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Effects were determined of some abiotic factors on the patterns of abundance and diversity of amphipods associated with Perna perna (L.) beds. Observations were conducted at 2 sites subjected respectively to h g h and low degrees of wave exposure. The sampllng program combined the following 4 environmental aspects-seasonal effects, wave a c t~o n , vertical zonation and tide level. Sixteen species were found, of which 7 occurred only at the exposed slte. The lowest values of density and number of species corresponded to the protected site; high values charactenzed samples from the exposed site. Jassa falcata dominated at the exposed s~t e but was absent at the protected mussel bed. The dominant species common to both stations were: Elasmopus brasiliens~s, E. pectenicrus and Hyale sp. 1. The distribution of arnphipods appeared to be a function of 3 main factors, as shown by correspondence analysis. structural heterogeneity of the mussel bed (related to wave action), verbcal zonation, and desiccation. Accordingly, cluster analysis pointed out wave action and zonation as the 2 key factors affecting similanty of samples. The cluster analysis of s p e c~e s showed few cases of high similarity and when so, the a m p h~p o d s differed In 11fe style. Thls niche partition partially explains coemstence of the amphipod assemblage in the mussel beds. Direct effects of wave impact seemed less important than its side effects (e.g. sedimentahon) as regards selection of specles, and t h~s was related to the physical protecbon of the mussel mats

The invasive golden mussel, Limnoperna fortunei (Dunker, 1857), was introduced into the La Plata River estuary and quickly expanded upstream to the North, into the Paraguay and Paraná rivers. An ecological niche modeling approach, based on limnological variables, was used to predict the expansion of the golden mussel in the Paraguay River and its tributaries. We used three approaches to predict the geographic distribution: 1) the spatial distribution of calcium concentration and the saturation index for calcium carbonate (calcite); 2) the Genetic Algorithm for Rule-Set Production (GARP) model; and the 3) Maximum Entropy Method (Maxent) model. Other limnological variables such as temperature, dissolved oxygen, pH, and Total Suspended Solids (TSS) were used in the latter two cases. Important tributaries of the Paraguay River such as the Cuiabá and Miranda/Aquidauana rivers exhibit high risk of invasion, while lower risk was observed in the chemically dilute waters of the middle basin where shell calcification may be limited by low calcium concentrations and carbonate mineral undersaturation.Keywords: golden mussel, ecological niche modeling, Paraguay River, Pantanal wetland. Modelagem da distribuição potencial do mexilhão dourado Limnoperna fortunei na Bacia do Alto Rio Paraguai usando variáveis limnológicas ResumoA espécie invasora mexilhão dourado, Limnoperna fortunei (Dunker, 1857), foi introduzida na bacia do Rio da Prata e rapidamente se expandiu em direção ao norte, nos rios Paraguai e Paraná. A modelagem de nicho ecológico com base em variáveis limnológicas foi utilizada para prever a expansão do mexilhão dourado na bacia do Alto Paraguai. Foram usados três métodos para prever a distribuição espacial do mexilhão dourado: 1) a distribuição espacial da concentração de cálcio e o índice de saturação do carbonato de cálcio (calcita); 2) a modelagem utilizando o algoritmo Genetic Algorithm for Rule-Set Production (GARP); e 3) a modelagem usando o Maximum Entropy Method (Maxent). Outras variáveis como temperatura da água, oxigênio dissolvido, pH e sólidos totais suspensos também foram utilizadas na modelagem. Importantes tributários do Rio Paraguai como Cuiabá e Miranda exibem alto risco de invasão, e menores riscos foram observados para águas mais diluídas na parte média da bacia, onde a calcificação das conchas pode ser limitada pela baixa concentração do cálcio e dos carbonatos minerais abaixo do nível de saturação.Palavras-chave: mexilhão dourado, modelagem de nicho ecológico, Rio Paraguai, Pantanal.