Crescimento populacional do mexilhão dourado (<i>L. fortunei</i>) em reservatórios de hidroelétricas: um estudo via modelagem matemática e computacional

José Carlos Rubianes Silva

Centro Federal de Educação Tecnológica “Celso Suckow da Fonseca”, Rio de Janeiro, RJ, Brasil

Claudia Mazza Dias

Universidade Federal Rural do Rio de Janeiro, Nova Iguaçu, RJ, Brasil

Dayse Haime Pastore

Centro Federal de Educação Tecnológica “Celso Suckow da Fonseca”, Rio de Janeiro, RJ, Brasil

Anna Regina Corbo Costa

Centro Federal de Educação Tecnológica “Celso Suckow da Fonseca”, Rio de Janeiro, RJ, Brasil

Raquel Medeiros Andrade Figueira

Hubz, Rio de Janeiro, RJ, Brasil

Humberto Freitas de Medeiros Fortunato

Hubz, Rio de Janeiro, RJ, Brasil

Charles Henrique Xavier Barreto Barbosa

Universidade Federal Rural do Rio de Janeiro, Nova Iguaçu, RJ, Brasil

Breylla Campos Carvalho

Fundação Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro, RJ, Brasil

Editor-in-Chief: Adilson Pinheiro

Associated Editor: Iran Eduardo Lima Neto

Location (see Figure 1)	Coordinates	Abundance	Weight (g)	T(°C)	O₂[diss]	pH	EC	TDS	Salinity
Tres Irmãos HPP (P1)	20°39'17.592” S; 51°18'20.094” W	3598	1732	30.0	8.8	8.8	253	162	0.08
Pereira Barreto Channel (P2)	20°36'47.286” S; 51°4'35.544” W	1325	562.5	27.6	8.4	8.2	227	148	0.07
Pereira Barreto Dock (P3)	20°38'40.872” S; 51°5'56.508 W	360	216	28.4	9.4	8.5	185	120	0.06

Parameter	Values	Units	Reference
$D_{A}$	1.2	$m^{2} / d a y$	Cangelosi et al. (2015)Cangelosi, R. A., Wollkind, D. J., Kealy-Dichone, B. J., & Chaiya, I. (2015). Nonlinear stability analyses of Turing patterns for a mussel-algae model. Journal of Mathematical Biology, 70(6), 1249-1294. PMid:24832542. http://dx.doi.org/10.1007/s00285-014-0794-7. http://dx.doi.org/10.1007/s00285-014-079...
$D_{M}$	0.0012	$m^{2} / d a y$	Montresor (2014)Montresor, L. C. (2014). Implicações ecotoxicológicas do controle químico de Limnoperna fortunei (Dunker, 1857) (Bivalvia: Mytilidae) (Tese de doutorado). Universidade Federal de Minas Gerais, Belo Horizonte.
$D_{L}$	0.012	$m^{2} / d a y$	van de Koppel et al. (2005)van de Koppel, J., Rietkerk, M., Dankers, N., & Herman, P. M. J. (2005). Scale-dependent feedback and regular spatial patterns in young mussel beds. American Naturalist, 165(3), E66-E77. PMid:15729660. http://dx.doi.org/10.1086/428362. http://dx.doi.org/10.1086/428362...
$b_{1}$	0.015*	$d a y^{- 1}$	Inferred
$b_{2}$	0.01	$d a y^{- 1}$	Inferred
$b_{3}$	0.0002	$m^{- 1} d a y^{- 1}$	Inferred
$λ$	0.03	$d a y^{- 1}$	Inferred
$r_{1}$	0.07	$m^{- 1} d a y^{- 1}$	Inferred
$r_{2}$	0.12	$d a y^{- 1}$	Inferred
$K_{L}$	20	$g / l$	Inferred
$K_{M}$	1732	$g / m^{2}$	Inferred
$K_{A}$	0.01	$g / l$	Inferred
c₁	0.001	$g / l$	Inferred
c₂	0.001	$g / l$	Inferred

Parameter	Formula	Reference
Chlorophyll-a (chl-a)	$N D C I = [R_{r s} (655) - R_{r s} (560)] / [R_{r s} (655) + R_{r s} (560)]$ $c h l_{a} = - 1.6954 + 2.4429 \times N D C I - 1.2405 \times {[N D C I]}^{2} + 0.239 \times {[N D C I]}^{3} - 0.0155 \times {[N D C I]}^{4}$	Mishra & Mishra (2012)Mishra, S., & Mishra, D. (2012). Normalized difference chlorophyll index: a novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117, 394-406. http://dx.doi.org/10.1016/j.rse.2011.10.016. http://dx.doi.org/10.1016/j.rse.2011.10.... This paper
Water Surface Temperature (WTS)	$L_{l} = M_{L} Q_{c a l} + A_{L}$ $W T S_{W C} = (K_{2} / \ln (K_1 / L_{l} + 1)) - 273.15$ $W T S = (0.7745 \times T_{S C}) + 9.6502$	U.S. Geological Survey (2021)U.S. Geological Survey – USGS. (2021). Using the USGS Landsat Level-1 Data Product. Retrieved in 2021, June 18, from https://www.usgs.gov/core-science-systems/nli/landsat/using-usgs-landsat-level-1-data-product https://www.usgs.gov/core-science-system...

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