Statistics and Ecologists Today

Statistics and Ecologists Today: More from the “Emperor Has No Clothes Chronicles”

In my opinion, the question most often asked by ecology practitioners today is “what statistical method should I use in my study?”.  Why? Because whether you are just beginning to learn about ecology, e.g., you are a first-year graduate student preparing your proposal or you are further on and thinking about publishing in a peer-reviewed journal, the pressure is on to understand and decide upon your statistical approach.  It is a modern paradigm that statistics are fundamental in ecology, i.e., most likely your supervisor and your journal editors/reviewers will demand statistical analyses be included in your publication.  The question of the method to use is both good and bad.  One of the paradigm’s great outcomes is the training of ecologists to design effective and meaningful studies (see also Prof. Kreb’s thoughts - https://www.zoology.ubc.ca/~krebs/ecological_rants/on-defining-a-statistical-population/#comments).  However, one massive failure is the mushrooming of complex statistical approaches and easy software packages that are neither as effective nor meaningful as some ecologists wish them to be.

The Covid-19 lock-down let me catch up on some statistics papers I’d tucked into my “to-read” folder.  My younger colleagues are very bright and doing very complex, statistical analyses that aren’t easy to understand, I’m reviewing work using these techniques for journals and funding agencies, and I wanted to invest time exploring and hopefully learning more about these emerging ideas about statistical approaches and applications.  There are many, very excellent papers describing methods and applications that are clearly written by intelligent people who have spent time thinking about statistical approaches in ecology, and more generally biology.  I’ve been reading about AAN, AIC, Bayes, CV-R², GAM, GLMM, LLM, PLS, RDA, and RF among others. 

My first conclusion is that there is a direct, positive correlation between the abundance and complexity of data arising from emerging sampling tools, e.g., remotely sensed data in my world, and the abundance and complexity of statistical analyses, e.g., Lortie et al. (2020).  I posit that the correlation began about the time that SAS and its 2000-page manuals hit our desks in the 1980s (SAS 1989).  Here is great quote that summarizes statistics in biology more broadly today:  “The suite of statistical tools available to biologists and the complexity of biological data analyses have grown in tandem…The availability of novel and sophisticated statistical techniques means we are better equipped than ever to extract signal from noisy biological data… [statistical] models are powerful yet complex tools.” (Harrison et al. 2018).  The quote is true regarding the much larger and complex data sets and the complex statistical analyses, analytical approaches, and packages today; however, the phrase “noisy biological data” glosses over the fact that it is the fundamental nature of biology to be messy and stay messy.  My second conclusion is that if you want to explain the fiery heat in your spicy chili, then trying to count and find patterns among the chili molecules isn’t a great investment of your time - ”Blackholes are simpler…But even if those equations could be solved for immense aggregates of atoms, they wouldn’t offer the enlightenment that scientists seek.” (https://aeon.co/ideas/black-holes-are-simpler-than-forests-and-science-has-its-limits).

It is the inherent nature of living things to be and stay messy.  All biological systems must have continuous variability and random or not opportunities to break moulds, otherwise selection for survival in a given environment can’t occur and life and lineages end.  This is evolution and broadly, natural selection with some mutations thrown in along the way.  It is this dynamic variability of living systems that jams up biology as it tries to fit into classical, physics-based definitions of the natural world (e.g., Egler 1986; Pigliucci 2002).  Biology has one law for certain (for now at least):  there will be lots of fluxing and variability and the occasional, and often unpredictable, mutations.  Stability is a major discussion point in ecology, but it is a temporal illusion because if you wait a few or 1,000,000 years, change will happen.

The rise of numeracy in biology is a great thing and there is no arguing that numbers are important and useful, especially in ecology.  The problem is that mathematics is bounded and as a consequence, it doesn’t always get along with the especially “noisy” data of ecology.  Counting, measuring, and summarizing are cornerstones of ecology.  Correlations and relations between measured factors and comparing groups are all very useful for developing an understanding of ecological systems.  The clash comes when ecologists, seeing their complex data become enthralled by complex number-busters of mathematics, e.g., statistics.

The clash occurs because 99% of mathematicians don’t understand that 99% of the rest of the world doesn’t get math.  Then add to this the rise of the machines.  Today there isn’t much in the way of statistical analyses that anyone with a few moments of ‘online help’ can’t do, e.g., the rise of “R” (Lortie et al. 2020; and a useful overview is https://blog.eduonix.com/software-development/rise-r-programming-language-usefulness-data-science/).  The story-line has been as follows: there is very beautiful mathematics (more on this later), it is translated through a machine with the virtual pressing of a button and with instant results, there is a growing throng of intelligent “applied statistics” crusaders, and voilà, we have the perfect recipe for disaster.  The crusaders are smart people, know there is a math issue, and sincerely also advise ecologists to ‘consult a statistician’.  But this is one of the disconnects: normal people (the 99%) don’t understand that mathematicians can’t conceive of a system not bounded by equations, and they are very happy that anyone is interested in mathematics and will dive into any math problem presented.

Taking a few steps back, I’m a math nerd and have been since I was 8 years old and calculating the least expensive set of groceries on a hand-held, pocket counter while wheeling the cart through the store.  I went to university to study mathematics, did two years in Canada’s elite mathematics’ programme, realized that math had other uses (leading to my mostly ecology career), and 30 years later I now teach statistics to undergraduate and graduate students in the environmental sciences.  I have been in the community of mathematicians and, while I am generalizing about them for literary purposes (apologizes to my math friends), I live at the mathematics-ecology nexus and I have happily added to the mathematics-ecology mash-up during my career, especially early on.

Math is cool even if 99% of us don’t get it.  Watching your hard work and collected data become a statistically significant regression or show statistically significant differences supporting your original hypothesis are powerful moments, especially early in your career.  Turning your very large and complex, interconnected data set into a 2-dimensional principle component space is amazing.  These analyses, among many math applications, have moved ecology far beyond counting and measuring, and the math can be very informative for advancing our understanding of complex systems.  Math gives ecologists many useful tools.  But – and there is a big but - mathematics has rules and ecological systems flaunt every one of those rules.  Ecology, i.e., the study of living things and their environments, is inherently variable across space and time, within and among individuals, families, groups, populations, species, communities, ecosystems, and at many more levels we can’t yet comprehend.  The biological and environmental information collected today will vary later today, be different tomorrow, and so on.  Natural systems want to change and have to change, but math needs stability and it has boundaries.    

I describe our current situation as statistics running amok over ecology at the beckon of ecology.  The math is beautiful and the people applying it and creating software to perform the complex computations are more intelligent than I, but the ever-increasing birds-nests of statistical analyses are mostly unnecessary as other more intelligent people than I have pointed out (see for example, Murtaugh 2007 and Amrheim et al. 2019).  Imagine I need to get from my home on the east coast of Canada to the west coast some 5,400 km away.  I used to drive a 1976 Chevy Nova which was the most standard car design on the road for a couple of decades.  I could successfully achieve my goal by driving that car across Canada with some simple assumptions that hold true: I have a paper map and there is gas and a mechanic who can fix any problem in every town.  Alternatively, I could drive a somewhat hypothetical, but close to reality automobile of today that is self-driving, GPS linked and controlled, electric fueling, and so on.  My assumptions are that self-driving is possible on all roads, all my computer systems don’t fail, the GPS satellites are detectable, Siri isn’t leading me astray, the computers that run the remote things don’t fail, and so on.  I also assume I can get these things fixed, but anyone who drives a lesser-imagined car today knows you can’t get it fixed unless you are at a big city dealer with a service computer plug-in for your car.  My analogy in statistical language: a simple t-test may not look pretty in today’s psychedelic statistical landscape, but it achieved the same result. 

I’m also sensing a negative impact on the advancement of ecology because we get distracted creating and promoting, more and more complex statistical analyses and software.  It is an ever-deepening rabbit’s hole because our inherently complex ecological systems are far beyond our current ability to comprehend and creating more complex statistical models and computational processes will never advance our understanding of the original question in ecology.  Trying to extract a signal we won’t recognize from guaranteed-to-be-increasingly-complex and noisy data is a never-ending do-loop. 

My take home message is what I try to instill in my young learners in the environmental sciences: “If your experiment needs statistics, you ought to have done a better experiment.”  A statement most often attributed to E. Rutherford, I explain that he (or whoever) wasn’t slamming statistics but was appealing for better experimental design.  I follow with Curry’s Corollary : “If you need statistics to tell something is significant, then it is not significant”.  This is about the natural variability you will face, that there is no magic bullet to overcome it, and that a clear question and good sampling design is the foundation you need to find or get you close to a solution, including which, if any, statistical approach you choose to use in your studies.  Statistics is just a tool, like many that we use in the environmental sciences.  It has value, but it remains just one tool in your toolbox.  I also emphasize ad nauseam, including with journal editors, that a far better tool is a well-thought out figure showing an effect/no effect.

I encourage and teach the use of mathematics, especially statistics, in all the environmental sciences.  These disciplines should be very grateful because statistic’s greatest gift has been the teaching to think about our questions, sampling design, and interpretation and presentation of data (there are many useful guides, e.g., Kass et al. 2016; Zuur and Ieno 2016).

A final note to readers.  There aren’t many references herein on purpose.  If you feel the need for a rebuttal, then you will already have many, very effective references at your fingertips to slam into your response.  Or you will get my point, smile, and contemplate your next study design a priori over a beverage of your choice.

Allen

References:

Amrhein V, Greenland S, McShane B.  2019.  Scientists rise up against statistical significance.  Nature 567(7748):305-307.

Egler, FE.  1986.  Physics envy in ecology.  Bulletin of the Ecological Society of America 67:233-235.

Harrison XA, Donaldson L, Correa-Cano ME, Evans J, Fisher DN, Goodwin CED, Robinson BS, Hodgson DJ, Inger R. 2018. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6:e4794.

Pigliucci, M.  2002.  Are ecology and evolutionary biology ‘soft’ sciences?  Annales Zoologici Fennici 39:87-98.

Kass RE, Caffo BS, Davidian M, Meng X-L, Yu B, Reid N.  2016.  Ten simple rules for effective

    statistical practice. PLoS Comput Biol 12(6):e1004961.

Lortie, CJ, Braun, J, Filazzola, A, Miguel, F.  2020.  A checklist for choosing between R packages in ecology and evolution. Ecol Evol 10:1098– 1105.

Murtaugh, PA.  2009.  Performance of several variable‐selection methods applied to real ecological data. Ecology Letters 12: 1061-1068.

SAS Institute Inc.  1989.  SAS STAT user's guide, version 6.  4th ed. SAS Institute Inc., Cary, N.C.

Zuur AF, Ieno EN.  2016.  A protocol for conducting and presenting results of regression‐type analyses. Methods Ecol Evol 7: 636-645.

Defining Oneself

Each year my new graduate students take a course that requires a synopsis of their supervisor.  Each time I’m asked to describe myself, to define who I am, and I struggle to know what to say.  Curiously, my son pointed this out to me, “when people ask, you always skirt around a description of what you do”.  When my PhD student insisted I describe myself in a few lines for a project he was working on, I felt it was time to put some effort into my self-understanding of my apparent, enigmatic storyline.

My first answer when asked who I am is typically, “I’m a natural historian”.  For my academic colleagues and my students, this statement evokes a look of uncertainty because this term evokes an image of a Victorian naturalist or Monk collecting rocks, plants, or animals and describing them in detail, and maybe the differences within and among.  So here is the long-winded explanation that goes with my natural historian self-moniker. 

I’m a professor of biology, forestry, and environmental management, and very clearly ensconced in the world of modern science.  I am an academic studying animals (fish and invertebrates) and their habitats (rivers, lakes, coastal zones).  I sit in a Department of Biology most days.  As a biologist, I’m in a community that has spent 50+ years divorcing itself from its “natural history” legacy (see for example Rickleff 2012 ) as the community pushed very aggressively to be a “hard science”, sometimes referred to as physics envy (e.g., Egler 1986 ).  For my biology colleagues and students who are well-trained a.k.a. indoctrinated, claiming to be a natural historian is tantamount to heresy.  As an aside, check out interesting read on science as a religion by Manson (2016)  and the many references therein.  Back to my colleagues and students, surely you must be an “ecologist”, even if you lower your position in the community by using the adjective “applied” ecologist because much of your science relates to answering questions that need answers today (e.g., Curry and Devito 1996; Monk et al. 2011, Freedman et al. 2012; Lento et al. 2018; O’Sullivan et al. 2019 ).  The term “applied” when used in the academic, biological sciences community is considered the lowest of castes because it is not “theoretical” to which all biologist must now strive to achieve.  Only theoretical approaches will advance of biology as a “hard science”; therefore, you are expected to do this for the community and your standing or rank is thusly judged.  This is a legitimate predicament for my biology students, they know their boss has a very successful career, but how is that possible as a “heretic” and most importantly for them, “how will that impact me, my studies, and my career?”.

I also live in the academy of physical sciences, e.g., my NSERC Discovery Grant comes from the Geosciences group (NSERC Discovery is the pinnacle of recognition for Canadian academics).   Describing my life is generally easy for these colleagues and students because they are not hung-up on a desire to be something else like my biology-type associates.  The dilemma for this community is the view that biology is a “soft”, lower caste of science.  How is it possible that a successful scientist could also be involved in such soft science, and for the students, “how will that impact me, my studies, and my career?”.

It is this apparent complexity of lives that confuses who I am to people outside science.  I am a professor, which invokes the typical “you work eight months of the year as a teacher”, which is partially true (some professors follow that model).  Once we dance around this for a few moments, I then get to describe what I “teach”.  I sometimes say I’m a biologist which is generally understood – medicine right?  I study fish, which is then quickly interpreted as “you are a marine biologist”, and some days I am.  You explain that most of your work is in freshwater, but that only confuses people because to them fish and fish-like creatures on TV and movies are Jaws, Free Willy, Flipper, etc.  You can’t often talk about invertebrates because these are, when you are lucky, just insects and they don’t live in water do they?, or they are seafood.   Hydrology is easier to explain because I can talk about flooding and invoke the popular topic of climate change.  Trying to explain that I study how water flows across and through landscapes is too deep, so how cutting down trees impacts stream temperatures is usually a good storyline.

Right now, my two biggest projects involve a large dam removal and the regional scale hydrology of New Brunswick (my province in Canada).  Dam removal is pretty easy to understand, unless I get asked for more details because it is hard to explain in one sentence the breadth of my work from the ecology of fish, invertebrates, and macrophytes to the hydrodynamic modelling of rivers and engineering of fish passage.  The regional hydrology study explanations are quickly consumed and transformed into a conversation lead by the questioner about the loss of buffer zones, too large cut blocks, poor forest roads, and always, industry is bad. 

Which brings me back to my description of myself as a natural historian.  I choose that description because I want to invoke the image of Charles Darwin or Charles Lyell.  Not because they were great scientists whom I think I am like, they are way out of my league, but because their era’s detailed studies and description of the natural world is what I do.  I’m interested in the very mundane, day-to-day structures and processes of the natural world, and what happens when we alter these.  I sometimes use a battlefield analogy to describe my work, an analogy where many may aspire to be majors and generals leading the way, but somebody still has to do the dirty work in the trenches, on the beaches, and door to door, or I do grunt work.

I’ve described myself as an explorer too.  I will go to the difficult places few or no others have gone before.  Eventually others may follow as pioneers and settlers.  I liked to turn over rocks as a kid because there are very intriguing things and creatures to discover.  It turns out the same discoveries occur when you turn over the “rocks” of our science world.  You discover that many of our modern ideas aren’t actually ours and we rarely care to acknowledge the science that came before us.  Our studies of the hydrology of landscapes is rapidly expanding with amazing new tools such as remote sensing with large and fine scale maps of surface and sub-surface attributes and stable isotopes that give us an idea of water age.  Long forgotten is the same work written eloquently by among others, Noel Hynes -A Stream and Its Valley (Hynes 1975 ), Tom Winter – Hydrological Landscapes (Winter 2001) , and Jack Stanford/James Ward  - Hyporheic Corridors (Stanford and Ward 1993).  And in the biological sciences, Charles Darwin wrote about many and arguably most of the new ideas proposed as “modern” biology theories (see among many reviews,  Boero 2015).

So, the next time you hear me being asked “Hey Allen, what do you do?”, know that all of these many storylines are streaming through my head as I try to decide which is the most appropriate response for the situation.  In the end, I remain a proud natural historian who is happy to use modern tools to explore nature, turn over rocks, get dirty, and find the best answers for today’s challenges today. 

REFERENCES
Boero, F.  2015.  From Darwin's Origin of Species toward a theory of natural history.  F1000prime reports, 7.
Curry, R.A. and K.J. Devito.  1996.  Hydrogeology of brook trout (Salvelinus fontinalis) spawning and incubation habitats: implications for forestry and land use development. Canadian Journal of Forest Research 26:767-772.
Egler, F.E.  1986.  Physics envy in ecology.  Bulletin of the Ecological Society of America 67:233-235.
Freedman, J.A., R.A. Curry, and K.R.M. Munkittrick.  2012.  Stable isotope analysis reveals anthropogenic effects on fish assemblages in a temperate reservoir. River Research and Applications 28:1804-1819.
Hynes, H.B.N.  1975.  The stream and its valley.  SIL Proceedings 1922-2010 19:1-15.
Lento, J., M.A. Gray, A.J. Ferguson, and R.A. Curry.  2018.  Establishing baseline biological conditions and monitoring metrics for stream benthic macroinvertebrates and fish in an area of potential shale gas development. Canadian Journal of Fisheries and Aquatic Sciences 999:1-15.
Manson, M.  2016. “The Subtle Art of Not Giving a F*ck: A Counterintuitive Approach to Living a Good Life.”  Harper Collins. 
Monk, W.A., D.L. Peters, R.A. Curry, and D.J. Baird.  2011.  Quantifying trends in indicator hydroecological variables for regime-based groups of Canadian rivers. Hydrological Processes 25:3086-3100.
O'Sullivan, A.M., T. Linnansaari, and R.A. Curry.  2019.  Ice Cover Exists (ICE): A quick method to delineate groundwater inputs in running waters for cold and temperate regions. Hydrological Processes 33: 3297– 3309.
Ricklefs, R.E.  2012.  Naturalists, natural history, and the nature of biological diversity (American Society of Naturalists Address).  The American Naturalist 179:423-435.
Stanford, J.A., and J.V. Ward.  1993.  An ecosystem perspective of alluvial rivers: connectivity and the hyporheic corridor.  Journal of the North American Benthological Society 12:48–60.
Winter, T.C.  2001.  The concept of hydrologic landscapes 1.  Journal of the American Water Resources Association 37:335-349.

Canada's Rivers Get a Report Card

After many years of very hard work, commitment, and perseverance, WWF Canada’s Freshwater Team has released their Freshwater Health Assessment (http://www.wwf.ca/conservation/freshwater/freshwaterhealth/).  This is our first, national effort to comprehensively report on the state of Canadian rivers.  The assessment or ‘report card’ will evolve to become an effective tool in the tool box used by river managers, community organizations, and all river enthusiasts across Canada.  It has the potential to transform our thinking and actions, including uniting in conservation, communities within and among watersheds across Canada.

The Freshwater Health Assessment (FHA) is a response to Canada’s water community’s desire for a clear, science-based picture of the health of our rivers.  While this should be a goal for our national government, we couldn’t wait any longer for action and unfortunately, the federal government is now reducing its potential to achieve environmental status reports.  The FHA was created over 2-years of extensive and intensive collaborations with Canada’s river science experts to build four key metrics that measure water quality, water flow, fish, and benthic macroinvertebrates (bottom dwelling animals).  The metrics were designed to be applied across the diversity of Canadian rivers.   The FHA was not designed to replace the many innovative assessment and management tools Canadians have already developed in watershed organizations across Canada.  Rather, the FHA will compliment ongoing initiatives and provide an effective linkage for river conservationists, managers, scientists, and everyone interested in rivers right across our great country.

The Canadian Rivers Institute has been and will continue to be a proud supporter of the FHA as it grows and evolves.

Thank you Tony Maas and the WWF Canada Freshwater Team!

Why you should care about government science with an epilogue by Dr. Seuss

The news headlines across Canada are filling with stories of the current federal government’s jettisoning of science programmes.  It is correctly an assault on federal government science consistent with the modus operandi of this government, e.g., their attacks on the environmental laws, environmental NGOs, and international aid programmes to name a few.  The attacks appear to begin as a goal derived from a decision on a perceived black or white issue followed by the “put your head in the sand and don’t stop the attack until the goal is achieved” assault.  Never deviate from the plan; our decision is correct, we are elected (regardless of electoral flaws or dubious manipulations) and therefore the chosen ones; it matters not what new information we are given; and in the language the current government probably best understands, damn the torpedoes, full speed ahead.

Not all of this behaviour is bad.  It is good to think about and discuss issues and try to find solutions including changing how we have done things in the past.  But it is a very serious problem with guaranteed consequences when we change things we don’t understand.  Most of us can attest that in a fit of frustration with a non-functional mechanical or electrical device, we have fought to open it up, dismantle it, and then reassemble it only to learn the expensive lesson that we pooched it forever.

This is what is happening with our “device” we call the federal government.  The device has some problems, the decision makers have decided to fix it, they have looked at a piece called federal science, they don’t understand how it works, fits, or functions, and so they are discarding it as non-essential.  And as we will learn in the near future, as we always do, there was a reason that piece was in the device.  It may need to be fixed, but the device can’t work without it.

To understand why federal science programmes are a part of a functional government device, we need to first understand how science works.  Science is the study of the world around us, or in simple terms our environment.  Art and other disciplines also study our world, but science is further defined by a process of advancement of knowledge we call a standardized method: observe the world around us and collect data, hypothesize about meaning and predict results of hypotheses, test predictions to confirm or reject hypotheses, and generate new questions and quests for data (the hypothetico-deductive method if you care to look it up).  Scientists are wrapped up in some or all of these activities within the realm of the world they study, i.e., the disciplines of science.  Science is also divided into questions that have immediate relevance to us often called “applied” sciences such as engineering and human medicine, and questions that are important for advancing understanding, but not necessarily of immediate need that are sometimes referred to as “pure” science, e.g., identifying all the species of the oceans.  These terms are not accurate descriptors, but I’ll keep them for this anecdote.  Pure science proceeds slowly and is driven by financial grants mostly from governments for scholarly activities such as writing journal articles about studies and experiments.  It typically happens at universities.  Much of applied science is driven by markets for products or services and therefore it can respond quickly and with significantly more funding than pure science.  It occurs in both universities and the private sector. 

Applied science also includes questions not driven by market forces but rather society needs, e.g., how much exposure of a chemical is safe for humans or how much industrial/human/agricultural effluent can we safely discharge to natural environments.  It also includes the undertaking of long-term monitoring of environmental conditions necessary for detecting change in natural systems, i.e., diagnosing when they become sick.  These are clearly very important questions for humans and their environment, but such science has little immediate market potential.  In fact, it can be mundane and lack innovation because it is driven by public policy and not novel inquiry and it sometimes requires 20+ years to gather enough data to test predictions.  Unfortunately, there is minimal private funding for this type of science.  It is unattractive to academic scientists at universities because the financial support models for their research depend on innovation, novel ideas, and regular publication of results.  This realm of applied science is as critical for us as any other science and therefore societies all over the world provide direct support via government research scientists and their teams.  This is why you should care about federal science.  It is one of the ways in which our society directly protects itself and prepares for living in the future.  And if you don’t believe such science is a fundamentally, economic issue, then think about the cost of cleaning up the environment after the Exxon Valdez oil spill (>$5B) or the Deepwater Horizon oil spill (>$37B directly and an estimated >$20B lost to local economies).    

Had the inner politicos of the current government asked why federal science programmes were required, they would have learned the essential function of this piece of the “device”, and thus why federal science can’t be jettisoned without consequences for our society.  The current government may have understood and accepted the risk of not controlling this science, but such behaviour is akin to running into the Canadian Boreal forest at midnight without a light and hoping that you won’t hit a tree.  You can say that someone else will shine a light into the forest, what the government says when it suggests the necessary science will be done at universities and in the private sector, but there is no guarantee that someone will build the appropriate light or shine it where you need it when you need it.  There is a better chance of missing trees if support for the light building and appropriate light-shining are being supplied, i.e., you planned and provided funding programmes (not the case now in Canada).  Even so, there is still no way to guarantee you will get the lighting you need when you need it to ensure your safety.  That is why we do this kind of science within government; to guarantee we are safe and protected in the future.

There is still time to stop the current assault on federal science before it incurs what is certainly a costly future for us.  Alas, the assault is unlikely to stop because the current government has blinkered itself and covered its ears to logic even when the truth affects their oft-stated bottom-line, i.e., the economy.  It is not wrong to make changes in how we do federal science including cost cutting; however, jettisoning something because you don’t understand it or don’t want to understand it is a fool’s folly.  Moreover, scrambling the communication spin of such political follies and then forcing the delivery on bureaucrats who know better but are forced by politics into this unpleasant position, and Ministers and party staff who are similarly intelligent enough to understand this folly is, in addition to being disingenuous to those of us who voted for these politicians, rather 'Grinchy'.

“The Grinch had been caught by this little Who daughter

Who'd got out of bed for a cup of cold water.

She stared at the Grinch and said, "Santy Claus, why,

"Why are you taking our Christmas tree? WHY?"

But, you know, that old Grinch was so smart and so slick

He thought up a lie, and he thought it up quick!

"Why, my sweet little tot," the fake Santy Claus lied,

"There's a light on this tree that won't light on one side.

"So I'm taking it home to my workshop, my dear.

"I'll fix it up there. Then I'll bring it back here." 

Seuss, Dr. 1957.  How the Grinch Stole Christmas! New York: Random House. 

Mining’s Dirty Little Secret.

Imagine your lovely house and home with its wonderful yard where you have gardens or your kids and pets play.  One day a person approaches you to buy the dirt from your front yard.  He will pay you a very good price for your dirt - guaranteed, cash up front, and more than you ever imagined dirt could be worth.  He promises to replace the dirt, returning your yard to its original state, and most importantly, he guarantees it will be restored on time and exactly as it was found.  He guarantees it with a post-dated cheque amounting to the original cost of the dirt and the estimated restoration costs: this is supposed to be your insurance policy.  You check out his company and find it is legitimate.  Your lawyer approves the contract for the dirt and reimbursements.  Confident you are protected, you sign the contract.

On the day he arrives to collect your dirt, you meet the company team.  They are most professional, they pay you cash, and you confidently head off to work.  When you return you find that all the dirt has been removed as planned and the team is gone.  To your surprise they don’t show-up the next day.  Exploring the hole more closely you notice that in addition to the dirt, they also broke through the main sewer line and the hole is filling with raw waste water, including some nasty industrial runoff that also collects in the pipe that is broken.  You are obviously unhappy so you call the company.  No answer?!  You go to the office address provided and read the sign on the door, “This Company has declared bankruptcy”.  What now?!  You go to the bank to cash the post-dated cheque which you discover is worthless.  You call your lawyer.  She informs you that your only recourse is trying to take the company to court, which will result in legal battles and fees extending for years and if you are really lucky you might get some of your money back.  An assessment of your front yard indicates the cost to fix the problem is 100x the amount you were paid for your dirt.  Now you have a gaping hole in your property filled with contaminated waste that you can’t use and you are left to wonder “where did I go wrong?”.    

That is mining in Canada: a litany of abandoned mine sites across this country, holes in the ground you may or may not see, piles of contaminated waste rock usually covered by water to keep them chemically “stabilized”, i.e., from killing living things directly, and most probably, toxic water leaching from the site.  The owner is long-gone and environmental bonds intended to fix problems inaccessible because of legal battles for company assets.  If there is an owner, then there are the legal battles to access bonds.  If a bond is accessed, rarely are there adequate funds to cover the current costs to restore the place to its original state.  And while the site sits constantly leaching toxins to the environment via water or wind, there is the cost of monitoring this pollution.  If there is no company, then ‘we’ (the government) pay.  Sometimes and mostly ‘we’ choose not to monitor at all because it is expensive.  Without a mine owner it is our responsibility to restore, i.e., ‘we’ pay (see the 351 mines of ‘our’ >22,000 known contaminated sites across Canada waiting to be restored at http://www.tbs-sct.gc.ca/fcsi-rscf/home-accueil-eng.aspx).  ‘We’ might get lucky and a company restores a site.  It has happened, but rarely do ‘we’ protect ourselves from any future failure of that restored site.

This is the legacy of mining in Canada and their dirty little secret – not my expression, but one used by some retired mining professionals.  In New Brunswick where I live, we have at least 48 known and possibly >250 abandoned mine sites.  Industry correctly claims that it is doing a significantly better job protecting the environment today by meeting and most times exceeding all government requirements (this is the environmental impact assessment process).  However, going from an “F” to a “D” on your grade school report card probably wouldn’t have impressed your parents much.  Interestingly, Canada just decided that companies can build small mines without a federal environmental impact assessment, i.e., Canadian politicians think the mining industry has been doing an acceptable job of protecting the environment on its own. 

Canadians and others have supported this Wall Street model for the mining industry that demands earning wealth for a company and its shareholders.  That is not necessarily a bad thing, but citizens and governments around the world have yet to charge companies for the environmental costs of their business, i.e., taxpayers have given them significant subsidies by not charging for the cost to the environment during operations or the restoration of their mine sites.  The mining industry is not going to change itself if that means reduced profits (don’t get mad at them; we as a society, i.e., company shareholders, are demanding more profits).  Only governments can demand the real cost of mining be incorporated into a mine’s business plan, and importantly including an upfront  guarantee of unfettered access to the money required to address environmental problems when they arise and eventually pay the restoration costs. 

Current governments across Canada are pushing hard for new mining ventures, including openly relaxing environmental regulations.  These ventures are repeatedly supported by the business community, newspaper editors, and journalists.  All of these parties argue that our economies need these mining ventures and moreover, regulations protecting the environment are enough on their own to change the mining industry.  While improving regulations may better protect the people most likely to be affected by mining activities, i.e., those living close to the mine, these regulations don’t address the actual issue which is the real cost of building and operating an environmentally-sound mine and then restoring the environment to a safe condition.  It is disingenuous for our community leaders to argue from an economic podium the case for mining development while continuing to suppress the true costs of mining. 

Canada has a wealth of natural resources.  Canadians from First peoples to today know the value of these resources.  We also have a wealth of science and engineering knowledge and experience capable of extracting and processing minerals including oil and gas while achieving a minimum impact on the environment.  This mining industry, in fairness, has invested millions of dollars on the issues of dealing with its waste and site restoration, but only after they have created negative impacts on the environment which they knew would occur (they report it in their environmental impact statements).  Extracting and processing natural resources, i.e., mining, is acceptable to most Canadians, but we should be investing in the research and development of environmentally-sound processes before we start. 

People might not like these real costs of getting the minerals into the products they consume, delaying consumption until we learn how to extract minerals innocuously, or earning less on investments, but we can’t sustain these secret costs any longer.  The current yet unspoken financial debt for the impact to the environment of all Canada’s past industrial activities is >$85B and growing.  ‘We’ have already spent about $1.3B of our tax dollars “paying down” this environmental debt, i.e., trying to fix the problems (http://www.oag-bvg.gc.ca/internet/English/parl_cesd_201205_03_e_36775.html#hd4b).  Ironically, everyone agrees that we need to reduce our current levels of indebtedness, so why continue to knowingly grow our debt by not charging the true cost of extracting and processing natural resources?  Is it ethical to grow these already nasty financial and environmental debts we are leaving to our children and grandchildren?

The evolving insignificance of significance

When I was an impressionable undergraduate in environmental sciences at the beginning of the 1980s, statistics was just hitting the main fashion runways of biology and especially ecology.  We didn't know why, but we were pummeled with Fisherian statistical training that required studying, cover to cover the works of authors such as Sokal, Rohlf, and Zar.  We learned techniques such as analysis of variance and its variants and the algebra of factor analyses and without computers I might add.  Computing was taking off so my cohort and those just ahead of us teaching statistics soon became well trained users of SAS and SPSS.  I worked in a research group with a variety of graduate students who discussed at length topics such as ANOVA, regression, and the emerging applications of multiple linear regression and PCA in biology and ecology.  These were heady days when such statistics were, I far as I knew, the perceived new elemental particle of ecology.

It wasn't until I became a graduate student in the mid-1980s that I realized that asking “why” had a wider required application than just my research.  I asked my mentors why statistics had become the all consuming, fashion in ecology.  The best answer or at least the one I understood to underlie the fashion was the perceived, absolute need for ecology to become a “hard” science like physics.  Ecology was considered “soft” also described as not rigorously absolute and therefore the discipline was perceived to border on non-science and this was not acceptable.  The explanation included the statements: ecology needed to move away from its natural history roots; we know enough about the natural world already; and, ecology needs to get structured, synthesize, and this rigorous mathematics is the ticket to salvation.  I didn't understand who would make such determinations nor did I understand fashion (as pictures from the time prove), but I did respect the status of my mentors and as a former mathematician I understood the rigorous nature of numbers.

I was an acceptable mathematician and computer programmer, so I fit well within this intensifying fashion and helped many colleagues including mentors with their struggles with these statistics.  My science grew as I churned through the modern scientific method of questions, hypotheses, predictions, and tests of predictions.  But I also watched several of my more senior mentors struggle to adjust to Fisher’s statistics which was creating a fundamental, philosophical change in how we studied natural environments.    

For about ten years I acceptingly immersed myself in the forced application of applied statistics in my natural history studies, which included both the biological and physical sciences of natural ecosystems.  I was publishing papers, theses, and reports with peer-reviewed and accepted statistical analyses.  Why questions endlessly nag me because of my nature, but the “why these statistics” question began to overwhelm me with my own recurring and other published results of statistical analyses that simply affirmed the obvious or where significance was biologically, physically, or chemically irrelevant for the ecosystem.  It took those ten years for me to begin to truly comprehend the statistics we were using in the environmental sciences and importantly, the insignificance of statistical significance.

That knowledge came from battles fought in my trenches of ecological research, i.e., the required long, hard hours in unrelenting weather collecting data about living creatures and their natural habitats, replicating such among cohorts, and then the battles with editors and reviewers arguing that 1-2 populations or years of replication is the first and probably the last set of data that we will achieve towards answering the asked question.  The take home messages of my battles were: Fisherian statistical zealotry was the environment we had built; fashion is more important than content; conform or be cast out.

I was in a minority group asking challenging questions about the foundations of the statistics driving our science.  The majority were caught up in the power and magic of numbers.  Those who could understand the analyses were the fashion gurus.  They used the mystification of mathematics, because the vast majority of humans don’t get mathematics, to drive our science ruthlessly though a maze of confusion that produced very little advancement and definitely not the Nobel-prizing winning inspirations most believed they were going to produce at any moment (this was the unwritten conclusion expressed in their publications and conference presentations).  But this was a lesson in “fashion”, a false human construct (yet interestingly arising from most probably an evolutionary process of selection) and in addition, my community of research was male-dominated so the essence of megalomania is well entrenched.  It was not in the best interests of the gurus to understand or inform on their weaknesses, e.g., the true assumptions of statistical analyses and probability theory, or to tame the zealots. 

The simple and definitive flaw for Fisher-based statistics as an elemental particle of environmental sciences is the natural environment itself.  Statistics was created within the bounded world of numbers.  Natural environments are variable and boundaries while seemingly apparent are in fact rather difficult and maybe impossible to define.  Moreover, living creatures exist only because of variability of form across all scales from genes to individuals to populations to communities to ecosystems.  You don’t have to be a scientist to realize the incongruity of applying an analytical process based on rigid boundaries to a highly variable system with indefinable boundaries.  This realization eluded me in the beginning because fashion can be alluring, and that became my first hard lesson in science.

I now teach what I consider to be hard-won rules about statistics in the environmental sciences.  Natural variability crushes all assumptions of any currently used, Fisher-based, statistical analyses.  Attempts to address this fact, i.e., “tests of assumptions” are used only to mollify your wrong application of the analysis.  I present a theorem:  If you need statistics, then you should have designed a better experiment (somebody else’s statement, most probably the physicist/chemist Lord E. Rutherford); and its corollary: If you need statistics to prove it is significant, then it isn't significant.  My students now should be talking about probabilities and multiple working hypotheses.

All this is not intended to instigate a movement to abolish Fisher’s statistics from the environmental sciences.  Indeed these statistics have some very useful purposes when applied properly and when the limits of analyses are well described.  In the realm of environmental regulation, constraints of our legal system force heavy dependence on understanding the state of “normal”, variability associated with normal, and defining what is not normal and thus, requiring structured applications of statistical analyses.  Perhaps Fisher’s greatest gift to the environmental sciences is the impact on what is now the foundation for sampling variable systems, i.e., the necessity to randomly sample among and within all possible habitats/locations (e.g., stratified random sampling).  There is no reason not to use tools such as analysis of variance or linear-based regression to help you understand and then explain a hypothesis, but that can’t be the only tool in your toolbox.  Statistic’s “null hypothesis” hasn't been as kind to science because it created wide-spread confusion about the logic of “falsifiability” which underlies our modern scientific method (another debatable statement, but the two concepts are not related).  But in the not too distant future, Bayesian statistics and probability theory might prove a saviour of environmental science once this community of researchers re-learns there are no absolutes in the natural world.      

I’m not sure if my world of science and research is changing its fashion, but there are fewer demands from journal editors and their reviewers to include meaningless statistical analyses and such demands are more easily refuted during the review process.  I still receive editor-level rejections based on the proposition that my studies in natural history lack sufficient replication.  Rejection based on poor judgement is difficult to accept, but you learn that in the short-term, fashion is more important than content and revolutions don’t happen over-night.  Anyway, I published those articles in journals with better impact factors which is another statistic created for fashion and a topic for a future essay.

Some interesting reading on this topic:

Cohen, J.  1994.  The earth is round (p<. 05).  American Psychologist 49:997-1003.

Fisher, R.A.  1956.  Statistical methods and scientific inferences.  New York, NY: Hafner

Lawrence, P.A.  2007.  The mismeasurement of science.  Current Biology 17(15):583-585.

Pigliucci, M.  2002.  Are ecology and evolutionary biology ‘soft’ sciences?  Annales Zoologici Fennici 39:87-98.

The American Psychology Association’s Task Force on Statistics: http://www.apa.org/science/leadership/bsa/statistical/tfsi-initial-report.pdf

An interesting essay on the reality of Fisherian statistics: 

http://www.creative-wisdom.com/computer/sas/math_reality.html

Allen evolving

Blogging - Maybe old dogs can learn new tricks.  My plan is to use this blog to rant and hopefully inform people about the inner workings of my corner of environmental sciences.  
Stay tuned.  Allen.  January 2, 2013.

Allen evolving by Ray Troll and Gary Larson.
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