Combined Effects of Impervious Surface and Vegetation Cover on Air Temperature Variations in a Rapidly Expanding Desert City

The goal of this study is to improve our understanding of the interac- tive function of impervious and vegetation covers at different levels of the local and intra-urban spatial scales in relation to air temperatures in an urban environment. A multiple regression model was developed using impervious and vegetation frac- tions at different scales to predict maximum air temperature for the entire Phoenix metropolitan area in Arizona, USA. This study demonstrates that a small amount of impervious cover in a desert city can still increase maximum air temperature despite abundant vegetation cover.

A space-for-time (SFT) substitution approach to studying historical phenological changes in urban environment.

Plant phenological records are crucial for predicting plant responses to global warming. However, many historical records are either short or replete with data gaps, which pose limitations and may lead to erroneous conclusions about the direction and magnitude of change. In addition to uninterrupted monitoring, missing observations may be substituted via modeling, experimentation, or gradient analysis. Here we have developed a space-for-time (SFT) substitution method that uses spatial phenology and temperature data to fill gaps in historical records. To do this, we combined historical data for several tree species from a single location with spatial data for the same species and used linear regression and Analysis of Covariance (ANCOVA) to build complementary spring phenology models and assess improvements achieved by the approach. SFT substitution allowed increasing the sample size and developing more robust phenology models for some of the species studied. Testing models with reduced historical data size revealed thresholds at which SFT improved historical trend estimation. We conclude that under certain circumstances both the robustness of models and accuracy of phenological trends can be enhanced although some limitations and assumptions still need to be resolved. There is considerable potential for exploring SFT analyses in phenology studies, especially those conducted in urban environments and those dealing with non-linearities in phenology modeling

Impact of climatic factors on genetic diversity of 'Stipa breviflora' populations in Inner Mongolia

Genetic diversity of 'Stipa breviflora' populations in the Inner Mongolian grasslands of China and its possible correlation with climatic factors was examined using geographic information systems and random amplified polymorphism DNA analysis. A total of 308 bands were produced with 28 arbitrary decamer oligonucleotide. Three major findings were demonstrated. First, the genetic diversity of 'S. breviflora' was high but lower than that of 'Stipa grandis' and 'Stipa krylovii'. Second, genetic distances between the populations analyzed using the unweighted pair group method and the Mantel test had a highly positive correlation with geographical distances, indicating that spatial separation of this species in the studied area produced genetic shift in the population. Finally, both canonical correspondence and Pearson's analyses revealed strong correlations between genetic differentiation and temperature in the area. We therefore conclude that temperature variations play an important role in genetic differentiations among the investigated 'S. breviflora' populations

Comparison of best phenological regression models for combined and historical data.

<p>Notes: Phenophases are same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051260#pone-0051260-t001" target="_blank">Table 1</a>. SE = standard error of the slope. Bold face font indicates significance at <i>p</i><0.05.</p>*<p>denotes phenophases for which combining historical and spatial data resulted in improvements.</p

Absolute slopes (Y-axis) of linear fits to phenological time-series (1963–2009) predicted based on phenology regression models from combined data and those constructed from historical records.

<p>All slopes are negative and significant at p<0.05. Numbers along the X-axis are sample sizes (top = combined and bottom = historical) used to develop phenology models. They decrease according to the elimination of one historical data point at a time, starting from the earliest. * indicate significance (p<0.05) of slope difference based on ANCOVA analysis. Phenophases are same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051260#pone-0051260-t001" target="_blank">Table 1</a>.</p

Scatterplots illustrating hypothetical situations when spatial data (star symbols) are combined with historical data (squares).

<p>Scatterplots illustrating hypothetical situations when spatial data (star symbols) are combined with historical data (squares).</p

Analysis of covariance (ANCOVA) testing differences between linear regression fits to historical and spatial phenological observations.

<p>Notes: Phenophases are same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051260#pone-0051260-t001" target="_blank">Table 1</a>. Numerator and denominator degrees of freedom are listed as subscripts for each F-value. Bold face font indicates significance at <i>p</i><0.05.</p

Comparison of mean AGDD (∑>5) accumulation (upper panel) and the associated standard deviation (lower panel) for the period of March,31– May,30.

<p>Historic data is the average of years for which phenology observations are available during 1963–2010, and spatial temperature data is the average of two years (2010–11) shown for three major land uses separately.</p

Map of study area showing phenology observations sites.

<p>The rectangular area in the upper right inset elevation map with major highways corresponds to the study area extent shown on the main map. Vegetation information is derived from the Normalized Difference Vegetation Index (NDVI) computed from July 28, 2010 Landsat TM image (Vegetation: NDVI = 0.5–0.7, Dense vegetation: NDVI >0.7).</p