write a short conclusion for this "Key findings
Ways of establishing collaborative co-design in a complex stakeholder environment.

Citizen science is not inherently collaborative. Only 4% of citizen science is co-designed, and conscious effort is required to made it so. Collaboration improves citizen science in several ways, it:
•	Allows greater access to local knowledge/data.
•	Broadens participation.
•	Increases motivation of participants.
•	Improves engagement with locally based stakeholders (Taylor et al, 2023).
•	Leads to longer lasting participation.
•	Can lead to pro-environmental behaviour change, and alterations to local land management.
•	Is more likely to lead to nature-based, locally appropriate solution selection.
There are potential drawbacks to co-design. It requires more time and effort, often on the part of both researcher and citizen scientists. It can also take decision making power away from researchers. However, the literature is overwhelmingly positive on the impact of co-design on citizen science. Effective co-design in citizen science requires collaboration at multiple stages of the project, from inception, to planning, to implementation, and ongoing usage of data gathered. Citizen science collaboration can be grouped into three broad stages: co-design, co-production of knowledge, and dissemination. The literature provides a checklist for co-designing a project:
•	Collective design of a methodology
•	Consider the needs of users and decision makers.
•	Translate user suggestions into designs (multiple, iterative cycles if necessary)
•	Design project tools (apps, surveys etc.) through a series of collaborative brainstorms
•	Create tool prototypes.
•	User testing
•	Review, further iterative designs (if necessary)
The next stage is co-production of knowledge. This is a two-way process where researchers must work to maintain scientific rigor of the project, and citizen scientists must (alongside data gathering) communicate emerging project needs to the researchers or steering committee. The dissemination stage is particularly important for the creation of communities of practice. This involves storing and sharing the information gathered in open access format, with open forums from participants to help decide how to use and interpret the data. Project specific social media apps have been successfully used to create these ongoing communities. 
How to develop and facilitate communities of practice/knowledge.
New communities of practice/knowledge can develop through citizen science projects. Existing groups can also be developed into stronger associations, or change their practices/worldviews through such projects. Intent, on the part of the researchers, is not enough to develop new communities of practice/knowledge. Such communities develop organically, but researchers can develop ideal conditions for them. 
To develop the best conditions for communities of practice to develop, the first step is co-design. This not only creates a sense of ownership, but it also allows the needs of communities to be fulfilled. Communities are more likely to form when there is a sense of partnership, as it a chance to learn something about their local area, rather than just provide data to researchers.
The next step to develop communities is knowledge management. During the data gathering stage, the utilization of multiple forms of knowledge (i.e, historical anecdotes and statistical data) helps the varied perspectives of participants feel included. Developing long-term communities can be achieved by effective storage and dissemination of knowledge, as mentioned in the previous section. Providing forums for the community to interact through also helps develop long term associations, both with researchers, and amongst themselves. These could be regular meetings/events, or social media apps dedicated to the project. Without such avenues, communities of practice tend to dissipate over time. 
Knowledge Application can improve community cohesion. This is partially because it gives further ownership and partnership of projects. Location-based communities of practice also tend to favour more nature-based, location specific solutions than researcher communities. 
Making citizen science effective and inclusive.
Exclusion tends to be most prominent in already disenfranchised groups. With those in the DE grade of the social grade system (Semi-skilled & unskilled manual occupations, Unemployed and lowest grade occupations) being least likely to participate. Though younger people are generally less likely to participate, their numbers have shown an upward trend in recent years.
As disenfranchised groups are more likely to have direct experience of degraded environments, their exclusion from citizen science can affect data quality. Below is a chart showing the effects of background on participation in citizen science projects:


Variable	Most likely cohort to participate (% of sampled group)	Least likely cohort to participate (% of sampled group)	
Gender	Men (8.3%)	Women (6.8%)	
Age	55-64 (9.3%)	25–34 (4.1%)	
Ethnicity	White (8.1%)	Asian (3.9%)	
Work Status	In education (10.6%)	Unemployed (3.9%)	
Social Grade	Grade B (16.7%)	Grade DE (3.3%)	
Area (Urban/Rural)	Urban (80.9%)	Rural (8.7%)	

There are multiple strategies for improving inclusion in citizen science, such as:
•	Inclusion focused recruitment strategies
•	Instituting flexible project structure
•	Accommodation those with different experience backgrounds
•	Providing forms of compensation for participants
•	Co-designing the project.
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