Matching with trait data

[wcornwell]

I'm thinking feeding guilds will be the most interesting, personally. But @Josh-Lee1 should make final call on what to look at

[Josh-Lee1]

That sound's good. Should the decision be based on what we expect will give a strong result, or what hasn't been done, or what might have implications for conservation? I will have a little look around the literature but otherwise feeding guilds could be a good one.

On Sat, Jul 18, 2020 at 12:20 PM Will Cornwell notifications@github.com wrote:

I'm thinking feeding guilds will be the most interesting, personally. But @Josh-Lee1 https://github.com/Josh-Lee1 should make final call on what to look at

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[coreytcallaghan]

Some mix of these, based on your hypotheses!

Let me know if you need help wrangling the trait data. This is where I have downloaded it from in the past: https://www.nature.com/articles/sdata201561

[Josh-Lee1]

Thanks Corey, I'm just looking around the paper but cant find a download? is getting hands on the data more complicated than that?

[coreytcallaghan]

Yeah. The link to figshare or something should be somewhere in there.

On Wed, Jul 22, 2020, 7:04 PM Josh-Lee1 notifications@github.com wrote:

Thanks Corey, I'm just looking around the paper but cant find a download? is getting hands on the data more complicated than that?

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[wcornwell]

Not easy to find....

http://dx.doi.org/10.6084/m9.figshare.1499292

On Wed, Jul 22, 2020, 7:36 PM Corey Callaghan notifications@github.com wrote:

Yeah. The link to figshare or something should be somewhere in there.

On Wed, Jul 22, 2020, 7:04 PM Josh-Lee1 notifications@github.com wrote:

Thanks Corey, I'm just looking around the paper but cant find a download? is getting hands on the data more complicated than that?

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[Josh-Lee1]

Got it! I'm happy to get going with feeding guilds. Looks like there are papers on the subject and I feel like it could be approached from a few angles. If this step is easy would it be possible to try with a few things? Might be fun to use @coreytcallaghan's urbanness index or something?

Shall I create a subset df with just food columns for the next bit of processing?

[Josh-Lee1]

Actually I would be interested in using feeding aggregation as well if its possible.

[coreytcallaghan]

Sounds good! Sounds like some hypotheses are forming. :)

On Thu, Jul 23, 2020 at 2:18 PM Josh-Lee1 notifications@github.com wrote:

Actually I would be interested in using feeding aggregation as well if its possible.

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[Josh-Lee1]

I stuck the trait data and the coefficients df together. In a pretty cluncky way but i think it works. Then made some boxplots to look at the data. Not completely sure I did this right. I just used the before.after coefficient (ignoring the intercept)?. I pushed if you want to have a look. Thanks.

[wcornwell]

Yeah ignore the intercept which is just "how common is that species" in general. Some interesting stuff in there, but I don't fully get the categories. and some are very small sample size

some fun with gghalves

[wcornwell]

[wcornwell]

library(gghalves)

# default
ggplot(values, aes(x = as.factor(X171_Food_Intertidal_invertebrates_10), y = Estimate, fill = X171_Food_Intertidal_invertebrates_10)) +
  geom_half_boxplot(nudge = -0.1, outlier.color = NA) +
  geom_half_point() +
  theme_light() +
  guides(color = guide_legend(nrow = 1))+
  theme(legend.position = "none")

[coreytcallaghan]

These are looking great!

I guess when we pick hypotheses/traits, we'll want something where we have some variability over...

On Fri, Jul 24, 2020 at 3:59 PM Will Cornwell notifications@github.com wrote:

[image: image] https://user-images.githubusercontent.com/5534161/88364534-8a8df800-cdc6-11ea-8674-efe77de4ea97.png

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[Josh-Lee1]

Cool! well if you think these are ok then I might play with some other things cause i didn't get anything dramatic and exciting.

[wcornwell]

I don't know--I think there is exciting stuff in there. But be good to write down some specific hypotheses (ideally from the literature) before hunting through lots of traits.

[Josh-Lee1]

Ahk cool.