Networks of citizen scientists (CS) have the potential to observe biodiversity and species distributions at global scales. Yet the adoption of such datasets in conservation science may be hindered by a perception that the data are of low quality. This perception likely stems from the propensity of data generated by CS to contain greater levels of variability (e.g., measurement error) or bias (e.g., spatio-temporal clustering) in comparison to data collected by scientists or instruments. Modern analytical approaches can account for many types of error and bias typical of CS datasets. It is possible to (1) describe how pseudo-replication in sampling influences the overall variability in response data using mixed-effects modeling, (2) integrate data to explicitly model the sampling process and account for bias using a hierarchical modeling framework, and (3) examine the relative influence of many different or related explanatory factors using machine learning tools. Information from these modeling approaches can be used to predict species distributions and to estimate biodiversity. Even so, achieving the full potential from CS projects requires meta-data describing the sampling process, reference data to allow for standardization, and insightful modeling suitable to the question of interest.

Item Type:	Refereed Article
Keywords:	volunteer data, statistical analysis, experimental design, linear models, additive models, species distribution models, biodiversity, reef life survey
Research Division:	Agricultural, Veterinary and Food Sciences
Research Group:	Fisheries sciences
Research Field:	Fisheries sciences not elsewhere classified
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the biological sciences
UTAS Author:	Bird, TJ (Mr Tomas Bird)
UTAS Author:	Bates, AE (Dr Amanda Bates)
UTAS Author:	Hill, NA (Dr Nicole Hill)
UTAS Author:	Thomson, RJ (Dr Russell Thomson)
UTAS Author:	Edgar, GJ (Professor Graham Edgar)
UTAS Author:	Stuart-Smith, RD (Dr Rick Stuart-Smith)
UTAS Author:	Wotherspoon, S (Dr Simon Wotherspoon)
UTAS Author:	Stuart-Smith, JF (Dr Jemina Stuart-Smith)
UTAS Author:	Pecl, GT (Professor Gretta Pecl)
UTAS Author:	Barrett, N (Associate Professor Neville Barrett)
UTAS Author:	Frusher, S (Professor Stewart Frusher)
ID Code:	86722
Year Published:	2014
Web of Science® Times Cited:	205
Deposited By:	Sustainable Marine Research Collaboration
Deposited On:	2013-10-16
Last Modified:	2017-11-15
Downloads:	0