I have a new post about my highlights of the MEDECOS and AEET joint meeting in the blog of Journal of Ecology, check it out: https://jecologyblog.wordpress.com/2017/02/07/aaet-medecos/
P-hacking, or researcher degrees of freedom, it’s a worrying issue in science. Specially, because p-hacking is not a black and white issue. On the blackest side there is deliberate p-hacking with the only purpose to advance your career. This is bad, but I hope it’s rare*. The grey area is more intriguing, because it concerns researchers not doing it consciously. I used to think that this include researchers that never had a proper statistical training, with too much pressure to publish too small datasets or that fool themselves thinking that this new analysis/subset of the data is what he/she should be testing in first place, so it doesn’t matter really the 200 previous analysis/subsets (which is false, they matter!). This is equally bad for science (even if the motivation is not as bad).
But then I read “against method” of Paul Feyerabend**. Despite some passages are really slow and repetitive, I liked it. A big part of the book explains Galileo Gallilei story. Galileo changed the paradigm based in incomplete theory, iffy data and measurement tools, and lots of propaganda. He used more its intuition than a proper scientific method. He was still right and most of his ideas were confirmed years later.
And that rang a bell. I’ve heard before scientists saying things like “well, we can’t measure it accurately, but trust me I know the system and this is what is happening”. From here to do a bit of conscious or unconscious p-hacking to support your hypothesis there is a small step. This researchers are using intuition, hours of thought and lots of knowledge. This scientists are putting forward their ideas. Ideas in which they believe, but they can’t just prove unequivocally with the data at hand because of the complexity of the problem.
Paul Feyerabend said that “everything goes” if it advances science. I am not justifying p-hacking to support something that it’s hard to prove but you think is true, but after reading Feyerabend I am also less worried about adding some subjectivity to the scientific method, because being completely objective and following the method strictly may also slow down science. Maybe the middle ground is being able to recognizing when something is an opinion, and not facts, and avoid sticking a p-value to this opinion, but defend it anyway in the light of the data available and try to push forward the agenda to get better data, better methods, or whatever you need to support it. It’s complicated.
*people that only want to advance their careers choosed politics in first place, not science, right? I know this is probably a wrong assumption.
**In a nutshell he praises that an objective scientific method is unattainable and rarely applied, and that we should free ourselves from using it as the single tool to do science. I liked for example the idea of aiming to create a plethora of theories (with no historical constraints or resistance from the status quo to accept compatible alternative explanations) that can cohabit and let time to do the thinning a posteriori. More on wikipedia.
This is a guest post by my PhD student Miguel Ángel Collado. My last post on preferring a preference indexes was not satisfactory to us, so we have better options now. Read Miguel Ángel solution below.
We are working on the ecological value of various habitats or sites. In addition to different classical biodiversity indexes, we want to know if we have some sites that are not specially diverse, but they have some ecologically important species attached to them, we could measure this through preference analyses, using null models to compare with our data.
We can define “preference” for an species if the presence of this species on a given site is bigger than expected by random. A way to know this is comparing to null models and establishing an upper threshold for preference, and a lower one for avoidance, this way we would know whether some species of interest have affinity for some sites or just use them as expected.
To see an example of this
A conversation today at lunch time made me think about some notes I took on this topic, which I reproduce here:
Jonathan Foley gave a pretty convincing talk at ESA 2013 showing that meat consumption is unsustainable for the environment (i.e. land use + CO2 emissions). This was “the straw that broke the camel’s back”* for me and since then I reduced my meat consumption quite drastically.
However, I read a few days ago this paper showing that changing meat for vegetables and fruits can be even worse if you take also into account water footprint and energy use (e.g. transport and storage). I skip the details, but the bottom line is that the story is complicated and the best way to save the world is to reduce calorie intake and eat lots of grains. Here is Figure 2 from the paper (the paper style and figures are quite poor, by the way).
It’s hard because even if you want to do the best is not easy. Is it better for the environment to use bacon or eggplant with my pasta? No idea!**. If I knew the Y axe of the following graph things would be easier.
*this is what google suggest for translating “la gota que colma el vaso”.
**Is the bacon from pigs next door? Is the eggplant from Nicaragua?
I explain it in this cool video. Pass it on!
This video was made with the support of Marie Curie CIG Action
This year was crazy in Seville with plants flowering 2-3 months earlier than last year. So we went to sample, and guess what: bees were there too. Despite expectations about phenological “mis-match” are raised here and there, we don’t find a big phenological mismatch between plants and pollinators*. I am not talking here of specific species, but taking a community approach. However, this is not the end of the story. Is good that plants and pollinators are in sync, but this alone doesn’t warrants a healthy ecosystem functioning.
Why not? My main worry is that after a mild January and beginning of February, we have now “normal cold days” again. Consequently, we also find little bee activity (today we are sampling at 14ºC just to make sure this is true). Hence, both plants and bees are likely to suffer. The demographic implications of this are hard to predict, maybe is not a big deal if it happens only one year, but if it happens often, I presume can be quite bad. All in all its hard to quantify, but I suspect that we need to go back to population dynamics if we want to understand climate change impacts beyond phenological overlaps.
*Don’t take this blog as word, there are plenty of good papers showing it (here and here), including my own (here and here), and very little showing a clear mismatch, most of those on specialized systems.