Tracking International Terrorism with Mycorrhizal Networks

Betty Reynolds1, and Agent F2

1 Department of Tree Sociology, Cramberry-Lemon University, Pittsburgh, PA, USA

2 Department of REDACTED, REDACTED University, Chantilly VA, United States.


It has been well established that trees talk to each other through underground chains of fungus called Common Mycorrhizal Networks (CMM). Affectionately called the Wood Wide Web, these networks allow for networks of trees to locally communicate and organize the transfer of water, carbon, nitrogen, local gossip and political pamphlets. Previous research suggested that these fungal networks only operated at a community level. Nutrient transfer back translation has shown this assumption is no longer valid in certain abnormal communities. In the woods of Germany, England, Wyoming, and many more locations, ISIS terrorist propaganda has been discovered in a handful of Douglas Fir communities and a growing prevalence has been seen in Birch populations. This paper will discuss the methodology, results and dangerous consequences of Islamic Radicalized tree communities in your backyard and how the terrorist organization has spread their radical message to the world’s forests.  

Keywords: Mycorrhizal Networks, Tryte Packet Communication, Language, Wood Wide Web, Flora-Political-Biosphere, Deciduous Sociology

1. Introduction

Ever since Mycorrhizal Networks and their positive roles in tree communities was discovered,  the Dendrology communities was forever changed. The newly discovered Wood Wide Web described by Giovannetti et al [1] comprising of mutualistic Arbuscular Mycorrhizal (AM) fungi have created a network which thrives around 80% of all land plant species. Several papers and TED talks later and it has been widely accepted by the academic community to be fact. 

What many of these papers never attempted to answer is what CMN tree community culture is actually like. With the technology to decode CMN tree language, very little work has been done in studying tree culture as noted in [2]. Such studies could only speculate on such questions. How do the communities really support each other? How do germination courtship rituals proceed in dense and loose tree communities? What family values do they hold? How can we even track and translate such information? The answers may surprise you. The goal of this study was to answer those questions.

Using a back translation method to translate nutrient transfer into a language that humans could understand, we were able to not only understand what trees were saying but who the trees really were. To the surprise of the research team, we found a simplistic culture among ~98% of tree communities and a dangerous community whose sole purpose was the establishment of a theocratic islamic state and to wage Salafi Jihadism around the world in an abnormal ~2%.     

2. Common Mycorrhizal Network Background

Understanding a tree takes patience and ingenuity. It took years of incremental research. It wasn’t until years after CMN communication was even discovered that scientists approached linguists and networks experts to tackle this problem. For centuries, the Egyptian language was lost until the Rosetta stone was discovered. With no tree Rosetta stone in site there was no telling if translating tree language would even be possible. Thankfully, with advances in machine learning, many of these tasks are now possible from scratch. There are many steps in decoding a language and they are much more do-able with modern technology.    

2.1 The Tree Syllable Alphabet 

The first major step in CMN translation occurred when Rawling discovered how to decode one tryte of CMN tree data [3]. Calling it a byte of data would even be deceiving. After tree and tri for three, bytes became trytes. Each tryte contained eight different molecular tree nutrients consisting of water, carbon or nitrogen. With those different combinations of three elements trees pass through CMN’s the equivalent of syllables could be exchanged across tree communities. These syllables were found to make up the tree alphabet.

Determining what made up the tree alphabet turned into an intellectual wrestling match. The tree translation community could not decide on what made up the alphabet. They could only agree that such an alphabet of trytes did indeed exist. Infamously, Franz and Jenkins debated the matter in each of their own argumentative papers [4] and [5]. It wasn’t until Franz and Jenkins finally were court ordered to share their data that they found out they were both right in [6]. Their collaboration published on their facebook friendiversary showed that the tryte syllable alphabet is location dependent. The alphabet adapted based on the local abundance of Carbon, Nitrogen and Water.    

2.2 Data Collection 

In general, tree surveillance technology use bunches of CMN highways which are grown into artificial hyphal branches through an organic router which logs each tryte into a database and then passes the tryte onto the next tree. After adapting wire shark for a tryte syllable database, the packet sniffing is trivial. Wire shark patched their software to handle trytes one month after the discovery of the communication packet. 

Unfortunately, broadly collecting data from CMN hyphal branching filamentous structures to build an international database is not trivial. Organic routers are not cheap and not every tree does a lot of communicating. To optimize organic router tryte packet sniffing, Sully determined that the best way is to find the mother tree in the community [7]. The most successful method for identifying the mother tree is to monitor a few randomly sampled hyphal bundles and monitor traffic. While monitoring, take a few team members to think negative feelings about each tree and slap around some trees individually as demonstrated in [8]. Because of the tree defense mechanisms, the mother tree will receive and create the most traffic.  

2.3 Syllable to Idea Association 

The next obvious step was associating those syllables to words and ideas. This became the most difficult step in tree language decoding. Centered at Stockholm Sweden is now the Center for Tree Language Translation (CTLT). There, an international effort has been underway over the last five years to collect raw tryte packet data across the world and tree species. With the CTLT’s massive database, an enormous cluster of computers have been using canned unsupervised machine learning techniques from the trendiest python libraries around the clock. The widely accepted opinion is that these canned ML functions work because the graphs they produce look really nice.

Again using the methods shown in [8], truth data was gathered using telepaths who worked tirelessly day and night thinking towards the community of trees, a predefined script of emotionally charged language which was likely to get the biggest rise from each community. With the data from the mother tree routers and the telepath truth data, we were able to finally begin matching combinations of tree syllables to ideas. After the language library was built our team of deciduous sociologists began international work of gathering cultural data blindly across the globe. 

Figure 1: Tree Language Translation configuration

Figure 1: Tree Language Translation configuration  

3. Analysis 

Using more canned machine learning libraries, we began taking tree data to map tree cultures across the world. Of our large database gathered by the CTLT, 98% of the tree communities were more or less the same. Below in figure 2 shows a typical word cloud produced from these tree communities. As was expected, most of the communication had to do with typical tree activities; fruiting, flowering, seasons, weather, nutrients, that sort of thing. The trees would also discuss local animals and other forest activity. If a threat was around, the CMN networks would always light up with ‘Axe!’ ‘Saw!’ or ‘Fire!’ and transfer nutrients where they can to avoid a communal loss.  

Figure 2: Normal Tree Community Word Cloud

Figure 2: Normal Tree Community Word Cloud

With a normal tree community modeled with our word clouds, we began to look for abnormalities. None of the tree sociologists could figure out how to get the dimensionality reduction libraries to work so we kept using the word cloud technique and looked for words and word sizes that were more than three sigmas away from our standard tree model. It’s nothing we’re proud of, but it worked. That’s when we found an abnormality in the tree communities that has turned out to be more of a danger to our forests than the pine beetle.        

4. Results

The abnormality we found appeared to be intense islamic radicalization appearing in small communities across the globe. None of the tree sociologist experts knew what was causing the radicalization but we can determine that the spread is aggressive. According to our other word clouds, tree cultures are never religious or even spiritual despite what has been theorized in [9]. Even in a laboratory setting, no known scientific research has found a way to introduce religion into a tree community. Latter Day Saint missionaries teamed up with the Wycliff Bible Translators have been working around the clock attempting to translate the new testament into a language that trees can understand. Likely due to cultural differences, the interdenominational campaign to spread salvation to the world’s forest has had no success. They couldn’t even get publishable results. There is something unkown about the militant Sunni jihadist movement that appears to be resonating with particular clusters of tree communities. Below in figure 2 is a word cloud from a typical tree colony that has been infected with radical islamic propaganda. The radicalized tree communities were so obsessed with the islamic faith so much that speech barely covered properly sharing resources or reproducing.

Figure 3: Radicalized Tree Community Word Cloud

Figure 3: Radicalized Tree Community Word Cloud

At first there were positive signs this problem would take care of itself. Due to the application of Sharia law, these tree clusters have appeared to die out at a slightly younger age due to fasting during Ramadan. Additionally, because the strict covering of reproductive organs had prevented flowering in the spring which caused these communities to never reproduce. If it weren’t for the physical interpretation of the concept of a Jihad, we would just let the communities die out. While nearby tree colonies of varying species may not accept the radical islamic message, the extremist trees began withholding nutrients through the CMN’s until all nearby trees began following the five pillars of Islam. The spread has only accelerated by fertilizer purchased internationally with captured Iraqi oil.

When the state department and interpol got involved we got the technical support and resources to begin tracking the spread at a more granular level. The going theory is that the mechanism for cross forest spread is a result of the CMN hyphal network picking up dark web information from buried google fibres. Because these frequencies occur in such a high spectrum we have been limited in our physical tracing.  According to the US’s Department it actually was using to across the ! With that revelation the Forces were able to start . Then . Before the communities had a chance to recover we then started . Almost makes you feel sorry for them. Most did not survive but we assume many more still exist.

5. Conclusion

Unless something like is done globally, some experts believe that most forests will be threatened by extremist islamic terrorist tree colonies and it’s not just the trees that suffer. Just two weeks ago a coordinated attack using fallen branches injured a dozen barbecuers at the rib and pulled pork events in a bbq competition in Dallas. Across metropolitan areas, root systems around some city parks are making sidewalks impossible for morning joggers in sports bras to get that cardio in. It’s freedoms like these  we have to protect from international terrorist tree colonies. 

Other than communication monitoring and , the more serious tree colonies we identified in the study have been destroyed using agent orange armed remotely piloted drones. Now it’s every American’s duty to know the warning signs and immediately report any suspicious tree behavior to your local police department. Whatever you do, don’t panic when your backyard maple loses its leaves during Ramadan. Just call the authorities.  


  1. Manuela Giovanetti et al 2006 At the Root of the Wood Wide Web :: Plant Signal Behavior  
  2. Hermit Joe 2008 The Canopy’s the Limit and What Tree Culture May have in Store for Humanity :: Self Published
  3. Rawling J. 2010 Basic Tryte Packet Structure for Tree Communities :: Journal of Bio-Comm
  4. Franz H. 2012 Definitive List of International Tryte Alphabets :: Journal of Forrest Languages 
  5. Jenkins R. 2012 A More Likely International Tryte Alphabet than Franz H’s :: Journal of Forrest Languages. 
  6. Franz H. and Jenkins R. 2013 Multi Cultural Tryte Alphabets and Why Available Tryte Components Matter :: Journal of Forrest Languages
  7. Sully J. 2017 Finding Mother Tree: The Hub of Life :: Journal of Greater Tree Sociology
  8. Belleci T and Imahara G. Plants Have Feelings :: Mythbusters Episode 61 
  9. Pocahontas 1995 Colors of the Wind :: Self Published

If you enjoyed this article please like, share, and subscribe with your email, our twitter handle (@JABDE6), or our facebook group here for weekly content.

Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: