dataesr/scientific-paragraphs-categorization huggingface dataset:
The maps of the fitted scores of the significant canonical axes in the PCNM analysis for Epigeic: live and feed on the soil surface; Endogeic: live and feed within the soil; Anecic: live within the soil and dig vertical or semi-vertical burrows to feed on the soil surface (after  [45, 46] ). Endo-anecic worms have characteristics of anecic (anterodorsal pigmentation, flattened rear end) and endogeic worms (horizontal burrowing). 2 Average biometric data for adults (g. f. w. indicates grams of fresh weight in 4% formalin, gut contents included). species (A), species assemblages and the whole community (B) are depicted in Additional file 2. The significant explanatory environmental variables that best described the multi-spatial structure varied for the earthworm community, species and species assemblages (Table  5 ).
Multivariate ordination analysis (CA) Species abundance (raw data) was analyzed using correspondence analysis (CA). When the species abundance was <5% of the total, it was removed from the species matrix. The extracted factorial axes allowed us to identify various species assemblages, i. e. these were defined according to the sum of the individuals of all species linked to positive or negative row scores of the three axes. Species assemblage patches and gaps SADIE (Spatial Analysis Distance IndicEs) analysis  [64, 65]  was used to assess the presence of significant patches and gaps within species assemblages. The index uses count data, i. e. , the total number of individuals who corresponded to any of the assemblages identified per sampling point. A global index of aggregation (I a ) is computed: I a ¼ D=Ea; where D is the distance moved to achieve the regular pattern for the observed data and Ea is the arithmetic mean distance to regularity for non-regular randomized samples  [64].
I a equals 1 for a random distribution, whereas it is >1 or <1 for either a clumped (aggregated) or regular spatial pattern, respectively  [65]. SADIE identifies clusters of high (patches) and low (gaps) mean density, respectively, and these clusters are categorized as v i (positive) and v j (negative cluster index). A patch or gap comprises at least one sample location where the cluster index (v i or v j ) is significant at the heuristic threshold of 1. 5 and -1. 5, respectively. Adjacent sample locations with significant index values form a single cluster  [65]. The observed v i or v j indices are tested using random permutations against the H 0 of complete spatial independence of counts  [66]. In this study, we used positive and negative row scores extracted from the CA to obtain count data and compute the SADIE v i and v j cluster indices.
Data were allocated to 11 distance classes with a minimum of 50 pairs of points for each distance class to compute the cross-correlogram. The significance of the correlogram is tested with a Monte Carlo simulation  [20] ; it is significant when at least one coefficient is lower than the Bonferroni corrected p′ of α′ = α/k for the k distance classes used  [76]. Data normality was tested with a Kolmogorov-Smirnov test; when the normality assumption was not confirmed, a Box-Cox transformation was used  [77]. The gstat and ncf packages of the R program 2. 15. 1  [78]  were used to compute the variograms and cross-correlograms and to depict the kriged maps.
) Figure  5  Sampling protocol used with a regular grid of 10 x 10 sampling points and a 5 m inter-sample distance. A total of 400 soil samples were collected for the various soil analyses and 100 soil monoliths for earthworm species counts. The location of tree species (>5 m diameter at breast height, DBH) within the surveyed 0. 2 ha plot are shown together with the soil pit where the earthworms were sampled, identified and counted, as well as the four soil cores taken for physical and chemical determinations from each of the 100 sampling points. which the relationship was significant. The lowest value of the Akaike information criterion (AIC) identifies the best spatial model. Partitioning the variation of a species response data table among two or more explanatory tables is performed using multivariate variation partitioning  [85] , which determines how much of the species variation is spatially structured and associated with the measured environmental variables  [57, 80].
The variation partitioning analysis is based on the adjusted R 2 statistic R a 2  [86] , and patterns on finer scales identified by the PCNM variables appear smoother compared to other spatially explicit models, such as nested variograms and filter kriging  [79]. The data matrix included count data for 6 earthworm species, 23 soil environmental variables (Additional files 3 and 4) and xy coordinates for 100 sampling points. It has been recommended that fauna data should be detrended and transformed during PCNM analysis. Contrary to correspondence analysis, earthworm abundance data were Hellinger transformed because PCNM has been found to be inappropriate for raw data that includes many null abundances  [87]. Earthworm spatial distribution is represented by patches, and clearly meaningful trends are rarely observed; consequently, the data were not detrended.
Acknowledgements This study was financed by the  DIVERSITAS program  and  CIAT (Soils Unit). We are particularly grateful for the useful comments provided by  R. J. Thomas  and  I. Rao  and for the continuous support of soil ecology studies by  E. Amézquita. We also gratefully acknowledge the help provided by field and lab research assistants at CIAT. We thank  Dr. J. Perry  at  Rothamsted  for kindly providing SADIE software and the  ARAID  foundation for support to the first author. We acknowledge support of the publication fee by the  CSIC Open Access Publication Support Initiative  through its  Unit of Information Resources for Research (URICI). Finally, we are grateful for the insightful comments and valuable suggestions provided by two reviewers, which improved an early version of the manuscript.
Several foraging metrics (e. g. dive duration, dive depth, descent/ascent rate, bottom time, post dive surface interval) can be calculated from TDR data and are used to classify dives into functional categories  [7, [16] [17] [18] [19] [20] , but typically they are not systematically associated with direct information on food intake  [21]. However, the greater complexity of dives revealed through both high resolution time-depth datasets and three-dimensional diving studies suggest that this method could lead to an over-simplification of diving behaviour  [22, 23]. When a seal is spending some time at a particular depth and travelling up and down while at this depth (''wiggles''), it is displaying vertical ARS, and this has been used as an index of foraging activity (not necessarily including prey capture), with several studies providing independent evidence for this in the form of changes in stomach or oesophageal temperature  [7, 21, [24] [25] [26].
Weddell seals are the second deepest phocid diver in the Southern Ocean after the southern elephant seal, attaining 900 m  [39]. They are opportunistic predators feeding mainly on fish, but also on cephalopods and crustaceans, in proportions that vary according to age, location and season  [40]. Weddell seal diving and foraging behaviour has been extensively studied during summer in the Ross Sea and the Weddell Sea  [29, 41]. However, because Weddell seals are opportunistic predators it is difficult to associate only one type of foraging dive to their overall behaviour  [42, 43]. We used high resolution TDR datasets from two SES that travelled to Antarctica during their post-breeding foraging trip and a high resolution TDR dataset covering six winter months from a Weddell seal to develop a new method for identifying the phases within a dive where the seals exhibited foraging behaviour.
Materials and Methods Fieldwork and data collection were undertaken with approval from the University of Tasmania animal ethics committee (permit A8523), and from IPEV (Institut polaire francais Paul Emile Victor) and TAAF (Terres Australes et Antarctiques Francaises) animal ethics committee. Two adult female SES (length: 266 and 255 cm) were captured at Kerguelen Island (49u209 S, 70u209 E) in early November before their post breeding trip. One adult female Weddell seal was captured in February 2008 after its annual moult at Dumont d'Urville (66u409 S, 140u00 E) (length 230 cm). Similar capture and tagging procedures were adopted for both species. The seals were approached by foot and temporarily restrained with a head bag and an intravenous injection of Zoletil (1:1 mixture of tiletamine and zolazepam, 0.
B: Original dive profile (B, black line) summarized by the optimal number of broken stick points (B, black data points) as estimated by mean distance represented above (A). The blue lines represent transit segments BS and the red lines represent hunting segments BS. The green dashed line represents the depth below which bottom time is calculated with the classical dive analysis method. A Weddell seal dive was used as an example for this graph. doi:10. 1371/journal. pone. 0099329. g002 respectively, using a two component industrial epoxy (Araldite AW 2101). Seals were observed during recovery from anaesthesia and allowed to enter the water when no longer sedated. The TDRs recorded time and pressure at 1 Hz. Acceleration was recorded in the 3 axis at 16 Hz.
e ascent, bottom and descent) dive analysis (CA) we used a method based on a broken stick algorithm (BS). This method selects the data points where the dive trajectory between two points changes the most rapidly (inflexion points). Any number of points can be chosen depending on the resolution required  [49]. We started with three points: (i) surface start point, (ii) maximal depth and (iii) surface end point (Fig. 1 A ). We then iteratively selected the data points of maximum difference between the original dive profile and the dive profile reconstructed by linear interpolation between the points selected during the previous iterations (Fig. 1 , Script S1 and Dataset S1). We then estimated the optimal number of broken stick points (from 6 to 33) that best summarize the dive shape. For this, we calculated a mean distance based on the average of the differences between each data point and its corresponding position on the line between the broken stick points (Fig.
1 , averaged depth differences between the black curve and the red lines). The mean distance was calculated for each dive summarised with 6 to 33 broken stick points (Fig. 2  A, Script S1 and Dataset S1). For each dive we plotted the mean distance for a range of broken stick points and we determined the inflexion point of this curve (i. e the point after which the amount of new information explained by increasing the number of segments BS began to decline). To do this in an automated way, each integrated distance curve was smoothed by fitting to a Gompertz model  [50]. The inflexion point of this curve was then determined by calculating the maximum distance between the Gompertz curve and the linear approximation between its start and end points (Fig. 2  A, Script S1 and Dataset S1). The number of corresponding broken stick points was then used to optimally describe each dive (Fig.
2 , Script S1 and Dataset S1). There was no trend in the relationship between the mean distance and the number of broken stick points per dive (mean 6 SD, 560. 02 m, min: 0. 3 m, max: 18 m and 1. 260. 8 m, min: 0. 15 m, max: 7. 8 m, for the SES and the Weddell seal respectively) (Fig. 3 ), showing that there is no bias associated with dive complexity. 3 Detection of intensive foraging within dives. Based on the definition of Area Restricted Search (ARS) in the horizontal dimension when animals are at the surface  [5] , we expected diving predators such as the SES and the Weddell seal to adjust their diving behaviour in order to increase the time spent in a patch of prey, by decreasing their vertical velocity and increasing the vertical sinuosity of their trajectory. Therefore for each segment between two broken stick points (hereafter segment BS ) we calculated, (i) the vertical descent/ascent rate (in m/s) and (ii) the vertical sinuosity (Script S1 and Dataset S1) adapted from  [7]  as:.
0. 9) from directed travel mode BS (0. 9# sinuosity $1) within each dive. Hunting mode BS was characterized by a more sinuous path, possibly indicating intra-patch movements, whereas directed travel mode BS showed a straighter path probably occurring during interpatch movements or when transiting from surface to/from depth. Successive broken stick segments of the same behavioural mode BS were then grouped in hunting or transiting phases BS allowing us to quantify the phases BS within each dive (Fig. 2 B ). For each dive, we characterized each phase BS using the behavioural mode BS (i. e. hunting vs transit), the number of broken stick segments making up each phase BS , its duration, its mean depth and its mean ascent/ descent rate (Script S1 and Dataset S1). For the SES data set, we also counted the number of prey capture attempts that occurred in each behavioural phase BS.
They were calculated from the concurrent high resolution acceleration data  [51, 52]. Briefly, acceleration data were used to identify rapid head movements that may be associated with prey encounter events and these are visible as spikes in the filtered acceleration profiles  [25]. Acceleration profiles with more than one spike above a given threshold (in m/s 2 ) visible both in the surge and heave axes were considered to be related to prey encounter events. A full description of the acceleration data filtration process and definition of the threshold for the spike occurrence are given in  [25]  and  [52]. 4 Comparison of the two behavioural modes BS. In the Weddell seal data set a number of segments BS showed very high vertical ascent/descent rates, which may result from depth measurement errors by the sensor.
Davis et al. [42]  used a velocity sensor recording swimming speed and observed mean maximum speeds up to 5. 161 m/s depending on the type of dive and location. We therefore removed dives containing segments BS with ascent/descent rates. 7 m/s (23 dives in the Weddell seal dataset). In SES the maximum ascent/descent rates of the broken stick segments was 3. 5 m/s, therefore all the SES dives were retained. We compared the number of prey capture attempts (when available), duration, depth, and ascent and descent rates between the two behavioural modes BS estimated with our method (i. e. hunting vs transit) using unilateral Welch tests on two datasets of 10% of the dives randomly selected for each behaviour. We also compared the time spent in hunting mode BS with the bottom time CA identified in the classical method, using unilateral Welch tests on two datasets of 10% of total dives randomly selected  [53]  [50].
Foraging behaviour 1 Comparison between the broken stick analysis and prey capture attempts in SES. Dives included an average of 1260. 02 (max: 16, SES 1), 1260. 02 (max: 17, SES 2) and 1262 (max: 17, Weddell seal) broken stick segments using the broken stick algorithm. However, the fit of the Gompertz model included in the method did not work for 6% of the SES dives and 4% of the Weddell seal dives which were removed from the dataset. For these dives, the relationship between the mean distance and the number of broken stick points was more linear (Fig. S1 ). Consequently, the model could not detect an inflexion point, which is necessary for determining the optimal number of Table  1. General information on tag transmission and diving behaviour. broken stick points needed to summarize the dive (Fig.
S1 ). In these cases, the number of broken stick points can be determined subjectively by the user (e. g could be determined to suit the averaged mean distance for all dives). SES dives were rarely associated with more than 40 prey capture attempts, therefore these dives with. 40 prey capture attempts were also removed from the dataset (0. 1% of the SES dives). Of the remaining SES dives, there were 1369 dives that were not associated with prey capture attempts (17% of the SES dives) but during which the SES spent 8613 min in hunting mode BS. These dives were, on average, 39366 m deep, 2062 min long and characterized by 560. 05 behavioural phases BS. The remaining dives (6814) were associated with an average of 1160. 1 prey capture attempts and on average 960. 05 min were spent in hunting mode BS.
Discussion In natural systems, predators perceive and react to environmental heterogeneity. These reactions are detected through changes in movement characteristics of animals (e. g. direction, speed, sinuosity)  [5, 54] , that are likely to reflect changes in the presence, or availability, of prey. We present a new method to quantify the within-dive complexity of diving predators, and demonstrate it using high resolution TDR datasets from two SES and a Weddell seal. We assessed within-dive behavioural phases BS (e. g. hunting vs transit) using concepts derived from ARS analyses developed for horizontal track analysis. Our results show: (i) the seals alternated between hunting and transit modes BS at the scale of a dive; (ii) the dives were mainly characterized by numerous behavioural phases BS instead of the three previously described phases CA (descent, bottom and ascent), of which only one (the bottom) was deemed to be involved in foraging; (iii) 77% of total SES actual prey capture attempts occurred in our identified hunting mode BS and intra-dive hunting phases BS were associated on average with four times more prey capture attempts (SES) than transit phases BS ; (iv) hunting mode BS was adopted two or three times in a dive and was shorter (SES) or longer (Weddell) than that classically estimated from bottom time CA.
doi:10. 1371/journal. pone. 0099329. g006 more predictable in these areas. Here, we identified ARS in the vertical dimension in order to identify those parts of the dive during which the seal increased its foraging activity. One limit of our study could be that it was based on data from three individuals, though this is compensated to some extent by the very large number of high resolution dives included in the analysis. Nonetheless, two behavioural modes BS were clearly identified in the vertical dimension according to the sinuosity of the dive segments BS identified with the broken stick method. In our study, 77% of the SES prey capture attempts measured independently occurred during hunting phases BS. Acceleration data cannot discriminate between successful prey capture attempts and unsuccessful ones, thus it doesn't give a true estimation of feeding success.
The seal then probably optimizes the time spent in that area by: (i) making ''wiggles''; (ii) decreasing its vertical speed and; (iii) horizontally meander at that depth, which cannot be detected with our dataset but which has been previously observed in 3D movements analysis studies  [42, 43, 58]. Thus, intensive foraging depths likely correspond to the depths where prey patches are located. Vertical sinuosity (or wiggles) is often used as an index of foraging effort and/or feeding success even when no independent information on prey capture is available  [16, 20, 56, 59 ]. In our study, non-foraging SES dives were also characterized by some hunting phases BS , but they only represented a minority of the dives performed. It is possible that in non-foraging dives SES captured their prey by suction which wouldn't be detected in acceleration data  [51].
Acknowledgments We are grateful to  V. Andrews-Goff ,  S. Blanc ,  A. Jacquet-Chaboisson ,  E. Antoine  and all volunteers from the 56th, 57th and 58th scientific missions at  Dumont d'Urville  who assisted in the field. We thank the  Institut Polaire Francais Paul Emile Victor (IPEV  programs  109  and  394 ),  Terres Australes et Antarctiques Francaises (TAAF)  and the  Australian Antarctic Division  for providing logistical support. We wish to thank  F. Roquet  for his help on the computation of the broken stick algorithm,  A. C. Dragon ,  Y. Ropert-Coudert ,  C. -A. Bost , and  J. Vaquie-Garcia  for their advices and support.
Hence, in the IoT, data become volatile since they are useful only when they are produced and processed, while requests become persistent since they are permanently executed. The continuous processing of sensed data has been extensively studied in the context of Wireless Sensor and Actuator Networks (WSAN) given the focus on the acquisition of data from the physical world. This has resulted in the introduction of dedicated Data Stream Management Systems (DSMS), which are, in the case of WSANs, tools to manage and process streams across a sensor network  [5]. Historically, DSMSs were part of relational database research, as extension of Data Base Management Systems (DBMS), establishing a theoretical background for data stream management. In contrast to DBMS, WSAN research focuses on very low-power http://www.
Typically, this class of devices can accommodate Web technologies provided the technologies are adequately revisited, knowing that only a subset of these technologies are useful for implementing Web services in order to achieve the Web of Things (WoT) vision. Hence, we argue that WSAN-and Web-based techniques need to be integrated within a fully-distributed streaming middleware that is able to run directly onto every type of average and smart Things. As a benefit, the IoT will be more interoperable, each Thing will be more autonomous and the need of proxies will be mitigated. Toward this end, this paper introduces a customizable distributed DSMS middleware, called Dioptase, whose contributions are as follows: • Dioptase adds flexibility to state of the art DSMS solutions for resource-constrained devices, by introducing a high-level application model that can map any IoT/WoT application onto the entities of the network (sensors, actuators, users, services, databases, etc.
g. Sun SPOT), smartphones, personal computers, servers and the cloud. • From a technical perspective, the flexibility of Dioptase is based on a lightweight domain-specific language (DSL) designed to express continuous processing tasks. The DSL syntax is specifically optimized to be interpreted on the huge number of average Things that are more powerful than small sensors but very limited compared to smart Things. This mechanism enables the dynamic deployment of tasks in isolated sandboxes which are naturally safer than arbitrary binary-code deployment  [16, 17]. As a benefit, developers can build applications composed of tasks deployed in the network at any time, using http://www. jisajournal. com/content/5/1/13 standard Web services. To achieve this, Dioptase features relevant optimizations of Web technologies (small Web server, subset of protocols, compression, etc.
g. , selection or union). The sensor network is then managed as a large database that can be queried using a SQL-like language, with some specific operations. The database may further be distributed (each node runs a part of the query), centralized (a powerful node collects all the data and applies queries) or partially centralized (with many powerful nodes)  [27]. From a practical perspective, queries are translated into query plans that are distributed in the network. State of the art DSMSs primarily differ with respect to: the expressiveness of the query language, the associated algebra, and assumptions made about the underlying networking architecture. A well-known DSMS is TinyDB  [28] , which exposes the sensed data as a relation (i. e. , table) on which it is possible to apply queries over the sensed values as well as the metadata associated with the sensors.