Predicting risks of invasion of macroalgae in the genus Caulerpa in Florida

    Authors

    C. G. Glardon; L. J. Walters; P. F. Quintana-Ascencio; L. A. McCauley; W. T. Stam;J. L. Olsen

    Comments

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    Abbreviated Journal Title

    Biol. Invasions

    Keywords

    Algae; Caulerpa taxifolia; chlorophyta; coastal; invasive species; prediction models; MEDITERRANEAN SEA; RACEMOSA CAULERPALES; BIOLOGICAL INVASIONS; LIGURIAN; SEA; E-COMMERCE; TAXIFOLIA; ALGA; CHLOROPHYTA; SEAGRASS; AQUARIUM; Biodiversity Conservation; Ecology

    Abstract

    There is worldwide concern about the aquarium strain of the green alga Caulerpa taxifolia (Vahl) C. Agardh that was introduced to the Mediterranean Sea in 1984. Since that time, it has flourished and now covers thousands of hectares of near-shore waters. More recently, aquarium strains of C. taxifolia invaded southern California and Australian waters. Our goal was to evaluate potential invasion of C. taxifolia to Florida's coastal waters. We looked for evidence of C. taxifolia-aquarium strain, as well as the present distribution of all species of Caulerpa, in Florida's near-shore waters. We surveyed 24 areas in six zones along the Floridian coastline, and evaluated the association of potential indicators for the presence of Caulerpa. Latitude, presence of seagrass beds, human population density, and proximity to marinas were the four variables simultaneously considered. Caulerpa taxifolia-aquarium strain was not found at any of our survey locations. However, 14 species of Caulerpa were found at 31 of the 132 sites visited. Percent correct for our model was 61.5% for presence and 98.1% for absence. There was a positive correlation between Caulerpa spp. and seagrass beds and proximity to marinas. There was a negative correlation with latitude and human population density. The parameters in the logistic regression model assessing the association of Caulerpa occurrence with the measured variables were then used to predict current and future probabilities of Caulerpa spp. presence throughout the state. This prediction model will allow resource managers to focus their efforts in future surveys.

    Journal Title

    Biological Invasions

    Volume

    10

    Issue/Number

    7

    Publication Date

    1-1-2008

    Document Type

    Article

    Language

    English

    First Page

    1147

    Last Page

    1157

    WOS Identifier

    WOS:000258704400020

    ISSN

    1387-3547

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