Blogging again

In the summer of 2014 I began a PhD in soil science and I am now drawing towards the end of it.  As I think is usual, the first two years of the PhD felt hectic, to say the least.  Now, as I begin my write up, I have time to reflect on my research and am bring the approach I developed as an undergraduate to bear on this new context.

And what a context.  Soil has been called “the poor [wo]man’s tropical rainforest” due the mind boggling diversity and complexity of its microbial, mesofaunal and macrofaunal communities and it has a complex 3d structure.  Unlike a tropical forest, if you want to investigate the effect of a disturbance event you don’t need to wait for a hurricane, climb trees and smoke out species or set up large scale deforestation plots, you can arrange tillage treatments with a farmer or simply take intact soil, pass it through a sieve and repack it.  If working with soil is like working with Arabidopsis or mice, it makes experiments in tropical forests look like working with redwoods or elephants.

So, some thoughts on the valuing our soils and what the valuing of this vitally important natural resource means for farming will follow, hopefully you will find them interesting.

Making Space to Fail

I’ve just finished a Master’s project investigating the movement of pathogens from the faeces of cattle to the sea where people swim.  The pattern of and processes involved are fairly poorly understood, especially the movement of pathogens from stream to sea.  It’s the first time I’ve been involved in studying such a complex and incompletely understood system and it’s been great.  With so much to learn and the need for both a better knowledge of the patterns of pathogen abundance and the need to better understand the processes responsible for I’ve found value in pursuing and range of questions and approaches.

A large part of my research work involved regular field work to collect data for a long term data set and the analysis of this data set.  I could be highly confident that this work would produce valuable insights.  With data collection and analysis planned out in advance I still had some spare time in which to speculate, producing and testing hypotheses.  I had room for many small failures.  With the large knowledge gaps, curiosity and the room to fail came a healthy attitude towards failure.  Being aware of my ignorance, and the ignorance of the larger scientific community meant that finding processes to be not significant genuinely felt like a valuable lesson learnt.  The cost of learning these lessons was low.  Materials were extremely cheap, the lab work for preliminary experiments took at most a few hours and I didn’t need to prove my hypotheses to be correct, on top of the long term dataset work any findings were a bonus.

Having benefitted from this set up I’m a big fan of differentiating one’s work into that which is extremely likely to produce valuable knowledge (even if only moderate amounts) and that which is more speculative.  Google do something similar with 20% time encouraging employees to spend 20% of their time working on curiosity driven projects not related to the rest of their work  (see Adapt by Tim Harford for an elaborated discussion of this idea or his talk on the subject).  Ideally research as a whole should be a space for failure but funders want results, significant results are more likely to be published and referenced than non-significant ones and unless we are extremely self-confident we struggle to do research without wanting to prove our ideas (and therefore ourselves) to be right.  One solution is to have so many ideas you can’t get attached to any of them, another is to view producing hypotheses like making bets, good bets often lose, to paraphrase Nicolas Nassim Taleb, a mistake is not something to be identified with hindsight but identified at the time with the information available.  In producing a hypothesis we place a bet using the information available.  The greater the rewards the more speculative our hypotheses can afford to be and the more speculative we are they more we have to expect to disprove our hypothesis.

Erich Fromm and Conservation Science

To be successful conservation must alter the actions of individuals.  To alter the actions of individuals it is helpful to first understand human nature.  This is the realm of philosophy and psychoanalysis, of ideas resulting from slow and deep thinking not necessarily tested through experiments.  Instead of writing about what conservation can learn from other sciences, today I will write about what conservation can learn from the social sciences, particularly from Erich Fromm.

Eric Fromm (1900-1980) was a psychoanalyst (the Freudian school of psychology) and a social psychologist.  His ideas were not the results of experiments and consequent adjustments.  He worked up from the principles he believed to govern human behaviour, principles from philosophy, psychoanalysis and his own experiences and observations.  In ‘To have or to be?’, his 1976 book, Fromm wrote that most individuals “identify themselves by the following formula: I am=what I have and what I consume” and, consequently, in a conservation between two individuals of differing opinions “Each identifies with his own opinion.  What matters to each is to find better, i.e, more reasonable, arguments to defend his position.  Neither expects to change his own opinion, or that his opponent’s opinion will change.  Each is afraid of changing his own opinion, precisely because it is one of his possessions, and hence its loss would mean an impoverishment”.

I was first reminded of this passage when reading a paper advocating that scientists should not limit themselves to one theory (to prevent them from becoming too attached), instead they should suggest as many possible theories as possible and design experiments with the aim of disproving them.  It makes intuitive sense that individuals get attached to their ideas; Fromm offers a framework for understanding this.  He suggests that people derive their value, their self-worth from the ideas they create and then consume.  To lose an idea (to scientific progress) is just like losing another possession.

Fromm’s theory also has powerful ramifications for understanding the public’s stance on and engagement with issues of science such as global warming and GM technologies.  According to Fromm, an idea’s value is not a function of the idea’s use as a tool for making sense of the world (its usefulness); instead an idea’s value is determined by the cost of disowning that idea if a new, incompatible one is adopted.  More useful (i.e. correct) ideas do not necessarily replace less useful ideas, this means the initial ideas one creates regarding a subject are self-reinforcing and therefore very important.

Recently, the Cultural Cognition Project has produced quantitative evidence to support Fromm’s theory (I’m not sure if they were aware of Fromm’s work or not).  Their research showed citizens presented with expert sources regarding climate change were more likely to judge the expert source to be “knowledgeable and trustworthy” when the expert’s view agreed with their own.  Another study (by the same research group) reports that “Members of the public with the highest degrees of science literacy and technical reasoning capacity were not the most concerned about climate change” (authors’ italics).  The polarised opinions of two groups, separated on the basis of their political values, diverged as scientific literacy and technical reasoning increased, exactly as Fromm’s theory (“what matters to each is to find better, i.e, more reasonable , arguments to defend his position”) predicts.  

Andrew Balmford showed that children are better at identifying pokemon than real species (see here), might this be partly due to an ability to take ownership of pokemon (via card and computer games) which does not exist for real species (other than pets)?  How could this ownership be best replicated for real species?

Fromm suggests that keeping an open mind and avoiding dogmatism is difficult and requires active effort.  I suggest that it is the scientist’s role to purposefully keep an open and unbiased mind whilst they practice science, in their personal life, as with the rest of the public, they are free to respond to evidence however they like.

Conservation, a subject or a method?

A few months ago I sat down with the aim of describing exactly what conservation science is with the hope of producing a sort of framework.  I struggled.  I struggled because, trying to describe a science, I attempted to outline the conservation science method.  Pathology has a method and economics has a method; conservation science (if it can be called a science) does not have a method.  This is because conservation is a subject in the same way that, for example, asking how a healthcare system could be improved would be a subject.  To improve a healthcare system one would need not only look at biomedical research but also how to engineer the best buildings, how to attract and train the best staff, how to alter the public’s behaviour (in an acceptable manner) such that the demands on the system were reduced and how to best use the available staff, buildings and other resources.  In this blog I will explore the different strands of research which could and do contribute to the field of conservation highlighting that, in such a complex subject, explicitly defining the question one is answering and the framework they are using is vitally important.

I completed my undergraduate studies at Cambridge where, without really realising the significance at the time, I was introduced to three academics using and investigating different, complementary conservation methods. Head of conservation research for the RSPB, Rhys Green lectured me on a problem solving, investigative method used to isolate the factor causing the decline of vultures in Asia.  This was the similar approach to my idea of science, based on lab science.  William Sutherland introduced me to horizon scanning, a method to identify the upcoming challenges for conservation in conjunction with policy makers.  This type of research is obviously well complemented by the problem solving approach Rhys Green expounded.  William Sutherland also introduced me to citizen science, a framework for harnessing the information generated, often without being captured, by conservation practitioners.  This framework also has the potential to complements the Rhys Green framework, generating the data necessary to find solutions. 

Andrew Balmford, in my opinion a person who excels at providing the framework in which problems are best considered also lectured me.  Above all else, his lectures highlighted to me the need to identify the correct currency with which to measure the success of conservation efforts and the inevitable trade-offs entailed.  If you want to know how successful a zoo in then you must define success, is it visitor numbers or changes in individuals’ attitudes towards nature or the number of successful reintroductions or profits?  Are zoos trying to maximise the same measure of success as conservation scientists?  This approach largely influenced my blog on Quality Adjusted Life Years (QUALYs), the metric used by the NHS to quantify the value of different treatments.  It is also largely through the work of Andrew Balmford that I was introduced to the ecosystem services approach, an attempt to find a common currency which will allow conservation to engage with economics and politics, the forces which, ultimately, shape human behaviour and therefore the fate of nature (again, my blog on QUALYs examines this issue).

This last year, since graduating, I have discovered two new methods which, I believe, have much to offer to conservation.  The first is behavioural economics.  I have stated that economics and politics shape human behaviour but economics does not provide a perfect prediction of human behaviour.  Understanding why individuals act ‘non-rationally’ (not as an economic model would predict) is the realm of behavioural economics.  I believe that it will prove to be extremely valuable by aiding our understanding of the decision making of individuals, such as farmers, whose actions determine the fate of nature.  Once these decision making processes are understood, policy makers will be able to offer farmers, and other stewards of nature, the rewards they will respond to in exchange for actions which benefit nature (identified via traditional research and Sutherland’s citizen science).  Here is a link to a blog I wrote on what behavioural economics can offer conservation.  The second is no so much a method as a way of thinking (at least to me, perhaps I am not intelligent enough to convert this new awareness into a method).  It comes from the books of Nicholas Nassim Taleb, ‘Fooled by Randomness’ and ‘The Black Swan’.  These books provide a way of thinking about randomness in complex systems, and, ultimately conclude that it is better to be broadly right when making ecological predictions and designing conservation interventions than attempting to make precise predictions and being wrong.  If policy makers can be convinced that they are better served by uncertain predictions which include a measure of the uncertainty entailed, then the conservation would, I believe, benefit.  I have also written this blog on this subject.

Though I have characterised the three Cambridge professors as each pursuing different methods, in reality they co-operate and their work overlaps greatly.  In such an interdisciplinary field, collaboration within and between departments offers so much.  I think I will return to my blog and try to bring these strands and methods together into some sort of framework.  If you have any comments or advice then please do leave your thoughts below.  Many thanks.

Portraying the opinion of the scientific community in media debates

Consider the case of GM foods or Climate Change.  I want to know why the public assess risk differently from scientists and other experts.  It is a problem with many components of which I will only investigate one: how is the balance of ‘opinion’ within science, communicated to the public?  The norm for televised discussions or debates is to have on one side of the table the individual representing the consensus position and on the other side an individual representing the non consensus position.  I don’t believe that this format communicates to the public an accurate reflection of what scientists think.

Take for example the recent IPCC report which states with 95% confidence that humans are the ‘dominant cause’ of global warming.  At the most basic level, I believe, that having one scientist advocating the consensus position and one individual representing the 5% of uncertainty in a debate leaves the public with the impression that something akin to equal weight should be given to each side of the debate.   I would like to see the physical set up of the discussion reflect the actual balance of confidence (which in turn reflects the evidence).  This could be achieved by giving 95% of the airtime to the consensus position but I would not advocate this approach as it would limits the opportunity of the ‘non-consensus’ position to challenge the consensus position.  I am all in favour of the consensus position being scientifically challenged, it is through challenging and improving many generations of old scientific ideas and models that we arrived at the scientific understanding we have today.  Instead I would be in favour of a more literal representation of the balance of opinion.

In the instance of the 95% confidence level, for every individual adopting the ‘non-conventional’ stance I would like to see 19 individuals on the other side of the table.  The 19 would be represented by one spokesperson, the only individual who would talk on behalf of that side of the debate.  The other 18 could show their support by raising their hands in support of the speaker or, at the end of a speaker’s statement by saying ‘I agree’.  This, I believe would much better represent the balance of opinion.  It would be necessary to ensure that there is no sense of bullying which I believe the single spokesperson would ensure.  If the person representing the non-conventional approach wished to have another person on their side of the table then they could, as long as 19 people joined the other side of the table.

As a caveat, I recognise that the 95% confidence does is not the view of 100% of scientists, 97% of scientists support the IPCC report.  Therefore, a better representation would be if the stance that humans are responsible for global warming had 95% x 97% (which equals 92.15%) representation.

So far this blog has dealt with an issue of confidence (a measure of scientists’ confidence in their model).  In this case it is not that 95% of scientists 100% hold stance ‘A’ and 5% hold stance ’B’ which actually makes it difficult to represent as all scientists who support the IPCC have some degree of uncertainty, it is not that 5% disagree with the others.  It is simpler when the question is framed such that each scientist takes a position 100%, for example if the debate was centered on the question ‘are the ecological risks posed by GM technologies outweighed by the benefits they offer?’. The same set up could be used with proportional representation of scientific opinion for a debate centering on this question

This blog is just from off of the top of my head but proportional (physical) representation of the opinion of the scientific community makes intuitive sense to me.  If you have any criticisms then please do leave them below.

Thanks

It is better to be broadly right instead of precisely wrong.

In this blog I will discuss a recent move by the National Trust to ‘macro-manage’ land in an attempt to get things broadly right over a large geographic scale.  Attempting to manage land for nature can be difficult because nature is so complex, in the face of such complex systems the National Trust’s strategy is not only cost efficient, it is extremely sensible.  To explain why I think this, I first need to write a little on the nature of randomness.  I will distinguish between three types of randomness, inherent, game and apparent randomness.  All three types of randomness are alike in a crucial way: they make the future uncertain.  Inherent randomness belongs in the domain of physics, more precisely quantum mechanics which states, amongst other things, that it is impossible to know both where a (sub-atomic) particle is and where it is going.  There is pure randomness at the root of reality.  Fortunately, as we move to larger scales e.g the size of a cell all of the randomness averages out in a predictable way hence this randomness has no influence on our lives.  Game randomness is the randomness we are most familiar with, the moment before the die is rolled we are uncertain about what the outcome will be, if we pick a card at random from a deck we do not know for certain which card we will pick.  Now, if the die is fair and the deck is a complete one then we can know with certainty the probability of rolling a ‘2’ (1/6) or picking an Ace (1/13), we know how uncertain the future is, how much we don’t know.  The last type of randomness, apparent randomness, is the most important to this topic.  Apparent randomness is the case of facing an uncertain future due to a lack of knowledge and understanding, it can be thought of trying to play cards with a weighted deck (say, one in which all clubs have been replaced by hearts) the composition of which the player does not know.  Here, if one knew the makeup of the deck then they would be able to predict the probability of different outcomes but the deck, the randomness generator, is not known to the player.  For example, though the weather may be determined by laws, just as the movement of a planet is determined by (Newton’s) laws of motion, our understanding of the weather laws is such that we are unable to predict it, thus, for all purposes, the future weather appears to us to be, at least slightly, random.  For another example consider the many precise (but often wrong) predictions made by the Bank of England regarding future inflation/interest rates/unemployment (see here for the result of a very quick Google search)

The weather is so difficult to predict because it depends upon many ‘units’ (water droplets, the solids around which they form, local air pressures and more) which can interact to form positive feedbacks.  The result is a system which is rendered hugely difficult to predict, in part because a tiny mis-estimation can have far reaching consequences.  In fact it was a study of the weather which gave birth to the mathematical field of ‘chaos theory’.  Chaos theory can be understood by imagining a tennis ball sat atop a large exercise ball in the middle of a sports hall.  If the perfectly balanced tennis ball is instead placed slightly to the right then it will roll away to the right, perhaps as far as the end of the sports hall and vice versa if the ball is placed slightly to the left.  Thus a tiny mis-estimation of the starting position of the tennis ball has far reaching consequences for predictions of the future position of the ball.  That the weather forms such an unpredictable system is a problem for ecology, conservation and agriculture because the weather plays such a large role in determining what happens at the very base of every foodweb.  Even if the weather was perfectly forecastable, predicting the precise impact of altering a complex foodweb via carrying out a conservation intervention would be nigh on impossible as is predicting the impact of a government’s economic intervention.

Conservation interventions (outside of academia) are normally carried out on the belief that doing A will result in a change in B.  If A is expensive then it will need to be justified.  The normal approach to this problem is to make a prediction about the change in B which will result from action A.  Unfortunately this usually means making predictions about complex systems which are not completely understood.  The more precise one attempts to be, the more likely one is to be wrong, herein lies the problem. 

There are two solutions to the difficulties of forecasting presents:

making vaguer predictions and making no predictions at all.

Both of these solutions run counter to our nature and also counter to the media’s handling of prediction making in the face of randomness.  Firstly we cannot help but attempt to predict the future by imagining various scenarios, not making predictions requires a great mental effort and self-restraint.  Secondly, when we cast our minds forwards we do so by imagining one possible scenario at a time.  Such a forecasting system is deeply flawed.  A useful forecast is not the sum of one or a few imagined scenarios, is the (weighted) average of all the possible scenarios which includes in it a measure of uncertainty.

The National Trust’s Wicken Fen (Cambridge) (see here) and High Peak Moors (Peak District) (see here and here) projects are both attempts to manage land whilst making the minimal possible number of predictions.  The National Trust is doing this by utilising fairly unspecific tools (i.e. livestock graze land instead of fine scale intervention by hand in Cambridge and blocking ditches in the Peak District) in the hope that these will bring about broad benefits to the ecosystem.  Precisely what those benefits will be, there has been little attempt to precisely predict.  Instead the introduction of the livestock, ditch blocking and other broad-stroke interventions, are forming lessons from which the National Trust will learn in order to inform future efforts.

Restraining the extent of our meddling with nature, basing this meddling on minimal ecological theorising and instead looking for examples of what worked in the past may seem a non-scientific approach but it isn’t.  Learning what works is scientific.  If broad and approaches can be shown scientifically to work best, scientists are obliged to put aside any inherent preference for complexity and evaluate these approaches in the same way as more complex ones. 

Being broadly right means dealing with uncertainty, with vagueness, a situation which may not sit well with us at first but it is surely better than being precisely wrong.

QALYs and conservation

I am interested in what conservation can learn from other scientific disciplines as it strives to tackle two of its biggest problems, a lack of knowledge and a lack of funds.  Adopting an evidence based approach to selecting conservation interventions, as has been achieved in medicine, has been proposed as a solution to tackling the lack of knowledge conservation suffers from.  With regards to funds, conservation must generate more funds and use these funds as effectively as possible.  Here again, the NHS offers a lesson.  The NHS’ budget has long been insufficient to offer all treatments to all patients, as a solution a scientific method has been put in place to ensure that the resources are used optimally, to save the most lives and increase life quality as much as possible.  I will explore what conservation can learn from how the NHS achieves this.

Finite resources force tradeoffs.  Tradeoffs entail valuation, either implicit or explicit.

When utilising finite resources, a decision to pursue one course of action/invest in one intervention means deciding not to invest in another option.  Thus, the intervention which is chosen is valued (implicitly or explicitly) more highly than the option(s) not chosen.  When different interventions require different amounts of resources then the situation becomes more complicated but the principle remains, the option which is chosen is judged to be of greater value than the options not chosen.

The NHS’ finite budget forces it to make tradeoffs between different treatments.

The NHS has a finite budget which it can spend on treatments.  This budget is not large enough to provide every proven treatment.  As a result, according to the NICE (the National Institute for Health Care and Excellence) website ‘The enormous costs (of different treatments) mean that choices have to be made’.  These choices are tough choices, choosing between offering a drug to extend the life of a cancer patient by a few months or years and using this money to provide other treatments.  As stated on the NICE website ‘It makes sense to focus on treatments that improve the quality and/or length of someone's life and, at the same time, are an effective use of NHS resources’ (my emphasis).  It is because health and life are so important that these choices are so difficult and it is because these choices are so difficult that a rigorous and scientific system needs to be in place to ensure the correct decisions are made.  In this blog I will examine in more detail how the NHS does this, paying particular attention to the two phrases I’ve highlighted in bold, considering similarities in and lessons for conservation science.

‘Focus on treatments that improve the quality and/or length of someone’s life’:

The NHS’ finite budget means that choices must be made between different treatments for the same and different conditions.  This raises the question ‘how do you compare the cost of losing a leg against the cost of losing sight in one eye or both eyes against the cost of chronic back pain taking into account the effect of the treatment on the patient’s life expectancy?’  What is required is a common currency which allows the benefits of different treatments to be compared.  The currency used is the QALY (Quality Adjusted Life Year). 

To calculate the QALY for a treatment the quality of life of an individual with and without treatment must first be quantified as between 0 (death), and 1 (that of a healthy individual).  By multiplying the additional quality of life resulting from the treatment against the patient’s life expectancy, the Quality Adjusted Life Years resulting from the treatment is calculated (more information can be found here).  The resulting QALY is a standardised measure of the benefit of a treatment and can be compared to any other treatment.

 ‘It makes sense to focus on treatments that ... are an effective use of NHS resources’

Not all treatments offer equally high QALYs and not all treatments are equally expensive.  A treatment which resulted in a great increase in the patient’s quality of life and/or life expectancy may not be offered on the NHS if the price is too high.  Again, NICE use a simple and explicit method to overcome this problem: the cost of the treatment is divided by its QALY value to give £ per QALY (the chosen measure of cost effectiveness) as you can see on their website here. 

I believe that QALYs offer conservation two lessons:

1.       When choosing between different conservation options a common currency is required to compare these different options

2.       The calculation of the benefits of pursuing each option (expressed in the common currency) must be made explicit

I will explore these lessons throughout this blog.

Optimally utilising a predetermined, finite budget.

So what would be the equivalent of a QALY in conservation?  I suggest that the simplest currency, which requires the least number of assumptions and arbitrary valuations, should be used to compare the costs and benefits of different options.  Take for instance a zoo deciding how best to use its resources.  The zoo may aim to maximise the number of different species in the zoo in which case the cost per extra species maintained in zoo could be used.  This might be adjusted to reflect preference for different species (in the same way different treatments are weighted depending on the change in quality of life they offer) e.g. an ape may be worth 3 (other) mammal species and each mammal may be worth 10 bird species (with the exception of penguins) which in turn may each be worth 10 invertebrate species.  Or perhaps, if the zoo’s aim was to reintroduce species to the wild, the zoo would use estimated cost per successful reintroduction of a species.  If the zoo aims to maximise visitor stay at the zoo then sum visitor time spent at enclosure per species could be used.  If education was the aim then a measure of change in knowledge and/or values resulting from visitors visiting the enclosure could be used.  I’m not suggesting how every zoo should be run, I’m trying to demonstrate that making your decision process explicit forces you to identify what it is that you base your decisions on and how you weight these decisions.  When public money is being spent then this is especially important.

Using economic valuation as the common currency

Suppose a conservation organisation has to choose between 3 projects, one will safeguard water quality, another will improve the health of pollinator populations and the third will increase carbon sequestration.  If all three projects cost the same, how should the organisation choose between the alternative uses of its resources?  I suggest that a common currency (like the QALY) is needed so that the projects can be compared.  This currency should require the minimal number of assumptions and arbitrary valuations.  The best attempt to achieve this so far is the Ecosystem Services approach which uses, as its common currency, monetary valuation.  For example, the avoided cost of building a new water treatment plant, the avoided cost of pesticide and benefit or larger crop yield and the avoided cost of climate change (calculated using carbon prices which already exist).  These conversions between different currencies, from water quality to avoided cost of a water treatment plant can be calculated scientifically, minimising the need for assumptions and arbitrary valuations whilst making those assumptions and arbitrary valuations explicit.

Comparing conservation to non conservation options, conservation must use the currency of decision makers.

Now suppose that a government has to decide between 3 project proposals concerning a plot of land, a large motorway verge for example.  The first project is a conservation one which will entail the planting native tree species leading to an increase in carbon sequestration and water quality, the second will build shops which will generate revenue via rent and VAT and the third will ensure that the land use, which has no net economic cost, does not change.  As when comparing different conservation options, a common currency is required to directly compare the 3 different projects.  The benefits of the second and third projects are easily estimated and expressed in monetary terms.  To compare these benefits to that of the first project either the economic benefit (or cost if the benefit is negative) of the second project must be converted into an equivalent amount of carbon sequestration and water quality improvement or vice versa.  As the first is very unlikely, the Ecosystem Services approach, by converting the value of the carbon sequestration and water quality improvement to monetary terms, allows comparison of all three projects such that the conservation option is included and well represented in the decision making process.

NICE doesn’t have to make decisions between increasing health or increasing GDP, that’s why they can stick to QALYs, a currency one step removed from explicit economic valuation.  But individuals and organisations comparing conservation with business opportunities use the currency of money.  Economic valuation is the language spoken.  This must be the QALY of conservation.  It is this which conservation must adopt in order to ensure the value of nature can be compared to the value of other options so that it may be included in the decision making process.  I acknowledge that occasionally, and only occasionally, the currency the government uses to make decisions is public support/protest.  In these instances public support for a conservation option/opposition to the alternative may influence a decision.  However, public support for conservation options has, historically, been hugely insufficient.  This is why I believe that economic valuation is required.

We are a long way off of accurately valuating Ecosystem Services.  In fact, before conservation ‘treatments’ can even been assigned a QALY value they must be demonstrated to be effective via a rigorous scientific valuation.  Many conservation ‘treatments’ are used without ever being effective; conservation’s knowledge problem.  This is why a transparent method, like that used by NICE, is so necessary.  By making our implicit valuations and assumptions explicit, we make criticism and adjustment in the face of new information simpler.  It is because nature, like our health, is so important, that a rational, scientific method is required when it comes to using our limited resources.  If you still disagree, if you believe that explicitly valuing nature is fundamentally wrong then please stop to consider that, if you live in the UK, every aspect of your health is valued relative to every other and has its value.  If you are unfortunate to contract renal cancer then the NHS won’t cover the full cost of treatment ‘sunitinib’, proven to be effective, because it is too expensive (http://news.bbc.co.uk/1/hi/health/7546879.stm).