Emily B. Schultz

Poplar maintains zinc homeostasis with heavy metal genes HMA4 and PCS1

Perennial woody species, such as poplar (Populus spp.) must acquire necessary heavy metals like zinc (Zn) while avoiding potential toxicity. Poplar contains genes with sequence homology to genes HMA4 and PCS1 from other species which are involved in heavy metal regulation. While basic genomic conservation exists, poplar does not have a hyperaccumulating phenotype. Poplar has a common indicator phenotype in which heavy metal accumulation is proportional to environmental concentrations but excesses are prevented. Phenotype is partly affected by regulation of HMA4 and PCS1 transcriptional abundance. Wild-type poplar down-regulates several transcripts in its Zn-interacting pathway at high Zn levels. Also, overexpressed PtHMA4 and PtPCS1 genes result in varying Zn phenotypes in poplar; specifically, there is a doubling of Zn accumulation in leaf tissues in an overexpressed PtPCS1 line. The genomic complement and regulation of poplar highlighted in this study supports a role of HMA4 and PCS1 in Zn regulation dictating its phenotype. These genes can be altered in poplar to change its interaction with Zn. However, other poplar genes in the surrounding pathway may maintain the phenotype by inhibiting drastic changes in heavy metal accumulation with a single gene transformation.

Plant‐based FRET biosensor discriminates environmental zinc levels

Heavy metal accumulation in the environment poses great risks to flora and fauna. However, monitoring sites prone to accumulation poses scale and economic challenges. In this study, we present and test a method for monitoring these sites using fluorescent resonance energy transfer (FRET) change in response to zinc (Zn) accumulation in plants as a proxy for environmental health. We modified a plant Zn transport protein by adding flanking fluorescent proteins (FPs) and deploying the construct into two different species. In Arabidopsis thaliana, FRET was monitored by a confocal microscope and had a 1.4-fold increase in intensity as the metal concentration increased. This led to a 16.7% overall error-rate when discriminating between a control (1μm Zn) and high (10mm Zn) treatment after 96h. The second host plant (Populus tremula×Populu salba) also had greater FRET values (1.3-fold increase) when exposed to the higher concentration of Zn, while overall error-rates were greater at 22.4%. These results indicate that as plants accumulate Zn, protein conformational changes occur in response to Zn causing differing interaction between FPs. This results in greater FRET values when exposed to greater amounts of Zn and monitored with appropriate light sources and filters. We also demonstrate how this construct can be moved into different host plants effectively including one tree species. This chimeric protein potentially offers a method for monitoring large areas of land for Zn accumulation, is transferable among species, and could be modified to monitor other specific heavy metals that pose environmental risks.

Consequences of Landsat Image Strata Classification Errors on Bias and Variance of Inventory Estimates: A Forest Inventory Case Study

Use of remotely sensed (e.g., Landsat) imagery for developing sampling frame strata for large-scale inventories of natural resources has potential for increasing sampling efficiency and lowering cost by reducing required sample sizes. Sampling frame errors are inherent with the use of this technology, either from user misclassification or due to flawed technology. Knowledge of these sampling frame errors is important, as they inflate the variance of inventory estimates, particularly poststratified estimates. Forest inventory estimates from the Mississippi Institute for Forest Inventory (MIFI) were utilized to study the extent to which Geographic Information System classification errors (sampling frame errors) affect forest volume and area mean and variance estimates. MIFIs high sampling intensity provided a unique opportunity to quantify the magnitude that different levels of misclassification ultimately have on mean and variance estimates. A variance calculator was developed to assess the impact of various levels of misclassification on least and most variable summary estimates of cubic meter volume percent and total area. The standard error estimates for mean and total volume decreased when plots were reallocated to their correct strata. The increased efficiency obtained from correcting misclassifications illustrates that the loss in precision due to misclassifying inventory strata is consequential. Knowledge and correction of these errors provides a natural-resource-based professional or investor using land classification/inventory data the best minimum risk information possible. A complete set of variance estimators for poststratified means and total area estimates with sampling frame errors are presented and compared to estimators without sampling frame errors.