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We used landscape maps to quantify landscape heterogeneity based on five metrics calculated using FRAGSTATS and compared these for elephant and randomly located ranges within the study areas.
Fig. 3 Bar plots of the six Layman's niche metrics calculated using the Bayesian statistics based on resampling (n = 10,000) of uncertainty in sample mean estimates.
We used a geographic information system to reconstruct past forest landscapes at four times (1947, 1962, 1975, and 1997) based on interpretation of aerial photographs and landscape metrics calculated using the FRAGSTATS software for spatial pattern analysis.
We tested the potential of using the above metrics to provide near real-time risk assessments with NOAA's operational products by evaluating Hot Snap, Cold Snap and Winter Condition metrics calculated using a 50 km, twice-weekly SST dataset.
Table 6 provides a summary of the ALE metrics calculated using several different read sets against both the M_zebra_v0 and M_zebra_UMD1 assemblies.
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As these metrics are proportions, the significance of differences in performance metrics was calculated using a two-sided test for equality of proportions (prop.test function in R).
Various landscape metrics of each zone, including area, patch density and size, edge, shape, nearest-neighbor, diversity, and contagion and interspersion metrics were calculated using the landscape structure analysis program FRAGSTATS.
Brain deformation metrics were calculated using the University College Dublin Brain Trauma Model.
Further quality metrics were calculated using the statistics software SPSS PASW Statisticsb.
By a landscape pattern analysis, a set of five landscape metrics was calculated using FRAGSTATS.
All textural metrics were calculated using the glcm package in the R statistical environment (Zvoleff 2014).
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