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Exploring carnivorous plant habitats based on imagesfrom social media

Rafael Blanco, Zujany Salazar, Tobias Isenberg

To cite this version:Rafael Blanco, Zujany Salazar, Tobias Isenberg. Exploring carnivorous plant habitats based on imagesfrom social media. IEEE VIS 2019: IEEE Conference on Visualization, Oct 2019, Vancouver, Canada.�hal-02196764�

Exploring Carnivorous Plant Habitats based on Images from Social MediaRafael Blanco*

Telecom SudParisZujany Salazar∗

Telecom SudParisTobias Isenberg∗

Inria

ABSTRACT

We visualize species habitat distribution information based on geo-lo-cated images posted on social media. For the example of carnivorousplants, we use published image data to produce interactive mapsof the spatial distribution of different species/genuses, histogramsof the elevations at which they grow, and plots of the temporaldistribution of the photographs. We further discuss the mismatchbetween our distribution maps and traditionally established maps aswell 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 Red List 2012; https://www.iucnredlist.org/).This status also applies to many carnivorous plants, and past stud-ies have quantified the conservation threats of the different genusand species [4], providing information to prioritize certain areas ofconservation. Yet information about the distribution of the differentspecies is often difficult to obtain. Researchers use environment dataand current habitat sightings, to apply species distribution modeling(SDM), which uses statistical models to generate an estimate of theplant distribution around the world. In contrast to dedicated cam-paigns to document species distribution in the wild, we explore theuse of geo-located images posted in social media. While the use ofgeo-located social media images and other posts has been exploredin the past for the study of popular places [3] and events [1, 5], weuse the social images posted on Panoramio and Flickr not as singu-lar events but as evidence for the existence of a specific plant at alocation, providing evidence for the habitat of the depicted species.

2 DATA ACQUISITION

We collected a dataset by searching Panoramio1 and Flickr for geo-tagged images whose label or description included at least onefrom a series of search terms. These search terms included theLatin genus and family names, the terms “carnivorous plant(s) orsimilar, as well as a number of common names for different speciesin several languages including Chinese, Danish, Dutch, English,French, German, Italian, Japanese, Norwegian, Polish, Portuguese,Russian, Spanish, and Swedish. This resulted in more than 28,700candidate images. For each of the found images, we looked at boththe image itself and its location on the map using an online satellitemap, to manually determine whether the images showed a truehabitat and whether the location data was believable (e. g., Fig. 24).For example, we removed image locations in the middle of urbanareas. As some people posted many images of plants kept at home orused the same location that was not plausibly a habitat location, weremoved certain user IDs and locations from consideration to speed-up the inspection. We then recorded resulting locations, resultingin a list of more than 8,800 entries. Out of these, approx. 4,600are within a radius of approx. 250 m another location with the samespecies and can thus be considered to be duplicates. For each plantlocation, we recorded its scientific name,2 its geographic positionon the map, its elevation (based on an elevation look-up for the

*e-mail: {rblancog25 | zujany}@gmail.com, tobias.isenberg@inria.fr1The Panoramio service has since been retired by Google.2We generally used the Latin names provided in the descriptions. If no

species name was provided or if it was wrong, we either classified the plantourselves if we knew the species well, or we only recorded the plant’s genus.

Figure 1: Screenshot of our data exploration interface, with markerscolored by genus and image thumbnails shown at the bottom.

geographic position), the time the picture was taken, the socialmedia service and the respective image ID, text description of thegeographic location such as area name and country, the name andID of the person who uploaded the images, and the image itself.

3 VISUALIZATION SYSTEM

We designed a map-based (using Google’s Maps API) data explo-ration interface that indicates the location of each plant in the datasetwith a marker. We provide two visualization modes: a general datapoint exploration mode and a plant species or genus distributionmode. The exploration mode (e. g., Fig. 1) has a filter panel to filterfor plant characteristics (genus, species) and/or image data (location,date it was taken, social network). In this mode we provide an imagecarousel that shows thumbnails of the images of the currently dis-played data points. In the distribution mode (Fig. 12), we use circlesaround plant locations to generate distribution maps (e. g., Fig. 3),without applying any statistical methods. We use circles whose sizecan be changed based on the current map magnification. In addition,as such maps could potentially be published, we add a small randomoffset to obfuscate the specific locations to prevent potential poach-ing. Furthermore, we allow users to generate interactive plots of theimage dates and elevation histograms using Plotly (e. g., Fig. 2).

Our application relies on Flask for Python, HTML5, CSS3, andJavaScript. We use SQLAlchemy to manage the database that hasthe information of each image of a plant. Also, we use the FlickrAPI to allow the users to look up each entry directly on Flickr.

4 DATA EXPLORATION STRATEGIES AND CASE STUDIES

Our visualization system allows us to investigate the collected datain interesting ways. Primarily it is straight-forward to examine thegeographic distribution of different species and to compare this withknown distribution maps. For example, we can find that the distribu-tion of Drosera rotundifolia in our data matches with the distributionmap on Wikipedia (Fig. 18). However, we also see that our datadoes not support the full distribution on the established maps. Thisfact is likely due to a strong bias in our data: we only have picturesfrom places that people are likely to live at or travel to (Fig. 21,21(c)). We thus have no evidence for the distribution of Droserarotundifolia in Siberia, for example. In another example, we can alsosee that our data supports the distribution of Sarracenia purpureain North America (Fig. 19)—including the isolated population inthe southern United States but excluding the less populated areas

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Figure 2: Examples of a generated data plots: (a) sightings of Sar-racenia purpurea by month and (b) elevation histogram for Nepenthes.

of central and northern Canada. Yet we can also see populations ofthe species in Europe—not only well known places where the plantwas introduced in Switzerland but also places in the UK, Scandi-navia, and the Netherlands. Similarly, our map of the distributionof Drosera arcturi (Fig. 3) shows habitats not only in Australia butalso in New Zealand and for Darlingtonia californica (Fig. 20) wesee also additional locations in western and central California.

In addition, we need to address the inherent unreliability of theposted data—in particular the geographic location could be intention-ally or unintentionally incorrect. We thus implemented verificationmechanisms that provide information about the reliability of thelocations. Specifically, we treat a location as more reliable if othersocial media users have posted pictures of other plants nearby. Basedon a user-selectable search threshold (we experimented, e. g., with1 km and 4 km; e. g., see Fig. 15 and 16), we search for nearby sitesposted by other users and depict sites with a species match in green,sites with a genus match in yellow, sites with only other-genus plantsnearby in orange, and the rest in red (other color maps for color-deficient users are possible). The process to compute these trust-worthiness values essentially uses a hashed comparison of the sitesusing 9-neighborhood “buckets” and thus takes about 10 seconds tocompute. We allow users to adjust the search radius and thus thetrust level they want to work with, which likely depends on a givensite and its number of data points. We also explored a verificationbased on the posts of a single person: We check if the same personposted multiple pictures from exactly the same GPS position—asign for manipulated coordinates—and color the markers based onhow many other pictures were posted a given location (Fig. 17).

In addition to the geographic distributions, we also analyzedabstract data. In particular, we support the analysis of the timethe picture was taken and of the elevation of the location (e. g.,Fig. 4 shows the number of entries in our dataset by year, and Fig. 5shows monthly detail since 2003). We also analyzed the dates ofthe pictures within a year (e. g., Fig. 6 for all entries), and saw thatsightings in the Northern Hemisphere with a peak in the middle ofthe calendar year are prevalent. Looking at specific plants, we canidentify the different growth periods between northern and SouthernHemisphere plants (e. g., Fig. 10). The elevation plots (e. g., Fig. 7,9) also show a quite different behavior of different species.

We note that our data relies on a correct classification of thespecies. While due to our manual process we are certain that we usethe correct genus name for all entries, the same cannot be said for thespecies. Here we relied on the classification provided in the socialmedia post (if any), and only re-classified some specific specieswhich we knew well. A sizable portion of our entries (approx. 12%)thus only contains the genus of the respective plants locations. Anexpert botanist with a research background on these plants wouldcertainly be able to address these challenges. The abstract data plotsas well as the geographic maps are likely to support such an analysis,identifying outliers or non-classified plants in specific regions.

Figure 3: Example of a generated habitat map for Drosera arcturi.

5 CONCLUSION AND FUTURE WORK

Our work showcases an interesting and, to the best of our knowledge,previously not explored use of social media data for longer-term“events” such as plant grows in a specific habitat/region. We exploredboth geographic as well as abstract data analysis techniques for thisdata, and it would be interesting in the future to also explore space-time representations [2]. We are also planning to investigate machinelearning approaches to be able to identify flowers on the pictures,which would allow us to plot flowering periods of the plants. We alsoplan to investigate if ML approaches could support genus or speciesidentification to be able to further check the data or automate the datacollection process. The latter should also generally be integrated inthe visualization tool, so that we can merge the currently separateprocesses of data collection, correction, and visualization.

We collected our dataset and built our visual exploration tool dueto an interest in carnivorous plants, it can thus be used by enthusiaststo visit habitats. Our tool also has the potential to be used by plantbiologists or botanists to study the habitat information we collectedas well as by conservationists to become aware of unknown sites aswell as the evolution of known sites. For the latter of the two appli-cations, however, we point out that our dataset is limited becausefor some species we only have very few entries (e. g., currently 110species with 5 entries or fewer each). So users need to be aware ofsmall-number statistics issues when analyzing the data. In addition,the previously mentioned data biases need to be considered. Basedon the protection status of many carnivorous plants, however, wewill not make the tool or data publicly available to prevent poachingand will only share it with researchers in the application domain.

REFERENCES

[1] G. Andrienko, N. Andrienko, P. Bak, S. Kisilevich, and D. Keim. Analy-sis of community-contributed space- and time-referenced data (exampleof Flickr and Panoramio photos). In Proc. VAST (Posters), pp. 213–214.IEEE Computer Society, Los Alamitos, 2009. doi: 10.1109/VAST.2009.5333472

[2] B. Bach, P. Dragicevic, D. Archambault, C. Hurter, and S. Carpendale.A descriptive framework for temporal data visualizations based on gen-eralized space-time cubes. Computer Graphics Forum, 36(6):36–61,2017. doi: 10.1111/cgf.12804

[3] D. J. Crandall, L. Backstrom, D. Huttenlocher, and J. Kleinberg. Map-ping the world’s photos. In Proc. WWW, pp. 761–770. ACM, New York,2009. doi: 10.1145/1526709.1526812

[4] D. E. Jennings and J. R. Rohr. A review of the conservation threats tocarnivorous plants. Biological Conservation, 144(5):1356–1363, 2011.doi: 10.1016/j.biocon.2011.03.013

[5] S. Kisilevich, M. Krstajic, D. Keim, N. Andrienko, and G. Andrienko.Event-based analysis of people’s activities and behavior using Flickrand Panoramio geotagged photo collections. In Proc. Information Visu-alisation, pp. 289–296. IEEE Computer Society, Los Alamitos, 2010.doi: 10.1109/IV.2010.94

Exploring Carnivorous Plant Habitats based on Images from Social Media

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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 duethe 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 whichapparently 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 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 plotshows 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 recentbins 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, orpotentially it is caused by when we conducted the searches for potential picture IDs on Flickr (which we then cached for later manual inspection).

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

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Figure 10: Comparison of the monthly sightings of two sundew species: (a) Drosera rotundifolia which occurs on the Northern Hemisphereand (b) Drosera arcturi from the Southern Hemisphere. The two plots clearly show the different growth periods in the Northern and SouthernHemispheres, 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 JeremyRiel (cbn CC BY-NC 2.0).

Figure 15: In exploration mode with the trust mapping enabled, a sitein the United States that shows the different categories of verficationfor a 1 km search radius (red: not verified by other users; orange:other plants by other users; yellow: other plants of same genus byother users; and green: other plants of same species by other users).Same map section as in Fig. 16.

Figure 16: In exploration mode with the trust mapping enabled, a sitein the United States that shows the different categories of verficationfor a 4 km search radius (red: not verified by other users; orange:other plants by other users; yellow: other plants of same genus byother users; and green: other plants of same species by other users).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 probablynot manipulated or we cannot make a good judgment (green).

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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 Sarracenia purpurea: (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-clopedia of Life group by 2012, providing an estimate of the expected globallikelihood for habitat image posts. Image by Roderic Page (cb CC BY 4.0).

(b) The same heatmap generated based on the currently available data of349,011 posts up to July 2019, generated with the same script by Roderic Page.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 (formerlyyellow) 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 ofanimals, plants, fungi, protists, and bacteria: Since they cover a similar subject matter but for all species in general, the maps can beused 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: 100–999images; 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., thebackground 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 insteadquery it only in irregular intervals. Flickr image 6855169886 by Franck Michel (cb 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; cbn CC BY-NC 2.0), (b) botanical gardens or similar (Flickr image 5086658928 byJane Nearing; cbn CC BY-NC 2.0), and (c) plants kept at home (Flickr image 10753181585 by Mike Linksvayer; cz CC0 1.0). Other reasonsfor rejecting entries include name collisions with geographic place names, GPS locations at unsuitable locations (e. g., farmland, streets), andimages that do not show any or not the right plants (e. g., name collisions with common or scientific names, general textual habitat descriptions inthe comments of the postings).

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Drosera tomentosaUtricularia flaccidaUtricularia bremii

Drosera ascendensUtricularia nanaRoridula dentata

Drosera occidentalisDrosera alba

Drosera trinerviaUtricularia calycifida

Utricularia neottioidesDrosera parvula

Utricularia tenellaDrosera darwinensis

Nepenthes pilosaGenlisea filiformisUtricularia stygia

Utricularia involvensDrosera bulbosa

Nepenthes distillatoriaUtricularia endresii

Nepenthes ventricosaNepenthes mirabilis x rafflesiana

Nepenthes northianaNepenthes stenophylla x reinwardtiana

Nepenthes villosa x rajahNepenthes murudensisNepenthes hurrelliana

Nepenthes lowii x rajahDrosera androsacea

Nepenthes alataDrosera esterhuyseniae

Nepenthes reinwardtiana x stenophyllaDrosera villosaDrosera hilaris

Byblis (unclassified)Utricularia geminiscapa

Nepenthes angasanensisNepenthes beccarianaNepenthes sumatrana

Drosera murfetiiNepenthes stenophylla x fusca

Drosera natalensisNepenthes tobaica

Nepenthes spectabilisNepenthes platychila

Utricularia laciniataNepenthes veitchii x stenophylla

Nepenthes rajah x burbidgeaeDrosera ordensis

Utricularia kimberleyensisNepenthes x hookeriana

Byblis filifoliaNepenthes vogelii

Nepenthes suratensisDrosera chrysolepisUtricularia menziesii

Drosera barbigeraDrosera scorpioides

Nepenthes macrovulgarisDrosera madagascariensis

Utricularia odorataDrosera macrophylla

Sarracenia x catesbaeiCephalotus follicularis

Drosera miniataPinguicula villosa

Utricularia heterosepalaUtricularia erectifloraUtricularia geoffrayi

Utricularia delphinioidesNepenthes ampullaria x reinwardtiana

Nepenthes maxima x eymaeUtricularia minutissima

Utricularia unifloraNepenthes muluensis

Heliamphora (unclassified)Drosera brevicornisDrosera stoloniferaUtricularia tricolor

Nepenthes chanianaDrosera hyperostigma

Drosera cistifloraUtricularia floridana

Byblis linifloraSarracenia oreophila

Nepenthes hirsutaNepenthes ramispina

Utricularia micropetalaNepenthes gymnamphora

Utricularia amethystinaPinguicula balcanicaUtricularia longifolia

Drosera monticolaUtricularia arenaria

Genlisea (unclassified)Nepenthes hamata

Drosera stenopetalaUtricularia multifidaPinguicula longifolia

Drosera dilatato-petiolarisPinguicula macroceras

Nepenthes singalanaNepenthes pitopangiiUtricularia welwitschii

Drosera giganteaDrosera rosulataUtricularia alpina

Drosera aliciaeUtricularia striatula

Utricularia graminifoliaUtricularia asplundiiPinguicula parvifolia

Drosera linearisDrosera ultramafica

Nepenthes fuscaUtricularia leptoplectra

Drosera capensisUtricularia striata

Drosera pallidaUtricularia resupinataUtricularia laterifloraSarracenia x moorei

Nepenthes kampotianaNepenthes aristolochioidesNepenthes x kinabaluensis

Nepenthes pervilleiUtricularia chrysantha

Utricularia caeruleaDrosera fulva

Nepenthes maximaNepenthes faizaliana

Drosera unifloraDrosera roraimaeSarracenia rubraPinguicula mundiUtricularia fulva

Utricularia reticulataPinguicula moranensis

Pinguicula calyptrataPinguicula corsica

Nepenthes lowiiDrosera aberransUtricularia radiata

Utricularia jamesonianaDrosera erythrorhiza

Drosera montanaNepenthes madagascariensis

Utricularia purpurascensUtricularia unifolia

Pinguicula leptocerasUtricularia simulans

Drosera x obovataUtricularia pusilla

Utricularia macrorhizaNepenthes edwardsiana

Utricularia junceaUtricularia foliosa

Nepenthes sanguineaDrosera menziesiiDrosera petiolaris

Utricularia intermediaPinguicula elongataNepenthes mirabilis

Pinguicula nevadensisNepenthes stenophylla

Utricularia aureaPinguicula ionantha

Nepenthes albomarginataNepenthes veitchii

Nepenthes bicalcarataDrosera glanduligeraUtricularia uliginosa

Sarracenia jonesiiNepenthes burbidgeae

Drosera pygmaeaPinguicula primulifloraDrosera neocaledonica

Drosophyllum lusitanicumUtricularia australis

Nepenthes macfarlaneiPinguicula crystallina

Utricularia minorDrosera whittakeriNepenthes gracilis

Utricularia dichotomaNepenthes reinwardtiana

Pinguicula caeruleaDrosera indica

Nepenthes tentaculataDrosera burmannii

Nepenthes vieillardiiPinguicula planifolia

Pinguicula vallisneriifoliaUtricularia gibba

Nepenthes villosaDrosera macrantha

Sarracenia (unclassified)Utricularia inflata

Utricularia purpureaNepenthes rajah

Pinguicula lusitanicaDrosera arcturi

Utricularia subulataDrosera binata

Utricularia cornutaDrosera brevifolia

Pinguicula grandifloraSarracenia alataPinguicula alpinaSarracenia minorDrosera capillarisPinguicula pumila

Pinguicula (unclassified)Pinguicula lutea

Nepenthes rafflesianaDrosera spatulata

Dionaea muscipulaSarracenia rosea

Nepenthes ampullariaUtricularia vulgaris

Sarracenia psittacinaUtricularia (unclassified)

Drosera peltataDrosera filiformis

Drosera anglicaSarracenia leucophylla

Sarracenia flavaNepenthes (unclassified)

Pinguicula vulgarisDarlingtonia californica

Drosera intermediaDrosera (unclassified)

Sarracenia purpureaDrosera rotundifolia

1111111111111111111111111111111111111111111111111111122222222222222222222233333333333333334444444555555556666666666777777777788888899999101010101111111112131414141415151616161616161617171717181818181920202020212122222324262626282828292930303234343435363839404142434450535355

6061626364

7475768080

939598

105107110

120141

157178

193205

244260

305322

468541

9701301

Figure 25: Species count in complete database of all species that we found, sorted by rank.

0 200 400 600 800 1000 1200

Utricularia welwitschiiUtricularia vulgarisUtricularia unifoliaUtricularia uniflora

Utricularia uliginosaUtricularia tricolorUtricularia tenella

Utricularia subulataUtricularia stygia

Utricularia striatulaUtricularia striata

Utricularia simulansUtricularia reticulata

Utricularia resupinataUtricularia radiataUtricularia pusilla

Utricularia purpureaUtricularia purpurascens

Utricularia odorataUtricularia neottioides

Utricularia nanaUtricularia multifida

Utricularia minutissimaUtricularia minor

Utricularia micropetalaUtricularia menziesii

Utricularia macrorhizaUtricularia longifolia

Utricularia leptoplectraUtricularia laterifloraUtricularia laciniata

Utricularia kimberleyensisUtricularia juncea

Utricularia jamesonianaUtricularia involvens

Utricularia intermediaUtricularia inflata

Utricularia heterosepalaUtricularia graminifolia

Utricularia gibbaUtricularia geoffrayi

Utricularia geminiscapaUtricularia fulva

Utricularia foliosaUtricularia floridanaUtricularia flaccida

Utricularia erectifloraUtricularia endresii

Utricularia dichotomaUtricularia delphinioides

Utricularia cornutaUtricularia chrysantha

Utricularia calycifidaUtricularia caerulea

Utricularia bremiiUtricularia australis

Utricularia aureaUtricularia asplundiiUtricularia arenaria

Utricularia amethystinaUtricularia alpina

Utricularia (unclassified)Sarracenia x moorei

Sarracenia x catesbaeiSarracenia rubraSarracenia rosea

Sarracenia purpureaSarracenia psittacinaSarracenia oreophila

Sarracenia minorSarracenia leucophylla

Sarracenia jonesiiSarracenia flavaSarracenia alata

Sarracenia (unclassified)Roridula dentata

Pinguicula vulgarisPinguicula villosa

Pinguicula vallisneriifoliaPinguicula pumila

Pinguicula primulifloraPinguicula planifoliaPinguicula parvifolia

Pinguicula nevadensisPinguicula mundi

Pinguicula moranensisPinguicula macroceras

Pinguicula luteaPinguicula lusitanicaPinguicula longifolia

Pinguicula leptocerasPinguicula ionantha

Pinguicula grandifloraPinguicula elongata

Pinguicula crystallinaPinguicula corsica

Pinguicula calyptrataPinguicula caerulea

Pinguicula balcanicaPinguicula alpina

Pinguicula (unclassified)Nepenthes x kinabaluensis

Nepenthes x hookerianaNepenthes vogelii

Nepenthes villosa x rajahNepenthes villosa

Nepenthes vieillardiiNepenthes ventricosa

Nepenthes veitchii x stenophyllaNepenthes veitchiiNepenthes tobaica

Nepenthes tentaculataNepenthes suratensisNepenthes sumatrana

Nepenthes stenophylla x reinwardtianaNepenthes stenophylla x fusca

Nepenthes stenophyllaNepenthes spectabilisNepenthes singalana

Nepenthes sanguineaNepenthes reinwardtiana x stenophylla

Nepenthes reinwardtianaNepenthes ramispina

Nepenthes rajah x burbidgeaeNepenthes rajah

Nepenthes rafflesianaNepenthes platychilaNepenthes pitopangii

Nepenthes pilosaNepenthes pervillei

Nepenthes northianaNepenthes murudensis

Nepenthes muluensisNepenthes mirabilis x rafflesiana

Nepenthes mirabilisNepenthes maxima x eymae

Nepenthes maximaNepenthes madagascariensis

Nepenthes macrovulgarisNepenthes macfarlaneiNepenthes lowii x rajah

Nepenthes lowiiNepenthes kampotianaNepenthes hurrelliana

Nepenthes hirsutaNepenthes hamata

Nepenthes gymnamphoraNepenthes gracilis

Nepenthes fuscaNepenthes faizaliana

Nepenthes edwardsianaNepenthes distillatoria

Nepenthes chanianaNepenthes burbidgeaeNepenthes bicalcarataNepenthes beccariana

Nepenthes aristolochioidesNepenthes angasanensis

Nepenthes ampullaria x reinwardtianaNepenthes ampullaria

Nepenthes albomarginataNepenthes alata

Nepenthes (unclassified)Heliamphora (unclassified)

Genlisea filiformisGenlisea (unclassified)

Drosophyllum lusitanicumDrosera x obovataDrosera whittakeri

Drosera villosaDrosera uniflora

Drosera ultramaficaDrosera trinervia

Drosera tomentosaDrosera stolonifera

Drosera stenopetalaDrosera spatulata

Drosera scorpioidesDrosera rotundifolia

Drosera rosulataDrosera roraimaeDrosera pygmaeaDrosera petiolaris

Drosera peltataDrosera parvulaDrosera pallida

Drosera ordensisDrosera occidentalis

Drosera neocaledonicaDrosera natalensis

Drosera murfetiiDrosera monticolaDrosera montanaDrosera miniata

Drosera menziesiiDrosera madagascariensis

Drosera macrophyllaDrosera macrantha

Drosera linearisDrosera intermedia

Drosera indicaDrosera hyperostigma

Drosera hilarisDrosera glanduligera

Drosera giganteaDrosera fulva

Drosera filiformisDrosera esterhuyseniae

Drosera erythrorhizaDrosera dilatato-petiolaris

Drosera darwinensisDrosera cistiflora

Drosera chrysolepisDrosera capillarisDrosera capensis

Drosera burmanniiDrosera bulbosa

Drosera brevifoliaDrosera brevicornis

Drosera binataDrosera barbigera

Drosera ascendensDrosera arcturiDrosera anglica

Drosera androsaceaDrosera aliciae

Drosera albaDrosera aberrans

Drosera (unclassified)Dionaea muscipula

Darlingtonia californicaCephalotus follicularis

Byblis linifloraByblis filifolia

Byblis (unclassified)

5110

143

213

153

167

1510

711

1643

142115

228

32

16477

11

1612

117

4226

3821

1016

3121

292

608

18

126

186446

1417

29

105970

1203

74205

21244

6341

1305

236

7623

356

18910

580

505

1519

6217

281110

304

6480

8121

3934

11

201

342111

1815

161

303

144

9315

18

113

118

28

142

261

117

134

329

79

1613

2220

17

12

10720

1260

31

426

1628

19

61135

952

130169

2217

1571

711

2411

414

217

22

406

46832

31

2068

1781

135

132

757

341

613

5521

53193

16

111

54198

32223

11

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