The Earth System is dynamic. It influences and is influenced by physical, chemical, and geological processes, but it may be the least understood of these systems. The biosphere interacts with the physical Earth System on diurnal and seasonal scales, and over decades and centuries. The living system interacts with itself and other systems at a variety of scales. At large, continental scales, exchange between biotic elements and the atmosphere and surface water control climate, hydrology, and productivity. At small scales plants interact with each other and exchange energy and matter with the atmosphere and soil. Understanding the Earth System requires comparable methods and analysis across scales and over decades. This is particularly true given that the Earth System is increasingly facing changes in climate and disturbances, the redistribution of species, and land-use change.
The National Ecological Observatory Network (NEON) is a platform designed to enable an understanding of the causes and consequences of change on ecology. By simultaneously measuring the drivers of change and ecological responses – organisms, atmosphere, and soil – it will enable the ecological community to better understand the nature of interactions and support forecasts of future states. This work describes questions, analysis, and testing for the development of the plant diversity observations to be made by NEON.
Models and forecasts require information from each of the sites that comprise NEON. The study design that directs spatial distribution of plots for sampling diversity relies on a random design that is stratified by land cover with replication intended to detect differences in trends between sites over thirty years. A classic power analysis that relied on prototype data and satellite imagery to parameterize temporal and spatial variability indicated that a sample size of 30 plots per site would sufficiently differentiate trends across sites. Results from multiple sites collecting data according to the design demonstrated that patterns of spatial variation were higher than expected and that a larger sample size would be required to satisfy the specified test.
Plant diversity data collected according to the design also must be comparable within and across sites. Variations in level of effort challenge the statistical comparison of plant species richness data. Comparing richness where the coverage - as defined by slope of the species accumulation curve – provides a statistically rigorous and biologically meaningful point of comparison. To sample such that species accumulation curves terminated at a slope of seven, plots were allocated proportional to the square-root of the strata area within each site. When comparing plant species richness data collected according to the proposed allocation from six it was found that only 30% of the within-site species accumulation curves terminated at a slope of seven, and only 33% of the species accumulation curves at the scale of the site terminated at a slope of seven.
Ensuring the creation of a design that generates data capable of describing extant status and future states will require iteration and continued evaluation. A method for ensuring plots are located such that change will be detected was evaluated by generating species distribution models of two invasive plant species, Pennisetum clandestinum and Holcus lanatus as predicted by topography and extant and future climate data. The models suggested that suitable habitat for Pennisetum clandestinum may decrease in extent while suitable habitat for Holcus lanatus may expand at the site over time. To adequately document and improve understanding of the causes and consequence of habitat expansion, additional sampling plots could be placed in areas vulnerable to by Holcus lanatus in the future. Similarly, any resources available for the control of plant species invasion may be better expended on Holcus lanatus. This is one example of the many uses of NEON data to assist land managers.
|Commitee:||Evangelista, Paul, Martin, Patrick, Morisette, Jeffery|
|School:||Colorado State University|
|School Location:||United States -- Colorado|
|Source:||DAI-B 79/02(E), Dissertation Abstracts International|
|Keywords:||Data comparability, Plant diversity, Study design|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be