Burrow fidelity in the blue crab, <i>Cardisoma crassum</i> Smith, 1870 (Brachyura: Gecarcinidae) from the Ponuga River, Veraguas, Panama

Lombardo, Roberto C.; Rojas, Maryory

doi:10.1590/2358-2936e2022033

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Original Article • Nauplius 30 • 2022 • https://doi.org/10.1590/2358-2936e2022033 copy

Burrow fidelity in the blue crab, Cardisoma crassum Smith, 1870 (Brachyura: Gecarcinidae) from the Ponuga River, Veraguas, Panama

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

From January to June 2021, mark-recapture and biometric data were collected from a population of blue crab, Cardisoma crassum Smith, 1870, from Ponuga, in Veraguas, Panama. Recapture histories of 163 crabs indicate strong burrow fidelity. During the dry season (January-March) burrow fidelity was 73.2 %, while in the rainy season (April-June) it was 50.7 %. Population was estimated at 2,569 crabs in 10.93^-ha with a density of 0.024 ind. m^-2. Cephalothorax width averaged 63.62 ± 6.37 mm for males (50.95-78.01 mm, N = 83) and 61.90 ± 5.47 mm for females (53.21-77.78 mm, N = 80). Blue crab reproductive season was traditionally known to take place just during the rainy season. Here, we report ovigerous females in February and March (dry season), suggesting reproduction may occur throughout the year in Panama. This is the first report of burrow fidelity and continuous biometry measurements in natural conditions for C. crassum. Burrow fidelity seems to be closely related to abiotic factors and is different between dry and rainy seasons.

Keywords:
Biometry; cephalothorax width; population estimate; recapture; sex ratio

Biometric variable	Parameter				t-test
Biometric variable	Maximum	Minimum	Mean	SD	t-test
♂ TW*	235.01	61.11	128.96	40.67	t = 2.91, P = 0.004
♀ TW	206.11	61.05	112.53	30.95	t = 2.91, P = 0.004
♂ CW	78.01	50.95	63.62	6.37	t = 1.85, P = 0.067
♀ CW	77.78	53.21	61.90	5.47	t = 1.85, P = 0.067
♂ CL	61.12	34.77	48.38	5.52	t = 1.49, P = 0.137
♀ CL	58.80	33.69	47.13	5.18	t = 1.49, P = 0.137
♂ QH*	43.45	13.90	31.32	5.93	t = 4.56, P < 0.001
♀ QH	41.23	14.31	27.06	5.94	t = 4.56, P < 0.001
♂ P3*	24.70	14.40	19.71	2.01	t = 2.49, P = 0.014
♀ P3	26.78	14.50	19.36	2.32	t = 2.49, P = 0.014
♂ C3	22.20	13.03	17.31	1.80	t = 1.58, P = 0.115
♀ C3	23.56	11.99	16.80	2.24	t = 1.58, P = 0.115
♂ P4*	22.69	14.14	18.97	1.98	t = 2.36, P = 0.019
♀ P4	24.56	14.58	18.54	2.00	t = 2.36, P = 0.019
♂ C4	21.40	11.80	17.02	1.90	t = 1.58, P = 0.115
♀ C4	22.46	11.14	16.53	2.06	t = 1.58, P = 0.115

Biometric variable (mm)	Max.	Mean	Size diff. with burrow		BD to variable ratio (max.)	Number of burrows > variable (max.)
Biometric variable (mm)	Max.	Mean	Max.	Mean	BD to variable ratio (max.)	Number of burrows > variable (max.)
BD	113.01	82.41
CW	70.80	64.35	42.21	18.06		16, P = 0.006
CL	54.90	47.60	58.11	34.81		19, P = 0.001
QH	39.79	31.94	73.22	50.47		20, P < 0.001
BD/CW					1.60
BD/CL					2.06
BD/QH					2.84

Sampling	M	Population estimate (N)			Survival (φ)			Immigration
Sampling	M	N	LCL	UCL	φ	LCL	UCL	B	LCL	UCL
Jan					0.51	0.36	0.67
Feb	24.2	49.7	39.4	59.9	0.90	0.75	1.05	13.60	3.10	24.00
Mar	38.8	58.3	49.4	67.1	0.72	0.58	0.87	23.50	17.50	29.40
Apr	39.0	65.7	60.6	70.8	0.52	0.38	0.65	51.10	41.40	60.90
May	33.6	85.0	73.9	96.1

Biometric variable	Parameter				PWC	Tukey test
Biometric variable	Maximum	Minimum	Mean	SD	PWC	Tukey test
TW	235.01	61.05	121.12	37.05	Apr-Jan	t = 2.89, P = 0.050
CW	78.01	50.95	62.84	6.01	Jun-Jan	t = 2.89, P = 0.039
CL	61.12	33.69	47.77	5.36	P = 0.110
QH	43.45	13.90	29.29	6.31	Mar-Jan	t = 3.93, P = 0.002
					Apr-Jan	t = 3.94, P = 0.002
					May-Jan	t = 3.66, P = 0.005
					Jun-Jan	t = 5.00, P < 0.001
					Jun-Feb	t = 3.17, P = 0.023
P3	26.78	14.40	19.56	2.17	Jun-Jan	t = 5.33, P < 0.001
					Jun-Feb	t = 4.42, P < 0.001
					Jun-Mar	t = 3.64, P= 0.005
					Jun-Apr	t = 3.86, P= 0.002
					Jun-May	t = 5.03, P< 0.001
C3	23.56	11.99	17.08	2.05	Jun-Jan	t = 4.21, P = 0.001
					Jun-Feb	t = 5.31, P < 0.001
					Jun-Mar	t = 4.92, P < 0.001
					Jun-Apr	t = 4.81, P < 0.001
					Jun-May	t = 4.83, P < 0.001
P4	24.56	14.14	18.79	2.02	Jun-Jan	t = 5.96, P < 0.001
					Jun-Feb	t = 4.66, P < 0.001
					Jun-Mar	t = 4.61, P < 0.001
					Jun-Apr	t = 4.56, P < 0.001
					Jun-May	t = 5.24, P < 0.001
C4	22.46	11.14	16.81	2.01	Jun-Jan	t = 4.96, P < 0.001
					Jun-Feb	t = 5.26, P < 0.001
					Jun-Mar	t = 4.58, P < 0.001
					Jun-Apr	t = 5.23, P < 0.001
					Jun-May	t = 5.10, P < 0.001

LWR	Linear regression equation	Intercept	Slope	r²	t-test for allometry (Ho: b = 3)	TW = a ∙ L^b
CW	log(TW) = -7.056 + 2.854∙log(CW)	-7.056	2.854	0.7757, P < 2.2^-16	t = -1.21, df = 162, P = 0.115	0.00086 ∙ L^2.854
♂	log(TW) = -7.056 + 2.854∙log(CW)	-6.907	2.824	0.7497, P < 2.2^-16	t = -0.97, df = 81, P = 0.168	0.00100 ∙ L^2.824
♀	log(TW) = -7.008 + 2.837∙log(CW)	-7.008	2.837	0.805, P < 2.2^-16	t = -1.03, df = 78, P = 0.153	0.00090 ∙ L^2.837
CL	log(TW) = -1.031 + 1.498∙log(CL)	-1.031	1.498	0.304, P= 2.1^-14	t = -8.43, df = 162, P = 9.02^-15	0.35651 ∙ L^1.498
♂	log(TW) = -0.971 + 1.492∙log(CL)	-0.971	1.492	0.273, P= 4.0^-7	t = -5.57, df = 81, P = 1.60^-7	0.37855 ∙ L^1.492
♀	log(TW) = -0.725 + 1.407∙log(CL)	-0.725	1.407	0.328, P= 2.9^-8	t = -6.98, df = 78, P = 4.28^-10	0.48452 ∙ L^1.407
QH	log(TW) = 2.107 + 0.789∙log(QH)	2.107	0.789	0.356, P < 2.2^-16	t = -26.42, df = 161, P = 9.9^-61	8.22279 ∙ L^0.789
♂	log(TW) = 1.731 + 0.899∙log(QH)	1.731	0.899	0.333, P= 1.1^-8	t = -14.87, df = 81, P = 3.6^-25	5.64573 ∙ L^0.899
♀	log(TW) = 2.449 + 0.684∙log(QH)	2.449	0.684	0.331, P= 2.9^-8	t = -20.92, df = 77, P = 8.4^-34	11.5733 ∙ L^0.684
P3	log(TW) = -0.914 + 1.909∙log(P3)	-0.914	1.909	0.4633, P < 2.2^-16	t =-6.76, df = 162, P = 1.18^-10	0.40104 ∙ L^1.909
♂	log(TW) = -1.386 + 2.081∙log(P3)	-1.386	2.081	0.4312, P= 1.58^-11	t = -3.46, df = 81, P = 0.0004	0.25005 ∙ L^2.081
♀	log(TW) = -0.320 + 1.693∙log(P3)	-0.320	1.693	0.5097, P= 1.06^-13	t = -6.95, df = 78, P = 4.93^-10	0.72615 ∙ L^1.693
P4	log(TW) = -1.118 + 2.004∙log(P4)	-1.118	2.004	0.4855, P < 2.2^-16	t = -6.14, df = 162, P = 3.06^-9	0.32690 ∙ L^2.004
♂	log(TW) = -0.830 + 1.919∙log(P4)	-0.830	1.919	0.3879, P= 3.25^-10	t = -4.03, df = 81, P = 6.18^-5	0.43609 ∙ L^1.919
♀	log(TW) = -1.295 + 2.052∙log(P4)	-1.295	2.052	0.6147, P < 2.2^-16	t = -5.15, df = 78, P = 9.37^-7	0.27403 ∙ L^2.052
C3	log(TW) = 0.726 + 1.422∙log(C3)	0.726	1.422	0.3023, P = 2.44^-14	t = -9.30, df = 162, P = 4.57^-17	2.06680 ∙ L^1.422
♂	log(TW) = 0.244 + 1.604∙log(C3)	0.244	1.604	0.261, P= 8.04^-7	t = -4.65, df = 81, P = 6.22^-6	1.27634 ∙ L^1.604
♀	log(TW) = 1.256 + 1.219∙log(C3)	1.256	1.219	0.335, P= 1.87^-8	t = -9.15, df = 78, P = 2.69^-14	3.51275 ∙ L^1.219
C4	log(TW) = 1.024 + 1.324∙log(C4)	1.024	1.324	0.2719, P = 8.14^-13	t = -9.85, df = 162, P = 1.60^-18	2.78292 ∙ L^1.324
♂	log(TW) = 1.155 + 1.292∙log(C4)	1.155	1.292	0.2001, P= 2.24^-5	t = -5.95, df = 81, P = 3.22^-8	3.17371 ∙ L^1.292
♀	log(TW) = 1.132 + 1.270∙log(C4)	1.132	1.270	0.3389, P = 1.47^-8	t = -8.61, df = 78, P = 3.07^-13	3.10247 ∙ L^1.270

Sociedade Brasileira de Carcinologia Instituto de Biociências, UNESP, Campus Botucatu, Rua Professor Doutor Antônio Celso Wagner Zanin, 250 , Botucatu, SP, 18618-689 - Botucatu - SP - Brazil
E-mail: editor.nauplius@gmail.com

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[1] CORRESPONDING AUTHOR
Roberto C. Lombardo roberto.lombardo@up.ac.pa