Baby bird communication (II)

This is Piolín:



(Piolín is pronounced [piɔˈli:n], and it is the Spanish name of Tweety)

My niece found Piolín in June, saving him (or her... I have here the same problem I had with Patxi) from certain death, as he fell from his nest on a very hot day (under the Sun it must have been around 40ºC), lying on one side of a road. You can see a small wound on his back, probably caused by the fall.

As I discovered later, after some research, and despite his aspect, Piolín was not an ET, but a baby sparrow. When we found him he was one to three days old. He could not be quite older than that, because he had not even opened his eyes yet.

We started feeding him with some water and wet bread, and, as this video shows, he was hungry:



I observed that Piolín, as every baby, used to sleep or rest a lot when there was no activity around. But when he felt my presence, he always opened his big mouth, asking for food. He was striving for life, and did not give up at all. Bird parents must be very hard workers, as these babies request a huge amount of food! But when Piolín's tummy was full, our friend simply started resting again.

There was a difference between resting alone and resting with mum: when he felt I was near, he used to tweet. When he felt he was alone, he used to keep quiet. Probably this is so, because he has to tell his parents in some way he is alive and OK. Otherwise, the parents could think he died, and so they would start worrying about the other babies.

But if you are a baby bird, and you are alone, you will get more chances to survive if you keep quiet, or otherwise you could be discovered by an undesired predator.

The experiment

Well, that was what I thought, but... was it really so? Or was just my imagination? After all this is a blog about experiments, no matter how simple they are. And every experiment needs an idea to check, predictions (we just guess something from our idea), measurements (to check if they fit what we predicted) and conclusions.

So, the idea to check here is:

A baby sparrow will tweet more often if he feels his parents' presence

The prediction is simple: if I touch Piolín, he will tweet more times per minute than if I don't. So, I started counting how many tweets he did per minute. In the next video you can see a sample:



Even if I was covering him with my hand, I was not pressing at all. I was just touching him.

And here are the measurements:

• On my hand (covering him) > 38, 42 and 54 tweets per minute in three different moments


• On my open hand (not covering him) > 18 tweets per minute


• Touching him directly, while being in his nest > 65 tweets per minute


• Touching him through some cotton in his nest > 63 tweets per minute

• In his nest, not touching him > 0 tweets per minute


• In his nest, not touching him, but with my hand near him (so he could perhaps feel my warmth) > 0 tweets per minute


• In his nest, covered with cotton, but not touching him > 0 tweets per minute


• In his nest, covered with cotton, two minutes after being touched by me > 26 tweets per minute

Every measurement has been made leaving enough time in between, so each time I started from "zero tweets per minute". This is important, as we will see soon that Piolín kept tweeting for a few minutes each time he had been touched.

And the conclusion: it is obvious that there is a big difference in his message if he feels the mild pressure of my fingers/hand than otherwise. By tweeting, he is trying to express something to his parents, probably just to let them know he is OK.

As I said, Piolín used to keep tweeting for a while after he was left alone (about two or three minutes). Here you can see him alone in the nest we made for him, a few minutes after the previous video (where he was in my hand):



What about Piolín?

Very unfortunately, this story has a very sad ending. He did not make it. The day later, while we thought he was sleeping, he started choking on his own vomit, and when we arrived it was too late. Everything happened in just five minutes. We felt really unhappy about this, but at least we gave him a chance and it has been a very enriching experience for us (even if it has been really exhausting, as they need to be fed every 20-45 minutes).


Piolín sleeping

However, if you ever find a baby sparrow, don't be discouraged, as you have many chances to help him become an adult, healthy sparrow. If you are in this situation, you can check these videos:

Video about a baby sparrow, with happy ending
Video about how to feed a baby sparrow
Video about how to make home made baby bird food
Eight baby sparrows asking for food!

Baby bird communication (I)

Last month my sister brought a baby duck to my mother. Its name is Patxi (pronounced [patʃi]), and apparently what he (or she, but I will say he until we know what he/she is) loves most in this world is... tomatoes.

He probably thinks he is a human, as he has always been among humans. So, when there are no humans around he starts feeling scared of this dangerous world, and asks for help. In his language "help me, I am alone!" is translated into a siren-like shrill sound, while "I am OK and happy with you" is a normal ducky chirp.

This gave the idea to check for this experiment:

If a baby duck (grown up among humans) is left alone, he starts making a siren-like shrill sound to ask for help

Apparently this is quite simple. As any experiment should have at least some objective measurements, I timed the siren-like sound.

You can see the result in the following video.



I don't mention timing in the video, but you can see easily with a stopwatch that Patxi used his siren-like sound for about 24 seconds in four sessions.

Then I found something interesting: there is an exception to this. When Patxi is home, he does not feel scared of being alone (for him home means the plastic blue box). Even if I leave, he just calls mummy for a few seconds (about six seconds) and then he stops and continues doing whatever he was doing before (usually just chirping, pecking at things or... ehm... leaving his droppings around). Everything is explained in the second video:



As I show at the end of the video, funnily enough both places are in close proximity to each other.

So, by way of conclusion, we can make the following diagram:



I repeated the experiment several times, and many times there was not even "panic interval" when I left Patxi alone at home.

There is another experiment about baby bird communication I made. But that will be for the next entry.

Eating uncooked lentils (postanalysis considerations)

If you think the previous experiment about eating uncooked lentils lacks of an essential condition every real experiment (either important or unsignificant) should have... In all likelihood you are right!

Everybody who agrees with the previous statement is invited to leave a comment in this entry telling us what was wrong in that experiment and why should it be indisputably repeated to be accepted by the very thorough referees of this blog (oh, yes, did you think anybody can write here? This blog is so tough that only experiments done by the referee himself are accepted!).

Eating uncooked lentils

I made an interesting experiment last year, but I never spoke about it. There is a lot of people who enjoy eating sprouts of different seeds. I was used to see in the local store soya bean sprouts and I sometimes ate them.

OK, but eating once more soya bean sprouts wouldn't be quite an interesnting experiment. I needed something different. So, let us try lentil sprouts. Huh? Am I forgetting something? Oh, yes... the hypothesis! A real experiment is just a way to check a first idea. Otherwise it is... OK, simply, it is something else. So here is the initial idea:

Lentils are edible without cooking.

Yes, lentils are rather hard if you don't prepare them. No good for teeth, for sure. So, I thought first I needed several days to grow them.

I remember I saw a friend who was growing sprouts: he used some kind of sieve in some kind of tupperware with water. This way the seeds kept the humidity and had space to grow at the same time.

I couldn't find something similar, but I had an idea: I could just use a clean towel on a pan. For some stupid reason I thought this was a wonderful idea. Don't do it, I will tell you later why...

Here is the set-up:



This was the aspect the lentils had on Day 0 (which was 3rd August 2008, at 9 pm):


Day 0; 9 pm

OK, nothing surprising for the moment. Everybody has seen raw lentils. On Day 1 I didn't take photos, so we will skip this, and we go straight to Day 2 (5th August):


Day 2; 5 pm

Here you can see the first white roots. They are alive! Nothing surprising: if 99.99% of the population has ever seen a raw lentil, probably 99.9% has already seen a growing lentil (leave a comment if I am wrong). On Day 3 (6th August) the experiment proceeded well:


Day 3; 5 pm

I remember I tried one of the lentils, and it was quite hard. Not edible yet. I was surprised, I thought it would take less time. "Give it another day" I thought. And this way we get into Day 4 (yeah, 7th August...)


Day 4; 5 pm

We can see on Day 4, for the first time, that most of the seeds have green tiny leaves. Photosynthesis is on! But seeds are still very hard. Hm... Am I going to need to cook this after all? So I left them there one more day.

This was what I got on Day 5 (8th August, just in case you lost count):


Day 5; 6 pm

At this point the plants were about ten times longer than the original seed diameter. You can check this wiht the first image of this post, as it has been taken on Day 5. Seeds were much softer. Here is what I got on Day 6 (ehm... 9th August):


Day 6; lunchtime

A wonderful lentil salad! I added some onions, tomatoes, parsley and dressed them with olive oil and salt. Probably pepper as well.

My impression: lentils are perfectly edible even when you don't cook them, but you will need around six days to be able to enjoy them. They have a strange texture in the mouth, I don't know how to explain it. It is as if they were kind of grainy when you chew them. But I think I could perfectly get used to this. And in some ways I think lentil salads are much better than lettuce salads.

Now... you remember when I said it was a stupid idea using a towel for this experiment? OK, here is why: I didn't know the roots of the plants were going to root so deep into the towel. They were amazingly strong! It was impossible to remove them completely, so some roots stayed in the towel. Additionally, the pan I used, after almost one week in contact with a humid towel, started to go rusty! This way, the towel blackened.

One less towel...

So if you want to reproduce this experiment... use a different system!

After this experiment I read a bit about lentil sprouts, and I discovered some wonderful properties they have. For example, they have a 25% of protein, lots of vitamins and three times more fibre than cooked (as they produce fibre while growing). Maybe I should restart growing them.


Enjoy your meal!

The International Space Station with the naked eye

Some years ago, translating into Spanish the book Out of the Blue, by John Naylor, I learned it was possible seeing artificial satellites (see section 13.3 of the book). Well, actually I didn't learn it, I just believed in what the author said. It's funny, but the author was encouraging us all the time to be less bookish and try to learn things through our own experience, but I didn't try this.

I remembered this last month. I found in 43things.com (here to be precise) two guys who said they had seen the International Space Station (presumably with the naked eye). In fact, the ISS is big enough to be easily observable with the naked eye, and apparently it can be seen with no problems from most of the places of the world (excluding paces above 63º N or below 63º S).

So I had probably the most essential ingredient an experiment needs: an idea to test. This idea was:

I can see the International Space Station with the naked eye.

For this experiment I used two tools:

• the website heavens-above.com (a website with loads of information about when and where you can see the ISS, including the path of it across the sky or the apparent magnitude of it).


• the alarm of my mobile phone.

I had some chances to see the ISS after sunset in late January, but some days I left work late, or I forgot about it, and other days it was cloudy or rainy. So I missed this period.

Recently it started being observable again before dawn (according to the mentioned website). I saw that 18th February was promising, as the ISS was going to have an apparent magnitude of -2.3, making it brighter than any other star in the sky (and even brighter than Saturn and Mercury, which were in the sky at that time). According to heavens-above.com, from El Goloso (I put El Goloso as my place, which is near, as Tres Cantos was not in their database of places) it could be seen from 7:08:37 (time at which the ISS was going to leave Earth's shadow) to 7:16:20 (time at which it would disappear behind the horizon).

I set the mobile alarm for 6:58, and when I got up, I saw the sky was clear, as I could see stars very well. I checked which window was providing a better sight. Apparently it was going to pass near Vega, and then through Cygnus, which were easily identifiable from my bedroom window even with the city light pollution. So I calculated I would see it from my window at 7:12.

Funnily enough, exactly at that time, a little bit under Cygnus, I saw a bright double light crossing the sky. But... wait! It had a double trail! I didn't think the ISS would leave any trails at all, as it is outside the atmosphere and it is not burning so much fuel. I was puzzled for a few seconds. After that I checked the sky near Vega, and I saw the real ISS (so the other object was with no doubt an aircraft).

It crossed the sky following exactly the path the website told me. I used also binoculars, and this is a drawing of what I saw more or less:


It moved at a constant pace, through Cygnus. After that, it continued to the horizon, quickly losing brightness and becoming smaller and smaller. At 7:16 it hid behind some buildings.

It has been a great experience!

Tomatoes with no light

We are growing tomatoes for the first time. It is very interesting looking after new living beings at home. Our salads are also a bit more interesting now. For some reason I started wondering what would happen if tomatoes get no light. Are they red because of light? What about the leaves? Checking the local library or wikipedia you can get fast answers. I am not an expert, but after all I am not completely green in this subject, I know the basic things everybody can remember from school: chlorophyll, chloroplasts and so on.

But there is something that the local library, wikipedia and my teenager memories cannot fulfill: they cannot give me my own answer.

And that's why I made this experiment.

As with every experiment, I need an...

Idea to test: Plants are green because of sunlight, so if they get no light they will not be green, as they cannot photosynthesise. That's what we expect. And what about tomatoes? Somebody told me they are red colour because of carotenoids, the organic pigments produced by plants along with chlorophyll. Carotenoids, especially lycopene, absorb blue light and reflect red light.

So, the idea is: if a leaf cannot photosynthesise, it will be any colour but green, and if the tomato cannot photosynthesise, it will be any colour but red or green. Maybe yellow, maybe brown, but not the usual colour. Here is the specific idea I want to test:

If I wrap a tomato leaf up in aluminium foil, after some days it will start losing it natural green colour, and if I do the same to a tomato it will also lose its red or green colour.

The material: Aluminium kitchen foil, bought in the local supermarket, and a tomato plant.

The set-up and measures: Well, I have not a "green-meter", so to compare the differences I just took photos from time to time. The experiment lasted 39 days.

3rd August

The experiment starts!

Unfortunately I didn't take a photo of the tomato leaf before wrapping it up, so you will have to believe me when I say that it was pretty the same than the other non-wrapped leaves you can see in this photo:



For the tomato, I chose one from a branch with four tomatoes, so I could compare better the differences between the selected tomato and its "brothers". Here is how this branch looked like in a previous photo I took on 28th July:



Here is a closer view:



And here is the same branch with the wrapped tomato on 3rd August:



8th August

This is how the leaf looked like some days later:



Its end was a bit damp (which probably was due to the fact that it had been in a closed space), but it looked as green as it was the first day.

Unfortunately I didn't take a photo of the unwrapped tomato, but there were not many changes as well: the four tomatoes were very green, and probably not very tasty.

11th August

The leaf looked as green as the other leaves, but touching it I could feel it was weaker, softer than the rest.



The tomato was a bit smaller than the other three (which could be casual), but no sensible changes were observed:



30th August

Here is how the leaf looked like almost four weeks later (obviously, the only moment when I removed the aluminium foil was while taking the photos):



there are no major changes in colour, but the wrapping is centainly affecting the leaf's health. Now the end of the leaf looks like burned, and perhaps a bit more yellowish.

However, the tomato looks perfect (it's the left one in this photo):



Note that one of the four brothers (the one at the back which can barely be seen) is getting redder and redder!

11th September

Last day. The "leaf disease" is in an advanced state and the "patient" will probably not last very long:



The colour is still green, maybe a bit more yellowish near the edge at the end.

Two of the four tomatoes were eaten in a great salad, but we left one brother to compare with the selected tomato:



They are both fairly red and start looking very tasty, but wait... what's at the bottom of our friend? Maybe we can see better from below:



oh, it looked so healthy, but actually was starting to rot away! More or less the same that happened to the leaf. But the colour is exactly the same as its brother's colour.

Conclusion: During these 39 days I have not seen too many changes in colour, which has been surprising. However, the aluminium foil has obviously affected both the leaf and the tomato, as they started to rot away. I can think of three reasons for this:

• lack of light weakened them, making them more vulnerable,
  
• closed space kept them wet, which contributed to the rotting process,
  
• the tight contact with aluminium, which is not quite natural for a tomato plant, could have triggered the rotting process.
  

Anyhow, colour didn't change discontinuously at the edge of the aluminium foil, as I thought. Why? Are chloroplasts distributed evenly through all the plant, regardless of the place where they photosynthesise? Maybe it can be interesting making another experiment covering the whole plant, to check how different is its colour from the colour of another plant with good sunlight. But for the moment... let's eat a salad!

Games about the Olympic Games

The 2008 Olympic Games are over, and as expected, China has been the country with most gold medals (but curiously, not the country with most medals!). It might be easy to think that statistically a country with over 1.3 billion people has to have at least some good athlets. There seems to be a not surprising correlation between population and results in the Olympics.

The idea: Good, we have the following belief

There is a strong correlation between the population of a country and the results in the Olympic Games

is that something real, or am I fooling myself?

The material: To do this, I didn't need too many things, as I just took data from wikipedia about the medal count and the population.

The set-up: I calculated the following score for each country:

• gold medal = 1
  
• silver medal = 0.5
  
• bronze medal = 0.25
  

and then I got the list you can find at the end of this post. If we order it by total score (I preferred to order it by score/million people, which is more interesting), the list is not quite different from the original medal count, which is ordered by gold medals, then by silver medals, and then by bronze medals. China is still the number one, but United States is a bit nearer.

Ordered by score/million people, we see that... Jamaica, with a score of almost 3 is leading! (It pays having good short distance runers) Then we have the Bahamas, Iceland... The first big country is Australia (which has been amazing about every sport that is related with water).

I have made the following graph showing the relation between my score and the population:



Clicking on the graph you will see the original size, with the name of each country for each cross. But anyway, you will not see too much, as most of the countries are piled up in one corner. Maybe you can see better in logarithmic scale:



I have added the linear fit calculated using Origin. If we have to trust in the Origin, the correlation coeficient is 0.398.

This means that there is a weak, direct correlation between results and population. It is what I expected... but not as much as I expected (personally I thought it would have been a correlation around 0.8).

Why is the correlation so weak? I think one reason could be the fact that in most cases the medals are just one or two, which is not enough to get good statistical results, as fluctuations can change a lot the score, so we have "good" information just about countries that have at least ~10 medals

The other reason could be the fact that population is not the only important factor here. For example, the economy of a country is also important.

It's late enough now, so I will not make a graph showing the score versus the GDP, but if somebody does, I am interested in seeing the result.But just let's make one more graph. I choose only the countries that have got at least ten medals (not because they are more important than the other, but because the statistics are more accurate), and here is the result:



Interesting, isn't it? The bigger the country, the smaller the score. And here the correlation is a rather strong: -0.807. It seems that a single athlete has more chances to win a medal if he is from a smaller country (probably because there is less internal competition to qualify for the Olympics). That makes me think of the half-Togolese half-French kayaker Benjamin Boukpeti, who preferred to defend the Togolese flag instead of the French one, because it was much more difficult to qualify as part of the French team. After all, he got the first medal for Togo!

Conclusion: So it comes out that

1) big countries have more chances to get medals, because they have more people to select and train, but the correlation is much weaker than expected.
2) for the individual, being in a big country can be counterproductive, probably because there is more internal competition to qualify for the Olympics.

Hm...

OK, here is the promised table (as I am European, I wanted to see what are the results of my "bigger country", so I have added the data for the European Union below).

For space reasons, the medals are shown in format gold/silver/bronze=total. Population is in millions. And remember, score = #gold + #silver/2 + #bronze/4. (I have made also a map that you can see on Wikipedia).

Country	Pop.	Medals	Score	Score/pop.
1. Jamaica	2.714	6/3/2=11	8	2.948
2. Bahamas	0.331	0/1/1=2	0.75	2.266
3. Iceland	0.316	0/1/0=1	0.5	1.582
4. Bahrain	0.76	1/0/0=1	1	1.316
5. Norway	4.778	3/5/2=10	6	1.256
6. Slovenia	2.029	1/2/2=5	2.5	1.232
7. Australia	21.394	14/15/17=46	25.75	1.204
8. Mongolia	2.629	2/2/0=4	3	1.141
9. Estonia	1.341	1/1/0=2	1.5	1.119
10. New Zealand	4.274	3/1/5=9	4.75	1.111
11. Belarus	9.69	4/5/10=19	9	0.929
12. Cuba	11.268	2/11/11=24	10.25	0.91
13. Georgia	4.395	3/0/3=6	3.75	0.853
14. Slovakia	5.402	3/2/1=6	4.25	0.787
15. Latvia	2.268	1/1/1=3	1.75	0.772
16. Trinidad and Tobago	1.333	0/2/0=2	1	0.75
17. Denmark	5.489	2/2/3=7	3.75	0.683
18. Netherlands	16.445	7/5/4=16	10.5	0.638
19. Hungary	10.043	3/5/2=10	6	0.597
20. Lithuania	3.361	0/2/3=5	1.75	0.521
21. Armenia	3.002	0/0/6=6	1.5	0.5
22. Great Britain	60.587	19/13/15=47	29.25	0.483
23. Czech Republic	10.403	3/3/0=6	4.5	0.433
24. South Korea	48.224	13/10/8=31	20	0.415
25. Switzerland	7.637	2/0/4=6	3	0.393
26. Croatia	4.555	0/2/3=5	1.75	0.384
27. Finland	5.317	1/1/2=4	2	0.376
28. Kazakhstan	15.422	2/4/7=13	5.75	0.373
29. Azerbaijan	8.467	1/2/4=7	3	0.354
-. (European Union)	498.248	87/101/92=280	160.5	0.322
30. Germany	82.218	16/10/15=41	24.75	0.301
31. Panama	3.343	1/0/0=1	1	0.299
32. France	64.473	7/16/17=40	19.25	0.299
33. Bulgaria	7.64	1/1/3=5	2.25	0.295
34. Ukraine	46.059	7/5/15=27	13.25	0.288
35. Russia	141.889	23/21/28=72	40.5	0.285
36. Canada	33.347	3/9/6=18	9	0.27
37. Italy	59.619	8/10/10=28	15.5	0.26
38. Romania	21.438	4/1/3=8	5.25	0.245
39. Sweden	9.215	0/4/1=5	2.25	0.244
40. Spain	46.063	5/10/3=18	10.75	0.233
41. Ireland	4.339	0/1/2=3	1	0.23
42. Kenya	37.538	5/5/4=14	8.5	0.226
43. United States	304.875	36/38/36=110	64	0.21
44. Mauritius	1.262	0/0/1=1	0.25	0.198
45. Zimbabwe	13.349	1/3/0=4	2.5	0.187
46. Poland	38.116	3/6/1=10	6.25	0.164
47. Dominican Republic	9.76	1/1/0=2	1.5	0.154
48. Belgium	10.585	1/1/0=2	1.5	0.142
49. Portugal	10.623	1/1/0=2	1.5	0.141
50. Kyrgyzstan	5.317	0/1/1=2	0.75	0.141
51. North Korea	23.79	2/1/3=6	3.25	0.137
52. Greece	11.147	0/2/2=4	1.5	0.135
53. Austria	8.341	0/1/2=3	1	0.12
54. Japan	127.69	9/6/10=25	14.5	0.114
55. Tajikistan	6.736	0/1/1=2	0.75	0.111
56. Singapore	4.589	0/1/0=1	0.5	0.109
57. Serbia	9.858	0/1/2=3	1	0.101
58. Uzbekistan	27.372	1/2/3=6	2.75	0.1
59. Tunisia	10.327	1/0/0=1	1	0.097
60. Argentina	40.302	2/0/4=6	3	0.074
61. Moldova	3.794	0/0/1=1	0.25	0.066
62. Ethiopia	79.221	4/1/2=7	5	0.063
63. Cameroon	18.549	1/0/0=1	1	0.054
64. Turkey	70.586	1/4/3=8	3.75	0.053
65. China	1325.544	51/21/28=100	68.5	0.052
66. Thailand	63.038	2/2/0=4	3	0.048
67. Chinese Taipei	22.99	0/0/4=4	1	0.043
68. Togo	6.585	0/0/1=1	0.25	0.038
69. Ecuador	13.341	0/1/0=1	0.5	0.037
70. Brazil	187.474	3/4/8=15	7	0.037
71. Israel	7.303	0/0/1=1	0.25	0.034
72. Chile	16.763	0/1/0=1	0.5	0.03
73. Morocco	31.224	0/1/1=2	0.75	0.024
74. Algeria	33.858	0/1/1=2	0.75	0.022
75. Mexico	106.683	2/0/1=3	2.25	0.021
76. Malaysia	27.17	0/1/0=1	0.5	0.018
77. Iran	70.496	1/0/1=2	1.25	0.018
78. Colombia	44.513	0/1/1=2	0.75	0.017
79. Sudan	38.56	0/1/0=1	0.5	0.013
80. South Africa	47.851	0/1/0=1	0.5	0.01
81. Indonesia	231.627	1/1/3=5	2.25	0.01
82. Afghanistan	27.145	0/0/1=1	0.25	0.009
83. Venezuela	27.954	0/0/1=1	0.25	0.009
84. Nigeria	148.093	0/1/3=4	1.25	0.008
85. Vietnam	87.375	0/1/0=1	0.5	0.006
86. Egypt	75.201	0/0/1=1	0.25	0.003
87. India	1136.75	1/0/2=3	1.5	0.001

PS: I have seen that a wikipedian has made some interesting maps showing:

• capita per gold medal.
  
• capita per silver medal.
  
• capita per bronze medal.
  
• capita per medal (in general).