Spreading from a common body, reaching out in search of light, intertwined but solitary, a mutual goal but separate journeys. All supporting a common trunk to be alive, to support a life, to stay alive.

Long-term deep emission reductions, including the reduction of emissions to net-zero, is best achieved through institutions and governance that nurture new mitigation policies, while at the same time reconsidering existing policies that support continued emission of GHGs (high confidence).


It’s all there. A multitude of pathways to reduce emissions. Many branches, a common goal: Keep the planet habitable. It requires systematic change in all sectors: energy, housing, transport, industry, land use, food production. All of the pathways that limit warming to ‘acceptable’ limits have one thing in common: they require change right now. Or to be more precise – the required change should have begun 2 years ago, or 10 years ago, or 20 years ago. But still, nothing changes. Since this last report has been released, several countries have released their new plans to drill for even more oil and gas. Business as usual; the trees will get chopped down, leaving limbs scattered around the corpses.



One of the last exercises in an introductory course to programming I teach is to implement a straight-forward approach for modeling population growth over discrete time steps with a logistic growth function: The population x of a species at time t+1 is determined as x(t+1) = r * x(t) * (1-x(t)) where x(t) is the population at time t, and r is a fixed reproduction parameter. The choice of r influences the long term behaviour of the resulting time series – thus, the growth of the species population; for example, for r < 1, the series tends towards zero – the species goes extinct. However, for r > 3 the series oscillates – it exhibits a periodic behaviour (for some values of r the series even becomes seemingly random without a fixed period, see e.g. here). The length of the period depends upon r, but it never reaches an equilibrium; like a pendulum, swinging around its only stable position in the middle. Like life pulsating between non-steady positions, but never reaching a balanced state.

Oscillations are present constantly. The term (1-x(t)) models the environmental restrictions that prohibit unlimited growth. Restrictions which prevent us to come to a rest. The fantasy of a steady state is a futile one. There are times where a stable position seems in reach; until external restraints pull us back into another direction. At the moment, it’s the direction of work; and hence, photography and blog posts are somewhat neglected. Winter already fades again, making way for summer. Left are only some solitary pictures of oscillating camera movements and colorless nature.

The Inevitability of Triviality

The Inevitability of Triviality

Triviale Maschinen haben nur einen Zustand: Sie liefern auf denselben Input immer den gleichen Output.

Heinz von Förster

The quote could be vaguely transcribed as ‘Trivial machines have a single state: Given the same input, they always produce the same output.’ In contrast, for non-trivial systems the output not only depends on the input but additionally on an inner, possibly unknown, state of the system. This inner state evolves with every given input and, thus, the same input can lead to different output. In other words: the output may seem random to an observer as it also relies on the complete history of inputs processed by the system, reflected by its internal state.

Using this bipolar framework to describe actual systems can be challenging: When I type 2+2 in my calculator it will always yield 4. It’s apparently trivial – until the environment acts upon it and the batteries run out, the circuit board becomes corrupt, or the display breaks. If my bike would always give the same output when I start pedaling, I would be much more satisfied and my local bike shop would go out of business. If computers really were trivial, a whole lot of IT assistants could look for a new job right now. Systems decay over time, they are error prone, they are subjected to the very same universe we are.

Another approach might be to not consider it as a binary decision, but a continuous scale of triviality where systems are ranked based on their robustness. In a probabilistic sense, the calculator is rather trivial as it gives the predicted output in a quantifiably large majority of cases. In contrast, living systems are on the other end of the scale and highly non-trivial since they exhibit wildly different behaviour in seemingly similar situations.

However, when system are ranked on such a scale of non-triviality, problems arise: How should I work on this very laptop when I assume that it could fail me anytime? If I would admit its non-triviality, I couldn’t work in the first place because it could give any output, independent from the keys I am pressing. This example seems a little daft, but when transferring it to human interactions, the exact same applies: How should I communicate with my colleague about work issues when assuming that the output will be determined by a non-trivial living system? How should I forward instructions if the output is uncertain anyway? How could I coherently speak with my partner about serious topics when my input has potentially little effect on the output?

We constantly trivialize the non-triviality around us. We do so because it is necessary. When I am typing in my calculator I expect a correct result. When I am asking a question to a friend, I expect to get an answer. Not because the answering system is trivial, but because I have to assume it is in order to ask the question in the first place. We trivialize machines, we trivialize humans, the reactions of strangers, friends, and partners. And if the output is unexpected, we don’t blame our foolish assumption of triviality, but we blame the system itself. And the scale isn’t really one that describes the non-triviality of systems, but rather a scale of how much an observer trivializes systems.

Where does this lead? Potentially nowhere; there might be other, potentially more useful, distinctions to draw. But when drawing this distinction, I am wondering in which cases it might be wise to begin to acknowledge the non-triviality of systems.

The Sixth Extinction

The Sixth Extinction

66 million years ago dinosaurs became extinct – an inconceivably long time span. However, they roamed the planet for even longer: astounding 165 million years. Our species has only been around for ~300.000 years now. When the dinosaurs died, three-quarters of all plant and animal species vanished with them, also known as the Cretaceous-Paleogene extinction event. The cause was, to our best knowledge, the impact of an asteroid with devastating effects. The precise number of such large extinction events is under debate, but by many the Cretaceous-Paleogene extinction is regarded as the fifth and latest one.

So, what does the title of this post refer to? It refers to right now: this millennium, this month, this day. Today, a lot of species are dying. Forever. They will be lost for all of the remaining time this planet exists. Estimates vary from 24 up to 150 species every day. It’s neither possible to precisely guess the actual number, nor it’s easy to evaluate how drastic the estimated numbers really are. The question at heart ist: How much larger is this decline of species than the background noise of species extinction that happens naturally? Some people argue it’s 100-1000 as much as it would be without human interference. Others say these numbers are inflated and global mass extinction is, for now, not as drastic as proposed (because the common assumption that decline in habitat area is highly correlated with decline in diversity might not hold true). One of the major hurdles in assessing the severity of the current biodiversity situation is not only that estimates of dying species vary widely, but also that the total number of species that are currently living is still largely unknown.

In 2010, the UN agreed upon 20 major goals for the upcoming decade regarding biodiversity, including specific plans for the conservation of nature and variety of species. The last decade was even termed the ‘United Nations Decade on Biodiversity’. So, how are we doing so far?

Pretty bad! And irregardless of how accurate the estimates on global diversity decline might be, some other numbers are well proven and unambiguous: There is a large decline in animal populations across most domains of life. Since 1970, populations show an average decline of 60%. In south America, due to deforestation of rain forests, the decline in biodiversity is already estimated at 94%. The number of insects in Germany has gone down 70-80% in the last 30 years alone. 25% of all plant, fungi, and animal species are endangered. To quote a rather optimistic assessment: If we presume a total of 8 million species, we will loose at least 1 million by the end of the century. And these effects can mostly be traced back to modern agriculture alone. As soon as climate change really hits (very soon), these numbers are expected to increase significantly again. And even if these changes do not necessarily mean a decline in global biodiversity, the effects of declining local biodiversity are the ones we will pay for.

So the UN agreement from 2010 didn’t turn out well – and by now, I doubt that the Kunming declaration from this year will cause any large-scale systemic change. For me, reading about these events evokes two opposing feelings. First, sadness and helplessness. That we, as a society, are responsible for this undesirable change. That I, as an individual, am responsible for this horrible change. And second, relief. At least five times life has recovered from the most harsh conditions imaginable. And it probably will continue to do so until the heat death of the universe. It’s unclear though whether the species Homo sapiens will survive this next great extinction; by several scientists, this threat is estimated to be even more dangerous than climate change.

Yesterday, the winners of the European Wildlife Photographer of the Year 2021 were announced. Check them out here. Stunning pictures all around! The winning picture of this year also entails a grave background story about our influence on precious nature.

Similarity / Dissimilarity

Similarity / Dissimilarity

We’ll mix it up this time. Correct: Not I, but We. Because you’re going to get involved in this one. One simple question, many answers. Take your time to think about it before reading on; here comes the question:

Are the following pictures similar?

Let’s start with the basics: They are digital pictures shown on a website, saved in the same digital format. Not known to you, they also have the same number of pixels along the long side. To be exact, 1000 pixels as all pictures here in order to not occupy too much space (and to not get stolen – but who will steal them anyway…). Color information is stored in Adobe RGB color space, however, since all are black and white, the information for red, green, and blue is identical for every pixel anyway.

Not so fast, you will intervene. And you are right in doing so: While the long side is always 1000 pixels, they do not share the same aspect ratio, nor the same orientation. And it won’t be a stretch to claim that between every two pictures no single pixel is identical. So already after these first simple investigations we see a problem emerging: They are similar to a specific degree, identical in some respects, but disparate with respect to other criteria.

Let’s dive deeper: Are the images we see here the actual images, let alone do they inherit or show some of our reality? First of all, they do not show the full data gathered, since the original pictures are much larger, also encode color, and a variety of additional information in their RAW format. And in any case, they are just some arbitrary representation of reality without any real connection to it. One of endless possible portrayals of reality. While this does not directly touch on the original question, it is important to keep this in mind when we search and interpret their similarity.

Lastly, how do the overall pictures appear? Even if the single pixels are different between all images, combined they create patterns that can be alike. The pixels combine to a variety of forms, which, in turn, are received differently by different viewers. Waves, scales, oscillations, geometrical forms. And are they really creating these patterns or does the viewer infer them? Can we infer different patterns from the same picture?

I could go on for a while, but it’s getting too long. Let’s move on to the second question: Now, we also need to quantify the difference between every pair of pictures. On a scale from 0 to 100, how different are these two?

And what about these?

I think you are getting my point. We can create an endless list of metrics and choose what we think is best. We can apply these metrics to these simple images, or we can gather more data, larger pictures, RAW data, and then apply the metrics. We can weight and combine metrics to generate an overall score of similarity, we can try to assess how it performs in comparison to other scores. We can compare pairs of pictures and create a hierarchy of similarity. But it will never be the same when done by different people. And in the end, it’s quite arbitrary. Do we look at pixels, color, form, format, derived patterns, povoked emotions?

Most of the day I am doing such arbitrary comparisons. Not between images, but between DNA strands. Instead of pixels I am looking at sequences of A, C, G, and T. Depending on the chosen metric, a variety of results emerges. There is no correct metric, no correct similarity measure. There is no correct way to describe reality, neither to analyze and exploit it. There is an infinite number and every single one creates another distinct result.

But fortunately, in the end, it somehow seems to work – at least sometimes, when it solves a problem in biology research or medicine; but most of the time I don’t get how.

Polyommatus Icarus

Polyommatus Icarus

Primrose Optimization 2.0: The common blue (Hauhechel Bläuling), or Polyommatus Icarus. Hiding the blue upper side, but showing the yellow spots underneath the tips of the wings. When flying high his wings don’t melt – when sitting still, a lovely subject for a photographic study during sunrise. His favorite plant: Lotus corniculatus; here: on wheat.

There is never a single point of view. The multiplicity of different perspectives can convey a sense of completeness; however, infinitely many other perspectives always remain unexplored. It’s important to be able to exclude, to simplify, to stop exploring. It’s also important to disconnect from everyday life, slow down, take a break, and relax. Even on vacation I struggle with this. There is always the drive to do stuff, otherwise I am afraid to miss something. I cannot switch off my brain and sit still; the limited time needs to be used to climb the next mountain, to find the next boulder, to photograph the changing landscapes. It remains unknown if and how I can resolve this constant struggle – I’ll let you know when I found a solution…

But still, I have to get up to find a butterfly and at least a hundred different perspectives, enjoy.

Photo Post: Printing

Photo Post: Printing

When we are not away on weekends I mainly do two things: Writing blog posts or printing photos (however, this weekend we also prepared our van for the upcoming holiday!). By now, I have stashed quite a few prints and I need to figure out what to do with them; I think we still have plenty of free space on our walls (my significant other does not agree…), but it also takes time and money to properly frame the prints. So far, I have created mainly A6 postcards on matte paper, A5 prints on semi-gloss paper, A4 matte prints with white margins, and occasionally a large A3+, all on fine art Hahnemühle-paper. I also tried some lower quality paper, but have to agree with their slogan: ‘Paper makes the difference’, visually as well as haptically it’s outstanding. While in the beginning I needed several tries for the right settings, now I can get (most) prints as I want them from the get-go. Additionally, I slowly figured out which pictures do work as print in the first place, and which pictures just do not translate to paper. And as mentioned earlier: The printing also changed my progress of photographing itself, regarding the settings, lighting, composition, and subjects. The first charge of cartridges is empty and already replaced for the next prints to come.

Cantharis fusca & Triticum aestivum

Cantharis fusca & Triticum aestivum

Exactly a month ago, in my post on botanical gardens, I made the promise to inform myself (and you) about one combination of plant & insect I am coming across. Thus, I think it is time to fulfill this promise even though I did not manage to visit the botanical garden in the mean time.

We stumbled across many individuals of this beetle species already on our hike on P23. However, we had no idea what kind of species it exactly is:

Last Friday, I finally managed to go outside again and did some macro photography. And, again, I saw multiple of these bugs in the grasses, weeds, and fields. It also felt like the first genuine summer evening: Warm air coated the landscape, undulating fields of barley stretched in golden rays, the city vanished behind endless rows of trees, and its inhabitants escaped the asphalt towards the deep blue bathing lake.

And I stood in the fields and waited. Waited for this bug, waited that it flies in front of my lens, and that I don’t miss to press the shutter. And then it came:

It is (presumably) a Cantharis fusca, a species within the family of Cantharidae, in English also known as soldier beetle or leatherwings. The last name refers to its soft body; this is also why it is called ‘Weichkäfer‘ in German. There are many different sister species and often they only differ by minuscule details, at least to the untrained eye. In Germany alone, there are 86 different described species; worldwide more than 4500 – for a single family of beetles! The diversity and complexity that nature creates can be mind-boggling. They are mostly colored red, black, or golden. A wonderful visual overview is given here.

The plant it was landing on seemed rather uninteresting; most of all because it is so common on the fields in our area. At least, that’s what I thought at first:

It’s simple wheat – isn’t it? By now, I am not even sure anymore. Wheat is one of the most cultivated crops and it is an important source of food in uncountably many countries. The first record of wheat seems to be around 9600 years BC. This means, today we are 2000(!) years closer to Abraham, the patriarch of several religions, than Abraham was to the first use of wheat. I find it difficult to comprehend such time scales. However, this also means that there are countless different cultivated wheat species by now, including Common wheat, Spelt (‘Dinkel’), Durum, Emmer, Einkorn (the wild form), and many many more. Genetically speaking, a large difference between these species is the number of copies of each chromosome they have in their cells. While humans and many animals are diploid (they have two copies), it’s rather common in plants to have even more than two copies of each chromosome – this is referred to as polyploidy. (It also makes our life more difficult when dealing with their DNA sequences; but more on that at a different time.) The wild form of wheat is also diploid, but the other species are mostly tetra- or hexaploid. I still think that what I photographed is the most common form Triticum aestivum, but there are several more detailed distinctions to be made within this species.

Also, all information here is pure speculation from dubious internet research, also see this post on information.

Decidability 1

Decidability 1

Life is about decisions, large and small ones. What should I study? Which bread do I buy? Should I reach out to a long lost friend? Which approach to life should I take? What values are important to me? Do I buy the next lens or do I save up the money? Do I go outside for sports? Do I keep working for another evening? How do I want to spend the limited time I have in my life?

Some questions seem irrelevant, others may determine several years of our future life. So, how can we decide all these questions? Or: Is it even possible to decide all these questions? How should we approach and deal with any possibly life changing matter and decide: This or that? Now or later? Yes or no?

The more difficult the questions become that I face, the more I am convinced that they are inherently undecidable at any given moment in time. We do not have enough information to know all outcomes, the uncertainties are always large, and we cannot weigh in all factors because of their multitude and complexity. This also won’t change in the future. Maybe the options we decide on shift. Maybe it’s too late for a decision and we did not even have the opportunity to deliberately decide it ourselves. Some things we were sure that we chose correctly turned out to be terribly wrong; other things work perfectly even though we thought we made the wrong turn earlier.

Decidability is also infamous in computer science. In its simplest form it is known as the Halting Problem and was presented by Alan Turing. The problem formulation is as follows: Given an arbitrary algorithm and its input, is it possible to find another algorithmic solution that decides whether the given algorithm stops on the given input, or continues to run forever? If a solution can be found, then the problem is decidable. If no solution exists, then the problem is undecidable. In the case of the Halting problem, it can be shown that no algorithmic solution exists that solves the stated problem; thus, it is inherently undecidable. If you’re interested, keep reading for the proof:

We proof the above statement by contradiction. Imagine there exists an algorithm that can decide our problem statement: Given, as input, an arbitrary algorithm and its input, it can always decide whether this algorithm stops on the input or not. We call our deciding algorithm h and our input x. Given h, we now define a new algorithm h* that is a modified version of h: If h determines that the input algorithm stops, then h* keeps running in a loop. If h determines that the input algorithm keeps running, then h* stops. What happens if we feed our algorithm h* as input to itself (of which our original deciding algorithm h is part of)? This can be seen as a self-referential operation. We refer to the h* that is the deciding algorithm to h(h*) and to the input h* as x(h*). Both, h(h*) and x(h*) are the same algorithm. We have two possible outcomes: Either h decides that its input x(h*) stops – however, in this case h(h*) would keep running: a contradiction because x(h*) and h(h*) are the same algorithm. Or h decides that its input x(h*) doesn’t stop – but now, h(h*) would stop: again, a contradiction. Thus, the halting problem is not decidable.

To be continued in one of the next blog posts about how to decide anyways.

Colors of the Morning

Colors of the Morning

On several occasions I have been asked by family and friends: ‘Do you edit your photos?’ I understand where the question comes from and probably would have asked other photographers the same question myself before I bought a camera. But when you start taking pictures the answer becomes irrelevant, and also more philosophical. The question assumes that the truth can be captured; that there is a real representation of the environment. And this is simply not true. It is impossible to depict the world as it is and every representation is disconnected from the environment and just represents itself.

Pastel colors illuminate receding mountain ranges separated by morning haze; 45 minutes before the rising sun.

Thus, when people ask this question, what they often really mean is: ‘Do your photos look like it did in reality?’ And again, there is no right answer. Different people see different things in reality, pay attention to different details, some see green and red, others only see browns instead. Additionally, the effect of a scene is not only influenced by sight, but also many other senses that cannot be transported in a simple photo. So maybe the photo looks somewhat like the reality to me, but not to you.

An additional layer that is often forgotten: The picture is already hugely edited in camera. The photo receptors just capture some limited amount of light which is very different from reality. Besides this raw data, cameras often produce JPEG-images with already applied color profiles that interpret the raw data. This profiles can boost colors, contrast, or luminosities; or decrease them. Let’s take one of the easier settings that is present in all modern cameras: the white balance. The idea is to tune the information received from red, green, and blue light photo cells to display neutral colors (white, grays, or black) as such. The automatic white balance often fails during sunrises or sunsets because there are no neutral colors in the frame: everything is tinted with deep blues, shy purples, defiant magentas, or lush yellows. Take a look at the following two pictures. Both show the exact same frame only with different in-camera white balance settings:

Two different settings of white balance on the same photograph; 20 minutes after sunrise.

So which one is more correct? Impossible to answer. It depends on what I want to show and what you want to see in the image. Because cameras are not as powerful as computers, it often makes much more sense to edit photographs in a specific software instead. Then, you can fine tune the picture without depending on the limited options that are present in the camera.

So, yes! I edit my photographs! Sometimes only in camera, sometimes heavily in software. It depends on what I want to show. Sometimes the edited photos look more accurate to my perceived reality than the unedited photos; sometimes I use editing deliberately to transfer a feeling or message and don’t care about a ‘realistic’ depiction. Sometimes the photo is perceived as unrealistic even though it’s straight out of camera. Sometimes a photo is perceived as realistic even though it’s heavily edited. Ultimately, photography is just one of many arts, I will create what appeals to me, and if it also appeals to someone else: even better.

The color of light is getting warmer with the rising sun and illuminates the meadows below.

All these photographs were taken on last Saturday. Again, my dad and I started at 4 a.m. in the morning to view the sunset. This time not in the Harz Mountains but from a smaller peak in Hessen close to Hoher Meißner. During the two hours on the peak we saw many different colors and I tried to represent all of them in the next pictures. Enjoy.