The natural history of numbers

I have made a few facetious comments in this blog about the tendency for ecologists to spend more time staring at spreadsheets than engaging directly with the organisms and habitats they are trying to understand.   There is, of course, a balance that needs to be struck.   We can learn a lot from analysing big datasets that would not have occurred to a biologist who spent all his or her time in the field.  And, I have to admit, somewhat grudgingly, there is a beauty to the numerical landscapes that becomes apparent when a trained eye is brought to bear on data.

I’ve been involved in a project for the European Commission which has been trying to find good ways of converting the ecological objectives that we’ve established for the Water Framework Directive into targets for the pressures that lead to ecosystem degradation.   The key principle behind this work is summarised in the graph below: if the relationship between the biology (expressed as an Ecological Quality Ratio, EQR) and a pressure (in this case, the phosphorus concentration in a river or lake) can be expressed as a regression line then we can read off the phosphorus concentration that relates to any point on the biological scale.   (Note that there are many other ways of deriving a threshold phosphorus concentration, but this simple approach will suffice for now.)

PvEQR_1pressure

Relationship between biology (expressed as an Ecological Quality Ratio, EQR) and phosphorus concentration for a hypothetical dataset.  The blue line indicates the least squares regression line, the horizontal green line is the position of the putative good/moderate status boundary and the vertical green line is the phosphorus concentration at this boundary position.  Coefficient of determination, r2= 0.89 (rarely achieved in real datasets!)

This is fine if you have a strong relationship between your explanatory and response variables and you are confident that there is a causal relationship between them.  Unfortunately, neither of these criteria are fulfilled in most of the datasets we’ve looked at; in particular, it is rare for the biota in rivers to be so strongly controlled by a single pressure.  This means that, when trying to establish thresholds, we also need to think about how a second pressure might interact with the factor we’re trying to control.   If this second pressure has an independent effect on the biota then we might expect some sites that would have had high EQRs if we just considered phosphorus might now be influenced by this second pressure, so the EQR at these sites will fall below the regression line we’ve just established.   When we plot the relationship between EQR and phosphorus taking this second pressure into account, our data no longer fits a neat straight line, but now has a “wedge” shape, due to the many sites where the second pressure overrules the effect of phosphorus.   If you were tempted to put a simple regression line through this new cloud of data, you would see the coefficient of determination, r2, drop from 0.89 to 0.35.  Note, too, how the change in slope means that the position of the phosphorus boundary also falls.   More worryingly, we know that, for this hypothetical dataset, the new line does not represent a causal relationship between biology and phosphorus.  That’s no good if you want to use the relationship to set phosphorus targets and, indeed, you now also need to think about how to manage this second pressure.

PvEQR_2pressures

The same relationship as that shown in the previous graph, but this time with an interaction from a second pressure.  The blue line is the regression line established when phosphorus alone was considered, and the red line is the regression between EQR and phosphorus in the presence of this second pressure.

My purpose in this post is not to talk about the dark arts of setting targets for nutrient concentrations that will support healthy ecosystems but, rather, to talk about data landscapes.  Once we saw and started to understand the meaning of “wedge”-shaped data, we started to see similar patterns occurring in all sorts of other situations.   The previous paragraph and graph, for example, assumed that the factor that confounded the biology-phosphorus relationship was detrimental to the biology, but some factors can mitigate the effect of phosphorus, giving an inverted wedge, as in the next diagram.  Once again, the blue line shows the regression line that would have been fitted if this was a pure biology versus phosphorus relationship.

PvEQR_2pressures_#2

The same relationship, but this time with a second factor that mitigates against the effect of phosphorus.  Note how the original relationship now defines the lower, rather than the upper, edge of the wedge. 

Wedge-shaped data crop up in other situations as well.  The next graph shows the number of diatoms I recorded in a study of Irish streams and there is a distinct “edge” to the cloud of data points.   At low pH (acid conditions), I rarely found more than 10-15 species of diatom whereas, at circumneutral conditions, I sometimes found 10-15 species but I could find 30 or more.   Once again, we are probably looking at a situation where, although pH does exert a pressure on the diatom assemblage, lots of other factors do too, so we only see the effect of pH when its influence is strong (< pH 5).

Ntaxa_v_pH_FORWATER

The number of diatom species recorded across a pH gradient in Irish streams.  Unpublished data.

In this case, the practical problem is that the link between species number and pH is weak so it is hard to derive useful information from the number of species alone.   It would be dangerous to conclude, for example, that the ecology at a site was impacted by acidification on the strength of a single sample.  On the other hand, if you visited the site several times and always recorded low species numbers, then you have a pretty good indication that there was a problem (not necessarily low pH; toxic metals would have a similar effect).   Whether such a pattern would be spotted will depend on how often a site is visited and the sad reality is that sampling frequencies in the UK are now much lower than in the past.

However, this post is not supposed to be about the politics of monitoring (evidence-based policy is so much easier when you don’t collect enough uncomfortable evidence) but about the landscapes that we see in our data, and what these can tell us about the processes at work.   Just as a field biologist can look up from the stream they are sampling and gain a sense of perspective by contemplating the topography of the surrounding land, so we should also be aware of the topography of our data before blithely ploughing ahead with statistical analyses.

with_Geoff_&amp;_Heliana

With Geoff Phillips and Heliana Teixaira – fellow-explorers of data landscapes in our project to encourage consistent nutrient boundaries across the European Union.

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The Imitation Game

About a year ago, I made a dire prediction about the future of diatom taxonomy in the new molecular age (see “Murder on the barcode express …“).   A year on, I thought I would return to this topic from a different angle, using the “Turing Test” in Artificial Intelligence as a metaphor.   The Turing Test (or “Imitation Game”) was derived by Alan Turing in 1950 as a test of a machine’s ability to exhibit intelligent behaviour, indistinguishable from that of a human (encapsulated as “can machines do what we [as thinking entities] can do?”).

My primary focus over the past few years has not been the role of molecular biology in taxonomy, but rather the application of taxonomic information to decision-making by catchment managers.   So my own Imitation Game is not going to ask whether computers will ever identify microscopic algae as well as humans, but rather can they give the catchment manager the information they need to make a rational judgement about the condition of a river and the steps needed to improve or maintain that condition as well as a human biologist?

One of the points that I made in the earlier post is that current approaches based on light microscopy are already highly reductionist: a human analyst makes a list of species and their relative abundances which are processed using standardised metrics to assign a site to a status class. In theory, there is the potential for the human analysts to then add value to that assignment through their interpretations.  The extent to which that happens will vary from country to country but there two big limitations: first, our knowledge of the ecology of diatoms is meagre (see earlier post) and, in any case, diatoms represent only a small part of the total diversity of microscopic algae and protists present in any river.   That latter point, in particular, is spurring some of us to start exploring the potential of molecular methods to capture this lost information but, at the same time, we expect to encounter even larger gaps in existing taxonomic knowledge than is the case for diatoms.

One very relevant question is whether this will even be perceived as a problem by the high-ups.  There is a very steep fall-off in technical understanding as one moves up through the management tiers of environmental regulators.   That’s inevitable (see “The human ecosystem of environmental management…“) but a consequence is that their version of the Imitation Game will be played to different rules to that of the Environment Agency’s Poor Bloody Infantry whose game, in turn, will not be the same as that of academic taxonomists and ecologists.  So we’ll have to consider each of these versions separately.

Let’s start with the two extreme positions: the traditional biologist’s desire to retain a firm grip on Linnaean taxonomy versus the regulator’s desire for molecular methods to imitate (if not better) the condensed nuggets of information that are the stock-in-trade of ecological assessment.   If the former’s Imitation Game consists of using molecular methods to capture the diversity of microalgae at least as well as human specialists, then we run immediately into a new conundrum: humans are, actually, not very good at doing this, and molecular taxonomy is one of the reasons we know this to be true.  Paper after paper has shown us the limitations of taxonomic concepts developed during the era of morphology-based taxonomy.  In the case of diatoms we are now in the relatively healthy position of a synergy between molecular and morphological taxonomy but the outcomes usually indicate far more diversity than we are likely to be able to catalogue using formal Linnaean taxonomy to make this a plausible option in the short to medium-term.

If we play to a set of views that is interested primarily in the end-product, and is less interested in how this is achieved, then it is possible that taxonomy-free approaches such as those advocated by Jan Pawlowski and colleagues, would be as effective as methods that use traditional taxonomy.   As no particular expertise is required to collect a phytobenthos sample, and the molecular and computing skills required are generic rather than specific to microalgae, the entire process could by-pass anyone with specialist understanding altogether.  The big advantages are that it overcomes the limitations of a dependence on libraries of barcodes of known species and, as a result, that it does not need to be limited to particular algal groups.  It also has the greatest potential to be streamlined and, so, is likely to be the cheapest way to generate usable information.   However, two big assumptions are built into this version of the Imitation Game: first, there is absolutely no added value from knowing what species are present in a sample and, second, that it is, actually, legal. The second point relates to the requirement in the Water Framework Directive to assess “taxonomic composition” so we also need to ask whether a list of “operational taxonomic units” (OTUs) meets this requirement.

In between these two extremes, we have a range of options whereby there is some attempt to align molecular barcode data with taxonomy, but stopping short of trying to catalogue every species present.  Maybe the OTUs are aggregated to division, class, order or family rather than to genus or species?   That should be enough to give some insights into the structure of the microbial world (and be enough to stay legal!) and would also bring some advantages. Several of my posts from this summer have been about the strange behavior of rivers during a heatwave and, having commented on the prominence and diversity of green algae during this period, it would be foolish to ignore a method that would pick up fluctuations between algal groups better than our present methods.   On the other hand, I’m concerned that an approach that only requires a match to a high-level taxonomic group will enable bioinformaticians and statisticians to go fishing for correlations with environmental variables without needing a strong conceptual behind their explorations.

My final version of the Imitation Game is the one played by the biologists in the laboratories around the country who are simultaneously generating the data used for national assessments and providing guidance on specific problems in their own local areas.   Molecular techniques may be able to generate the data but can it explain the consequences?  Let’s assume that method in the near future aggregates algal barcodes into broad groups – greens, blue-greens, diatoms and so on, and that some metrics derived from these offer correlations with environmental pressures as strong or stronger than those that are currently obtained.   The green algae are instructive in this regard: they encompass an enormous range of diversity from microscopic single cells such as Chlamydomonas and Ankistrodesmus through colonial forms (Pediastrum) and filaments, up to large thalli such as Ulva.   Even amongst the filamentous forms, some are signs of a healthy river whilst others can be a nuisance, smothering the stream bed with knock-on consequences for other organisms.   A biologist, surely, wants to know whether the OTUs represent single cells or filaments, and that will require discrimination of orders at least but in some cases this level of taxonomic detail will not be enough.   The net alga, Hydrodictyon(discussed in my previous post) is in the same family as Pediastrumso we will need to be able to discriminate separate genera in this case to offer the same level of insight as a traditional biologist can provide.   We’ll also need to discriminate blue-green algae (Cyanobacteria) at least to order if we want to know whether we are dealing with forms that are capable of nitrogen fixation – a key attribute for anyone offering guidance on their management.

The primary practical role of Linnaean taxonomy, for an ecologist, is to organize data about the organisms present at a site and to create links to accumulated knowledge about the taxa present.    For many species of microscopic algae, as I stressed in “Murder on the barcode express …”, that accumulated knowledge does not amount to very much; but there are exceptions.  There are 8790 records on Google Scholar for Cladophora glomerata, for example, and 2160 for Hydrodictyon reticulatum.  That’s a lot of wisdom to ignore, especially for someone who has to answer the “so what” questions that follow any preliminary assessment of the taxa present at a site.  But, equally, there is a lot that we don’t know and molecular methods might well help us to understand this.   There will be both gains and losses as we move into this new era but, somehow, blithely casting aside hard-won knowledge seems to be a retrograde step.

Let’s end on a subversive note: I started out by asking whether “machines” (as a shorthand for molecular technology) can do the same as humans but the drive for efficiency over the last decade has seen a “production line” ethos creeping into ecological assessment.   In the UK this has been particularly noticeable since about 2010, when public sector finances were squeezed.   From that point on, the “value added” elements of informed biologists interpreting data from catchments they knew intimately started to be eroded away.   I’ve described three versions of the Imitation Game and suggested three different outcomes.  The reality is that the winners and losers will depend upon who makes the rules.  It brings me back to another point that I have made before (see “Ecology’s Brave New World …”): that problems will arise not because molecular technologies are being used in ecology, but due to how they are used.   It is, in the final analysis, a question about the structure and values of the organisations involved.

References

Apothéloz-Perret-Gentil, L., Cordonier, A., Straub, F., Iseili, J., Esling, P. & Pawlowksi, J. (2017).  Taxonomy-free molecular diatom index for high-throughput eDNA monitoring.   Molecular Ecology Resources17: 1231-1242.

Turing, A. (1950).  Computing machinery and intelligence.  Mind59: 433-460.

Some like it hot …

My reflections on algae that thrive in hot weather continued recently when I visited a river in another part of the country.  As this is the subject of an ongoing investigation, I’ll have to be rather vague about where in the country this river flows; suffice it to say it is in one of those parts of the country where the sun was shining and your correspondent returned from a day in the field with browner (okay, redder) arms than when he started.   Does that narrow it down?

A feature of some of the tributaries, in particular, was brown, filamentous growths which, in close up, could be seen to be speckled with bubbles of oxygen: a sure sign that they were busy photosynthesising.  These were most abundant in well-lit situations at the edges of streams, away from the main flow.   Under the microscope, I could see that these were dominated by the diatom Melosira varians, but there were also several filaments of the cyanobacterium Oscillatoria limosa, chains of the diatom Fragilaria cf capucina and several other green algae and diatoms present.

Melosira varians is relatively unusual as it is a diatom that can be recognised with the naked eye – the fragile filaments are very characteristic as is its habitat – well lit, low-flow conditions seem to suit it well.   It does seem to prefer nutrient-rich conditions (see “Fertile speculations …”) but it can crop up when nutrient concentrations are quite low, so long as the other habitat requirements are right for it.  The long chains of Melosira (and some other diatoms such as Fragilaria capucina and Diatoma vulgare) help the cells to become entangled with the other algae.   I could see this at some sites where the Melosira seemed to grow around a green alga that had been completely smothered by diatoms and was, I presume, withering and dying.  In other cases, the Melosira filaments are much finer and seem to attach directly to the rocks.   Neither arrangement is robust enough for Melosira to resist any more than a gentle current which is why it is often most obvious at the edges of streams and in backwaters.   As is the case for Ulva flexuosa, described in the previous post, I suspect that the first decent rainfall will flush most of this growth downstream.   Another parallel with Ulva is that, despite this apparent lack of adaptation to the harsh running water environment, Melosira varians is more common in rivers and streams than it is in lakes.

Melosira varians-dominated filaments at the margins of a stream.  Top photograph shows the filaments smothering cobbles and pebbles in the stream margins (frame width: approximately one metre); bottom photograph shows a close-up (taken underwater) of filaments with oxygen bubbles (frame width: approximately one centimetre).

Algae from the filaments illustrated above: a. and b.: Melosira varians; c. Fragilaria cf capucina; d. Oscillatoria limosa.  Scale bar: 20 micrometres (= 1/50th of a millimetre).  

The graphs below support my comments about Melosira varians preferring nutrient rich conditions to some extent.  Many of our records are from locations that have relatively high nutrient concentrations; however, there are also a number of samples where M. varians is abundant despite lower nutrient concentrations.   How do we explain this?   About twenty years ago, Barry Biggs, Jan Stevenson and Rex Lowe envisaged the niche of freshwater algae in terms of two primary factors: disturbance and resources.   “Resources” encompasses everything that the organism needs to grow, particularly nutrients and light, whilst “disturbance” covers the factors such as grazing and scour that can remove biomass.   They used this framework to describe successions of algae, from the first cells colonising a bare stone through to a thick biofilm.   As the biofilm gets thicker, so the cells on the stone get denser and, gradually, they start to compete with each other for light, leading to shifts in composition favouring species adapted to growing above their rivals (see “Change is the only constant …”).

The relationship between Melosira varians and nitrate-nitrogen (left: “NO3-N”) and dissolved phosphorus (right: “PO4-P”).   The vertical lines show the average positions of concentrations likely to support high (red), good (green), moderate (orange) and poor (red) ecological status (see note at end of post for a more detailed explanation).

They suggested that filamentous green algae were one group well adapted to the later stages of these successions but these, in turn, create additional opportunities for diatoms such as M. varians which can become entangled amongst these filaments and access more light whilst being less likely to being washed away.   If there is a period without disturbance then the Melosira can overwhelm these green algal filaments.   Nutrients, in this particular case, do play a role but, in this case, are probably secondary to other factors such as low disturbance and high light.  Using the terminology I set out in “What does it all mean?”, I would place M. varians in the very broad group “b”, with the caveat that the actual nutrient threshold below which Melosira cannot survive in streams is probably relatively low.   Remember that phosphorus, the nutrient that usually limits growth in freshwater, comprises well under one per cent of total biomass, so a milligram of phosphorus could easily be converted to 100 milligrams of biomass in a warm, stable, well-lit backwater.

Schematic diagram showing the approximate position of Melosira varians on Biggs et al.’s conceptual habitat matrix.

The final graph shows samples in my dataset where Melosira varians was particularly abundant and this broadly supports all that has gone before: Melosira is strongly associated with late summer and early autumn, when the weather provides warm, well-lit conditions with relatively few spates.

The case of Meloisra varians is probably a good example of the problem I outlined in “Eutrophic or euphytic?”  I have seen similar growths of diatoms in other rivers recently, due to the prolonged period of warm, dry conditions.  It is easy to jump to the conclusion that these rivers have a nutrient problem.  They might have, but we also need to consider other possibilities.   Like Ulva flexuosa in the previous post, Melosira varians is an alga that is enjoying the heatwave.

Distribution of Melosira varians by season.   The line represents sampling effort (percent of all samples in the dataset) and vertical bars represent samples where M. varians forms >7% of all diatoms (90th percentile of samples, ranked by relative abundance). 

Reference

Biggs, B.J.F., Stevenson, R.J. & Lowe, R.L. (1991). A habitat matrix conceptual model for stream periphyton. Archiv für Hydrobiologie 143: 21-56.

Notes on species-environment plots

These are based on interrogation of a database of 6500 river samples collected as part of DARES project.  Phosphorus standards are based on the Environment Agency’s standard measure, which is unfiltered molybdate reactive phosphorus.  This approximates to “soluble reactive phosphorus” or “orthophosphate-phosphorus” in most circumstances but the reagents will react with phosphorus attached to particles that would have been removed by membrane filtration. The current UK phosphorus standards for rivers that are used here are site specific, using altitude and alkalinity as predictors.  This means that a range of thresholds applies, depending upon the geological preferences of the species in question.  The plots here show boundaries based on the average alkalinity (50 mg L-1 CaCO3) and altitude (75 m) in the whole dataset.

There are no UK standards for nitrate-nitrogen in rivers; thresholds in this report are based on values derived using the same principles as those used to derive the phosphrus standards and give an indication of the tolerance of the species to elevated nitrogen concentrations.  However, they have no regulatory significance.

 

 

The Catchment Data Explorer

One of the things I like to do on this blog is to draw out the links between the microscopic and human worlds, and also to explain how we measure the extent of human impacts on the aquatic environment, and what we can do to reverse significant negative impacts.   My professional life is largely concerned with how the evidence for these evaluations is gathered and used to arrive at decisions.  Lip service has always been paid to the importance of transparency in this process but it has not always been easy to find information about the condition of your local environment.  So a few months ago I was pleased to find a new website from the Environment Agency that makes this process a lot easier.

The Catchment Data Explorer starts with some intuitive navigation panes that let you search for your part of England, and then to locate particular streams, rivers and lakes and see how these match up to current environmental targets.   Navigating to my local river, the River Wear, and, more specifically, to the section closest to my house (“Croxdale Beck to Lumley Park Burn”), I find a table with drop-down tabs that give a brief overview of its state.    I see from this that the overall condition of the river is “moderate” and, then, by opening-up further levels, see that the various components of the ecology are all good (I’m not sure that I agree with that for the microscopic algae but that’s a story for another day) but that “physico-chemical supporting elements” are “moderate”.   Classification of rivers and lakes follows the “one out, all out” rule, so it is the lowest class that is measured that determines overall status.   In this case, opening up the physico-chemical elements levels in the table, I see that all is well, except for phosphorus, which is moderate and, therefore, determines the classification.

The home page of the Catchment Data Explorer

From here we can also download a file of “reasons for not achieving good status” in order to understand why phosphorus levels are elevated which tells us that it is waste water treatment and urban drainage that is the most likely source of phosphorus in the catchment.    Control that and, in theory, all should be well.   However, these are just two rows of 147 in a spreadsheet which deals with the lower Wear catchment and its tributaries, so the scale of overall challenge facing the Environment Agency becomes clear.    Moreover, the Wear has already had over £7M investment to install phosphorus stripping from the larger sewage works, to comply with the Urban Wastewater Treatment Directive, so the potential for further improvement is already limited.   Go back to the original table and look at the right hand column, which is labelled “objectives”.   The ecological target is “good by 2027”; however, if you hover the cursor over this, a box pops up telling you that this is “disproportionately expensive” and “technically infeasible”, invoking the WFD’s notorious “Get Out of Jail Free” card which lets countries bypass the need to achieve good status in certain specified situations (clause 4 paragraph 5 – “Less Stringent Objectives”).

Water body classification information from the Catchment Data Explorer for the River Wear, between Croxdale Beck and Lumley Park Burn.

All good so far.   The problems come when you start burrowing deeper into the Catchment Data Explorer and, in particular, when you download data.   There is a lot of information in Excel spreadsheets (which is great); however, it is riddled with jargon and not very well interpreted.   Then there are some apparent contradictions that are not explained. I searched for one stream that interested me, and found the overall ecological status to be moderate, despite the status of the fish being poor.  There is probably a good reason for this (perhaps there was low confidence in the data for fish, for example) but, again, it is not very well explained.

Then there are those water bodies that are, apparently, “good status” but, when you delve deeper into the Catchment Data Explorer, you find that there is no evidence to support this.   This is a surprisingly common situation, not just in the UK but across Europe.  The phrase “expert judgement” is invoked : probably meaning that someone from the local Environment Agency office went along for a look around and could not see any obvious problems.   It seems to be used, in the UK at least, mostly for smaller water bodies and is probably a pragmatic decision that limited resources can be better used elsewhere.

These are relatively minor niggles when set against the positives that the Catchment Data Explorer offers.   There is already quite a lot of information in the Help pages, and there is also a Glossary, so you should be able to work out the situation for your local water bodies with a little patience.   A struggle with terminology is, perhaps, inevitable, given the complexities of managing the environment.  We would all do well to remember that.

 

Eutrophic or euphytic?

A paper has just been published that should be required reading for anyone interested in the management of nutrients in in ecology.   It is a follow-up of a 2006 paper with the catchy title “How green is my river” that set out to provide a conceptual framework for how rivers responded to enrichment by nutrients.   That original paper contained several good ideas but, crucially, not all of them were underpinned by evidence.  A decade on, several of the predictions and statements made in that original paper have been tested, and the time has come to re-examine and modify that original conceptual model.

My reaction to the 2006 paper was that it was very interesting but not fully reflective of the rivers in my part of Britain, whose rougher topography produced quite different responses to nutrient enrichment than that proposed in their original model.   That criticism has been addressed in the revised version, which places greater emphasis on the physical habitat template, which means that it is more broadly applicable than the original version.   But that, in turn, got me wondering about the continued relevance of a term such as “eutrophication” to rivers.

People have been using the term “eutrophic” to describe lakes with high concentrations of nutrients since early in the 20th century.   As the century progressed, evidence of a causal relationship between inorganic nutrients and algal biomass, and the consequences for other components of lake ecosystems grew.   With this foundation, it has then become possible to predict the benefits of reducing nutrients and there are plenty of case studies, particularly from deep lakes, that demonstrate real improvements as nutrient concentrations have declined.

Attempts to apply the same rationale to rivers have, however, met with far less success.   Legislation to reduce nutrients in rivers has been in force in Europe since 1991 (the Urban Wastewater Treatment Directive, followed by the Water Framework Directive) and whilst this has led to reductions in concentrations of phosphorus in rivers (see  “The state of things, part 2”), there has, in most cases, not been a corresponding improvement in ecology.   There are a number of reasons for this but, at the heart, there was a failure to understand that the tight coupling between nutrients and biology that was the case in deep lakes did not also occur in running waters.   What was needed was recognition of fundamental differences between lakes and rivers, and “How green is my river?” and, now, this new paper have both contributed to this.

However, one consequence of recognising the importance of the physical habitat template alongside nutrients is to challenge the relevance of the term “eutrophic” when describing rivers.   “Eutrophic” literally means “well-nourished” so is appropriate in situations where high nutrients cause high plant or algal biomass.   This high biomass (strictly speaking, the primary production arising from this biomass) then creates problems for the rest of the ecosystem (night-time anoxia caused by plants consuming oxygen being a good example).   If high biomass can arise due to, let’s say, removal of bankside shade or alteration to the flow regime, perhaps (but not always) in combination with nutrients, then perhaps we need a term that does not imply a naïve cause-effect relationship with a single pressure?

My suggestion is to shift the focus from nutrients to plant growth by using the term “euphytic” (“too many plants”) as this would shift the emphasis from simply driving down nutrient concentrations (expensive and not always successful) towards reducing secondary effects.  It is possible that strategies such as planting more bankside trees, for example, or altering the flow regime or channel morphology (see “An embarrassment of riches …”) may be just as beneficial, in some cases, as reducing nutrient concentrations.   That said, we also have to bear in mind that nutrients may have an effect well downstream, so focus on amelioration of effects within a particular stream segment will never be a complete solution.

I should emphasise that a lot of work has been done in recent years to understand the concentrations of nutrients that should be expected in undisturbed conditions, and also to understand the nutrient concentrations that lead to changes in community structure in both macrophytes and algae.   These show that many rivers around Europe do have elevated concentrations of nutrients and I am not trying to side-step these issues.  I do, however, think it is important that regulators can prioritise those rivers in greatest need of remediation and, in most cases, they do this without considering the risk of secondary effects.

It is, largely, a matter of semantics.   I have been involved in many conversations over the past couple of decades about how to improve the state of our rivers.  Many of those have centred on the importance of reducing nutrient concentrations (which would be, indisputably, a major step towards healthier rivers).  But there is more to it than that.  And Mattie O’Hare and colleagues are helping to open up some new vistas in this paper.

Note: the photograph at the top of this post shows the River Wear at Wolsingham.  This stretch of the river captures many of the challenges facing river ecologists: nutrient concentrations are relatively low and there is good bankside shade.  However, the flow of the river is highly altered due to impoundments upstream and a major water transfer scheme.  How do all these factors interact to create the often prolific algal growths that can be seen here, particularly in winter and spring?

References

Hilton, J., O’Hare, M., Bowes, M.J. & Jones, J.I. (2006).  How green is my river?  A new paradigm of eutrophication in rivers.   Science of the Total Environment 365: 66-83.

O’Hare, M.T., Baattrup-Pedersen, A., Baumgarte, I., Freeman, A., Gunn, I.D.M., Lázár, A.N., Wade, A.J. & Bowes, M.J. (2018).  Responses of aquatic plants to eutrophication in rivers: a revised conceptual model.   Frontiers in Plant Science.   9: 451

That’s funny …

The most exciting phrase to hear in science, the one that heralds new discoveries, is not “Eureka!” but “That’s funny”
Attributed to Issac Asimov

I have visited Croasdale Beck, in western Cumbria, twenty-eight times since 2015 and I thought I was beginning to understand it’s character (see “A tale of two diatoms” and “What a difference a storm makes”).   It is the unruly sibling of the River Ehen which, usually, offers a far less amenable environment for freshwater algae.  Last week, however, as we walked down the track towards the stream, we were confronted with the unexpected sight of a river bed that was bright green.  Our measurements, too, showed that not only was there a lot of algae in absolute terms, but there was far more here than we had measured in the River Ehen.  Usually, the situation is reversed, with the Ehen having more than Croasdale Beck.

Croasdale Beck at NY 087 170 looking upstream in April 2018.   The position of the gravel bar has shifted over the time that we have visited, with the wetted channel originally being at the right hand side, rather than being split into two.

It was hard to capture the extent of the algae growing on the river bed in a photograph, but the macroscopic image below captures the colour of the growths well, and you’ll have to use your imagination to scale this up to cover half of the stream bed.  Under the microscope, these growths turned out to be virtual monocultures of the green alga Draparnaldia glomerata.  This is common in clean rivers in spring time, and I often find it in the nearby River Ehen (see “The River Ehen in February”).  What my images do not show is the mucilage that surrounds the filaments.   In some cases, the growths can be almost jelly-like, so prolific is this mucilage.   One of the roles of this mucilage plays is to serve a matrix within which enzymes released by the fine hairs at the end of the filaments can act to release nutrients bound into tiny organic particles (see “A day out in Weardale …”).

Growths of Draparnaldia glomerata in Croasdale Beck (NY 087 170) in April 2018.  The upper image shows the filaments growing on submerged stones and the lower image shows the bushy side-branches growing from a central filament.  Scale bar: 100 micrometres (= 1/10th of a millimetre).

We also sample a site a couple of kilometres downstream on Croasdale Beck and, here again, the river bed was smothered in green growths.  I assumed that this, too, was Draparnaldia glomerata but, when I examined the filaments under the microscope, it turned out to be a different alga altogether: Ulothrix zonata (see “Bollihope Bhavacakra” and links therein).   There is little difference between the two sites that might explain this: the latter is slightly lower and is surrounded by rough pasture whilst the other is closer to the fells.   However, I have seen both Ulothrix zonata and Draparnaldia glomerata at several other sites in the vicinity, and a simplistic interpretation based on agricultural enrichment does not really work.

There were also a few obvious differences in the diatoms that I saw in the two samples.   In both cases, we sampled stones lacking green algae but, instead, having a thick brown biofilm.  Several taxa were common to both sites – Odontidium mesodon, for example (broadly confirming the hypothesis in “A tale of two diatoms …”) and Meridion circulare was conspicuous in both.   However, the lower site had many more cells of “Ulnaria ulna” than the upper site.   Again, there is no ready explanation but, at the same time, neither green algae or diatoms at either site suggests anything malign.

Filaments of Ulothrix zonata at Croasdale Beck (NY 072 161).   The upper filament is in a healthy vegetative state (although the cell walls are not as thickened as in many populations).  The lower filament is producing zoospores.   Scale bar: 25 micrometres (= 1/40th of a millimetre).

Diatoms in Croasdale Beck, April 2018.   a. upper site: note the abundance of Odontidium mesodon, plus cells of Gomphonema cf exilissimum, Achnanthidium minutissimum and Meridion circulare; b. lower site: note the presence of “Ulnaria ulna” as well as several of the taxa found at the upper site.   Scale bar: 25 micrometres (= 1/40th of a millimetre).  

So where does this take us?  I talked about the benefits of repeat visits to the same site in “A brief history of time wasting …” and I think that these data from Croasdale are making a similar point.  By necessity, most formal assessments of the state of ecology are based on very limited data, from which, at best, we get an estimate of the “average” condition of a water body over a period of time.  Repeat visits might lead to a more precise assessment of the “average” state but also give us a better idea of the whole range of conditions that might be encountered.  Here, I suspect, we chanced upon one of the extremes of the distribution of conditions.   Cold, wet weather in early spring delayed the growth of many plants – aquatic and terrestrial – as well as the invertebrates that graze them.   Then the period of warm, dry conditions that preceded our visit gave the algae an opportunity to thrive whilst their grazers are still playing “catch-up”.  I suspect that next time we visit Croasdale Beck will have its familiar appearance.   It is, nonetheless, sobering to think that this single visit could have formed fifty-percent of the evidence on which a formal assessment might have been made.

 

A brief history of time-wasting …*

Having talked about diversity on a microscale in the previous post, I thought it would be interesting to place this in context by looking at the variations that I have observed in the River Wear at Wolsingham over the past decade or so.   The River Wear has seen some significant improvements in water quality over this period, but those have mainly affected sections of the river downstream from Wolsingham.  Most of the changes at Wolsingham are, therefore, giving us some insights into the range of natural variation that we should expect to see in a river.

I’ve got 31 samples from the River Wear at Wolsingham on my database, collected since 2005.  Over this period, nine different diatom species have dominated my counts: Achnanthidium minutissimum on 21 occasions, Nitzschia dissipata twice and Cocconeis euglypta, Encyonema silesiacum, Gomphonema calcifugum, Navicula lanceolata, Nitzshia archibaldii, N. paleacea and Reimeria sinuata once each.   I also have records for non-diatoms during 2009, during which time the green alga Ulothrix zonata, and two Cyanobacteria, Phormidium retzii and Homeothrix varians were the dominant alga on one occasion each.   In total, I have recorded 131 species of diatom from this one reach, although only I’ve only found 91 of them more than once, and only 59 have ever formed more than one percent of the total.   I’ve also got records of 22 species other than diatoms.

This – along with my comments in “The mystery of the alga that wasn’t there …” raises questions about just how effective a single sample is at capturing the diversity of algae present at a site.  .    In 2009 I collected a sample every month from Wolsingham and the graph below shows how the total number of species recorded increased over that period.   Typically, I find between 20 and 30 species in a single sample, and each subsequent month revealed a few that I had not seen in earlier samples.   Importantly, no single sample contained more than 40 per cent of the total diversity I observed over the course of the year.  Part of this high diversity is because of the greater effort invested but there is also a seasonal element, as I’ve already discussed.   The latter, in particular, means that we need to be very careful about making comments about alpha diversity of microalgae if we only have a single sample from a site.

Increase in the number of diatom taxa recorded in successive samples from the River Wear at Wolsingham.  In 2009 samples were collected monthly between January and December whilst in 2014 samples were collected quarterly. 

This seasonal pattern in the algal community also translates into variation in the Trophic Diatom Index, the measure we use to evaluate the condition of streams and rivers.  The trend is weak, for reasons that I have discussed in earlier posts, but it is there, nonetheless.   Not every river has such a seasonal trend and, in some cases, the community dynamics results in the opposite pattern: higher values in the summer and lower values in the winter.  It is, however, something that we have to keep in mind when evaluating ecological status.

Variation in the Trophic Diatom Index in the River Wear at Wolsingham between 2005 and 2015, with samples organised by month, from January (1) to December (12).   The blue line shows a LOESS regression and the grey band is the 95% confidence limits around this line.

All of these factors translate into uncertainty when evaluating ecological status.   In the case of the River Wear at Wolsingham, this is not particularly serious as most of the samples indicate “high status” and all are to the right of the key regulatory boundary of “good status”.  However, imagine if the histogram of EQRs was slid a little to the left, so that it straddled the good and moderate boundaries, and then put yourself in the position of the people who have to decide whether or not to make a water company invest a million pounds to improve the wastewater coming from one of their sewage treatment plants.

At this point, having a long-term perspective and knowing about the ecology of individual species may allow you to explain why an apparent dip into moderate status may not be a cause for concern.  Having a general sense of the ecology of the river – particularly those aspects not measured during formal status assessments – should help too.  It is quite common for the range of diatom results from a site to encompass an entire status class or more so the interpretative skills of the biologists play an important role in decision-making.   Unfortunately, if anything the trend is in the opposite direction: fewer samples being collected per site due to financial pressures, more automation in sample and data analysis leading to ecologists spending more time peering at spreadsheets than peering at stream beds.

I’ve never been in the invidious position of having to make hard decisions about how scarce public sector resources are used.  However, it does strike me that the time that ecologists used to spend in the field and laboratory, though deemed “inefficient” by middle managers trying to find cost savings, was the time that they learned to understand the rivers for which they were responsible.  The great irony is that, in a time when politicians trumpet the virtues of evidence-led policy, there is often barely enough ecological data being collected, and not enough time spent developing interpretative skills, for sensible decisions to be made.   Gathering ecological information takes time.   But if that leads to better decisions, then that is not time wasted …

Ecological Quality Ratio (EQR: observed TDI / expected TDI) of phytobenthos (diatoms) at the River Wear, Wolsingham) between 2005 and 2015.   Blue, green, orange and red lines show the positions of high, good, moderate and poor status class boundaries respectively.

* the title is borrowed from the late Janet Smith’s BBC Radio 4 comedy series