I have data consisting of seasonal transects of herbivore counts, which I wish to analyse using the kilometric abundance index. I want to determine how changes in prey abundance over the study period has affected demographic rates of various lion prides. I have used the outline of lion home ranges to divide the transects into transects per pride. However, due to seasonal changes in flooding and access to roads, some prides have more transects than others within each year. My main questions are:
1) How do I determine whether the differences in distance covered have a significant effect on the outcome of the kilometric abundance? And, if it does have an effect, how do I account for it?
2) I have both lean season and abundant season data for most years, but in two years have only abundant season data. If I want to use lean season data , how do I deal with the two years where I have only abundant season data?
TRY to use the population software or sigmastat, both are good in solving such issue
Had you tryied relative abundance? ( relativize the abundance of each "sampling" per total km of that specific sample). Then you can use any statistics that uses percentages.
Where possible, I would recommend using a distance sampling approach which would allow for transects of different lengths as well as accounting for animals that you may have missed by forming a probability of detection distribution based on the distance between the animal and observer. However, this type of analysis does make some very strong assumptions about how the data were collected (for example constant speed along transects, reasonably homogeneous vegetation cover etc.). Where these assumptions are not met I would absolutely agree with Silvia; convert your data into relative abundances, or perhaps better relative encounter rates which would help control for differences in transect length. I would analyse this data using generalised linear or additive models - the exact form of these models will depend on the data. The book "Mixed effects models amd extensions in Ecology with R" by Zuur et al. is an excellent introduction to using these types of anlayses.
Thank you for you contributions.
IMADEDDIN MOH'D ALBABA - I have had a look at sigmastat and it seems to be a very useful tool in future analysis. Thank you for introducing me to it.
Silvia López-Casas and Angus I Cameron - relativizing the data seems like it will be the best fit. If my understanding is correct, this will best highlight how differences in prey abundance may have affected survival rates of individuals in the different prides. I plan to analyse using generalized linear models - thank you for the book reference I will definitely look it up.
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