Showing papers by "Brian J. Enquist published in 2008"

Microbes on mountainsides: Contrasting elevational patterns of bacterial and plant diversity

Abstract: The study of elevational diversity gradients dates back to the foundation of biogeography. Although elevational patterns of plant and animal diversity have been studied for centuries, such patterns have not been reported for microorganisms and remain poorly understood. Here, in an effort to assess the generality of elevational diversity patterns, we examined soil bacterial and plant diversity along an elevation gradient. To gain insight into the forces that structure these patterns, we adopted a multifaceted approach to incorporate information about the structure, diversity, and spatial turnover of montane communities in a phylogenetic context. We found that observed patterns of plant and bacterial diversity were fundamentally different. While bacterial taxon richness and phylogenetic diversity decreased monotonically from the lowest to highest elevations, plants followed a unimodal pattern, with a peak in richness and phylogenetic diversity at mid-elevations. At all elevations bacterial communities had a tendency to be phylogenetically clustered, containing closely related taxa. In contrast, plant communities did not exhibit a uniform phylogenetic structure across the gradient: they became more overdispersed with increasing elevation, containing distantly related taxa. Finally, a metric of phylogenetic beta-diversity showed that bacterial lineages were not randomly distributed, but rather exhibited significant spatial structure across the gradient, whereas plant lineages did not exhibit a significant phylogenetic signal. Quantifying the influence of sample scale in intertaxonomic comparisons remains a challenge. Nevertheless, our findings suggest that the forces structuring microorganism and macroorganism communities along elevational gradients differ.

On estimating the exponent of power-law frequency distributions

Abstract: Power-law frequency distributions characterize a wide array of natural phenomena. In ecology, biology, and many physical and social sciences, the exponents of these power laws are estimated to draw inference about the processes underlying the phenomenon, to test theoretical models, and to scale up from local observations to global patterns. Therefore, it is essential that these exponents be estimated accurately. Unfortunately, the binning-based methods traditionally used in ecology and other disciplines perform quite poorly. Here we discuss more sophisticated methods for fitting these exponents based on cumulative distribution functions and maximum likelihood estimation. We illustrate their superior performance at estimating known exponents and provide details on how and when ecologists should use them. Our results confirm that maximum likelihood estimation outperforms other methods in both accuracy and precision. Because of the use of biased statistical methods for estimating the exponent, the conclusions of several recently published papers should be revisited.

The relationship between stem and branch wood specific gravity and the ability of each measure to predict leaf area.

Abstract: A few trait axes that represent differential biomass allocation may summarize plant life-history strategies. Here we examine one of these axes described by wood specifi c gravity. Wood specifi c gravity represents the location of a species on a continuum of the rate of growth vs. the likelihood of mechanical failure, ranging from rapid volumetric growth/increased probability of mechanical failure to slow volumetric growth/decreased probability of mechanical failure. Wood specifi c gravity has been quantifi ed primarily using three separate methods: a section from terminal branch, a section from the main stem or from a trunk wood core. What is unclear is how comparable these methods are and whether one or the other is a better predictor of other important plant traits such as leaf area. Here we measured stem and branch wood specifi c gravities from individual trees and shrubs in a tropical rain forest, quantifi ed their relationship and determined their ability to predict leaf area. Stem and branch measures were highly correlated with each measure having a weak correlation with leaf area in trees and strong correlation with leaf area in shrubs. These results indicate that various methodologies for measuring wood specifi c gravity are comparable, and thus less destructive methods than are currently used are available to determine values for this important trait.

Higher taxa as paleoecological and paleoclimatic indicators: A search for the modern analog of the Florissant fossil flora

Abstract: We used higher taxonomic composition of 241 modern forest plots from across the New World to identify the closest modern analog of the Florissant fossil flora and to infer Late Eocene paleotemperature for Florissant. Non-metric multidimensional scaling (NMS) based on both genus and family presence-absence placed Florissant in a no-analog taxonomic space surrounded by North American warm temperate broadleaved forests, Mexican humid pine-oak forests, and subtropical moist forests from Florida, Mexico, and Argentina. The most similar site to Florissant, as indicated by the mean of Euclidean distances in genus and family NMS space, was a subtropical moist forest in southern Florida, followed by the humid pine-oak forests of central and northeastern Mexico, and the broadleaved deciduous forests of eastern North America.

Using network analysis to characterize forest structure

Abstract: Network analysis quantifies different structural properties of systems of interrelated parts using a single analytical framework. Many ecological phenomena have network-like properties, such as the trophic relationships of food webs, geographic structure of metapopulations, and species interactions in communities. Therefore, our ability to understand and manage such systems may benefit from the use of network-analysis techniques. But network analysis has not been applied extensively to ecological problems, and its suitability for ecological studies is uncertain. Here, we investigate the ability of network analysis to detect spatial patterns of species association in a tropical forest. We use three common graph-theoretic measures of network structure to quantify the effect of understory tree size on the spatial association of understory species with trees in the canopy: the node degree distribution (NDD), characteristic path length (CPL), and clustering coefficient (CC). We compute the NDD, CPL, and CC for each of seven size classes of understory trees. For significance testing, we compare the observed values to frequency distributions of each statistic computed from randomized data. We find that the ability of network analysis to distinguish observed patterns from those representing randomized data strongly depends on which aspects of structure are investigated. Analysis of NDD finds no significant difference between random and observed networks. However, analysis of CPL and CC detected nonrandom patterns in three and one of the seven size classes, respectively. Network analysis is a very flexible approach that holds promise for ecological studies, but more research is needed to better understand its advantages and limitations.