Estimation of functional diversity and species traits from ecological monitoring data
Channel: Complexity Papers
Date: 2022-10-18

This video summarizes the following paper:

Alexei Ryabov, Bernd Blasius, Helmut Hillebrand, Irina Olenina and Thilo Gross
PNAS to appear (2022)  https://doi.org/10.1073/pnas.2118156119
arXiv: 2107.13792

SYNOPSIS
The rampant loss of biodiversity is starting to be recognized as a global crisis rivalling the climate emergency. To address this crisis scientists need robust methods to measure the diversity in a system. Importantly, these methods should not only count species but capture the variety of different functions that the species in a system can perform. In the paper summarized here, we propose a machine learning method by which existing data from ecosystem monitoring can be reanalysed to reveal changes of functional biodiversity over time. The method is based on the combination of a manifold learning method (diffusion map) with a functional diversity metric (Rao's quadratic entropy) and a measure for the dissimilarity of similar species (Spearman correlation across samples). 
We show that this approach can be used to robustly reveal functional diversity in monitoring datasets and to some extent may allow to infer species traits from abundance data. 


BACKGROUND READING, DATA AND CODE

- The diffusion map was proposed in
 Coifman et al. PNAS 102, 7426 (2005) doi.org/doi:10.1073/pnas.0500334102

- We use a particular variant of the diffusion map described in  
Barter and Gross, Proc. Roy. Soc. A 475, 20180615 (2019)
doi.org/10.1098/rspa.2018.0615

- Rao's diversity index was proposed in
 Rao,  Theor. Pop. Biol. 21, 24 (1982) doi.org/10.1016/0040-5809(82)90004-1

- A brief introduction to Rao's formula is given in 
Botta-Duk, Vegetation Sci.16, 533 (2005) doi.org/10.1111/j.1654-1103.2005.tb02393.x 

- Spearman Correlation was proposed in
Spearman, Am. J. Psy. 100, 441 (1904) doi.org/10.2307/1422689

- Regarding the ultimate outcome of the observed gains and the potential for future biodiversity loss see 
Blowes et al. Science 366, 339 (2019) doi.org/10.1126/science.aaw1620

- The Baltic Sea Phytoplankton Data was published as 
Olenina et al HELCOM Balt. Sea Environ. Proc.. 106 (2006)

- Code for the computationa experiment can be downloaded from
https://github.com/AlexRyabov/Metacommunity-model

- Code for the diffusion map analysis can be downloaded from 
https://github.com/AlexRyabov/Diffusion-Map

CONTENtTS
00:00 Introduction
00:53 Functional diversity
01:37 Comparing is hard
03:09 Diffusion maps
06:06 Comparing unknown species
08:15 Predation and competition
10:05 Numerical experiment
12:42 Biodiversity gains on the Lithuanian coast


SOURCES 
- "Aerial photo of boat on sea" (13:00) by Pok Rie via Pexels
 https://www.pexels.com/photo/aerial-photo-of-boat-on-sea-1655166/
 (The photo has been cropped to fit the figure)
- Plankton photographs (03:00, 06:30, 08:05) by Nils Henrik Hintz
- Diagrams (10:22, 12:10, 12:40, 13:40, 14:00) and Legends (14:30) by Alexei Ryabov
- Maps (13:00-15:00) generated from Data by OpenStreetMap contributors (openstreetmap.org)

The paper summarized in the video is available free of charge (open access) from PNAS or via the arXiv preprint server (https://arxiv.org/abs/2107.13792)