Climate variability on somatic growth
Multi-decadal daily resolved growth increments reveal climate effect on the growth of a highly migratory shark in the North Atlantic by Zhou
Background Substantial progress has been made in identifying large-scale climate effect on somatic growth through the use of bio-chronologies developed from hard parts.
Problem statement However, temporal resolution is a missing dimension in current long term climate effect studies, especially in marine environments. Not being able to examine biological responses to climate variability at a sufficiently high temporal resolution substantially constrains our understanding of climate change effects.
Contribution This study presents a novel modelling approach to combine high temporal resolution with long-term coverage in order to examine the climate effect on the somatic growth of a highly migratory shark in the North Atlantic. Results indicate the growth response of blue sharks to the NAO pattern occurred at a daily scale with a time-lag. Non-parametric modelling reveals an optimal response curve around the historical average of the NAO, and a significant negative response for large positive NAO anomalies, probably an evolutionary adaptation to the natrual climate variability in this region.
Recommendations The presented modelling approach is broadly applicable to a large body of existing tag recapture databases, especially for managed marine stocks to understand the effects of climate change on their productivity.
This is the first publication in this series of studies to identity the biological response of marine animals to climate variability, be it natrual or anthropogenic in origin. When I say this approach is broadly applicable in the abstract, I mean it, seriously. Just about every dataset (either public access or restricted access datasets that happen to sit on my computer) with 1k plus records that I have looked at, the climate effect is significant! Obviously, I don't have time to write an article for each single one of them. Since I am primarily interested in the methodology part, applying the same thing over and over again just is not my thing. If you are interested in colabs with your own data, let me know through email or PM on GitHub.
But why is that just about everywhere I see, there is climate effect? A quantitative answer is provided in the second study of this series, which is currently under review. Intuitively, combining a daily time scale with long term coverage is like examining climate effects through a high power microscope 🔬. It is undeniable that the growth of a wild animal is under the influence of environmental conditions. Sometimes you can't see it because your "microscope" is not BIG enough! The tools provided in this study is (to the best of my knowledge) the BIGGEST "microscope" so far for detecting climate effect on the growth of wild animals.
Somatic growth is a platform on which climate effects are studied. The focus of this study is on climate effects, not somatic growth per se. I am sorry if I didn't cite many of those studies that estimate growth parameters from GI data. Those studies are equivalent to the baseline models in this study. You can think of this case as the active gradient and inactive gradient of your medicine, both of which are essential but only the active gradient deserves a detailed description on the bottle.
I would call the model presented in this article version 1.0, and some of the directions that I said in this article that deserve further research at the time of that writing have already been done. With that said there are still open research questions that need to be addresssed. I plan to address them later if no one else beat me to it 😂. Right now, I have some articles that I promised to write but haven't started yet! I am perfectly fine if you want to extend this study. In fact, the full source code with an example have been made available on GitHub. You are welcome to fork it or clone it. You may want to tell me your general idea about ways to extend this study to see if it has already been done by me or someone else. I don't want you to waste your time 😎. Considering that the public datasets I used here have been available for decdades and no one else has done anything like it, I say the chance that someone else would do it now is pretty slim. Do prove me wrong here.
I hope I have provided enough motivation for you to download the full paper and read it yourself. Here is the link:
Cite this paper if you like it ❤️
or leave me a comment 👇