dataesr/scientific-paragraphs-categorization huggingface dataset:
We included the Angola dataset as an example in the package (meta_angola object), with abundances built from biomasses in 1986 and 2003. We also represented the difference network between the two dates using the diff_plot function with the 'TL-tsne' layout (β = 0. 05) in Fig. 4. We also computed a profile of extended Moran index along beta values to select optimal β (see Fig. S2 , Fig. S3 ).
Norway soil trophic network Norway soil trophic network dataset was extracted from  Calderón-Sanou et al. (2021). It consists in a soil expert-knowledge metaweb and environmental DNA data sampled in the Varanger region in Northeastern Norway. The metaweb has 40 groups and 204 interactions with several available aggregation levels (trophic group, trophic class and kingdom). The groups have relative abundances given by their mean abundances in environmental DNA samples. The Fig. 5  shows the metaweb at the group level using the 'TL-tsne' layout with the diffusion parameter β = 0. 006 (Fig. 5 ). The metaweb has two basal resources: plant and organic material. They have the lowest x-axis values in the 'TL-tsne' layout. The channel starting from plants corresponds to the green energy channel while the channel starting from organic materials is the brown channel  (Polis & Strong 1996 , Moore et al.
Discussion We have presented metanetwork, a R package dedicated to handling and representing trophic metanetworks. These metanetworks are built from a metaweb, an abundance    S1  for credits. for trophic networks was sorely lacking. We developed the 'TL-tsne' network layout algorithm, which constitutes the main methodological development of the present paper. This new layout combines the computation of trophic levels, using the Laplacian matrix on the x-axis, with a non-linear dimension reduction of the graph diffusion kernel on the y-axis. Besides representing two different features, it allows reading the network along fixed axes. Our diffusion kernel method not only relies on edges, which corresponds to paths of length 1, but also on paths of arbitrary long length. As a result, our layout is less sensitive to the deletion of an edge or, more generally, to the mistakes in edge specification compared to force-based layouts that are very sensitive, as pointed out in  Pocock et al.
Data Availability This paper uses a simulated data set available as a vignette of the package documentation available online (https://marcohlmann. github. io/metanetwork/articles/pyramid. html). It also uses three datasets that are already available in the package: • Angola coastal network: dataset is extracted from Web of Life (https://www. web-of-life. es/map. php?type=7), is attached to metanetwork and analysed in a vignette (https: //marcohlmann. github. io/metanetwork/articles/angola. html) • Norway soil network: this dataset from is attached to metanetwork and analysed in a vignette (https://marcohlmann. github. io/metanetwork/articles/norway. html) • European vertebrate metaweb: this dataset from is attached to metanetwork and analysed in a vignette https://marcohlmann. github. io/metanetwork/articles/vertebrates.
1. Automatic monitoring of wildlife is becoming a critical tool in the field of ecology. In particular, Radio-Frequency IDentification (RFID) is now a widespread technology to assess the phenology, breeding, and survival of many species. While RFID produces massive datasets, no established fast and accurate methods are yet available for this type of data processing. Deep learning approaches have been used to overcome similar problems in other scientific fields and hence might hold the potential to overcome these analytical challenges and unlock the full potential of RFID studies. 2. We present a deep learning workflow, coined "RFIDeep", to derive ecological features, such as breeding status and outcome, from RFID mark-recapture data. To demonstrate the performance of RFIDeep with complex datasets, we used a long-term automatic monitoring of a long-lived seabird that breeds in densely packed colonies, hence with many daily entries and exits.
3%. 4. RFIDeep has unfolded the potential of artificial intelligence for tracking changes in animal populations, multiplying the benefit of automated mark-recapture monitoring of undisturbed wildlife populations. RFIDeep is an open source code to facilitate the use, adaptation, or enhancement of RFID data in a wide variety of species. In addition to a tremendous time saving for analyzing these large datasets, our study shows the capacities of CNN models to autonomously detect ecologically meaningful patterns in data through visualisation techniques, which are seldom used in ecology.
RFID technology is also exposed to two major constraints because of the impossibility to detect multiple tags at the same time with a single antenna and the impossibility to install several antennas at the same place, due to electromagnetic interference. By increasing probability of missing detections, the tag and reader collision problems create a trade-off between the number of deployed tags (the size of the dataset) and the probability of undetected individuals (the completeness of the dataset). This leads to challenges in inferring missing detections to correct the locations and movement patterns of individuals. Like in other automated data processes, such data imperfections need to be considered and if possible repaired with suitable algorithms. Here, we demonstrate that non-explicit detection data from fixed observation points contain enough information to infer individual behaviour.
Taking advantage of the recent developments in deep learning methods, we develop the "RFIDeep" workflow to automatically extract breeding status and outcome from detection data acquired by RFID antennas using convolutional neural networks. We illustrate how deep learning methods detect biological features in RFID data with very high classification accuracy, and demonstrate the use of a visualisation method not yet commonly implemented in ecology. RFIDeep was initially developed for an "archetypal" real-life dataset with a ca. 25 years-long RFID detection time series collected on known-age/history king penguins (Aptenodytes patagonicus)  (Gendner et al. , 2005). Unlike flipper bands used until then, which are detrimental to the individuals  (Gauthier-Clerc et al. , 2004; Dugger et al. , 2006) , the recording of every transit between the colony and the sea of RFID-tagged birds, throughout their life, allowed a more accurate and unbiased description of the reproductive patterns of the species  (Descamps et al.
, 2002) , and of the population's demographic parameters  (Le Bohec et al. , 2008). In these previous studies, all RFID detections were manually analysed by human experts and none of them used the entire dataset of RFID-tagged penguins. Since breeding king penguins exhibit highly stereotyped movement patterns  (Descamps et al. , 2002) , they were good candidates for artificial intelligence classification. Based on direct field observations and molecular sexing data, we trained several CNN to infer RFID-tagged penguins' sex, breeding status and outcome (Breeding vs. Non-Breeding; Success vs. Failure), and breeding dates. We developed RFID-specific data augmentation steps to account for biological variance and data acquisition imperfections. We trained our classification process with field observation data and tested it manually with annotated data to compare the performance of automatic classification with the human experts.
RFID data acquisition Here, we used data collected from the colony of king penguins (Aptenodytes patagonicus) named 'La Grande Manchotière' and located at Possession Island, Crozet Archipelago (46°25S, 51°45E). A sub-area of the colony of ca. 10,000 breeding pairs has been electronically monitored since 1998 with RFID technology. As of 2022, four pathways between the sea and the colony (the only ways in or out of the colony) are equipped with permanent automatic identification systems (the detailed information of the field site and systems are described in  Gendner et al. (2005) ). In short, these automatic systems are composed of paired antennas that record the direction of each commuting bird that has been implanted subcutaneously with an RFID tag. Patterns of presence and absence of ca.
Start of a breeding cycle (breeding date) can also be determined, it is defined as the beginning of the pattern characteristic of the courtship and incubation period, i. e. , the first long sojourn at the colony following the annual moult and exceeding 10 days  (Descamps et al. , 2002). Additionally, the sex of an individual can also be derived from presence/absence patterns at the colony. An automatic sex determination has great potential application for many species where sex determination is challenging (i. e. , monomorphic species like king penguins;  (Kriesell et al. , 2018) ). C) Model deployment. 7) Building the testing datasets: a testing step is used to remove biases induced during parameterisation with comparison of model classifications and manual techniques of data processing (i.
e. , human expert classifications). 8) Testing the CNN models: model tests using expert-labelled datasets assess performance but also ensure that model performances are consistent according to classes and individual characteristics (e. g. , age, sex, life stage). 9) Application: classifications are applied to all detection data after pre-processing and formatting (i. e. , after correction of missing detections and building of vectors), and results are represented in Sphenotron for each individual (successful breeding cycles in green, failed breeding cycles in red). 6.
RFID data pre-processing Input data To prepare the detection data in an appropriate format, we chose to represent absence and presence time-series for each breeding cycle with two vectors providing the location of the individual at the end of 12-hour periods (states 0 and 1) and the number of detections occurring during the 12 hours (Figure  S8 ). For one individual and one given year n, we built vectors encompassing the breeding cycle. For the King penguin, vectors start October 1st of the year Y and end January 31st of the year Y+2 to cover the entire >1-year breeding cycle of the species (Figure  S7 ). We obtained two vectors of 974 elements for each individual and each year. This step can be tailored to match other RFID acquisition systems and species, for instance by dropping the first vector when no location ('in' or 'out') is defined but only simple detection are recorded, e.
Building the training dataset To build a training/ground truth dataset, we visually monitored 295 RFID-tagged individuals over 9 years  (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) , assessing their breeding status and behaviour directly through field observations. Birds were monitored from the beginning of the breeding season (November-January), thereby we were able to detect early breeding failures that may have been difficult to distinguish from non-breeding behaviour using RFID detections alone. Breeding outcomes (S: Success; F: Failure) from these study birds was determined according to the survival of their chicks until they fledged. The sex of individuals was determined with the observation of their first period in the colony, as females leave the breeding site right after egg laying, while males care for the egg  (Barrat, 1976).
We used a random offset between -30 days and 30 days to cover large biological variability in the phenology of our species (but this can be easily changed in the code, and this step can be turned off). The second augmentation process consisted in simulating missing RFID detections. In the actual dataset, the most frequent problem is the loss of a single detection, which is solved by our correction algorithm. Therefore, we chose to remove 10% of the detections at each iteration, before applying our correction algorithm, allowing a complete recovery of the original detections for at least 50% of individuals (see Supplement C) and leaving uncorrected detections and erroneous locations to improve training generality. Models for determining the breeding status were trained with and without these data augmentation processes to assess the benefits of this step.
We ran this algorithm on all breeding cycles in our dataset and obtained a graph of importance value for each element of the vector (each 12-hour period in our example) for a particular class of interest. We computed these activation plots and compared to the raw input detection data for 1) the Breeding vs. Non-Breeding model, restricting the dataset to breeding cycles classified as Breeding in order to identify the features used by the algorithm to detect a breeding cycle, and 2) the Success vs. Failure model, including only breeding cycles classified as successful in order to identify the regions of the breeding cycle that were indicative of a success.
Testing the CNN models To compare the overall classification performance, we used a global accuracy metric of the different models given by:  (Powers, 2020). Since our ground truth datasets were well balanced across classes (168 Non-Breeding; 131 Failure; 164 Success), the global accuracy metric did not approach its limits through class unbalance, and it provided a simple and effective metric of overall classification performance. N correctpredictions N predictions To provide a measure of classification accuracy for all possible classification thresholds, we also used the AUC-ROC score (Area Under the Receiver Operating Characteristic Curve)  (Fawcett, 2006). To assess the accuracy of breeding date determination, we used a threshold of 5 days between the true breeding date and the predicted date to define whether or not a breeding date was correctly predicted (see Supplement E).
To quantify an unbiased estimate of model performance, we used a testing dataset encompassing 917 breeding cycles of penguin individuals that were never used in model training  (Kuhn et al. , 2013). These breeding cycles were blind-labelled, i. e. , breeding status and breeding date were not determined through field observations, but by human experts who examined the RFID detections of individuals using our custom-designed Sphenotron software (see Supplement A). Human experts, with a strong knowledge and experience of the species in the field, were trained using the ground truth dataset, blindly examining detection data to infer breeding cycles, and cross-checking previously assigned breeding cycles. Two human experts were chosen to label the same dataset, and we tested our models with both classifications.
We also computed the global accuracy metric between the datasets labelled by the two human experts to assess human variability in classification. The performance of the lifetime sexing method was compared to a molecular sexing dataset of 6,196 birds (molecular sexing method adapted from  Griffiths et al. (1998)  showing 100% accuracy in typical cases  (Purwaningrum et al. , 2019) ). Because sex was estimated with a variable number of breeding cycles between individuals (we used all available breeding cycles for each bird), we also tested whether the accuracy 10 of pooled sexing increased when including additional breeding cycles. Finally, we computed the accuracy of the models for each age class and for males and females separately to test whether the performance of our models was consistent over the whole dataset.
Model deployment The trained models were used to predict the breeding status and dates of all RFID-tagged individuals since 1998 (i. e. , 85,524 breeding cycles from 14,795 different individuals). On the laptop computer used here, prediction (from raw RFID data to classification) of breeding status and sex of all birds required 140 seconds, but it took 1. 1 hours for the determination of the breeding date due to the number of predictions needed (320 for each breeding cycle). In comparison, it took a human about 1 minute to make the same decision as for Prediction vs. Dataset 1, see Figure  S11 ). As expected, models with data augmentation consistently performed better than models without any transformation of the input data. The lifetime classification sexing procedure yielded an 88. 6% accuracy compared to the molecular data (AU C sex = 0.
930 see Figure  S12 ). Before pooling lifetime sex probabilities, the global accuracy of sexing was only 81. 7%. As expected, we also found that sex classification accuracy from the pooling of lifetime sex probabilities increased with the number of breeding cycles used to determine the sex of an individual (see Figure  S12 ). According to dataset 1, predictions were slightly better for males than for females for the breeding status (males: 94. 5% vs. females: 93. 2%) and inversely for the breeding dates (males: 85. 2% vs. females: 88. 9%). The breeding dates also appeared to be less predictable for young individuals (see Figure  S13 ).
We also developed a post-processing step to extract dates of breeding, and, with a visualisation technique, we identified the regions of the dataset used by the models to classify the breeding cycles. Such a framework can be used beyond our example dataset and help to quickly classify the breeding activities of many individuals, even more so for long-term projects for which preprocessing analysis is very time-and labor-consuming (in our example, we worked on ca. 15,000 individuals over ca. 25 years). Certainly, we do anticipate a wide application for other colonial birds with their many ins and outs of their colonies and with their complex patterns of phenology. For non-colonial species, we anticipate a good performance of our model with likely simpler RFID data (e. g. simple detection at a feeding site), if detection rate is high enough to capture regular patterns.
While it is still challenging to successfully transfer pre-trained deep learning from a study case to another  (Marcus, 2018) , RFIDeep workflow is tailored for any 13 study classifying behaviours based on RFID-tagged animal detections. RFIDeep was successfully tested and used on another species, the Adélie penguin (Pygoscelis adeliae), for which breeding is markedly different from our first dataset example, yet monitored with a similar automatic RFID setup. Given that our model performed well for these contrasting datasets (global accuracy of 95. 2% for breeding outcome determination and 93. 5% for sex, see Supplement F for details), we argue that any RFID-monitored species with stereotyped movements during a given life stage could certainly benefit from the RFIDeep workflow, such as bumblebees  (Molet et al.
, 2008) , Leach's Storm-petrels  (Zangmeister et al. , 2009) , hummingbirds  (Bandivadekar et al. , 2018) , as well as other penguin species  (Kerry et al. , 1993; Chiaradia & Kerry, 1999; Horswill et al. , 2014). Furthermore, the missing detection correction and data augmentation algorithms implemented in RFIDeep have great potential to tackle uncompleted and/or low-quality datasets, such as those produced by mobile RFID antennas temporarily deployed  (Cristofari et al. , 2018). We are confident that the RFIDeep workflow will help biologists to adopt deep learning applications more easily, either by using the codes directly or by adapting it for their requirements. Both validation and testing steps showed the high performances of the RFIDeep models, on the one hand, in reference to the ground truth data and, on the other hand, from a human-machine comparison point of view.
While data augmentation is usually done by adding random noise to the dataset (e. g. , in pictures for 2-D CNN classification with image rotations for instance,  (Pawara et al. , 2017) ), here we aimed to mimic biological variance and technical limitations of the RFID data acquisition systems. Doing so, we covered a large variance in breeding dates, enabling us to anticipate breeding seasons that could begin earlier or later 14 than those existing in our ground truth data, because of environmental shifts already observed or expected in the coming years/decades  (Visser et al. , 2021). This applies not only for the Sphenisciformes species used in this study, but likely also for other species with a high variance in breeding phenology  (De Villemereuil et al. , 2020). Another interesting aspect of the automatic classification of breeding cycles is the independence among predictions.
Indeed, each breeding cycle was analysed without supplement information about the year (e. g. , average breeding success, phenological data), the individual (e. g. , age, body condition), and previous and future breeding cycles. The breeding classification of lifetime datasets by human experts can induce bias for quantifying the inter-individual and intra-individual heterogeneity in breeding cycles since they are usually not classified independently. However, while there may be an advantage to having independent classifications, the lifetime information may also be beneficial, for instance to better determine the breeding date of the very first breeding seasons that tend to be less predictable for numerous species (see Figure  S13 ). It would also be useful to train CNN models with mixed data (e.
g. , RFID detections and automated weighting at the detection point) to increase the classification accuracy and/or complexity, and to refine further some of the analyses (e. g. , the stage of breeding failure), as it has also been done in other fields  (Ahsan et al. , 2020). With visualisation techniques, we showed which parts of the datasets are mostly used to perform classification. They provided a peek into the deep learning 'black box', making the process more transparent for the user, a shortcoming that often prevents its use by ecologists  (Borowiec et al. , 2022). We argue that such a step can help expand the potential of deep learning to describe and analyse ecological big data. In our example, while activation maps are primarily used by CNN for classification, their visualisation allows the detection of the specific breeding activities or features, such as seasonal phenology.
In our application on king penguins, the CNN models showed that the presence or absence of pre-and post-winter chick feeding patterns were the most important criteria for predicting breeding outcome. Although these regions can be used for distinguishing between failure and success, it reinforces our interest in using these visualisation techniques not only to understand how our deep learning models work, but also to detect regions of interest in our datasets. It also highlights the use of CNN models that are not frequently found in ecological studies but have great potential, for instance, to detect hidden patterns in large datasets. Moreover, to cope with the recent explosion of big data acquisition due to increasingly sophisticated, miniaturised, autonomous, and powerful data collection instruments  (Williams et al.
Supplement A Sphenotron software Sphenotron is an open-source software written in Python. The current version of Sphenotron has been developed to interact with MySQL databases composed of several tables compiling all known information on each RFID-tagged individual, such as biometric or phenological data, sampling and recapture events if any, past breeding territories/coordinates, etc. , in addition to detections (see Figure  S1 ). Sphenotron was initially developed to interact with databases related to specific species (order Sphenisciformes). Yet, the code can be reused and adjusted to manage and interact with databases of similar or different formats of other species. The complete Sphenotron software and codes, with examples of database, are downloadable at the following link (https://github.