Summary of Ai-based Mapping Of the Conservation Status Of Orchid Assemblages at Global Scale, by Joaquim Estopinan et al.
AI-based Mapping of the Conservation Status of Orchid Assemblages at Global Scale
by Joaquim Estopinan, Maximilien Servajean, Pierre Bonnet, Alexis Joly, François Munoz
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As machine learning educators write for technical audiences, we can summarize this research paper abstract as follows: Recent advances in deep learning enable reliable mapping of the conservation status of species assemblages on a global scale. The study introduces a new Deep Species Distribution Model trained on 1M occurrences of 14K orchid species to predict their assemblages at global scale and at kilometre resolution. The model assesses and maps the conservation status of the iconic orchid family, discussing insights conveyed at multiple scales. The paper proposes two main indicators of the conservation status: (i) the proportion of threatened species and (ii) the status of the most threatened species in the assemblage. By applying these indicators to global and interactive maps available online, researchers show sharp spatial variations at all scales. Key findings include the highest level of threat found at Madagascar and neighbouring islands, as well as good correspondence between protected areas and conservation indicators in Sumatra. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary To explain this research to curious high school students or non-technical adults, we can say that this study helps us understand which parts of the world have a higher risk of losing certain plant species. The scientists used special computer models to create detailed maps showing whether different groups of plants are at risk. They found that some places, like Madagascar and its surrounding islands, are in greater danger than others. This information can help conservation efforts by identifying where we need to focus our protection efforts. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
