Out of my depth …

Castle_Eden_Dene_March19

I was about to start writing up an account of my latest visit to Castle Eden Dene, when I realised that I had forgotten to describe my previous visit, back in March.   I’ve already described a visit in January, when the stream was dry (see “Castle Eden Dene in January” and “Tales from a dry river bed”) and promised regular updates through the year.   It seems that, amidst all the travel that filled my life over the last three months, I overlooked the post that I should have written about the visit that I made in early March.

Whereas the river was dry in January, rain during February meant that, when I returned to the Dene on 11 March, some rather turbid water was flowing down the channel on its short journey to the North Sea.   There is, finally, something more like a stream habitat from which I can collect some diatoms.

Many of the diatoms that I found in March belonged to taxa that I had also seen in January; however, the proportions were quite different.   In some cases, species that were common in January were less common now (e.g. Humidophila contenta*) but there was a small Nitzschia species with a slightly sigmoid outline that was very sparse in the January sample but which was the most abundant species in the March sample.  I’ve called this “Nitzschia clausii” but the Castle Eden Dene population does not fit the description of this perfectly.   A lot can change in a couple of months, especially when dealing with fast-growing organism such as these, as my posts on the River Wear showed (see “A year in the life of the River Wear”).  Castle Eden Burn’s highly variable discharge just adds another layer of complication to this.

CED_diatoms_Mar19

Diatoms from Castle Eden Dene, March 2019:   a. – e.: Nitzschia cf clausii; f. Tabularia fasiculata; g. Tryblionella debilis; h. Luticola ventricosa; i. Luticola mutica; j. Ctenophora pulchella.  Scale bar: 10 micrometres (= 1/100thof a millimetre).   The picture at the top of the post shows Castle Eden Burn at the time that the sample was collected.   

Nitzschia clausii is described as being “frequent in brackish freshwater habitats of the coastal area and in river estuaries, as well as in inland waters with strongly increased electrolyte content”.   A couple of the other species from this sample – Ctenophora pulchella and Tabularia fasiculata (both illustrated in the diagram above) – have similar preferences.    My experience is that we do often find a smattering of individuals belonging to “brackish” species in very hard water, as we have in Castle Eden Burn.  Average conductivity (based on Environment Agency records) is 884 µS cm-1; however, values as high as 1561 µS cm-1.   The fluctuating discharge plays a role here, as any evaporation will serve to concentrate those salts that are naturally present in hard freshwater.   This should probably not be a big surprise: life in brackish waters involves adapting to fluctuating osmotic regimes so species that can cope with those conditions are also likely to be able to handle some of the consequences of desiccation.

Average values of other chemical parameters from 2011 to present, based on Environment Agency monitoring are: pH: 8.3; alkalinity: 189 mg L-1 CaCO3; reactive phosphorus: 0.082 mg L-1; nitrate-nitrogen: 1.79 mg L-1; ammonium-nitrogen: 0.044 mg L-1.   There is some farmland in the upper catchment, and the burn also drains an industrial estate on the edge of Peterlee but, overall, nutrient concentrations in this stream are not a major concern.   The Environment Agency classifies Castle Eden Burn as “moderate status” due to the condition of the invertebrates but does not offer any specific reason for this. I suspect that the naturally-challenging habitat of Castle Eden Burn may confound assessment results.

I’ve also been given some data on discharge by the Environment Agency which shows how patterns vary throughout the year.  The two sampling locations are a couple of kilometres above and below the location from which I collect my samples and both have more regular flow.  However, we can see a long period between April and September when discharge is usually very low.   The slightly higher values recorded in July are a little surprising, but are spread across a number of years.   It is also, paradoxically, most common for the burn to be dry in July too: clearly, a month of extremes.  As my own visits have shown, it is possible for the burn to be dry at almost any time of the year, depending on rainfall in the preceding period   The dots on the graph (representing ‘outliers’ – records that exceed 1.5 x interquartile range) show that it is also possible to record high discharges at almost any time during the year too.  I should also add that, as I am not a hydrologist, I am rather outside my comfort zone when trying to explain these patterns.  I would have said ‘out of my depth’ though that’s not the most appropriate phrase to use in this particular situation.

CED_discharge

Discharge in Castle Eden Burn, as measured by the Environment Agency between 2007 and present.   Measurements are from NZ 4136 2885 (‘upstream’) and NZ 45174039 (‘downstream’).  

* Note on Humidophila contenta:it is almost impossible to identify this species conclusively with the light microscope as some key diagnostic characters can only be seen with the scanning electron microscope.   However, all members of this complex of species share a preference for intermittently wet habitats so these identification issues are unlikely to lead to an erroneous ecological interpretation.  It is probably best to refer to this complex as “Humidophila contenta sensu lato” rather than “Humidophilasp.” order to distinguish them from those species within the genus that can be recognised with light microscopy.

Reference

Lange-Bertalot, H., Hofmann, G., Werum, M. & Cantonati, M. (2017).  Freshwater Benthic Diatoms of Central Europe: over 800 Common Species Used in Ecological Assessment. English edition with updated taxonomy and added species.  Edited by M. Cantonati, M.G. Kelly & H. Lange-Bertalot.  Koeltz Botanical books, Schmitten-Oberreifenberg.

More algae from Shetland lochs …

Lamba_Water_May19

I’m taking you back in the Shetland Islands for this post, and onto the remote moorlands of northern Mainland.   When I visited this particular loch in 2016, I noticed a lot of slippery filaments of Batrachospermum attached to the sides of the cobbles in the littoral zone (see “Lucky heather …”).   This time around, I explored further around the edge of the loch and, in the south-west corner noticed prolific growths of algae in the shallow peaty water.  Closer inspection showed that these, too, were the red alga Batrachospermum and, though they were not fertile, Dave John suggests that they are likely to be B. turfosum Bory.

Batrachospermum_Lamba_Water_May19

Tufts of Batrachospermum turfosumin the littoral zone of Lamba Water, north Mainland, Shetland Islands, May 2019.   The picture frame is about 15 centimetres across. 

If you have a hand lens you can just about make out a bead-like structure when observing Batrachospermum in the field; however this becomes much clearer with higher magnification.   I think it looks like a bottle-brush when seen under the microscope at low magnification, with whorls of side-branches arising from the central filament.  At higher magnification, these filaments can be seen to have a bead-like structure, with cell size gradually reducing with distance from the centre.

What you cannot do in the field is separate Batrachospermumfrom the closely-related genus Sheathia(see “News about Batrachospermum… hot off the press”).   I usually tell people that, for a general overview of the condition of a stream or lake (for example, as part of the UK macrophyte survey technique), then simply recognising that you have “Batrachospermum” (meaning Batrachospermum or Sheathia) should be enough.   In my experience, the presence of Batrachospermumis usually a good indication that the water body is in a healthy condition.  However, I have been told that Batrachospermumis often found growing prolifically in very enriched conditions in southern chalk streams, which would challenge this assumption.   This may be because the species that are found in southern chalk streams are different to those that I encounter in my more usual haunts in northern England and Scotland.  But it is also possible that the factors I described in “The exception that proves the rule …” pertain in those cases too.

Batrachospermum_turfosum_Lamba_Water

Filaments of Batrachospermum turfosum from Lamba Water, north Mainland, Shetland Islands, May 2019.   The upper photograph shows a low magnification view of a filament (about 350 micrometres, or 0.35 millimetres, wide) whilst the lower image shows a whorl of side branches arising from the main stem.  Scale bar: 20 micrometres (= 1/50thof a millimetre).  

We often run into this dilemma with filamentous freshwater algae: it is reasonably straightforward to identify the genus but we need reproductive organs to determine the species.  As they seem to survive quite happily in the vegetative state our understanding of the ecology of individual species (rather than the genus as a whole) is scant so it is hard to tell whether there is value in that missing information or not.   In a few cases – this is one – better taxonomic understanding has revealed that we may not even be dealing with a single genus but the lists used for applied ecological surveys still persist with the old concepts.

This creates a toxic spiral of consequences: it is hard to split into species so most people don’t bother. Because we don’t bother, our interpretations are based on generalisations drawn from the behaviour of the genus.  This means we don’t generate the data needed to demonstrate the value (or otherwise) of the effort required to go from genus- to species-level identifications.   So we carry on lumping all records to genus (or, in this case, a pair of genera) and accept a few records that our out of line with our expectations as “noise”.  The situation is probably worse in the UK than in many places because there are very few people in universities specialising in these organisms and, as a result, no-one is producing the data that might break us out of this spiral.

We found Batrachospermum turfosum in a few other locations during our visit, but nowhere, even in nearby lochs, was it in such quantity as we saw in Lamba Water.   Chance might play a part in determining its distribution on a local scale but that ought to be the explanation of last resort rather than the go-to answer when we are worryingly short of hard evidence.

 

 

Beyond the Tower of Babel …

Danube_at_Vienna_May19

A week after I return from China, I was off on my travels again; this time to Vienna for a workshop between molecular ecology specialists and ECOSTAT, the committee of Member State representatives who oversee ecological aspects of Water Framework Directive implementation.   As ever, I found some time to visit some art galleries around the meeting and, as Vienna has one of the most impressive collections of paintings by Pieter Brueghel, I could not resist spending some time in front of his “Tower of Babel”.  A few years ago I cheerfully included this picture in a talk on EU ecological assessment methods, as we tried to make sense of the myriad national approaches.   Three years after the Brexit vote, however, it seems to better reflect UK domestic politics where, ironically, language is one of the few things that all protagonists do have in common.

The River Danube seems to encapsulate the reasons why Europe needs collaborative thinking on the state of the environment.  It is the second longest river in Europe, after the Volga, and flows through ten countries, with tributaries extending into nine more.   Eight of the nine countries through which the river flows are members of the EU (the ninth, Serbia, is in the process of joining) so the river represents a case study, of sorts, on whether EU environmental policies actually work.   This is not just an academic question: ecologists are generally in favour of integrated management of entire catchments whilst the EU operates on a principle of “subsidiarity”, which means that decision-making is devolved to the lowest competent authority (individual Member States in the case of the environment).   Finding the right balance between these principles takes a lot of patient discussion and is one reason why EU decision-making can appear to be agonisingly slow.

Breughel_Tower_of_Babel

Pieter Bruegel’s “Tower of Babel” in the Kunsthistorisches Museum in Vienna.

And there are more problems: the Water Framework Directive evaluates the sustainability of water bodies by their naturalness yet very large rivers such as the Danube have been very heavily modified by human use for centuries.   The river has been broadened, deepened and impounded, and its banks have been straightened and strengthened in order to make it navigable, and there is a huge human population, with associated industry, living on its banks.  The stretch of the Danube along which I walked on my last morning in Vienna was also lined with embankments to protect the surrounding land from flooding but these, at the same time, cut the river off from the ecological benefits of the floodplain.

What hope for a large river such as the Danube in the face of all these challenges?   First of all, when dealing with rivers such as these we need to adjust our expectations, recognising that they are so central to the economic life of the regions through which they flow that there are limits to their capacity to ever resemble truly natural rivers.   Once we have done this, we can start to unpick the challenges that can be addressed by individual Member States.  In the case of water quality, in particular, the story for the Danube is encouraging and European environmental legislation has played its role in this process.  By the time the Danube reaches the borders with Romania, for example, nutrient concentrations are low enough for many of the benthic algal-communities to meet criteria for “good ecological status”.

You can see this in the graph below, from a paper that we’ve published recently.   The Romanian sites are largely clustered at the top left hand side of the graph, relative to data from other countries – indicating low phosphorus concentrations and good ecology (expressed as “ecological quality ratios”, EQRs).   Thanks to an extensive exercise that took place a few years before I started grappling with the Romanian data, we already had a consensus view of the EQR boundaries for high and good status, and most of the Romanian data fits into the band representing “good status”.  That’s encouraging and whilst these communities are just one element of a much more complex ecosystem, but it is a clear step in the right direction.

RO_VLR_intercalibration

The relationship between dissolved phosphorus and ecological status of the phytobenthos (expressed as the Ecological Quality Ratio, EQR, based on the intercalibration common metric (which gives a harmonised view of status between Member States).   Horizontal lines show the average position of “high” (blue) and “good” (green) status boundaries.   RO = Romanian data; XGIG = data from other Member States.   See Kelly et al. (2018) for more details.  

Romania is, of course, a long way downstream from where I was standing in Vienna.  Before the Danube gets there it has to cross Slovakia, Hungary and Serbia.  The river also forms the boundary between Romania and Bulgaria for about 300 kilometres, so it is important that there is joined-up thinking between those responsible for water quality on the two opposite banks.  That’s why the EU is so important for the environment on a pan-European scale.  It is easy for those of us crammed onto our insignificant archipelago in the north-west corner of the continent to overlook this, but the Danube is really a great success stories for European environmental collaboration and, indeed, a reason for staying with this ambitious project into the future.   Too late, I know, but it needs to be said.

Reference

Kelly, M.G., Chiriac, G., Soare-Minea, A., Hamchevici, C. & Birk, S. (2018).  Defining ecological status of phytobenthos in very large rivers: a case study of practical implementation of the Water Framework Directive in Romania.  Hydrobiologia 828: 353-367.

Vienna_sights_May19

Sightseeing in Vienna: Stefansdom, the historic cathedral in the city centre and the Ferris wheel at the Prater amusement park, which played a starring role in Graham Greene’s The Third Man.

Life out of water …

Last time I wrote, I mentioned that those diatom genera that did not have to be permanently submerged in order to thrive (so-called “aerophilous diatoms”) often appeared together in samples.   Having seen some Luticola muticaearly in my analysis of the sample from Castle Eden Burn, it was no surprise to find Diadesmisand Simonsenialater in the same analysis.   If anything, the biggest surprise was that I did not also find Hantzschia amphioxys, another habitué of the damp fringes of diatom society.

A quick analysis of my database puts these thoughts into context.   There are 6500 samples in my database, so we can see, from the total number of records of each of the aerophilous genera that these are relatively scarce in the samples I encounter.  That is largely because my sampling approaches are biased against the habitats where these thrive (more about this below).   Aerophilous diatoms are more common than you might think; it is scientists with a yearning to learn more about them that is in short supply.

Hantzschiaand Simonseniaare both less frequent and less abundant than the other two genera, never occurring in numbers exceeding ten per cent of the total but, when they form more than one per cent of the total, there is a very high chance that you will also find other aerophilous taxa in the sample.   Humidophilaand Luticolaare sometimes found in higher numbers, and when this is the case, then the proportion of other aerophilous taxa is also often high: 75 per cent of samples where Humidophilais abundant, for example, have at least one other aerophilous taxon present at one per cent or more.

Frequency of other aerophilous genera in samples with Hantzschia, Humidophila, Luticolaand Simonsenia.    Each genus is represented by two rows: records where it formed 10 per cent or more of the total number of valves and records where it formed more than one per cent.   Similarly, records for other aerophilous genera are also stratified into those where they comprise more than 10 per cent of the total and those where they comprise more than one per cent.  

Genus number of records   other aerophilous genera
>10% >1%
Hantzschia 147 >10% n/a n/a
>1% 0.50 0.70
Humidophila 248 >10% 0.25 0.75
>1% 0.09 0.29
Luticola 630 >10% 0.09 0.35
>1% 0.05 0.16
Simonsenia 61 >10% n/a n/a
>1% 0.50 1.00

Over the years, I have come to use this information informally as a way of knowing whether the results of an analysis are likely to be giving me useful insights into ecological condition.   Many of the samples I analyse are collected by other people and sent to me.   These samplers should have been working to protocols that ensure that they check that the stones they choose were fully submerged for some time prior to their visit.  However, the person collecting the sample may have to make a judgement about river and lake level fluctuations in the period before their visit.  Finding lots of cells of aerophilous taxa in a sample is a good hint that something is awry.

The German method for ecological status assessment actually uses the proportion of aerophilous taxa as a check on the reliability of an assessment.    I suspect that they are not the only ones, but They have a list of 46 species that they regard as aerophilous taxa, and use a threshold of five per cent in a sample as a threshold.   The genera I’ve discussed all feature prominently, along with representatives of 19 other genera. Most of these are represented by only one or two species, although there are seven species of Nitzschia, five of Pinnulariaand six of Stauroneis.   I suspect that some species on this list are more tolerant of desiccation than others. We do not know enough of the physiological mechanisms behind this tolerance but it would seem that a few genera (Hantzschia, Humidophila, Luticiola) have definitely got this hard-wired into their genotypes, whilst other genera have members which are mostly aquatic in their habit but with a few exceptions able to survive out of water for some time.   I, personally, would trust the five per cent threshold if it was restricted to the hardcore aerophilous genera, with other taxa on the list providing supporting evidence. I would also add the proviso that there should be more than one aerophilous taxon contributing to that five per cent.  I would be happier, too, if there were a few experimental studies behind these lists and thresholds but, as ever with the world of diatoms, taxonomists are several steps ahead of the physiologists and so we are heavily dependent on anecdotal information when interpreting results.

List of taxa regarded as aerophilous in the German system for assessing ecological status in rivers. 

Name Authority
Achnanthes coarctata (Brébisson) Grunow in Cleve & Grunow 1880
Chamaepinnularia parsura (Hustedt) C.E.Wetzel & Ector in Wetzel et al. 2013
Cosmioneis incognita (Krasske) Lange-Bertalot in Werum & Lange-Bertalot 2004
Denticula creticola (Østrup) Lange-Bertalot & Krammer 1993
Diploneis minuta Petersen 1928
Eolimna subadnata  (Hustedt) G. Moser, Lange-Bertalot & Metzeltin 1998
Fallacia egregia (Hustedt) D.G. Mann 1990
Fallacia insociabilis (Krasske) D.G. Mann 1990
Fistulifera pelliculosa (Brébisson ex Kützing) Lange-Bertalot 1997
Halamphora montana (Krasske) Levkov 2009
Halamphora normanii (Rabenhorst) Levkov 2009
Hantzschia abundans Lange-Bertalot 1993
Hantzschia amphioxys (Ehrenberg) Grunow 1880
Hantzschia elongata (Hantzsch) Grunow 1877
Hantzschia graciosa Lange-Bertalot 1993
Hantzschia subrupestris Lange-Bertalot 1993
Hantzschia vivacior Lange-Bertalot 1993
Humidophila aerophila (Krasske) Lowe, Kociolek, Johansen, Van de Vijver, Lange-Bertalot & Kopalová, 2014
Humidophila brekkaensis (J.B.Petersen) D. Lowe, Kociolek, Johansen, Van de Vijver, Lange-Bertalot & Kopalová, 2014
Humidophila contenta (Grunow) Lowe, Kociolek, Johansen, Van de Vijver, Lange-Bertalot & Kopalová, 2014
Humidophila perpusilla (Grunow) Lowe, Kociolek, Johansen, Van de Vijver, Lange-Bertalot & Kopalová, 2014
Luticola cohnii (Hilse) D.G. Mann 1990
Luticola dismutica (Hustedt) D.G.Mann1990
Luticola mutica (Kützing) D.G. Mann 1990
Luticola nivalis (Ehrenberg) D.G. Mann 1990
Luticola nivaloides (W.Bock) J.Y.Li & Y.Z.Qi 2018
Luticola paramutica (W. Bock) D.G. Mann 1990
Luticola pseudonivalis (W.Bock) Levkov, Metzeltin & A.Pavlov 2013
Luticola saxophila (W.Bock ex Hustedt) D.G.Mann 1990
Mayamaea nolensoides (W. Bock) Lange-Bertalot 2001
Melosira dickiei (Thwaites) Kützing 1849
Muelleria gibbula (Cleve) Spaulding & Stoermer 1997
Neidium minutissimum Krasske 1932
Nitzschia aerophila Hustedt 1942
Nitzschia bacillarieformis Hustedt 1922
Nitzschia disputata J.R. Carater 1971
Nitzschia harderi Husedt 1949
Nitzschia modesta Hustedt 1950
Nitzschia terrestris (J.B. Petersen) Hustedt 1934
Nitzschia valdestriata Aleem & Hustedt 1951
Orthoseira dendroteres (Ehrenberg) Genkal & Kulikovskiy in Kulikovskiy et al. 2010
Orthoseira roseana (Rabenhorst) Pfitzer 1871
Pinnularia borealis Ehrenberg 1843
Pinnularia frauenbergiana E. Reichardt 1985
Pinnularia krookii (Grunow) Hustedt 1942
Pinnularia largerstedtii (Cleve) Cleve-Euler 1934
Pinnularia obscura Krasske 1932
Simonsenia delognei (Grunow) Lange-Bertalot 1979
Stauroneis agrestis J.B. Petersen 1915
Stauroneis borrichii (J.B.Petersen) J.W.G.Lund 1946
Stauroneis gracillima Hustedt 1943
Stauroneis lundii Hustedt 1959
Stauroneis muriella J.W.G. Lund 1946
Stauroneis obtusa Lagerstedt 1873
Surrirella terricola Lange-Bertalot & Alles 1996
Tryblionella debilis Arnott ex O’Meara 1873

Reference

Schaumburg, J., Schranz, C., Steizer, D., Hofmann, G., Gutowski, A. & Forester, J. (2006).  Instruction protocol for the ecological assessment of running waters for implementation of the EC Water Framework Directive: macrophytes and phytobenthos.  Bavarian Environment Agency

A year in the life of the River Wear …

After six bimonthly visits to the River Wear at Wolsingham during 2018, I can now step back and have a look at the complete dataset to see what patterns emerge.   Over the course of the year, I have visited the site six times and recorded a total of 107 species: 5 Cyanobacteria, 32 green algae, 69 diatoms and one red alga.  The true figure is probably higher than this, as the green algae include a number of “LRGT” (see “Little round green things …”) and certainly did not receive the same level of attention as the diatoms.

This crude enumeration of species, however, disguises some interesting seasonal patterns with, as I described in “Summertime Blues” and “Talking about the weather …”, abundant growths of green algae during the heatwave and associated low flow periods.  This can be seen clearly in the bar chart showing the seasonal changes in the river: diatoms predominate in the early part of the year whilst green algae are very scarce.  The bloom of the green filamentous alga Ulothrix zonata that I expected to see in March was missing due, I suspected, to the hard weather we experienced in late Feburary (see “The mystery of the alga that wasn’t there …”) but, by the summer, the river had taken on a very different complexion and was dominated by small green algae.   The last sample of the year, collected in November, showed a return to diatom dominance with a late autumn showing of Ulothrix zonata(see “The River Wear in November …”).

wear_summary_2018

Relative proportions (by approximate biovolume) of the main groups of algae found in the River Wear at Wolsingham during 2018.  

Looking back at records of a similar exercise in 2009, I see that the beginning and end of the year were quite similar, with thick biofilms dominated by diatoms; however, the algae in the summer of 2009 were very different to those I found in 2018.  My 2009 exercise involved visits every month rather than every other month and I see that I recorded more Cyanobacteria in June and July 2009 than I found in Summer 2018.  These were mostly filaments of Phormidium retziiand tufts of Homoeothrix varians, which I assumed to be a consequence of intense grazing (there is evidence that invertebrates find Cyanobacteria to be less palatable than other algae).  By July, Cyanobacteria comprised over half the total biovolume of algae; however, there was a major spate soon after my visit.  I was surprised to find, when I visited in August, a noticeably thicker biofilm smothering the rocks and, when I looked closely, this was dominated by the small motile diatom Nitzschia archibaldii.   The Cyanobacteria had disappeared almost completely.   I attributed this change to the invertebrate grazers being washed away by the spate, allowing the algae to grow unhindered.  As the biofilm grew in thickness, so the algal cells start to shade each other, and a diatom that can glide through the biofilm has an advantage over any that are stuck to one place.  Diatoms remained dominant for the remainder of the year, although my November sample came just after another storm and the stones I sampled were completely bare.

wear_summary_2009

Relative proportions (by approximate biovolume) of the main groups of algae found in the River Wear at Wolsingham during 2009.   A sample was collected in November but no living algae were recorded from it.

Overall, however, the similarities between the years outweighed the differences in the summer assemblages, whilst the composition of communities between late autumn and late spring was remarkably similar across the two years.   The changes in summer 2018 extended beyond just a shift in the balance of algae in favour of greens: there were also changes in the composition of diatoms too.  In fact, the changes in diatoms proved to be quite powerful mirrors of the changes in the community as a whole.  I have demonstrated this in datasets spanning a number of sites in the past but it is reassuring to see that they are also reflecting patterns within one site.   On the other hand, if I only had examined the diatoms, I would have missed some of the most interesting changes in the river over the course of the year.

Another observation is that no single sample from 2018 contained more than a quarter of the total algal diversity that I recorded over the course of the year.  Every month saw some new arrivals and some departures (or, more likely in some cases, a few taxa that were present had dropped below my analytical detection limit).  Some of these were expected (the seasonal dynamics of Ulothirx zonata, for example); others not (e.g. dominance by Keratococcus bicaudatusin the summer).  I discussed this in “A brief history of time-wasting …” and, in honour of that post, am not going to repeat myself here. In an age when our environmental regulators are cutting back on the amount of data that they gather, I shall go into 2019 reflecting on Yuval Noah Harari’s comment that “the greatest scientific discovery was the discovery of ignorance”.

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.

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.