Seahorses are not just beautiful creatures that fascinate young and old; they are flagship species,…
Note: I was unable to get this post to publish with images, so I’ve proceeded to publish it, image free.
As a dog lover, I am always trying to further my understanding of my K9 companions. Why wouldn’t I, dogs literally surround me, at all times. My goldendoodle Paws (named by my 3.5-year-old daughter) heads off to my office with me, and my Siberian Husky Niko keeps watch over the house, and attaches to my hip, as soon as I come home. Recently, I enrolled in a course offered by Duke University online, that explores dog evolution, cognition and intelligence. It seems as though scientists who study animal cognition, have determined that dogs are much smarter than we previously thought. Some scientists are working to determine how dogs, whom are descended from wolves, made the evolutionary jump to being man’s best friend, and planet Earth’s second most successful mammalian predator.
During the course, I was introduced to dognition. Dognition is a website and database, which is used by animal cognition scientists, to gauge dog intelligence, by setting up a series of experiments (games) for dog owners to conduct with their K9 companions. Some of the experiment games are as simple as putting a treat in a seal container, and gauging your dog’s reaction, on up to complex games that measure a dog’s capacity for memory and empathy. Working through some of the course material, I was awestruck by how a similar online system would be of benefit to both aquarists, and the marine livestock industry, in finding answers to a host of questions about aquaria.
What about an experiment where aquarists log the feeding reaction of their fish, to a host of different prepared fare? Or an experiment that has aquarists measuring the time it takes specific species to adjust (or not adjust) to captive life. Aquarium scientists could decide on 10 factors to measure in both aquarium fish and corals. Perhaps: ease of introduction to captive life, feeding response to commonly available fare, reaction and bond to other fish species, or even reaction and bond to the aquarist. For corals we could measure: vitality under specific lighting profiles (detailed right down to lighting brands and specific settings), requirement for food beyond photosynthesis, aggression toward other coral (or invertebrate) species. In reality, the possibilities for citizen scientist study are endless, and to make such a program work, it would be vital that scientists plan the categories to be measured, setting specific ground rules for conducting experiments.
What we have now:
Sadly, much of the information available online, for aquarists contemplating a new fish or coral species, or even a new tank methodology, is anecdotal. It’s forum chatter, which smashes together a host of scenarios, without any real controls or experiments. This chatter is often the ramblings of novice or intermediate aquarists, making basic observations and generating assumption based hypothesis. I’m not saying forum chatter is a worthless way to gather information, but pointing out that it has no standard: as the good is mixed right up with the bad. It’s sort of like filling a blender with fresh fruits and vegetables, fish oil and niacin: with the hopes of creating a healthy shake. At the end, just before you press the blend button, you pour in a tub of Breyers ice-cream and a few packs of Reese’s peanut butter cups. Yes, there is a lot of good stuff in there, but it’s laced with plenty of bad. And just like misinformation on an aquarium forum, sometimes the bad is a bit easier to swallow, than the good.
A system like dognition for aquarists (and aquarium scientists) wouldn’t be an open discussion, but a series of controlled experiments, which aquarists could perform at home. To reward the aquarist, at the end of the experiments, a program which analyzes the data could compute an aquarium profile, which details an individual’s tank, and makes suggestions and predictions, based on a pool of data and reported outcomes.
There is an entire other layer, this type of aquatic, collective database could peel back. In addition to collecting data about how animals respond to the captive environment, it could also collect data about the types of species aquarists are keeping, and where they came from. This could be as detailed as right down to the specific vendor, and oceanic region of origin. Using this, aquarists could report back the survival rate, health and vitality and longevity of specific species, from specific vendors and parts of the world. It could begin to demystify questions about what regions and vendors provide healthy and long-lived species, or what specific species simply have an utterly dismal rate of survival in home aquariums.
Collecting, organizing and analyzing this type of data, straight from private aquarists, could allow marine scientists and researchers to provide aquarists with real, data based answers, about some of their most pressing questions. How will this species behave in my tank, should I seek out a species from this region of the world, or will this species thrive under the conditions my schedule allows me to provide. The framework of the entire program, would be experiments constructed by aquarium scientists, with controls simple enough that any home aquarist could conduct them.
Some potential hurdles:
The first and foremost hurdle, would be ensuring that the data coming back was valid. This is why it’s important that the experiments are easy enough for anyone to perform, with controls that aren’t too rigorous or require expensive scientific equipment. Another would be vetting subscribers, in an attempt to ensure that the data inputted is correct and honest. A simple approach would be charging a subscription fee for the service, which would potentially weed out people simply wanting to play around and pass off sub-par data. The subscription could pay for the aid of aquarium scientists, and the infrastructure of the website.
Perhaps though, the biggest hurdle may be, the reaction of the marine livestock industry. They may not be keen on a data pool, which outputs information such as, the survival rate of wrasse species X, collected around island Y in Indonesia, is less than 35%. We see attempts to suppress negative information, shared on forums, all the time. Often forums will remove, or edit, posts that suggest one of their sponsors or vendors, is providing something that may not yield the advertised results. In order for a program as described above to work, it would have generate its own steam, meaning that it couldn’t rely on industry level sponsors, whom then have a vested interest (and level of control) over the data collected and shared. If industry level sponsors were required, there would need to be a contract between the sponsor and the website, that ensures they have no control over content recorded, or content shared. In this regard, a sponsor could be subconsciously praised for taking part, as they are openly endorsing a candid way of collecting data from unbiased home aquarists, and making that data available to the public, in hopes of increasing overall marine aquaria success rates.
No small task:
Implementing the aquatic version of dognition, would be no small task. For example, dognition has a team of over 6 phd level scientists and animal cognition experts, that both generate experiments and analyze data. Many of these scientists are working through university grants, specifically out of animal and K9 cognition departments. An aquatics system would need the same (if not a higher) degree of commitment from the scientific community. With today’s technology, such a system could be a reality, putting the power of data into the hands of everyday aquarists, and making others active citizen scientists. Throughout a host of scientific fields, citizen scientists are making startling discoveries. Just look at the discoveries brought forth via the Planetary Society of citizen scientists. The reward for the scientific community, is having a huge pool of data at their fingertips, which can aid them in solving complex problems, currently prohibited by a limited lack of data.