Enhanced visualization of microbial biofilms by staining and environmental scanning electron microscopy.

Bacterial biofilms, i.e. surface-associated cells covered in hydrated extracellular polymeric substances (EPS), are often studied with high-resolution electron microscopy (EM). However, conventional desiccation and high vacuum EM protocols collapse EPS matrices which, in turn, deform biofilm appearances. Alternatively, wet-mode environmental scanning electron microscopy (ESEM) is performed under a moderate vacuum and without biofilm drying. If completely untreated, however, EPS is not electron dense and thus is not resolved well in ESEM. Therefore, this study was towards adapting several conventional SEM staining protocols for improved resolution of biofilms and EPS using ESEM. Three different biofilm types were used: 1) Pseudomonas aeruginosa unsaturated biofilms cultured on membranes, 2) P. aeruginosa cultured in moist sand, and 3) mixed community biofilms cultured on substrates in an estuary. Working with the first specimen type, a staining protocol using ruthenium red, glutaraldehyde, osmium tetroxide and lysine was optimized for best topographic resolution. A quantitative image analysis tool that maps relief, newly adopted here for studying biofilms, was used to compare micrographs. When the optimized staining and ESEM protocols were applied to moist sand cultures and aquatic biofilms, the smoothening effect that bacterial biofilms have on rough sand, and the roughening that aquatic biofilms impart on initially smooth coupons, were each quantifiable. This study thus provides transferable staining and ESEM imaging protocols suitable for a wide range of biofilms, plus a novel tool for quantifying biofilm image data.

Remotely sensed data for ecosystem analyses: combining hierarchy theory and scene models.

Remotely sensed data have been used extensively for environmental monitoring and modeling at a number of spatial scales; however, a limited range of satellite imaging systems often constrained the scales of these analyses. A wider variety of data sets is now available, allowing image data to be selected to match the scale of environmental structure(s) or process(es) being examined. A framework is presented for use by environmental scientists and managers, enabling their spatial data collection needs to be linked to a suitable form of remotely sensed data. A six-step approach is used, combining image spatial analysis and scaling tools, within the context of hierarchy theory. The main steps involved are: (1) identification of information requirements for the monitoring or management problem; (2) development of ideal image dimensions (scene model), (3) exploratory analysis of existing remotely sensed data using scaling techniques, (4) selection and evaluation of suitable remotely sensed data based on the scene model, (5) selection of suitable spatial analytic techniques to meet information requirements, and (6) cost-benefit analysis. Results from a case study show that the framework provided an objective mechanism to identify relevant aspects of the monitoring problem and environmental characteristics for selecting remotely sensed data and analysis techniques.