.. a myth up there with the unicorn and guaranteed ways to lose weight without eating less or exercising more. But that doesn’t stop people from espousing their version of this myth (Peters 1991, Paine 2010, Likens and Lindenmayer 2011). The usual form is to attack some now trendy but supposedly horrendous version of science and then mildly conclude that the way the author does science is the only really good way to do science. In the latest version of this archetype, two esteemed ecologists, David Lindenmayer and Gene Likens (hereafter L&L) penned an almost vitriolic piece attacking “Open-Access Science”, “Big Science” and I don’t know what all else (that I’m going to call for short hand “new-fangled ecology” for now)*. Now I respect the work of both of these scientists. Gene Likens is deservedly a member of the US National Academy of Sciences. Likewise, Lindenmayer is a fellow of the Australian Academy of Science and were he American and if the US NAS did not have a bias against conservation biology you could make a good argument he would be in the US NAS. These are people who have earned the right to be listened to. So I don’t want to engage in just a slam down on their slam down.
But they did call me a “parasite” and implied it is likely I am doing “context-free, junk science”, which I feel at least entitles me to a response 🙂 And their arguments are oft repeated (although they’re usually expressed in a politer fashion) so the polite versions of the claims do deserve a thoughtful response. Stripped of vitriole, some of their arguments are even made by my own fellow blogger (but of course there the dialogue was civil even when I disagreed in the comments) (UPDATE from Jeremy: I think Brian actually meant this old, not very good post of mine). So let me briefly respond to three points they raise in their piece, then I want to use their piece as a launching point to talk about larger issues.
Three commonly repeated concerns about “new-fangled ecology” that are raised in especially vivid terms by L&L are:
- People who analyze data without collecting data aren’t pulling their fair share (or in L&L terms “There is also the emerging issue of a generation of what we term here as “parasitic” scientists who will never be motivated to go and gather data because it takes real effort and time and it is simply easier to use data gathered by others.”) – Ouch. Full confession – by their definition I am a parasite. I have never published on a dataset I personally have collected. But I respectfully disagree. The usual counterarguments are valid and usually are along the lines of noting that ecology is at the extreme end of the spectrum in terms of believing in individual ownership of data. Meteorology, astronomy, particle physics, even economics all see the data as a public good to a much greater degree than ecology. Of course in their cases, the collection of the data is funded by the government, wholly unlike in ecology 🙂 And taxonomy didn’t exactly go down the tubes when journals required placing sequences in GenBank before you could publish. Notwithstanding all of that I am sympathetic to the plight of somebody who has spent years collecting a dataset. I personally don’t think compelling an individual to share is the right path (as distinct from institutions like LTERs or NEON which are and should be compelled to share their data). So to me the most compelling reason I am not a parasite is that I have students and faculty (from my institution and literally from across the world) emailing me and coming to my office to ask me questions about what kind of statistics to use on their hard-won empirical data, or how to set up a simulation or what theoretical context they can place their data in. Happens almost daily, certainly multiple times a week. Sometimes I can answer in 5 minutes but often its an hour. Occasionally I am formally on their committee and thus in some sense obligated and compensated for doing it. And sometimes it turns into a full-blown collaboration where I am a coauthor. But the vast majority of the time, neither of these apply. Should I start calling my friends, colleagues and students parasites? Hardly! This is called being a good scientific citizen. If you want to spend time setting up a formal credit system where every hour of my consulting time earns me 100 lines of a dataset go ahead. Personally, I’m pretty happy with the current system. Academics don’t live in a world where everything can be put into one unit of currency and traded. We work much better in a spirit of generosity and openness and freely giving (and yes taking) building synergies across our distinct skill sets and circumstances.
- Data before questions (or in L&L terms “do[ing] science backwards … now that we have all the data, what question shall we ask? … junk science”) – From the tone of the article one envisions a couple of troll-like ecoinformaticians skulking into a secret room at ESA and chuckling about the data they’ve stolen from the poor field ecologists and then saying “now that we’ve captured the data, what do you think we should do with it?” and the other troll replying – “Gee questions are hard – we should have stolen the questions from the field scientists too”. OK a flight of fancy there. In all seriousness, I don’t know what secret rooms of troll-like ecoinformaticians L&L hang around with but I’ve been in a lot of those rooms at ESA and NCEAS and etc and I have NEVER heard a conversation that went like “here’s some data, what question can I ask”. If it happened, then I would agree that that was junk science, but it doesn’t happen. I have also edited at least 100 ecoinformatic papers and I’ve never seen a hint of this thought process in those papers either. The conversations I hear go much more like “I am asking question X and I just cannot for the life of me find the appropriate data – do you have any suggestions?” (exactly proving that even ecoinformaticians put primacy on the question). But “what kind of question can I ask now that I have data X?”. Literally never heard this among ecoinformaticians. There’s really nothing left to say on this topic except show me some hard facts and stop criticizing how you imagine other people do science. On the other hand, I have had a few (just a few) students who showed up in my office with a dataset they collected after two years in the field who didn’t seem too clear on which question motivated them to collect the data … 🙂 (I’m just saying!).
- Using data without a detailed knowledge of how it was collected and the ecology of the organsims is dangerous (or in L&L terms “Our extensive experience from a combined 80 years of collecting empirical data is that large data sets are often nuanced and complex, and appropriate analysis of them requires intimate knowledge of their context and substance to avoid making serious mistakes in interpretation.”) – As you might expect by the more moderate tone of the language from L&L, this is probably the most reasonable concern. Who would be opposed to users of data having more detailed knowledge of the data collection and the organisms? No one. But L&L go on to say “There is an increasing number of examples where increased knowledge is missed or even where substantially flawed papers are being published, in part because authors had limited or no understanding of the data sets they were using, nor any experience of the ecosystems or other entities about which they have written.” Sadly they don’t provide any citations to support this claim which makes it hard to refute. Surely if this were a scourge of ecology there would be a few dozen examples? Certainly there have been some meta-analyses published where people dispute the authors’ interpretations (they discuss one such an example later but fail to recognize that discussion is still ongoing and not decisively settled as flawed). But these disputed interpretations are found in all areas of ecology. And if you talk to an experienced ecoinformatician, they go to great care to know the data. I can guarantee that you you don’t want to be part of the some of the conversations I have had about the details of datasets like the BBS, US Forest Inventory or even the Barro Colorado 50 ha tropical tree data. They get into incredibly dry boring detail about survey methods, variations between years, spatial heterogeneity, species that are well or poorly sampled, and etc. Ethan White has even set up a Wiki to capture such knowledge in public form. Lack of knowledge about the context of data is not evident! But ultimately, I think this point is misguided because it is not part of a one-directional goal (“more knowledge=better”) but part of a trade-off – more knowledge=smaller spatiotemporal scales and fewer parts of the world and taxonomic groups covered. If I am comparing 10 regions (or 10 orders of organisms), it is unavoidable that I will know less about each specific dataset. First because it is really unlikely that one person could collect all that data. Or even if they did, they’ve probably forgotten quite a lot about the first dataset by the time they’ve collected the 10th dataset 12 years later. Such cross-region and cross-taxa comparisons are obviously important for the advancement of science. Does somebody who spent 1000 hours collecting a dataset really want to argue that no general principles can be drawn from it and that it cannot meaningfully be compared and contrasted with a dataset from another part of the world or part of the taxonomic realm? Down this road ultimately lies a Simberloffian (2004) view that there are no general principles in ecology and the best we can do is spend our whole lives studying one place. That may work for some people (and more power to them – we need that view), but it’s not why I got into science (and I doubt it is why agencies are giving me funding).
So this brings me to the larger point I want to make that goes beyond the L&L piece, beyond the handful of papers I cited in the beginning, to what I perceive as an unfortunately all too common attitude in ecology. I call it the “not my kind of science=bad science” attitude. The bottom line is we throw around the “bad science” label at other ecologists way too often.
Try this thought experiment. Imagine a congressional staffer (or worse a congressperson) reading the L&L piece. What do you think their reaction is? Do you think it made them more or less likely to increase funding for ecology? Nobody knows for sure. There might be a few percent who actually thought “I don’t know if those L&L guys are right or wrong but at least they’re policing each other and having a strong internal debate about what is good science over in ecology”. But I’m pretty sure that would be an exceedingly rare response. I’m pretty sure much more common responses are “Scientists always say they have special methods for finding truth but they cannot even agree amongst themselves” or “Those ecologists are always bickering amongst themselves over petty philosophical disagreements and never stepping up all hands on deck to solve the problems society needs to solve”.
I have been told that in the 1970s during the strong environmental movement in the US (that is when the clean air and water acts and endangered species acts were passed) there was a move afoot in congress to create an NIE (national institute of the environment) similar to the NIH (national institute of health – which by the way is the major funding agency for all medical-related research in the US). But during congressional hearings, different ecologists started showing up and arguing about whose type of ecology was rigorous or not. This story is hearsay, but it sounds very credible to me.
Truly good science is a rare thing everywhere and junk science happens everywhere, so being able to find not-good-science in a field is a poor reason to label a field as “bad science”. Not only is there no one true route to good science, but good science inherently involves many independent routes converging. We as ecologists need to stop shooting ourselves in the foot and pulling out the mantra “bad science” as a way to put down the other side in our divides (theoretical vs empirical, ecosystem vs population, animal vs plant, and etc). It might help win a battle but it is losing the war (for funding, for respect, for scientific progress). Fields like physics and astronomy have been vastly more successful at attracting funding for fields which, at this point, probably are of less immediate urgency to society than ecology. There are many reasons, but at least one of them is they work together. Beyond the prosaicness of funding, the healthiest branches of science making the most progress are those were people reach across diverse fields and value the multiple perspectives and approaches, using each to their strength. I would like to see ecology become such a field. But its not going to be as long as we keep pulling out the “bad science” card every time we see a few extra dollars or pages in a good journal going in a direction different than our own.
* So here goes the world’s longest footnote – feel free to skip it if you aren’t interested:
As I mentioned, I am not clear exactly what it is that L&L are critiquing (they mention open data and big science, but some of their critiques seem not relevant to either of those). It does seem to me that several distinct ideas have been conflated into some sort of “new-fangled ecology” that L&L and others have been criticizing of late. So let me unpack “new-fangled ecology” into 5 distinct ideas. Each can be done alone or in any combination with one or more of the other ideas.
- Big-science – This is a project that requires many people of diverse skills to perform. Big here is # of participants. Physics with >1000 PhDs searching for the Higgs Boson is the best example, but NEON is no wilting violet either. Big science is an inexorable trend in all fields of science and this is probably a good thing. The days of Einstein dreaming up 3 Nobel-worthy papers while working as a patent clerk, or MacArthur reinventing theoretical ecology while being bored in the army, are over.
- Big-data – This involves really large datasets. As our capacity to store and process large data has grown exponentially, so has our capacity to fill such data storage. However, I would argue that ecology does not have any truly big-data. Big data is measured in terabytes and petabytes and exabytes. Gigabytes barely qualify. Yet most “big” datasets in ecology are under 100MB. They fit in memory. Ecologists are however collecting data at increasingly large spatiotemporal scales and this is noteworthy but probably needs its own name (Big-scale?) from Big-data which is well-claimed already by the computer scientists.
- Data-mining – Using machine learning to find patterns in the data. This is the source of the data before the questions idea. But genuine data-mining in ecology is exceedingly rare. Despite my post praising exploratory statistics, I see data-mining as one step further and one step too far. Good exploratory statistics still starts with clear questions and even tentative theories (that will not be formally tested) in mind. The one place you find data-mining in ecology is in applied purely predictive contexts. EG what will the malaria-carrying mosquito population be next year. I have no problem with saying that data-mining should stay in this limited domain. And it is seriously misguided to think that all (or most) data-oriented ecologists are doing true context-free data-mining.
- Open-data/Metadata – The push to have datasets be: 1) clearly documented (aka metadata) and 2) available for public access (e.g. downloadable on the internet or in journal archives). This is not black and white – one can argue for more or less open-data requirements. It would be hard to argue that ecology wouldn’t benefit from more open-data, but that doesn’t have to mean every single dataset has to be immediately published on the internet the day after it is collected. Also most pushers of open-data are strong advocates of appropriate methods for giving credit to data collectors.
- Meta-analysis/synthesis – The push to do NCEAS-style analysis across many datasets to assess the generality of many individual research projects. This goes all the way back to some of the first meta-analyes on all the competition projects by Gurevitch 1992 and Goldbergon&Barton 1992. Again, nobody is saying all science should be synthetic. On the contrary, meta-analysis implicitly assumes the need for individual data-collection experiments. But it’s hard to argue that its not good to stop once in a while and summarize all the data we’ve collectively gathered in a formal, rigorous, quantitative way.
To repeat – you can have any one of these alone or in any combination. Which of these involve good science? All of them (even Data-mining in the right context). Which of these involve junk science? All of them. In which way is this different from experimental ecology, observational ecology, phylogenies, analytical model development or etc? Its not! All subdisciplines of ecology (and of science) and all distinct methodologies involve good science and junk science.
“national institute of the environment”
There are similar rumours that when the Australian government was handing out large infrastructure grants in the mid-2000s, the funding set aside for ecology was delayed because the meetings with the govt (who were trying to _give away_ research funds) always ended with the ecologists squabbling about scientific priorities. A contrast was made with the astronomers, who had thrashed out their differences in private, and arrived with a coherent voice.
Why do they say cooperation is impossible in congress? “Every senator looks in the mirror and sees a president”. Ecologists are also prima donnas, and that’s partly why we’re so late to the big-data table. We refuse to compromise on our system or species or theory of interest (if I’m interested in species X in location Y, then by god that’s what I’m going to gather data on).
Interesting to hear about similar stories in Australia. From an American perspective it always seems to me like Australian ecology has its act together much better than we do.
Since I’m in the choir, clearly my response to the post is ‘hallelujah’. More seriously though, these divides and the increasingly vituperous attacks on other scientific approaches have been worrying me too. I suspect part of it is the ‘cowboy culture’ of ecology: the lone ecologist, acting independently, trekking out into the wilderness, communing with nature image. But I think part of the increasing nastiness is due to the increasingly scarce funding and high rejection rates in journals from Ecology on up. Resources that go to other scientific approaches are not available for those who conduct your approach. The real question is what to do about it. This is where I get stumped and this worries me because I completely agree with you that we are doing our science a grave disservice right now.
I think you have nicely summarized the problem. The big question in my mind is that other fields also are facing decreasing resources and increasing competition for journal space, but don’t fight like this. If we could figure out why, we’d be on to something.
totally agree with your comments, some enforcement for specific things:
regarding point 1: yes, “field work is so difficult, and you just sit at the computer and want our data for free” … apart from the stats advice, one thing I usually point out at this point is that most field people are very happy to use R-packages for free and without reciprocity, and the idea wouldn’t cross their mind that this is “parasitism” on statisticians.
regarding point 2: I have totally the same experience, we are responsible for stats advice here in the faculty, so I get from time to time students in here with their data, and the first thing I usually ask is: “what is your question?”, and it’s amazing how many of them do “field-mining” with the vague idea that some of the many things they measured should be correlated, and if so, they would like to know which.
regarding point 3: Personally, I think if you need the data collector to make sense of the data, you should have a better documentation. Moreover, having worked in the field gives you some background knowledge that is otherwise typically undocumented, but there is also the danger that you will get biased. We don’t call it bias of course, we call it “ecological intuition”, or “field knowledge”, and apparently you get it by hanging around in the field for 10 years and watching the trees grow. I’m not saying that there is no truth in this kind of intuition, but I think we should be skeptical when saying this intuition allows you to see the data in a different light; we all know how easy our brain sees patterns where there are none.
Finally, I get it that field ecologists complain, currently there seems to be more payoff for the same amount of work in analyzing the data that is already there, at least in terms of citations and results created, this of course affects funding and jobs, and this may seem unfair … but really, does that mean we should not do these analyses? Clearly, this would be insane. Rather, we have to find ways to ensure fair attribution of work. Plus, I think for making field ecology more relevant again, also for funding, one should not avoid, but rather encourage data being used by other people, which also includes discussing which data would be most relevant for the stats / theory people, as it is routinely done in other scientific fields.
Good points! You mention software, but ironically I have had field researchers ask me for datasets like the BBS as if I should freely share them. They are of course available for download on the internet as raw, unfiltered CSV files. But most of those datsets I have invested at least a person month or more into making them usable.
You call this ironic, but this is exactly what we ask (and should ask) field-ecologists to do: share data that they collected/processed for further use by the scientific community. I see what you’re saying, but would you then suggest it is better that all scientists invest the same person month in parallel? It would be more convenient to share the usable data, without pre-empting your own publication of course.
I really like your post, by the way, but this point doesn’t entirely make sense to me.
I take your point. And in fact I have shared my post-processed data with many many people, although the data is of limited shareability because the post-processing tends to be geared towards one scientific question and one data analysis environment. (Computer code is a different story and I’ve always published). I guess my point was one of attitude. I often get asked very glibly. I would never approach a field scientist who had a dataset I wanted with an attitude that they had put no value into it, but which is often how I’m approached. Somehow I feel as if my skills are devalued – as if once the field data collector has shared the data everything of work and value has been done and shared. Which is ironic because on some level they must appreciate the value I’ve added or they wouldn’t be asking me instead of downloading it themselves. In the end I guess this is the point of the post – we always recognize the hard work and challenge in the things we do and tend to think as trivial the things others do. Except I don’t think most ecoinformaticians think of field work as trivial.
I sort of though that is what you meant, but it is good you explained explicitly. It reminds me of how much work it is to make next-generation sequencing data public. I have seen so many people around me struggling to get their data into the format of the recommended program, and very few succeed. Most end up begging Genbank to please be allowed to upload in another format “just this once”. Fortunately they are usually very helpful, but I think the scientific community as a whole should indeed value and respect the work of others and not take sharing for granted, even when it is compulsory.
That’s really interesting. I never thought submitting to Genbank would be hard by the time you had your data sequenced and aligned for analysis. Even thought my whole post was arguing for this point of, the comments have been really educational to me exactly how much work and skill is involved in all aspects of our field. Your point about Genbank is a great example. I think in these comments I was guilty of being a bit too glib about how easy analytical modelling is (even though I myself have spent two years stewing over getting a model just right). Bottom line is anybody who has or is getting a PhD is a bright hard working person and all the work we do is a challenge.
And “field-mining” that is a coinage I intend to keep around!
Field mining! Love it. 🙂
I do think such coinages have a positive role to play in discussions, by calling attention to real issues. As long it’s clear that the coinage is meant light-heartedly and that it’s directed at scientific mistakes rather than at scientists personally. “Zombie ideas” and “statistical machismo” are in the same vein. Also things like “data dredging”, “P hacking”, etc.
Awesome post Brian. I don’t have much to add other than to affirm what you’ve said as another practicing “parasite”:
1. I and members of my lab spend *a lot* of time consulting on statistical, modeling, and data management practices. We also write software and build websites that other folks use. We’re happy when people take advantage of our accumulated knowledge and the tools we build.
2. We do not have a secret server that is quietly mining all of the available ecological data for correlations with p < 0.05, and it is *definitely not* located in Room 243.
3. This tradeoff is really important and I'm surprised at how poorly understood this is. I also think that informatics folks bring their own unique set of knowledge to data that is complementary to field based expertise. I have yet to work with a dataset produced by someone else and not discovered errors in the dataset within the first day or so of work. This isn't a criticism of the providers, these sorts of errors are difficult to avoid, but it suggests that by having data worked with by folks who work with data for a living we will more rapidly track down these issues and prevent them from influencing results that get into the scientific literature. Thanks for the plug for the Ecological Data Wiki.
Finally, to follow up on Florian’s point regarding the perception that doing computer based ecology is faster/easier/more impactful, I’d really like to see some evidence for this as well. I think the perception is often that somehow these new-fangled approaches just cut out the data collection step and are therefore inherently faster/easier. But I think this misses the important point that much of the work being done in informatics simply replaces this effort with different difficult things (finding, understanding, and cleaning up data; training in advanced statistical methods or developing new ones; lots and lots and lots of coding). Without meaningful evidence that primarily informatics based PhDs produce more than field based PhDs (or for that matter theory based PhDs) I’ll continue to be skeptical of this idea.
I really have to agree with your last point. Analytical theoretical work may be faster. But data work has a lot of drudgery and I doubt it is faster than field work
Re: analytical theory, it depends. Speaking as someone who’s done a bit of analytical theory, yes, it can be quick. But not necessarily. It literally took me *years* to understand the Price equation well enough to write my first paper on it. Now, you could say that’s different, it took me that long because I was learning something new, doing the math itself was fast once I understood it. Which is fair enough. But myself, I’m not sure I’d draw a hard and fast line between learning the math and doing the math. Sometimes when you’re doing analytical modeling, it takes a lot of time to understand the problem well enough to be able to set up the math.
And sometimes the order is reversed–the math is quick to do, but then it takes you a long time to understand the implications of the math. I have a colleague who’s working on a paper like this. He’s super smart, and he’s had the (quite simple) math done for years, and I think it’s a really deep and important result–but he’s still working on fully understanding it and the best way to explain it to readers.
And of course, technically difficult derivations can be time-consuming too, though I’m not sure how common such derivations are in an ecological context. You’d have to ask a proper theoretician, which I am not.
And of course, there are those who view microcosm experiments as the really quick and easy thing to do! And I can totally appreciate why it might seem that way to an outsider, but take it from me…
Great response (the main post), Brian!
You could say mathematics is a theoretician’s wider study area (with different branches of maths representing different habitats) and algebraic symbols are our focal organisms. We still need to work damn hard to make sure we choose the right methodological tools, the right species combinations and record the right response variables when asking the questions all ecologists are interested in!
I’ve been banging my head against a brick wall trying to derive a particular, hopefully elegant (but perhaps not simple) analytical result for some years now. I’ve tried to parasitise some more mathematically accomplished colleagues’ knowledge to help in the past, but still haven’t got where I want to with the problem. But I still believe collaboration remains the key to getting the best science done!
Great post Brian, and nice comments so far! I couldn’t agree more that this divide, which often pits theorists/statisticians against field ecologists, is bad for everyone. In my mind, the greatest progress in ecology comes from synergy between complementary approaches and skill-sets. Moreover, the idea of specializing and collaborating to promote efficiency is the foundation of our modern economies. Field ecologists saying that theorists or biostatisticians are parasites is analogous to someone from the manufacturing branch of a company reaming the marketing or accounting branches for being parasites. That kind of myopic egocentric attitude would never be tolerated in business, and shouldn’t be tolerated in science either.
Great post Brian. I love the footnote. Maybe our new slogan should be “Dynamic Ecology: even our footnotes are thought-provoking”. 🙂
Ok, in seriousness:
I think one unfortunate effect of pieces like L&L’s is that such polemics tend to crowd out more nuanced and worthwhile discussions on the same topics. When people feel like they’re under attack and that the other side is seeking to start a war rather than a discussion, they naturally react defensively. And then reasonable discussion and productive debate becomes much harder, because all concerned are primed to see anything with which they don’t fully agree as an attack of some sort (perhaps as “concern trolling” or whatever). I worry about this in part because I’m a big believer in the value of serious, productive debates, including debates over methodology, and I know you are too. I don’t like unproductive mud-slinging any more than you do. But I guess I worry a bit that framing the issue as one of “why can’t ecologists all just get along?” runs some risk of throwing the baby out with the bathwater.
Re: the high level of coordination in astronomy and some fields of physics when it comes to funding requests, yes. But doesn’t that spring from the fact that those fields depend on hugely expensive shared equipment like satellites and the Large Hadron Collider? Everybody has to come together and agree on what pieces of kit to prioritize, because they’re only going to get a very small number of pieces of kit that more or less the entire field is going to have to share. Which immediately suggests an ecological analogy: NEON. You note, correctly, that many ecologists aren’t big fans of NEON. But on the other hand, clearly enough ecologists (mostly ecosystem ecologists) are fans of it that they were able to coordinate and make a $400+ million dollar project happen.
NutNet is an interesting project to consider in this context. In some ways it’s “new school”–centrally-coordinated, involving a big international team, based on data sharing among a bunch of investigators. But yet in other ways it’s “old school”–collection of new field data, funded by a single, garden-variety NSF grant (old post here: https://dynamicecology.wordpress.com/2011/10/20/thoughts-on-nutnet/) And of course, at the level of individual investigators, there are plenty of people, including me, who’ve collected their own data as well as done synthetic work based on compiling and analyzing data collected by others. Perhaps one way to lower tensions among opposing camps here is to point out that the lines between camps are perhaps blurrier than you’d think from just reading L&L.
One further thought on coordination: I think the sort of central coordination that goes into making coordinated funding requests for expensive pieces of kit or to support big collaborative networks of researchers goes well beyond “people just being able to appreciate the value of different approaches to science”. I appreciate the value of everything you listed in this post–but I mostly don’t do any of it, don’t want to, and wouldn’t be great at it if I did do it. In particular, I’m under no illusions that I’m another Einstein and I’m going to revolutionize ecology all on my lonesome. But I’d like to think that the research ideas I have on my own, and that I can pursue on my own, are worth pursuing. I absolutely have no problem with others who prefer to work in a different way. And I certainly recognize that most fields of science are moving towards work performed by big coordinated teams. But there is absolutely a (hopefully productive!) debate that needs to be had about the appropriate mix of individual-investigator-based work and large-coordinated-team-based work.
You and Morgan seem to share the sense of rising internal tensions in ecology over different approaches. Can you both talk more about where that sense comes from? Is it just from the fact that a few famous senior people are making noise?
Re: fields in which there’s less internal disagreement being more successful in bending the ear of policy makers and funding agencies, maybe, but I’m not convinced. There are lots of historical reasons for the different levels of funding that different fields receive.
For anyone interested in reading further here are a few more responses to L&L or related discussions:
Jeremy – I have the same impression as Brian and Morgan and, at least for me, some of this impression is driven by conversations and interactions on Twitter. I agree with Morgan that this polarization is likely driven in part by increasing competition for limited resources.
One more response to a previous piece of L&L’s. Be sure to turn on the “highlight sarcasm” option in your browser before reading it. 🙂
Re: conversations on Twitter giving the impression of increasing polarization: is that perhaps in part because Twitter doesn’t really lend itself to nuanced discussion, and/or because the people on Twitter are a non-random sample of ecologists (e.g., non-random with respect to the strength of their opinions on certain topics, or just the strength of their opinions in general)? That is, are Twitter conversations perhaps an upwardly-biased estimate of the level of polarization in ecology as a whole? Honest question, I have no idea, not really using Twitter myself.
It’s certainly plausible that increasing competition for funding leads to an increasing mutual suspicion among those doing different sorts of ecology.
It’s possible that it’s a biased subset of folks certainly. That said I think that one of the interesting things about Twitter is that the conversation tends to be a little more off the cuff and therefore you also tend to get people reacting with how they think instead of how they feel they should present themselves.
Also, thanks for pointing out that those links are to the previous L&L piece.
Apparently 2011 wasn’t enough. They had to repeat it in stronger terms in 2013. 🙂
One further thought (and I’m still trying to decide if I’m kidding or serious about this): one possible response to name-calling is for those who are being called names to take up the name as a badge of honor. You totally undermine or even reverse the force of the intended name-calling by taking it as a compliment. For instance, this randomly-googled site from a woman who calls herself a “bitch”: http://amazingwomenrock.com/proud-to-be-a-bitch.
I wonder what would happen if folks whose work is based on data collected by others stopped denying they were parasites, and instead started taking “parasite” as a compliment?
The more I think about it, the more I’m inclined to take this seriously. One way to do it might be to run with the point that nobody knows everything and all of us depend on the expertise of others. We’re all parasites! Oh man, if you could get “we’re all parasites” to become a meme, then the fun would really start! You could start handing out “I’m a parasite!” badges at the ESA! 🙂
Ok, you’ve totally got to do this Brian! You could rope Meg in to help, she studies parasites (ok, diseases, but same difference), I’ll bet she’d have all sorts of great ideas about how being a “parasite” is actually a highly-admirable thing to be! 🙂
You’ve figured out my secret strategy! I’ve had people tell me they want t-shirts proclaiming “I do exploratory statistics” and now we’ve had a series of commentors happily announcing they’re data parasites.
I wish I was cool enough to be a parasite! Sadly, I’m just a lowly host.
I know this only half serious, but I’m not sure it would actually be productive in this case because I think it conveys an antagonistic relationship between the scientist who knows/has something and the scientist who doesn’t. In reality I think we’re much better off thinking of this as a mutualism where we (and, perhaps more importantly, science) can all benefit from each others collective knowledge and experience.
You are right of course that emphasizing the mutual benefits is the right way to go.
Oh, you people are no fun. What with your taking the high road and your boring thoughtful professionalism. Party poopers. 😉
In semi-seriousness, the connotation of antagonism that Ethan notes occurred to me as well. That’s actually why I suggested talking about how *everybody* is a “parasite”. Meaning that everybody gets some benefits from others, and not always by “paying” for those benefits in some way. Does this stretch or alter the antagonistic connotation of the word “parasite”? Sure–that’s the whole point. Just as women claiming the word “bitch” are actually stretching and altering the connotation intended by the men who use the word as an insult.
But clearly I’ve lost this debate and I’m going to have to settle for a boring ol’ “I’m a mutualist” t-shirt. Sigh. 🙂
Good post. I agree wholeheartedly. It’s sad that there’s so much infighting about whose type of ecology is best. I do both field biology and macroecology, and have had people dismiss each in turn as the “bad way” to do science. As a first semester grad student, having theoretical ecologists tell me that my field studies were “just collecting” data and that studies of natural history for its own sake should never be funded was an eye-opening experience. (These same people liked broad macroecological studies that necessarily drew upon data collected by natural historians). Fast forward a couple years to when I was presenting a macroecological project at a conference, only to be verbally attacked in the Q&A by a very angry elderly professor who was literally shaking at the idea I would analyze broader patterns across all birds. Both types of criticisms, especially when they’re so rudely phrased and directed at young graduate students, are counter-productive. We each may prefer to do science in a certain way (hopefully playing to our own talents and skillsets and interests), but we learn so much more by bringing together knowledge from across sub-disciplines and methodologies.
I have my own story in a similar vein. My very first ever 1 hour presentation after my dissertation diffense (i.e. my first seminar talk as postdoc) was on macroecology and afterwards a very senior well-known scientist got up and said “I’ve been trained that anything that isn’t about population processes isn’t good science” (there goes the not my science=bad science idea again). I totally agree that it is one thing for the titans to clash with each other (although I certainly think it would be better if it was done less and in a more polite fashion), but to do this to grad students is not acceptable.
I have an old post that makes the same points in the context of different ways of working (e.g., do you use social media? do you blog? do you use journals as a means of filtering the literature? etc.) I think it’s actually only a small minority of people who are prepared to insist on their own ways of working and rip others for not doing the same. But man, are the people in that small minority loud and annoying.
Thanks. I even read the footnote.
One additional thing I would add to the Footnote would be Open Source. I think having the tools used to conduct “new fangled ecology” openly available is implied by what you have written, but developing tools to deal with the new challenges posed by larger datasets, different analyses, and different questions is a significant enough of challenge to warrant inclusion in its own right.
And again thanks. I have been thinking of trying to write something similar (new-fangled, not so much a critique of L&L), but would not have been able to do it as well. I can take that off my to-do list now and just point to your post!
You’re quite right that open-source is a major thrust of new-fangled ecology. I was trying to list things that I thought L&L were attacking, and that wasn’t one. But it opens the question to what else is part of new-fangled ecology that wasn’t on my list?
That is a fun point to stress: Why aren’t L & L parasites when they use a software package that they didn’t develop? Or a statistical test that they didn’t discover?
A semi-facetious thought:
To the extent that ecologists need to stop the infighting and unify in order to win funding that would otherwise go to other fields, doesn’t that amount to saying we need to stop slamming each other so that we can slam the cell smashers and the gene jockey, or those fool astronomers who care more about exoplanets than their own planet, or whoever?
I’m kidding, of course, but not entirely. Back in the 1960s folks like MacArthur and Wilson quite self-consciously pushed new ways to do ecology on their fellow ecologists, in part because they thought that otherwise ecology would just be steamrollered by molecular biology.
It’s basically a group-selectionist argument. What selects for individuals to work altruistically towards the shared interests of the entire group? Competition with other groups, in which the most unified group wins.
That’s like the “we won’t have a world-government” until there are aliens to fight against idea. I think there is some truth to it. But ecologists ARE in a battle against other fields for funding. And I guess the point of my post is we are failing to recognize the group-selection fitness landscape and adopting suboptimal strategies for that landscape.
I would add that when different fields battle, it is less direct and when it is direct it is with less technical jargon and so easier for non-experts to judge. If an ecologists says: “you should give me more money than the astrophysicists because who cares about other planets?” that is an argument that doesn’t really undermine science as a whole because it is clear to most people how to evaluate it (“do I care about general knowledge about other planets or my own?”) because it is more obviously a subjective judgement. Within a field though, there is more focus on methodology and saying that “your approach to doing something is wrong” as opposed to “the thing you are looking at is not of interest”. As such, to someone who can’t understand the details of the methodologies being debated, or how to evaluate the merit of a methodology it just sounds like “they are divided over methods, so all of their methods are bad.” In game theory terms: I think inside a field the bickering is almost always zero-sum, but between fields it is more often non-zero-sum. Brian discussed this a bit in his congress-person thought experiment.
I wouldnt dismiss all the points raised by L&L. I dont see that they object to OA science in general but they point out some risks that we need to be aware of. Ecology is getting more and more competitive and “the system” favours fast publications with results that can be generalized (lots of data) – and yes, limited resources is the driver behind this debate. Will the field ecologist survive in the long run? I think we want the science to decide that, not politics/career opportunities (survival). This is an important topic and the focus should probably be on how we can benefit from different approaches and how to acknowledge different types of expertise (stats/field/theory etc).
Hi Gustaf – thanks for stopping by. First off, I think it is valid to dismiss points that are presented in such an over the top fashion. I would welcome a more reasoned discussion of the same points (which I think you are trying to do). And as I noted I think Jeremy has made some of these points to. But its hard to have a reasonable conversation when flames are being thrown.
Do you honestly think we are in danger of field ecology being eliminated? To be honest I cannot fathom that or anything close to that happening in my lifetime (and would fight against it if it were).
If you look at who is the US National Academy almost every one of them (Simon Levin is the only exception I can think of) is a very intensive field ecologist (albeit they were often the ones who successfully integrated theory to set themselves apart). I got eliminated from at least two jobs where I interviewed because I didn’t have a field system. Other than a theoretical ecology position, I doubt many field ecologists get eliminated for not having enough data or analytical skills? I think it would be very hard to make a case that careerist filters are biasing against field ecologists.
But this is by no means the first time I’ve heard these fears. Which to me is all the more reason we need to work on valuing diversity (of methods, approaches, systems, perspectives, scales and etc) in ecology.
Dont know much about the system in the US so cant really comment on that, but this seems to be hot topic. I dont think field ecologist will go extinct (beware of remote sensing though 😉 ) but a gap between, on one end desk ecologists and on the other field ecologists, calls for, as you point out, more collaboration and methodological integration. Co-authorship, citable data sets, friendly pre-reviews of manuscripts, etc…the classic, be a part of solution and not the problem.
When I interviewed at Michigan, I was surprised at how excited people were that I am a muddy boots ecologist. There was concern that the department’s historical strengths in field ecology have diminished, and there’s now a push to make sure we don’t lose that strength. There is a reason the ad for the position that’s open now includes the sentence “We seek applicants with a strong field component in her or his research program.”
At the same time, if the department only had muddy boots ecologists, people would be concerned about that. I agree that we need a diversity of approaches. In fact, I have a post in the queue for Monday saying that. 😉
That’s great. If every department was looking to hire more people not like them I’d be very happy. In the two cases I mentioned though, it was definitely a case of looking to hire more people just like themselves. I’m looking forward to your post!
I think this perceived divide could be partially remedied by redesigning curriculum to better train theorists and macroecology-types in natural history and field ecologists in mathematical modeling and statistics. The theoreticians (and biologists in general) I most respect seem to always have a broad, extra-academic interest in natural history.
100% agree. And as you note we are part way there. I know plenty of field ecologists who are theory friendly and wish they knew more theory and plenty of theoretical ecologists who are field friendly and wish they spent more time/knew more field stuff. Those two groups are the types I like to hang out with and I think not at all coincidentally generally do the best work. I am obviously biased being a macroecologist but I can think of very few macroecologists who don’t fit into one of these two spanning categories which is why I have gravitated to that field.
Brian, great post here.
To be honest, it sounds like L&L are primarily concerned about bad statistical practices rather than about open data, ecoinformatics, etc. They speculate on a rising number of publications that make mistakes in multiple comparisons, in ignoring heterogeneity in data, in failing to have well-posed questions first. This blog has often spoken to the issue of pervasiveness of statistical errors in the ecological literature, which is certainly neither a new problem. As many commenters have pointed out, those ecologists not collecting their own data are perhaps not the leading culprits in making these statistical errors.
I completely appreciate the deep knowledge of context that comes with first-hand collection of data, but at least to the extent that the context informs the conclusions, that context should be made part of the metadata. Do we not move towards, not away, from enabling junk science when we permit conclusions to be drawn only by those who designed the experiment and collect the data?
It is too easy to read L&L as a defense of data hugging. You can’t use my data because you cannot understand it. If publications and citations didn’t play the role they do in our academic economy, I suspect we would hear the exact same sentiments about publications themselves: if I publish my ideas, people who lack my superior understanding of context will misunderstand and misuse them! When these contributions are rewarded, it is easier to appreciate the benefits having these ideas be seen and debated among the scientific community outweigh the risk of misintepretation. The same might be said of data.
Open data is often represented as benefiting the parasites (through “free” publications) at a cost to the original contributors (in “lost” publications). If the production and citation of one’s original datasets became the major metric of scientific contribution, this would reward all those collecting data while leaving the parasites without any recognized contribution — a valuation that sounds more in line with how L&L view the reliatve merits of these groups. Given their position, it seems they would be great advocates for data publication and citation, which rewards the things they value most, and overhauling the current publication system so favorable to the existence of parasites.
I think there’s an element of ego in this though. Assuming “I know my system best”, what if I share that data and someone comes up with a better idea? An example might be that I publish a paper in the Canadian Journal of Fisheries, and you turn around and use my data with some fancy tipping points models to publish a paper in Nature (sounds like classic Carl to me!). So even if the data is cited, you still have gained “more” in the academic world than I have, despite the fact that I put in all the labor.
On another aside, I don’t think all the context can be put in metadata. I know that for my plankton manuscript (https://peerj.com/preprints/75/) we had all the data with metadata but it was only with being able to talk to the data collector that we learned things like: “oh yeah, on that day it was windy so we just sampled from the edge and didn’t take the boat out.” So in this case I see L&L’s point to a certain extent. This is one of the idiosyncrasies of ecological data that I’m not sure has any easy solutions.
I think another reason for some of the antagonism and divide is that “big science” requires a whole new skill set that I think most ecologists are not excited to learn. Take data from NEON as an example. Yes you’ll be able to get small data sets and supplement your own research, but the real power of the project (I think anyway) will come at being able to leverage the tremendous amounts of standardized data that come out. The skills checklist for that is something like this:
– Informatics / data standards / machine automated synthesis
– webnative data gathering (e.g. httr in R, requests in Python)
– skills to parse data streams (more python!)
– database skills (SQL, and maybe some noSQL like CouchDB)
All of these are programming skills on top of knowing the right stats / machine learning approaches on top of domain knowledge. My hope is that there are people 10 years younger than me just thinking about grad school that can code me under the table, but even within my (can I say youngish (30’s)?) cohort of new faculty / post-doc friends, computational literacy in ecology is so atrocious it might as well be 1950. Working with people from NCAR (National Center for Atmospheric Research) and disciplines other than ecology I’ve seen that basic computational literacy is a requirement in most other sciences, not something to scoff at.
Clearly there’s a push to move ecology in the direction of big science with all the money thrown behind DataONE and NEON. But with this transition comes the need for the aforementioned skills (and more). Pieces like L&L smack of conservative dogma that things were just better way back when I got wear my boots in the mud and I don’t think add much to the discussion. I wish that ecologists would embrace the potential of open data, NEON, and other projects, and look as at exciting opportunity to learn something new, not wish we could all just go back and count beetles alone in our offices. (Full disclosure, I work at NEON)
That’s a daunting checklist of skills, Ted. Doesn’t this get to Jeremy’s earlier point:
“I’d like to think that the research ideas I have on my own, and that I can pursue on my own, are worth pursuing … there is absolutely a (hopefully productive!) debate that needs to be had about the appropriate mix of individual-investigator-based work and large-coordinated-team-based work.”
There are strong trade-offs within any individual’s skill set because their intelligence and time are limited. As a result, there’s no way an individual can assemble and refine the skill-set that a cohesive, multidisciplinary team can offer. (That’s without even considering the benefits of different cultural, gender and personal perspectives, or the stimulating nature of ongoing conversations, etc). In an otherwise equal contest, team-based research is therefore always going to outperform individual efforts.
There are two benefits of individual-based research I can come up with. The first is a decreased transaction cost, because time spend in meetings is time away from the lab/computer/field. The second is the value of multiple, uncorrelated ideas about a question. A hierarchical group with a strong leader (remind you of any scientific labs?) can lead to unproductive group-think. But if we were somehow to calculate the group size that optimally balanced the various benefits of small and large, I doubt the maxima would fall near n=1.
“As a result, there’s no way an individual can assemble and refine the skill-set that a cohesive, multidisciplinary team can offer…In an otherwise equal contest, team-based research is therefore always going to outperform individual efforts. ”
An implicit assumption here is that all those skills are required to address a given problem. That’s precisely what I’m denying when I say that there are research ideas that I can pursue on my own that are worth pursuing.
I don’t think there’s a single optimal group size. The optimum for science a whole is a mixed strategy.
“there are research ideas that I can pursue on my own that are worth pursuing”
I see, then I’m off on a tangent, talking about optimality. Sure, there are certainly still questions that an individual can make significant solo progress on. I guess I would just add that in almost all cases, the presence of a collaborator would ensure faster and deeper progress (assuming one could find the right collaborator, scientifically & interpersonally).
I totally agree Mike, an individual will never be able to have all the same skills as a big team will. But the problem is similar to one that might happen when you have a statistician working with a field ecologist who hides when they see equations. The communication differences can be so great that collaboration is very difficult. I’m not saying that every ecologist needs to be able to be able to be an amazing programmer / informaticist, statistician, and know everything about 3 toed sloths. But I think for collaboration to happen you need a strong background in 1 and familiarity with the other 2. I see it almost everyday, the difficulties that come from field ecologists being resistant to the use of technology and how hard it is to communicate with programmers because of that. I think those new skills will open up new questions that can be answered with big data. I don’t think everyone has to do this, but it would be helpful to facilitate collaboration.
Ted, Mike, et al might be interested in reading Sean Eddy on this in a PLOS Comp Bio perspective called “Antedisciplinary Science”, which speaks to the importance of the polyglot in a vision of big team science that seems to still emphasize merly combining people from different silos http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0010006 We need interdisciplinary teams, but we also need interdisciplinary people.
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This was an interesting perspective on the best-known failure of ecologists to work together:
Click to access 10AronovaBakerOreskes_HNS.pdf
(perhaps everyone else in the world has already read it, but it was new to me). Actually it seems that the IBP was so widely perceived as a failure that many ecologists of my generation haven’t even heard of it.
Wow, thanks, that looks very interesting Matt. It is indeed true that the IBP was and is widely perceived as a failure by many ecologists, probably especially population and community ecologists. But I confess I’m largely ignorant of the history myself. In particular, I wasn’t aware that the IBP can be seen as having led to the LTER program. Will look forward to reading that.
That’s a great and thoughtful paper. Naomi Oreskes is probably the best thinker about the science-policy interface out there.
I agree it was widely perceived as a failure among population and community ecologists. But then they were against it from the beginning because it had an ecosystem/biogeochemical perspective.One of my PhD committee members regularly chuckled about the cycling model on the North American prairies that kept predicting we should be knee deep in dung and they never figured out how to fix it.
But I don’t know and would be really curious to hear the perspective of ecosystem ecologists about the success or failure of IBP. In general I think ecosystem ecologists have been better at banding together, keeping their squabbles internal and getting funding than population and community ecologists in the spirit of the original post. I also wonder if there isn’t an echo of the split around IBP in the current NEON effort.
Oh, there’s definitely an echo of the IBP split in reactions to NEON.
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Here’s the tl;dr version, from Robert MacArthur in 1972:
“It is a pity that several promising young ecologists have been wasting their lives in philosophical nonsense about there being only one way – their own way, of course – to do science. Anyone familiar with the history of science knows that it is done in the most astonishing ways by the most improbable people and that its only real rules are honesty and validity of logic, and that even these are open to public scrutiny and correction,”
Yep – that pretty much nails it already 50 years ago. I assume he was railing against people who criticized his work (theoretical but also often observational) by saying only field experiments are valid which is kind of ironic in relation to my original post. Funny how there’s nothing new under the sun.
I’d have to go and look up the full context, but given when he was writing I doubt he was pushing back against people who thought field experiments were the only way forward. I think 1972 was too early for that to have been his target? But I’m just speculating. And the line is from an edited volume I’ve never heard of, so I’m not sure if our library will have a copy. Could email Jay Odenbaugh (the author of the article I linked to) and ask who MacArthur’s target was. It may be unclear–Jay characterizes the quoted passage as “strange”, which suggests MacArthur’s target isn’t clear.
This is great. Thanks for passing it along.
Also interesting that MacArthur refers specifically to “several promising young ecologists” in that quote. Clearly he has specific individuals in mind. I wonder who?
Also interesting that he’s worried by “young” ecologists rather than senior ones. These days, the prominent public voices calling for ecologists to do things in one particular way seem to mostly be senior people (e.g., Lindenmayer & Likens; Mark Bertness in comments here). But I don’t know if I’d read much into that, since we’re talking about small numbers of people. I want a lot more data to go on before I leaped to the conclusion that ecologists who think there’s only one right way to do ecology were all young in the 1970s and are all senior today!
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Great admirer of Lindenmayer as someone who did the hard yards in Australian forests, then went on to do more. His criticism may not have been well put i.e.the p word, but I’m sure some of the other criticisms were valid. Comments about Australian ecology, from more of an observer point of view at this stage, are that I think it has its s… together pretty well, but I am also not concerned that American ecology is about to keel over. In fact South/Central American ecology is just up and running. Look forward to the same from other parts of the world.
Now, the IBP, this can hardly be considered a failure of ecology. This is a failure to get things right because it is so difficult and complex, no doubt exactly what Prof Lindenmayer was on about in his own unique way. By the way, the Chinese whose ecological and other sciences are also up and running have you noticed, are re-doing the IBP from what I heard. They will probably succeed, if anyone can, through sheer force of detailed organisation.
And we must keep trying, the planet has a finite future, any number of things may end the possibility for life on Earth. We must be able to export it, as what I believe is probably the only planet in the Universe with this phenomena. It is a bit depressing to hear such a valid experiment being talked about in this way, because it didn’t work first up. How many experiments do? And this one was huge and complex.
To their credit the CERN thing has worked for the physicists, but not until a number of teething problems were sorted out. It may be that other disciplines are more internally organised, but that may be a factor of ecology being so complex and difficult to pin down to one or the other theory,and the highly competitive nature of those who wish to do so. Agree there is no room for aggression, and there is a need for unity to apply for programs etc. But realistically if you have a hostile government as we currently have in Australia, you are not going to get anywhere no matter how much your bunch becomes a smooth talker. It’s like begging from Enron for environmental benefits, not worth your time.
About the maths/stats/computer bit, think I’m on the edge of that one, re-doing studies to upgrade skills. SQL was a douzy, hit my laptop over one problem, never the same again! Did some stats because of being poor in that area, and had a go at higher maths with no real success, certainly not Einstein either! You did not emphasize maths, but in all the other blogs on this site there is this maths and that maths involved in nearly every theory,and from what I can gather about Australian ecologists, they are becoming excellent statisticians, even mathematicians, because they have to be.
However the vast majority of young ecology students probably have no idea how far off the mark they will be if they don’t do a strong component of stats for analysis, and maths to even understand the theory. For example, since mouthing off about dynamical systems in another post on this site, I had another look at it on Scholarpedia. Managed to grasp the maths, just. Also looked up stochastic resonance- first time I’d ever heard of it, discussed on this site somewhere. I like all the chaos theory, stochastic stuff so gave it a go, but it led to other stuff and reams of maths involved, and now I realise that I am a long way off understanding what I want to without having the maths background.
Your site has failed to discuss entropy from what I’ve seen so far, but there is a big overlap between the maths involved in ecological theory, and the physics and chemistry basis for that maths. There have been a couple of great ecology books on entropy by Harte and Ichuri Aoki that I know of. But none of this is for the faint-hearted or mathematically disinclined!
There needs to be integration, 100%, with ecological studies- not only computer technology, but maths and stats, in the universities. So far I have found there is however a lack of enthusiasm from some maths people to set up mathematical ecology courses in Australia, and the same might apply to statisticians, but there needs to be combined studies now, one cannot go back in time. I did a search trying to find a higher degree in both maths/stats and ecology in Australian universities, with little success a while back. I also did a search for postgrad network theory in biology and also got nowhere, but I now think that may not be sophisticated enough to solve ecological problems, not sure. So the ecological scientists in Australia kind of get through it on their own I suppose,agreed that probably includes with help from professionals such as on this blog page, and agreed there should be more respect (especially for temperamental SQL programmers) between the two camps. But the youngsters won’t have a clue what they are up against.
Errata mistook IBP for the Biosphere experiment sorry! IBP is clearly a different story, whoops!
Funny you should mention Biosphere – I just visited this week and am working on a positive blog post about it. I expect we probably agree on that.
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