Category Archives: Nature

Baby Bison Born!

I’m a long time Bisonophile and enthusiastic supporter of restoring wild Buffalo herds to North America. I’m particularly happy with the strong role of various Native America tribes, working through the political and technical barriers, and finding land to host the new herds. It almost goes without saying that this restoration has immense symbolic and cultural significance for the peoples who once lived with the Buffalo.

There has been a steady stream of reintroductions, notably to Banff earlier this year and  Blackfeet Reservation and Ft. Peck Reservation in earlier years This month was marked by another milestone, the birth of a calf on the Eastern Shoshone Wind River Reservation.

The birth of the bison calf catalyzes important conversations to be had about tribal protection of this spiritually important ungulate on tribal lands. CREDIT COURTESY OF JASON BALDES

You go little guy!

As part of a twenty year project to restore buffalo to tribal lands, the Eastern Shoshone received ten buffalo last fall. The new baby is a welcome sign that the Bison are settling in, and a promise of a permanent presence in the future.

Jason Baldes considers this to be more than wildlife management, for him it is a form of restorative justice. He commented on Yellowstone Public Radio,

What happened to Native people similarly happened to buffalo and we’re now isolated on former pockets of our once vast territories, you know, Indians on reservations and buffalo on national parks and refuges. And we’re kinda in a time now where we can handle that different.

At a time when knuckle draggers and latter day Medicis in Washington are plunging down a deeply destructive path, we can only hope that this little guy and his small tribe of buffalos can survive and thrive.

I’ll end with a culturally mixed welcome to the young one in Lakota, Taŋyáŋ yahí.

(I know very well that Lakota is not the same as Shoshone. But I have an online translator for Lakota, and this was an opportunity to learn a new word. I’m sure Lakota people are happy at the birth as well.)


  1. Brie Ripley, Eastern Shoshone Tribe Celebrate First Baby Buffalo Born On Reservation In Over A Century Yellowstone Public Radio.May 8 2017, http://ypradio.org/post/eastern-shoshone-tribe-celebrate-first-baby-buffalo-born-reservation-over-century

 

Weidensaul on “The New Migration Science”

Of all the cool things about birds (they fly! they sing! they have feathers! they are living dinosaurs!) one of the most profound is their astonishing seasonal migrations.

Scott Weidensaul writes for the Cornell Lab of Ornithology about technologies that are coming on line that enable scientists to gain unprecedented information about bird migrations.

[T]oday really is a truly exceptional time for migration science, with so many new avenues for documenting the journeys of birds.

First on the list are twenty first century leg bands, one gram geolocation recorders. Some larger birds can carry a satellite tag that tracks their travel and reports by radio. A cheaper and lighter option is a recording tag that logs the data, to be recovered when the bird is recaptured.

A third option are tiny radio transmitters that can be picked up by a network of collaborating receivers. With standardized signals and networked databases, a receiver can pick up and report any pings in its area, no matter who tagged the animal. The bird does not have to be recaptured, so there is much higher probability of encountering the tagged inividuals.

Weidensaul reports that both DNA and chemical isotope analyses can be made from a single feather or scrap of tissue. DNA can help sort out subpopulations, and isotope analysis can identify geographical history, e.g., of what the bird has eaten or drunk recently.

Recent improvements in data processing have enabled the routine use of NEXRAD weather radar to detect migrating flocks of birds each night. High resolution weather radar can also detect individual birds and reveal details of behavior. These studies, combined with remote sensing of vegetation and water, are enabling a detailed understanding of critical way stations where migratory birds rest for the day, and then continue.

With decades of archived NEXRAD, scientists are also studying trends over time. (The main trend is “down”, as we all might expect.)

Digital networks enable the combination of data from all these soruces, The internet also has automated the centuries-old traditions of collaboration among birders, creating massive crowdsourced datasets of observations.

Weidensaul reports current efforts to deploy cameras to automatically identify birds in cities. With today’s powerful visual analytics, it seems likely that inexpensive digital cameras will soon routinely identify and report individual birds.

Finally, inexpensive microphones on mobile devices or not can record high quality digital sound, which soon will enable a detailed picture of all the unseen birds in the area.


All of these digital technologies were developed for purposes other than ornithology. Almost no one develops complex and expensive technology just for observing birds. But birders will not be denied! These are some excellent examples of repurposing technology, and using powerful general purpose tools such as image and signal processing algorithms and machine learning.

And, of course, birders have been collaborating and crowd sourcing for centuries, long before computer scientists got into the game. Birders are some of the original citizen scientists, and, just as our feathered friends have persisted from dinosaur days, the global collaborative community of bird enthusiasts has survived centuries.  Now we have picked up digital technology and put it to good use.


  1. Bird Studies Canada. Motus Wildlife Tracking. 2017, http://motus.org/.
  2. Cornell Lab of Ornithology. eBird – Birding in the 21st Century. 2017, http://ebird.org/content/ebird/.
  3. Scott Weidensaul, The New Migration Science, in All About Birds. 2017, Cornel Lab of Ornithology: Ithaca. https://www.allaboutbirds.org/the-new-migration-science/

The Biological Computation in a Lizard’s Skin

Many plants and animals display fascinating multicolor geometric patterns, spots, stripes, swirls, and so on. How do these patterns develop? How does one bit of skin know it is supposed to be one color, and the bit next door another color?

At the dawn of the computer age, this question was recognized as a form of “computation”, in which the molecules, cells, and other structures of the organism somehow “calculate” a geometric equation.

Sensei Alan Turing himself explored the mathematics of reaction-diffusion (RD) equations, which describe some chemical reactions. He proved that some RD systems can produce patterns such as spots or swirls that resemble patterns seen in nature.

Around the same time, the other founding Olympian of computing, Johnny Von Neuman, explored cellular automata. These discrete systems are closely related to digital technology, and have become familiar through John Conway’s Game of Life. These systems can also produce geometric patterns that resemble natural biological systems.

Intuitively, these two systems seem similar (at least to me), but the math is not in any way similar. Like many interesting computer science problems, we have a continuous and a discrete “solution”, with a conceptual abyss between. They may both be correct, but it isn’t easy to get from one to the other.

This month Liana Manukyan, Sophie A. Montandon and a multidisciplinary team from University of Geneva and other institutions published a very beautiful study in Nature [1]. They examined the skin color of the ocellated lizard (Timon lepidus). The young lizard is brown with white spots, and as it matures, the skin becomes black with green spots. More interesting, the spots are a pattern of colored scales, not a continuous patterns of cells.  I.e.,  “clumps” in a nearly hexagonal grid. This looks like a cellular automaton, at the level of these scales. How does this develop?

Credit: Michel C. Milinkovitch

The team observed maturing lizards for several years, and discovered an additional wrinkle: the pattern changes continuously, with scales becoming green, and then switching to black over time, maintaining the spotted pattern. (I.e., when a scale turns gree, the surrounding scales turn black.  Clearly, there is some sort of dynamic process that is calculating “rules” analogous to a cellular automaton.

scale colour change in ocellated lizards follows a probabilistic CA process” ([1], p. 176)

Attacking this problem with computer simulations, the team found that the pattern could indeed be described as a cellular automaton (CA).  But what short of physical or chemical process could produce these “rules”?

Calling in the heavy cavalry from the Math department (or maybe they are the Pros from Dover), the team discovered that a RD system could produce this CA when the continuous functions are constrained by “interactions (cell–cell contacts) are substantially reduced between scales compared to within scales” (p. 177).

In other words, the 3D nubbiness of the skin moderates the continuous physical diffusion processes to create a discrete “computation”.  The scales are thicker in the middle, forming small islands with troughs between. This geography forms natural boundaries, and turns out to be a critical feature.

I love this study for many reasons.

The result is a deep and brilliant description of this biological process. As Phys.org put it,

The highly multidisciplinary team of researchers had closed the loop in this amazing journey, from biology to physics to mathematics … and back to biology.” [2]

It is also a beautiful example of both computational thinking (conceiving the lizard’s spots as a biological computation) and computational methods (simulations of several types were essential).

Achieving this kind of beautiful result was only possible with a multidisciplinary collaboration, which is the great strength of major research universities. (And, by the way, this is much maligned “curiosity driven research”, with no commercial spin off in sight.)

Finally, the entire enterprise depended on a careful and long term observation of the natural system in question. All the theory and computation could not even begin without the solid empirical observations of the biologists.

Starting from the new- born stage (about 2 weeks after hatching), animals were scanned for a period of 3–4 years and with a frequency of two weeks to four months” ([1])

Google might have the math and computing resources, but they are unlikely to observe live lizards for four years as a run up.


  1. Liana Manukyan, Sophie A. Montandon, Anamarija Fofonjka, Stanislav Smirnov, and Michel C. Milinkovitch, A living mesoscopic cellular automaton made of skin scales. Nature, 544 (7649):173-179, 04/13/print 2017. http://dx.doi.org/10.1038/nature22031
  2. Phys.org. How to color a lizard: From biology to mathematics. 2017, https://phys.org/news/2017-04-lizard-biology-mathematics.html.

UK Dawn Chorus Research

Our planet is awash in birdsong, and probably has been for hundreds of millions of yearsBirds are singing mostly to each other, and we humans can pick out individual songs and exchanges, if we work at it. But it’s a giant cocktail party problem, and it’s not easy for computers to separate out what’s going on.

In recent years there has been significant progress using machine learning techniques to recognizes and separate individual bird sounds from the natural chaos [2]. This is an interesting research tool, because it makes possible further studies of what birds are saying, and who is talking to whom [1].

This year this technology is being applied in a UK research project to study and decode the “dawn chorus”, the glorious time when all the songbirds wake and chatter at once [3].  This work seeks to extend earlier investigations on individual and small groups to this much more complex natural setting.

In addition to the boldness of the goal (and who doesn’t love the phrase “dawn chorus”), what caught my eye is that this project has a citizen science component, which has been building a large dataset of samples collected by a mobile phone app.

An earlier version of this concept was called Warbler, which records bird sounds and uses machine classification to identify the species.

Unfortunately, I’m not sure what this app does because I can’t get it.

The press release and the web site talk about “citizen science” and the general public, it turns out that they really mean “The Great British public”.  No foreigners allowed.

While I suspect that the project is most interested in recordings from their target areas in the UK, I’m pretty sure that the reason the app is restricted is political. These days, all UK research software is “free for Britons, not available for anyone else”. Sigh.

[Rambling complaints about the stupidity of this policy deleted.]

For the record, this project is based on research that depends on analysis of datasets openly available from around the world, and data from earlier studies is freely available.  It is software that the seems to be blockaded, no doubt under the mistaken impression that it is somehow commercially valuable.  Double sigh.

Anyway, I look forward to the study of the Dawn Chorus, and hope that we’ll be allowed to see the results beyond the press release.


  1. Dan Stowell, Lisa Gill, and David Clayton, Detailed temporal structure of communication networks in groups of songbirds. Journal of The Royal Society Interface, 13 (119) 2016. http://rsif.royalsocietypublishing.org/content/13/119/20160296.abstract
  2. Dan Stowell and Mark D. Plumbley, Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ.2014, 2488 https://doi.org/10.7717/peerj.488
  3. The Engineering and Physical Sciences Research Council. Research teaches machines to decipher the dawn chorus. 2017, https://www.epsrc.ac.uk/newsevents/news/dawnchorus/.
  4. Warblr. Warblr: the birdsong recognition app. 2016, https://warblr.net/.

 

Whale Watching From The Shore

Whales are really cool! I don’t live anywhere remotely close to whale watching of any kind, so I only get to see whales if I’m on vacation or traveling near the ocean.

The Whale Trail  organization (not the video game) is providing interpretive signs and other aides to promote shore based whale and marine mammal watching on the West Coast of North America. (This group believes that watching from shore is less invasive than chasing them with boats.)

The trail is built on local knowledge. As Executive Director Donna Sandstrom says, local people know the best locations and times, and what to expect to see, and can help people recognize passing wildlife.

I think this effort lies at the intersection of “entertainment”, “education” and “citizen science”, (There may be political component, too, when conservation policy is contended.)

This group has built a community of people, through a combination of digital networking and local, in person, experiences. The second is by far the most important. There is a website with useful information—how else would I know about it, 3,000km inland?—but the heart of the matter is local sites. You have to be here, now. Furthermore, each location is different.

I haven’t visited any of the official Whale Trail sites (as far as I know), though I have visited some of the places, and I have seen whales from shore (and once, very memorably, from directly above in a propjet approaching an airport!)

I can also testify to the magnificent success of the 1972 US law that protects marine mammals. As a boy, I visited the coast of Oregon before the law, and I saw only a few marine mammals, in rather bedraggled colonies. In the years since, I have seen more, and more seals, sea lions, and, of course whales. And they are bold and unafraid of humans. It has been an amazing success.

I’m going to bookmark The Whale Trail, and check it out the next time I visit that coast.


  1. The Whale Trail. the whale trail | To inspire appreciation and stewardship of whales and our marine environment. 2017, http://thewhaletrail.org/.
  2. Tom Wilmer, The Whale Trail—from B.C. to Baja—great whale watching spots identified, in Journeys of Discovery with Tom Wilmer. 2017. http://kcbx.org/post/whale-trail-bc-baja-great-whale-watching-spots-identified

 

Bear Lake UNESCO Site

Peter Kujawinski wrote a nice piece in the NYT aboutTsá Tué, a new UNESCO preserve at Bear Lake in Canada. This vast area is inhabited by the Sahtuto’ine people, who have lived there for generations.

Snow- and ice-covered bushes along the shore of Great Bear Lake. It’s the eighth largest lake in the world. Credit Christopher Miller for The New York Times
Snow- and ice-covered bushes along the shore of Great Bear Lake. It’s the eighth largest lake in the world. Credit Christopher Miller for The New York Times

Unlike many other nature preserves, Tsá Tué has been placed under the formal authority of the local people, to sustain and protect for all humanity.

This approach aligns the traditional cultural connections of the local people with the lake and it’s environs, and places our faith in people who feel a deep, deep link to the area. (And for once, the priorities of the local people are treated with respect and not to be overrun by outsider powers.)

Tsá Tué caught my attention because I had dreamed of such approach to combat poaching and destruction of forests. I imagined forming a protective force run by inigenous people, including defensive bands of rangers. Who better to selflessly care for a forest or park, than peoples whose identity and culture are one with the natural setting?

Kujawinski was lucky enough to visit Deline and the lake, and reports on the beauty he found there. He also spoke to his hosts about their views and hopes. The residents and now stewards of the area speak of their language and history, and an identity inextricably tied to the lake.

He notes that many of the people are inspired by the prophecy o Sahtuto’ine elder Eht’se Ayah, who taught that the lake will be one of the last clean areas on Earth, and people would come North for refuge. Other stories recount the beating heart of the lake, and suggest that Bear Lake is connected to all the other lakes and waters of the world.

Whether you take these stories literally or metaphorically, they illustrate a commitment to defend the natural environment, a commitment that is not motivated solely by self-interest.

Not everything is honky dory, of course. Bear Lake is way up North, but hardly far enough to miss out on the twentieth century. For several decades, there was a large Uranium mine on the lake, which supplied Uranium for the earliest nuclear weapons. Many local people worked in the mine, and many died from the work. The mine is closed now, but who knows how much residual damage is there to plague the future?

I also have to think that the idea that Bear Lake, Tsá Tué, will remain pristine, and be a refuge from environmental disaster is too optimistic. Even today, the seasons are changing, which will surely stress the wildlife and waters. I’m also confident that the snow, lake, and living things already contain measurable traces of chemicals from the smoke down south. The preserve may be relatively empty, but I’m sorry to say that it is still part of the far from pristine world.

I don’t think I’ll be visiting Bear Lake soon, it’s too far away. But I’m glad to know that it is in good hands.


  1. Peter Kujawinski, Guardians of a Vast Lake, and a Refuge for Humanity, in New York times. 2017: New York. https://www.nytimes.com/2017/02/07/travel/great-bear-lake-arctic-unesco-biosphere-canada.html
  2. United Nations Educational, Scientific and Cultural Organization. Tsá Tué. 2016, http://www.unesco.org/new/en/natural-sciences/environment/ecological-sciences/biosphere-reserves/europe-north-america/canada/tsa-tue/.

 

CoralNet: Computer Beats Crowd

In recent years there has been a fluorescence of “crowd sourced science”, a la Galaxy Zoo and it’s large family of descendants.

These projects all share one basic rationale, that some tasks are hard for computers, yet easy for people. Galaxy Zoo is an early, successful, and influential project asks people to perform several image classification tasks, such as categorizing the shape of galaxies. The idea is that computer algorithms are ineffective at this task compare to humans, and there is so much data to process that professional astronomers cannot even dream of looking at it all.

Their studies indicate that large numbers of untrained people (i.e., volunteers from the Internet) provide data that is useful and comparable to alternatives such as machine processing. (Crowdsourcing may also give faster turn around.) Similar methods have been applied to a variety of image processing tasks in a number of domains, including interpretation of handwriting from old documents.

In all these cases, the whole enterprise hinges on the claim that human processing beats the computer, at least at the price point (which is generally around zero dollars.) These claims are clearly contingent on the specific task, and on the state of technology (and funding).

For example, recent advances in face recognition algorithms (driven, no doubt by well financed national security needs) have dramatically changed this calculus in the realm of analysis of digital imagery of human faces.  Low cost, off the shelf software can probably beat human performance in most cases.

This is actually one of the continuing technological stories of the early twentyfirst: the development of algorithms to meet and exceed human perception and judgment. Part of the “big” news in “Big Data” is the ways that it can outperform humans.

One example of these developments is CoralNet, from U. C. San Diego [1, 2].

It is now possible to survey large areas of coral reef quickly, generating large amounts of data. From this data, it is import to identify the type of coral and other features, which are important to understand the ecology of coral and the associated ecology, and to monitor changes over time. It isn’t feasible to hand annotate this data, so automated methods are needed.

The CoralNet system annotates digital imagery of coral reefs, identifying the type of coral and state of the reef. The basic idea is to use machine learning techniques to train the computer to reproduce the classifications of human experts. How well does that work?

The Silicon Valley approach would be to assert that they have “disrupted” coral identification, and rush out a beta. Real scientists, however, actually study the question, and publish the results.

In the case of CoralNet, there have been several studies over the past few years, including. For example, Oscar Beijbom and colleagues published detailed analysis of the performance of human experts and the automated system [2]. Additional details appear in Beijbom’s Thesis [1].

The study found variability among human analysts (to be expected, but often overlooked), and determined that the automated system performed comparably to human raters. These papers is a good example of the careful work that is needed to validate digitally automated science.

Since the 2014 study, the software has been improved and updated. CoralNet 2 improved the speed to the point that it is 10 to 100 times faster than human classification. This speed up is significant, making data available quickly enough to understand changes to the reefs. Combined with automated data collection (e.g., autonomous submarines), it is now possible to continuously monitor reefs around the world.

It seems obvious to me that crowdsourcing a la zooniverse would not be warranted for this case. The computer processing is now good enough that human raters, even thousands of them, are not needed.

I note that even in the domain of ocean ecology, there are many examples of simple analysis tasks. For example, in “Seafloor Explorer” crowdsourced identification of images of the seafloor, identifying material and species.  This is basically the same task as CoralNet automates, though looking for different targets.

I’m pretty sure that machine learning algorithms could catch or exceed the crowdsourced results of CoralNet. (It may or may not be feasible to develop the system, of course.)

The point is that crowdsourcing science is not a panacea, nor are their any problems that, for certain and always will be done better by Internet crowds. My own suspicion is that crowdsourcing (at least the “galaxy zoo” kind) will fade within a decade, as machine learning conquers every perceptual task.

And since I brought it up, I’ll also note the challenges these techniques pose to reproducibility. Human crowdsourcing is, by definition, impossible to reproduce. Classification vial machine learning may be difficult to reproduce as well, especially if the algorithm is updated with new examples.


  1. Oscar Beijbom, Automated Annotation of Coral Reef Survey Images, Ph.D. Thesis in Computer Science. 2015, University of California, San Diego: San Diego.  http://www.escholarship.org/uc/item/0rd0r3wd
  2. Oscar Beijbom, Peter J. Edmunds, Chris Roelfsema, Jennifer Smith, David I. Kline, Benjamin P. Neal, Matthew J. Dunlap, Vincent Moriarty, Tung-Yung Fan, Chih-Jui Tan, Stephen Chan, Tali Treibitz, Anthony Gamst, B. Greg Mitchell, and David Kriegman, Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation. PLOS ONE, 10 (7):e0130312, 2015. http://dx.doi.org/10.1371%2Fjournal.pone.0130312