BACKGROUND The Republic of Madagascar is home to a unique assemblage of taxa and a diverse set of ecosystems. These high levels of diversity have arisen over millions of years through complex processes of speciation and extinction. Understanding this extraordinary diversity is crucial for highlighting its global importance and guiding urgent conservation efforts. However, despite the detailed knowledge that exists on some taxonomic groups, there are large knowledge gaps that remain to be filled. ADVANCES Our comprehensive analysis of major taxonomic groups in Madagascar summarizes information on the origin and evolution of terrestrial and freshwater biota, current species richness and endemism, and the utilization of this biodiversity by humans. The depth and breadth of Madagascar’s biodiversity—the product of millions of years of evolution in relative isolation —is still being uncovered. We report a recent acceleration in the scientific description of species but many remain relatively unknown, particularly fungi and most invertebrates. DIGITIZATION Digitization efforts are already increasing the resolution of species richness patterns and we highlight the crucial role of field- and collections-based research for advancing biodiversity knowledge in Madagascar. Phylogenetic diversity patterns mirror that of species richness and endemism in most of the analyzed groups. Among the new data presented, our update on plant numbers estimates 11,516 described vascular plant species native to Madagascar, of which 82% are endemic, in addition to 1215 bryophyte species, of which 28% are endemic. Humid forests are highlighted as centers of diversity because of their role as refugia and centers of recent and rapid radiations, but the distinct endemism of other areas such as the grassland-woodland mosaic of the Central Highlands and the spiny forest of the southwest is also important despite lower species richness. Endemism in Malagasy fungi remains poorly known given the lack of data on the total diversity and global distribution of species. However, our analysis has shown that ~75% of the fungal species detected by environmental sequencing have not been reported as occurring outside of Madagascar. Among the 1314 species of native terrestrial and freshwater vertebrates, levels of endemism are extremely high (90% overall)—all native nonflying terrestrial mammals and native amphibians are found nowhere else on Earth; further, 56% of the island’s birds, 81% of freshwater fishes, 95% of mammals, and 98% of reptile species are endemic. Little is known about endemism in insects, but data from the few well-studied groups on the island suggest that it is similarly high. The uses of Malagasy species are many, with much potential for the uncovering of useful traits for food, medicine, and climate mitigation. OUTLOOK Considerable work remains to be done to fully characterize Madagascar’s biodiversity and evolutionary history. The multitudes of known and potential uses of Malagasy species reported here, in conjunction with the inherent value of this unique and biodiverse region, reinforce the importance of conserving this unique biota in the face of major threats such as habitat loss and overexploitation. The gathering and analysis of data on Madagascar’s remarkable biota must continue and accelerate if we are to safeguard this unique and highly threatened subset of Earth’s biodiversity. Emergence and composition of Madagascar’s extraordinary biodiversity. Madagascar’s biota is the result of over 160 million years of evolution, mostly in geographic isolation, combined with sporadic long distance immigration events and local extinctions. (Left) We show the age of the oldest endemic Malagasy clade for major groups (from bottom to top): arthropods, bony fishes, reptiles, flatworms, birds, amphibians, flowering plants, mammals, non-flowering vascular plants, and mollusks). Humans arrived recently, some 10,000 to 2000 years (top right) and have directly or indirectly caused multiple extinctions (including hippopotamus, elephant birds, giant tortoises, and giant lemurs) and introduced many new species (such as dogs, zebu, rats, African bushpigs, goats, sheep, rice). Endemism is extremely high and unevenly distributed across the island (the heat map depicts Malagasy palm diversity, a group characteristic of the diverse humid forest). Human use of biodiversity is widespread, including 1916 plant species with reported uses. The scientific description of Malagasy biodiversity has accelerated greatly in recent years (bottom right), yet the diversity and evolution of many groups remain practically unknown, and many discoveries await.
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Machine learning predicts large scale declines in native plant phylogenetic diversity
Though substantial effort has gone into predicting how global climate change will impact biodiversity patterns, the scarcity of taxon‐specific information has hampered the efficacy of these endeavors. Further, most studies analyzing spatiotemporal patterns of biodiversity focus narrowly on species richness. We apply machine learning approaches to a comprehensive vascular plant database for the United States and generate predictive models of regional plant taxonomic and phylogenetic diversity in response to a wide range of environmental variables. We demonstrate differences in predicted patterns and potential drivers of native vs nonnative biodiversity. In particular, native phylogenetic diversity is likely to decrease over the next half century despite increases in species richness. We also identify that patterns of taxonomic diversity can be incongruent with those of phylogenetic diversity. The combination of macro‐environmental factors that determine diversity likely varies at continental scales; thus, as climate change alters the combinations of these factors across the landscape, the collective effect on regional diversity will also vary. Our study represents one of the most comprehensive examinations of plant diversity patterns to date and demonstrates that our ability to predict future diversity may benefit tremendously from the application of machine learning.
- NSF-PAR ID:
- 10456219
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 227
- Issue:
- 5
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 1544-1556
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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