The popularity of both citizen science and participatory modelling has given way to a growing number of case studies that all outline the benefits of more inclusive forms of conservation planning. Citizen science offers volunteers the opportunity to engage in environmental research while participatory modelling engages individuals in community-level environmental decisionmaking. Although both of these public-science collaborations are often said to lead to improved environmental decision-making, evidence for these outcomes in the peer reviewed literature remains sparse. We suggest that combining these fields has promise for developing communitysupported research leading to conservation action. To demonstrate this approach, we present the infrastructure and use of a participatory modelling software called Mental Modeler (http://www.mentalmodeler.org/), used with a citizen science web portal (www.citsci.org) that allows citizen scientists, scientists, and managers to: (1) collaboratively define local conservation issues of shared concern; (2) model and represent assumptions, evidence, and existing information about these issues; (3) run scenarios to discuss potential research or management options; and ultimately (4) co-develop citizen scientific research and conservation plans. Using case study data from two community groups working on locally-defined issues related to land management practices in the US, we demonstrate how coordinated learning through modelling practices can lead to the development of self-organized and co-created conservation action. We conclude that the development of online modelling tools holds strong promise for the fields of both citizen science and conservation biology.

This study explores the motivations and barriers for participation and persistence in an innovative citizen science pilot project with Virginia Master Naturalist volunteers. The project combines self-guided online training, in-person meetings, and collaboration through social networking and "mental modeling" to support on-the-ground development and execution of citizen science projects developed by participants. Results suggest that the two strongest motivators for volunteers to participate in the project were an interest in the environment and an interest in protecting a local natural resource. Our findings indicate that volunteers with more prior experience participating in citizen science projects and those with higher gross incomes were more likely to persist in the project. Our data also suggest that decisions to persist or drop out of the project were influenced by volunteers' time commitment, their ability to use the online tools, the perceived relevance of the resources, and the saliency of the project. Projects that arose from pre-existing environmental issues seemed to be more salient and may have enhanced volunteer persistence. We discuss the influence of our findings and the implications for the development of future citizen science projects.

Sensorless adaptive optics optical coherence tomography (AO-OCT) is a technology to image retinal tissue with high resolution by compensating ocular aberrations without wavefront sensors. In this Letter, a fast and robust hill-climbing algorithm is developed to optimize five Zernike modes in AO-OCT with a numerical aperture between that of conventional AO and commercial OCT systems. The merit function is generated in real time using graphics processing unit while axially tracking the retinal layer of interest. A new method is proposed to estimate the largest achievable field of view for which aberrations are corrected uniformly in sensorless AO-OCT.

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