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Niche Modelling: a comparison between modelling methods best applied for Cnidaria niche dispersion studies

Authors: Lima, Alessandra Vallim;

Niche Modelling: a comparison between modelling methods best applied for Cnidaria niche dispersion studies

Abstract

Nas ultimas décadas, modelagem de nicho ecológico vem recebendo maior atenção em diversas áreas da biologia devido a evolução dos computadores pessoais e aumento dos dados disponíveis utilizados para a modelagem. Os resultados obtidos podem ser utilizados em ações preventivas, tais quais manejo de espécie e acompanhamento da distribuição de espécies invasoras. Desde o aumento dessa popularidade, diversos algoritmos estão disponíveis e testes estão em andamento para averiguar suas performances em relação a diferentes filos. Invertebrados marinhos, mais especificamente cnidários, apresentam poucos estudos nesse ramo, devendo receber mais atenção nos próximos anos devido ao aumento global das populações de aguas vivas (blooms), e branqueamento em quase todos os recifes de corais. Devido a essa lacuna em informação, este grupo foi escolhido para comparar três algoritmos. Utilizamos o MAXENT, GARP e AquaMaps em suas formas de desktop e os selecionamos baseado em outros estudos comparando algoritmos. Utilizamos diferentes organismos do filo cnidária, Lychnorhiza lucerna, Chrysaora lactea, Phyllorhiza punctata, Tamoya haplonema, Ceriantheomorphe brasiliensis e Mussismilia hispida, para comparar os algoritmos e averiguar qual demonstrou melhor performance. Nossos resultados mostram que o MAXENT superou os outros algoritmos tanto com relação a Área Sob a Curva ROC (AUC), quanto com relação aos mapas de distribuição. O GARP apresentou resultados variados com mapas generalizados e AquaMaps foi o menos confiável. Nossos resultados são similares aqueles encontrados em diversas publicações, significando então, que o MAXENT é o algoritmo mais confiável em se tratando da modelagem de nicho desses organismos.

Recently, ecological niche modelling has been receiving more attention in several areas in biology, due to the evolution of personal computers, and the increasing availability of data used in modelling. The results obtained can be used in preventive actions such as species management and invasive species distribution. Since its increasing popularity, several algorithms are available and undergoing tests regarding their performance towards different phylum. Marine invertebrates, more specifically cnidarians, present few studies on this field, and should receive closer attention in the next years due to worldwide increases in jellyfish population (blooms), and bleaching in almost every known shallow water coral reef. Because of this gap of information, we chose this still poor studied group to compare three algorithms. We used MAXENT, GARP and AquaMaps in its desktop form and selected them based on other studies comparing algorithms. Our aim was to, based on different organisms of the phylum Cnidaria, Lychnorhiza lucerna, Chrysaora lactea, Phyllorhiza punctata, Tamoya haplonema, Ceriantheomorphe brasiliensis and Mussismilia hispida, compare those algorithms and examine which one performed the best. Our results shown that MAXENT outperformed the other algorithms both regarding de Area Under the ROC Curve (AUC) and the map distribution. GARP show varying results with generalized maps and AquaMaps was the least accurate of them. Our results are similar to those found in other papers, thus meaning that MAXENT is the most reliable software when it comes to modelling these animals.

Pós-graduação em Biodiversidade Aquática - São Vicente

Country
Brazil
Keywords

Cnidaria, Comparison of algorithms, Modelagem de Nicho, Niche Modelling, Comparação de algoritmos

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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