mobiodiv / elev_gradient Goto Github PK
View Code? Open in Web Editor NEWMoB analysis of communities along elevation gradient
MoB analysis of communities along elevation gradient
Ideally we want to develop an analysis that is completely reproducible based only the raw data file. Therefore any intermediate steps of processing the data should be coded in R.
With that in mind, we need an R script that imports the raw master data file: ./data/180926-AllRawData.xlsx
using one of the R packages that allows reading of excel sheets. That script should then output the two .csv
files that other analyses will depend on.
This script should also fix a mistake that I have detected in the field called Code
in the All
sheet of 180926-AllRawData.xlsx
. Specifically, it appears that CATALOOCHEE
needs to be replaced with CATA
. Otherwise a look up for that sites coordinates will not work. I'll double check with Nate to make sure this is correct.
Hey @T-Engel I'm going to try to merge your k-NCN branch with the master branch. I'm going to just cherry pick the three commits from Oct 12 onward - the other commits seem to be already in the master branch. I wanted to give you a quick heads up before I do this. After the merge please make sure you update your local branches. If you want to improve an aspect of the code just push to a newly created branch not the old k-NCN one. Thanks!
Hey @T-Engel, nice job kicking off the analysis! I did want to suggest that we do keep samples without individuals because this is ecologically informative about patterns of patchiness. For the two-scale analysis, I know it will result in some Inf values for beta diversities which can be annoying but for the delta analysis we'll want those zero abundance plots retained so ideally let's just keep the same mob_in
object for both analyses. Here's the line of code I would suggest changing so that you remove the rowSums > 0 check.
minor issue on this line of code it needs to be run after importing dat
but before dropping the site ids from dat
I'm having a little trouble with thinking about how to interpret Sander's et al. fisher's alpha results. They interpret the strong negative correlation between fisher's alpha and temperature to indicate that extinction dynamics are playing a role in maintaining diversity is sites with more individuals. This interpretation seems flawed to me because fisher's alpha is independent of N and therefore is a richness estimate that is corrected for numbers of individuals. Its strong correlation actually indicates the opposite that temperature is not modulating S by sampling effects (i.e., N effects). Here's what they write specifically:
It is unlikely that sites with higher temperature have more
species only because local assemblages are simply sampling from
the regional species pool, as the sampling mechanism predicts.
Fisher’s α, which removes the effect of sampling, still tracked
temperature. This result suggests that some sites are more species
rich because those sites have higher abundance, and thus lower
probabilities of extinction. In fact, if we tally the number of
occurrences (the number of 1-m2 quadrats a species is detected
in) as an estimate of abundance (following previous authors:
Kaspari et al., 2000a,b, 2003; Longino et al., 2002) and plot richness
against abundance, the relationship is strong and positive at each
spatial grain and for Fisher’s α (Table 3), indicating that some
sites have more species because the probability of local extinction
is reduced. This result, along with the positive relationship
between Fisher’s α and temperature, supports the abundance–
extinction mechanism.
Can you please let me know if you disagree with my interpretation.
Our own results show N effects are minimal based on patterns of S_n and the estimated N effect from the multi-scale analysis. Thanks!
The analysis of the IBR is limited to the site with the smallest number of individuals. One site (NODI) has only 6 individuals which means that the scale for the IBR is quite limited. If we drop NODI from the analysis then we get 12 individuals but this is still rather limited. We should think about if there is a creative solution that allows us to consider larger N values for the IBR. This could be done via extrapolation for example. Alternatively, sample size could just be sacrificed at larger values of N. This is relevant because how important you judge the SAD effect depends in part on how large of a scale you consider. This also isn't straightforward to solve because N decreases along the gradient and thus dropping low N sites means that the computed regression are from a narrower lower elevation portion of the gradient thus complicating interpretation.
Here is our remaining todo list:
mobr
PR for dev
branch to integrate recent changes made to graphics and analysismobr
repo dev
branchmobr
univariate code to be better for continuous variables right now we have to hack it together in our own analysismobr
on dev
branch (@T-Engel can you tackle this?)
mobsim
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.