I’m BACK everyone!! Major blogger hiatus, not because of a lack of motivation (hah, surprisingly), but because of all the adventures I’ve been going on! Woo!! Suffice to say, I’m taking that New Year’s Resolution seriously, and enjoying every moment of it 🙂 I’ll be posting pictures soon from New York City, Omaha, our camping trip, and some of my local LA wanderings.
But for nowwww, I would like to share this awesome article about shark tooth weapons! My advisor at Columbia University, Joshua Drew, published this paper in PLOS ONE yesterday and it’s generated quite a bit of media buzz, which is always awesome for scientists and even better for the marine conservation effort as a whole! During his postdoc, Dr. Drew poked around these collections of shark tooth weapons from the Gilbert Islands, housed at Chicago’s Field Museum of Natural History, and made some interesting discoveries. By identifying the species of shark used for these 18th century weapons, he was able to essentially look into the past, getting a glimpse of what reef biodiversity looked like centuries ago – something he calls “shadow diversity”. After identifying all shark species whose teeth were used for the formidable weapons, Dr. Drew ultimately discovered that there are at least two species of sharks that have disappeared from the Gilbert Islands within the last two centuries! This is a great study showing how baselines can shift over time, leading to a skewed impression of what the reefs “should” look like in their pristine state. One of the major requirements of marine conservation success is determining a baseline, so that future efforts can work towards preserving/restoring that level of biodiversity – but if the baseline data only comes from 40 or 50 years back, it may not be the best representation. Utilizing museum collections is a superb way of trying to form a more accurate baseline for a particular area, and this kind of study (while also super cool in that it looks at badass weapons!) is the perfect example of how we can come to better conclusions using non-traditional forms of data.