[R] model non-nested random effects in nlme library

[mwege]

Season	Individual	FT	FTLength	CurvIndex	dir.lin
2009W	GW522	1	20	0.538931977	1.8884631
2009W	GW522	2	28	0.498651384	0.8379838
2010W	A184	1	17	0.492549537	1.23907
2010W	A184	2	23	0.630582873	1.3010501
2010W	A184	3	27	0.478359129	0.6488715
2010W	OO456	1	19	0.533888959	2.1471811
2011S	A142	1	5	0.777146676	2.1168675
2011S	A142	2	9	0.875245592	1.6422436
2011S	A142	3	7	0.912760026	1.7903588
2011S	A142	4	8	0.368177237	1.8827868
2011S	A142	5	12	0.790110963	1.723052
2011S	A142	6	17	0.530349103	2.104049
2011S	GW503	1	9	0.822049259	1.1624394
2011S	GW503	2	9	0.805705677	1.4590542
2011S	GW503	3	8	0.764926849	1.3209942
2011S	GW503	4	6	0.802072344	1.3903048
2011S	GW503	5	11	0.651539153	1.8380583
2011S	GW503	6	10	0.830663679	1.588408
2011S	A188	1	9	0.710436628	1.648512
2011S	A188	2	9	0.718584921	1.8092618
2011S	A188	3	11	0.641348724	1.6436084
2011S	A188	4	13	0.68348373	1.9246773
2011S	A188	5	16	0.652572155	1.8031925
2011S	LB504	1	8	0.852924101	1.3636355
2011S	LB504	2	13	0.823136776	1.4618325
2011S	LB504	3	12	0.75605543	1.3690891
2011S	LB504	4	18	0.478978335	1.4824566
2011W	A168	1	14	0.281223063	0.9328787
2011W	LB491	1	16	0.610471298	1.968323
2011W	LB491	2	19	0.510918052	1.4519552
2011W	LB491	3	22	0.503229145	0.8539206
2011W	LB491	4	50	0.538715522	0.9241864
2011W	OO440	1	17	0.565258152	1.7838117
2011W	OO440	2	33	0.561548705	1.0129331
2011W	OO440	3	67	0.272020469	0.4717046
2009W	GW495	1	36	0.501137969	1.5722404
2009W	FB515	1	37	0.573009216	-1.6528193
2009W	FB513	1	25	0.63702884	-2.0009436
2009W	FB513	2	117	0.393231124	-1.213341
2010W	A164	1	27	0.734281437	1.3515392
2010W	A164	2	48	0.46142686	0.8394697
2010W	A164	3	32	0.649801375	0.8179546
2010W	OO455	1	22	0.697236934	1.8904179
2010W	OO455	2	20	0.458066447	2.7011468
2010W	OO455	3	31	0.406230997	2.3547132
2010W	OO478	1	28	0.666138495	1.6501422
2010W	OO478	2	36	0.682536815	1.5002367
2010W	OO478	3	12	0.545977976	1.264352
2010W	OO478	4	57	0.408892851	0.8349835
2010W	A160	1	36	0.744976923	1.2087431
2010W	A160	2	47	0.552217128	0.8730726

So here I have copied a small subset of my data.  It has to do with seals
swimming to and from an island to forage as obtained from sattelite
telemetry data.  Season is 2009W (2009 winter), 2011s = 2011 summer etc.
Then there is individual.  FTLength is foraging trip length...i.e. how many
days the animal was out to sea. FT is the number of foraging trip. I.e. the
first or the second. CurvIndex is what I call the curvilinear index...which
basically measures how straight or loopy the animal travels to and from the
island. This is my independent variable and varies between 0 and 1, where 1
is straight and 0 is very undirectional...or "loopy" and then dir.lin is the
mean direction from the island which was a circular variable but I converted
to a linear one.

So I basically want to model the following:

lmer(Curvindex~FTLenght+FT+dir.lin + (1|Individual) + (1|Season) 

I used Individual and Season as seperate random effects because although I
have repeated measurements for some individuals, they are within a year and
no individual was tracked in more than one year. You said in your previous
message that if I have good and bad years I might want to nest individual in
year. Well since I only have 3 years, I dont think its really that
feasible...or what  I know from the data...none of the years were
particularly different from the rest or "bad" in a sense.

Is my thinking correct? How would you model my above example in the nlme
library?

Thanks again


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