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ESA: Regime Shifts in the Anthropocene

Last year I was supposed to present this talk at ESA100 but a delayed visa made me miss the opportunity to share the main results of my PhD with the ecological society of America. This year and with the support of the PlosONE early career travel awards, I’m presenting my talk Regime Shifts in the Anthropocene at ESA101 in Fort Lauderdale. Here are the slides and the abstract of my talk.

 

Abstract:

Human action is driving worldwide change in ecosystems. While some of these changes have been gradual, others have led to surprising, large and persistent ecological regime shifts. Such shifts challenge ecological management and governance because they substantially alter the availability of ecosystems services, while being difficult to predict and reverse. Assessing whether continued global change will lead to further regime shifts, or has the potential trigger cascading regime shifts has been a central question in global change policy. Addressing this issue has, however, been hampered by the focus of regime shift research on specific cases or types of regime shifts. To systematically assess the global risk of regime shifts we conducted a comparative analysis of 25 types of regime shifts across marine, terrestrial and polar systems; identifying their main drivers, and most common impacts on ecosystem services. We use network analysis to demonstrate that regime shifts share clusters of direct and indirect drivers that shape opportunities for management.

While climatic change and food production are common drivers of regime shifts, drivers’ diversity undermine blue print solutions. Drivers co-occurrence vary with management scale and ecosystem type. Subcontinental regime shifts have fewer drivers related to climate; aquatic regime shifts share more drivers, often related to nutrient inputs and food production; while terrestrial regime shifts have a higher diversity of drivers making their management more context dependent. Given this variety of drivers, avoiding regime shifts requires simultaneously managing multiple types of global change forces across scales. However, there are substantial opportunities for increasing resilience to global drivers, such as climate change, by managing local drivers. Such coordinated actions are essential to reduce the risk of ecological surprises in the Anthropocene. Because many regime shifts can amplify the drivers of other regime shifts, continued global change can also be expected to increase the risk of cascading regime shifts. Nevertheless, the variety of scales at which regime shift drivers operate provides opportunities for reducing the risk of many types of regime shifts by addressing local or regional drivers, even in the absence of rapid reduction of global drivers.

Book review: Phase Transitions

IMG_0382

Phase Transitions by Ricard Solé was one of those books that nurtured my curiosity and motivated me to carry on with my PhD. Ana, my girlfriend at the time (2011), always suggested me to bring nice books for holidays that would distract me from work, books with stories or authors from the places we were visiting . But with Solé it was difficult to leave it at home. Most of the book was read in 2012-13 on the beaches and bars of Barcelona, Solé’s home; and believe or not, it did distracted me from work by making me looking it from a different perspective.

Phase Transitions is the concept that physicist like Solé use to describe changes in dynamic systems with bifurcations – changes between different states of organisation in complex systems. It’s the same as ‘critical transitions’ or critical phenomena, as other authors like Marten Scheffer prefer to use; or ‘regime shifts’ as ecologist often call them. But that’s just jargon. I read the book too long ago to be able to give a fair summary and highlight its most important lessons. However, this review will be more from an emotional perspective, what I like and dislike from that bunch of math.

The book is an amazing resource for teaching. It’s structured in 16 very short chapters, most of them don’t exceed the 10 pages. Yet they cover as many disciplines as you can imagine, it’s like brain candy for an interdisciplinary inclined mind. Chapter 1-5 set up the basics: what are phase transitions, analysis of stability and instability, bifurcations, percolations and random graphs. Solé keeps the mathematics to a minimum, any student without a strong maths background like me follows and enjoy more the story that the mathematical subtleties. He also guide you on how the math or the set of equations that helps you understand something, say percolations, are also useful to understand what looks like unrelated topics  such as cancer dynamics or lexical change in a language.

And that is exactly what I like of the book. Chapters 6 – 16 takes you on a journey of where phase transitions have applications in different fields in science: the origins of life (6), virus dynamics (7), and cell structure (8) for the biology inclined.  For the medicine inclined: epidemic spreading (9), gene networks (10), and cancer (11). For someone like me: ecological shifts (12), social collapse (16), information and traffic jams (13) and collective intelligence (14). And my absolute favourite: language (15) because it surprised me how phase transitions can be used to understand change in language, and also because it introduced a very peculiar model called the hypercube. Now what I dislike of the book was the incomplete list of references, imagine if the one missing is the one you want to follow up!

I took the book out of the shelf today and look at it with nostalgia. Last week I read a paper that studies depression as a critical transition using models of symptoms networks with thresholds (co-authored by Scheffer, the author of the book that inspired this blog), and today I accidentally ended up watching the video below on how music can also have basins of attraction. That feeling of déjà vu, that two disparate fields can have something fundamental in common, that we can learn music and better understand depression or cancer and viceversa; that’s what makes me in love with science. That’s what I enjoyed the most of Solé’s book, it opened the horizon of what I was actually doing on my PhD and helped me feel less afraid of exploring; otherwise how does one make the nice connections?

 

Writing advise

Few words of advise from Steven Pinker.

 

Sustainability science

There is a lot of talk about what is, what is not or how should it be. As a note to the self: Juan remind that ‘sustainability’ is an adjective, the noun is ‘science’. So make it science first and then add the adjective that comes more handy: social, natural, inter, trans… blah blah. Adjectives is a matter of taste, but first it has to be science.

Coming back to the ‘sustainability’ side, there has been a number of editorials in Nature and Science clarifying the need and challenges of the new international program Future Earth. Exciting news for a guy on the sustainability science business like me. However, the editorial of Nature (March 3, Vol. 531, p8) explains why the world remains skeptical of such effort, it is questioned whether the program will be able to deliver on something that is fashionable but conceptually unproven. In other words, it is calling for useful and empirically based science that helps making difficult decisions… not another conceptual framework but more jargon than substance.

Future sustainability research, no matter how interdisciplinary, should build on that heritage and focus on finding and closing knowledge gaps. In doing so, scientist involved in Future Earth can provide an invaluable service to society. And researchers in niche disciplines – paleoclimatology or behavioural science, say – who work to fill those gaps will get a welcome chance to put their work into a broader context.

Future Earth might also become a showcase for linking natural and social sciences – a real necessity given that human activity is altering the planet at worrying speed. But sustainability research must not become tied in the straitjacket of conceptualism and utilitarianism. Scientist are not merely service providers. As in any other field of science, sustainability research must remain at its core a curiosity-driven affair.

The author clarifies at the beginning of the editorial that sustainability science does not have the luxury of timelessness. Sustainability science is one of the urgent science that deals with issues with socio-economic stakes, where high decisions are needed (aka. a post-normal science), and where time, funding or ethics do not allow for the perfect experiment or traditional scientific methods.

What to do when you miss conferences because of visas? – Keep an eye on Twitter

Screw up visas. It arrived last night, just two weeks late to be able to attend the ESA meeting – Ecological Society of America who this year celebrate their centenary anniversary. I missed the opportunity to present my work and get feedback from the ecologist community; and more importantly, get to know what other ecologist are doing. It’s a pity. Earlier this summer I also missed the International Conference of Computational Social Sciences and another meeting in the US regarding the Arctic Resilience Assessment. Last year I also missed the European Conference of Complex Systems, my mum got sick and I had to travel home to take care of her. Luckily my mum is better now, and with so many exiting academic events that one cannot attend either due to visa restrictions, lack of funding, or unfortunate life events; one has to come with some alternative solution to get to know what is going on. Here is my solution: I mine twitter.

Twitter is not a perfect source of data, but at least is free and gives you a flavour of what the digital conversation is about. At the end of the day, humans are sensors of that reality you’re missing and leave traces of what they find interesting on the digital world. Twitter is not a perfect source of data because it’s biased: only people with access to smartphones  or internet connection tweets, twitter is mostly used on certain age groups that might not represent what is going on in the whole community (in this case of mostly ecologist), and you never have certainty on how well  is your data sampled. Anyways, is free and you don’t need a visa to play with twitter data, although some restrictions do exist.

At ESA people was asked not to tweet unless speakers allowed for tweeting at the beginning of their talks. Despite the non-twitter policy of the #ESA100 meeting (that’s was the official twitter hashtag), I managed to recover over 18000 tweets from 2589 twitter users. That’s huge!! Just to put perspective to those numbers, other conferences I’ve observed on twitter without the non-twitter policy include:

  • International Conference of Computational Social Science: #ICCSS2015, 2288 tweets from 570 users.
  • Network Science conference 2015: #NetSci15, >2000 tweets, ~550 users (of which I analysed 801 from 195 users)
  • European Conference of Complex Systems: #ECCS14, 2330 tweets, 399 users
  • EAT Forum Stockholm: #EAT2015, 897 tweets, 560 users (I missed the first day of data)
  • Resilience conference 2014: #Resilience2014, 2042 tweets, 442 users
  • World Water Week 2014: #wwweek, 1599 tweets, 793 users

So by comparison, not only was #ESA100 huge, it was also full of virtual activity despite its non-tweeting policy. Tweeting activity is nevertheless quite predictable at least in time. You would expect burst of activity around the plenary sessions in the mornings and afternoons, less so during nights and before / after the conference. Here is how it looks:

timelineESA100

As you can see from the figure above, I don’t have data for the tweets before the conference. That’s one of the limitation of the Twitter API, you can ask for tweets but they decide which ones and which time period you get. Previously (last year) there was a window of 4 days that you could look in the past. Now it allows you to go further in the past and harvest more data but still it is not perfect. And since I only do this as hobby, I’m not up to date with the constant API terms of use changes. As expected there is peaks of activity during the day and valleys at night, in some days you can even observe the lunch break between two peaks. Is good that people mingle together and put down the phone from time to time. But who is this people? who is talking to who and about what?

The figure below depicts a mention network, one node connects to another if the first mention the second on her/his tweet. Therefore is a directed network where 16% of the links are reciprocated. The node size is scaled by the degree aka. the number of connections in the network. One could also use the number of followers in twitter but since I’m interested on the conversation and who is the interesting people to keep an eye on while missing the conference, not on how popular they are on twitter, degree on the mention network is a good proxy of the quality of the tweets content. Although is not depicted on the graph, note that there is also link weights given that one user can mention another many times through different tweets. Thus some links are stronger than others. But besides the visual appealing of the picture, is not very informative: few people have lots of links while most of people have fewer links. This could be because some people is simply more active on twitter, or they tweet more interesting stuff that is worth mention / retweeting, or simply some other underlying process that is unknown from the data alone; for example that the person tweeting is a very famous ecologist or that it mentions president Barak Obama, or both. Anyways, extracting the core of nodes that everyone is talking about is good if you want to filter the information that the network as a whole is signalling as more important, instead of reading the whole +18000 tweets. You can extract them on a list and keep an eye on the most trending stuff.

ESAMentionNetwork

Who are they? Plotting the names on the graph would make it just messier. So here is the list of the top 50 #ESA100 twitter users given the number of times some one mention them. The number following the name is the number of links they have in the network, so the number of people who mention them.

  1. PLOS    248
  2. JacquelynGill    230
  3. leafwarbler    217
  4. srsupp    193
  5. DrEmilySKlein    174
  6. ESA_org    173
  7. ethanwhite    164
  8. SPBombaci    162
  9. ucfagls    154
  10. katteken    137
  11. DJPMoore    136
  12. openscience    134
  13. matthewgburgess    126
  14. skmorgane    123
  15. jhpantel    123
  16. annamgroves    121
  17. commnatural    113
  18. noamross    112
  19. polesasunder    108
  20. LeahAWasser    107
  21. DrNitrogen    106
  22. sjGoring    105
  23. sesync    104
  24. treebiology    102
  25. algaebarnacle    102
  26. tpoi    101
  27. ElenaBennett     98
  28. NEONInc     90
  29. jonbkoch     90
  30. MethodsEcolEvol     88
  31. PLOSEcology     86
  32. colindonihue     86
  33. tewksjj     82
  34. INNGEcologist     82
  35. jessicablois     81
  36. ESAOpenSci     81
  37. JoshGalperin     79
  38. elitabaldridge     78
  39. cjlortie     78
  40. GrunerDaniel     75
  41. MorphoFun     74
  42. JCSvenning     73
  43. bjenquist     72
  44. PLNReynolds     70
  45. fluby     69
  46. nceas     67
  47. wildwonderweb     66
  48. esanathist     66
  49. RallidaeRule     64
  50. davidjayharris     64

What were they talking about?

Since is the 100 ESA anniversary, here are the top 100 most retweeted tweets:

[1] “Theoreticians: stop telling us not to be scared of your equations. I’m not. Explain them well, like I do my methods, then continue #ESA100”          

  [2] “Watch President Obama wish the Ecological Society of America a happy 1OOth birthday on @Vimeo #ESA100 https://t.co/nhyaYmkt7C”                       

  [3] “#esa100 is a good time to announce that @uofa is looking for 5 new hires in Ecosystem Genomics | global to microbes http://t.co/OZAyoDyO86”          

  [4] “First speaker to #ESA100 recognises ESA’s contribution to the environment: President Obama. Am impressed! http://t.co/oDSMNXYPg4”                    

  [5] “Know of anyone looking for a PhD in ecology? Fully funded (!) at Wisconsin to work on bats and insects http://t.co/q3rGh9roZr #esa100”               

  [6] “#ESA100 friends, please read and RT my article on how to live-tweet scientific conferences! http://t.co/fMhDWivy9c #SciComm”                         

  [7] “Exciting news from @ESA_org Council Meeting: all ESA members will get free online access to ESA journals. #ESA100”                                   

  [8] “One of the nicest things you can do at meetings is to acknowledge the students trying to catch your eye and introduce themselves. #ESA100”           

  [9] “Tenure track job in ecological modelling with @JaneElith & the rest of us at @qaecology https://t.co/44jCNxRBiZ #ESA100”                         

[10] “weird that tweeting talks at #ESA100 with permission only. If you don’t want people discussing your work you should not present it.”                 

[11] “Scicomm resource guide to eco-communication #ESA100 http://t.co/h6nEbjaq9S http://t.co/Xv5qe0HxIS”                                                   

[12] “What were we Tweeting about at #ESA100? (H/T again @fmic_ for Twitter stats code http://t.co/SlyQHL0yDE) http://t.co/eWmUxQJDhP”                     

[13] “Of course Terry Pratchett already wrote everything I think about science and sci fi, and better than I could #ESA100 http://t.co/fMmM04NpHM”         

[14] “A few thoughts on #SciComm at #ESA100: Sharing science, stories & art; and @ESA_org’s social media confusion: http://t.co/7crijzElJ2”            

[15] “This is what students see: fewer women speaking. Imagine gender equality for ESA 2016. #ESA100 @ESA_org #WomenInSTEM http://t.co/irs3QmStKD”         

[16] “Slides from my #ESA100 talk on comparing different approaches to forecasting diversity. http://t.co/LoHIxgbidc w/links to code + grant”              

[17] “Our #ESA100 centennial paper out in Ecospehre: Climate change & microbial-plant interactions @ESA_org http://t.co/fTLU0xtTOL”                    

[18] “Top tweeps at the #ESA100 meeting.  (H/T @fmic_ for Twitter stats code http://t.co/SlyQHL0yDE) Good work, team! http://t.co/zOpkQO4PaS”              

[19] “#ESA100 \nThe world is big. Scientists are relatively small. Collaborate.”                                  

[20] “Overheard conversation by a bronycon goer: I think these are ecology people, there are a lot of Hawaiian shirts. #ESA100”                            

[21] “Test your talk in a simulator like ColorOracle first! http://t.co/PNhsJQsApv #esa100  https://t.co/aVlYUz1TED”                                       

[22] “#ESA100 1.2M publications in ecology (or more). A total of 40% captured by 4 terms: interactions, biodiversity, climate change, & gradients.”    

[23] “Happy #ESA100 & #BronyCon! Hasbro, DM me if you want to discuss marketing. #mylittlesturgeon #mylittlestudyspecies http://t.co/GhY4KD56yb”       

[24] “Too many talks that I can’t understand because the figures are not colourblind-friendly #ESA100”                                                     

[25] “Secrets to successful scientific networks: trust, time and early-career scientists. @e_seabloom @e_borer #ESA100”                                    

[26] “Let’s make ecology in the field safer for all: come to @Drew_Lab and my free workshop on Tues: http://t.co/icSjpaQE06 #ESA100 All welcome!”          

[27] “Our Postdoc Fellowship Program is now accepting applications! Pre-screening submissions due October 26: http://t.co/8EkdBzxZjX Attn: #ESA100”        

[28] “Yes, that’s @POTUS! RT @LPZ_UWI: Obama celebrates the #ESA100 centennial with us! http://t.co/M1MMpbKolD”                                            

[29] “Hello #ESA100 The @calacademy is hiring new biodiversity scientists! Lots of ’em! Do science, change the world! http://t.co/F9DvM3e1Hp”              

[30] “At our blog, you can submit your own “seed” of a Good Anthropocene: http://t.co/rIBLhUGGPF #ESA100”                                                  

[31] “A surprise birthday message to @ESA_org from @POTUS \”The health of our nation depends on the health of our environment\” #thanksobama #ESA100″      

[32] “Speakers: promote #openscience ! \nDon’t forget to tell your audience if you are OK live tweeting! #ESA100”                                          

[33] “#VirginiaTech is hiring a stream ecologist! Come talk to me at #ESA100 if you have questions: https://t.co/aNOXL7l2yN”                               

[34] “We’re seeking time-series data for a #biodiversity study. Do you have data to share? http://t.co/6PfCHWZMNI @maadornelas @mioconnor #ESA100”         

[35] “Research News at #ESA100 “Increase in red spruce growth tied to the Clean Air Act” @atkinsjeff http://t.co/yDm3w8vxUa http://t.co/rb9HO0ih9u”        

[36] “One thing clear at #esa100: The Anthropocene as an idea has won.”                                 

[37] “Dr Erwin: Change is the observable dynamic of the fossil record – there is not empirical evidence for equilibrium. \n\nYes!! #ESA100 #esapl2”        

[38] “Loss: Cat predation:  2.4 billion birds killed by cats in the US every year 70% from feral cats #ESA100″                                             

[39] “We might just need better (realistic, detailed, radical) visions of positive future. #GoodAnthropocenes #ESA100”                                     

[40] “Sketching your notes at #ESA100 ideas for creative expression from #ESASciComm @commnatural http://t.co/22kL1reSc7 http://t.co/kPYoVV74wu”           

[41] “When Science is Not Enough: Communicating the Scientific Consensus on #ClimateChange @samillingworth #scicomm #ESA100 http://t.co/eLDAzp6sr2”        

[42] “Hi #ESA100, please favorite this if you are interested in finding a way to convince the society to give a budget line to support @ESA_SEEDS.”        

[43] “\”we use statistics to hide the instability of our arguments\” http://t.co/R7BC2f1UMr #ESA100 #ecology #biology”                                     

[44] “Not sure I understand the no tweet policy at #ESA100. I mean why would you want it? You are already sharing your research w professionals”           

[45] “Scientists have a hard time talking about race. We also have a hard time listening. These are uncomfortable but vital conversations #ESA100”         

[46] “#ESA100 program change: new COS at 1050AM Fire alarm impacts on ecologist community dynamics http://t.co/hPW60xD55K”                                 

[47] “Beautiful data, carefully curated and presented, made available to the world in multiple formats. Surely this is the future. #ESA100”                

[48] “#ESA100 slide makers: allow me to recommend this color scale for your graphs in the future: http://t.co/FoTnVldbGL”                                  

[49] “Strong argument for allowing tweeting of conference talks & posters. #ESA100 #gsa2015  https://t.co/9bxHXga6dJ”                                  

[50] “You never know someone’s personal pronouns unless you ask. Some folks at #ESA100 write them on their badges. It’s always worth checking.”            

[51] “Access now! Functional Ecology Special Feature: Urban Ecology: http://t.co/IHEVetopyL  #ESA100 #UrbanEcology”                                        

[52] “Could #ESA100 moderators ask speakers if tweeting talk is okay?I bet most are okay with it but don’t know assent is required. @ESA_org”              

[53] “Diverse group of people better solve problems. Benefit of diversity to science goes up as problems get harder #esa100 @ESA_SEEDS”                    

[54] “What happens when the fire alarm goes off during talks at #ESA100 http://t.co/G1zjTLJEAA” 

[55] “My take from #ESA100 so far: Ecology is actually a loose collection of disciplinary silos that barely communicate.”                                  

[56] “Ecologists with mad data skills will catapult ecology into its next 100 years! #ESA100 #hackingecology”                                              

[57] “Coding is becoming crucial for #Ecology @MethodsEcolEvol Applications explain new software, equipment & tools #ESA100 http://t.co/FWgSo232hX”    

[58] “#ESA100: I’m adding to dataset on who asks ?’s after talks. Want to help? Just note gender of speaker & ppl asking them ?’s in your program.”    

[59] “.@KathiJoJo \”China alone is firing up a new coal plant every eight to 10 days\” #ESA100 https://t.co/ywkc5WSi4r”                                    

[60] “Anyone can tweet about my #ESA100 poster if they want: it’s up on @figshare and @github too. \nhttps://t.co/fwHlmI9o5y”                              

[61] “Slides from my #ESA100 talk on @nceas and @DataONEorg provenance tools in #rstats for reproducibility and #opendata https://t.co/vlsHXsT4YF”         

[62] “New blog post: Thoughts about #SciComm, #openscience, sharing, & social media confusion at #ESA100. http://t.co/7crijzElJ2”                      

[63] “Check out the highlights of my talk on Nitrogen fixation in tropical dry forests #ESA100 featured @PLOSEcology! \nhttps://t.co/i7kU68NcD9”           

[64] “From the audience: calculus is the *wrong* math. We’d be better off teaching stats & probability (& computing) #ESA100 #HackingEcology”      

[65] “So far very few talks have given permission to tweet. I wonder if bc they actively don’t want them shares or it’s not on their radar #ESA100”        

[66] “Conducting ecosystems research? Check out our methods, models, tools, & databases: http://t.co/2ihCI5Db1R\n#ESA100”                              

[67] “#ESA100 save a postdoc’s self esteem, live tweet a talk.”                                                       

[68] “@BarackObama helps celebrate the @ESA_org centennial! #ESA100 #POTUS http://t.co/gz0W91hujg”                                                         

[69] “Beginning wk of special #ecology #climatechange coverage for #ESA100; get the rundown at http://t.co/FNpye6kbhm http://t.co/ekg6oi4HNW”              

[70] “At #ESA100 @jagephart applies a climate change vulnerability framework to #foodsecurity in @PLOSEcology by @atkinsj http://t.co/rT5Di6yliT”          

[71] “The Gund Institute in #Vermont seeks 5 PhD students. Do great work in beautiful #BTV: http://t.co/KvXdx7jVoQ #ESA100 http://t.co/vU14Qy9MT5”         

[72] “Science is worthless unless it’s shared with others, yet academics incentivized to focus only on peer rev journals @JulieReynolds88 #ESA100”         

[73] “Fascinating ignite talk Rachel Vannette (http://t.co/lWuzTXhE1c): microbial effects on plant-pollinator interactions #ESA100”                        

[74] “Another reason for #ESA100 talks to be open to live tweeting: we have a global audience unable to attend conference! https://t.co/8kVQXsC3fR”        

[75] “Best #ESA100 fundraising #frisbee #secchidisk for @ESA_SEEDS by @duffy_ma @Drew_Lab @ESAAquatic @limnojess! http://t.co/wttjyh9Lm6”                  

[76] “All materials, slides, sources, code on @github & under CC-BY #openscience #ESA100 #rstats https://t.co/ocffOZsKL5 https://t.co/eJSMR0VmdQ”      

[77] “To tweet or not to tweet at conferences? Confusion at #esa100 http://t.co/9PA66JBVTO\n\n@Drew_Lab @ewanbirney @_Jni_ @ta_wheeler @ESA_org”           

[78] “Lenore Fahrig: \”All habitat has value, no matter how small\”. Major review shows habitat loss NOT fragmentation hurts biodiversity #ESA100″         

[79] “Powerful to hear Susan Harrison tell us her 15-yr field site was consumed by wildfires just 30 minutes ago. Here’s to new directions #ESA100”        

[80] “#BrightSpots, seeds of a #GoodAnthropocene: Pockets of a better future \nthat are already in existence today #ESA100  http://t.co/rIBLhUGGPF”        

[81] “#ESA100,Pres David Inouye,Scientific Plenary during #POTUS greeting video,Whooa, ESA and US Pres, doesn’t get better! http://t.co/i1ThOuwkyS”        

[82] “Can you guys at #ESA100 help me spread the good word on #sciart? https://t.co/opvO8isK70 Thanks! http://t.co/gG5S8gO2Q7”                             

[83] “How to educate all when we don’t value outreach &esp social justice work? When we pretend the meritocracy works? @RushHolt @ESA_org #ESA100”     

[84] “Lovejoy 2 degree Warming target chosen not for its ecological merit; means a world w/out tropical reefs e.g. #esa100 http://t.co/nQsHj8jbK9”         

[85] “Climate change shapes drought/flood frequency & severity @allingon on @PLOSONE @PLOSBiology papers & #ESA100 sessions http://t.co/B3EqPAvWx0”

[86] “New @PLOSEcology \”All Eyes on the Oceans: James Hansen & Sea Level Rise http://t.co/vIRFonX1Rb @sashajwright #ESA100 http://t.co/ZWmbW5OsSo”    

[87] “Another fun animation of an Am Nat Classic foundations of ecology in rhyme no less!  http://t.co/H6dcb2WbXb #esa100″                                 

[88] “.@ESA_org Another good way to keep important secrets is to *not* include them in presentations to groups of strangers #ESA100”                       

[89] “Best live-tweet advice so far: When tweeting 2+ times per talk, reply to your 1st to create a chain of tweets. Thx @PlantTeaching! #ESA100”          

[90] “The BronyCon people have red, yellow, & green tags on their name tags.  These are how willing they are to talk. Chat w/ green only #ESA100″      

[91] “Ecology from treetop to bedrock: human influence in earth’s critical zone #ESA100 – Ecotone (blog) http://t.co/WFSnddb6un”                           

[92] “@colindonihue #ESA100 Most favorited users (among users who tweeted 5+ times, excludes retweets). http://t.co/otms4ndCix”                            

[93] “Ecology in a Changing World: the #ESA100 centennial video http://t.co/ByaaYIhXlB”               

[94] “Conservation fuels ecological discovery, not just vice versa says Bill Fagan #ESA100”             

[95] “Slides from my #ESA100 ignite talk on \”Hacking ecology: Facilitating data-intensive research in ecology\” http://t.co/TD5BZYy3f0″                   

[96] “Fot those interested in R: a new R package called cati #ESA100  http://t.co/gVMwoKtReG”    

[97] “@Drew_Lab \”We didn’t have a tardis, but we had a museum collection!\” Going back in time to look at fish diversity in Bootless Bay. #ESA100″        

[98] “Time matters. Learn about temporal ecology and ecosystems at #ESA100 Thursday morning | http://t.co/dLmN9ZWESR http://t.co/e7c8BiVgsi”               

[99] “.@polesasunder created an #rstats package to analyze community time series data: codyn #HackingEcology #ESA100”                                      

[100] “.@ethanwhite on the cultural changes needed to get more scientists creating software tools:\nTrain\nHire\nCollaborate\nReward\n#esa100” 

The search produce slightly different results when one look on tweets that have been previously retweeted. It include tweets that are not listed above, for example:

“RT @flypod2: Know of anyone looking for a PhD in ecology? Fully funded (!) at Wisconsin to work on bats and insects http://t.co/q3rGh9roZr …”

“RT @PLNReynolds: 50 notable papers in #Ecology, all currently #OpenAccess! #ESA100 #ReadingList  http://t.co/Y1WYsYdVKA http://t.co/O6KDr3v…”

As you see, lots of job offers going on, president Obama was mentioned quite a bit, and gladly I was not the only one doing twitter analytics🙂 The first tweet was retweeted 92 times and the last one only 11. One of the topics that got retweeted a lot was about the live-tweeting policy, see tweets 6 and 10 as example with 53 and 44 retweets respectively. Do you think people was generally happy with the conference despite this tweeting policy?

Reading the >18000 tweets to figure it out is not a pleasurable read even if you couldn’t attend the conference like me. To answer such question one can use sentiment analysis, a text mining technique that ranks pieces of text (tweets in this case) given the presence of words that have been previously labeled as common when expressing positive or negative emotions. The labelled lexicon (~6800) was developed by Minqing Hu and Bing Liu, two computer scientist from University of Illinois and Microsoft respectively. You can download their lexicon and learn more about their work here.

The figure below shows the results of the sentiment analysis for the #ESA100 dataset. If a tweet got a zero score, it’s emotion content is neutral, if the score is positive is dominated by positive words and if the score is negative the opposite. The plot shows that the distribution of tweet emotions as learned from the Hu & Liu training lexicon are skewed towards the positive side. The top 10 positive tweets are:

  1. Come and see us at Booth 328. Play our game to win an exciting prize and enter our prize draw for $100 worth of books! #ESA100
  2. Wow! What an absolute pleasure to meet @kwren88! #ESA100 keeps getting better and better!
  3. Super excited to see that #sketchyourscience happened again at #ESA100! Good work #ESASciComm people! (#WishIWasThere)
  4. Du is making it easy for us by being super clear about whether results matched his predictions. Good thing b/c it’s late on Day 4 of #ESA100
  5. RT @srsupp: Scanga: You need to find a strong support network. Family friendly work, backup at home (and money can help). #earlycareer #esa…
  6. RT @JoshGalperin: .@uedlab – Ecologists look at the way #citites work, but they can work with designers to make cities work better. #ESA100…
  7. Jackson: interdisciplinary work is tough! Takes time and the right attitude/aptitude – they do work but still a major challenge #ESA100
  8. SeJin Song’s #ESA100 ignite talk was gorgeous, w/ vivid clear visuals. #ESASciComm would love to talk w/ her re design decisions!
  9. Big thank you to @leafwarbler for the great #ESA100 live tweeting — SO great for those who can’t be there! (like me😦 …)
  10. .@MCFitzpatrick: Realized niches overlap less and less as you go back in time. How well does this work and can it work better? #ESA100

And the top 10 + negative tweets are:

  1. @k_a_christopher Buddhism: suffering stems from greed, hatred, and/or delusion. Ecological problems often have same origins. #ESA100
  2. Comparing areas with american seagrass vs invasive asiatic sand sedge… Invasive areas are NOT more susceptible to erosion #ESA100
  3. Brown: sustainable development is thermodynamically unsustainable. A catastrophic crash seems almost inevitable #ESA100 very provocative.
  4. Cause of all environmental problems? Greed, hatred, and/or delusion says @ElBeeddha #ESA100
  5. #ESA100 poster 188: Alyssa Gehman #OdumSchool-Influences on infection by an invasive castrating parasite, 8:30-10:30 am 8/14 Exhibit Hall
  6. Scientists have a hard time talking about race. We also have a hard time listening. These are uncomfortable but vital conversations #ESA100
  7. Jim Brown: risk of a catastrophic earth collapse is >99.99%. I don’t see any way out. Time for ecologists to step up. #ESA100
  8. I see a problem with this picture: http://t.co/zMGVI42K1n hint: it’s the same problem the #ESA100 plenary suffered from …
  9. We lose minority STEM students after second univ year at alarming rates. What are we doing wrong? Focus on intro courses #esa100 @ESA_SEEDS
  10. Sorry about the fire alarm folks. The conv center sprinkler system activated; cause unknown #ESA100

15. RT @LauraEllenDee: Agreed! “@BonnieKeeler: Bummed to miss the #ESA100 Shark Tank. Hoping there will be live tweeting” https://t.co/005rayit…
sentimentESA100

As you can see (dear ESA organiser) there was not hard feelings against the policy, although tweets on both sides of the distribution point out to people sad of missing the conference and glad to see so much twitting activity. ehemmm just saying.

Another technique that I’ve used on my work to understand large amounts of unstructured data such as text is topic mining. Again, is not practical to read all tweets but thankfully there are methods out there to simplify noisy data and extract more valuable meaning. In topic mining by using the frequency distribution of words across documents one can fit the probability of a word belonging to a topic, and the probability of a topic explaining the contents of a document. A common technique to do so is called Latent Dirichlet Allocation. First I cleaned up the dataset creating a corpus without stop words, punctuation, the conference hashtags (#ESA100, #ESA2015), the twitter names of people mentioned and links to other webpages. That leave me with words that hopefully capture the topics of the twitter conversation. To better capture the variability of words I also get rid of overly popular words and extremely rare words that doesn’t contribute much when differentiating one topic from another.

TopicsWordClouds

Although the machine learning algorithm does its job, I’m not completely happy with the result. Each word cloud above summarises the most common words of 30 topics characterising the conversation of the 2589 twitter users. Each word is scaled according to how frequent they are in each topic. The problem with twitter data and topic models is that one ends up with more documents than words on them. Once the dataset is clean many tweets have few words or none at all, therefore the document term matrix is too sparse. A way to solve the issue would be to change the unit of analysis, the documents, from individual tweets to all tweets written by an user assuming that each person has a particular interest on the conference. If I’d have attended the conference, I’d probably look for talks related to regime shifts and methods to study them. Inherently each attendee has intentions and interest captured on their tweeting behaviour. But that’s probably for next blog post. If you want to play yourself with the topic model data, you can check this interactive visualisation.

Credits:

All this work was done in R following blogs by others and also scientific papers. If you are interested on this type of analysis just drop me a line and I can point you out towards some sources. The libraries I used are:

Jeff Gentry (2015). twitteR: R Based Twitter Client. R package version
1.1.9. http://CRAN.R-project.org/package=twitteR

Jeff Gentry and Duncan Temple Lang (2015). ROAuth: R Interface For OAuth.
R package version 0.9.6. http://CRAN.R-project.org/package=ROAuth

Ingo Feinerer and Kurt Hornik (2015). tm: Text Mining Package. R package
version 0.6-2. http://CRAN.R-project.org/package=tm

Bettina Gruen, Kurt Hornik (2011). topicmodels: An R Package for Fitting
Topic Models. Journal of Statistical Software, 40(13), 1-30. URL
http://www.jstatsoft.org/v40/i13/.

Jonathan Chang (2012). lda: Collapsed Gibbs sampling methods for topic
models.. R package version 1.3.2. http://CRAN.R-project.org/package=lda

Ian Fellows (2014). wordcloud: Word Clouds. R package version 2.5.
http://CRAN.R-project.org/package=wordcloud

Butts C (2008). “network: a Package for Managing Relational Data in R.”
_Journal of Statistical Software_, *24*(2). <URL:
http://www.jstatsoft.org/v24/i02/paper&gt;.

Carter T. Butts (2014). sna: Tools for Social Network Analysis. R package
version 2.3-2. http://CRAN.R-project.org/package=sna

Carson Sievert and Kenny Shirley (2015). LDAvis: Interactive
Visualization of Topic Models. R package version 0.2.
http://CRAN.R-project.org/package=LDAvis

Ramnath Vaidyanathan, Karthik Ram and Scott Chamberlain (). gistr: Work
with ‘GitHub’ ‘Gists’. R package version 0.3.1.9100.
https://github.com/ropensci/gistr

Without them it wouldn’t be as fun to play with Twitter data in R. Thanks guys!

Video: Ecology on a changing world

A nice video for the 100 anniversary of the Ecological Society of America. Ecology in a Changing World from Benjamin Drummond / Sara Steele on Vimeo.

What are the main drivers of regime shifts globally?

That was one of the key questions that inspired my PhD work. There is a buzz both in the media and the scientific literature that we are approaching a dangerous zone where the stability of the world ecosystems are at stake. Coral reefs are struggling and under a 2ºC warming scenario they will most likely disappear from many areas of the world. Every summer we hear of new ice free records in the Arctic while last few months there has been a consensus that Antarctica is also warming at a higher rate than previously expected. As today, boreal forest in Canada is burning at remarkably higher rates than usual. This year droughts have impacted strongly California and Brazil, with potential impacts on US food production and carbon storage in the Amazon respectively. Things are happening as ‘we speak’ and yet our knowledge about critical transitions in ecosystems is limited and often confined to well understood case studies (e.g. Jamaican coral reefs) and theoretical models. To the best of my knowledge, comparison of regime shifts exist for a handful of systems such as climate, agricultural landscapes, hydrological regime shifts, coral reefs and marine ecosystems.

Yesterday our paper Regime Shifts in the Anthropocene: Drivers, Risks, and Resilience was published in PlosONE. It address the question ‘What are the main drivers of regime shifts?’ by studying co-occurrence patterns of drivers reported by the regime shift database. It is the first large comparison of regime shift and their drivers, in fact we analysed 25 regime shifts types in marine (blue), terrestrial (green) and polar/subcontinental (orange) ecosystems. The figure below shows a network of drivers (57) on the left and regime shifts (25) on the right. The bigger the dot, the higher is the number of connections, which is is a proxy of the number of drivers a regime shifts has reported, or the number of regime shifts a driver is reported to cause. While nodes in the bottom show idiosyncratic drivers and regime shifts, the ones on the top are generalist, this is the most common drivers and the regime shifts with higher drivers diversity.

Screen Shot 2015-08-13 at 11.46.46

The main results of our work is that drivers related to climate change (e.g. droughts, floods, green house emissions) and food production (e.g. fishing, crops, use of fertilisers) are the main responsible for regime shifts globally. They co-occur together in patterns that one wouldn’t expect by pure chance, and this associations help us envisage management opportunities and challenges. The opportunities center around the knowledge base. We found that if two regime shifts share certain attributes such as occurring on the same ecosystem type, similar space and temporal scales, and impact similar ecosystem services; we can assume that they are caused by similar sets of drivers and therefore transfer successful management strategies from well-understood regime shifts to less understood ones. The challenge is to embrace drivers diversity. Addressing only well understood variables won’t preclude regime shifts from happening. Our work shows that these phenomena are often caused by a diversity of drivers and addressing them imply co-ordinated actions across scales, especially at the international level.

If you want to know more about our work, just follow the link above. The paper is on a open access journal and the data is also publicly available both in the regime shifts database and the public scientific repository Figshare. I happy to reply to questions or comments here or on the journal’s website.

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