Guild richness associations with various metrics.

<p>(Top row): correlation bar plots of the most predictive metrics of species richness by guild. White bars represent a positive correlation and grey indicate a negative correlation. (Bottom rows): correlation comparisons between comparable patch-based metrics with and without considering the vertical patches and edges for the woodland and forest edge guild. The left panels show traditional metrics without accounting for height-heterogeneity; the right panels are height-structured counterparts. The black dots indicate a negative correlation and the grey ones indicate a positive correlation.</p

Predictive ability of multivariable models.

<p>A, B, C, and D are the four habitat metric sets, and 4BPHMs are the four best predictive height-structured metrics. Each of the top panels shows four linear models with whiskers giving 95% confidence interval of adjusted-r² values. The length of the bar represents the mean adjusted-r² for these models. The lower panels show the explained variance of the comparable random forest (RF) models. Uniquely the top bars at lower pannels are the results from the models employed all metrics from the four metric sets.</p

Distribution of BBS routes through the primarily forested ecoregions in the U.S.

<p>The richness models for the woodland guild were built using data from both eastern and western forested ecoregions. The forest edge and interior forest bird richness was modeled in the eastern forested ecoregions only.</p

The Influence of Vegetation Height Heterogeneity on Forest and Woodland Bird Species Richness across the United States

<div><p>Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r² = ∼0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r² = ∼0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r² values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness.</p></div

List of all metrics developed in the study.

<p>List of all metrics developed in the study.</p

Random Forest model results.

<p>(Top row): Modeled vs. actual species richness for three guilds using all-inclusive random forest models. (Below the scatter plots): variable importance plots show the percent increase in mean square error (%IncMSE) of the top 20 most influential metrics in the woodland guild richness model and the forest edge guild richness model (note different scales on X-axes). The metrics characterizing vegetation height heterogeneity are plotted with triangles and the rest of the metrics are circles.</p

Losing a jewel—Rapid declines in Myanmar’s intact forests from 2002-2014

<div><p>New and rapid political and economic changes in Myanmar are increasing the pressures on the country’s forests. Yet, little is known about the past and current condition of these forests and how fast they are declining. We mapped forest cover in Myanmar through a consortium of international organizations and environmental non-governmental groups, using freely-available public domain data and open source software tools. We used Landsat satellite imagery to assess the condition and spatial distribution of Myanmar’s intact and degraded forests with special focus on changes in intact forest between 2002 and 2014. We found that forests cover 42,365,729 ha or 63% of Myanmar, making it one of the most forested countries in the region. However, severe logging, expanding plantations, and degradation pose increasing threats. Only 38% of the country’s forests can be considered intact with canopy cover >80%. Between 2002 and 2014, intact forests declined at a rate of 0.94% annually, totaling more than 2 million ha forest loss. Losses can be extremely high locally and we identified 9 townships as forest conversion hotspots. We also delineated 13 large (>100,000 ha) and contiguous intact forest landscapes, which are dispersed across Myanmar. The Northern Forest Complex supports four of these landscapes, totaling over 6.1 million ha of intact forest, followed by the Southern Forest Complex with three landscapes, comprising 1.5 million ha. These remaining contiguous forest landscape should have high priority for protection. Our project demonstrates how open source data and software can be used to develop and share critical information on forests when such data are not readily available elsewhere. We provide all data, code, and outputs freely via the internet at (for scripts: <a href="https://bitbucket.org/rsbiodiv/" target="_blank">https://bitbucket.org/rsbiodiv/</a>; for the data: <a href="http://geonode.themimu.info/layers/geonode:myan_lvl2_smoothed_dec2015_resamp" target="_blank">http://geonode.themimu.info/layers/geonode:myan_lvl2_smoothed_dec2015_resamp</a>)</p></div

Boxplot comparing mean canopy cover as measured by Hansen et al. (2013) for intact and degraded forests in different forest regions of Myanmar between 2002 and 2014.

<p>Northern Sagaing = mostly mixed deciduous and evergreen forest; Southern Sagaing = mixed deciduous and dry dipterocarp forest; Tanintharyi = tropical evergreen forest. (A) Canopy cover values for intact forest; (B) Canopy Cover values for degraded forest; (C) Location of Landsat tiles used for comparison.</p

Deforestation hotspots between 2002 and 2014.

<p>Deforestation hotspots between 2002 and 2014.</p

Land cover and land use categories used for forest mapping in Myanmar.

<p>Land cover and land use categories used for forest mapping in Myanmar.</p