Functional trait data can greatly enhance biodiversity science and global change ecology by providing insights into the mechanistic links between environmental changes and biodiversity and ecosystem responses, and by providing ‘common currencies’ through which we can compare systems that share few species. This knowledge is urgently needed to understand the consequences of global climate and environmental change, and to support evidence-based conservation and management. Consequently, trait-based research and data has been accumulating rapidly over the past decades. However, as is often the case, there are considerable geographic and taxonomic biases in these data, and many biodiversity hotspots are currently severely underrepresented.
This increased focus on functional traits in ecology has also created a demand for training in trait-based approaches. In 2015, we had the opportunity to develop a new field course focusing on plant functional traits. At the same time, we had climate change experiments running in the Gongga Mountains, Sichuan Province, China, at the edge of the Tibetan Plateau. This is a region of highly diverse mountain grasslands, where trait-based data were lacking. Collecting plant functional traits data requires considerable resources – as in – many hands on deck. Especially when working in a diverse flora. What if we combined the course with a traits data campaign?
In 2015 and 2016, we ran the two first International Plant Functional Traits Courses in Moxi, Sichuan Province, China. Our students were trained in the theory and practice of trait-based ecology, and together we collected a comprehensive dataset: We collected trait data associated with plant resource use, growth, and life history strategies (leaf dry mass, leaf area, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, CN ratio and P content and C and N isotopes) from local populations and from experimental treatments along a 1100 m altitudinal gradient. The resulting database consists of 6,671 plant records, and 36,743 trait measurements. We are proud to report that these two courses alone increased the trait data coverage of the regional alpine flora by 500%, reporting trait data from 193 plant taxa, ca. 50% of which have no previous published trait data, across 37 plant families.
As a group of over 40 researchers and students from seven countries and five continents, we encountered and tackled many challenges that will no doubt be familiar for others involved in large international, collaborative, field courses and data campaigns. Many people were involved, at different career stages, and with different knowledge of and experience with field and lab work in general and with trait-based campaigns in particular. As the course was not purely for educational purposes, but also aimed to collect a real research-grade dataset, it was important for us to do everything we could to ensure that we collected high-quality data. A first step is to use established and state-of-the art methods for collecting the data. To ensure that we applied these methods in consistent ways, a second important step is to work together to develop good and clear field and lab protocols. However, errors can and will always occur, and a third step was therefore to make sure that any errors or weaknesses in our data are properly documented. Further, we developed approaches for data cleaning and data management that were reproducible, and we developed our field, lab, and data protocols to avoid as many as possible of the manual measurements and transcriptions that may create human errors in the data. Good, transparent methodologies and data documentation is critical to high-quality science, especially as open science practices are catching on and data are increasingly shared and reused. In the process of developing this course and data campaign we have been realizing that these aspects are critical, but often under-communicated, learning outcomes from field and lab training in ecology. Publishing this data paper together has been a very nice way of wrapping the work up. The publication process has provided the added value of giving the students credit for the hard work they put in during and after the course, and also sharing the data from this understudied region openly with the world.
The motivation for making our database open is to provide global change ecologists and conservation biologists access to the traits, vegetation, and experimental data from this understudied region.
In our publication we describe our field methods for collecting the observations presented in the database, we summarize the contents of the database, and we provide the raw and cleaned data, as well as the code for cleaning and summarizing the data. Also, the experiences from these courses inspired us to conduct similar course-based field campaigns in other parts of the world – we eagerly look forward to sharing the next waves of plant functional trait data from elevation gradients and experiments in the Puna grasslands of Peru, in the high arctic of Svalbard, and beyond!