Hi everyone,

I'd like to compare vegetation in forest fragments of various sizes (1-,
10, and 100-ha) with vegetation where nine species of birds forage. I
consider the bird samples to be independent, even within the same species.
I have, however, several samples within each fragment.

I'd like to analyze the vegetation variables using JAGS but before I even
get that far I'm not sure how to code the data.

Below is what I have so far. "code" identifies the individual fragment and
bird species,  "type" is fragment size and bird species (so there's nothing
different between "code" and "type" for birds except I added a 9 in type
that was absent in code).

Is there a better way to code a column that has a mixture independent
samples and nested samples?

Thanks,

Jeff

 structure(list(code = c(1112L, 1112L, 2107L, 2107L, 2107L, 2107L,

2108L, 2108L, 2108L, 2108L, 3114L, 3114L, 3114L, 3114L, 3114L,

3114L, 4101L, 4101L, 4101L, 4101L, 1212L, 1212L, 1212L, 1212L,

1212L, 1212L, 1212L, 1212L, 2206L, 2206L, 2206L, 2206L, 2206L,

2206L, 2206L, 2206L, 3209L, 3209L, 3209L, 3209L, 3209L, 3209L,

3209L, 3209L, 4201L, 4201L, 4201L, 4201L, 4201L, 4201L, 4201L,

4201L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,

2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,

3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,

4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,

7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,

8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L,

9L, 9L, 9L, 9L, 9L, 9L, 2303L, 2303L, 2303L, 2303L, 2303L, 2303L,

2303L, 2303L, 2303L, 2303L, 2303L, 2303L, 2303L, 2303L, 2303L,

3304L, 3304L, 3304L, 3304L, 3304L, 3304L, 3304L, 3304L, 3304L,

3304L, 3304L, 3304L, 3304L, 3304L, 3304L, 3304L), type = c(1L,

1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,

1L, 1L, 1L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,

10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,

10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 91L, 91L, 91L, 91L,

92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 92L,

93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L,

93L, 93L, 93L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L,

94L, 94L, 94L, 94L, 94L, 94L, 94L, 95L, 95L, 95L, 95L, 96L, 96L,

96L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 98L, 98L, 98L, 98L,

98L, 98L, 98L, 98L, 98L, 98L, 98L, 98L, 98L, 98L, 98L, 98L, 98L,

98L, 98L, 98L, 98L, 98L, 98L, 99L, 99L, 99L, 99L, 99L, 99L, 99L,

99L, 99L, 99L, 99L, 99L, 100L, 100L, 100L, 100L, 100L, 100L,

100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L,

100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L, 100L,

100L, 100L, 100L), leafno = c(1.8, 2.65, 1.25, 2.6, 1.55, 2.35,

2.3, 1.7, 2.2, 3.2, 3.1, 1.25, 2.35, 1.9, 1.75, 2.8, 1.95, 2.45,

1.55, 1.65, 0.85, 1, 1.85, 1.45, 2.55, 4.05, 3.4, 2.1, 2.05,

1.95, 2.75, 2.6, 1.95, 2.15, 2.2, 2.1, 1.7, 2.2, 1.65, 2.2, 2.95,

2, 2.05, 1.45, 1.55, 0.7, 1.45, 2.05, 1.6, 1.4, 1.25, 2.25, 2.35,

2.4, 2.15, 1.65, 1.8, 2.25, 1.45, 2.15, 2.05, 1.5, 2, 1.7, 1.15,

1.25, 1, 1.15, 1.95, 1.05, 2.4, 1.1, 1.75, 1.7, 1.7, 1.75, 1.1,

1.05, 1.8, 1.45, 2.05, 1.9, 1.15, 2, 1.15, 2.9, 1.6, 2.65, 2.1,

2.35, 1.25, 1.8, 0.85, 1.6, 2.35, 1.75, 1.5, 1.75, 1.3, 1.8,

1.05, 1.45, 0.85, 0.9, 1.7, 2.05, 1.55, 1.55, 1.4, 1.8, 1.5,

1.2, 1.3, 2, 1.4, 2.6, 2.05, 1.05, 2.55, 2.4, 0.4, 2.1, 1.65,

1.65, 1.5, 1.5, 1.55, 2.4, 1.8, 2.2, 1.7, 1.05, 1.45, 1.5, 1.1,

0.95, 0.6, 1.3, 1.95, 1.3, 1.9, 1.55, 1.75, 1.65, 2.35, 3.2,

1.55, 1.65, 1.05, 2.6, 1.75, 1.6, 2.35, 1.2, 1.35, 2.5, 0.95,

2.15, 2.15, 2.55, 1.6, 2.1, 1.9, 0.95, 2.1, 2.4, 2.4, 1.7, 2.45,

1.85, 1.15, 1.25, 0.65, 1.35, 1.8, 1.4, 1.15, 2.75, 2.25, 2,

1.75, 1.4, 2.25)), .Names = c("code", "type", "leafno"), class =
"data.frame", row.names = c(NA,

-183L))
********************************************************
Jeffrey A. Stratford, PhD
Department of Biology and Health Sciences
Wilkes University, PA 18766 USA
570-332-2942
http://web.wilkes.edu/jeffrey.stratford/
********************************************************

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