We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Modelling of the potential distribution of Limnoperna fortunei (Dunker, 1857) on a global scale.
- Authors
de Cássia Souza Campos, Mônica; Alves de Andrade, André Felipe; Kunzmann, Bárbara; Diniz Galvão, Danielle; Alcísio Silva, Fabiano; Valadão Cardoso, Antônio; David Carvalho, Marcela; Mota, Helen Regina
- Abstract
Predictive modelling of species' distributions is an important tool in biogeography, evolution, ecology, conservation, and invasive-species management. In this study we applied four different algorithms: Mahalanobis Distance, Domain, GARP and MAXENT, using them to predict the potential distribution of Limnoperna fortunei, a freshwater mussel native to Southeast Asia and a major fouling pest of water supply systems in Hong Kong, Japan, and South America. For model input, we compiled native and invaded occurrence data from Asia (71 points) and South America (248 points) from the literature and BIOCLIM's environmental layers related to air temperature and precipitation. To evaluate model quality we used different "training" and "test" data sets. On the Mahalanobis Distance and Domain algorithms, three sets of training data were used: 1) Asia points; 2) South America points; 3) Asia and South America points. For MAXENT the combinations were: 1) South America points (25% test data/75% training data); 2) Asia points (25% test data/75% training data); 3) South America training data/Asia test data; 4) Asia training data/ South America test data; 5) Asia + South America points (25% test data/75% training data). Comparing the responses of the four types of algorithms used, it was found that MAXENT was the most conservative model (i.e. it produced a smaller area of suitable habitats) followed in order by GARP, Domain and Mahalanobis Distance, which proved to be the widest. In general, the best results corresponded to models in which the points of occurrence covered a greater environmental variability (Asia+South America 25% test data/75% training data). They showed better performance for predicting correctly the occurrence of regions already known to host the species. An ensemble map was produced based on the best scenarios for each algorithm. This tool performed well in assessing the potential global distribution of L. fortunei even though it was generated from climatic macro variables without the use of locale-specific abiotic variables, which are more difficult to obtain.
- Subjects
MUSSELS; INTRODUCED species; ALGORITHMS; SIMULATION methods &; models; HABITATS
- Publication
Aquatic Invasions, 2014, Vol 9, Issue 3, p253
- ISSN
1798-6540
- Publication type
Article
- DOI
10.3391/ai.2014.9.3.03