Os resultados ressaltam a importa¢ncia de estudar as comunidades para obter uma perspectiva mais ampla sobre asmudanças ambientais ostanto no passado quanto no presente.
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)
Um exemplo de rede com conexões entre várias formas de vida marinha antiga. Este quadro éuma ferramenta de ensino e não éa rede real construada pelos pesquisadores no estudo. Crédito: Drew Muscente.
Ao aplicar um algoritmo semelhante ao que o Facebook usa para fazer sugestaµes de amigos, os pesquisadores identificaram comunidades de vida antiga no registro fa³ssil e acompanharam como seus números mudaram atravanãs de cada uma das extinções em massa do planeta.
Como esperado, o número de comunidades osum grupo de espanãcies diferentes que vivem na mesma área geral oscaiu durante os eventos de extinção em massa. Mas a taxa em que as comunidades desapareceram nem sempre acompanhou a perda geral de vida e biodiversidade durante uma extinção, um resultado que sugere que os impactos ecola³gicos de uma extinção nem sempre estãoligados ao número de espanãcies que perecem.
“Houve momentos em nossa história em que houve grandes eventos que viram tremendasmudanças nas comunidades, mas muito poucas espanãcies desapareceramâ€, disse o principal autor Drew Muscente, que conduziu o estudo quando era pesquisador de pa³s-doutorado na Universidade do Texas em Escola Jackson de Geociências de Austin. “E houve eventos em que muitas espanãcies desapareceram e comunidades e ecossistemas foram pouco afetadosâ€.
Muscente éagora professor assistente no Cornell College. O estudo foi publicado recentemente na revista Geology .
Os resultados ressaltam a importa¢ncia de estudar as comunidades para obter uma perspectiva mais ampla sobre asmudanças ambientais ostanto no passado quanto no presente.
“Tentamos entender como asmudanças nessas comunidades levam a transformação fundamental de ecossistemas inteirosâ€, disse o coautor Rowan Martindale, professor associado da Jackson School.
Identificar comunidades no registro fa³ssil énotoriamente difacil. A maioria das pesquisas sobre paleocomunidades se concentra na comparação de amostras e coleções de fa³sseis que foram retirados de rochas de várias idades e locais. E embora os manãtodos computacionais convencionais possam ser usados ​​para agrupar amostras em paleocomunidades, eles funcionam melhor com conjuntos de dados relativamente pequenos de apenas algumas centenas ou milhares de coleções de fa³sseis. Devido a essa limitação, os manãtodos convencionais são podem ser aplicados a dados de regiaµes e períodos de tempo específicos, ao contra¡rio do registro inteiro.
Os pesquisadores conseguiram superar esses desafios e examinar todo o registro fa³ssil aplicando um algoritmo de detecção de comunidade baseado em manãtodos de análise de rede . As empresas de madia social são conhecidas por usar esses tipos de manãtodos para conectar usuários, mas estãose tornando cada vez mais aplicadas em várias disciplinas cientaficas.
Os pesquisadores analisaram conexões entre 124.605 coleções de fa³sseis, representando
toda a história da vida animal marinha, e descobriram que elas podem ser agrupadas
em 3.937 paleocomunidades (mostradas aqui como pontos
coloridos). Crédito: Muscente et al.
De acordo com Muscente, este estudo éa primeira vez que a análise de rede foi aplicada para detectar paleocomunidades em todo o registro fa³ssil da vida animal marinha osdesde quando a vida animal apareceu pela primeira vez atéa era geola³gica atual.
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Matthew Clapham, professor de paleobiologia da Universidade da Califórnia em Santa Cruz que não esteve envolvido no estudo, disse que outra vantagem do manãtodo de análise de rede éa aªnfase na visualização de conexões, em vez de apenas os tipos de animais presentes em um ecossistema.
"Isso aproxima a análise da forma como as comunidades realmente funcionavam porque as comunidades e as interações entre as espanãcies são redes", disse ele.
Com base em um banco de dados de 124.605 coleções de fa³sseis de animais marinhos de todo o mundo e representando 25.749 grupos de animais vivos e extintos, ou gêneros, o algoritmo identificou mais de 47 milhões de ligações entre essas amostras e organizou-as em 3.937 paleocomunidades distintas.
O estudo acompanhou as comunidades e a biodiversidade nos últimos 541 milhões de anos. A pesquisa mostrou que, embora os eventos de extinção em massa tenham afetado ambos, o grau de declanio a s vezes diferia.
Algumas extinções afetaram mais as comunidades do que a biodiversidade. Alguns afetaram mais a biodiversidade do que as comunidades. E alguns afetaram tanto sobre o mesmo.
Além disso, os pesquisadores não encontraram uma ligação entre a causa de uma extinção e se ela teve um grande impacto nas comunidades ou na biodiversidade.
Os resultados indicam que os maiores impactos ecola³gicos da extinção são mais sobre quais espanãcies são perdidas do que sobre o número de espanãcies perdidas. Se os atores-chave de um ambiente forem preservados, as comunidades podem permanecer intactas. Mas se muitos desses jogadores forem removidos, a comunidade desmorona com ele.
Muscente disse que espera que os manãtodos de análise de rede aplicados pela primeira vez neste estudo possam ser aprimorados e usados ​​para estudar os ecossistemas modernos.
"Eu gostaria de tentar preencher a lacuna do registro de rock atéo presente", disse ele.