Population Structure of Geosmithia morbida, the Causal Agent of Thousand Cankers Disease of Walnut Trees in the United States

The ascomycete Geosmithia morbida and the walnut twig beetle Pityophthorus juglandis are associated with thousand cankers disease of Juglans (walnut) and Pterocarya (wingnut). The disease was first reported in the western United States (USA) on several Juglans species, but has been found more recently in the eastern USA in the native range of the highly susceptible Juglans nigra. We performed a comprehensive population genetic study of 209 G. morbida isolates collected from Juglans and Pterocarya from 17 geographic regions distributed across 12 U.S. states. The study was based on sequence typing of 27 single nucleotide polymorphisms from three genomic regions and genotyping with ten microsatellite primer pairs. Using multilocus sequence-typing data, 197 G. morbida isolates were placed into one of 57 haplotypes. In some instances, multiple haplotypes were recovered from isolates collected on the same tree. Twenty-four of the haplotypes (42%) were recovered from more than one isolate; the two most frequently occurring haplotypes (H02 and H03) represented 36% of all isolates. These two haplotypes were abundant in California, but were not recovered from Arizona or New Mexico. G. morbida population structure was best explained by four genetically distinct groups that clustered into three geographic regions. Most of the haplotypes isolated from the native range of J. major (Arizona and New Mexico) were found in those states only or present in distinct genetic clusters. There was no evidence of sexual reproduction or genetic recombination in any population. The scattered distribution of the genetic clusters indicated that G. morbida was likely disseminated to different regions at several times and from several sources. The large number of haplotypes observed and the genetic complexity of G. morbida indicate that it evolved in association with at least one Juglans spp. and the walnut twig beetle long before the first reports of the disease

Tropical mammal functional diversity increases with productivity but decreases with anthropogenic disturbance

A variety of factors can affect the biodiversity of tropical mammal communities, but their relative importance and directionality remain uncertain. Previous global investigations of mammal functional diversity have relied on range maps instead of observational data to determine community composition. We test the effects of species pools, habitat heterogeneity, primary productivity and human disturbance on the functional diversity (dispersion and richness) of mammal communities using the largest standardized tropical forest camera trap monitoring system, the Tropical Ecology Assessment and Monitoring (TEAM) Network. We use occupancy values derived from the camera trap data to calculate occupancy-weighted functional diversity and use Bayesian generalized linear regression to determine the effects of multiple predictors. Mammal community functional dispersion increased with primary productivity, while functional richness decreased with human-induced local extinctions and was significantly lower in Madagascar than other tropical regions. The significant positive relationship between functional dispersion and productivity was evident only when functional dispersion was weighted by species' occupancies. Thus, observational data from standardized monitoring can reveal the drivers of mammal communities in ways that are not readily apparent from range map-based studies. The positive association between occupancy-weighted functional dispersion of tropical forest mammal communities and primary productivity suggests that unique functional traits may be more beneficial in more productive ecosystems and may allow species to persist at higher abundances

Population Structure of <i>Geosmithia morbida</i>, the Causal Agent of Thousand Cankers Disease of Walnut Trees in the United States

<div><p>The ascomycete <i>Geosmithia morbida</i> and the walnut twig beetle <i>Pityophthorus juglandis</i> are associated with thousand cankers disease of <i>Juglans</i> (walnut) and <i>Pterocarya</i> (wingnut). The disease was first reported in the western United States (USA) on several <i>Juglans</i> species, but has been found more recently in the eastern USA in the native range of the highly susceptible <i>Juglans nigra</i>. We performed a comprehensive population genetic study of 209 <i>G. morbida</i> isolates collected from <i>Juglans</i> and <i>Pterocarya</i> from 17 geographic regions distributed across 12 U.S. states. The study was based on sequence typing of 27 single nucleotide polymorphisms from three genomic regions and genotyping with ten microsatellite primer pairs. Using multilocus sequence-typing data, 197 <i>G. morbida</i> isolates were placed into one of 57 haplotypes. In some instances, multiple haplotypes were recovered from isolates collected on the same tree. Twenty-four of the haplotypes (42%) were recovered from more than one isolate; the two most frequently occurring haplotypes (H02 and H03) represented 36% of all isolates. These two haplotypes were abundant in California, but were not recovered from Arizona or New Mexico. <i>G. morbida</i> population structure was best explained by four genetically distinct groups that clustered into three geographic regions. Most of the haplotypes isolated from the native range of <i>J. major</i> (Arizona and New Mexico) were found in those states only or present in distinct genetic clusters. There was no evidence of sexual reproduction or genetic recombination in any population. The scattered distribution of the genetic clusters indicated that <i>G. morbida</i> was likely disseminated to different regions at several times and from several sources. The large number of haplotypes observed and the genetic complexity of <i>G. morbida</i> indicate that it evolved in association with at least one <i>Juglans</i> spp. and the walnut twig beetle long before the first reports of the disease.</p></div

Unrooted phylogenetic tree of <i>Geosmithia</i> species based on ITS/BT sequences.

<p>A Bayesian analysis was performed for 1,500,000 generations by using a GTR-gamma distributed model of evolution (invariant sites). Bayesian percentages (≥50%) are depicted above each branch, and maximum likelihood bootstrap values (≥500) obtained by using PhyML (default parameters) are shown below most branches. <i>Geosmithia morbida</i> haplotypes are color coded according to their genetic cluster assignment (four-cluster-MLST-DAPC model, as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112847#pone-0112847-g003" target="_blank">Figure 3</a>) and haplotypes sharing the same ITS and BT sequences are co-located. Leaves pertaining to the same branch were arranged together according to their cluster assignment. GenBank accession numbers of other <i>Geosmithia</i> spp. are identified within parenthesis.</p

Distribution of 57 MLST-based <i>Geosmithia morbida</i> haplotypes in the United States.

<p>The size of wedges in each pie chart is proportional to the number of isolates. Haplotype colors relate to genetic clusters identified in the four-cluster-MLST-DAPC model where cluster 1 = shades of blue, cluster 2 = shades of red/brown, cluster 3 = shades of yellow, and cluster 4 = shades of green. Callouts are color-coded according to the three-region geographic-Hudson’s Permtest model, where: 1) blue = NM_AZ, central CA, northern CA, northern CO, and TN; 2) green = central AZ; and 3) red = southwestern CA, OR_WA, and southern CO. Callouts in white indicate regions not assessed by using Hudson’s Permtest. Counties are color-coded as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112847#pone-0112847-g001" target="_blank">Figure 1B</a>. (U.S. map adapted from US Census Bureau at <a href="https://www.census.gov/" target="_blank">https://www.census.gov/</a>).</p

Locations, hosts, haplotypes and genetic clusters based on the four-cluster-MLST-DAPC model, for <i>Geosmithia morbida</i> isolates.

<p>* Geographical regions are depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112847#pone-0112847-g001" target="_blank">Fig. 1B</a>.</p><p>** Isolates selected in the first trial of MLST analysis.</p>☆<p>Isolates deposited at Centraalbureau voor Schimmelcultures as CBS 124663 (1217) and CBS 124664 (1218).</p><p>Isolates underlined were subjected to SSR analysis.</p>A-P<p>Common letters indicate isolates from different cankers of the same tree.</p><p>Collectors are as follows: R. M. Bostock (RMB), T. W. Coleman (TWC), W. Cranshaw (WC), P.L. Dallara (PLD), G. Durham (GD), E. Fichtner (EF), S. Fraedrich (SF), A. D. Graves (ADG), K.J. Greby (KJG), B. Hammon (BH), J.E. Henrich (JEH), S.M. Hishinuma (SMH), D. Leatherman (DL), C. Leslie (CL), A. Liu (AL), J. McKenna (JM), L.M. Ohara (LMO), J. Pscheidt (JP), M. Putnam (MP), S. Schlarbaum (SS), S. J. Seybold (SJS), N. Tisserat (NT), C. Utley (CU), D.L. Wood (DLW). All isolations were made in the laboratory of NT with the exception of isolate 1513, which was made in the laboratory of RMB.</p><p>Locations, hosts, haplotypes and genetic clusters based on the four-cluster-MLST-DAPC model, for <i>Geosmithia morbida</i> isolates.</p

<i>Geosmithia morbida</i> molecular variance determined by AMOVA of Bayesian (Structure), DAPC, and Hudson’s Permtest analyses.

<p>* 1) central AZ; 2) NM_AZ; 3) northern and central CA; 4) southwestern CA; 5) OR_WA; 6) northern CO; 7) southern CO; and 8) TN.</p><p>** 1) central AZ; 2) NM_AZ; 3) northern and central CA and northern CO; 4) southwestern CA; 5) OR_WA and southern CO; and 6) TN.</p><p>*** The three “macro” regions were: 1) NM_AZ, central CA, northern CA, northern CO, and TN; 2) central AZ and 3) southwestern CA, OR_WA, and southern CO.</p><p><i>Geosmithia morbida</i> molecular variance determined by AMOVA of Bayesian (Structure), DAPC, and Hudson’s Permtest analyses.</p

Coordinates of 57 (A) and 55 (B) haplotypes of <i>Geosmithia morbida</i> from the MLST-DAPC model.

<p>The most distant cluster (cluster 4 in green) comprised of haplotypes H32 and H33 is identified <b>(A)</b>, as well as the coordinates of all remaining haplotypes when H32 and H33 were excluded <b>(B)</b>. A comparison between the assignments of the MLST-DAPC and MLST-STRUCTURE models are shown in detail. Pie charts give the probability of assignment of haplotypes to the four genetic clusters obtained in the four-clusters-MLST-STRUCTURE model. They are represented by colors, cluster 1 = blue, cluster 2 = red, cluster 3 = yellow and cluster 4 = green. Haplotypes in the box (in <b>B</b>) were amplified for better resolution.</p

Coordinates of the SSR profile of 112 isolates of <i>G. morbida</i> based on DAPC analysis: of a theoretical <i>K</i> = 6 (A); and the same, but excluding the distant isolates in green, which resulted in <i>K</i> = 3 (B).

<p>Coordinates of the SSR profile of 112 isolates of <i>G. morbida</i> based on DAPC analysis: of a theoretical <i>K</i> = 6 (A); and the same, but excluding the distant isolates in green, which resulted in <i>K</i> = 3 (B).</p

Primers tested on MLST analysis of <i>Geosmithia morbida</i> isolates.

<p>* BT Geo was designed based on <i>G. morbida</i> genome, inwardly oriented after amplification by using BT22 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112847#pone.0112847-ODonnell1" target="_blank">[36]</a>.</p><p>Primers tested on MLST analysis of <i>Geosmithia morbida</i> isolates.</p