,075 individuals belonging to 1,204 morphospecies.
ACKNOWLEDGMENTS We would like to thank  Rampal Etienne  and  Franck Jabot  for taking the time to review the mathematical approaches used in this study. Furthermore,  Daniel Sabbatier  and  Jean-François Molino  are supported by an " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-0025 ).
Summary difference m. given versus m. est and range SD of estimations Dataset One-stage est. Inference method G st statistic two-stage (Etienne) Δm SD range Δm SD range Δm SD range Δm SD range Guyana/Suriname. 044. 032-. 06. 0075. 009-. 016. 0200. 043-. 382 - - French Guiana. 071. 033-. 060. 0078. 009-. 016. 0240. 044-. 325 - - Ecuador. 132. 022-. 061. 0070. 008-. 018. 0160. 043-. 418. 004. 017-. 046 1. 0 0. 8 Estimated m parameter 0. 4 0. 6 0. 2 One-stage (Etienne) Inference method 0. 0 Gst statistic Two-stage (Etienne) 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 Input migration parameter F I G U R E 1 LOESS regressions of the migration parameter used for input versus the estimated migration from the spatially implicit simulations. Results from each method indicated by color with broken lines indicating the 95.
Estimates of migration based on a semi-spatially explicit neutral model. Probability of migration was determined from adjacent plots (m. adj), the metacommunity (i. e. , all other plots except the local and adjacent plots; m. meta) or the local plot. Number of plots was 400 with a runtime of 1e 8 for all datasets Results from the spatially implicit model-based estimates of m using the three separate field datasets POS et al. | 4261 Dataset T A B L E 2 Spatial semi-explicit Simulation parameters and yielded variables Inference method G st statistic m2 SD m3 SD Guyana/Suriname. 075. 050 0. 046. 044 French Guiana. 22. 085 0. 11. 058 two-stage (Etienne) m4 SD. 084. 074. 170. 062 Cor. Plot Geometry m5 CI. 071. 055-. 088. 103. 088-. 119 T A B L E 3 Parameter estimation for the three field datasets.
For the corrected plot geometry method by Chisholm and Lichstein (2009), the following parameters were used: Guyana/Suriname w = 100, d = 15-25 m, French Guiana, w = 100 m, Estimated migration d = 25-35 m, Ecuador, w = 100 m and Ecuador. 26. 153 0. 17. 152. 246. 114. 147. 133-. 160 Inference method d = 40-50 m G st statistic dataset Nr. sp. Nr. sing m. local m. adj m. meta m2 SD m3 SD 1 1,777 244 0 0 1. 00. 990. 028 1. 011. 0057 2 0. 5 1,088 37. 79. 20. 01. 140. 015 0. 156. 0012 Guyana/Suriname 3 1,529 142. 79. 01. 20 French Guiana. 209. 021 0. 210. 0015 Ecuador 4 0. 4 1,542 147. 75. 05. 20. 244. 024 0. 247. 0017 5 6 7 8 9 10 Estimated migration 0. 2 0. 3 1,282 1,093 1,609 1,277 1,077 1,666 73 48 169 74 50 182. 75. 69. 69. 65. 59. 59. 20. 30. 01. 30. 40. 01. 05. 01. 30. 05. 01. 40. 200. 197.
310. 260. 254. 416. 019. 019. 027. 023. 021. 034 F I G U R E 3 Estimated migration 0. 205 0. 215 0. 312 0. 270 0. 277 0. 419 probability from each empirical dataset (GS,. 0014. 0013. 0020. 0017. 0016. 0024 FG, and EC) for the Inference method, G st statistic, Two-stage Sampling estimation and the Corrected Plot Geometry method. 11 0. 1 1,315 97. 55. 40. 05. 325. 027 For the first three methods, whiskers 0. 341. 0019 12 1,056 36. 49. 50. 01. 310. 028 indicate standard deviation of the 0. 330. 0019 13 14 0. 0 1,690 1,301 Inference Method 186 96. 49. 45 Gst Statistic. 01. 50 T wo-stage Sampling. 50. 05 Core Plot Geometry. 512. 380. 040 estimation. For the corrected plot geometry 0. 517. 0028. 032 0. 400 confidence interval for the estimation. 0023 method, they are representative of the 15 1,706 187.
39. 01. 60. 615. 046 0. 621. 0034 16 1,727 220. 29. 01. 70. 716. 050 0. 721. 0039 17 T A B L E 4 Dataset 1,748 224 Method. 19 Migration. 01 Metacommunity. 80 Plots. 819 Species. 050 0. 822 Singletons. 0042 Fisher's alpha Guyana/Suriname - - - 67 1,042 210 198 0. 4 0. 6 0. 8 1. 0 Guyana/Suriname Joint estimated migration probability Inference method Inference method Gst statistic Guyana/Suriname G st statistic Guyana/Suriname Two-stage Etienne Guyana/Suriname Cor. Plot Geometry French Guiana -French Guiana Inference method French Guiana G st statistic French Guiana Two-stage Etienne French Guiana Cor. Plot Geometry Ecuador -Ecuador Inference method Ecuador G st statistic. 075. 046. 084. 071 -. 220. 110. 170. 103 -. 260. 170 MC-low MC-low MC-low MC-low -MC-low MC-low MC-low MC-low -MC-high MC-high Joint estimated migration probability 1.
In 2015, an emergency state was declared in Bolivia when Poopó Lake dried up. Climate variability and the increasing need for water are potential factors responsible for this situation. Because field data are missing over the region, no statements are possible about the influence of mentioned factors. This study is a preliminary step toward the understanding of Poopó Lake drought using remote sensing data. First, atmospheric corrections for Landsat (FLAASH and L8SR), seven satellite derived indexes for extracting water bodies, MOD16 evapotranspiration, PERSIANN-CDR and MSWEP rainfall products potentiality were assessed. Then, the fluctuations of Poopó Lake extent over the last 26 years are presented for the first time jointly, with the mean regional annual rainfall. Three main droughts are highlighted between 1990 and 2015: two are associated with negative annual rainfall anomalies in 1994 and 1995 and one associated with positive annual rainfall anomaly in 2015.
Atmospheric correction is arguably the most important step in pre-processing Landsat data to monitor change detection when considering multitemporal time series images  [9] [10] [11]  but not when working on single date  [11]. Today, the United States Geological Survey (USGS) service provides an atmospherically corrected version of each LandSat scene: the Landsat Surface Reflectance (LSR) product. Here, we used field spectral measurement to quickly assess LSR estimates for the Landsat-8 OLI products for the first time over the Altiplano. The Surface Reflectances (SRs) obtained after applying the fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) atmospheric correction were also considered for comparisons. The method used to build a time series of the fluctuations of lake extent is based on detecting changes in the pixel's state from one Landsat scene to another.
Finally, based on the most efficient Atmospheric model correction and SDI, a 26-year temporal series from 1990 to the 2015 disappearance of the lake is presented for the first time. After validation, the MOD16 evapotranspiration (ET) products, the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Multi-Source Weighted-Ensemble Precipitation (MSWEP) reanalysis rainfall products are confronted to the Poopó Lake extent fluctuations. Rainfall and ET general trends are used to understand the potential influence of the climatic variability and increasing irrigated quinoa culture over the watershed during the last decades. This study provides a preliminary analysis of the hydrological behavior of Poopó Lake over the last decades and could be used as a guideline by national authority to consider more sustainable use of water resources over the region.
Field Spectral Measurements Spectral radiometric measurements were acquired from the field, using TriOS RAMSES Sensors on 6 September 2014. A total of 69 points from 5 transects located on the north, east, south and west lakeshores and in the shallow part of the lake were analyzed (Figure  1b ). At each point, the lake depth and SR in the spectral domain between 450 and 950 nm were registered. These data were acquired.
Field Spectral Measurements Spectral radiometric measurements were acquired from the field, using TriOS RAMSES Sensors on 6 September 2014. A total of 69 points from 5 transects located on the north, east, south and west lakeshores and in the shallow part of the lake were analyzed (Figure  1b ). At each point, the lake depth and SR in the spectral domain between 450 and 950 nm were registered. These data were acquired using the methodology established by  [21]. Hereafter, we refer to Ground Control Points (GCPs) when referring to the in situ measurements. Spectral radiometric and Landsat measurement differ in term of spatial resolution (point vs. Pixel). This is an important feature to be considered to avoid inconsistent comparison. To account for this, spectral radiometric measurements were done on homogeneous area in term of water depth and water-soil representation to avoid mixed pixel.