I'm an MSc student and not particularly statically minded and I would really appreciate some help on going about analysing count data! Please try to keep is simple!
I have two species, the common frog and marsh frog and I have count data on both species since 1980. The marsh frog is non-native and I am trying to ascertain whether since the introduction of the marsh frog they are negatively impacting/outcompeting the common frog. If they were outcompeting you would expect to see the population counts of the marsh frog increase whilst the population of common frogs decrease. I want to statistically test this "model/hypothesis"!
How is it best to go about this? I have already ascertained that counts for both species is non-normally distributed so I'm assuming non-parametric tests. I don't really know where to start, maybe a Mann Whitney U test or Kruskal-Wallis test? Is it best to group the data counts i.e 1980-1984, 1985-1989, 1990-1994 etc!
(Sorry I cant upload the actual data to a public forum, I have not got permission for this but please find attached fabricated data!)
Many thanks in advance!
I have two species, the common frog and marsh frog and I have count data on both species since 1980. The marsh frog is non-native and I am trying to ascertain whether since the introduction of the marsh frog they are negatively impacting/outcompeting the common frog. If they were outcompeting you would expect to see the population counts of the marsh frog increase whilst the population of common frogs decrease. I want to statistically test this "model/hypothesis"!
How is it best to go about this? I have already ascertained that counts for both species is non-normally distributed so I'm assuming non-parametric tests. I don't really know where to start, maybe a Mann Whitney U test or Kruskal-Wallis test? Is it best to group the data counts i.e 1980-1984, 1985-1989, 1990-1994 etc!
(Sorry I cant upload the actual data to a public forum, I have not got permission for this but please find attached fabricated data!)
Many thanks in advance!