Training sessions
Three training sessions were offered prior to the conference. The following trainings were provided:
- From the biophysical mapping to the accounting of ecosystem services in physical and monetary terms
- Developing your skills for academic life – Early Career Workshop
- ARIES (Artificial Intelligence for Ecosystem Services)
Date: 20 October 2019
Language: English
Venue: Leibniz Universität Hannover – Institute of Physical Geography and Landscape Ecology, Schneiderberg Str. 50 30167 Hannover
More information per training is provided below.
1. From the biophysical mapping to the accounting of ecosystem services in physical and monetary terms
Organised by: JRC team (D.3- Natural Capital)
Time: 09:15-17:30, Room 203
Number of participants: 20
The purpose of this training is to introduce the attendants to the theory and practice of ecosystem services accounting. Practical cases will be shown for selected ecosystem services to introduce the conceptual workflow, from the configuration of the biophysical modelling to the setting up of the accounting methodology.
Hands-on exercises will illustrate the following steps:
- What is potentially delivered by the ecosystem
- What is demanded by users
- What is actually supplied as ecosystem service
- How accounting tables are structured and compiled
Different approaches are possible: from (simpler) adapting statistics to (more complex) modelling. The course will provide examples for both cases, making sure the basic definitions and concepts of ecosystem services accounting are clearly explained.
The training is structured as follows:
- Description of what is really ecosystem services accounting
- concepts and definitions
- linkages to the system of national accounts
- Practical case studies
- example with national statistics for timber production: disentangling the ecosystem contribution
- example for the biophysical modelling for outdoor recreation
- Hands on exercises on timber production and outdoor recreation
Participants will work with their own laptop. Basic knowledge of QGIS or ArcGIS is strongly recommended.
QGIS version 3.x
ArcGIS version 10.x
2. Developing your skills for academic life – Early Career Workshop
Organised by: YESS – Young Ecosystem Services Specialists
Time: 09:00-17:30, Room 205
Number of participants: 30
Early career researchers (ECRs) are often faced with specific challenges pertaining their academic career. Critical challenges include understanding the academic publishing system, how to network with their scientific peers and to communicate their science to a heterogeneous audience, among other factors. This 1-day workshop provides a training opportunity to help addressing such challenges, drawing on inputs from experienced researchers, peer-learning and facilitated discussions. The topics of ethics, open-access and paper reviewing in scientific publishing, along with networking and science communication will be addressed in a safe learning space. Participants will be able to interact directly with researchers experienced in scientific publishing like journal editors, learn from each other in working groups, and receive constructive feedback on their own work. This will help ECRs developing important skills needed for their academic life. The workshop will itself constitute a networking opportunity for ECRs, while allowing first time conference goers a smooth start into the conference.
3. ARIES (Artificial Intelligence for Ecosystem Services)
Organised by: Basque Centre for Climate Change (BC3) and Integrated Modelling Partnership
Time: 15:30-18:30, Room 309
Number of participants: 50
The training session ARIES (Artificial Intelligence for Ecosystem Services) will show how its web interface k.explorer works. The session will be facilitated by experts from the ARIES Team in a lecture + live demo format. No laptops will be required for this training. However, should the participants like to install k.explorer and use it, you will be able to do so!
After attending this 3 hours training, participants would have acquired the necessary skills so as to run k.explorer and test the existing models on their context case studies.
