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• W2893432681 abstract "Monitoring understory plant diversity is important, allowing managers to track current diversity status and trends both spatially and temporally at a landscape-scale. Improving precision in quantifying patterns in understory plant diversity improves efficiency in monitoring design and more accurate measures of success of management intervention over time. Patterns of species diversity are dependent upon the scale in which they are examined – an increase in small-scale diversity across a gradient can convert to a decrease in large-scale diversity across that same gradient. Using two extensive datasets including both mined historical data and supplemental experimental data, we performed an additive partitioning of plant diversity to elucidate the hierarchical spatial patterns of understory plant species richness, and independent measures of alpha and beta diversity in the species-rich longleaf pine ecosystem at Eglin Air Force Base in northwestern FL, USA. This analysis allowed us to identify the spatial scale that most effectively captures plant diversity to inform monitoring efforts by using measures of species turnover, specifically beta diversity. We found that while species richness and alpha diversity increased with spatial scale, beta diversity began to reach an asymptote at smaller (1 m2) scales. Furthermore, we found the sampling effort at this 1 m2 scale required as few as 60 plots to effectively estimate plant diversity within management blocks. While our results are attributable to Eglin AFB specifically, these scaling analyses can help to streamline monitoring efforts in other ecosystems that seek to elucidate the individual contributions of diversity components." @default.
• W2893432681 created "2018-10-05" @default.
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• W2893432681 date "2019-01-01" @default.
• W2893432681 modified "2023-10-13" @default.
• W2893432681 title "Maximizing the monitoring of diversity for management activities: Additive partitioning of plant species diversity across a frequently burned ecosystem" @default.
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• W2893432681 doi "https://doi.org/10.1016/j.foreco.2018.09.022" @default.
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