Here we used an extensive fish dataset to create a large pool of fish community metrics which were screened to create multimetric indices (MMIs) in eight ecoregions covering the conterminous U.S. Candidate metrics were tested for metric range, corrected for natural gradients using boosted regression trees, and then tested for repeatability and sensitivity to landscape disturbance.
Image metrics were tested for rank correlation to composite scores and clinical liver function parameters.
Four C-Q metrics were tested: concentration-discharge (C-Q), concentration-cumulative discharge (C-ΣQ), load (L -discharge (L -dischargemalized concentration-normalized dischande (normalized
To confirm this statement, a thorough multivariate regression analysis was performed and several estimation models combining these three metrics were tested, being the most accurate, a linear model including, from the three candidates, only THRU metrics.
The behavior of several landscape pattern metrics were tested against various pattern scenarios generated by neutral landscape models, including number of classes, scale-map extent, scale-resolution, class proportion, aggregation level RULE, and aggregation level SimMap.
The metrics were tested using both two-dimensional and three-dimensional synthesized images that mimicked clinically significant pathologies: two of the three metrics were scale invariant, additive, and produced tortuosity values tailored to be independent of the resolution of the imaging system.
Our metrics are tested on a large-scale corpus of public, online, user-generated groups.
Both metrics are dedicated to synthesis-related artifacts without compression-related artifacts and both metrics are tested using IRCCyN/IVC DIBR images dataset.
Spatial analysis was used to assemble a range of landscape metrics that were tested for associations with the spatial distribution of groundwater-driven refuges.
Differences among sites for metrics and indices were tested using crossed ANOVAS, with site and year as factors.
In this article, Allometric models and the traditional multiple linear regression models for estimating the linkage between landscape metrics and water quality were tested in Sihu Basin, Hubei Province, China.
