鸭绿江后记 Notes after Yalujiang Estuary Shorebird Investigation
Resights of ringed/flagged birds 2017 spring (个人旗标记录汇总)
To find out what color-combined flags represent what location information, browse the protocol.
每个月两次大潮，会有大批游客和观鸟爱好者前往近丹东港的2号点观鸟。那时潮水会赶着上万只鸻鹬类（主要是斑尾塍鹬（Limosa lapponica）和大滨鹬（Calidris tenuirostris））到地势相对较高的东西两侧。梅爷爷多次建议保护区，在那个时候做些科普、宣传，未果。当然不会有结果，因为保护区不希望外界知道这里鸟的数量下降了，最好什么也不要知道。某一次大潮数鸟，我们偶遇保护区的副局长老王，跟他聊了很久。他给我们讲述年初他在西澳大利亚与Chris Hassell等人的水鸟环志之旅（新西兰一NGO出资培训保护区职员），讲网捕大杓鹬的不幸溺水，给我们看手机里绚丽的西澳风光。我知道，老王不是坏人，我们都一样，喜欢天高任鸟飞的唯美。想必是评价体系的问题吧，虽然鸟变少并非全部归咎于保护区管理不当（我们也不明原因，揣测丹东港西航道的封堵可能改变了原有沙粒组成），但在那个职位上，人总要为上级负责。我敢说，环保局、林业局、村知书、农民、渔民、利益集团，没有一个是坏人，虽然我还不清楚这一整套错综复杂体制的运作纠葛。
尽其所能让公众知晓情况，理当是生态保护学家的义务，然而这在鸭绿江也受到了挑战。我被告知，当被问到那些鸟吃什么时，切忌说到蛤，因为滩上的白蚬子（Mactra veneriformis）多是渔民放养的，如果渔民知道鸟会吃他们的蚬子，则他们可能会采取极端的手段来维护自己的经济利益，比如下毒。有谁知道滩上的鸟吃蛤？我们科研团队，司机隋师傅，也许帮我们挖泥的刑师傅也知道。我们的终极理想是什么？每个人都知道或可以查阅到生态系统中的每一环。然而现在呢？一道难以跨越的障碍横亘其间……戏剧性的是，僵局在四月中下旬被打破了——4号点滩涂的老板下滩捡起一坨鸟粪，发现是碎砚壳，恍然明白鸟吃了他养的四角蛤蜊（Mactra veneriformis）。老板刚投了800万元的蚬苗（指甲盖大小），全给野鸟当饲料了咋整？于是他开始雇人每天下滩放炮赶走鸟儿（买炮、雇人的开销小于白蚬子被鸟吃掉的损失）。
Current situations of Yalujiang Estuary Wetland National Nature Reserve
Only one bottom line of the reserve: no further reclamation is permitted. Within this bottom line, people can do anything not against the law (including not against “Wildlife Protection Act”).
Since 1980s' reclamation (sea burying -> field), the coast of Town Beijingzi has been pushed toward the sea by 5 km. Now the reserve consists of 2 major parts: agricultural & fishery land. The former is the area which locates over 200 meters off the sea wall. The latter comprises the marine product cultivating ponds inside the sea wall, and the mudflat and waters outside. Fishermen cultivate bivalves (Mactra veneriformis), Thomas snails (Umbonium thomasi), etc. on the intertidal zone, harvest mantis shrimps and fish on the fixed net 2 km off the sea wall, fish on the subtidal zone by fishing vessel.
Coordinating fishermen’s livelihood and international scientific protection, regulation enforcement and supervision, facility maintenance, outreach, etc., all that we expect a nature reserve to operate is next to none in here Yalujiang estuary. On the contrary, the land ownership doesn’t belong to Dandong Environmental Protection Bureau, but to the contractors who are in charge of each village’s sea product cultivation respectively. In order to video birds and dig mud, we scientists ought to inform both the reserve and the contractors. The critical process in negotiating with the contractors of 5 villages respectively is to send each one a carton of cigarettes, thus guaranteeing this season’s passport. After all, it’s their land for money raising. We would have no way if they simply say no. Look, when mud snails (wild) prospered in late May, they no longer let us go onto the mudflat in case we stepped onto the mud snails so caused damage. Then we had to talk with the village head until they rendered us the permission. In return, we gave them delicate photos for the town’s image construction.
To judge the development level of an area, I like to see its popularity among the public. The eco-protection of Yalujiang estuary wetland hasn’t reached folk. Most people have no idea of the birds, their habit, where they’re from and to. Fishermen readily throw garbage on mudflat. Each time altering sea products, farmers remove the remaining creatures in the pond by chemical poison. An afternoon, a young fisherwoman who lived on the embankment didn’t realize so large-scaled flock and there were more than one species until she used my telescope to watch the shorebirds on the mudflat and in the artificial channel. Birds' plumage mixed in vast mudflat, not surprising that average people know little without lens and awareness.
During the spring tide twice every month, groups of tourists and bird lovers come to Site 2 nearby Dandong Port, when the tide drives dozens of thousands of shorebirds (mainly bar-tailed godwits (Limosa lapponica) and great knots (Calidris tenuirostris)) to the higher east and west. Grandpa Mel has more than once suggested to the reserve that they hand out leaflets about the birds, but achieved no reaction. Of course they won’t take action because the reserve don’t expect the outside to know about the decline of birds in here. Knowing nothing is the best. Once a time we bumped into Mr. Wang the vice-president of the reserve when counting birds, and had a long chat with him. He told us about his bird banding trip with a group led by Chris Hassell in Western Australia early this year (a New Zealand NGO contributes to training the reserve’s staff), storying the far eastern curlew (Numenius madagascariensis) drowned, showing us the pictures in his cell phone of splendid WA scenery… I know Mr. Wang isn’t a bad man. We are same, both yearning for the aesthetic bird flying sky. Supposed to be the problem of the evaluation system, isn’t it? The decrease of birds shouldn’t be totally owing to the reserve’s improper management (we don’t why either, perhaps the block of Dandong Port west channel causes the grain composition to change), but on that position, people have to be responsible for higher-ups, and have to watch their own official career. I bet no one is a bad guy, not the environmental protection bureau, forestry bureau, village secretary, peasants, fishermen, interest groups, although I’m unclear of the complex system’s operation.
It should be conservationists' duty to inform the public as much as possible, but this guide line faces a challenge in Yalu River. I was told not to say mactra when asked about the birds' menu. Because the fishermen would possibly take extreme means such as poisoning birds to protect their properties, the mactra, semi-cultivated. Who knows those birds eat mactra? We scientific team, driver Mr. Sui, maybe Mr. Xing too who helps benthos survey. What’s our ultimate dream? Everyone knows or can refers to each element in an ecosystem. But what about now? An unconquerable barrier lies between… Dramatically, the deadlock was broken in mid-late April - the contractor of Site 4 went onto his mudflat and picked up a bird poop… “Isn’t it my white mactra?” shocked him finally understood that the birds had been eating his sea products. He just had planted CNY 8 million mactra juveniles (about a nail’s size), couldn’t be devoured by birds! So he employed people to drive away birds by fireworks since then everyday (the fee is lower than the loss without action).
I cherish a system in which it’s taken into consideration and solved all potential problems in design. If any bug occurs during operation, then it gonna solve it once and for all and spare no effort to maintain. I ask when China can build such a national park system like North American and Oceania do. Jason says 50 years. Grandpa Mel and I have no idea.
- 真高兴，机器人（电脑）绝对干不了我现在做的事情。 重复……
Potential techniques that push ecological researches
We video foraging Charadriiformes, then quantify how many times they peck the mud, eat, poop, vomit, conflict during a period, plus information on food types. These data need to be recorded by manually watching videos and striking keys. If the hundreds of 5-minute videos are replayed in 0.2 speed, then the time for analysis will require at least 5 times original total length – 200 hours! If the mass, boring working load could be accomplished by a machine, then we’d be far relieved.
A cover article on “Nature” illustrated analyzing a flock’s trace in a video to find who the head bird is. So artificial intelligence (AI) already has been applied on ethology. Analysis on videos of foraging shorebirds, I think, should also be switched from human to machine. Extracting frame by frame, computers can note each time a beak pecks the mud, and identify each mollusk or worm eaten. The computer’s performance is stable, free of mood, while a human types when staring at the screen, mistakes inescapable. We also scan the flock for their behaviors, learning the proportion of foraging ones at different time, which can also be analyzed by machine. A computer can identify in what posture the birds are foraging or resting, and get the number and species.
What is artificial intelligence / neural networks? In common words, we feed a machine 10 photos of shoes and tell it they are shoes. Then we show the machine the 11th shoes, it will tell us that’s shoes. Someone may ask why we let the machine identify what a man with even a little common knowledge can identify. So what if when an object is too ambiguous to distinguish, for example, telling whether a sound track is a bird’s song or a man’s whistle (or listen to a poem recitation and tell whether it’s a man’s sound or a machine’s), or when the information is too multiple to immediately calculate the result, for example, in which part of the library a newly-cataloged book should be assigned, then it’ll be reasonable to have a machine do it. On the just-ended ImageNet contest, the algorithm hit 97.3% accuracy in object discriminating, beating humans’ 94.9%. I believe, after accumulating enough data, one day iNaturalist can automatically identify species according to photos, off the unnecessary manual ID. When machines come better than humans in some aspect, they can replace humans in that role. But pessimistically speaking, how soon this AI tech can be universally applied on ecological researches, or analyzing bird foraging videos, it’s difficult to predict at present. The reason is the lack of economic drive. To make an AI product good, a great deal of resources should be devoted to training and debugging during the development phase. Samples of only 10 pairs of shoes are far from ideal. What Google and Amazon make use of are big data from global users. Efficiency and quality of sample training depend on the GPUs on cloud and the datasets. Even resources of labs of graduate schools can’t rival that of big corporations, so the former’s developing efficiency is supposed beaten by decades by latter’s. Eco-protection isn’t an industry with a promising market and big corporations’ support as autopilot and smart home. Even autopilot and smart home haven’t been as developed and public as imagined, so from now, it seems still a faraway dream to free labors by revolving techs. Honestly I know nothing about neural networks at all. All that I write in here is heard and summarized from the IT engineers around, so there must be a lot of mistakes. Nonprofessional please don’t lay superstition. Professional please point any mistakes you find. Thank you!
If we set the obstacle aside and further envisage, then it’s better to have machines manage all fieldwork. Everyday we had at most 4 people going onto the mudflat to shoot birds. We carried a tripod, a telescope and other equipment to search for flocks. After succeeding in getting close, we would do a behavior scan, count birds and video individuals, and write down time, location, etc. If there were Unmanned Aerial Vehicles (UAV), fixed cameras on mudflat and auto-controlled spy cameras united carrying out the task, then UAV would survey birds’ distribution quick, and robots could be set to video targets with the instant information from UAV. The compose of the flock, scale, time, coordinates, etc. would have been detected and quantified by UAV, so the cruise cameras would just need to focus on video taking. If the videos are uploaded to cloud, then the flock’s foraging situations would be analyzed and quantified at once. If a spy camera finds a banded bird, then it would also note it down. The fixed cameras would wait for their prey, and they could be specifically set according to the results of benthos survey. An advantage of auto videoing would be avoiding anthropogenic disturbance on birds’ behavior. Particularly in the late stage when both birds and food lacked, it’d be very hard to get close to a flock, as they would suddenly fly away with no clue. Sometimes the flock saw guys coming, they would forage towards the opposite direction. As for benthos survey, machines could dig and filter the mud, identify worms and live shellfishes, same technique. If machines hadn’t been advanced enough to do benthos survey, then in assistant ways, AI would be expected to optimize routes. Combining environmental factors as landform, tide, wind, etc. and difficulty levels of different types of mud, whether to bypass a fishnet, to cross a channel safe and sound, the computer could schedule the path for benthos survey. When any change occurs on real time circumstances (e.g. wind influences where the water line is), or the surveyor doesn’t follow the scheme or has a change of the plan then needs a new guide, AI should be flexible and resourceful. This calls for an integrated product with a critical function on synchronizing (Google Home?). We went to survey the mudflat where we took videos yesterday, we would need to note down its coordinates (usually the guy took videoed wasn’t the guy dig mud). It’d be embarrassing if the one doing benthos survey forgot to note the coordinates, or found the 49 sampling points haven’t been imported to her/his GPS device from the laptop when the new task came.
Perhaps somebody may ask, “if all the things are assigned to machines, what are we human beings supposed to do?” We definitely always have things to do! From the invention of tools in primitive society to today’s information system, the revolution of technology pushes forward productivity while we humans have been always working. In ecology, the process of research and protection never feels too fast. We don’t know what we’ll do after machines replace us, because our recognition is limited by the lifestyle nowadays. At last may I cite the repeating mode summarized in book “The Inevitable” (Kevin Kelly 2016):
- A robot (computer) isn’t able to do my job.
- Well, it can do a lot, but it can’t do all my job.
- Well, it can do all what I do, but bugs often occur, so I need to handle at that time.
- Well, it never makes a mistake in usual work, but I need to train it to learn new tasks.
- Well, let it do what I did, as that work shouldn’t be done by a human.
- Wow, robots are doing what I did, and my new job isn’t only more fun, but also more well-paying!
- Happy, a robot (computer) definitely isn’t able to do what I’m doing now. [repeating…]