Biodiversity index | Wikipedia audio article
Channel: wikipedia tts
Date: 2019-05-03

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Diversity_index


00:05:20 1 Richness
00:06:08 2 Shannon index
00:07:28 2.1 Rényi entropy
00:08:14 3 Simpson index
00:08:23 3.1 Inverse Simpson index
00:15:50 3.2 Gini–Simpson index
00:17:42 4 Berger–Parker index
00:21:45 5 See also
00:22:49 6 References
00:24:26 7 Further reading



Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago.

Learning by listening is a great way to:
- increases imagination and understanding
- improves your listening skills
- improves your own spoken accent
- learn while on the move
- reduce eye strain

Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone.

Listen on Google Assistant through Extra Audio:
https://assistant.google.com/services/invoke/uid/0000001a130b3f91
Other Wikipedia audio articles at:
https://www.youtube.com/results?search_query=wikipedia+tts
Upload your own Wikipedia articles through:
https://github.com/nodef/wikipedia-tts
Speaking Rate: 0.9262083404559696
Voice name: en-US-Wavenet-E


"I cannot teach anybody anything, I can only make them think."
- Socrates


SUMMARY
=======
A diversity index is a quantitative measure that reflects how many different types (such as species) there are in a dataset (a community), and simultaneously takes into account how evenly the basic entities (such as individuals) are distributed among those types.
When diversity indices are used in ecology, the types of interest are usually species, but they can also be other categories, such as genera, families, functional types or haplotypes. The entities of interest are usually individual plants or animals, and the measure of abundance can be, for example, number of individuals, biomass or coverage. In demography, the entities of interest can be people, and the types of interest various demographic groups. In information science, the entities can be characters and the types the different letters of the alphabet. The most commonly used diversity indices are simple transformations of the effective number of types (also known as 'true diversity'), but each diversity index can also be interpreted in its own right as a measure corresponding to some real phenomenon (but a different one for each diversity index).Many indices only account for categorical diversity between subjects or entities. Such indices however do not account for the total variation (diversity) that can be held between subjects or entities which occurs only when both categorical and qualitative diversity are calculated.
True diversity, or the effective number of types, refers to the number of equally abundant types needed for the average proportional abundance of the types to equal that observed in the dataset of interest (where all types may not be equally abundant). The true diversity in a dataset is calculated by first taking the weighted generalized mean Mq−1 of the proportional abundances of the types in the dataset, and then taking the reciprocal of this. The equation is:

  
    
      
        
          

          
          
            q
          
        
        
        D
        =
        
          
            1
            
              M
              
                q
                −
                1
              
            
          
        
        =
        
          
            1
            
              
                
                  
                    ∑
                    
                      i
                      =
                      1
                    
                    
                      R
                    
                  
                  
                    p
                    
                      i
                    
                  
                  
                    p
                    
                      i
                    
                    
                      q
                      −
                      1
                    
                  
                
                
                  q
                  −
                  1
                
              
            
          
        
        =
        
          
            (
            
              
                ∑
                
                  i
                  =
                  1
                
                
                  R
                
              
              
                p
                
                  i
                
                
                  q
                
              
            
        ...