@article {KNZ001847,
	title = {Different clades and traits yield similar grassland functional responses},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {114},
	year = {2017},
	pages = {705 - 710},
	abstract = {Plant functional traits are viewed as key to predicting important ecosystem and community properties across resource gradients within and among biogeographic regions. Vegetation dynamics and ecosystem processes, such as aboveground net primary productivity (ANPP), are increasingly being modeled as a function of the quantitative traits of species, which are used as proxies for photosynthetic rates and nutrient and water-use efficiency. These approaches rely on an assumption that a certain trait value consistently confers a specific function or response under given environmental conditions. Here, we provide a critical test of this idea and evaluate whether the functional traits that drive the well-known relationship between precipitation and ANPP differ between systems with distinct biogeographic histories and species assemblages. Specifically, we compared grasslands spanning a broad precipitation gradient (\~{}200{\textendash}1,000 mm/y) in North America and South Africa that differ in the relative representation and abundance of grass phylogenetic lineages. We found no significant difference between the regions in the positive relationship between annual precipitation and ANPP, yet the trait values underlying this relationship differed dramatically. Our results challenge the trait-based approach to predicting ecosystem function by demonstrating that different combinations of functional traits can act to maximize ANPP in a given environmental setting. Further, we show the importance of incorporating biogeographic and phylogenetic history in predicting community and ecosystem properties using traits. },
	keywords = {LTER-KNZ, biogeography, Ecosystem function, Functional traits, grasslands. phylogenetics},
	issn = {0027-8424},
	doi = {10.1073/pnas.1612909114},
	url = {https://www.pnas.org/content/114/4/705},
	author = {Forrestel, Elisabeth J. and M.J. Donoghue and Edwards, Erika J. and Jetz, Walter and du Toit, Justin C. O. and M.D. Smith}
}
@article {KNZ001481,
	title = {Measuring genetic diversity in ecological studies},
	journal = {Plant Ecology},
	volume = {213},
	year = {2012},
	pages = {1105 -1115},
	abstract = {<p>There is an increasing interest in how genetic diversity may correlate with and influence community and ecosystem properties. Genetic diversity can be defined in multiple ways, and currently lacking in ecology is a consensus on how to measure genetic diversity. Here, we examine two broad classes of genetic diversity: genotype-based and genome-based measures. Genotype-based measures, such as genotypic richness, are more commonly used in ecological studies, and often it is assumed that as genotypic diversity increases, genomic diversity (the number of genetic polymorphisms and/or genomic dissimilarity among individuals) also increases. However, this assumption is rarely assessed. We tested this assumption by investigating correlations between genotype- and genome-based measures of diversity using two plant population genetic datasets: one observational with data collected at Konza Prairie, KS, and the other based on simulated populations with five levels of genotypic richness, a typical design of genetic diversity experiments. We found conflicting results for both datasets; we found a mismatch between genotypic and genomic diversity measures for the field data, but not the simulated data. Last, we tested the consequences of this mismatch and found that correlations between genetic diversity and community/ecosystem properties depended on metric used. Ultimately, we argue that genome-based measures should be included in future studies alongside genotypic-based measures because they capture a greater spectrum of genetic differences among individuals.</p>},
	keywords = {LTER-KNZ, Community diversity, Ecosystem function, Genome diversity, Phenotype, SGDC},
	doi = {10.1007/s11258-012-0069-6},
	url = {https://link.springer.com/article/10.1007\%2Fs11258-012-0069-6},
	author = {M.L. Avolio and Beaulieu, J. and Lo, E. and M.D. Smith}
}
