function [data, auxData, metaData, txtData, weights] = mydata_Ictalurus_furcatus_JR 

%% set metaData
metaData.phylum           = 'Chordata'; 
metaData.class            = 'Actinopterygii'; 
metaData.order            = 'Siluriformes'; 
metaData.family           = 'Ictaluridae';
metaData.species          = 'Ictalurus_furcatus_JR'; 
metaData.species_en       = 'Blue catfish'; 

metaData.ecoCode.climate  = {'Cfa', 'Dfa'};
metaData.ecoCode.ecozone  = {'THn'};
metaData.ecoCode.habitat  = {'0iFr', '0iFl', '0iFe'};
metaData.ecoCode.embryo   = {'Fnm'};
metaData.ecoCode.migrate  = {'Ms'};
metaData.ecoCode.food     = {'biO'};
metaData.ecoCode.gender   = {'D'};
metaData.ecoCode.reprod   = {'O'};

metaData.T_typical        = C2K(20); % K, body temp
metaData.data_0           = {'ab'; 'ap'; 'am'; 'Lb'; 'Lp'; 'Lwi'; 'Wd0'; 'Wwp'; 'Wwi'; 'Ri'}; 
metaData.data_1           = {'t-L'; 'L-Ww'; 'L-N'; 'L_GSI'; 'Ww-JO'}; 

metaData.COMPLETE         = 3.0; % using criteria of LikaKear2011

metaData.author           = {'Vaskar Nepal'};   
metaData.date_subm        = [2022 07 08];  
metaData.email            = {'vnepalkc@vims.edu'}; 
metaData.address          = {'Virginia Institute of Marine Science, 23062, USA'};

metaData.curator     = {'Bas Kooijman'};
metaData.email_cur   = {'bas.kooijman@vu.nl'}; 
metaData.date_acc    = [2022 07 10]; 

%% set data
% zero-variate data;

data.ab = 6 + 3; units.ab = 'd'; label.ab = 'age at birth'; bibkey.ab = {'BioKidsUmichWebsite'};
  temp.ab = C2K(27); units.temp.ab = 'K'; label.temp.ab = 'temperature'; 
  comment.ab = 'Egg to hatch + hatch to exogenous feeding';
data.tp = 7*365; units.tp = 'd'; label.tp = 'time since birth at puberty';
  bibkey.tp = 'NepaFabr2021a'; temp.tp = C2K(17.3);  units.temp.tp = 'K';
  label.temp.tp = 'temperature'; comment.tp = 'Youngest mature; mean = 7.7';
data.am = 25*365; units.am = 'd'; label.am = 'life span for females'; bibkey.am = 'NepaFabr2020';
  temp.am = C2K(17.3);  units.temp.am = 'K'; label.temp.am = 'temperature'; 
  comment.am = 'max I have ever seen in literature regardless of sex is 34';
data.amm = 20*365; units.amm = 'd'; label.amm = 'life span for males'; bibkey.amm = 'NepaFabr2020';
  temp.amm = C2K(17.3);  units.temp.amm = 'K'; label.temp.amm = 'temperature'; 
  comment.amm = 'max I have ever seen in literature regardless of sex is 34';

data.Lb  = 1.6 * 16.786 / 19;  units.Lb  = 'cm';  label.Lb  = 'fork length at birth';    bibkey.Lb  = 'SimoWall2003';
  comment.Lb = '16-17.6 mm TL (p. 95-96); 16 mm (p. 22); convert 1.6 cm TL to FL for larval fish based on picture';
data.Lp  = 31.5;  units.Lp  = 'cm';	label.Lp = 'fork length at puberty'; % smallest = 31.5 cm 
  bibkey.Lp =  'NepaFabr2021a'; comment.Lp = 'Smallest for James (Mean = 34.3 cm FL)';
data.Li = 105.5;   units.Li  = 'cm';	label.Li  = 'ultimate fork length for females';
  bibkey.Li = 'NepaFabr2020'; comment.Li = 'Wiki accessed 2020 02 16 says 145 but dont know sex';
data.Lim = 110.2;   units.Lim  = 'cm';	label.Lim  = 'ultimate fork length for males';
  bibkey.Lim  = 'NepaFabr2020'; comment.Lim = 'Wiki accessed 2020 02 16 says 145 but dont know sex';

data.Wd0 = 7.093/ 1000;  units.Wd0 = 'g'; label.Wd0 = 'egg dry mass'; bibkey.Wd0 = 'NepaFabr2021a';
  comment.Wd0 = 'Mean egg dry weight for James';
data.Wwp = 398;   units.Wwp = 'g';	label.Wwp = 'somatic wet weight at puberty';
  bibkey.Wwp = 'NepaFabr2021a'; comment.Wwp = 'Somatic Wet weight of the shortest mature fish in James';
data.Wwi = 25725; units.Wwi = 'g';	label.Wwi = 'ultimate somatic wet weight for females';
  bibkey.Wwi = 'NepaFabr2020'; comment.Wwi = 'Wiki on 2020 02 16 says 64864; world record but dont know sex';  
data.Wwim = 29630; units.Wwim = 'g'; label.Wwim = 'ultimate somatic wet weight for males';
  bibkey.Wwim = 'NepaFabr2020'; comment.Wwim = 'Wiki on 2020 02 16 says 64864; world record but dont know sex';

data.Ri = 328; units.Ri  = '#/d'; label.Ri  = 'maximum reprod rate'; % 148468/365
  bibkey.Ri = 'NepaFabr2021a'; temp.Ri = C2K(17.3); units.temp.Ri = 'K';
  label.temp.Ri = 'temperature';  comment.Ri = 'Extrapolation for 25725 g fish based on Fec ~ SomaWt gam relationship';
 
% uni-variate data

% Age-Length_Weight Early from Connelly 2001
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Early (1998 - 2000) Adults; columns: Age (d), FL (cm), Weight (g), River, Sex
tLW0 = [ ...
808	808	1016	1195	1249	1381	1381	1381	1435	1747	1889	1889	1926	1951	2159	2254	2254	2254	2254	2269	2275	2275	2291	2311	2311	2311	2311	2619	2619	2619	2634	2640	2640	2640	2640	2656	2985	2985	2999	2999	3005	3365	3365	3365	3365	3365	3365	4362	396	676	676	676	764	793	1041	1041	1195	1392	1785	1889	2149	2165	2254	2254	2254	2275	2275	2275	2291	2311	2311	2477	2477	2640	2640	2640	2640	373	373	394	394	407	415	533	551	681	702	738	738	738	772	772	772	772	772	772	772	772	772	795	795	795	795	807	807	843	844	844	844	870	898	916	916	916	919	1046	1067	1069	1069	1069	1069	1103	1103	1103	1138	1145	1161	1209	1235	1235	1235	1235	1263	1263	1263	1263	1263	1407	1411	1808	1876	2137	2165	2696	2931	2945	3274	3645	3645	3645	3645	3681	3681	4004	114	114	373	394	394	394	394	394	394	478	478	478	478	478	478	478	479	479	479	501	533	533	533	676	676	681	681	702	702	702	723	738	738	738	738	738	759	780	780	780	795	795	795	795	795	807	843	843	843	843	843	843	866	866	870	870	870	870	898	898	916	916	916	919	957	972	978	978	1046	1046	1046	1067	1067	1067	1069	1069	1069	1069	1069	1103	1145	1145	1161	1161	1172	1231	1231	1231	1231	1231	1231	1235	1235	1235	1235	1235	1235	1235	1235	1263	1263	1282	1284	1343	1343	1383	1411	1411	1432	1435	1510	1512	1575	1629	1647	1647	1647	1647	1650	1650	1855	1940	1966	2012	2015	2165	2305	2331	2359	2579	2585	2670	2945	2945	2951	2951	3645	; % AgeD
21.7	25.1	34.6	38	32.9	39	39	46.2	44.7	46.2	38	40.5	63.5	57.8	35	43.6	47.5	70.1	70.2	34.9	65.6	77.3	64	72.5	75.2	77	77.5	45.4	56	68.8	42.2	58.6	70.3	73	80.9	60	72	79.7	64	74.7	78	64.5	74	74	79.6	80.3	81.3	53.7	16.8	24.1	24.1	29.9	30.5	40.5	32.5	33.5	36.5	47.5	50.2	43	54.1	51.7	47.8	51	65	62	62	63.4	63	68	73.3	49	49	55.6	64	64	67.5	19	21	13.3	15.7	14.1	16	18.5	18.9	17	16.2	23.8	24.5	26.6	19	19.1	19.8	20	22	22	23	23	23.3	20.8	21	21.5	22.5	19	23	23.7	22.4	23	23.7	21	36.8	22.5	22.6	23.4	24.1	23.5	16.2	23.8	24	25	25.6	27.9	28	28	26.6	27.6	25.6	26.3	28.1	28.7	29.2	32.1	25	26.8	27	28.5	32	31.8	25	28	30.5	31.8	38.7	37.5	84.5	57.1	80	60.5	91	91	93.8	70.5	70.5	80	10.4	11.8	20.1	11.6	13.2	13.2	13.5	14	14	15.3	15.8	15.8	15.8	16.6	16.9	17.2	15.2	16	17.5	20.5	14.7	18	19	17.5	18.3	15.5	18	15.5	16.3	17.2	14.3	21.3	24.6	25.2	25.2	27	17.8	11.8	18.1	18.5	21.1	21.2	22.2	23	23.6	20	17.7	20.2	20.5	21.5	23.4	23.4	23.5	25.2	20.5	22.9	23.4	24.9	22.2	23.5	23.2	25	25.6	24.7	25.2	24	24	26.5	22	22	25.4	15.7	23.6	25.5	22.6	23.5	25.2	25.3	25.6	23.3	25.8	26.5	24	24.7	20.5	21	21	27.4	29.3	29.3	29.8	25	27	27.4	27.7	27.9	28.3	28.8	28.8	29.2	34.9	24.1	25.5	27	30.7	25	25.5	27.9	27.3	31.6	28.4	34.1	44.2	28.9	29.7	29.8	29.8	31.2	27	32	36.5	32.5	32	31.1	29.8	37	41.6	32.4	42.5	52.3	38.5	33.6	75	78.5	43	43	75.4	; % FL
NaN	NaN	599.2	719.3	492.1	908.1	908.1	1345.7	1434.7	1345.7	806.7	1094.8	4200	3280	NaN	1228.2	1666.1	5650	6550	NaN	4704.8	8665.7	3950	7114.1	7370.9	8861.7	6515.4	1525.7	2750	6100	NaN	3464.9	6577.1	6423.9	10187.3	3850	9250	9450	NaN	NaN	9669.1	NaN	NaN	NaN	NaN	NaN	NaN	2765.2	58.79	204.6	204.6	424.37	345.46	883.68	548.42	650.42	649.13	1441.91	1781.69	1172.95	2521.65	2035.94	1362.16	2099.09	3047.42	3200.74	3636.12	3904.58	4130.79	4403.01	6254.06	1828.84	1828.84	2548.67	4164.92	4164.92	4217.27	89.8	124.8	27.1	38.7	41.6	49	78.7	87.5	56.5	50.6	163.8	222.5	266.6	91.6	85.4	116.7	92.9	139.8	140.8	156.8	138.2	134	117.6	119	124.7	148.4	106.6	161.3	NaN	150.1	145.6	173.9	106.5	235.2	138.4	189.4	164.1	193	160.9	50.6	214.6	234.7	256.8	269.9	268.7	276.3	276.3	246.6	282.7	235.2	241.6	280.7	322.7	309.6	455.3	227.8	252.7	272.7	335.6	405.9	516.7	183.9	449.9	359.5	516.4	890	790.5	15450	3450	15000	4545	14450	14450	19750	6100	6100	15000	17.6	20.4	105.74	17.1	26.9	29.2	31.3	30.1	34.23	53.8	51.44	58.66	58.66	64.8	63.2	62.16	42.4	52.3	48.45	101.72	40.89	77.85	86.8	86	87.1	43.04	65.03	47.6	53.6	60.4	39	126.4	191.84	217.16	217.16	274.92	71	94.32	66.48	74.35	133.8	122.6	149	156.2	179.3	114.68	75.8	102.3	118.3	147.3	180.1	157.1	161.01	217.79	101.85	182.1	175.71	190.53	130.52	153.1	181.1	182.8	201.7	199.81	209.85	194.64	171.22	232.87	137.47	137.47	190.64	52.4	150.71	188.18	187.75	180.969	255.7	292.352	252.052	176.35	241.04	267.27	103.3	224.2	129.5	147.03	147.03	253.37	349.44	349.44	297.57	200.99	286.8	265.47	263.4	275.32	356.5	299.22	291.38	379.37	195.3	175.9	216.38	269.32	342.53	268.4	235.32	313.75	257.34	429.795	311.94	621.49	1264.85	296.52	314	336.7	336.7	382.2	266.78	444.29	675.08	465.28	440.24	372.9	366.64	900.562	1015.37	419.9	1063.21	2278.92	702.44	473.5	8236.2	10376.67	1173.9	1173.9	7449.1	; % Weight
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	; % James
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1]'	; % Female

% Age-Fork length James Female
data.tL0       = tLW0(tLW0(:, 4) == 1 & tLW0(:, 5) == 1, [1 2]);   % 1 is James in column 4; 1 is Female in column 5
data.tL0       = rmmissing(data.tL0);
data.tL0(:, 1) = data.tL0(:, 1);    % age (years)
data.tL0       = sortrows(data.tL0, 1);   % Sort it in ascending order based on age; makes it easier in the predict file (function ismembc)
units.tL0      = {'d', 'cm'};  label.tL0 = {'time since birth', 'Fork length', 'females, early period'};  bibkey.tL0 = 'Conn2001';
temp.tL0       = C2K(17.3);  units.temp.tL0 = 'K'; label.temp.tL0 = 'temperature';
comment.tL0    = 'Females James early period';
% Males
data.tL0_m       = tLW0(tLW0(:, 4) == 1 & tLW0(:, 5) == 0, [1 2]);   % 1 is James in column 4; 1 is Female in column 5
data.tL0_m       = rmmissing(data.tL0_m);
data.tL0_m(:, 1) = data.tL0_m(:, 1);    % age (d)
data.tL0_m       = sortrows(data.tL0_m, 1);   % Sort it in ascending order based on age; makes it easier in the predict file (function ismembc)
units.tL0_m      = {'d', 'cm'};  label.tL0_m = {'time since birth', 'Fork length', 'males, early period'};  bibkey.tL0_m = 'Conn2001';
temp.tL0_m       = C2K(17.3);  units.temp.tL0_m = 'K'; label.temp.tL0_m = 'temperature';
comment.tL0_m    = 'Males James early period';

% Length-weight James females;
data.LWw0    = tLW0(tLW0(:, 4) == 1 & tLW0(:, 5) == 1, [2 3]);   % 1 is James in column 4; 1 is Female in column 5
data.LWw0    = rmmissing(data.LWw0);
units.LWw0   = {'cm', 'g'};   label.LWw0 = {'fork length', 'wet weight (somatic)', 'males, early period'};  bibkey.LWw0 = 'Conn2001';
comment.LWw0 = 'Females James early period (somatic weight)';
% Males
data.LWw0_m    = tLW0(tLW0(:, 4) == 1 & tLW0(:, 5) == 0, [2 3]);   % 1 is James in column 4; 1 is Female in column 5
data.LWw0_m    = rmmissing(data.LWw0_m);
units.LWw0_m   = {'cm', 'g'};   label.LWw0_m = {'fork length', 'wet weight (somatic)', 'males, early period'};  bibkey.LWw0_m = 'Conn2001';
comment.LWw0_m = 'Males James early period (somatic weight)';


% Recent (2015-2017) from Nepal et al. 2020 and Nepal and Fabrizio 2021
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% columns: Time since birth, FL, Weight, River, Sex
tLW1 = [ ...
    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% Age, days
    17.4	20.1	11.5	14.3	12.5	12.1	11.8	12.2	11.3	11.4	12.2	12.7	12.4	15.8	12.2	13.8	14.5	14.8	14.2	14.6	14.4	14.5	13.6	12.4	14.7	11.2	15.7	14.4	17.4	17	16.5	15	14.5	12.9	16	14.2	16.9	14.1	14.6	15	12.5	15.7	16.7	20.2	19.7	20.7	25.8	21.9	19.1	15.4	13.2	22.5	19.3	18.5	17.6	19	19.2	17.1	21.7	19.4	18	19.7	21.5	18.5	18.5	18.5	20.6	18.2	17.5	18.5	18.7	20.4	21.3	21.9	17.5	20.4	18.8	17.7	19.1	18.2	19.9	18.2	18.9	19.1	18.2	18.8	19.3	20.9	21.2	18.8	18.3	19.1	18.6	20.9	21.5	21.3	18.8	20.9	18.3	18.3	19.8	19.6	16.7	20.8	17.3	17.9	19.7	19.2	22.9	18.6	19.2	18.8	20.9	20.4	21.6	22	22.7	24.5	24.7	23	29.3	25.3	28.6	23.8	27.3	21.4	25	23.9	24.9	23.6	23.4	25.7	26	24.6	23.9	23	26.1	21.2	26	24.7	28.6	21.8	25.1	22.9	27.2	25.4	26	23.5	26	27	26	25.3	24.9	24.3	25.2	26.5	25.5	26.3	25.2	24.5	25.6	26.2	25.7	26.6	22.8	23.2	25	23.5	24.7	21	22	22.6	23.4	20.4	22.3	23	24.3	24	23.4	24.6	22.8	23.7	23.8	23.2	24.1	22.7	22.7	23.5	21.7	21.4	24.1	23.6	23.3	23.6	22.2	22.9	23.8	22.1	22.8	23.4	22.9	23.7	23.7	24.1	24.8	22.8	21	23.8	24.5	22.6	24.1	23.3	23.8	24.8	21.6	23.7	21	22.4	24.6	24.9	22.6	24.1	21.7	23.1	23	24.3	23	23.4	24	22.6	23.1	23.4	24.2	23.4	22.8	23.6	24.4	22.2	25.6	21.4	24.2	24.1	23.5	24.1	23.6	22.9	23.8	22	22.4	23.6	32.7	28	28.8	24.8	28.3	25.5	23.8	26.8	24.1	25.2	35.6	26.1	26	27.8	30.7	29.2	28	30	29.5	35.9	27.4	33.8	35.8	34.8	29.3	29.5	27.5	26.1	24.8	24.1	24.4	31.8	29.9	28.8	28	29.1	29.4	31.7	29	26.7	29.5	31.6	29	33.1	30.4	33	30.8	30.8	26.8	26	32.1	28.2	30.5	28.5	30.4	39.3	27.1	29.4	27.1	28.2	27.9	27.2	29.5	29	29.6	27.3	28.2	27.1	27.6	27	27.2	26.6	26.4	29.3	36.4	37.2	28	36.4	27.5	29.5	27.6	26	24.7	26.2	24.2	24.6	22.2	25.4	23.2	26.2	21.7	26.5	34.6	36.5	28.8	26.4	35	39.6	38.2	37.5	36.7	31.6	39.5	35.4	36.8	43.1	35	33.1	31	35.4	36.2	34	39.1	36	33	37	33	37	37	35.5	31.2	33.6	38	34.5	36.4	39.6	37.6	40.8	31.4	38.5	34.8	37.5	34	28.9	37.9	35.1	38	32.6	34	33.2	34.2	33.4	31.4	28	31	33.8	32.8	31.8	34.7	33.5	34.5	32	32	33.4	35.3	33	34.3	30.8	31.8	28.2	30.2	26.5	33.4	29.6	31.2	30	27.7	29.3	30.4	30.8	30.2	30.5	28.5	30.4	30.3	29.3	29.8	28.6	27.9	40	32	31.7	31.5	37.6	41.2	42.8	45.6	39.6	35.2	41.5	40	37	34.5	36.1	38	38.6	41	35.8	36.5	39	38.5	34.5	35.3	46.2	40.5	38.7	39.3	36.3	38.4	39.1	36.7	35	36.2	34.9	33.1	32.1	32	34.5	32.9	31.9	33	30.5	31.2	31	32.7	28.9	35.3	31.6	31.8	38.7	46.5	41	35.5	40	33.9	35.8	35.9	31.2	31.6	34.1	31	30	31	30.4	31.2	30	45.1	30.8	36.7	41.3	41	43	39.9	42.6	39	44.3	41.3	32.7	30.5	38.5	40.3	49.5	44.9	41.5	49	38.3	36.8	38.2	36.9	43.8	44.1	41	40.7	37	45	38.4	35	36.2	37	39.3	42	40	39.9	39.3	34.8	49.2	39.9	33.8	38.2	37.1	38.4	33.9	36.8	40.9	44.4	39.2	33.5	37.4	38.5	44.3	45	39.2	42.3	41.2	35.6	48.3	35.9	37.9	37.8	34.5	37.9	35.9	35.6	39.5	40.1	40.7	41.5	29.9	50.6	44.8	37.4	47.1	44.5	35.6	44.1	35.5	37.7	41.6	45	44	44.7	51	43.9	37.7	46	33.8	36.2	38.3	38.6	36.4	38.7	35	47.8	48.4	40	39	34.5	39.3	37.3	41.8	49.3	48.7	40.3	46.5	40.7	45.2	40.3	47.9	41.7	44	41	43.3	45.6	43	42.5	41	42.3	40.1	37	36.5	36.4	40	41.5	37.5	38.1	43	39	41.6	42.5	42.8	38	37	42.4	40.3	40	45.2	50.3	48	57.4	38.4	35.7	32	42.2	38.8	42	46.9	33.4	43.9	34.2	41.2	33.9	34.8	36.5	45.3	34	38.1	50	39.3	45.2	47.3	45.4	37	45	38.5	36.6	38.1	40.9	40.6	43.3	45	39.1	42	61.4	45.7	46.7	40.3	38.1	41.9	37.7	42.8	55	46.2	37.5	38	44.1	44.9	43.4	41.5	49.1	46.8	49.7	51.2	51	47.8	41.4	41.3	55.3	51.4	45.5	56.9	50	41.3	43.4	39	39.9	42.1	50	42.5	46.7	56.6	56.7	54.3	37.4	34.3	39	36.7	37.2	34.1	33.5	36.4	33.9	46	43.8	39	54.1	47.4	48.5	46.7	39.6	40.2	38.8	44.3	35.4	40	39.3	42.7	37.9	41.1	48.5	38.2	54	52.5	52.1	48.9	44.8	41.8	41.5	43.2	47	39.2	45.7	46.7	39.2	45.4	46.2	42.5	49.7	48.5	53.8	46	37.5	42	41	40.2	40	37	48.5	38.2	38	41.1	40	38	40.8	39.5	40.5	40.1	42.3	39.5	44.7	47	42.4	39.8	38.1	29.8	41.5	38.7	39.1	42	48.7	45.4	53.3	51	46.5	37.9	41	36	37.3	31.7	50.9	41.3	49.4	46	50.5	49.7	39.5	38.5	41	40.2	49.4	41.2	41.7	44.3	42.8	45.4	43.8	42	41.2	38.4	44.8	50.7	47.3	47.9	40.1	41.6	42	39	41.3	49.2	31.5	38	45.6	50.1	43.1	48.2	46.3	39	37.8	43.1	42.2	32.8	32.3	35.2	44.3	51.6	49.7	52.2	36.5	46.5	36.1	38.8	36	49.1	58.5	40.5	44.1	56	54	42.8	52.9	53.9	47.5	51.2	51.2	56.1	57.1	48.1	49.5	50.5	56.6	58	35.4	46.5	49.6	52.4	54.8	51.5	56.7	39.1	51.5	44.3	40.4	63.8	41.9	58	60	51.4	59.2	51	51.5	56	54.3	47	38	39.4	34.8	37.4	36.1	36.2	43	35.8	32.6	41	46.4	35.5	54.8	37.1	39.5	50.8	55.9	47.6	45.8	64.7	44.3	45.5	53.2	45.4	55.1	48	52.3	51.6	43.9	43.1	35.8	55.5	49.9	45.6	58	36.7	37.5	38.9	46.8	40.7	41.9	53.2	60.4	49.4	41.1	52.1	52.5	49.1	50.5	51.3	49	39.5	37	39.5	43.4	38.4	38.5	38.4	35	50.9	49	52.8	45.5	47.3	47.4	45.5	38	38.5	39.8	41	39.5	48	52.1	44	40.6	42.3	40.9	40.6	46.2	43	41.5	40.3	41	43.9	41	47.5	42.9	37	44.3	46.8	57.7	52.2	38.3	65.7	47	41.8	35.4	55.7	51.5	51	51.5	53.5	54.7	49.3	48.7	52	45.5	49.6	45	38.9	52.5	47.7	47.1	43	37.5	40.5	46	40.6	42.3	40.3	45.4	42.1	41.6	41.2	40.1	45	39.2	51	53.6	65.1	64	57.6	52.1	74	46	77.7	61.2	54.7	50.5	36.8	39.7	42	45.4	56.7	54	44.8	50.9	51.1	48.7	46.3	61.2	52.2	42	46.8	51.1	45	44.5	36	47	46	44	41.8	44.5	53.3	57.9	51	62.8	57	58.9	59.8	52.2	55.3	55.7	50.6	58	53.4	47.1	55.8	50.5	45.5	47.5	49.7	57.6	53.4	45.3	56.8	44.6	51.9	40.6	45.5	46.1	56	50	53.4	49.4	46.8	57.8	61.6	57.8	59.7	65.1	70.2	64.9	57.9	44.5	43.4	56	43.4	42.2	36.1	42.7	37.7	37.9	34.2	35.4	38.1	38.9	34.1	41	43.9	42.5	50.7	47.8	51	59.8	51.1	50.9	47.5	50	56.1	52.5	66.3	47.7	53.1	44.3	50.2	43.2	36.4	58.7	49.7	52.6	51.5	51.3	58.9	59	45.5	37.7	46.8	58.9	61.2	49.5	43.4	47.8	44.2	51.8	55.7	57.1	49.9	47.4	50	52	47.9	50.5	52	39.6	42	46.3	46	43.4	39.5	63.9	37	50	38.5	43	43.4	58.5	57	47	50	50.7	49	52	46.3	46.1	49.9	57.1	60	37	40.5	38.6	50.4	61	52	55.5	45.1	49.5	38.9	49.3	45.1	40.5	52.4	39.7	41.4	37.1	42.1	46.5	39.3	44.5	44.5	48.7	54.5	63.7	34.4	51.3	42.4	38.4	43.9	44	51.2	40.5	49.9	48	63.7	49.3	46.1	43.9	57.5	66	53.4	61	42.6	39	38.6	42.7	49.9	46.5	54.4	53.5	46.1	50.5	45.8	37	46	40.9	43.4	42.6	53.5	40.9	47.6	42.6	37.1	38.8	58.5	47.1	54.4	45.3	43.9	53.7	70.3	59.6	71	41.1	40.1	54.8	64.3	53	54.3	45.2	59.4	47.3	48.8	54.8	44.4	63.2	54.7	53.6	56.4	46.7	59.5	50.3	48.1	67.4	49.1	45	49.2	42.9	45.3	51.6	52.3	56.4	59.2	58.4	49.8	52.3	49	50.5	63.4	53.3	49.5	54.9	61.5	49.1	46.2	52.7	55	56.5	69.6	74	52.5	45	62.6	64.7	46.4	37	63.8	41	56	45.1	40.9	51.9	45.2	60.1	46.4	49.8	52.7	53	51.4	46.2	48.1	48.3	55.3	38	46.3	38.1	51.8	53.5	48.6	55.5	40.4	40.9	41.9	55.1	55.5	42.4	43.1	72.8	50.4	54.3	57	46.3	49.4	47.3	46	53.8	56	50.5	50.8	44.2	38.1	66.2	46.9	46	54.2	42.4	59.5	48	40	36	47.9	40	47.3	45	39.2	43.5	43.5	37.5	40.5	40	39.5	37	38.5	37	39.1	65	57.8	63.8	59.1	57.4	53	56	43.5	40	40.5	41.5	40.9	51	54.5	50.3	48	54	49.5	47.9	43.7	60.4	74.2	48.3	40	42.5	46.8	61.5	51.9	47.4	54.5	55.1	41.6	46	39.9	42.8	39.2	37.9	44.5	46.6	48.6	43.1	51	44.3	49.6	46	52.9	56.5	52.9	52.8	50.6	51.8	49.5	53	49.2	56.1	55.1	49.5	66	50.5	53	45.1	39.1	43.1	68.1	38.3	34.1	46.1	55.6	50.2	45.7	48	49.2	49.3	51.6	51.5	50	48	38.5	46.4	44.4	38.3	35.7	48	38	45.2	47.1	72.5	66.6	76.7	71	76	45.3	63.2	51.2	65.1	43.5	51	46.4	53.1	53.8	59.4	62.3	73.8	63.8	52.5	48.8	38.5	36.7	51.5	40.4	55.2	45.8	54.3	38.7	48.1	60.6	82.7	47.4	63.7	50.6	46.9	56.2	58	48.7	53.3	69.8	57	53	63	41.5	58.7	52	57.6	45.2	55	44.1	45	51.7	55.5	46.9	45.8	51.2	60	40.2	57.2	53.8	49.8	52.3	61	61	59.6	46.2	56.2	47.9	41.6	37	41.1	44.2	37.6	52.9	55.5	56.5	56.5	52.3	44	46.7	55.6	61.3	48.2	54	56	58	47	54.7	54.4	51.6	50.8	54.8	39.4	42.6	53.1	65.6	43.8	50	50.7	49	46.5	57	42.8	37.7	43.4	37.9	39.1	42.7	40	47.2	67.7	63	46.2	46.1	45.6	71	64.8	60.3	42.6	43	65.8	51.7	50.5	68.2	53.7	45.4	46	53.3	76.4	68	53.6	73.9	52.8	70.7	73.7	57	60.8	65.5	55.6	58.5	59	44.8	48.4	47.1	50.7	48.4	52.3	58.6	64.5	56	57.5	47.5	56	49.1	41.6	57	50	45.2	50.7	55.1	52.8	43.1	43.1	94.7	62.9	61	65	72	86.9	52.2	47.8	54.5	55.4	39.2	67.5	61.9	67.3	58.5	77.6	81	68	70.2	47.7	58.5	52.4	50.3	49.9	75	93	68	54.4	51.3	65.2	52.7	38.9	61.3	53	49.8	63.2	53.7	88.8	57.3	53.1	105.5	53.4	90.1	62.6	56.7	79.6	51.4	56.7	56.8	81.2	49.9	69.4	56.5	91.2	77.8	69.8	103	93.2	66.9	66	71.1	61.2	64	57	57.8	110.2	76.7	74	77	64.3	78.5	81	52.3	67.5	67	57	51.7	64.5	51	71	58.8	94.5	58	105	86	97.1	103.6	102.3	100.5	48.5	56	74	60.5	55.2	98.5	105.3	88.7	103	112.5	65.5	93.3	96.7; % Fork length, cm
    58.76	92.8	17.6	31.08	21.8	20.58	19.6	21.98	15.9	17.39	19.7	25.48	22.39	48.27	20.99	38.98	38.68	39.17	43.3	36.58	38.6	33.4	29.88	22.69	38.58	NaN	47.4	32.08	57.9	57.76	54.86	40.3	34.8	23	47.2	35.28	55.5	35.48	36.27	41.3	NaN	41.09	NaN	103.8	107.8	122.8	197	135.8	99.8	40.2	28.1	148.5	NaN	NaN	NaN	NaN	75.3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	115	126.8	148.8	190	175.82	136	285.62	NaN	279	158	221.6	NaN	NaN	NaN	NaN	NaN	NaN	193.7	203	170.4	186.8	NaN	197.9	108.7	188.2	166.1	285.7	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	449	291.5	277.7	181.8	275.5	202	158.7	239	175	199.7	576.5	241	238.8	286.7	393.7	303	289	324	339.5	556.58	257.7	442.56	592.44	524.43	202	273	246.59	201	NaN	NaN	NaN	360	335	311	288.63	293	319.59	408.68	315.7	234	304	396.7	303.65	452.5	356.4	NaN	373.6	353.8	230.3	NaN	NaN	NaN	369.1	NaN	327.6	316.3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	523.2	560	318.6	249.5	490	796	745.91	797	665.08	417.56	874.61	586.99	560.02	1129.34	578	427.58	376.56	538.4	644.2	463.2	770	639.5	523.57	779.67	560.8	670.2	656.58	587	446.68	510.56	715.8	545	606	796	742.3	895.47	NaN	745.5	472.77	596.9	478.66	271.28	617.06	516.9	506.6	477.8	408.4	468.7	512.88	410.3	401.57	264.63	348.58	NaN	466.7	NaN	NaN	407.6	NaN	400.7	NaN	NaN	583.2	457.9	NaN	342.6	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	847	386	427.7	390.7	672.76	874.64	1200	1336	777.57	540.19	954.9	913.2	756	610.6	659	845.62	779.97	922	534.91	638.3	758.8	778.4	538.77	563.4	1262.5	897	670.8	725.5	761.3	617.4	860.5	727.4	559.1	632.43	506	450.34	405	389	528.48	426.29	383.32	402.84	377.33	364	347	422	274.5	NaN	360.8	392.4	914	1522.13	718.97	NaN	811.8	477.7	611.6	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1255.07	392.1	632.6	922	915	1125	872.59	921.43	782.16	1068	1039.99	453	379.89	738.68	792	1528	1309	1021.8	1765.03	724	602	621	563	1150	1032.62	874	927	727.4	1288.03	697.12	518.4	556.13	626.7	840.5	941.47	752.55	742.6	654.91	559.04	1413.4	786.32	606	648.6	718.7	759.81	493	644.66	855	1057	764	476.8	716.57	863	1150.24	1184.63	830.55	874.51	841.4	510.63	1403.83	565.2	685.3	741	498.1	620.9	NaN	544.9	NaN	NaN	849.7	963.9	NaN	1796.4	1256	663	1356.47	1103	582	1048.99	585.2	841.98	1010	1239.13	1076.21	1097.45	1957.34	1130	642.71	1433	427.35	594.8	808	695.07	658	724.54	576	1624	1464.79	930	823	532	673.41	670.19	991	1655.48	1466	823	1359	916.76	1332	906.34	1619	951	1063	1003.46	1044	1145.85	1153.2	959.28	893.22	966.5	708.05	638.24	609.5	648.4	780.57	820.2	551.35	662.6	977.3	697.32	807.5	965.35	924.22	642.6	598.2	1068	771.14	731	1195.5	1794	1529.6	2610.5	737.8	663.5	484.6	782.8	820.2	1041.2	1353.43	456	1096	472.6	864	NaN	507.69	586	1175.23	422.4	680.58	1688.97	963.37	1276.84	1399.19	1028.8	586.47	1118.97	814.03	495.25	NaN	950.7	NaN	NaN	1566.8	NaN	NaN	3496.3	1231.4	1575	894	737.57	958.26	672.88	982.9	2579	1308	693	674.04	1226.1	1236.4	1141	894.87	1703	1402.09	1751	1618.12	2062	1664.68	1056.4	950.7	2252.15	1615.36	1312.13	2679	1723	878.47	1144.3	913	798	990	1815	1237	1463	2475	2539	2257	707.79	450.06	628.7	686.92	605	505.03	507.72	698.26	493.28	1370	1108.58	851.85	2294	1549	1793	1482.39	852.46	792.42	817.85	1260	589.53	817	740	1016.2	610	939.68	1616	675	2184	2105	1878.57	1428	1293.73	837	862.15	1255	1342.56	789	1086	1375.6	828.6	1178	1424	1239	1758	1568.56	2483.3	1355.1	586.4	1058.5	856.31	851.5	898.4	653.8	1693.5	665.15	684.7	691.3	734.02	601	707.2	779.14	768.05	847.4	951.2	711.56	1203.9	1390.8	807.9	767.6	658.2	530.3	845.1	647.9	738.6	1001.55	1532.88	1221.7	1941.55	1800.3	1346.28	708.82	912	577.92	615.63	398.21	2037.27	876.43	1784.94	1601.1	1722.97	1484.96	745.8	624.48	765.32	710.69	1467.94	776.33	886.27	1027.29	1097.1	1050.6	1229.46	850.75	796.12	674.83	1008.46	1313.66	1389.88	1455.48	680.72	903.5	979.5	714.67	765.49	1287.02	507.22	581.32	1152.81	1792.35	1182.7	1464.19	1477	947	710	1121.65	956.23	512.11	447.02	655.43	1261.75	2066.67	1575.6	1832	665	1509	828	756.78	591.87	1784.6	2306.2	927.2	1374	2551	2483	960.56	2284	2246	1529.5	1879.52	1901.3	2630	2230.61	1787.16	1811.39	1803.56	2593	3118.1	556.58	1085.11	1822	1986.08	2190	1952	2646	752.3	2078	1042.29	919.11	4090	1050	2999	3081	1926	3028	1930	1907	2580	2360	1487	737	663.2	635.27	676.03	622	641.67	941	530	417.09	828.19	1435	625	2252	690	838	1758	2408	1360.79	1259	4782	1232	1373	2327	1224	2385.71	1394	2151	1743.84	1042	982.99	561.4	2419.29	1738	1201	2769	639.85	654	803.99	1407.94	929.33	1080.91	2233	3316.21	1604	937.51	2105.47	2266	1482.92	1697	2057	1779.7	1017.1	625.6	821.74	946.9	713.31	651.4	774.8	508.3	1976.61	1413.3	1823.2	1278.4	1490.51	1321.19	1516.3	608.17	639.4	753.97	857.2	799	1402.46	1793.91	1092.21	859.34	985	754.8	797	1283.8	1045.3	973.7	775.02	747.87	971.5	820	1327.4	956.9	656.1	1091	1409.12	2683.1	2174.3	662.53	4833	1514.7	NaN	538.01	1971.2	2045.5	1865	2100.88	2272.72	2185.62	1992.3	1724.02	1991.18	1522.08	1606.88	1079.83	653.3	2385.46	1081.33	1309.04	994.39	584.66	864.99	1222.99	794.03	858.47	725.21	1209.85	831.88	956.3	832.69	723.54	1057.06	688.65	1774.13	NaN	NaN	NaN	2820	1803.4	8087	1215	10196	3976	2393	2259	597.19	862.83	1086	1346	2337	2262	1262	1711.8	1810	1471.41	1145.48	3572	1901.9	1213	1414	1909	1302.7	1406	634.67	1398.89	1380.3	1025.31	813.38	1203.62	2210.96	2774.33	2074.14	4122	3168	3337	3410	1798.19	2698	2488	2055.44	3003.38	1975.57	1506.6	2389.72	1830.5	1114.51	1473.9	1550	2806.96	1773.66	1373	2436.6	1372	2043	945	1403	1322	2316	2075	2383	1825.99	1210	2955	3348	2520	3177	4628	6390	3585	2547	1014.75	1276	2256	1127.05	968	744.56	893	636.1	634.38	505	602.89	638.83	807.86	525.32	829.28	1116	1047	1510.82	1360.6	1716.13	3420	2043	1819	1521.5	1748	2697.1	1999	3988	1514	2142.87	1006	1786	1033.06	554	2814.67	1477	1988.88	2025.45	2031	2978.78	3100	1184.35	617.13	1531.88	3181	4553	1760	1107	1628	1315.42	1858	2447.18	2757.62	1744	1452	1828.9	1841.7	1731.7	1982.1	2380.8	860.5	880.7	1472.2	1245.64	1043.6	800.1	4045.9	646.8	1679.9	855.5	1063.9	977.8	2359.6	3190.6	1400.3	1791.1	1891.1	1850	1714.6	1379.94	1129.31	1740	2966	3255.4	637	806	731.27	1421.56	2711.6	1939.91	2379.3	1139.6	1610.1	764.3	1651.1	1334.2	857.7	2027.2	694.2	757.8	631.3	742.9	1300.6	779.7	1297.16	1266.2	1756.2	2912.39	3877.4	519.54	1954.7	956	691.74	1045.6	1068.46	1886.69	895.83	1775.44	1995.17	3931.5	1613.58	1714.21	938.3	2761.69	3877.72	2210.72	3657.86	904.81	697.54	618.96	895.57	1524.18	1305.82	2209.12	2039.52	1301.32	1593.02	1167.51	563.89	1427.96	914.01	981.6	992.4	1635.32	735.96	1303.88	914.5	625.91	787	3010.59	1299.87	2277.5	NaN	NaN	NaN	5866.7	3029	5100	851	1074	2150.37	3884.06	1988	1958.8	1181.28	2790	1528.5	1454.7	2418.6	1131.8	3856	1950.43	2140.4	2674	1346.9	3166	1731	1427	4361.5	1877.8	1438	1691	1209.22	1321.2	2192	2408	2887.61	3332	3283	2049	2302	1633.06	1799	3793	2010.13	1516.85	2042.3	2553.91	1274.06	1341	1971.21	2253	2572.6	4833	6241	1741.28	1368	3513	4484	1290	625	3652.47	767.04	2393	1168	965	2196	1112.37	3285	1617	1762.61	2037	2045	1956	1260	1547.48	1616	2490	731.47	1264	689	2010	2557.08	1613.31	2500	870.67	912.44	995.73	2239.34	2941.88	956	1137	7038.93	1727.71	2325	2793	1332	1793	1434	1361.84	2216.93	2674.16	2079.33	1651	1143.79	729.2	4545.7	1280.3	1353.4	2752.2	1128.8	2669.5	1629.62	843.4	781.1	1541	839.8	1340.4	1237.7	694.7	1013.3	918.4	750.5	917.3	734.56	899.7	679.1	715.6	663.94	684.5	3802.79	2473.7	3000	3225.7	2707.8	1999.1	1974.07	920.6	771	819.11	907.3	746.71	1593.67	1930.49	1508.66	NaN	1850.97	1306.89	1746.9	927.19	2972	6225	1585.9	898	1210.7	1394.02	3919.4	2147.5	1627.3	2347	2129	NaN	1299	780.99	962	738.44	734	1072.5	1390	1620.52	1165.22	1603.04	1204.89	1840	1316.1	1882.78	2672	1782.52	2132.98	2050.2	2162.53	1701.71	1531.88	1439.5	2211.14	2312.43	1628.98	3990.76	1489.91	1953.01	1115.12	680.02	865.35	5386.1	659.85	477.08	1484.61	2389.85	1546.88	1245.49	1563.5	1881.47	1632.99	1898.62	1981.9	1428.34	1319.99	603.82	1034.7	1013.82	684.1	486.6	1408.2	704.09	1005.32	1206.44	6716.2	NaN	7332.8	5558	8019.36	1406	4296	1930.37	4239.52	1055.72	1957.6	1294	1965	1736.1	2710.16	4331	6011.57	3196	2295	1609	832.15	607	1766.25	1003	2322.03	1268	2398	802	1545.94	4095	10434.1	1626	4675.84	1877	1095.18	2488	2514.46	1761	2061	5757.21	2934.14	2034.6	5326	954.8	2864.2	2288.98	2252	1391.5	2316.02	1031.5	1256.4	2004.24	2453.2	1442.99	1297.64	1853.48	2077.7	755.6	2847.2	2411.6	1616.04	1565.9	3324.8	3944.87	2874.24	1481	2775	1522	894.81	NaN	905.96	1025.73	683.7	1742.52	2616.54	2862.92	2617.49	2153.84	1263.59	1340.68	2768.24	3626.95	1871.94	2121.19	2172.69	2423.56	1724.3	1868.66	2165.97	2089.06	1833.77	2254.45	682.6	989.75	1594.23	3762.72	1024.64	1901.31	1434.56	1704.4	1274.27	2330.68	948.3	723.2	NaN	628.09	703.73	1134.9	750.76	1338.84	4559.1	NaN	1274.5	1296.78	1100.8	5263.12	4075.05	2779.57	1204.77	1180	3955.25	1928	1747	5831	2150	1270	1309.16	2171	6269	4131	2213	6404	2071	6024	8193.3	2828.1	2882.75	3891	2673.94	2796.1	2968.35	1281.9	1585.5	1281.58	1884.1	1679.42	2335.32	2735.73	3719.73	2545.85	2875.43	1765.99	2290.83	1463.51	789.45	2494.92	1621.47	1367.8	1906.56	2028.28	1972.6	1029.18	1053.59	14865.49	4281.1	NaN	NaN	6496	10268	1681.73	1313.03	2405.84	2288.32	730.45	4249.8	2947.97	4842	2840.69	7937	10003	4135	5901	1327.3	3128.95	2202	1846	1696.91	6810	14652	4947.9	1861.95	1741.5	4917	1483.2	984	3547.56	1745.55	1179.23	3927.2	2079.64	13618.7	2412.49	1815.87	25725.23	2215.03	14305.2	4105.1	NaN	9775.1	2254	2778.04	2326.76	8531.63	1803.27	4634.18	2139.65	13055.8	6950.12	4581.7	21574	15468.3	NaN	3798.4	6562.2	3316.57	4027.39	2151.74	2545.38	29610.4	NaN	6910	7804	3561.17	9740.3	10276.2	1910.1	4603.88	4645.58	2559.24	1702.55	4671.1	1549.2	4199.41	2631.95	17070	3056.31	25623	12494.4	19856	21018.7	29308	21408.3	1366.7	NaN	NaN	NaN	2139.07	18305.8	22210.54	NaN	19832.44	29348	3545.4	15779.8	19239.14; % Somatic weight of fish; g
    0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	1	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	0	0	0	0	1	1	0	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	1	0	0	1	1	1	0	0	0	0	0	1	1	1	1	1	1	0	1	0	0	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	0	0	0	0	0	1	1	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	1	1	1	1	1	0	1	1	0	1	1	1	1	1	1	1	1	0	0	1	1	1	1	1	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	0	0	1	1	1	1	1	0	0	1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	0	1	0	1	1	1	1	1	1	1	1	0	0	0	0	0	1	1	1	1	0	0	0	1	1	1	1	1	0	1	1	1	0	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	1	1	1	1	1	0	1	1	0	0	1	0	0	0	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1	1	1	1	1	1	0	1	1	1	0	0	0	0	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	0	0	0	0	0	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	1	1	1	1	1	0	0	0	0	0	0	1	1	0	0	1	1	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	0	0	1	1	0	1	1	1	1	1	1	0	0	1	1	1	1	1	0	0	0	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	0	0	0	0	0	0	0	0	0	1	0	0	1	1	1	0	0	0	0	0	0	1	0	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	0	0	0	0	1	1	1	0	0	0	0	0	0	0	1	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	0	0	0	1	0	0	0	0	1	1	1	1	0	1	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	1	1	1	1	1	0	1	1	0	0	0	1	0	1	1	1	1	0	0	1	1	0	1	1	0;   % River; 1 = James, 0 = York
    1	0	0	1	0	1	0	1	0	1	0	1	1	1	1	1	1	1	0	1	0	0	1	1	1	1	0	1	0	1	1	0	0	0	0	1	0	1	1	0	1	1	1	1	1	1	0	1	1	0	0	1	1	1	1	1	0	1	1	1	0	0	1	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	1	0	1	0	1	0	1	0	0	1	0	1	1	1	1	1	1	1	1	1	0	0	1	0	1	1	0	0	1	1	0	1	0	1	1	0	0	0	1	0	1	0	0	1	1	0	0	0	1	0	0	0	0	0	1	1	1	1	1	0	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	1	1	1	1	0	1	1	1	1	0	1	0	1	0	1	0	1	1	1	0	0	0	1	0	0	0	1	0	1	1	0	1	0	0	1	1	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	1	1	1	0	0	0	0	0	0	0	1	0	0	1	1	1	1	1	1	1	1	0	0	1	1	1	0	1	1	1	1	0	1	0	0	1	1	0	1	1	1	0	0	0	0	1	1	1	1	1	0	0	1	0	1	0	0	0	0	0	1	0	1	1	1	0	0	1	1	0	0	1	0	0	0	1	1	1	0	1	0	0	1	1	1	0	0	1	0	1	0	0	0	0	0	1	1	0	0	0	1	0	1	0	0	1	1	1	1	0	1	0	0	0	1	0	1	1	1	0	1	1	0	0	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	0	1	1	0	0	0	0	1	1	1	0	1	0	1	1	1	0	0	0	0	0	1	0	1	1	1	1	0	1	1	0	1	0	1	1	0	0	1	0	1	1	1	1	1	1	1	1	0	0	1	0	0	1	0	0	1	1	1	1	1	1	0	1	1	1	1	0	0	1	1	0	1	0	0	0	1	1	0	1	0	0	1	1	0	1	0	0	0	0	1	0	0	0	1	0	1	0	0	1	1	1	1	1	0	0	0	0	1	1	1	0	1	1	1	0	0	0	1	1	1	1	0	1	0	0	1	0	1	0	1	0	0	0	1	1	1	0	1	0	1	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	1	0	1	1	0	1	1	1	0	1	0	1	0	0	0	1	1	0	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1	1	0	0	1	1	0	1	1	0	0	1	1	1	0	1	1	1	1	0	1	0	1	1	0	1	0	1	0	0	1	0	0	0	1	1	0	1	0	0	0	1	0	1	0	0	0	1	0	1	1	1	1	0	1	1	0	0	1	0	1	0	0	1	0	1	1	0	0	0	1	1	1	0	1	0	0	1	0	0	0	1	1	0	0	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	0	1	1	0	1	0	0	1	1	1	1	0	0	0	1	0	0	0	1	0	1	0	1	0	1	0	0	1	1	1	0	0	1	1	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	1	1	0	1	1	0	0	0	1	1	1	1	0	1	0	0	1	0	1	0	0	0	0	1	0	1	0	1	0	1	0	0	1	1	0	0	0	0	1	1	0	0	0	1	0	0	0	0	1	0	1	1	0	1	1	1	0	0	1	0	0	0	0	0	0	1	0	0	1	1	0	1	0	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	1	1	0	0	1	0	0	0	1	0	0	1	1	1	0	1	1	1	1	0	0	1	1	0	1	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	1	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	1	1	1	1	0	0	1	0	1	1	1	1	0	1	0	1	1	0	1	0	0	0	0	1	1	1	1	1	0	0	1	0	0	0	1	1	0	1	1	1	1	0	1	1	1	1	0	0	1	0	0	0	1	1	0	0	0	1	0	0	1	0	0	1	0	0	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	0	0	0	1	1	0	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	1	1	0	1	1	1	1	1	1	0	0	0	0	1	0	0	1	1	1	0	1	0	1	0	0	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	1	1	0	1	1	1	1	1	0	0	1	1	1	0	0	0	1	0	1	0	0	0	1	0	0	1	0	1	0	1	1	0	1	0	1	1	1	0	0	0	0	0	1	0	1	1	0	0	0	1	0	0	0	0	0	0	1	1	0	1	1	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	1	1	1	1	1	1	0	0	1	1	1	0	1	0	1	1	1	0	0	1	0	0	1	0	1	0	0	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	0	0	0	1	0	1	1	0	1	1	0	0	0	0	1	0	1	1	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	1	1	1	1	1	1	1	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	1	0	0	0	1	1	0	1	1	1	1	1	0	0	1	1	0	0	0	0	0	1	1	1	1	0	1	0	1	1	1	0	0	1	1	0	1	0	0	1	0	1	1	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	1	1	1	1	1	1	1	0	1	0	0	1	1	0	1	1	1	0	0	0	1	0	1	0	1	0	0	0	1	1	1	1	0	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	0	1	0	0	1	1	1	1	0	0	1	1	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0	1	0	1	0	1	0	1	0	0	1	1	1	1	0	0	1	1	0	1	1	0	1	1	1	1	1	0	0	1	0	1	0	0	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	1	0	1	0	1	0	0	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	0	0	1	1	1	1	1	1	1	0	1	0	0	0	0	1	1	0	0	1	0	0	0	1	1	0	0	0	1	1	1	1	1	0	1	1	1	1	0	0	1	0	0	1	1	1	1	1	1	0	1	1	0	0	1	1	1	1	1	1	1	0	1	0	0	1	1	1	0	1	1	1	1	0	1	1	1	0	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1]'; % Sex; 1 = female; 0 = male

% Time since birth - Fork length James
data.tL1       = tLW1(tLW1(:, 4) == 1 & tLW1(:, 5) == 1, [1 2]);   % 1 is James in column 4; 1 is Female in column 5
data.tL1       = rmmissing(data.tL1); % Remove rows with missing (i.e., NaN) values
data.tL1(:, 1) = data.tL1(:, 1);    % age (d)
data.tL1       = sortrows(data.tL1, 1);   % Sort it in ascending order based on age
units.tL1      = {'d', 'cm'};  label.tL1 = {'time since birth', 'fork length', 'females, recent period'};  bibkey.tL1 = 'NepaFabr2020';
temp.tL1       = C2K(17.3);  units.temp.tL1 = 'K'; label.temp.tL1 = 'temperature';
comment.tL1    = 'Females, James recent period';
% males
data.tL1_m       = tLW1(tLW1(:, 4) == 1 & tLW1(:, 5) == 0, [1 2]);   % 1 is James in column 4; 1 is Female in column 5
data.tL1_m       = rmmissing(data.tL1_m);     % Remove rows with missing (i.e., NaN) values
data.tL1_m(:, 1) = data.tL1_m(:, 1);    % age (years)
data.tL1_m       = sortrows(data.tL1_m, 1);   % Sort it in ascending order based on age
units.tL1_m      = {'d', 'cm'};  label.tL1_m = {'time since birth', 'fork length', 'males, recent period'};  bibkey.tL1_m = 'NepaFabr2020';
temp.tL1_m       = C2K(17.3);  units.temp.tL1_m = 'K'; label.temp.tL1_m = 'temperature';
comment.tL1_m    = 'Males, James recent period';

% Length-weight James females;
data.LWw1    = tLW1(tLW1(:, 4) == 1 & tLW1(:, 5) == 1, [2 3]);   % 1 is James in column 4; 1 is Female in column 5
data.LWw1    = rmmissing(data.LWw1);
units.LWw1   = {'cm', 'g'};   label.LWw1 = {'fork length', 'wet weight (somatic)', 'females, recent period'};  bibkey.LWw1 = 'NepaFabr2020';
comment.LWw1 = 'Females, James recent period (somatic weight)';
% males
data.LWw1_m    = tLW1(tLW1(:, 4) == 1 & tLW1(:, 5) == 0, [2 3]);   % 1 is James in column 4; 1 is Female in column 5
data.LWw1_m    = rmmissing(data.LWw1_m);
units.LWw1_m   = {'cm', 'g'};   label.LWw1_m = {'fork length', 'wet weight (somatic)', 'males, recent period'};  bibkey.LWw1_m = 'NepaFabr2020';
comment.LWw1_m = 'Males, James recent period (somatic weight)';


% Fork length - Fecundity
%%%%%%%%%%%%%%%%%%%%%%%%%
% columns: Fork length (cm), fecundity (#), River (1 = James, 0 = York)
LN1 = [...
    341	383	385	386	389	390	391	392	394	402	410	410	412	412	416	417	420	423	425	426	426	427	428	431	438	439	440	441	443	445	450	450	451	454	454	455	457	460	461	461	465	467	467	469	471	474	477	480	485	487	491	492	493	494	495	495	497	497	498	499	500	502	503	504	505	507	507	508	509	509	510	510	512	516	518	520	520	520	521	521	522	522	523	525	528	529	529	530	530	531	533	535	536	537	538	540	543	544	545	547	547	547	551	551	555	555	555	556	557	557	560	560	560	560	561	565	565	565	567	570	570	570	571	573	575	575	578	579	580	580	585	586	610	613	619	630	632	637	643	645	656	660	660	660	670	674	681	698	315	323	338	343	352	354	354	355	356	357	360	360	360	366	368	370	370	370	370	371	374	375	377	377	379	379	380	380	380	384	384	385	385	385	386	386	388	388	389	390	390	391	392	392	392	394	395	400	400	400	401	401	403	405	405	405	406	406	408	409	410	410	411	411	412	412	413	413	416	416	418	420	421	422	423	426	426	427	428	428	430	431	431	431	434	434	434	435	435	437	440	440	444	447	447	448	450	450	454	455	456	456	458	460	460	461	462	464	465	465	470	471	471	472	473	473	476	479	480	480	482	483	492	495	500	505	507	510	510	514	528	534	535	538	548	552	553	570	576	585	595	617	639	648	655	698	707	710	760	785	810; % Fork length, mm
    5164	8789	7974	6140	8799	12540	6928	3505	7268	10893	8495	8767	8211	11859	6949	7151	14714	10423	6042	8227	11529	13622	8590	11713	8391	8945	7933	9590	8260	7976	5478	8121	9313	9802	15814	15540	17214	11391	11436	6766	15418	13756	10808	8032	17708	8856	15845	10187	14287	11736	19499	12142	11333	13394	7815	16789	19052	14945	10222	18736	20283	13578	7251	9792	14026	11841	12753	12559	17453	10238	12019	20294	16715	13719	13438	17984	11062	16483	15436	10844	23251	20819	20703	15775	17078	13259	14071	9813	14877	13705	18957	25151	22971	17212	11544	10392	17325	15223	9380	24324	15553	16123	31714	23339	17233	13177	15384	17153	16449	15375	13406	26646	16179	17531	26432	15137	17540	9245	19947	11343	21073	19755	27042	28700	30439	17355	27174	29489	21401	17330	12286	29543	29079	31145	20345	24189	24186	36024	34675	50424	23197	34417	31106	32233	28126	41312	31495	35043	8686	6792	4347	5495	9196	8406	2613	7103	7788	7025	9234	6214	6266	7408	8497	7848	4059	11005	7730	10421	5405	7085	7777	7833	8821	9209	9538	5499	6983	5580	8152	4777	11759	11780	7880	7225	10986	4949	8535	6179	8436	6586	10305	4301	8354	6361	5771	5069	12622	8826	12281	7269	9170	14942	10580	9252	12019	10931	6040	10533	8431	12863	9711	7591	11714	10036	9719	10537	14611	9060	11277	13662	12087	9048	9813	13243	11593	7275	13136	8974	8878	11812	11470	9973	9256	9264	9013	11900	7060	9842	12566	12263	14476	8052	13595	17456	12901	11497	12738	15639	16952	14336	15792	14527	13796	15222	15579	12296	17452	15794	10379	13937	14095	18237	6701	18003	15180	13314	18457	20106	15481	16324	13952	19830	16806	19962	22419	20180	13984	22699	22605	21462	20292	11155	24826	18598	21173	26791	15911	26982	16983	39533	24287	41449	30651	49399	36341	51267	63040	60395	68356; % Fecundity, #
    0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1]'; % fecundity, # 
data.LN1 = LN1(LN1(:, 3) == 1, [1 2]);   % 1 is James
data.LN1(:, 1) = data.LN1(:, 1)./10;    % Fork length (cm)
units.LN1 = {'cm', '#'}; label.LN1 =  {'fork length', 'fecundity'};  
temp.LN1 = temp.tL1;  units.temp.LN1 = 'K'; label.temp.LN1 = 'temperature'; 
bibkey.LN1 = 'NepaFabr2021b';

% FL - GSI for James (Gravid Females only)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Columns: FL (mm); GSI = Gonad weight / total weight; River (1 = James; 0 = York)
L_GSI1 = [...
    383	385	386	389	390	391	392	394	402	410	410	412	412	416	417	420	423	425	426	426	427	428	431	438	439	440	441	443	445	450	450	451	454	454	455	457	460	461	461	465	467	467	469	471	474	477	480	485	487	491	492	493	494	495	495	497	497	498	499	500	502	503	504	505	507	507	508	509	509	510	510	512	516	518	520	520	520	521	522	522	523	525	528	529	529	530	530	531	533	535	536	537	538	540	543	544	545	547	547	547	551	551	555	555	555	556	557	557	560	560	560	560	561	565	565	565	567	570	570	570	571	573	575	575	578	579	580	580	585	586	610	613	619	630	632	637	643	645	656	660	660	660	670	674	681	698	377	377	379	379	380	380	380	384	384	385	385	385	386	386	388	388	389	390	390	391	392	392	392	394	395	400	400	400	401	401	403	405	405	405	406	406	408	409	410	410	411	412	412	413	413	416	416	418	420	421	423	426	426	427	428	428	430	431	431	434	434	434	435	435	437	440	440	444	447	447	448	450	450	454	455	456	456	458	460	460	464	465	465	470	471	471	472	473	473	476	479	480	480	483	492	495	500	505	507	510	510	514	528	534	535	538	552	553	570	576	585	595	617	639	648	698	710; % Fork length, mm
    0.178	0.165	0.161	0.169	0.183	0.133	0.042	0.156	0.156	0.182	0.166	0.197	0.161	0.173	0.112	0.162	0.14	0.137	0.088	0.125	0.144	0.146	0.141	0.097	0.174	0.136	0.191	0.098	0.129	0.128	0.107	0.149	0.122	0.113	0.137	0.106	0.08	0.116	0.066	0.141	0.087	0.169	0.154	0.146	0.107	0.172	0.139	0.107	0.123	0.172	0.119	0.057	0.15	0.137	0.082	0.145	0.113	0.141	0.132	0.133	0.114	0.096	0.138	0.072	0.094	0.148	0.07	0.141	0.045	0.146	0.169	0.08	0.063	0.056	0.063	0.118	0.077	0.107	0.161	0.15	0.083	0.119	0.148	0.131	0.168	0.17	0.117	0.106	0.098	0.126	0.097	0.131	0.053	0.123	0.152	0.149	0.121	0.136	0.133	0.136	0.127	0.103	0.1	0.098	0.079	0.099	0.106	0.162	0.065	0.168	0.121	0.07	0.167	0.049	0.119	0.075	0.067	0.093	0.132	0.075	0.143	0.142	0.108	0.114	0.092	0.119	0.158	0.141	0.078	0.135	0.091	0.083	0.119	0.058	0.109	0.116	0.119	0.144	0.128	0.141	0.123	0.15	0.115	0.095	0.066	0.085	0.164	0.168	0.148	0.171	0.163	0.123	0.119	0.123	0.108	0.138	0.133	0.183	0.152	0.184	0.169	0.052	0.109	0.155	0.122	0.145	0.171	0.124	0.136	0.165	0.073	0.117	0.177	0.137	0.179	0.136	0.159	0.147	0.156	0.121	0.178	0.123	0.145	0.185	0.145	0.178	0.154	0.148	0.126	0.15	0.152	0.153	0.122	0.168	0.136	0.161	0.116	0.141	0.149	0.06	0.146	0.143	0.118	0.15	0.143	0.178	0.172	0.089	0.151	0.171	0.149	0.155	0.15	0.17	0.109	0.146	0.145	0.145	0.142	0.126	0.137	0.17	0.145	0.116	0.132	0.144	0.135	0.154	0.133	0.111	0.128	0.143	0.144	0.119	0.115	0.117	0.093	0.162	0.137	0.116	0.118	0.156	0.132	0.147	0.155	0.179	0.08	0.138	0.14	0.111	0.143	0.17	0.076	0.116	0.157	0.064	0.122	0.143	0.159	0.121	0.14	0.134	0.118; % GSI, - 
    0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1]'; % River
data.LGSI1 = L_GSI1(L_GSI1(:, 3) == 1, [1 2]);   % 1 is James
data.LGSI1(:, 1) = data.LGSI1(:, 1)./10;      % Fork length (cm)
units.LGSI1 = {'cm', '-'};   label.LGSI1 = {'Fork length', 'GSI'};  bibkey.LGSI1 = 'NepaFabr2021a';
temp.LGSI1   = temp.tL1; units.temp.LGSI1 = 'K'; label.temp.LGSI1 = 'temperature';
comment.LGSI1 = 'Data discussed, but not presented in NepaFabr2021a; Data obtained from lead author';


% Wet weight - O2 consumption
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Columns: FL (cm); Total wet weight (g); Specific Oxygen Consumption (mg O2/kg/h); Sex (1 = Female; 0 = Male)
% WwJO = [...
% 18.8	19.1	19.2	19.3	19.31	19.79	19.8	19.81	20	20.3	20.39	20.4	20.41	20.6	20.8	20.81	21	21.1	21.39	21.4	21.41	21.42	21.45	21.5	21.6	21.7	21.8	21.81	22.1	22.2	23.3	23.6	; % FL, cm
% 76.9	79.9	92	76	79.4	86.9	91	96.1	91.9	103.9	93.2	96.2	109.4	114.3	104.1	105.1	123.5	105.2	108.7	110.4	114.7	119.7	122.3	110.8	113.8	130.4	115	123.6	125.8	138.8	157	152.7	; % Wet Wt, g
% 71.6	82.1	73	67.5	69.2	68.9	79.1	59.3	55.5	73	74.3	85	84.8	74.5	64.9	69.7	65.2	67.8	65.4	64.4	57.1	83.9	62.2	82.9	85.4	61.4	63.4	67	63.6	58.8	57.5	55.6	; % mg O_2/kg/hr
% 0	0	0	1	0	0	0	1	1	1	1	0	0	1	1	0	0	0	1	0	1	0	1	1	0	1	1	0	1	1	1	0	]'; % Sex 0 = male; 1 = female
% WwJO(:, 3) = WwJO(:, 3) .* WwJO(:, 2) ./ 1000 * 24;      % Convert to mg O_2/d
% data.LwJO = WwJO(WwJO(:, 4) == 1, [2 3]);      % Females
% data.LwJO = sortrows(data.LwJO, 1);   % Sort it in ascending order of the independent variable
% units.LwJO = {'g', 'mg O_2/d'};  label.LwJO = {'wet weight', 'O_2 consumption'}; 
% temp.LwJO  = C2K(20); units.temp.LwJO = 'K'; label.temp.LwJO = 'temperature';
% bibkey.LwJO = 'NepaFabr2021b';
% comment.LwJO = 'Female Juveniles only; Data discussed, but not presented in NepaFabr2021b; Data obtained from lead author';
% 
% data.WwJO_m = WwJO(WwJO(:, 4) == 0, [2 3]);      % Males
% data.WwJO_m = sortrows(data.WwJO_m, 1);   % Sort it in ascending order of the independent variable
% units.WwJO_m  = {'g', 'mg O_2/d'};  label.WwJO_m = {'wet weight', 'O_2 consumption'}; 
% temp.WwJO_m   = C2K(20); units.temp.WwJO_m = 'K'; label.temp.WwJO_m = 'temperature';
% bibkey.WwJO_m = 'NepaFabr2021b';
% comment.WwJO_m = 'Male Juveniles only; Data discussed, but not presented in NepaFabr2021b; Data obtained from lead author';

WwJO = [...
76	76.9	79.4	79.9	86.9	90.7	90.9	91	91.01	91.9	92	93.2	94.9	96.1	96.2	96.9	97.5	102.7	103.9	104.1	105.1	105.11	105.2	106	108.7	109.1	109.4	109.7	110.4	110.8	110.81	113.8	114	114.3	114.7	114.71	115	116.6	119.5	119.51	119.7	122.3	123	123.5	123.6	125	125.1	125.8	125.81	126	127	130	130.4	132.3	138.8	140	142.3	152.7	157; % Wt, g
67.5	71.6	69.2	82.1	68.9	64.8	59.5	70.3	79.1	55.5	73	74.3	68.5	59.3	85	68.5	75.2	62.5	73	64.9	66.2	69.7	67.8	77.1	65.4	71.8	84.8	79.1	64.4	79.7	82.9	85.4	63.1	74.5	62.9	57.1	63.4	64.7	65.6	57	83.9	62.2	60.2	65.2	67	64.7	61.1	69.2	63.6	62.8	62.6	68.8	61.4	64.2	58.8	67.8	67	55.6	57.5; % SMR mg O_2/kg/h
1	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	0	1	1	0	0	0	0	1	0	0	1	0	0	1	0	0	1	0	1	1	0	1	0	0	1	0	0	0	1	1	0	1	1	1	0	1	0	1	1	0	0	1]'; % Sex; 1 = female; 0 = male
WwJO(:, 2) = WwJO(:, 1) .* WwJO(:, 2) ./ 1000 * 24;      % Convert to mg O_2/d
data.WwJO = WwJO(WwJO(:, 3) == 1, [1 2]);      % Females
data.WwJO = sortrows(data.WwJO, 1);   % Sort it in ascending order of the independent variable
units.WwJO = {'g', 'mg O_2/d'};  label.WwJO = {'wet weight', 'O_2 consumption', 'juvenile females'}; 
temp.WwJO  = C2K(20); units.temp.WwJO = 'K'; label.temp.WwJO = 'temperature';
bibkey.WwJO = 'NepaFabr2021b';
comment.WwJO = 'Female Juveniles only; Data discussed, but not presented in NepaFabr2021b; Data obtained from lead author';

data.WwJO_m = WwJO(WwJO(:, 3) == 0, [1 2]);      % Males
data.WwJO_m = sortrows(data.WwJO_m, 1);   % Sort it in ascending order of the independent variable
units.WwJO_m  = {'g', 'mg O_2/d'};  label.WwJO_m = {'wet weight', 'O_2 consumption', 'juvenile males'}; 
temp.WwJO_m   = C2K(20); units.temp.WwJO_m = 'K'; label.temp.WwJO_m = 'temperature';
bibkey.WwJO_m = 'NepaFabr2021b';
comment.WwJO_m = 'Male Juveniles only; Data discussed, but not presented in NepaFabr2021b; Data obtained from lead author';

%% set weights for all real data
weights = setweights(data, []);
weights.Ri    = 0.3 * weights.Ri;
weights.Wwi  = 0.3 * weights.Wwi;
weights.Wwim = 0.3 * weights.Wwim;
weights.amm  = 0.3 * weights.amm; % Because size- (and so, age-) selective harvesting may be happening
weights.Lb    = 0 * weights.Lb;

%% set pseudodata and respective weights
[data, units, label, weights] = addpseudodata(data, units, label, weights);
%weights.psd.k_J = 0; 
data.psd.k = 0.3; weights.psd.k = 0.1; units.psd.k  = '-'; label.psd.k  = 'maintenance ratio';

%% pack auxData and txtData for output
auxData.temp = temp;
txtData.units = units;
txtData.label = label;
txtData.bibkey = bibkey;
txtData.comment = comment;

%% Group plots
set1 = {'tL0','tL1'}; subtitle1 = {'early female, recent female'};
set2 = {'LWw0','LWw1'}; subtitle2 = {'early female, recent female'};
set3 = {'tL0_m','tL1_m'}; subtitle3 = {'early male, recent male'};
set4 = {'LWw0_m','LWw1_m'}; subtitle4 = {'early male, recent male'};
set5 = {'WwJO','WwJO_m'}; subtitle5 = {'female, male'};
metaData.grp.sets = {set1, set2, set3, set4, set5};
metaData.grp.subtitle = {subtitle1, subtitle2, subtitle3, subtitle4, subtitle5};

%% Discussion points
D1 = 'This entry only concerns with the James River population in Virginia, USA. This is a genetically distinct population.';
D2 = 'Males are assumed to differ from females by {p_Am} and h_a only';
D3 = 'Arrhenius temperature T_A calculated based on flathead catfish (Bourret et al. 2008)';
D4 = 'd_V set to 0.26 g/cm^3 based on dry weight to wet weight ratio of immature fish';
D5 = 'd_E set to 0.42 g/cm^3 based on dry weight to wet weight ratio of eggs';
D6 = 'Lb is ignored because isomorphy is hard to combine with abj model';
D7 = 'In view of low somatic maintenance, pseudodata k_J = 0.002 1/d is replaced by pseudodata k = 0.3';
metaData.discussion = struct('D1',D1, 'D2',D2, 'D3', D3,'D4', D4, 'D5',D5, 'D6',D6, 'D7',D7);

%% Links
metaData.links.id_CoL = '3PB7V'; % Cat of Life
metaData.links.id_ITIS = '163997'; % ITIS
metaData.links.id_EoL = '995103'; % Ency of Life
metaData.links.id_Wiki = 'Ictalurus_furcatus'; % Wikipedia
metaData.links.id_ADW = 'Ictalurus_furcatus'; % ADW
metaData.links.id_Taxo = '44002'; % Taxonomicon
metaData.links.id_WoRMS = '275329'; % WoRMS
metaData.links.id_fishbase = 'Ictalurus-furcatus'; % fishbase

%% References
bibkey = 'Kooy2010'; type = 'Book'; bib = [ ...  % used in setting of chemical parameters and pseudodata
'author = {Kooijman, S.A.L.M.}, ' ...
'year = {2010}, ' ...
'title  = {Dynamic Energy Budget theory for metabolic organisation}, ' ...
'publisher = {Cambridge Univ. Press, Cambridge}, ' ...
'pages = {Table 4.2 (page 150), 8.1 (page 300)}, ' ...
'howpublished = {\url{http://www.bio.vu.nl/thb/research/bib/Kooy2010.html}}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];

bibkey = 'Wiki'; type = 'Misc'; bib = [...
'howpublished = {\url{https://en.wikipedia.org/wiki/Blue_catfish}},'...
'note = {Accessed : 2020-02-16}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];

bibkey = 'BioKidsUmichWebsite'; type = 'Misc'; bib = [...
'howpublished = {\url{http://www.biokids.umich.edu/critters/Ictalurus_furcatus/}},'...
'note = {Accessed : 2020-05-18}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];

bibkey = 'SimoWall2003'; type = 'Book'; bib = [ ... 
	'title = {Reproductive biology and early life history of fishes in the Ohio River drainage},'...
	'volume = {3},'...
	'publisher = {CRC Press},'...
	'author = {Simon, Thomas P. and Wallus, Robert},'...
	'year = {2003},'...
	'pages = {94--100}'];
metaData.biblist.(bibkey) = ['''@', type, '{', bibkey, ', ' bib, '}'';'];

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