Potential invasion of exotic ambrosia beetles Xyleborus glabratus and Euwallacea sp. in Mexico: A major threat for native and cultivated forest ecosystems

We analyze the invasive potential of two Asian ambrosia beetles, Xyleborus glabratus and Euwallacea sp., into Mexico and the southern United States. The fungal symbionts of these beetles have been responsible for damage to trees of the family Lauraceae, including Persea americana and other noncultivated tree species on both coasts of the United States. We estimate their potential threat using ecological niche modeling and spatial multi-criteria evaluation protocols to incorporate plant and beetle suitabilities as well as forest stress factors across Mexico. Mexico contains higher climatic and habitat suitability for X. glabratus than for Euwallacea sp. Within this country, the neotropical region is most vulnerable to invasion by both of these species. We also identify a corridor of potential invasion for X. glabratus along the Gulf of Mexico coast where most Lauraceae and native Xyleborus species are present; dispersal of either X. glabratus or Euwallacea sp. into this region would likely lead to major disease spread. However, the overall potential damage that these beetles can cause may be a function of how many reproductive hosts and how many other ambrosia beetles are present, as well as of their capacity to disperse. This work can also alert relevant managers and authorities regarding this threat

The integration of empirical, remote sensing and modelling approaches enhances insight in the role of biodiversity in climate change mitigation by tropical forests

Tropical forests store and sequester high amounts of carbon and are the most diverse terrestrial cosystem. Studies show potentially important effects of biodiversity on carbon storage and equestration, but a complete understanding of this relationship across spatiotemporal scales relevant for climate change mitigation needs three approaches: empirical, remote sensing and ecosystem modelling. Here, we review the contribution of these individual approaches to the understanding of the relationship of biodiversity with carbon storage and sequestration, and find short-term and long term benefits of biodiversity at both broad and fine spatial scales. We argue that enhanced understanding is obtained by combining approaches, i.e., by using output from one approach to improve another approach and thus results in better input, validation and comparison between approaches. This can be further improved by integrating approaches through using ‘boundary objects’(i.e., variables) that can be understood and measured by all approaches, such as the diversity of leaf traits of the upper canopy and forest structure indices. Combining and especially integrating approaches will therefore lead to a better understanding of biodiversity effects on climate change mitigation. This is crucial for making sound policy decisions

Soil Organic Carbon Across Mexico and the Conterminous United States (1991–2010)

Soil organic carbon (SOC) information is fundamental for improving global carbon cycle modeling efforts, but discrepancies exist from country‐to‐global scales. We predicted the spatial distribution of SOC stocks (topsoil; 0–30 cm) and quantified modeling uncertainty across Mexico and the conterminous United States (CONUS). We used a multisource SOC dataset (>10 000 pedons, between 1991 and 2010) coupled with a simulated annealing regression framework that accounts for variable selection. Our model explained ~50% of SOC spatial variability (across 250‐m grids). We analyzed model variance, and the residual variance of six conventional pedotransfer functions for estimating bulk density to calculate SOC stocks. Two independent datasets confirmed that the SOC stock for both countries represents between 46 and 47 Pg with a total modeling variance of ±12 Pg. We report a residual variance of 10.4 ±5.1 Pg of SOC stocks calculated from six pedotransfer functions for soil bulk density. When reducing training data to define decades with relatively higher density of observations (1991–2000 and 2001–2010, respectively), model variance for predicted SOC stocks ranged between 41 and 55 Pg. We found nearly 42% of SOC across Mexico in forests and 24% in croplands, whereas 31% was found in forests and 28% in croplands across CONUS. Grasslands and shrublands stored 29 and 35% of SOC across Mexico and CONUS, respectively. We predicted SOC stocks >30% below recent global estimates that do not account for uncertainty and are based on legacy data. Our results provide insights for interpretation of estimates based on SOC legacy data and benchmarks for improving regional‐to‐global monitoring efforts.Key PointsMultisource topsoil organic carbon prediction and prediction variance in Mexico and the conterminous United StatesCalculated stocks of 46–47 Pg of SOC (0‐ to 30‐cm depth, years 1991–2010) using a simulated annealing regression frameworkPredicted stocks >30% below recent global estimates that are largely based on legacy dataPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154249/1/gbc20950_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154249/2/gbc20950-sup-0003-2019GB006219-ts02.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154249/3/gbc20950.pd

Permutation entropy scatter plot in terms of <i>β</i> value.

<p>Entropy reaches highest values around the beta values range for which pink noise is defined.</p

Complexity as a quadratic function of Permutation Entropy.

<p>Complexity as a quadratic function of Permutation Entropy.</p

Our proposal for measuring the dynamic dimension of ecosystemic health is based on the idea of criticality as the combination of scale invariance and balance between adaptability and robustness (pink noise).

<p>By combining a scale invariance index based on BIC values with the value of the scalar coefficients (beta) in power spectra, we propose an Ecosystemic Health Index, whose maximum for beta values equals 1, and that is associated with a balance between adaptability and robustness. In this way, an ecosystem may lose health by losing robustness and exhibiting white-noise dynamics, or by losing adaptability leading to Brownian-noise dynamics.</p

Values of our Ecosystemic Health Index for all ecosystem types (IGBP).

<p>The color of each data point corresponds to the type of noise (white, pink and brown).</p

Permutation entropy boxplot in terms of noise color.

<p>Permutation entropy boxplot in terms of noise color.</p

Evolution of Fisher information for the Harvard Forest site (US-Ha1) from 1991 to 2003.

<p>Pink points are 1 year time series with a pink noise kind of dynamics (0.5 < <i>β</i> < 1.5) and blue point correspond to 1 year time series with a white noise dynamics (<i>β</i> ≤ 0.5).</p

As expected, most ecosystems fall into pink noise.

<p>In general, we found that ecosystems out of criticality are older forests or have been altered by human activity or events such as wildfires.</p