Optic disc classification by the Heidelberg Retina Tomograph and by physicians with varying experience of glaucoma

PurposeTo compare the diagnostic accuracy of the Heidelberg Retina Tomograph's (HRT) Moorfields regression analysis (MRA) and glaucoma probability score (GPS) with that of subjective grading of optic disc photographs performed by ophthalmologists with varying experience of glaucoma and by ophthalmology residents.MethodsDigitized disc photographs and HRT images from 97 glaucoma patients with visual field defects and 138 healthy individuals were classified as either within normal limits (WNL), borderline (BL), or outside normal limits (ONL). Sensitivity and specificity were compared for MRA, GPS, and the physicians. Analyses were also made according to disc size and for advanced visual field loss.ResultsForty-five physicians participated. When BL results were regarded as normal, sensitivity was significantly higher (P<5%) for both MRA and GPS compared with the average physician, 87%, 79%, and 62%, respectively. Specificity ranged from 86% for MRA to 97% for general ophthalmologists, but the differences were not significant. In eyes with small discs, sensitivity was 75% for MRA, 60% for the average doctor, and 25% for GPS; in eyes with large discs, sensitivity was 100% for both GPS and MRA, but only 68% for physicians.ConclusionOur results suggest that sensitivity of MRA is superior to that of the average physician, but not that of glaucoma experts. MRA correctly classified all eyes with advanced glaucoma and showed the best sensitivity in eyes with small optic discs

Enhanced Migratory Waterfowl Distribution Modeling by Inclusion of Depth to Water Table Data

In addition to being used as a tool for ecological understanding, management and conservation of migratory waterfowl rely heavily on distribution models; yet these models have poor accuracy when compared to models of other bird groups. The goal of this study is to offer methods to enhance our ability to accurately model the spatial distributions of six migratory waterfowl species. This goal is accomplished by creating models based on species-specific annual cycles and introducing a depth to water table (DWT) data set. The DWT data set, a wetland proxy, is a simulated long-term measure of the point either at or below the surface where climate and geological/topographic water fluxes balance. For species occurrences, the USGS' banding bird data for six relatively common species was used. Distribution models are constructed using Random Forest and MaxEnt. Random Forest classification of habitat and non-habitat provided a measure of DWT variable importance, which indicated that DWT is as important, and often more important, to model accuracy as temperature, precipitation, elevation, and an alternative wetland measure. MaxEnt models that included DWT in addition to traditional predictor variables had a considerable increase in classification accuracy. Also, MaxEnt models created with DWT often had higher accuracy when compared with models created with an alternative measure of wetland habitat. By comparing maps of predicted probability of occurrence and response curves, it is possible to explore how different species respond to water table depth and how a species responds in different seasons. The results of this analysis also illustrate that, as expected, all waterfowl species are tightly affiliated with shallow water table habitat. However, this study illustrates that the intensity of affiliation is not constant between seasons for a species, nor is it consistent between species

Integrating biodiversity, remote sensing, and auxiliary information for the study of ecosystem functioning and conservation at large spatial scales

Assessing patterns and processes of plant functional, taxonomic, genetic, and structural biodiversity at large scales is essential across many disciplines, including ecosystem management, agriculture, ecosystem risk and service assessment, conservation science, and forestry. In situ data housed in databases necessary to perform such assessments over large parts of the world are growing steadily. Integrating these in situ data with remote sensing (RS) products helps not only to improve data completeness and quality but also to account for limitations and uncertainties associated with each data product. Here, we outline how auxiliary environmental and socioeconomic data might be integrated with biodiversity and RS data to expand our knowledge about ecosystem functioning and inform the conservation of biodiversity. We discuss concepts, data, and methods necessary to assess plant species and ecosystem properties across scales of space and time and provide a critical discussion of outstanding issues