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Shabani, Farzin
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Given Name
Farzin
Farzin
Surname
Shabani
UNE Researcher ID
une-id:fshaban2
Email
fshaban2@une.edu.au
Preferred Given Name
Farzin
School/Department
School of Environmental and Rural Science
1 results
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- PublicationAre research efforts on Animalia in the South Pacific associated with the conservation status or population trends?Analyses of knowledge gaps can highlight imbalances in research, encouraging greater proportionality in the distribution of research efforts. In this research we used generalized linear mixed models (GLMM) with the aim to determine if research efforts for the period 2005–2015 for terrestrial vertebrates of Amphibia, Aves, Mammalia and Reptilia in the South Pacific region were correlated with conservation status (critically endangered (CR), endangered (EN), vulnerable (VU), least concern (LC) and near threatened (NT)) or population trends (increasing, stable, decreasing and unknown) through the International Union for Conservation of Nature (IUCN) database. Our results showed that research distribution was uneven across different classes. Out of 633623 investigated papers, the average number of publications per species was 43.7, 306.7, 717.6 and 115.3 for Amphibia (284 species), Aves (1306 species), Mammalia (243 species) and Reptilia (400 species), respectively. Consistently, the lower publication effort on Amphibia compared to other taxonomic classes was revealed as significant by GLMM analysis. There was no significant differences in research effort among levels of conservation status. However, we found significantly different publication efforts among population trends of all examined species in that species with "unknown" population trends gained significantly lower researchers' attention compared to species with "decreasing" trend. Results also indicated that, although it was not significant, the highest attention is given to species with "increasing" population trend over all taxonomic classes. Using the Information Theoretic approach we also generated a set of competing models to identify most important factors influencing research efforts, revealing that the highest ranked model included taxonomic class and population.