This blog is a summary of a very dynamic workshop that took place at the end of the NZ Ecological Society Conference last week in Christchurch. Amazingly, around 30 scientists, students, academics, teachers, consultants and project coordinators summoned their remaining energy to discuss some key citizen science topics.
Earlier in the year, a call was put out by the organisers of NZES2015 (the annual NZ Ecological Society conference), for symposia and workshops. I thought it high time to capitalise on the gathering momentum of citizen science in New Zealand, and the initial 5 talks proposed rapidly grew to 9. Projects from NZ and Chile were presented, with Karen James and Caren Cooper beaming in from the US showcasing projects they are involved with.
The workshop was based around 4 interdependent questions that rose out of my PhD research:
- How can we ensure that community groups collect quality data?
- What can community-generated data be used for?
- How can community-generated data be integrated with agency data?
- What do we need to grow citizen science in NZ?
So when you put around 30 intelligent and inquiring people in a room after 4 long days at a conference… move them around 4 tables each facilitated by a dynamic leader (Jon Sullivan, Heidi Kikillus, Colin Meurk and Peter Handford) for some short, sharp discussions…. you get best practice for citizen science in New Zealand:
Creating a solid foundation by strengthening data quality.
From the outset, good design to determine the fitness of the study for its intended purpose along with a ‘data quality risk assessment’ is essential. This will guide the selection of protocols and equipment suited to community users and promote standardisation. Technology can play a strong role through user-friendly interfaces for data entry. Being able to determine the skill level of participants, and allowing participants to state their uncertainty will help data analysis along with the provision of evidence to validate the data (e.g., photos, sounds, video) and meta data. Pilot studies help iron out any issues.
Participants will need to be encouraged and supported in diverse ways: describing the purpose of the data collection and study; training in data collection and other aspects of the study participants may become involved with, along with expert supervision and the provision of constructive feedback. In fostering community expertise, participants can be motivated to engage more fully in the study.
Many of the factors that contribute to data quality support data use and integration with other data sets.
Species distribution (limits & gaps), identifying new species, collecting baseline data to record changes over time, and answering ‘big questions’ (e.g. the effects of climate change on phenology), are some well-known uses of citizen science data. Communities may act as watchdogs, alerting agencies of the need to strengthen their monitoring activities e.g. during biosecurity events, and in the case of natural disasters and phenomena such as beech masting. Many community groups already collect 5-Minute Bird Counts, photopoint, vegetation plot and Residual Trap Catch Index data, all of which could be more widely used, if monitoring objectives are shared between data collectors and agencies.
Issues were raised around the complexities of data ownership and access given the attractiveness of iconic native species to poachers and clashes around data use through Resource Management Act consent processes. The need for national guidelines was put forward. Of importance, is the use of standard formats to enable wide data use, such as through the Global Biodiversity Information Facility (although taxonomic name changes can challenge data amalgamation).
Growing citizen science in New Zealand requires social, economic, cultural and ecological considerations
While there is a need for coordinators, facilitators and mentors, remuneration is also needed to support these positions. Citizen science should not be considered a replacement for professional data collection. There’s plenty we don’t know about citizen science in New Zealand, and so it represents a rich area for both social and ecological research: participant behaviour, environmental and scientific literacy; determinants of project success; project outcomes to name a few. As a research method and social movement, a semi-professional/professional approach to project development and implementation is necessary.
Overall, leadership is required so that best practices can be shared and expanded upon. There are many initiatives underway so there may be a need for simplifying choices – unifying messages and portals, as crowdsourcing needs a crowd! There is also plenty of room for engaging society more widely, e.g., incapacitated citizens that could can contribute to online projects and schools. Virtual field trips for example could incorporate citizen science projects and nationwide projects could engage in huge linked learning exercise comparing data and having inter-school competitions.
A few messages:
- It takes a diverse set of expertise ranging from ecological to social to develop robust citizen science studies with engaged participants
- Good quality communication is required that bridges disciplinary and experiential barriers
- Demystifying science is a means toward democratising science, and fundamental to creating a society that is engaged, scientifically as well as environmentally literate
- Scientists need to become mentors for community members
- Mutual respect and trust must be woven into citizen science projects, along with reciprocity: what can volunteers receive in return for their time and effort?
- Ensure that the first experiences are positive ones!
While these findings aren’t necessarily new, having this sort of discussion with such varied participants in New Zealand is. As citizen science evolves in New Zealand, then so will definitions and frameworks. The NZ Landcare Trust will be coordinating further discussions as a part of a new citizen science project – I’m involved and you can be too – contact firstname.lastname@example.org for more information.