In brief: Who is in to create an Open Trait Data repository?
In this same moment at least 10 researchers (but mostly undergrads) are compiling trait data for some exciting analysis. That includes myself. In fact, most trait analysis are hampered by the quality of the traits, which are often lumped to the species level, and hence do not capture the natural variation, or info for some species is based on just one population with the hope that it is representative. Paradoxically, I think this trait data is very abundant, but not available. Thousands of researchers have measures, for example, of body size for a bunch of specimens of his/her preferred taxa. This data is just not accessible or is scattered on the net.
There are some databases (some open, some not) with traits for some groups (plants, birds and mammals) but not a joint effort to capture all this knowledge like the GenBank initiative. So I propose to create a TraitBank. The technology is easy to implement (from a SQL server liked to a web, to a simple Google spreadsheet), but the key aspect would be to enroll the community to make trait data deposition encouraged upon manuscript acceptance. Do you think that the leading journals will ask authors to deposit any morphological or life history measurement reported in the paper? It will also be important that a well-known independent organisation host the data. Any idea on who to contact? would Figshare be an option?
The fields should be very delimited to allow an easy search and compilation of information; as a first pass I would propose:
– Publication associated with the data and/or author
– Species taxonomy (full taxonomy can be retrieved from ITIS)
– Measurement is in wild or captive populations
– Region and Lat/Long of the measurement
– Category (morphological;life history; or ecological trait)
– Subcategory (e.g. body mass; clutch size; survival; phenology…)
– Mean value, SE and n: Units should be fixed by the subcategory.
A form and an option to upload a large csv should be enough. An API that allow connecting to R would be a blast. So how can we move that idea forward?
I read this paper (Food webs: reconciling the structure and function of biodiversity; Thomson et al. 2012 Trends in Ecology & Evolution, 27(12):689-697) with great interest because the title is really promising. Indeed it is nice overview of what’s out there in terms of network and functioning, but not much reconciliation. First I have the feeling that community ecologists (even if they don’t use network metrics) are already (and have been for a long time) on the framework they describe in Table 1C. But my main concern is that I missed an answer to the question: What can a network approach add to the study of ecosystem functioning?
Well, I have two ideas that can help answering that.
1) Network approach can be very useful when the function itself is defined by the network. If you are studying pollination or pest control, the actual function delivered is contained in the network structure, hence species richness, diversity or composition (or new metrics, like FD) can be unable to fully explain functionality because what confers high levels of function (or stability) to the community is the network properties (e.g if it’s modular, generalized or well connected). I know some pople is on that path, so I am looking forward to see what they find.
2) Another situation where networks can make the difference is when indirect interactions modulate the function, but are too complex to track them one by one. Networks can describe better phenomenons like apparent competition or cascading effects than any other classical approach. If this type of complex interactions are relevant for the level of functioning measured (e.g. productivity of the basal level), then, adding the network perspective can be more informative than classical approaches.
May be what I am saying is too obvious, so the authors didn’t cover it, or I may be missing something, but this is the direction I would like to see things moving.
Today is my birthday. It’s also the birthday of a close friend. What an incredible coincidence! Or wait, may be is just expected. One more time R comes into our help, because it has a built-in function to answer our question.
Which is the probability of two coincident anniversaries among a group of 17 people? (yes we have a mailing list, so I can count my friends semi-objectively without the fear of not counting them all). Just type:
pbirthday(n= 17, classes = 365, coincident = 2)
The answer is approximately 0.3, that is 3 of every 10 friend groups (of that size) have at least two anniversaries that coincide. Not that impressive, isn’t it?. But the beauty of stats is that stats are here to correct your intuition. To have an impressive coincidence (and statistical significant) you will need a group of 47 people, none of them with coinciding birthdays. And then, probably nobody will be amazed.
qbirthday(prob = 0.95, classes = 365, coincident = 2)
Anyway, happy birthday to all readers celebrating today (if any)!