This document discusses a global study examining the impacts of storms on freshwater habitats and phytoplankton assemblages. It outlines the study's goals of analyzing data from over 30 lakes to identify how storms affect ecosystems, biodiversity, and community resilience. The study faces challenges in dealing with heterogeneous data from different disciplines and origins. It employs adapted team management, data compilation strategies, and analytical methods like meta-analysis and trait-based approaches to standardize data and facilitate comparisons across sites. Initial results are available on the study website.

1. Dealing with heterogeneous data to improve our knowledge of biodiversity
dynamics and ecosystem function: perspectives from synthesis projects.
Global Evaluation of the Impacts of Storms on
freshwater Habitat and structure of
phytoplankton Assemblages
Orlane Anneville1, Vijay Patil2, Jason Stockwell3 , Jonathan Doubek3, Nico
Salmaso4 , Gaël Dur5, James A. Rusak6, Patrick Venail7, Shin-Ichiro Matsuzaki8,
Lars Gosta Rudstam9 and GEISHA/Storm-blitz contributors10
1- INRA, UMR CARRTEL, France, 2- US Geological Survey, University of Alaska
Fairbanks, USA, 3- Rubenstein Ecosystem Science Laboratory, University of Vermont,
USA, 4- Research and Innovation Centre, Fondazione Edmund Mach (FEM),Via E. Mach
1, 38010 San Michele all’Adige, Italy, 5- Creative Science Course (Geosciences), Faculty
of Science, Shizuoka University, Japan, 6- Dorset Environmental Science Centre, Ontario
Ministry of the Environment and Climate Change, Dorset, ON, Canada, 7- Département
F.A. Forel, University of Geneva, Switzerland, 8- Center for Environmental Biology &
Ecosystem Studies National Institute for Environmental Studies (NIES), Japan, 9- Cornell
University, USA, 10- GEISHA/Storm-blitz contributors: http://www.geisha-stormblitz.fr/

2. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
2
GLEON* Project
* GLEON: Network of
scientists interested in lakes -
http://gleon.org/
GEISHA
Co-PI: Jason Stockwell (Univ. Vermont, USA)
Vijay Patil (USGS, Univ. Fairbanks, USA)
Orlane Anneville (INRA, France)

3. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
3
Climate change
Increased concern about the impacts of Extreme Climatic Events (Van de Pol et al., 2017)

4. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
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Climate change
Increased concern about the impacts of Extreme Climatic Events (Van de Pol et al., 2017)
Storms in LakesCredit: Sam Nanchen

5. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
5
Climate change
Increased concern about the impacts of Extreme Climatic Events (Van de Pol et al., 2017)
Ecosystem services
Light
Nutrient
Storms in Lakes
Temperature
Phytoplankton
Credit: Sam Nanchen

6. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
6
Ecosystem services
Light
Nutrient Storms
Temperature
Phytoplankton
Credit: Eric Leibensperger / SUNY-Plattsburgh
Water column thermal structure (Lake Champlain, USA)
Muggelsee (Germany) October 2017

7. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
7
Ecosystem services
Light
Nutrient Storms
Temperature
Phytoplankton
Changes in species composition and production (eg., Kasprzak et al., 2017, etc…)
Credit: Eric Leibensperger / SUNY-Plattsburgh
Water column thermal structure (Lake Champlain, USA)
Muggelsee (Germany) October 2017

8. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
8
Credit: Eric Leibensperger / SUNY-Plattsburgh
Identify mechanisms that lead to new
phytoplankton assemblages or
community resilience
Storms
Ecosystem
Function
Biodiversity
Physical
Habitat
Water column thermal structure (Lake Champlain, USA)
Evaluate the impact of storms by using
data from long-term surveys
Muggelsee (Germany) October 2017

9. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
9
Data commitment from > 30 lakes

10. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
Data commitment from > 30 lakes

11. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
Data commitment from > 30 lakes
Low-frequency in-situ data: Every 2 weeks; sampled period > 5 years
High-frequency in-situ data (buoy data): on chla, phycocyanin, other limnological
variables; coupled with low-frequency data

12. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
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Many data providers who need guarantees
Rita Adrian (Germany)
Orlane Anneville (France)
Yaron Be’eri-Shlevin (Israel)
Laurence Carvalho (UK)
Chih-Yu Chiu (Taiwan)
Elvira de Eyto (Ireland)
Claudia Dresti (Italy)
Werner Eckert (Israel)
Marieke Frassl (Australia)
Mark O. Gessner (Germany)
Scott Girdner (USA)
Josef Hejzlar (Czech Republic)
Scott Higgins (Canada)
Irina Izaquirre (Argentina)
Stéphan Jacquet (France)
Monika LaPlante (USA)
Shin-Ichiro Matsuzaki (Japan)
Chris Navitsky (USA)
Peeter Nõges (Estonia)
Ho-Dong Park (Japan)
Don Pierson (Sweden)
Pomati (Switzerland)
Frédéric Rimet (France)
Michela Rogora (Italy)
Lars Gosta Rudstam (USA)
Jim Rusak (Canada)
Steve Sadro (USA)
Angela Shambaugh (USA)
Dietmar Straile (Germany)
Hilary Swain (USA)
Stephen J. Thackeray (UK)
Wim Thiery (Belgium)
Nicole Van Lizpig (Belgium)
Michael Vanni (USA)
Piet Verburg (New Zealand)
Valerie Wentzky (Germany)
Tanner Williamson (USA)
Horacio Zagarese (Argentina)
Tamar Zohary (Israel)

13. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
13
Many data providers who need guarantees
Rita Adrian (Germany)
Orlane Anneville (France)
Yaron Be’eri-Shlevin (Israel)
Laurence Carvalho (UK)
Chih-Yu Chiu (Taiwan)
Elvira de Eyto (Ireland)
Claudia Dresti (Italy)
Werner Eckert (Israel)
Marieke Frassl (Australia)
Mark O. Gessner (Germany)
Scott Girdner (USA)
Josef Hejzlar (Czech Republic)
Scott Higgins (Canada)
Irina Izaquirre (Argentina)
Stéphan Jacquet (France)
Monika LaPlante (USA)
Shin-Ichiro Matsuzaki (Japan)
Chris Navitsky (USA)
Peeter Nõges (Estonia)
Ho-Dong Park (Japan)
Don Pierson (Sweden)
Pomati (Switzerland)
Frédéric Rimet (France)
Michela Rogora (Italy)
Lars Gosta Rudstam (USA)
Jim Rusak (Canada)
Steve Sadro (USA)
Angela Shambaugh (USA)
Dietmar Straile (Germany)
Hilary Swain (USA)
Stephen J. Thackeray (UK)
Wim Thiery (Belgium)
Nicole Van Lizpig (Belgium)
Michael Vanni (USA)
Piet Verburg (New Zealand)
Valerie Wentzky (Germany)
Tanner Williamson (USA)
Horacio Zagarese (Argentina)
Tamar Zohary (Israel)
1- Adapted Team Management

14. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
14
Data from:
- different disciplines (meteo, physic, biology….)
- various origins (in terms of type of lakes, sampling protocols….)
Phytoplankton data from microscopic counts
- Methods, equipment, magnification, number of counted individuals….
- High diversity -> different level of expertise (different among periods and lakes)
- Diverse and changing taxonoming nomenclature

15. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
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Data from:
- different disciplines (meteo, physic, biology….)
- various origins (in terms of type of lakes, sampling protocols….)
2- Adapted Data acquisition and Compilation strategies
3- Method Development to deal with heterogeneous datasets
Phytoplankton data from microscopic counts
- Methods, equipment, magnification, number of counted individuals….
- High diversity -> different level of expertise (different among periods and lakes)
- Diverse and changing taxonoming nomenclature

16. 1- Adapted Team Management
16

17. 17
1- Adapted Team Management
82participants
32 data providers
22 investigators (Core Group)
28 GLEON members

18. 18
Team
science
best
practices
Stimulate emotional
engagement and understanding
Engage members in
work and discussions
Teamwork
exercices
Short talks on
Teamworking
strategies
Surveys on expectations,
involvments
« Unstructured
time »
Maintain the team
culture
Our «Thorn, Buds and
roses» moments
Communication and confidence
1- Adapted Team Management
Improve effective
communication
Help to understand that conflict is part of
the process of « working together »
Working Rules
& policies
Cheruvelil et al. Creating and maintening high-performing collaborative research teams: the importance of diversity and interpersonal
skills. Front Ecol Environ, 2014; Goring et al. Improving the culture of interdisciplinary collaboration in ecology by expanding measures of
success. Front Ecol Environ, 2014; Read et al. Building the team for team science. Ecosphere, 2016, etc…

19. 1- Adapted Team Management
19
> Data collection: Not publicly available, appropriate acknowledgement, contact data
providers for other use….

20. 1- Adapted Team Management
20
> Data collection: Not publicly available, appropriate acknowledgement, contact data
providers for other use….
> Metadata, derived data, storage and use: Stored on the CESAB ftp site,
publicly available

21. 1- Adapted Team Management
21
> Data collection: Not publicly available, appropriate acknowledgement, contact data
providers for other use….
> Metadata, derived data, storage and use: Stored on the CESAB ftp site,
publicly available
> Co-authorship and authors responsibilities: Authors contributions

22. 2- Adapted Data acquisition and Compilation strategies
22

23. 2- Adapted Data acquisition and Compilation strategies
23
Reduce the Heterogeneities in the datasets / Get data which are « comparable »

24. 2- Adapted Data acquisition and Compilation strategies
24
Data Templates
Controlled vocabulary
Reduce the Heterogeneities in the datasets / Get data which are « comparable »

25. 2- Adapted Data acquisition and Compilation strategies
> Phytoplankton Data Template Name of the taxa
Biomass Abundance BiovolumeDate

26. 2- Adapted Data acquisition and Compilation strategies
26
> Phytoplankton Data Template Name of the taxa
Biomass Abundance BiovolumeDate
> Phytoplankton Controlled Vocabulary

27. 2- Adapted Data acquisition and Compilation strategies
27
Metadata files
Data Templates
Controlled vocabulary
Reduce the Heterogeneities in the datasets / Get data which are « comparable »

28. 2- Adapted Data acquisition and Compilation strategies
28
Metadata files
- Geographical location
- Properties and characteristics
- Location of sampling stations
- Sampling & sample processing
methods
- Devices
- Contact name and email address
Data Templates
Controlled vocabulary
Reduce the Heterogeneities in the datasets / Get data which are « comparable »

29. 2- Adapted Data acquisition and Compilation strategies
29
Quality Assurance, Quality Control protocols
- Identifying outliers
• Check the frequency distribution of data points in
JMP and R -> If a data point is clearly outside
limits = Value is set to NA
• Create depth profile plots
of all variables and
examine for extreme
values through R

30. 2- Adapted Data acquisition and Compilation strategies
30
Use Algaebase.org to find currently accepted names; in this
example, Kirchneriella contorta changed to Raphidocelis danubiana
- Need to standardize names among lakes
Quality Assurance, Quality Control protocols

31. 2- Adapted Data acquisition and Compilation strategies
31
Taxonomic diversity is matched by diversity in naming conventions among lakes
Original Name Standardized Name
> No unique abbreviations
among lakes and mis-spelled
abbreviation (e.g., “sp.”, ”sp”,
“spp”, “c.f”, “cf.”’)
> Change in name since
dataset created
> Mis-spelled names
Of 2,604 unique names from 19 lakes (so far), 1,057
were the correct standardized name (i.e., had to
“correct” 1,547 names)

32. 3- Method Development to deal with heterogeneous
datasets
32

33. 3- Method Development to deal with heterogeneous
datasets
33
Meta-analysis Intensity of the response « effect size » / explanatory
factors « covariates » or « moderators »

34. 3- Method Development to deal with heterogeneous
datasets
34
Meta-analysis Intensity of the response « effect size » / explanatory
factors « covariates » or « moderators »
Morpho-functional traits approaches Facilitate comparisons among
sites and identify general
patterns

35. Morpho-functional traits approaches (MFG- Salmaso, Padisak 2007, CSR- Reynolds, 1988)
Advantages:
- Based on measurable characteristics
- Insensitive to taxonomic change/errors
- Reduced dataset complexity
- Mechanistic predictions of phytoplankton
response to storm effects
3- Method Development to deal with heterogeneous
datasets
35

36. 36
Morpho-functional traits approaches (MFG- Salmaso, Padisak 2007, CSR- Reynolds, 1988)
Advantages:
- Based on measurable characteristics
- Insensitive to taxonomic change/errors
- Reduced dataset complexity
- Mechanistic predictions of phytoplankton
response to storm effects
3- Method Development to deal with heterogeneous
datasets
Reynolds, 1988

37. 37
Morpho-functional traits approaches (MFG- Salmaso, Padisak 2007, CSR- Reynolds, 1988)
Advantages:
- Based on measurable characteristics
- Insensitive to taxonomic change/errors
- Reduced dataset complexity
- Mechanistic predictions of phytoplankton
response to storm effects
Physical traits Eco/physio functions
Control response toAssociated with
Storm effects
Salmaso et al. 2015; Madgwick et al. 2006
3- Method Development to deal with heterogeneous
datasets
Reynolds, 1988

38. 38
The algaeClassify R package
Package objectives:
- Standardize species names
- Verify names w/ online database
- Species lists ↔ functional groups (MFG, CSR)
- Functional groups ↔ functional traits
- Integrated database of trait-based MFG and CSR
classifications
607 taxa and counting!
3- Method Development to deal with heterogeneous
datasets
Preliminary code at github.com/vppatil/GEISHA_phytoplankton/algaeClassify
Salmaso &
Padisak 2007
Reynolds,
1988

39. 39
The algaeClassify R package
Package objectives:
- Standardize species names
- Verify names w/ online database
- Species lists ↔ functional groups (MFG, CSR)
- Functional groups ↔ functional traits
- Integrated database of trait-based MFG and CSR
classifications
607 taxa and counting!
Example Workflow:
3- Method Development to deal with heterogeneous
datasets
Preliminary code at github.com/vppatil/GEISHA_phytoplankton/algaeClassify
Reynolds,
1988
Salmaso &
Padisak 2007

40. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
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Adapted Team Management
Communication and confidence
Adapted Data acquisition and
Compilation strategies
Method Development to deal with
heterogeneous datasets
Reduce the Heterogeneities in
the datasets
Morpho-functional and Meta-
analysis approaches
Starting to analyse data…
1st Results on http://www.geisha-stormblitz.fr/

41. Global Evaluation of the Impacts of Storms on freshwater
Habitat and structure of phytoplankton Assemblages
Acknowledgments
The GEISHA team is grateful to the numerous technicians,
engineers and scientists who collected, analyzed the samples
and gathered the data from each lake over the past 45+ years,
and to various agency for funding long-term ecological surveys:
http://www.geisha-stormblitz.fr/
We thanks all the GEISHA contributors and members of the
core-group.
41