text:
Home » "Le Mythe de Sisyphe" or "The Myth of Sisyphus"
"Le Mythe de Sisyphe" or "The Myth of Sisyphus"
On of the major playwrights during this period was Jean-Paul Sartre. Sartre had been imprisoned in Germany in 1940 but managed to escape, and become one of the leaders of the Existential movement. Other popular playwrights were Albert Camus, and Jean Anouilh. Just like Anouilh, Camus accidentally became the spokesman for the French Underground when he wrote his famous essay, "Le Mythe de Sisyphe" or "The Myth of Sisyphus". Sisyphus was the man condemned by the gods to roll a rock to the top of a mountain, only to have it roll back down again.
For Camus, this related heavily to everyday life, and he saw Sisyphus an absurd" hero, with a pointless existance. Camus felt that it was necessary to wonder what the meaning of life was, and that the human being longed for some sense of clarity in the world, since "if the world were clear, art would not exist". "The Myth of Sisyphus" became a prototype for existentialism in the theatre, and eventually The Theatre of the Absurd. Sisyphus is the absurd hero. This man, sentenced to ceaselessly rolling a rock to the top of a mountain and then watching its descent, is the epitome of the absurd hero according to Camus.
In retelling the Myth of Sisyphus, Camus is ble to create an extremely powerful image with imaginative force which sums up in an emotional sense the body of the intellectual discussion which precedes it in the book. We are told that Sisyphus is the absurd hero "as much through his passions as through his torture. His scorn of the gods, his hatred of death, and his passion for life won him that unspeakable penalty in which the whole being is exerted toward accomplishing nothing. " (p. 89). Sisyphus is conscious of his plight , and therein lies the tragedy.
For if, during the moments of descent, he nourished the hope that he would yet succeed, then his abour would lose its torment. But Sisyphus is clearly conscious of the extent of his own misery. It is this lucid recognition of his destiny that transforms his torment into his victory. It has to be a victory for as Camus says: I leave Sisyphus at the foot of the mountain! One always finds one's burden again. But Sisyphus teaches the higher fidelity that negates the gods and raises rocks. He too concludes that all is well. This universe henceforth without a master seems to him neither sterile nor futile.
Each atom of that stone, each mineral flake of that night-filled mountain, in itself forms a world. The struggle itself owards the heights is enough to fill a man's heart. One must imagine Sisyphus happy. (p. 91). Sisyphus' life and torment are transformed into a victory by concentrating on his freedom, his refusal to hope, and his knowledge of the absurdity of his situation. In the same way, Dr. Rieux is an absurd hero in The Plague, for he too is under sentence of death, is trapped by a seemingly unending torment and, like Sisyphus, he continues to perform his duty no matter how useless or how insignificant his action.
In both cases it matters little for what reason they continue to struggle so long as they testify to man's llegiance to man and not to abstractions or 'absolutes'. The ideas behind the development of the absurd hero are present in the first three essays of the book. In these essays Camus faces the problem of suicide. In his typically shocking, unnerving manner he opens with the bold assertion that: There is but one truly serious philosophical problem and that is suicide. (p. 3). He goes on to discover if suicide is a legitimate answer to the human predicament.
Or to put it another way: Is life worth living now that god is dead? The discussion begins and continues not as a metaphysical cobweb but as a well reasoned tatement based on a way of knowing which Camus holds is the only epistemology we have at our command. We know only two things:This heart within me I can feel, and I judge that it exists. This world I can touch, and I likewise judge that it exists. There ends all my knowledge, and the rest is construction. (p. 14)With these as the basic certainties of the human condition, Camus argues that there is no meaning to life.
He disapproves of the many philosophers who "have played on words and pretended to believe that refusing to grant a meaning to life necessarily leads to declaring that it is not worth living. (p. 7) Life has no absolute meaning. In spite of the human's irrational "nostalgia" for unity, for absolutes, for a definite order and meaning to the "not me" of the universe, no such meaning exists in the silent, indifferent universe. Between this yearning for meaning and eternal verities and the actual condition of the universe there is a gap that can never be filled.
The confrontation of the irrational, longing human heart and the indifferent universe brings about the notion of the absurd. The absurd is born of this confrontation between the human need and the unreasonable silence of the world. p. 21)and further:The absurd is not in man nor in the world, but in their presence together… it is the only bond uniting them. (p. 21)People must realize that the feeling of the absurd exists and can happen to them at any time. The absurd person must demand to live solely with what is known and to bring in nothing that is not certain.
This means that all I know is that I exist, that the world exists ,and that I am mortal. Doesn't this make a futile pessimistic chaos of life? Wouldn't suicide be a legitimate way out of a meaningless life? "No. " "No. " answers Camus. Although the absurd cancels all hances of eternal freedom it magnifies freedom of action. Suicide is "acceptance at its extreme", it is a way of confessing that life is too much for one. This is the only life we have; and even though we are aware, in fact, because we are aware of the absurd, we can find value in this life. The value is in our freedom, our passion, and our revolt.
The first change we must make to live in the absurd situation is to realize that thinking, or reason, is not tied to any eternal mind which can unify and "make appearances familiar under the guise of a great principle," but it is: … learning all over gain to see, to be attentive, to focus consciousness; it is turning every idea and every image, in the manner of Proust, into a privileged moment. (p. 20)My experiences, my passions, my ideas, my images and memories are all that I know of this world – and they are enough. The absurd person can finally say "all is well".
I understand then why the doctrines that explain everything to me also debilitate me at the same time. They relieve me of the weight of my own life, and yet I must carry it alone. (p. 41)Camus then follows his notions to their logical conclusions and insists that people must substitute quantity of xperience for quality of experience. The purest of joys is "feeling, and feeling on this earth. " This statement cannot be used to claim a hedonism as Camus's basic philosophy, but must be thought of in connection with the notion of the absurd that has been developed in the early part of the essay.
Man is mortal. The world is not. A person's dignity arises from a consciousness of death, an awareness that eternal values and ideas do not exist, and a refusal to give in to the notion of hope or appeal for something that we are uncertain of. In the following essays, Camus presents examples of the absurd person. We are given Don Juan, the actor, and the conqueror as examples of people who multiply their lives in an attempt to live fully within the span of their mortality. But more important is the creator who is discussed in the essay "Absurd Creation". The absurd joy par excellence is creation. " For in creating a work of art the creator is living doubly in as much as his creation id a separate life. "The artist commits himself and becomes himself in his work. " Works of art become, then, the one means for a person to support and sustain a lucid consciousness in the face of the absurdity of the universe. The resent and the succession of presents before an ever conscious mind, this is the ideal of the absurd man. (p. 81)Art is for Camus an essential human activity and one of the most fundamental.
It expresses human aspirations toward freedom and beauty, aspirations which make life valuable for each transient human being. Art defies that part of existence in which each individual is no more that a social unit or an insignificant cog in the evolution of history. In The Myth of Sisyphus then we find the philosophical basis for the stranger, the doctor, and the judge-penitent. This is the starting point of Camus's thought. Camus is concerned here as in his other works with persons and their world, the relationships between them, and the relationships between persons and their history.
In The Myth of Sisyphus he opposes himself to the rationalism of classical philosophy which seeks universal and enduring truths or a hierarchy of values which is crowned by God; he believes that truth is found by a subjective intensity of passion; he maintains that the individual is always free and involved in choice; he recognizes that persons exist in the world and are naturally related with it;he is deeply concerned with the significance of death, ts inevitability and its finality. The absurd is a revolt against tomorrow and as such comes to terms with the present moment.
Suicide consents to the absurd as final and limitless while revolt is a an ongoing struggle with the absurd and brings with it man's redemption. One can see now why Sisyphus is the absurd hero. He is conscious of his plight: it was his scorn of the gods, hatred of death, and passion for life that won him the penalty of rolling a rock to the top of the mountain forever, and he does not appeal to hope or to any uncertain gods. His is the ultimate absurd, for there is not death at the end of his truggle. All is not chaos; the experience of the absurd is the proof of man's uniqueness and the foundation of his dignity and freedom.
All that remains is a fate whose outcome alone is fatal. Outside of that single fatality of death, everything, joy or happiness, is liberty. A world remains of which man is the sole master. What bound him was the illusion of another world. The outcome of his thought , ceasing to be renunciatory, flowers in images. It frolics – in myths, to be sure, but myths with no other depth than that of human suffering and like it inexhaustible.
Not the divine fable that amuses and blinds, but the terrestial face, gesture, and drama in which are summed up a difficult wisdom and an ephemeral passion. p. 87)One could do worse than to consider the myths-retold in the works of Camus. Sisyphus is the absurd hero. This man, sentenced to ceaselessly rolling a rock to the top of a mountain and then watching its descent, is the essence of the absurd hero according to Camus. In retelling the Myth of Sisyphus, Camus is able to create an extremely powerful image with imaginative force, which sums up in an emotional sense the body of the discussion. We are told that Sisyphus is the absurd hero "as much through his passions as through his torture.
His scorn of the gods, his hatred of death, and his passion for life won him that unspeakable penalty in which the whole being is exerted toward accomplishing nothing. (p. 120). Sisyphus is conscious of his dilemma, and in that lays his tragedy. For if, during the moments of descent, he nourished the hope that he would yet succeed, then his labor would lose its torment. Nevertheless, Sisyphus is clearly conscious of the extent of his misery. It is this logical recognition of his destiny that ransforms his torment into his victory.
It has to be a victory for as Camus says: I leave Sisyphus at the foot of the mountain! One always finds one's burden again. But Sisyphus teaches the higher fidelity that negates the gods and raises rocks. He too concludes that all is well. This universe henceforth without a master seems to him neither sterile nor futile. Each atom of that stone, each mineral flake of that night-filled mountain, in itself forms a world. The struggle itself towards the heights is enough to fill a man's heart. One must imagine Sisyphus happy. (p. 123).
Sisyphus' life and torment are urned into victory by concentrating on his freedom, his refusal to hope, and his knowledge of the absurdity of his situation. It matters little for what reason he continues to struggle so long as he continues on this absurd path and not venture on to the path of dreaming or wishing. The ideas behind the development of the absurd hero are present in the first three essays of the book. In these essays, Camus faces the problem of suicide. In his typically shocking, unnerving manner he opens with the bold statement that: There is but one truly serious philosophical problem and that is suicide. (p. 3).
Essay Examples. (May 2020). "Le Mythe de Sisyphe" or "The Myth of Sisyphus". Retrieved from https://benjaminbarber.org/le-mythe-de-sisyphe-or-the-myth-of-sisyphus/
""Le Mythe de Sisyphe" or "The Myth of Sisyphus"." benjaminbarber.org. 05 2020. 02 2023. <https://benjaminbarber.org/le-mythe-de-sisyphe-or-the-myth-of-sisyphus/>.
Essay Examples. May 2020. "Le Mythe de Sisyphe" or "The Myth of Sisyphus". [online]. Available from: https://benjaminbarber.org/le-mythe-de-sisyphe-or-the-myth-of-sisyphus/ [Accessed 02 February 2023].
Essay Examples. "Le Mythe de Sisyphe" or "The Myth of Sisyphus" [Internet]. May 2020. [Accessed 02 February 2023]; Available from: https://benjaminbarber.org/le-mythe-de-sisyphe-or-the-myth-of-sisyphus/.
Jean-Paul Sartre Biography
The Stranger, Albert Camus
Otherness In American Psycho And The Outsider Essay
Free Will In The Stranger, By Albert Camus Essay
Mythology – Sisyphus
Albert Camus The Myth Of Sisyphus
Sisyphus and Mersault
Sisyphus Was Given A Punishment By The Gods
Albert Camus Writing Style
Existentialism In The Stranger
Existentialism and Theatre
Madison: War commander or legislator?
Plato's Philosophy of Sex


Add 13 new species of extinct trees are found around the world.
Ancient Trees 1.7.10 is a mod that adds a total of 13 new species of trees in Minecraft. But these trees will not appear in the generation of worlds, since it comes from trees that were once extinct in the world after a cataclysm. To get these trees you will have to look for the sprouts hidden around the world.
To replant these "extinct" trees you can take the sprout wherever you want, maybe in your house, or in your nearest forest. As these are new tree desings and new kinds of wood, you will have new wood decorative blocks with designs and colors unique for each one of these 13 types of trees.
How to install Ancient Trees Mod for Minecraft 1.7.10?
Paste the "Kore Sample" downloaded file into .minecraft / mods folder.
It's working! Let's find the most ancient trees in Minecraft!


New & used spare parts for Jaguar XJ. OEM & aftermarket parts for XJ V6, V8, V12 & XJR. XJ40, X300, X308, X350, X358 & X351.
New & used spare parts for your Jaguar S-Type V6, V8 & S-Type R. The S-Type was produced from 1999 to 2008.
New & spare parts for your Jaguar XK. XK8 et XKR. The XK was produced from 1996 to 2006, and from 2006 to 2014.
New & used spare parts for your Jaguar XJS L6 & V12. The XJS was produced from 1975 to 1996.
Take good care of your Jaguar!


text:
What the fatal Tesla Autopilot crash means for driverless cars
Tesla Model S 70-D electric car is test driven in Detroit.
Image: Carlos Osorio/AP
By Nick Jaynes 2016-07-01 18:15:14 UTC
The fact that a driver was killed in a crash inside his Tesla on Wednesday while the semi-autonomous Autopilot system was engaged is tragic. However, the collision will have ripple effects across Tesla and the rest of the automotive industry.
That's because it brings into question people's trust in self-driving systems. While it's fair for people to feel wary of Tesla Autopilot and other semi-autonomous driving systems, it's important people don't conflate it with truly autonomous cars, or use the incident to write off self-driving vehicles altogether.
SEE ALSO: Elon Musk casually reveals that Tesla Model S is also a boat
Tesla's blog post explaining the crash that took the life of one of its drivers while its Autopilot system was engaged is impressively detailed. From the description, we learn it was essentially a perfect storm of circumstances.
Whether or not the driver was distracted is only one factor to take into account.
According to Tesla, the Model S was traveling on a divided highway when a tractor trailer crossed in front of the car. The side of the truck apparently lined up perfectly with the skyline in both position and color, and because of that neither Autopilot nor the driver saw it. Autopilot missed it because the car was lined up with the middle of the truck, where there are no wheels, and the Tesla's low-mounted sensors only look downward toward the road, not above it. That allowed the car to drive underneath the trailer, killing the driver.
Many are saying, "Clearly, this driver wasn't watching the road, like he was supposed to. How can he miss that truck?" However, we weren't there. Maybe any one of us, too, would have not seen the truck — as unlikely as that sounds. That said, it's possible (perhaps even probable) he was distracted.
Turns out, though, whether or not the driver was distracted is only one factor to take into account when put in the context of autonomous driving. Obviously, carmakers need to get the tech right before putting it in the hands of drivers. And even when they do, there's a larger question of whether that tech — specifically semi-autonomous systems — might lull drivers into a false sense of security.
Beta testing with cars
Tesla clearly states that Autopilot is in beta. Anyone with an iPhone or PC probably isn't bothered by that — public betas happen all the time. Apple's Siri is a great example of a beta project that was put in the hands of customers.
That's essentially what's going on here with Autopilot. It's in beta, meaning it's not finished. That's why Tesla warns drivers that they still need to pay attention while Autopilot is engaged. But it's becoming fairly clear that the better your semi-autonomous system is, the more drivers are tempted to "check out" from the experience.
This is troubling because Tesla is the only automaker beta testing its driving systems on customers. Every other established brand does its testing with its employees and engineers. All of the comparable systems on the road today from other leading carmakers was developed and tested in labs and controlled environments years ago.
BMW's CEO Harald Krüger said late last year that applying the app industry approach to beta testing is "absolutely impossible with safety features in a car." Adding, "our technology must be 100 percent reliable."
In the world of autonomous driving, there are levels, measured from 0 to 5. Zero autonomy is traditional driving. Level 1 is autonomous braking — where the car can independently hit the brakes to prevent an imminent collision — and it's slowly being added to most new cars. Level 2 is automatic cruise control and some automated steering. Level 3 is complete autonomous driving in certain situations, like on the freeway. Level 4 is full autonomy, driving from your garage to your destination without you touching a steering wheel or a pedal. Above that, there's a Level 5, where the human can't even intervene (think Google's steering wheel-less cars).
Tesla's Autopilot is considered Level 2. It uses cameras and radar to see lanes and cars and can steer the car through most everyday freeway scenarios. Some other manufacturers, like Mercedes and Volvo, have deployed Level 2 systems as well.
The better your semi-autonomous system is, the more drivers are tempted to "check out" from the experience.
The difference between Level 2 and Level 4 is immense. Full autonomy requires more than the forward-facing sensors like those on the Tesla Model S; it incorporates 360-degree computer vision across many parts of the spectrum, enabled by cameras and LIDAR (laser-based radar), as well as car-to-car communication. Autopilot does not have most of this tech.
What's more, these systems and more — including the car's battery, for example — need to have backups, should one of them fail the car can still handle itself. Only with all these things onboard should a driver be allowed to really disengage from driving, because the whole idea is, no matter what happens, the car can handle it. That's why Volvo has said it will accept liability for its cars when they are in fully autonomous mode; a carmaker wouldn't make that claim if it wasn't fully confident in its car's capabilities.
Despite being a Level 2, many drivers treat Tesla's Autopilot as a Level 3 or 4. As I've experienced first-hand, and what we've seen from Tesla drivers, people feel like Autopilot is so in control that they disengage — they look away, start texting or even take a nap. Behind the wheel of an Autopilot-fitted Tesla, you feel like you can pretty much check out. That is, if you don't know any better.
Tesla did say in its blog post that Autopilot "makes frequent checks to ensure that the driver's hands remain on the wheel and provides visual and audible alerts if hands-on is not detected." I never experienced this when I tested the system, but that was prior to an update to Autopilot that Tesla pushed out in February. It's unclear whether these alerts didn't happen, the driver ignored them, or simply tapped the wheel now and then without looking up. Or, again, maybe he was paying attention — we just don't know yet.
Other semi-autonomous systems from Volvo, Honda and Mercedes are as technically advanced as Autopilot. But for safety reasons, they don't allow you to remove your hands from the wheel for long periods of time. Those other automakers have also purposely limited the car's ability to correct itself, instead relying on the driver to do things like keep the car from drifting into another lane. That's partly why those systems aren't considered beta, like Tesla's is.
In fact, the Mercedes-Benz E-Class's Drive Pilot system is so good, it earned the first autonomous driving license ever given to a production car in Nevada. But Mercedes didn't put that advanced system into its production car. Instead, it retarded it significantly so that drivers weren't just told but knew they were ultimately in charge of the driving, not the car.
Setback for driverless cars?
Let's not forget that this crash was human tragedy. The driver was a Tesla enthusiast with a family, and whatever the fallout is in the car industry, it's terrible this happened at all. But there's also a real fear that the incident will derail self-driving tech. A Jaguar engineer not too long ago told me he worried an incident with Autopilot could set back autonomous driving by a decade.
The endgame of autonomy is zero road fatalities.
Ultimately, though, the crash isn't a condemnation of automated driving, which has real potential to save lives. Going by average statistics, 87 people died on the road in America yesterday, and 90% of those were due to human error. Using one death to suggest that semi-autonomous tech isn't safe is misinformed at best. The endgame of autonomy is zero road fatalities.
But it's a fair question whether a sufficiently powerful semi-autonomous system — like Autopilot — instills too much false confidence in the car's ability to drive itself, leading to drivers disengaging more than they should. During testing of its self-driving cars, Google found that its drivers — who were paid to pay attention to the road — would disengage since they essentially thought, "The car's got it." If disengagement is the norm for Level 2 systems (as I suspect it is for many drivers) this tragic crash could be just the first.
The NHTSA is investigating the fatal wreck and Autopilot's role. When it's done, the government may be forced to write more laws that pertain to self-driving systems. That could mean the end of questionable practice of beta testing semi-autonomous driving systems, and a wider acknowledgement that those systems have to do everything possible to ensure drivers keep their eyes on the road.
Topics: autopilot, Cars, driverless cars, self-driving cars, Tech, Tesla, Transportation

redpajama_set_name: RedPajamaCommonCrawl


All Standard Hilman Rollers carry at least a 1 year warranty against manufacturer defects.
Super Heavy Duty Series Hilman Rollers (SHD Series) are the highest capacity rollers that Hilman offers as standard models. It is essentially an extension of the OT/NT/T Series in terms of top configurations and capacity. SHD Series rollers range in capacity from 100 tons to 1000 tons. There are several different models of 150 ton and 200 ton SHD models.
By no means is 1000-tons the highest capacity roller weve ever made. Presently the single most highest capacity Hilman Roller ever made is rated at 5000 metric tons. If you have an application that requires the capacity of a single roller to be in excess of 1000 tons we encourage you to contact us to discuss the nature of your application in greater depth.
OT-Series Rollers have an Oversized top that extend over the sides of the roller frame.
The relationship between the SHDB Series and the SHD Series is precisely the same as the relationship between the OTB/NTB/TB Series and the OT/NT/T Series Rollers. The addition of two end-blocks on the roller frame allows for the addition of an external guidance system (Accu-Roll). Note that there are some dimensional differences in the top plate from the SHD counterparts.
NT-Series Rollers have a Narrow top that extends over the ends of the roller frame. The tops of the OT & NT-Series rollers have standard hole patterns.
T-Series Rollers have tops that are flush with the roller frame on all sides. These are thought to be ideal for cavity-mount type applications.
200-TON CAPACITY XOT-WIDE STYLE ROLLER WITH BACKING BLOCKS ** SHIPPING WEIGHTS - 389 LBS./176 KGS.
200-TON CAPACITY XNT-WIDE STYLE ROLLER WITH BACKING BLOCKS ** SHIPPING WEIGHTS - 374 LBS./170 KGS.
200-TON CAPACITY XT-WIDE (WITH CENTER DIVIDE) STYLE ROLLER WITH BACKING BLOCKS ** SHIPPING WGT = 396 LBS./180 KGS.


text:
How Wearing A Strap-On Cost A Founder $500,000
Megan Rose Dickey
Dec 6, 2012, 12:09 AM
Photo: Bravo
Sibling entrepreneur duo Ben and Hermione Way haven't had much luck landing a $500,000 investment for their health startup Ignite. If you've been following Bravo's "Start-Ups: Silicon Valley," you have probably seen the Ways blow their pitch to Dave McClure of 500 Startups and unsuccessfully convince Jeff Clavier of SoftTech VC to invest.
But things don't seem to be getting much better for the sibling duo.
On this week's episode, Hermione celebrated her 27th birthday at her house in San Francisco with friends and two investors, Michael Gale and Amy Swartz, from Gramercy Ventures.
Everything seemed to be going well until Hermione opened a gift from her co-stars, which happened to be a strap-on dildo.
Hermione immediately put it on and started chasing people around The Villa while wearing the sex toy.
Her brother Ben and Gale appeared to be mortified.
"As funny as that was," Ben later said to Hermione, "that just cost us half a million dollars."
We don't hear directly from the investors in this episode, but based on the preview for next week's episode, it looks like Ben was right.
"I don't really care about losing the money," Gale said in a meeting with the Ways. "I care about looking stupid."
randi zuckerberg sai-us start-ups: silicon valley startups


Maligne Lake is located 48 km's south from the town of Jasper. The drive to the lake follows along the Maligne Range glacial valley. It offers beautiful scenery as well as many opportunities for spotting wildlife. Some animals to keep your eye out for are grizzly and black bear, bighorn sheep, white-tail deer, elk, moose and mountain goat.
The drive along Maligne Lake road is worth taking in itself. You may want to take a picnic lunch and stop along the way to eat it or wait until you get to the lake. There is a cafeteria style restaurant located there. They have sandwiches, soups and pastries. There are many stops of interest on the way to Maligne Lake. The most notable ones being Maligne Canyon, Medicine Lake and Marie's Rock.
This is one of my favorite lakes to come to. It always feels so peaceful. There are some nice hiking trails around the shoreline. We always seem to come across some form of wild life near the lake. Sometimes bears, or a tiny peka. This time there were a pair of Canadian geese. As the sun was starting to set the female gathered up her babies and nestled them in for the night. The peka is a small creature that sort of resembles a guinea pig. They make a high pitched squeaking sound. They are timid little critters that usually make their homes amongst large areas of rock. They usually scurry away quickly, I was quite pleased to be able to get so close on this occasion to get a picture of one.
Many of the lakes and waterways in this mountain valley drain through a network of underground caves. Medicine Lake is one of them. Visitors who come here in the summer have no idea the large lake they are viewing is gone in the fall. Every autumn this large body of water completely disappears. It acts like a large bathtub when the plug is pulled. Left behind is a giant mudflat. During the glacier runoff period of the summer there is enough water flowing into the lake to surpass what is draining.
Apparently in the past two attempts were made to plug the drain on the lake. Believe it or not old mattresses were dumped into the lake on the first attempt and loads of magazines on the second one. No effect was gained on either occasion. Are these old mattresses still in the lake?
The lake got its name from the early native people in the area. They believe it to have magical powers because of how it disappeared. Therefore they named it Medicine Lake.
When you get to the lake the options to view more than what you see from this part of the shoreline are limited. I am not aware of any groomed trails that you can hike on to get to other parts of the lake. The other two options are to rent a canoe, row boat or kayak from Curly Phillips Boathouse or to take the 90 minute boat trip with Maligne Tours.
We have visited the lake in the past but had never done the boat tour before. If you read the reviews about the tour they are mixed, some people feel it is not worth the money. To be honest we did not pay for the boat trip, we got it complementary because my husband works for Fairmont and the boat company is associated with them. We had intended to pay full price, but on arrival found out about the complimentary tickets. The cost for an adult is $55 plus tax, it is a lot of money, but the scenery was spectacular.
The only complaint we had was not having much time on Spirit Island. Normally you only get about ten minutes on the island. We were the last boat trip of the day so they stretched it to about twenty. It was not long enough, there appeared to be trails that went around the island. It would have been such a great place to explore and hike around. I guess the option to do this would be renting a canoe, kayak or row boat to get there yourself, but it would be a long ways to self propel! The lake is 22km's long and Spirit Island is farther than half way down it.
Update: Malgine Lake Tours now offers a cruise with an extended stay on Spirit Island. It is called The Through The Lens Cruise. A local professional photographer will assist you in getting some of your own fantastic photos.
The boat tour takes you past glacier studded mountains, the ones at the farther end of the lake that you view from Spirit Island were the most stunning. Some reviews of the boat tour had people saying you could see scenery the same in many other places around Jasper. We had done a fair bit around the area and have not seen anything quite the same. We don't do anything other than smaller day hikes though. I'm sure there are views as spectacular, but you probably have to work for them. You probably won't want to waste any of your precious time on the island by going to the washroom. If you must, you will get to go in a quarter of a million dollar facilit y. Yes that's right, Maligne Lake Tours has spent a small fortune to put a biffy on the island. There is a full functioning flush toilet that is solar operated and completely eco-friendly. A special system ensures everything breaks down and causes no impact to the environment.
From Maligne Lake there is a hike called the Skyline Trail. It is dubbed as on the best hikes in the Canadian Rockies. It is usually done over a several day period ranging from 2 to 6 days. 25km's of the trail is above the treeline, so the views offered are incredible. The hike finishes near the town of Jasper.
Maligne Lake has a self sustaining population of rainbow and brook trout. The lake is fed and drained by the Maligne River. It enters on the south side of the lake and drains it from the northern end. The largest rainbow trout in Alberta was caught in Maligne Lake. It was just over 20 pounds. The largest brook trout caught here is the third largest recorded one in the world!
Come back and visit us at Maligne Lake again!
Thanks, I have made the correction for the washroom facilities. Good to know about the extended stay option on Spirit Island. We will have tom come back and experience it!


text:
Research ArticleCHEMICAL ECOLOGY
Marine plastic debris emits a keystone infochemical for olfactory foraging seabirds
Matthew S. Savoca1,2,*,
Martha E. Wohlfeil1,2,
Susan E. Ebeler3 and
Gabrielle A. Nevitt1,2,*
1Department of Neurobiology, Physiology, and Behavior, University of California, Davis, Davis, CA 95616, USA.
2Graduate Group in Ecology, University of California, Davis, Davis, CA 95616, USA.
3Department of Viticulture and Enology, University of California, Davis, Davis, CA 95616, USA.
↵*Corresponding author. Email: msavoca13{at}gmail.com (M.S.S.); ganevitt{at}ucdavis.edu (G.A.N.)
Science Advances 09 Nov 2016:
Vol. 2, no. 11, e1600395
DOI: 10.1126/sciadv.1600395
Matthew S. Savoca
Department of Neurobiology, Physiology, and Behavior, University of California, Davis, Davis, CA 95616, USA.Graduate Group in Ecology, University of California, Davis, Davis, CA 95616, USA.
ORCID record for Matthew S. Savoca
For correspondence: msavoca13@gmail.com ganevitt@ucdavis.edu
Martha E. Wohlfeil
Susan E. Ebeler
Department of Viticulture and Enology, University of California, Davis, Davis, CA 95616, USA.
ORCID record for Susan E. Ebeler
Gabrielle A. Nevitt
Plastic debris is ingested by hundreds of species of organisms, from zooplankton to baleen whales, but how such a diversity of consumers can mistake plastic for their natural prey is largely unknown. The sensory mechanisms underlying plastic detection and consumption have rarely been examined within the context of sensory signals driving marine food web dynamics. We demonstrate experimentally that marine-seasoned microplastics produce a dimethyl sulfide (DMS) signature that is also a keystone odorant for natural trophic interactions. We further demonstrate a positive relationship between DMS responsiveness and plastic ingestion frequency using procellariiform seabirds as a model taxonomic group. Together, these results suggest that plastic debris emits the scent of a marine infochemical, creating an olfactory trap for susceptible marine wildlife.
foraging ecology
plastic debris
sensory ecology
Trophic interactions in the pelagic marine environment are mediated, in part, by infochemicals, including dimethyl sulfide (DMS). DMS and its chemical precursor, dimethylsulfoniopropionate (DMSP), are ideal candidate molecules for this investigation in that they serve as infochemicals for microfauna to macrofauna in foraging cascades (1–3) and have also received considerable attention as a potential contributor to global climate regulation (4). In pelagic ecosystems, DMS is produced by the enzymatic breakdown of DMSP in marine phytoplankton, which increases during zooplankton grazing (5), thus triggering foraging activity in a variety of marine organisms, including tube-nosed seabirds (order: Procellariiformes) (6). The procellariiform seabirds include the albatrosses, petrels, and shearwaters; members of this order are highly olfactory, pelagic, and wide-ranging, foraging over vast expanses of open ocean (7). Results from controlled experimental studies performed at sea have demonstrated that some procellariiform species respond to DMS and use it as a cue to localize prey, whereas other species are more responsive to odors associated with higher trophic interactions (6, 8–10). Recently, it has been further established that behavioral attraction to DMS is an adaptation for locating zooplankton grazers on phytoplankton (2), suggesting that DMS serves as a "keystone" infochemical in marine trophic interactions (11, 12).
Plastic debris may provide a substrate for biota that produce infochemicals, such as DMS or DMSP, because floating plastic debris is known to be an excellent substrate for biofouling (13–16). If plastic floating on the ocean surface is easily fouled by DMS-producing biota, then plastic debris might also acquire a chemical profile that attracts DMS-responsive species, eventually leading them to consume it. To test this hypothesis, we examined whether preproduction plastic beads experimentally deployed within the photic zone acquire a biologically relevant DMS signature. We then investigated whether plastic ingestion can be explained by behavioral attraction to DMS using the procellariiform seabirds as a model group. We chose this group because species within this order are severely affected by plastic consumption (17–20), and sufficient data are already in place to test hypotheses about whether plastic ingestion may be linked to olfactory foraging across this phylogenetic group (6, 9, 21–23). Moreover, because DMS sensitivity is likely an ancestral trait that coevolved with burrow-nesting behavior (24), this relationship allows us to extend our hypothesis to test whether burrow-nesting procellariiforms have a higher incidence of plastic ingestion than surface-nesting species. Our final aim was to use the results of this mechanistic investigation to predict how different species are being negatively affected while accounting for unequal sampling effort to inform future monitoring and conservation efforts.
We first examined whether exposure to the photic zone changes the sulfur signature of plastic beads (diameter, 4 to 6 mm) made from the three most common types of microplastic and mesoplastic debris: high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polypropylene (PP). These polymers are buoyant and make up more than 60% of global plastic production (25). We tested for a sulfur signature on clean beads that were never exposed to marine conditions (that is, virgin plastic; n = 10 for each plastic type) and also deployed replicate 3-g samples of microbeads contained in Nitex mesh bags (n = 12 bags of each plastic type) at two oceanographic buoys in the California current [Bodega Marine Laboratory (BML) and Hopkins Marine Station (HMS)] for approximately 3 weeks during the upwelling season (fig. S1). On dates when plastics were deployed at and retrieved from the buoys, we also collected replicate samples of seawater (n = 20 samples per site) within 10 m of the buoys. To measure headspace sulfur volatiles, we used solid-phase microextraction (SPME). After odor extraction via SPME, samples were manually injected into a gas chromatograph (GC) coupled to a sulfur chemiluminescence detector (SCD).
We did not find a DMS signature on any virgin plastic samples (Fig. 1A). In contrast, we detected DMS in the headspace of every plastic sample from both sites after marine exposure (concentration range, 0.6 to 28 μg of DMS per gram of plastic; Fig. 1B). These results confirm that three common varieties of plastic acquire a DMS signature after less than a month of marine exposure at concentrations that procellariiform seabirds can detect (26) (see figs. S2 and S3 for calibration curves).
Fig. 1 DMS concentration in plastic debris headspace.
(A) DMS was not detected on any of the virgin plastic samples tested (n = 10 samples each for HDPE, LDPE, and PP, N = 30 total). (B) DMS was detected on every plastic sample after marine exposure (n = 12 bags of each plastic type; each bag subsampled five times). Box plots illustrate DMS concentrations on marine-seasoned plastic of each plastic type across sites (HPDE = 8.31 ± 2.25 μg of DMS per gram of plastic; LDPE = 8.90 ± 1.34 μg of DMS per gram of plastic; PP = 9.56 ± 2.33 μg of DMS per gram of plastic). Points represent each bag's average DMS quantification; the error bars represent the SE of the five subsamples of each bag. Site averages by plastic type are as follows: for BML, x̄HDPE = 13.45 ± 2.41 μg g−1, x̄LDPE = 11.76 ± 1.65 μg g−1, and x̄PP = 4.99 ± 0.98 μg g−1; for HMS, x̄HDPE = 3.16 ± 1.07 μg g−1, x̄LDPE = 6.05 ± 1.39 μg g−1, and x̄PP = 14.13 ± 2.33 μg g−1.
DMS serves as a foraging cue for many pelagic marine organisms, including the highly olfactory procellariiform seabirds. Among procellariiform seabirds, a variety of experimental evidence suggests that DMS tracking is limited to a subset of species that specialize on phytoplankton grazers (for example, krill) (2) and that these seabird species also share certain life history traits, such as burrow-nesting (24). Behavioral detection of DMS has been demonstrated to be as low as 10−12 M in laboratory studies, suggesting that these birds should be able to use natural biogenic emissions of DMS to locate zooplankton grazers (27). Therefore, we could use this species-specific response to test whether DMS behavioral sensitivity (that is, DMS responsiveness) can be used to predict plastic ingestion within the procellariiform order.
We analyzed plastic ingestion data from 55 studies that sampled a total of 13,350 individuals among 25 procellariiform species (fig. S4 and database S1). For our analyses, we used generalized linear mixed models (GLMMs) with a binomial distribution (binomial GLMMs) where the original sample size of each study was preserved to account for uneven sampling effort between species and between studies (28, 29). We first used Akaike Information Criterion corrected for sample size (AICc) to rank models [see Materials and Methods (30)]. Models with DMS responsiveness as a predictor received >99% of the total AICc weight and offered the most explanatory power of any predictor we considered (see Materials and Methods and table S1). We next confirmed that DMS responsiveness had a significant effect on the frequency of plastic ingestion in the top-ranked model (binomial GLMM, z1,139 = 3.891, P < 0.0001; table S1). When we further examined the strength and direction of this effect while controlling for collection location and sampling effort (see Materials and Methods), we found that DMS responsiveness had a significant positive effect on the frequency of plastic ingestion (48.05% for DMS responders as compared to 7.52% for non–DMS responders; binomial GLMM, z1,139 = 3.897, P < 0.0001; Fig. 2A). We used "leave-one-out" cross-validation, which confirmed a high predictive performance of the model (r = 0.86, r2 = 0.73; fig. S5).
Fig. 2 Effects of DMS responsiveness and nesting behavior on plastic ingestion in procellariiform seabirds.
(A) Maximum likelihood estimate (±SEM) of the DMS-responsive and non–DMS-responsive species groups, illustrating a significantly higher frequency of plastic debris ingestion in DMS-responsive species (binomial GLMM, P < 0.0001). (B) Maximum likelihood estimate (±SEM) of burrow- and surface-nesting species groups, illustrating a significantly higher frequency of plastic ingestion in burrow-nesting species (binomial GLMM, P < 0.05). Burrow-nesting behavior is used here as a proxy for DMS responsiveness.
Our next step was to use a Bayesian approach to generate predictions of plastic ingestion patterns to inform future research (see Materials and Methods). Parameter estimates were used to generate predictions for both group-level (DMS-responsive or non–DMS-responsive) and species-level plastic ingestion frequencies (Fig. 3). The group-level result suggests that DMS-responsive species ingest plastic five times as frequently as non–DMS-responsive species [48.40%; 95% confidence interval (CI), 23.04 to 74.51% versus 8.73%; 95% CI, 2.78 to 21.53%, respectively; Fig. 3].
Fig. 3 Plastic ingestion and DMS responsiveness among procellariiform seabirds.
Colored circles and horizontal lines represent the mean and 95% CIs for each species' model-predicted plastic ingestion prevalence. Vertical dashed lines are model-predicted mean plastic ingestion frequency for each group (DMS-responsive and non–DMS-responsive). Vertical dotted lines represent the 95% CIs of the plastic ingestion frequency for each group.
We then extended our approach to examine our hypothesis on a much larger data set by using burrow-nesting behavior as a proxy for DMS responsiveness. This expanded our analysis to 20,852 individuals from 62 procellariiform species [fig. S6 and database S1; diving petrels (Pelecanoides sp.) were excluded from this analysis because they are the only burrow-nesting group that is not behaviorally responsive to DMS (8, 31)].
Multimodel selection on this larger data set found that nesting behavior was the best predictor of plastic ingestion across the procellariiform order. To examine the effect of nesting behavior on plastic ingestion, we analyzed the top-ranked model (table S2). When controlling for sampling effort, the frequency of plastic ingestion in burrow-nesting species was 25.01%, compared to 8.56% for surface-nesting species (binomial GLMM, z1,247 = −2.37, P < 0.05; Fig. 2B). Assessment of the predictive performance of the model using leave-one-out cross-validation was r = 0.86 (r2 = 0.74; fig. S7). To generate out-of-sample predictions, we used the same framework as that described for the DMS analysis. These findings suggest that burrow-nesting species ingest plastic nearly three times as frequently (25.64%; 95% CI, 13.49 to 41.19%) as surface-nesting species (9.20%; 95% CI, 3.58 to 18.26%) (Fig. 4).
Fig. 4 Relationship of plastic ingestion and nesting behavior.
Colored circles and horizontal lines represent the mean and 95% CIs for each species' model-predicted plastic ingestion frequency. Vertical dashed lines are model-predicted mean plastic ingestion frequency for each group (burrow- and surface-nesting). Vertical dotted lines represent the 95% CIs of the plastic ingestion frequency for each group.
We show first that three different types of plastic (HDPE, LDPE, and PP) take on the odor signature of DMS at concentrations of 10−5 to 10−8 M. This process can occur in less than 1 month of exposure at the offshore sites we tested (Fig. 1 and fig. S1). The DMS headspace concentrations we measured were higher than those that have been reported in association with frontal zones where DMS-responsive procellariiform species have been reported to aggregate (10−11 M) (8) and are at least four orders of magnitude above the behavioral detection threshold for Antarctic prions (Pachyptila desolata; 10−12 M) (26). The DMS concentrations we measured also overlap with values reported by classic laboratory studies that measured DMS emitted during phytoplankton grazing by zooplankton (10−7 to 10−9 M) (5). These laboratory measurements also served as a basis for identifying DMS as a keystone odorant in marine ecosystems (3, 11).
We next demonstrate the idea that behavioral attraction to DMS is a strong predictor of plastic ingestion within the procellariiform seabirds (n = 25 species; Fig. 2A). This result is further supported in a more comprehensive analysis using burrow-nesting behavior as a proxy for DMS responsiveness (n = 62 species; Fig. 2B). Although it is frequently assumed that marine organisms consume plastic debris because it is visually mistaken for prey (18, 19, 32, 33), our results suggest that chemical cues may also be mediating this maladaptive foraging behavior. The implication is that the odor signature plastic debris acquires in the photic zone hijacks a foraging response to a keystone marine infochemical. Thus, in addition to presenting a visual stimulus, our results provide evidence that marine plastic also presents an olfactory stimulus for species that have evolved to use DMS as a foraging cue. Our results (Figs. 3 and 4) suggest that many species that are predicted to be most vulnerable to this sensory trap may be overlooked and understudied in this regard.
DMS attraction is highly correlated to those species with diets rich in pelagic crustaceans (for example, krill), suggesting that behavioral sensitivity to DMS is an adaptation for locating nutrient-rich prey patches (2). This relationship presents the interesting possibility that the presence of a keystone infochemical on biofouled plastic may, in some cases, enhance attraction to an otherwise lower-quality foraging patch, creating an olfactory trap. This situation may be particularly problematic for species [such as prions (Pachyptila sp.)] that are highly sensitive to DMS and forage primarily by straining water through modified lamellae rather than by visual detection of individual prey items (34). Our analysis further indicates that some of the most affected species (for example, Puffinus griseus and Puffinus tenuirostris) migrate between the Southern Hemisphere and the Northern Hemisphere on a semiannual basis, suggesting that movement patterns may encourage contact with plastic debris on a global rather than a strictly regional basis.
Plastic debris has been found in pelagic environments worldwide (35, 36) and is rapidly increasing in marine ecosystems (37). As of 2014, a global analysis reported a quarter of a billion metric tons of plastic suspended in the global oceans (36). More than 200 species of marine fish (38–40), marine mammals (41, 42), sea turtles (43, 44), and seabirds (18–20) have been found to consume plastic at sea. Seabirds are especially at risk; a recent projection model concluded that greater than 99% of all seabird (n > 300) species will have ingested plastic debris by 2050 (17). The negative consequences of plastic ingestion have received considerable study and range from physical obstruction to chemical toxicity (45–48).
Therefore, elucidating the mechanisms that drive plastic consumption by marine fauna is urgently needed to help direct mitigation efforts. With limited resources, studies of plastic ingestion routinely focus on those species that are most common, charismatic, or frequently monitored because of interactions with fisheries. In contrast, our analysis incorporates plastic debris into the marine food web, suggests that procellariiform species that are adapted to locate prey by tracking DMS might be especially predisposed to consume plastic debris, and points to the need for a more focused monitoring effort informed by sensory foraging theory.
Globally, DMS and DMSP have been implicated as infochemicals across a wide range of marine biota, from zooplankton (1) to baleen whales (49). The explanation we provide for plastic ingestion patterns seen in procellariiform seabirds should be explored in other groups, including sea turtles (50), penguins (51), various species of marine fishes (52, 53), and even marine mammals (54), all of which have been shown to either detect or use these compounds in foraging contexts. Our results propose a novel mechanism for why plastic ingestion is becoming so prevalent among marine fauna and point to remediation strategies (including increasing antifouling properties of consumer plastics and using sensory ecology to identify at-risk species) that may aid future mitigation efforts.
Preproduction plastic beads of HDPE, LDPE, and PP (hereafter beads), 4 to 6 mm in diameter, were donated by SABIC Innovative Plastics (Saudi Basic Industries Corp.). Plastic beads (3 g) of each plastic type (HDPE, LDPE, and PP) were separately enclosed inside Nitex bags (1020-μm mesh size). Nine such Nitex bags (three of each plastic type) were zip-tied to a PP rope. We made four ropes, two for each of the two deployment sites, each with nine Nitex bags (each bag containing one type of plastic bead for ocean deployment). The ropes (n = 2 ropes per site, n = 18 Nitex bags per site; fig. S1) were attached to an oceanographic monitoring buoy located approximately 1 km offshore at two locations along the central California coast (fig. S1). One buoy was in the Bodega Marine Reserve maintained by the University of California's BML (38°18′58″N 123°04′29″W). The other buoy was in the Monterey Bay National Marine Sanctuary, maintained by the Monterey Bay Aquarium Research Institute, located offshore from the HMS of Stanford University (36°37′19″N 121°54′06″W).
Beads were deployed for 19 to 21 days (19 days at BML: 24 April 2014 to 13 May 2014; 21 days at HMS: 14 May 2014 to 4 June 2014). The beads from each bag were subsampled by weight into five 10-ml silanized headspace vials (Restek Co.) and sealed with a polytetrafluoroethylene (PTFE; Teflon) septum screw cap (18 mm thread; Restek Co.). Each vial was then wrapped externally in Parafilm M (Bemis Co. Inc.) to prevent gas exchange and stored frozen (−20°C) until analysis.
To check whether local seawater did not contain unusually high levels of DMS-producing algal or bacterial cells that might have affected our results, seawater samples were collected within 10 m of the buoys at the same time that beads were deployed (BML: 24 April 2014; HMS: 14 May 2014) and retrieved (BML: 13 May 2014; HMS: 4 June 2014) from the ocean. Surface seawater was collected in two 1-liter glass bottles that were washed three times with 95% ethanol and rinsed with ultrapure water (type 1, EMD Millipore) subsampled into 1-ml samples in 10-ml silanized headspace vials, sealed with an 18-mm-thread screw cap with a PTFE septum, wrapped externally in Parafilm M to prevent exchange, and stored frozen (−20°C) until analysis.
Analytical chemistry procedures
SPME description. SPME was used for odor extractions using a 50/30-μm Divinylbenzene/Carboxen/Polydimethylsiloxane-coated fiber (Sigma-Aldrich). SPME has been shown to be an ideal method to sample volatiles from a polymer matrix (55). Fibers were conditioned according to the manufacturer's instruction before use.
GC-SCD methods. After odor extraction, the SPME fiber was manually injected into a 5890 Hewlett Packard GC (Hewlett Packard/Agilent), equipped with a J&W GS-GasPro PLOT (60 m × 0.32 mm) column (Agilent), connected to a Sievers 355 SCD (Agilent). The GC was set for splitless injection, with an inlet temperature of 250°C and using a deactivated glass SPME injection liner (Sigma-Aldrich) with a diameter of 0.7 mm. The split flow was opened at 3 min following the injection and closed again at 13 min after injection. The carrier gas was helium at a volumetric flow of 2.8 ml min−1 with a constant column head pressure of 20 psi (~140 kPa). The oven temperature program started with an initial setting of 40°C followed by an immediate ramp of 10°C min−1 to 260°C, followed by a hold of 3 min. The SCD burner temperature was 800°C with a hydrogen flow rate of 100 ml min−1 and an air flow rate of 40 ml min−1. The SCD pressure was 6 torr, with the controller at 190 torr. The SPME fiber was left in the inlet for at least 10 min, retrieved, and exposed to the headspace of the following sample. These methods were adapted from previous studies of sulfur headspace compounds in food products (56, 57).
Solution procedures. To quantify the concentration of DMS in our plastic and seawater samples, we used ≥99% anhydrous diethyl sulfide (DES; Sigma-Aldrich) to generate an internal calibration standard. We performed a two-step serial dilution of DES to achieve a standard solution with a concentration of 10 parts per million (ppm) DES by volume (μl liter−1). First, we made a 1:100 dilution of neat DES in anhydrous (200 proof) ethanol (EtOH; Koptec) to yield a solution with a concentration of 1 × 104 μl of DES per liter. Ethanol was chosen as a solvent for the first dilution because DES is more soluble in EtOH than in water. Then, we made a 1:1000 dilution in ultrapure water of the first dilution to create a solution with a concentration of 10 μl of DES per liter. Corrected for the density of DES (0.837 g ml−1), this solution has a DES concentration of 8.37 mg liter−1.
Similarly, for DMS, our analyte of interest, we performed two-step serial dilutions of ≥99% anhydrous DMS (Sigma-Aldrich) to achieve a standard solution with a concentration of 10 ppm (μl liter−1) DMS by volume. First, we made a 1:100 dilution of neat DMS in anhydrous EtOH to yield 1 × 104 μl of DMS per liter of solution. Ethanol was chosen as a solvent for the first dilution because DMS is more soluble in EtOH than in water. Then, we made a 1:1000 dilution in ultrapure water of the first dilution to create a solution with a concentration of 10 μl of DMS per liter. Corrected for the density of DMS (0.846 g ml−1), this has a DMS concentration of 8.46 mg liter−1.
DMS standard solutions of differing concentrations were created via the same serial dilution method (varying the dilution in the second step according to the final DMS concentration desired) used to create the DES dilutions described above. The final concentration of a solution of DMS (10 μl liter−1) is slightly different from the same concentration by volume of DES because of the difference in density between DES and DMS.
External calibration curve procedures. To obtain the most accurate quantitative estimate of DMS dissipating from the bead samples, we generated two different external calibration curves. The first calibration curve was generated with lower concentrations of DMS (fig. S2A). We added 10 μl of DMS (Sigma-Aldrich) at one of five known concentrations—1, 2, 5, 10, and 20 μl liter−1 (corresponding concentrations corrected for density: 0.85, 1.69, 4.23, 8.46, and 16.92 mg liter−1)—to 0.5 g of clean beads contained in a headspace vial. We also added 10 μl of DES solution (10 μl liter−1) (corresponding concentration corrected for density: 8.37 mg liter−1) as an internal calibration standard. The second calibration curve was generated with higher concentrations of DMS (fig. S2B). We added 10 μl of DMS at one of six known concentrations—5, 10, 100, 200, 400, and 800 μl liter−1 (corresponding concentrations corrected for density: 4.23, 8.46, 84.60, 169.20, 338.40, and 676.80 mg liter−1)—to 0.5 g of clean beads contained in a headspace vial. We also added 10 μl of DES (10 μl liter−1) (corresponding concentration corrected for density: 8.37 mg liter−1) solution as an internal calibration standard. We chose this DMS concentration range because it encompasses the typical DMS concentrations we expected from our samples based on preliminary trials from summer 2013. Each DMS concentration was replicated four times to verify the precision of the GC-SCD.
Using peak area ratio calculations (DMS-integrated peak area/DES-integrated peak area) from these standard runs, we were able to generate a linear regression model with which unknown DMS concentrations from our plastic samples could be estimated from the formula of the regression line (fig. S2).
To produce an external calibration curve for seawater, we needed to use a matrix that closely resembled our seawater samples. To do so, we obtained filtered (25-μm filter size) seawater from BML. To ensure that all biological activity that might produce a DMS signature was eliminated, the seawater was heated to 60°C, well above the boiling point of DMS (35°C), in a precision economy oven (Thermo Fisher Scientific Inc.) for 12 hours. We then analyzed six samples of that water and confirmed that there was no detectable DMS in its headspace.
Because we expected the DMS in the headspace of a seawater sample to be at extremely low concentrations, we needed to determine the GC-SCD's sensitivity thresholds to DMS. A 3:1 signal-to-noise ratio was deemed to be the GC-SCD's limit of detection (LOD), whereas a 6:1 signal-to-noise ratio was considered to be the limit of quantitation (LOQ). Through repeated trials, we determined that the instrument's LOD for DMS in a seawater matrix was 0.5 μl liter−1 (4.23 ng of DMS per gram of seawater; maximum concentration in headspace, 7 × 10−10 M), with an LOQ of 0.75 μl liter−1 (5.29 ng of DMS per gram of seawater; maximum concentration in headspace, 1 × 10−9 M).
To create a standard curve, we added 10 μl of DMS at one of four known concentrations—0.75, 1, 2.5, and 5 μl liter−1 (corresponding concentrations corrected for density: 0.423, 0.529, 0.846, 2.115, and 4.230 mg liter−1)—to 1 g of seawater contained in a headspace vial. We also added 10 μl of solution of DES (10 μl liter−1) (corresponding concentration corrected for density: 8.37 mg liter−1) as an internal calibration standard. Each DMS concentration was measured no fewer than six times to verify the repeatability of the GC-SCD measurement. Using peak area ratio calculations from these standard runs of varying DMS concentrations while holding the internal calibration standard (DES) constant, we were able to generate a linear regression model with which unknown DMS concentrations from our seawater samples could be estimated from the formula of our regression line (fig. S3).
Plastic and seawater sample GC-SCD procedures. We used GC-SCD to analyze 10 virgin samples of each plastic type (PP, HDPE, and LDPE) to confirm that there were no sulfur-based compounds in the headspace of the plastic beads before marine exposure. We placed 0.5 g of HDPE, LDPE, or PP beads in a 10-ml silanized headspace vial (Restek Co.) with 10 μl of 10 ppm DES solution (internal standard) and sampled volatiles for 30 min at room temperature (20°C).
On the day of analysis, a bead or seawater sample was removed from the freezer and allowed to thaw for 30 min. This allowed the sample to reach a temperature of 12° ± 5°C, approximately the temperature of the water at the time the beads were floating in the ocean (fig. S8). Then, 10 μl of 10 ppm DES solution (internal standard) was added to each sample. An additional 30 min was given to allow for volatile compounds to come to equilibrium within the headspace vial. Then, the SPME fiber was injected into the closed 10-ml vial through the PTFE septa, and volatiles were extracted for 30 min at 15°C. Finally, the SPME fiber was removed from the headspace vial and manually injected into the GC-SCD.
Statistical analyses and results of plastic sulfur chemistry
To determine whether there was a significant difference in the DMS signature from the beads of different plastic types or at different sites, we ran GLMMs and performed analyses of variance (ANOVAs) on the fit models. To fit the GLMMs, we used the package nlme in R version 3.1. Our sampling unit was the bag of plastic beads (N = 36 total, n = 18 per site). The response variable for each bag was the average headspace concentration of DMS measured from the five subsamples within each bag (N = 180 samples total, n = 60 each for HDPE, LPDE, and PP). The DMS headspace concentration was reported as micrograms of DMS per gram of plastic.
For our exploratory model, the main effect predictors were plastic type (HDPE, LDPE, and PP), site (BML and HMS), an interactive effect of plastic type by site, and a nested effect of bag, within rope, within site. This nested effect was included to test for systematic differences between the two ropes at each site. If the nested effect was significant, then it would be included as a random effect in the final model.
After evaluating the factorial ANOVA, we ran a Tukey's post hoc test to investigate differences between groups. This analysis showed that there was a difference in DMS response between ropes at the BML site; thus, the final model included plastic type, site, an interactive effect of plastic type by site, and the random nested effect of bag, within rope, within site.
At the HMS site, PP exhibited a significantly higher DMS concentration than HDPE (Tukey's post hoc test, P < 0.01) and LDPE, although this difference was not significant (Tukey's post hoc test, P = 0.06). However, at the BML site, PP exhibited significantly lower DMS concentrations than HDPE (Tukey's post hoc test, P = 0.04).
DMS was only detectable in seawater samples in one instance (at HMS on 4 June 2014; 12.95 ± 1.60 ng of DMS per gram of seawater; n = 10). Headspace concentrations of DMS from all other seawater samples analyzed (n = 30) were below our instrument's LOQ. These DMS concentrations associated with seawater samples were one to three orders of magnitude lower than the DMS concentrations from every marine-seasoned plastic sample analyzed (Fisher's exact test, P < 0.0001).
Seabird plastic ingestion analyses
Plastic ingestion database. We used Web of Science to search the scientific literature for publications from 1960 to 2014 using the keywords "procellariiform," "plastic," and "ingestion" found anywhere in the publication. We retained publications that reported original plastic ingestion data and any referenced publications. We did not include gadfly petrels (Pterodroma spp.) in our analysis because of insufficient behavioral and plastic ingestion data available for this clade. This resulted in a total of 73 studies containing data on 20,922 individuals representing 65 procellariiform species (database S1). Although probably not exhaustive, we believe to have collected most of the studies documenting plastic ingestion in procellariiform seabird species.
Statistical methods. To analyze the effect of DMS responsiveness on plastic ingestion, we filtered our database to include only species groups where DMS responsiveness has been tested experimentally (6, 26, 58–60). Applying this filter left a total of 55 studies containing data on 13,350 individuals representing 25 procellariiform species. There were plastic ingestion records for 13 species of DMS-responsive procellariiform species (8485 total individuals) and 12 non–DMS-responsive procellariiform species (4865 total individuals). To examine the effect of nesting behavior on plastic ingestion, we analyzed the entire database excluding only diving petrels (Pelecanoides sp.) because diving petrels are burrow-nesting but not DMS-responsive (8, 31). The resulting data set contained data from 73 studies, which included 20,852 individuals from 62 procellariiform species. There were ingestion records for 39 burrow-nesting procellariiform species (8157 total individuals) and 23 surface-nesting procellariiform species (12,695 total individuals).
For the analyses of DMS responsiveness and nesting behavior on plastic ingestion, all individuals of the same species within the same study were combined for analysis. Frequency of occurrence (FO) of plastic ingestion was reported for every study; therefore, the response variable for all analyses was FO of plastic ingestion (n ingesting plastic debris/total N studied) for each species within each study. Candidate models were GLMMs with a binomial distribution where the response variable's sample size is preserved. We performed multimodel selection using AICc to rank models (30). Our model set included models with every additive combination of DMS responsiveness, collection location [wrecked (that is, dead or moribund) on beach, sampled at sea, and sampled at breeding colony], breeding phenology (breeding versus nonbreeding), study region (Arctic, North Pacific, South Pacific, Equatorial Pacific, North Atlantic, South Atlantic, Equatorial Atlantic, Indian, and Southern oceans), and decade of study (1960s, 1970s, 1980s, 1990s, 2000s, and 2010s) as main effect predictor variables. This design allowed us to determine whether there were other ecologically relevant predictors that could explain the variability in the data better than DMS responsiveness. Other studies have shown that plastic ingestion within seabirds is correlated to capture strategy (termed "foraging behavior," for example, pursuit diving, surface seizing, etc.) and "diet" (18). We did not include these predictors in our analyses because capture strategy does not align with "species" among seabirds (61), and we have previously demonstrated that DMS tracking is an adaptation for foraging on crustaceans (2). Consequently, "DMS responsiveness" and "diet" are highly correlated and cannot be disentangled in our model.
Further, to account for uneven sampling effort, we included "species" and "study" as random effects. To control for the effect of phylogeny on the response variable, we also included a set of models with "family" as a random effect. To test for the effect of nesting behavior on plastic ingestion prevalence, we used the same procedure described above, except here, we replaced "DMS responsiveness" with "nesting behavior." We also removed "family" as a random effect because of issues of multicollinearity with nesting behavior. All models were analyzed using package lme4 in R version 3.1. The full model included DMS responsiveness (species level binary: yes or no), collection location, breeding status, study region, and decade of study as main effects. Random effects, controlling for phylogeny and unbalanced sampling effort by species and study, were also included.
Of the 14 models that received AICc weight, 13 contained DMS responsiveness as a significant predictor of plastic ingestion. Even more powerfully, these 13 models received >99% of total AICc weight (table S1). To examine the sole effect of DMS responsiveness on plastic ingestion, we used the top-ranked model and removed "collection location" as a main effect predictor while retaining it as a random effect. We then preformed a second multimodel selection between this model and a "control" null model. The model that included DMS responsiveness as a predictor received 99% of AICc support (table S3). To derive Bayesian predictions from the model with DMS responsiveness as a predictor, we generated 10,000 sample parameters from the posterior of the model using a random binomial distribution, which accounts not only for the mean and variance of the parameters but also for sample size differences in the observed data. This Bayesian framework allowed us to account for uncertainty in parameter estimates and covariance among parameters (29). These parameter estimates were then used to generate predictions for both group-level (DMS-responsive or non–DMS-responsive) and species-level plastic ingestion occurrence.
For the analysis of the effect of nesting behavior on plastic ingestion, we used burrow-nesting behavior as a proxy for DMS responsiveness (24). In this more comprehensive analysis, 13 of the 19 models that received AICc weight contained "nesting behavior" as a predictor of plastic ingestion (table S2). These 13 models received 84% of total AICc weight (table S2). The top model included only "nesting behavior" as a significant main effect and "species" and "study" as random effects (table S2).
Here, again, we performed a second multimodel selection between the top model and a "control" null model. The model that included "nesting behavior" as a predictor received 82% of AICc weight as compared to only 18% support for the null model (table S4). Using the same methods described above, parameter estimates from the model that included "nesting behavior" were then used to generate predictions for both group-level (burrow- or surface-nesting) and species-level plastic ingestion prevalence.
To assess model performance, we conducted leave-one-out cross-validation (62). In this method, the data set was iteratively spilt into "1 to n − 1" observations, holding back one observation and generating a prediction for the omitted observation based on the "1 to n − 1" data set. Once an independent prediction has been made for the iteratively omitted observation, the predicted versus observed values are compared using Spearman's correlation coefficient (r).
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/11/e1600395/DC1
fig. S1. Field sites and experimental design for plastic deployments.
fig. S2. DMS standard curves with a plastic bead matrix.
fig. S3. DMS standard curves with a seawater matrix.
fig. S4. Plastic ingestion and DMS responsiveness among procellariiform species in the database.
fig. S5. Cross-validation results for the DMS responsiveness model.
fig. S6. Plastic ingestion and nesting behavior among procellariiform species in the database.
fig. S7. Cross-validation results for the nesting behavior model.
fig. S8. Data from oceanographic monitoring buoys.
table S1. Model selection table for the plastic ingestion meta-analysis in relation to DMS responsiveness.
table S2. Model selection table for the plastic ingestion meta-analysis in relation to nesting behavior.
table S3. Model selection table testing only DMS responsiveness as predictor against the null model.
table S4. Model selection table testing only nesting behavior as predictor against the null model.
database S1. Procellariiform plastic ingestion database (.xlsx).
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
J. R. Seymour,
R. Simó,
T. Ahmed,
R. Stocker
, Chemoattraction to dimethylsulfoniopropionate throughout the marine microbial food web. Science 329, 342–345 (2010).
M. S. Savoca,
G. A. Nevitt
, Evidence that dimethyl sulfide facilitates a tritrophic mutualism between marine primary producers and top predators. Proc. Natl. Acad. Sci. U.S.A. 111, 4157–4161 (2014).
R. K. Zimmer,
C. A. Butman
, Chemical signaling processes in the marine environment. Biol. Bull. 198, 168–187 (2000).
R. J. Charlson,
J. E. Lovelock,
M. O. Andreae,
S. G. Warren
, Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature 326, 655–661 (1987).
J. W. H. Dacey,
S. G. Wakeham
, Oceanic dimethylsulfide: Production during zooplankton grazing on phytoplankton. Science 233, 1314–1316 (1986).
G. A. Nevitt,
R. R. Veit,
P. Kareiva
, Dimethyl sulphide as a foraging cue for Antarctic Procellariiform seabirds. Nature 376, 680–682 (2002).
J. Warham, The Petrels: Their Ecology and Breeding Systems (Academic Press, 1990).
, Olfactory foraging by Antarctic procellariiform seabirds: Life at high Reynolds numbers. Biol. Bull. 198, 245–253 (2000).
G. Nevitt,
K. Reid,
P. Trathan
, Testing olfactory foraging strategies in an Antarctic seabird assemblage. J. Exp. Biol. 207, 3537–3544 (2004).
, Sensory ecology on the high seas: The odor world of the procellariiform seabirds. J. Exp. Biol. 211, 1706–1713 (2008).
R. P. Ferrer,
R. K. Zimmer
, Community ecology and the evolution of molecules of keystone significance. Biol. Bull. 223, 167–177 (2012).
, Molecules of keystone significance: Crucial agents in ecology and resource management. Bioscience 63, 428–438 (2013).
D. K. A. Barnes
, Biodiversity: Invasions by marine life on plastic debris. Nature 416, 808–809 (2002).
D. Lobelle,
M. Cunliffe
, Early microbial biofilm formation on marine plastic debris. Mar. Pollut. Bull. 62, 197–200 (2011).
T. Artham,
M. Sudhakar,
R. Venkatesan,
C. Madhavan Nair,
K. V. G. K. Murty,
M. Doble
, Biofouling and stability of synthetic polymers in sea water. Int. Biodeter. Biodegr. 63, 884–890 (2009).
A. Trishul,
M. Doble,
K. Suresh Kumar,
S. Syed Jahan,
D. Inbakandan,
R. R. Viduthalai,
V. R. Umadevi,
P. Sriyutha Murthy,
R. Venkatesan
, Biofouling and biodegradation of polyolefins in ocean waters. Polym. Degrad. Stab. 92, 1743–1752 (2007).
C. Wilcox,
E. Van Sebille,
B. D. Hardesty
, Threat of plastic pollution to seabirds is global, pervasive, and increasing. Proc. Natl. Acad. Sci. U.S.A. 112, 11899–11904 (2015).
P. G. Ryan
, The incidence and characteristics of plastic particles ingested by seabirds. Mar. Environ. Res. 23, 175–206 (1987).
M. L. Moser,
D. S. Lee
, A fourteen-year survey of plastic ingestion by western North Atlantic seabirds. Colon. Waterbirds 15, 83–94 (1992).
L. B. Spear,
D. G. Ainley,
C. A. Ribic
, Incidence of plastic in seabirds from the tropical pacific, 1984–1991: Relation with distribution of species, sex, age, season, year and body weight. Mar. Environ. Res. 40, 123–146 (1995).
T. C. Grubb
, Smell and foraging in shearwaters and petrels. Nature 237, 404–405 (1972).
C. Verheyden,
P. Jouventin
, Olfactory behavior of foraging procellariiforms. Auk 111, 285–291 (1994).
M. Losekoot,
H. Weimerskirch
, Evidence for olfactory search in wandering albatross, Diomedea exulans. Proc. Natl. Acad. Sci. U.S.A. 105, 4576–4581 (2008).
R. W. Van Buskirk,
, The influence of developmental environment on the evolution of olfactory foraging behaviour in procellariiform seabirds. J. Evol. Biol. 21, 67–76 (2008).
A. L. Andrady
, Microplastics in the marine environment. Mar. Pollut. Bull. 62, 1596–1605 (2011).
F. Bonadonna
, Sensitivity to dimethyl sulphide suggests a mechanism for olfactory navigation by seabirds. Biol. Lett. 1, 303–305 (2005).
F. Bonadonna,
S. Caro,
P. Jouventin,
, Evidence that blue petrel, Halobaena caerulea, fledglings can detect and orient to dimethyl sulfide. J. Exp. Biol. 209, 2165–2169 (2006).
B. M. Bolker,
M. E. Brooks,
C. J. Clark,
S. W. Geange,
J. R. Poulsen,
M. H. H. Stevens,
J.-S. S. White
, Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).
R. McElreath, Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC Press, 2016).
C. M. Hurvich,
C.-L. Tsai
, Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).
G. B. Cunningham,
H. Weimerskirch,
, A comparison of the olfactory abilities of three species of procellariiform chicks. J. Exp. Biol. 206, 1615–1620 (2003).
Q. Schuyler,
B. D. Hardesty,
K. Townsend
, To eat or not to eat? Debris selectivity by marine turtles. PLOS ONE 7, e40884 (2012).
Q. A. Schuyler,
K. Townsend,
N. J. Marshall
, Mistaken identity? Visual similarities of marine debris to natural prey items of sea turtles. BMC Ecol. 14, 14 (2014).
G. R. Martin,
P. A. Prince
, Visual fields and foraging in Procellariiform seabirds: Sensory aspects of dietary segregation. Brain Behav. Evol. 57, 33–38 (2001).
A. Cózar,
F. Echevarría,
J. I. González-Gordillo,
X. Irigoien,
B. Úbeda,
S. Hernández-León,
Á. T. Palma,
S. Navarro,
J. García-de-Lomas,
A. Ruiz,
M. L. Fernández-de-Puelles,
C. M. Duarte
, Plastic debris in the open ocean. Proc. Natl. Acad. Sci. U.S.A. 111, 10239–10244 (2014).
M. Eriksen,
L. C. M. Lebreton,
H. S. Carson,
M. Thiel,
C. J. Moore,
J. C. Borerro,
F. Galgani,
P. G. Ryan,
J. Reisser
, Plastic pollution in the world's oceans: More than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLOS ONE 9, e111913 (2014).
C. J. Moore
, Synthetic polymers in the marine environment: A rapidly increasing, long-term threat. Environ. Res. 108, 131–139 (2008).
C. M. Boerger,
G. L. Lattin,
S. L. Moore,
, Plastic ingestion by planktivorous fishes in the North Pacific Central Gyre. Mar. Pollut. Bull. 60, 2275–2278 (2010).
P. Davison,
R. A. Asch
, Plastic ingestion by mesopelagic fishes in the North Pacific Subtropical Gyre. Mar. Ecol. Prog. Ser. 432, 173–180 (2011).
C. A. Choy,
J. C. Drazen
, Plastic for dinner? Observations of frequent debris ingestion by pelagic predatory fishes from the central North Pacific. Mar. Ecol. Prog. Ser. 485, 155–163 (2013).
R. W. Baird,
S. K. Hooker
, Ingestion of plastic and unusual prey by a juvenile harbour porpoise. Mar. Pollut. Bull. 40, 719–720 (2000).
R. Fernández,
M. B. Santos,
M. Carrillo,
M. Tejedor,
G. J. Pierce
, Stomach contents of cetaceans stranded in the Canary Islands 1996–2006. J. Mar. Biol. Assoc. U.K. 89, 873–883 (2009).
K. A. Bjorndal,
A. B. Bolten,
C. J. Lagueux
, Ingestion of marine debris by juvenile sea turtles in coastal Florida habitats. Mar. Pollut. Bull. 28, 154–158 (1994).
J. Tomás,
R. Guitart,
R. Mateo,
J. A. Raga
, Marine debris ingestion in loggerhead sea turtles, Caretta caretta, from the Western Mediterranean. Mar. Pollut. Bull. 44, 211–216 (2002).
M. R. Gregory
, Environmental implications of plastic debris in marine settings—Entanglement, ingestion, smothering, hangers-on, hitch-hiking and alien invasions. Philos. Trans. R. Soc. London Ser. B 364, 2013–2025 (2009).
C. M. Rochman,
R. L. Lewison,
H. Allen,
A.-M. Cook,
S. J. Teh
, Polybrominated diphenyl ethers (PBDEs) in fish tissue may be an indicator of plastic contamination in marine habitats. Sci. Total Environ. 476–477, 622–633 (2014).
E. Hoh,
T. Kurobe,
, Ingested plastic transfers hazardous chemicals to fish and induces hepatic stress. Sci. Rep. 3, 3263 (2013).
K. Tanaka,
H. Takada,
R. Yamashita,
K. Mizukawa,
M.-a. Fukuwaka,
Y. Watanuki
, Accumulation of plastic-derived chemicals in tissues of seabirds ingesting marine plastics. Mar. Pollut. Bull. 69, 219–222 (2013).
J. G. M. Thewissen,
J. George,
C. Rosa,
T. Kishida
, Olfaction and brain size in the bowhead whale (Balaena mysticetus). Mar. Mammal Sci. 27, 282–294 (2011).
C. S. Endres,
K. J. Lohmann
, Perception of dimethyl sulfide (DMS) by loggerhead sea turtles: A possible mechanism for locating high-productivity oceanic regions for foraging. J. Exp. Biol. 215, 3535–3538 (2012).
K. L. B. Wright,
L. Pichegru,
, Penguins are attracted to dimethyl sulphide at sea. J. Exp. Biol. 214, 2509–2511 (2011).
J. L. DeBose,
S. C. Lema,
, Dimethylsulfoniopropionate as a foraging cue for reef fishes. Science 319, 1356 (2008).
A. D. M. Dove
, Foraging and ingestive behaviors of whale sharks, Rhincodon typus, in response to chemical stimulus cues. Biol. Bull. 228, 65–74 (2015).
S. Kowalewsky,
M. Dambach,
B. Mauck,
G. Dehnhardt
, High olfactory sensitivity for dimethyl sulphide in harbour seals. Biol. Lett. 2, 106–109 (2006).
M. Hakkarainen
, Solid phase microextraction for analysis of polymer degradation products and additives. Adv. Polym. Sci. 211, 23–50 (2007).
R. E. Miracle,
S. E. Ebeler,
C. W. Bamforth
, The measurement of sulfur-containing aroma compounds in samples from production-scale brewery operations. J. Am. Soc. Brew Chem. 63, 129–134 (2005).
J. Herszage,
S. E. Ebeler
, Analysis of volatile organic sulfur compounds in wine using headspace solid-phase microextraction gas chromatography with sulfur chemiluminescence detection. Am. J. Enol. Vitic. 62, 1–8 (2011).
K. Haberman
, Behavioral attraction of Leach's storm-petrels (Oceanodroma leucorhoa) to dimethyl sulfide. J. Exp. Biol. 206, 1497–1501 (2003).
G. Dell'Ariccia,
A. Célérier,
M. Gabirot,
P. Palmas,
B. Massa,
, Olfactory foraging in temperate waters: Sensitivity to dimethylsulphide of shearwaters in the Atlantic Ocean and Mediterranean Sea. J. Exp. Biol. 217, 1701–1709 (2014).
G. L. Hunt
, Olfactory sensitivities of foraging procellariid seabirds in the Aleutian Islands. Chem. Senses 21, 649–650 (1996).
E. A. Schreiber, J. Burger, Biology of Marine Birds (CRC Press, 2002).
R. Kohavi, in International Joint Conference on Artificial Intelligence (IJCAI-95), Montreal, Quebec, 20 to 25 August 1995.
Acknowledgments: We are indebted to K. Van Alstyne for conducting preliminary experiments to establish feasibility at the Shannon Point Marine Center of Western Washington University. We thank G. Susner and D. Dann of the BML and K. Nickols and P. Leary at HMS for technical support, G. Humphries for assistance with fig. S1, N. Willits and G. Humphries for statistical consultation, and M. Losekoot for editorial assistance. We also appreciate comments from two anonymous reviewers; their feedback improved the manuscript. Funding: This work was supported by the University of California, Davis (CA-D-NPB-3509-H to G.A.N.) and the NSF (GRF 1148897 to M.S.S. and PLR 1142084 to G.A.N.). Author contributions: M.S.S. conceived and designed the study, compiled and analyzed data, interpreted results, and drafted the manuscript. M.E.W. compiled and analyzed data and edited the manuscript. S.E.E. analyzed and interpreted chemical data and edited the manuscript. G.A.N. conceived and designed the study, interpreted data, and helped write the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.
Copyright © 2016, The Authors
Vol 2, No. 11
Thank you for your interest in spreading the word about Science Advances.
You are going to email the following Marine plastic debris emits a keystone infochemical for olfactory foraging seabirds
Message Subject (Your Name) has forwarded a page to you from Science Advances
Message Body (Your Name) thought you would like to see this page from the Science Advances web site.
By Matthew S. Savoca, Martha E. Wohlfeil, Susan E. Ebeler, Gabrielle A. Nevitt
Science Advances 09 Nov 2016 : e1600395
An infochemical found on marine plastic debris elucidates a novel mechanism for plastic ingestion by marine wildlife.
Stay Connected to Science Advances
Science Advances ISSN 2375-2548.


text:
Q: Bash - waiting on child processes, differences between loop types The below script contains two loops that fork processes to the background. They behave differently in that the for loop produces the expected results (waits for 10 child processes to complete) however the while loop does not produce the same results. Based on the output of pstree, it is clear that the while loop does not cause the child processes to be children of the current shell. I suspect this is due to the pipeline?
$ /tmp/wtf
1499921102 - for loop - start
wtf(2797)─┬─pstree(2810)
          ├─sleep(2800)
          ├─sleep(2801)
          ├─sleep(2802)
          ├─sleep(2803)
          ├─sleep(2804)
          ├─sleep(2805)
          ├─sleep(2806)
          ├─sleep(2807)
          ├─sleep(2808)
          └─sleep(2809)
1499921102 - for loop - waiting
1499921112 - for loop - done
1499921112 - while loop - start
wtf(2797)───pstree(2826)
1499921112 - while loop - waiting
1499921112 - while loop - done

$ cat /tmp/wtf
#!/bin/bash

set -m
set -e
set -u

printf "%s - for loop - start\n" "$(date +%s)"
for i in `seq 1 10`
do
  sleep 10 &
done
pstree -pl $$
printf "%s - for loop - waiting\n" "$(date +%s)"
wait
printf "%s - for loop - done\n" "$(date +%s)"

printf "%s - while loop - start\n" "$(date +%s)"
seq 1 10 | while read i
do
  sleep 10 &
done
pstree -pl $$
printf "%s - while loop - waiting\n" "$(date +%s)"
wait
printf "%s - while loop - done\n" "$(date +%s)"

How might I get the current process to wait on processes that are not direct children? An attempt to pass the process ids into wait was not successful:
wait $(seq 1 10 | while read i
do
  sleep 10 &
  echo $!
done | tr '\n' ' ')

/tmp/wtf: line 22: wait: pid 2866 is not a child of this shell
/tmp/wtf: line 22: wait: pid 2867 is not a child of this shell
/tmp/wtf: line 22: wait: pid 2868 is not a child of this shell
/tmp/wtf: line 22: wait: pid 2869 is not a child of this shell
/tmp/wtf: line 22: wait: pid 2870 is not a child of this shell
/tmp/wtf: line 22: wait: pid 2871 is not a child of this shell
/tmp/wtf: line 22: wait: pid 2872 is not a child of this shell
/tmp/wtf: line 22: wait: pid 2873 is not a child of this shell
/tmp/wtf: line 22: wait: pid 2874 is not a child of this shell
/tmp/wtf: line 22: wait: pid 2875 is not a child of this shell

A: Some experiments...
This while-based version seems to work:
seq 1 10 | while read i; do sleep 10 &  echo $!; done | xargs sh -c wait

This also works:
sh -c wait $(seq 1 10 | while read i; do sleep 10 &  echo $!; done)

Notes:

*

*The tr is needless.

*Since wait is a shell builtin command, xargs can't see it without
the sh -c.

*Evidently the sh -c is necessary, (even without xargs), so it's a builtin problem...

A: After doing some more research I confirmed my suspicion that the loop inside of a pipeline creates a subshell. What I learned is that wait will only wait for children of the current shell, so the cleanest solution I could think of was to ensure the backgrounded tasks and the wait are in the same subshell. I achieved this by wrapping the loop and the wait in a set of curly-braces inside the pipeline.
printf "%s - while loop - start\n" "$(date +%s)"
seq 1 10 | {
  while read i
  do
    sleep 10 &
  done
  pstree -pl $$
  printf "%s - while loop - waiting\n" "$(date +%s)"
  wait
}
printf "%s - while loop - done\n" "$(date +%s)"

redpajama_set_name: RedPajamaStackExchange