Amanda Greer

University of Canterbury | UC · School of Biological Sciences

Hi all, 

I am looking to have compound specific stable carbon and nitrogen isotope analysis of feather amino acids carried out. I tried UC Davis but ran into terminal permitting issues. Now I would like to find a facility in NZ or Oz to avoid such problems. 

If anyone out there knows of a NZ/AUS lab carrying out CSIA I would be very grateful if you could post their name here for me so I can start enquiries. 

Cheers! 

Hello,

I am wondering whether the area enclosed by an ellipse drawn around data points on an x - y graph where both axes are % (or in my case involving stable isotopes, ‰) takes a unit or is dimensionless.

From what I have seen most researchers report these areas sans unit. However, I came across a thesis recently with ‰² which started me thinking - why not? 

I am sure someone knows and can explain the answer to this for me. I would greatly appreciate any thoughts! And thanks in advance. 

In spite of the classical notion that interspecific competition constrains niche width it is possible to envision circumstances under which it could cause niche width to increase.

For example, reducing the availability of a valuable resource forces an animal to compensate in some fashion: through increased exploitation of the other resources it currently consumes (as classical theory would dictate), or potentially, through exploiting new resources which can mitigate the loss. If more than one alternative resource is incorporated, an animal will have widened their niche as a direct result of interspecific competition.

Does anyone know of any studies which have shown such effects? Or any papers in which this or other possible ways interspecific competition could widen niche are discussed?

Hi All,

I am trying to best analyse a set of foraging ecology data with >10 behaviour categories (DVs) and 3 levels of IV (season, sex, age). The time which an animal spent engaged in a behaviour was recorded and then divided by the total time spent in sight of the observer, so my data are proportional. As is typical, not all animals engaged in all behaviours and there are a large number of zeros in my dataset which is severely over-dispersed. I had initially analysed all the data in R using the glm function (family = quasibinomial, followed by anova. The intention was then to use the false discovery rate alpha to account for the large number of analyses. However, it has since been suggested that a multivariate approach might be better so I have been trying to figure out (a) if it's possible to run a quasi-binomial multivariate analysis of proportion data (b) how to go about it.

In the R Documentation quasi-binomial family function page (linked) it is stated that if multivariate response = TRUE the response matrix should be binary. This seems a pretty straightforward indictment of my idea to run this analysis on my proportion data, but I am wondering why - is this just not possible and why not; or is there a particular package that could help? If anyone could provide me with an answer or some much needed guidance on this topic I would be very grateful.

Thanks,

Amanda