Carbon lost and carbon gained: a study of vegetation and carbon trade-offs among diverse land uses in Phoenix, Arizona.

Human modification and management of urban landscapes drastically alters vegetation and soils, thereby altering carbon (C) storage and rates of net primary productivity (NPP). Complex social and ecological processes drive vegetation cover in cities, leading to heterogeneity in C dynamics depending on regional climate, land use, and land cover. Recent work has demonstrated homogenization in ecological processes within human-dominated landscapes (the urban convergence hypothesis) in soils and biotic communities. However, a lack of information on vegetation in arid land cities has hindered an understanding of potential C storage and NPP convergence across a diversity of ecosystem types. We estimated C storage and NPP of trees and shrubs for six different land-use types in the arid metropolis of Phoenix, Arizona, USA, and compared those results to native desert ecosystems, as well as other urban and natural systems around the world. Results from Phoenix do not support the convergence hypothesis. In particular, C storage in urban trees and shrubs was 42% of that found in desert vegetation, while NPP was only 20% of the total NPP estimated for comparable natural ecosystems. Furthermore, the overall estimates of C storage and NPP associated with urban trees in the CAP ecosystem were much lower (8-63%) than the other cities included in this analysis. We also found that C storage (175.25-388.94 g/m2 ) and NPP (8.07-15.99 g·m-2 ·yr-1 ) were dominated by trees in the urban residential land uses, while in the desert, shrubs were the primary source for pools (183.65 g/m2 ) and fluxes (6.51 g·m-2 ·yr-1 ). These results indicate a trade-off between shrubs and trees in arid ecosystems, with shrubs playing a major role in overall C storage and NPP in deserts and trees serving as the dominant C pool in cities. Our research supports current literature that calls for the development of spatially explicit and standardized methods for analyzing C dynamics associated with vegetation in urbanizing areas.

Bivariate zero-inflated regression for count data: a Bayesian approach with application to plant counts.

Lately, bivariate zero-inflated (BZI) regression models have been used in many instances in the medical sciences to model excess zeros. Examples include the BZI Poisson (BZIP), BZI negative binomial (BZINB) models, etc. Such formulations vary in the basic modeling aspect and use the EM algorithm (Dempster, Laird and Rubin, 1977) for parameter estimation. A different modeling formulation in the Bayesian context is given by Dagne (2004). We extend the modeling to a more general setting for multivariate ZIP models for count data with excess zeros as proposed by Li, Lu, Park, Kim, Brinkley and Peterson (1999), focusing on a particular bivariate regression formulation. For the basic formulation in the case of bivariate data, we assume that Xi are (latent) independent Poisson random variables with parameters λ i, i = 0, 1, 2. A bi-variate count vector (Y1, Y2) response follows a mixture of four distributions; p0 stands for the mixing probability of a point mass distribution at (0, 0); p1, the mixing probability that Y2 = 0, while Y1 = X0 + X1; p2, the mixing probability that Y1 = 0 while Y2 = X0 + X2; and finally (1 - p0 - p1 - p2), the mixing probability that Yi = Xi + X0, i = 1, 2. The choice of the parameters {pi, λ i, i = 0, 1, 2} ensures that the marginal distributions of Yi are zero inflated Poisson (λ 0 + λ i). All the parameters thus introduced are allowed to depend on co-variates through canonical link generalized linear models (McCullagh and Nelder, 1989). This flexibility allows for a range of real-life applications, especially in the medical and biological fields, where the counts are bivariate in nature (with strong association between the processes) and where there are excess of zeros in one or both processes. Our contribution in this paper is to employ a fully Bayesian approach consolidating the work of Dagne (2004) and Li et al. (1999) generalizing the modeling and sampling-based methods described by Ghosh, Mukhopadhyay and Lu (2006) to estimate the parameters and obtain posterior credible intervals both in the case where co-variates are not available as well as in the case where they are. In this context, we provide explicit data augmentation techniques that lend themselves to easier implementation of the Gibbs sampler by giving rise to well-known and closed-form posterior distributions in the bivariate ZIP case. We then use simulations to explore the effectiveness of this estimation using the Bayesian BZIP procedure, comparing the performance to the Bayesian and classical ZIP approaches. Finally, we demonstrate the methodology based on bivariate plant count data with excess zeros that was collected on plots in the Phoenix metropolitan area and compare the results with independent ZIP regression models fitted to both processes.