Environmental Management

, Volume 50, Issue 5, pp 969-978

First online:

Can Volunteers Collect Data that are Comparable to Professional Scientists? A Study of Variables Used in Monitoring the Outcomes of Ecosystem Rehabilitation

  • John GollanAffiliated withAustralian MusuemSchool of the Environment, University of Technology, Sydney Email author 
  • , Lisa Lobry de BruynAffiliated withEcosystem Management, School of Environmental and Rural Science, University of New England
  • , Nick ReidAffiliated withEcosystem Management, School of Environmental and Rural Science, University of New England
  • , Lance WilkieAffiliated withAustralian Musuem

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


Having volunteers collect data can be a cost-effective strategy to complement or replace those collected by scientists. The quality of these data is essential where field-collected data are used to monitor progress against predetermined standards because they provide decision makers with confidence that choices they make will not cause more harm than good. The integrity of volunteer-collected data is often doubted. In this study, we made estimates of seven vegetation attributes and a composite measure of six of those seven, to simulate benchmark values. These attributes are routinely recorded as part of rehabilitation projects in Australia and elsewhere in the world. The degree of agreement in data collected by volunteers was compared with those recorded by professional scientists. Combined results showed that scientists collected data that was in closer agreement with benchmarks than those of volunteers, but when data collected by individuals were analyzed, some volunteers collected data that were in similar or closer agreement, than scientists. Both groups’ estimates were in closer agreement for particular attributes than others, suggesting that some attributes are more difficult to estimate than others, or that some are more subjective than others. There are a number of ways in which higher degrees of agreement could be achieved and introducing these will no doubt result in better, more effective programs, to monitor rehabilitation activities. Alternatively, less subjective measures should be sought when developing monitoring protocols. Quality assurance should be part of developing monitoring methods and explicitly budgeted for in project planning to prevent misleading declarations of rehabilitation success.


Benchmark Citizen science Cost-effective Data collection Data credibility