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Exploring carnivorous habitats based on images from social media Rafael Blanco, Zujany Salazar, Tobias Isenberg

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Rafael Blanco, Zujany Salazar, Tobias Isenberg. Exploring habitats based on images from social media. IEEE VIS 2019: IEEE Conference on Visualization, Oct 2019, Vancouver, Canada. ￿hal-02196764￿

HAL Id: hal-02196764 https://hal.inria.fr/hal-02196764 Submitted on 29 Jul 2019

HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Exploring Carnivorous Plant Habitats based on Images from Social Media

Rafael Blanco* Zujany Salazar∗ Tobias Isenberg∗ Tel´ ecom´ SudParis Tel´ ecom´ SudParis Inria

ABSTRACT We visualize habitat distribution information based on geo-lo- cated images posted on social media. For the example of carnivorous , we use published image data to produce interactive maps of the spatial distribution of different species/genuses, histograms of the elevations at which they grow, and plots of the temporal distribution of the photographs. We further discuss the mismatch between our distribution maps and traditionally established maps as well as further possibilities for research with our data.

1 MOTIVATION AND RELATED WORK Many species of plants and animals are endangered today (e. g., see the IUCN List 2012; https://www.iucnredlist.org/). Figure 1: Screenshot of our data exploration interface, with markers This status also applies to many carnivorous plants, and past stud- colored by and image thumbnails shown at the bottom. ies have quantified the conservation threats of the different genus and species [4], providing information to prioritize certain areas of geographic position), the time the picture was taken, the social conservation. Yet information about the distribution of the different media service and the respective image ID, text description of the species is often difficult to obtain. Researchers use environment data geographic location such as area name and country, the name and and current habitat sightings, to apply modeling ID of the person who uploaded the images, and the image itself. (SDM), which uses statistical models to generate an estimate of the plant distribution around the world. In contrast to dedicated cam- 3 VISUALIZATION SYSTEM paigns to document species distribution in the wild, we explore the use of geo-located images posted in social media. While the use of We designed a map-based (using Google’s Maps API) data explo- geo-located social media images and other posts has been explored ration interface that indicates the location of each plant in the dataset in the past for the study of popular places [3] and events [1,5], we with a marker. We provide two visualization modes: a general data use the social images posted on Panoramio and Flickr not as singu- point exploration mode and a plant species or genus distribution lar events but as evidence for the existence of a specific plant at a mode. The exploration mode (e. g., Fig. 1) has a filter panel to filter location, providing evidence for the habitat of the depicted species. for plant characteristics (genus, species) and/or image data (location, date it was taken, social network). In this mode we provide an image 2 DATA ACQUISITION carousel that shows thumbnails of the images of the currently dis- 1 played data points. In the distribution mode (Fig. 12), we use circles We collected a dataset by searching Panoramio and Flickr for geo- around plant locations to generate distribution maps (e. g., Fig. 3), tagged images whose label or description included at least one without applying any statistical methods. We use circles whose size from a series of search terms. These search terms included the can be changed based on the current map magnification. In addition, Latin genus and family names, the terms “carnivorous plant(s) or as such maps could potentially be published, we add a small random similar, as well as a number of common names for different species offset to obfuscate the specific locations to prevent potential poach- in several languages including Chinese, Danish, Dutch, English, ing. Furthermore, we allow users to generate interactive plots of the French, German, Italian, Japanese, Norwegian, Polish, Portuguese, image dates and elevation histograms using Plotly (e. g., Fig. 2). Russian, Spanish, and Swedish. This resulted in more than 28,700 Our application relies on Flask for Python, HTML5, CSS3, and candidate images. For each of the found images, we looked at both JavaScript. We use SQLAlchemy to manage the database that has the image itself and its location on the map using an online satellite the information of each image of a plant. Also, we use the Flickr map, to manually determine whether the images showed a true API to allow the users to look up each entry directly on Flickr. habitat and whether the location data was believable (e. g., Fig. 24). For example, we removed image locations in the middle of urban 4 DATA EXPLORATION STRATEGIES AND CASE STUDIES areas. As some people posted many images of plants kept at home or used the same location that was not plausibly a habitat location, we Our visualization system allows us to investigate the collected data removed certain user IDs and locations from consideration to speed- in interesting ways. Primarily it is straight-forward to examine the up the inspection. We then recorded resulting locations, resulting geographic distribution of different species and to compare this with in a list of more than 8,800 entries. Out of these, approx. 4,600 known distribution maps. For example, we can find that the distribu- are within a radius of approx. 250 m another location with the same tion of rotundifolia in our data matches with the distribution species and can thus be considered to be duplicates. For each plant map on Wikipedia (Fig. 18). However, we also see that our data location, we recorded its scientific name,2 its geographic position does not support the full distribution on the established maps. This on the map, its elevation (based on an elevation look-up for the fact is likely due to a strong bias in our data: we only have pictures from places that people are likely to live at or travel to (Fig. 21, *e-mail: {rblancog25 | zujany}@gmail.com, [email protected] 21(c)). We thus have no evidence for the distribution of Drosera 1The Panoramio service has since been retired by Google. rotundifolia in , for example. In another example, we can also 2We generally used the Latin names provided in the descriptions. If no see that our data supports the distribution of purpurea species name was provided or if it was wrong, we either classified the plant in (Fig. 19)—including the isolated population in ourselves if we knew the species well, or we only recorded the plant’s genus. the southern but excluding the less populated areas 4000—4100 Panoramio 3900—4000 3800—3900 Flickr 3700—3800 3600—3700 3500—3600 3400—3500 3300—3400 200 3200—3300 3100—3200 3000—3100 2900—3000 2800—2900 2700—2800 2600—2700 2500—2600 150 2400—2500 2300—2400 2200—2300 2100—2200 2000—2100 1900—2000 1800—1900 1700—1800 1600—1700 100 1500—1600 1400—1500 1300—1400 1200—1300 1100—1200 1000—1100 900—1000 800—900 50 700—800 600—700 500—600 400—500 300—400 200—300 100—200 0—100 -100—0 0 (a) Jan. Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (b) 0 20 40 60 80 100 120

Figure 2: Examples of a generated data plots: (a) sightings of Sar- racenia purpurea by month and (b) elevation histogram for . Figure 3: Example of a generated habitat map for . of central and northern Canada. Yet we can also see populations of 5 CONCLUSIONAND FUTURE WORK the species in —not only well known places where the plant was introduced in Switzerland but also places in the UK, Scandi- Our work showcases an interesting and, to the best of our knowledge, navia, and the Netherlands. Similarly, our map of the distribution previously not explored use of social media data for longer-term of Drosera arcturi (Fig. 3) shows habitats not only in but “events” such as plant grows in a specific habitat/region. We explored also in and for (Fig. 20) we both geographic as well as abstract data analysis techniques for this see also additional locations in western and central . data, and it would be interesting in the future to also explore space- In addition, we need to address the inherent unreliability of the time representations [2]. We are also planning to investigate machine posted data—in particular the geographic location could be intention- learning approaches to be able to identify flowers on the pictures, ally or unintentionally incorrect. We thus implemented verification which would allow us to plot flowering periods of the plants. We also mechanisms that provide information about the reliability of the plan to investigate if ML approaches could support genus or species locations. Specifically, we treat a location as more reliable if other identification to be able to further check the data or automate the data social media users have posted pictures of other plants nearby. Based collection process. The latter should also generally be integrated in on a user-selectable search threshold (we experimented, e. g., with the visualization tool, so that we can merge the currently separate 1 km and 4 km; e. g., see Fig. 15 and 16), we search for nearby sites processes of data collection, correction, and visualization. posted by other users and depict sites with a species match in green, We collected our dataset and built our visual exploration tool due sites with a genus match in yellow, sites with only other-genus plants to an interest in carnivorous plants, it can thus be used by enthusiasts nearby in orange, and the rest in red (other color maps for color- to visit habitats. Our tool also has the potential to be used by plant deficient users are possible). The process to compute these trust- biologists or botanists to study the habitat information we collected worthiness values essentially uses a hashed comparison of the sites as well as by conservationists to become aware of unknown sites as using 9-neighborhood “buckets” and thus takes about 10 seconds to well as the evolution of known sites. For the latter of the two appli- compute. We allow users to adjust the search radius and thus the cations, however, we point out that our dataset is limited because trust level they want to work with, which likely depends on a given for some species we only have very few entries (e. g., currently 110 site and its number of data points. We also explored a verification species with 5 entries or fewer each). So users need to be aware of based on the posts of a single person: We check if the same person small-number statistics issues when analyzing the data. In addition, posted multiple pictures from exactly the same GPS position—a the previously mentioned data biases need to be considered. Based sign for manipulated coordinates—and color the markers based on on the protection status of many carnivorous plants, however, we how many other pictures were posted a given location (Fig. 17). will not make the tool or data publicly available to prevent poaching In addition to the geographic distributions, we also analyzed and will only share it with researchers in the application domain. abstract data. In particular, we support the analysis of the time the picture was taken and of the elevation of the location (e. g., REFERENCES Fig. 4 shows the number of entries in our dataset by year, and Fig. 5 [1] G. Andrienko, N. Andrienko, P. Bak, S. Kisilevich, and D. Keim. Analy- shows monthly detail since 2003). We also analyzed the dates of sis of community-contributed space- and time-referenced data (example the pictures within a year (e. g., Fig. 6 for all entries), and saw that of Flickr and Panoramio photos). In Proc. VAST (Posters), pp. 213–214. sightings in the Northern Hemisphere with a peak in the middle of IEEE Computer Society, Los Alamitos, 2009. doi: 10.1109/VAST.2009. the calendar year are prevalent. Looking at specific plants, we can 5333472 identify the different growth periods between northern and Southern [2] B. Bach, P. Dragicevic, D. Archambault, C. Hurter, and S. Carpendale. Hemisphere plants (e. g., Fig. 10). The elevation plots (e. g., Fig. 7, A descriptive framework for temporal data visualizations based on gen- 9) also show a quite different behavior of different species. eralized space-time cubes. Computer Graphics Forum, 36(6):36–61, We note that our data relies on a correct classification of the 2017. doi: 10.1111/cgf.12804 [3] D. J. Crandall, L. Backstrom, D. Huttenlocher, and J. Kleinberg. Map- species. While due to our manual process we are certain that we use ping the world’s photos. In Proc. WWW, pp. 761–770. ACM, , the correct genus name for all entries, the same cannot be said for the 2009. doi: 10.1145/1526709.1526812 species. Here we relied on the classification provided in the social [4] D. E. Jennings and J. R. Rohr. A review of the conservation threats to media post (if any), and only re-classified some specific species carnivorous plants. Biological Conservation, 144(5):1356–1363, 2011. which we knew well. A sizable portion of our entries (approx. 12%) doi: 10.1016/j.biocon.2011.03.013 thus only contains the genus of the respective plants locations. An [5] S. Kisilevich, M. Krstajic, D. Keim, N. Andrienko, and G. Andrienko. expert botanist with a research background on these plants would Event-based analysis of people’s activities and behavior using Flickr certainly be able to address these challenges. The abstract data plots and Panoramio geotagged photo collections. In Proc. Information Visu- as well as the geographic maps are likely to support such an analysis, alisation, pp. 289–296. IEEE Computer Society, Los Alamitos, 2010. identifying outliers or non-classified plants in specific regions. doi: 10.1109/IV.2010.94 Exploring Carnivorous Plant Habitats based on Images from Social Media

Additional material

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Figure 4: Date histogram by year and service, complete dataset. Interestingly, the number of posts per year declines after 2014, possibly to due the fact that people post their pictures only after some time and not directly when taking them and/or due to Flickr’s changed upload policies which apparently have let quite a number people to delete their images from the service (see Fig. 23 for a graph that supports this hypothesis).

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Figure 5: Date histogram by month and service, complete dataset, ignoring pictures from before 2003. 4000—4100 Panoramio 3900—4000 1400 3800—3900 Flickr 3700—3800 3600—3700 3500—3600 3400—3500 1200 3300—3400 3200—3300 3100—3200 3000—3100 2900—3000 2800—2900 1000 2700—2800 2600—2700 2500—2600 2400—2500 2300—2400 2200—2300 800 2100—2200 2000—2100 1900—2000 1800—1900 1700—1800 1600—1700 600 1500—1600 1400—1500 1300—1400 1200—1300 1100—1200 400 1000—1100 900—1000 800—900 700—800 600—700 500—600 200 400—500 300—400 200—300 100—200 0—100 -100—0 0 Jan. Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 500 1000 1500

Figure 6: Aggregated date histogram, by month, complete dataset. Figure 7: Elevation histogram, complete dataset.

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Figure 8: Histogram of Flickr ID bins for the identified plausible habitat images, complete dataset. Each bin contains 108 Flickr IDs. This plot shows some interesting patterns: We are not clear about the reason for the drop at around 120 · 108 and why fewer entries exist in the more recent bins since all bins are of equal size. These effects may be caused by the employed search strategies for the images using our set of keywords, or potentially it is caused by when we conducted the searches for potential picture IDs on Flickr (which we then cached for later manual inspection). 4000—4100 4000—4100 4000—4100 3900—4000 3900—4000 3900—4000 3800—3900 3800—3900 3800—3900 3700—3800 3700—3800 3700—3800 3600—3700 3600—3700 3600—3700 3500—3600 3500—3600 3500—3600 3400—3500 3400—3500 3400—3500 3300—3400 3300—3400 3300—3400 3200—3300 3200—3300 3200—3300 3100—3200 3100—3200 3100—3200 3000—3100 3000—3100 3000—3100 2900—3000 2900—3000 2900—3000 2800—2900 2800—2900 2800—2900 2700—2800 2700—2800 2700—2800 2600—2700 2600—2700 2600—2700 2500—2600 2500—2600 2500—2600 2400—2500 2400—2500 2400—2500 2300—2400 2300—2400 2300—2400 2200—2300 2200—2300 2200—2300 2100—2200 2100—2200 2100—2200 2000—2100 2000—2100 2000—2100 1900—2000 1900—2000 1900—2000 1800—1900 1800—1900 1800—1900 1700—1800 1700—1800 1700—1800 1600—1700 1600—1700 1600—1700 1500—1600 1500—1600 1500—1600 1400—1500 1400—1500 1400—1500 1300—1400 1300—1400 1300—1400 1200—1300 1200—1300 1200—1300 1100—1200 1100—1200 1100—1200 1000—1100 1000—1100 1000—1100 900—1000 900—1000 900—1000 800—900 800—900 800—900 700—800 700—800 700—800 600—700 600—700 600—700 500—600 500—600 500—600 400—500 400—500 400—500 300—400 300—400 300—400 200—300 200—300 200—300 100—200 100—200 100—200 0—100 0—100 0—100 -100—0 -100—0 -100—0 0 50 100 150 200 250 0 5 10 15 20 25 0 1 2 3 4 (a) (b) (c)

Figure 9: Comparison of three elevation histograms with different characteristics for (a) , (b) Dionaea muscipula, and (c) aplina.

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Figure 10: Comparison of the monthly sightings of two sundew species: (a) Drosera rotundifolia which occurs on the Northern Hemisphere and (b) Drosera arcturi from the Southern Hemisphere. The two plots clearly show the different growth periods in the Northern and Southern Hemispheres, respectively, but could also be influenced by when people travel to visit the respective habitats. Figure 11: Our tool in exploration mode.

Figure 12: Our tool in distribution mode, showing both our distribution maps and maps from Wikipedia or other sources. Figure 13: In exploration mode, narrowed in onto a particular site, with pictures of the site shown below.

Figure 14: In exploration mode, looking at a particular picture from the site shown in Fig. 13. The image is Flickr image 28504089402 by Jeremy Riel (c b n CC BY-NC 2.0). Figure 15: In exploration mode with the trust mapping enabled, a site Figure 16: In exploration mode with the trust mapping enabled, a site in the United States that shows the different categories of verfication in the United States that shows the different categories of verfication for a 1 km search radius (red: not verified by other users; orange: for a 4 km search radius (red: not verified by other users; orange: other plants by other users; yellow: other plants of same genus by other plants by other users; yellow: other plants of same genus by other users; and green: other plants of same species by other users). other users; and green: other plants of same species by other users). Same map section as in Fig. 16. Same map section as in Fig. 15.

Figure 17: Verification based on the images of a single poster. If four or more images of the same poster are at exactly the same GPS coordinates, we assume the GPS coordinates to be manipulated (red). If three images are at the same location, then the coordinates are likely manipulated (orange). If two images have the same location, then the coordinates are possibly manipulated (yellow). Otherwise the coordinates are probably not manipulated or we cannot make a good judgment (green). (a) (b)

Figure 18: Comparison of distribution maps for Drosera rotundifolia: (a) based on our social media data, (b) map from Wikipedia (p).

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Figure 19: Comparison of distribution maps for : (a) based on our social media data, (b) map from Wikipedia (p).

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Figure 20: Comparison of distribution maps for Darlingtonia californica: (a) based on our social media data, (b) map from Wikipedia (p). (a) Heatmap of the geographic distribution of all 135,578 posts in the Ency- (b) The same heatmap generated based on the currently available data of clopedia of Life group by 2012, providing an estimate of the expected global 349,011 posts up to July 2019, generated with the same script by Roderic Page. likelihood for habitat image posts. Image by Roderic Page (c b CC BY 4.0). The overall distribution pattern did not change compared to 2012.

(c) Same image as in (b), only with the less-than-ten-images-per-region class (formerly yellow) shown transparently, to better emphasize the locations with multiple image posts.

Figure 21: Analysis of geographic distribution data based on the Encyclopedia of Life Flickr group which collects images and videos of animals, plants, fungi, protists, and : Since they cover a similar subject matter but for all species in general, the maps can be used as an indication of where it is more or less likely to get a good distribution coverage (see https://iphylo.blogspot.com/2012/06/ where-is-in-crowdsourcing-mapping-eol.html). Color map: yellow: 1–9 images; light orange: 10–99 images; medium orange: 100–999 images; dark orange: 1,000–9,999 images; red: more than 10,000 images. Notice that in those regions that have no color assigned (i. e., the background map is visible) not a single image was posted to the Encyclopedia of Life Flickr group.

Figure 22: Heatmap for our data, generated with the same script by Roderic Page. Same color map as in Fig. 21. Figure 23: Flickr’s changed upload policies from January 2019 apparently have let quite a number people to delete their images from the service. This fact may have let us to “loose” some images in the process (see Fig. 4) since we do not continuously scape the Flickr database but instead query it only in irregular intervals. Flickr image 6855169886 by Franck Michel (c b CC BY 2.0).

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Figure 24: Examples for images which our search found but which we manually excluded because they do not show plausible plant habitats: (a) fake/artificial plants (Flickr image 9479368060 by Craig Moe; c b n CC BY-NC 2.0), (b) botanical gardens or similar (Flickr image 5086658928 by Jane Nearing; c b n CC BY-NC 2.0), and (c) plants kept at home (Flickr image 10753181585 by Mike Linksvayer; c z CC0 1.0). Other reasons for rejecting entries include name collisions with geographic place names, GPS locations at unsuitable locations (e. g., farmland, streets), and images that do not show any or not the right plants (e. g., name collisions with common or scientific names, general textual habitat descriptions in the comments of the postings). Drosera rotundifolia 1301 Sarracenia purpurea 970 Drosera (unclassified) 541 468 Darlingtonia californica 322 305 Nepenthes (unclassified) 260 244 Sarracenia leucophylla 205 193 Drosera filiformis 178 Drosera peltata 157 (unclassified) 141 120 110 107 105 Dionaea muscipula 98 95 93 Pinguicula lutea 80 Pinguicula (unclassified) 80 Pinguicula pumila 76 75 74 64 63 Pinguicula grandiflora 62 61 60 Drosera binata 55 53 Drosera arcturi 53 50 44 43 42 Sarracenia (unclassified) 41 40 39 38 Pinguicula vallisneriifolia 36 Pinguicula planifolia 35 Nepenthes vieillardii 34 Drosera burmannii 34 Nepenthes tentaculata 34 Drosera indica 32 Pinguicula caerulea 30 Nepenthes reinwardtiana 30 29 29 Drosera whittakeri 28 28 Pinguicula crystallina 28 Nepenthes macfarlanei 26 26 lusitanicum 26 Drosera neocaledonica 24 Pinguicula primuliflora 23 Drosera pygmaea 22 Nepenthes burbidgeae 22 21 21 Drosera glanduligera 20 20 20 20 Pinguicula ionantha 19 18 Nepenthes stenophylla 18 Pinguicula nevadensis 18 18 Pinguicula elongata 17 17 Drosera petiolaris 17 17 16 16 16 Nepenthes edwardsiana 16 16 16 Drosera x obovata 16 15 Pinguicula leptoceras 15 Utricularia unifolia 14 Utricularia purpurascens 14 Nepenthes madagascariensis 14 Drosera montana 14 Drosera erythrorhiza 13 Utricularia jamesoniana 12 11 Drosera aberrans 11 Nepenthes lowii 11 Pinguicula corsica 11 Pinguicula calyptrata 10 Pinguicula moranensis 10 Utricularia reticulata 10 Utricularia fulva 10 Pinguicula mundi 9 9 Drosera roraimae 9 9 Nepenthes faizaliana 9 8 Drosera fulva 8 8 Utricularia chrysantha 8 Nepenthes pervillei 8 Nepenthes x kinabaluensis 8 Nepenthes aristolochioides 7 Nepenthes kampotiana 7 Sarracenia x moorei 7 7 7 Drosera pallida 7 7 7 Utricularia leptoplectra 7 Nepenthes fusca 7 Drosera ultramafica 6 Drosera linearis 6 Pinguicula parvifolia 6 Utricularia asplundii 6 6 Utricularia striatula 6 6 6 Drosera rosulata 6 Drosera gigantea 6 Utricularia welwitschii 5 Nepenthes pitopangii 5 Nepenthes singalana 5 Pinguicula macroceras 5 Drosera dilatato-petiolaris 5 Pinguicula longifolia 5 Utricularia multifida 5 Drosera stenopetala 5 Nepenthes hamata 4 (unclassified) 4 4 Drosera monticola 4 Utricularia longifolia 4 Pinguicula balcanica 4 4 Nepenthes gymnamphora 3 Utricularia micropetala 3 Nepenthes ramispina 3 Nepenthes hirsuta 3 Sarracenia oreophila 3 liniflora 3 3 Drosera cistiflora 3 Drosera hyperostigma 3 Nepenthes chaniana 3 3 Drosera stolonifera 3 Drosera brevicornis 3 (unclassified) 3 Nepenthes muluensis 3 Utricularia uniflora 3 2 Nepenthes maxima x eymae 2 Nepenthes ampullaria x reinwardtiana 2 Utricularia delphinioides 2 Utricularia geoffrayi 2 Utricularia erectiflora 2 Utricularia heterosepala 2 Pinguicula villosa 2 Drosera miniata 2 follicularis 2 Sarracenia x catesbaei 2 Drosera macrophylla 2 Utricularia odorata 2 Drosera madagascariensis 2 Nepenthes macrovulgaris 2 Drosera scorpioides 2 Drosera barbigera 2 2 Drosera chrysolepis 2 Nepenthes suratensis 2 Nepenthes vogelii 2 Byblis filifolia 1 Nepenthes x hookeriana 1 Utricularia kimberleyensis 1 Drosera ordensis 1 Nepenthes rajah x burbidgeae 1 Nepenthes veitchii x stenophylla 1 Utricularia laciniata 1 Nepenthes platychila 1 Nepenthes spectabilis 1 Nepenthes tobaica 1 Drosera natalensis 1 Nepenthes stenophylla x fusca 1 Drosera murfetii 1 Nepenthes sumatrana 1 Nepenthes beccariana 1 Nepenthes angasanensis 1 1 Byblis (unclassified) 1 Drosera hilaris 1 Drosera villosa 1 Nepenthes reinwardtiana x stenophylla 1 Drosera esterhuyseniae 1 Nepenthes alata 1 Drosera androsacea 1 Nepenthes lowii x rajah 1 Nepenthes hurrelliana 1 Nepenthes murudensis 1 Nepenthes villosa x rajah 1 Nepenthes stenophylla x reinwardtiana 1 Nepenthes northiana 1 Nepenthes mirabilis x rafflesiana 1 1 Utricularia endresii 1 1 Drosera bulbosa 1 Utricularia involvens 1 1 Genlisea filiformis 1 Nepenthes pilosa 1 Drosera darwinensis 1 1 Drosera parvula 1 Utricularia neottioides 1 Utricularia calycifida 1 Drosera trinervia 1 Drosera alba 1 Drosera occidentalis 1 dentata 1 Utricularia nana 1 Drosera ascendens 1 1 Utricularia flaccida 1 Drosera tomentosa 1

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Figure 25: Species count in complete database of all species that we found, sorted by rank. Byblis (unclassified) 1 Byblis filifolia 1 3 Cephalotus follicularis 2 Darlingtonia californica 322 Dionaea muscipula 98 Drosera (unclassified) 541 Drosera aberrans 11 Drosera alba 1 Drosera aliciae 6 Drosera androsacea 1 Drosera anglica 193 Drosera arcturi 53 Drosera ascendens 1 Drosera barbigera 2 Drosera binata 55 Drosera brevicornis 3 Drosera brevifolia 61 Drosera bulbosa 1 Drosera burmannii 34 Drosera capensis 7 Drosera capillaris 75 Drosera chrysolepis 2 Drosera cistiflora 3 Drosera darwinensis 1 Drosera dilatato-petiolaris 5 Drosera erythrorhiza 13 Drosera esterhuyseniae 1 Drosera filiformis 178 Drosera fulva 8 Drosera gigantea 6 Drosera glanduligera 20 Drosera hilaris 1 Drosera hyperostigma 3 Drosera indica 32 Drosera intermedia 468 Drosera linearis 6 Drosera macrantha 40 Drosera macrophylla 2 Drosera madagascariensis 2 Drosera menziesii 17 Drosera miniata 2 Drosera montana 14 Drosera monticola 4 Drosera murfetii 1 Drosera natalensis 1 Drosera neocaledonica 24 Drosera occidentalis 1 Drosera ordensis 1 Drosera pallida 7 Drosera parvula 1 Drosera peltata 157 Drosera petiolaris 17 Drosera pygmaea 22 Drosera roraimae 9 Drosera rosulata 6 Drosera rotundifolia 1301 Drosera scorpioides 2 Drosera spatulata 95 Drosera stenopetala 5 Drosera stolonifera 3 Drosera tomentosa 1 Drosera trinervia 1 Drosera ultramafica 6 Drosera uniflora 9 Drosera villosa 1 Drosera whittakeri 28 Drosera x obovata 16 Drosophyllum lusitanicum 26 Genlisea (unclassified) 4 Genlisea filiformis 1 Heliamphora (unclassified) 3 Nepenthes (unclassified) 260 Nepenthes alata 1 Nepenthes albomarginata 20 Nepenthes ampullaria 107 Nepenthes ampullaria x reinwardtiana 2 Nepenthes angasanensis 1 Nepenthes aristolochioides 7 Nepenthes beccariana 1 Nepenthes bicalcarata 20 Nepenthes burbidgeae 22 Nepenthes chaniana 3 Nepenthes distillatoria 1 Nepenthes edwardsiana 16 Nepenthes faizaliana 9 Nepenthes fusca 7 Nepenthes gracilis 29 Nepenthes gymnamphora 3 Nepenthes hamata 4 Nepenthes hirsuta 3 Nepenthes hurrelliana 1 Nepenthes kampotiana 7 Nepenthes lowii 11 Nepenthes lowii x rajah 1 Nepenthes macfarlanei 26 Nepenthes macrovulgaris 2 Nepenthes madagascariensis 14 Nepenthes maxima 8 Nepenthes maxima x eymae 2 Nepenthes mirabilis 18 Nepenthes mirabilis x rafflesiana 1 Nepenthes muluensis 3 Nepenthes murudensis 1 Nepenthes northiana 1 Nepenthes pervillei 8 Nepenthes pilosa 1 Nepenthes pitopangii 5 Nepenthes platychila 1 Nepenthes rafflesiana 93 Nepenthes rajah 44 Nepenthes rajah x burbidgeae 1 Nepenthes ramispina 3 Nepenthes reinwardtiana 30 Nepenthes reinwardtiana x stenophylla 1 Nepenthes sanguinea 16 Nepenthes singalana 5 Nepenthes spectabilis 1 Nepenthes stenophylla 18 Nepenthes stenophylla x fusca 1 Nepenthes stenophylla x reinwardtiana 1 Nepenthes sumatrana 1 Nepenthes suratensis 2 Nepenthes tentaculata 34 Nepenthes tobaica 1 Nepenthes veitchii 20 Nepenthes veitchii x stenophylla 1 Nepenthes ventricosa 1 Nepenthes vieillardii 34 Nepenthes villosa 39 Nepenthes villosa x rajah 1 Nepenthes vogelii 2 Nepenthes x hookeriana 1 Nepenthes x kinabaluensis 8 Pinguicula (unclassified) 80 Pinguicula alpina 64 Pinguicula balcanica 4 Pinguicula caerulea 30 Pinguicula calyptrata 10 Pinguicula corsica 11 Pinguicula crystallina 28 Pinguicula elongata 17 Pinguicula grandiflora 62 Pinguicula ionantha 19 Pinguicula leptoceras 15 Pinguicula longifolia 5 Pinguicula lusitanica 50 Pinguicula lutea 80 Pinguicula macroceras 5 Pinguicula moranensis 10 Pinguicula mundi 9 Pinguicula nevadensis 18 Pinguicula parvifolia 6 Pinguicula planifolia 35 Pinguicula primuliflora 23 Pinguicula pumila 76 Pinguicula vallisneriifolia 36 Pinguicula villosa 2 Pinguicula vulgaris 305 Roridula dentata 1 Sarracenia (unclassified) 41 Sarracenia alata 63 Sarracenia flava 244 Sarracenia jonesii 21 Sarracenia leucophylla 205 Sarracenia minor 74 Sarracenia oreophila 3 Sarracenia psittacina 120 Sarracenia purpurea 970 Sarracenia rosea 105 Sarracenia rubra 9 Sarracenia x catesbaei 2 Sarracenia x moorei 7 Utricularia (unclassified) 141 Utricularia alpina 6 Utricularia amethystina 4 Utricularia arenaria 4 Utricularia asplundii 6 Utricularia aurea 18 Utricularia australis 26 Utricularia bremii 1 Utricularia caerulea 8 Utricularia calycifida 1 Utricularia chrysantha 8 Utricularia cornuta 60 Utricularia delphinioides 2 Utricularia dichotoma 29 Utricularia endresii 1 Utricularia erectiflora 2 Utricularia flaccida 1 Utricularia floridana 3 Utricularia foliosa 16 Utricularia fulva 10 Utricularia geminiscapa 1 Utricularia geoffrayi 2 Utricularia gibba 38 Utricularia graminifolia 6 Utricularia heterosepala 2 Utricularia inflata 42 Utricularia intermedia 17 Utricularia involvens 1 Utricularia jamesoniana 12 Utricularia juncea 16 Utricularia kimberleyensis 1 Utricularia laciniata 1 Utricularia lateriflora 7 Utricularia leptoplectra 7 Utricularia longifolia 4 Utricularia macrorhiza 16 Utricularia menziesii 2 Utricularia micropetala 3 Utricularia minor 28 Utricularia minutissima 2 Utricularia multifida 5 Utricularia nana 1 Utricularia neottioides 1 Utricularia odorata 2 Utricularia purpurascens 14 Utricularia purpurea 43 Utricularia pusilla 16 Utricularia radiata 11 Utricularia resupinata 7 Utricularia reticulata 10 Utricularia simulans 15 Utricularia striata 7 Utricularia striatula 6 Utricularia stygia 1 Utricularia subulata 53 Utricularia tenella 1 Utricularia tricolor 3 Utricularia uliginosa 21 Utricularia uniflora 3 Utricularia unifolia 14 Utricularia vulgaris 110 Utricularia welwitschii 5

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Figure 26: Species count in complete database of all species that we found, sorted by species name.