Escalating threat: increasing impact of the polyphagous shot hole borer beetle, Euwallacea fornicatus, in nearly all major South African forest types

Abstract
The polyphagous shot hole borer (PSHB; Euwallacea fornicatus), is an invasive ambrosia beetle and poses a significant threat to a wide range of tree species globally. Despite its potential impact, research on the beetle’s spread and impacts in natural ecosystems remains limited. This study examines the interactions between PSHB and native forest ecosystems in two regions in South Africa. Over 5 years, PSHB invaded all but one forest type with colonization being recorded on numerous native tree species, often resulting in severe infestations and sometimes mortality. Many tree species and families had higher than expected infestation rates. An increase in PSHB-attacked trees and infestation severity was observed over the course of the study with trees having a ~ 7.5% increased chance of PSHB infestations per year and PSHB holes increasing by over 10% annually. Significant temporal and regional effects on PSHB infestations were also noted. There were higher infestation levels in the tropical KwaZulu-Natal region than in the more temperate Western Cape. Monitoring plots closer to PSHB source populations had higher infestation rates. Higher tree species richness resulted in lower PSHB attacks, whereas higher densities of competent host species led to increased infestations. This study underscores the importance of extended monitoring of invasive species and provides key insights for the potential management of PSHB in natural forest ecosystems. The ecological effects of this invasion may be severe, with many important tree species sustaining infestations. Over time this invasion could have adverse effects to ecosystem functioning and resilience.

Introduction
Forests are a fundamental part of many natural environments and healthy forests provide humans with essential ecological, social, and spiritual benefits (FAO 2022). Healthy forests are spatially and temporally heterogeneous and encompass a network of successional patches that include all stages of naturally occurring disturbance and recovery (Trumbore et al. 2015; Sambaraju et al. 2024). Natural disturbances such as drought, fire, insect damage and diseases are essential parts of forest functioning and create a wide array of habitats that promote diversity (Winder and Shamoun 2006; Grossiord et al. 2014; Burkle et al. 2015; Silva Pedro et al. 2016). In the twenty-first century global forest health is at risk from multiple mega disturbances such as climate change, overexploitation, and invasive species, which are increasing in frequency, extent, and severity (Dale et al. 2001; Millar and Stephenson 2015; Guégan et al. 2023). Understanding the role of disturbance, the difference between natural and human-induced disturbances, and how much disturbance forests can tolerate is important in protecting global forest health (Raffa et al. 2009; Thom and Seidl 2016).

One of the biggest threats to forest health is invasive species (Aukema et al. 2010; Castello and Teale 2011; Ramsfield et al. 2016; Freer-Smith and Webber 2017). The introduction and range expansion of insect, plant, and fungal pests has caused substantial disturbance to many forest ecosystems globally (Sturrock et al. 2011; Ploetz et al. 2013; Ayres and Lombardero 2018; Guégan et al. 2023). Introduced non-native organisms can remain undetected during their establishment, allowing them to multiply and spread unchecked (Simberloff 2009), and once established, the management of pests in natural forests is difficult and costly. Some of the most invasive organisms in forests are bark and ambrosia beetles (Coleoptera: Curculionidae, Scolytinae) (Brockerhoff et al. 2006; Ploetz et al. 2013; van Wilgen et al. 2020). Introductions of these insects have significantly increased over the past century (Pureswaran et al. 2004; Cudmore et al. 2010; Ploetz et al. 2013), and surveys conducted at borders and ports of entry around the world have shown the majority of Coleopteran interceptions involve scolytine beetles (Haack 2006; Brockerhoff et al. 2006; Ploetz et al. 2013). These insects are highly destructive and may carry pathogenic fungal symbionts that can have devastating effects on the trees they attack (Fraedrich et al. 2007; Ploetz et al. 2013; Duan et al. 2018). The invasion patterns of bark and ambrosia beetles have been studied extensively (Haack and Rabaglia 2013; Lantschner et al. 2020) and the tree diseases caused by them are some of the most economically and ecologically damaging known to man (Ploetz et al. 2013; Kirkendall et al. 2015; Rassati et al. 2016). Due to increased invasions by these beetles, there is a growing body of research concerning them and their vectored pathogenic symbionts (Hulcr and Dunn 2011; Kirkendall et al. 2015).

The polyphagous shot hole borer (PSHB), Euwallacea fornicatus (Eichoff), is an ambrosia beetle native to Southeast Asia. Over the past decade, it has become highly invasive in the United States of America and Israel as well as being introduced into Australia and parts of South America (Eskalen et al. 2013; Cook and Broughton 2023; Ceriani-Nakamurakare et al. 2023). In 2017 the PSHB was discovered in Pietermaritzburg, South Africa, and has since been confirmed in all but one province of the country. It has been recorded attacking ornamental street trees, some agricultural species, and native tree species (Paap et al. 2018; van Rooyen et al. 2021; Engelbrecht et al. 2024; Townsend et al. 2024). Once female beetles bore into a host tree they release their fungal symbiont, Neocosmospora euwallaceae (previously Fusarium euwallaceae) (Hypocreales; Nectriaceae), which they cultivate in galleries as a source of food. If the fungus establishes within a host tree the female beetle begins laying eggs after which she and her brood will create tunnel systems within the tree, spreading the fungus (Eskalen et al. 2013; Cooperband et al. 2016). The fungus is pathogenic and grows within the xylem vessels of a host tree and can cause a disease known as Fusarium dieback which can lead to tree death (Freeman et al. 2013). Currently, the PSHB beetle has been recorded on 162 tree species in South Africa, 78 of which are indigenous. Of these 162 species recorded 84 are “competent” and 78 are “Fusarium colonised” host species (discussed below) (Paap et al. 2018; van Rooyen et al. 2021; Townsend et al. 2024). If this beetle continues to spread it poses a major risk to agriculture, urban trees, and indigenous forests throughout Africa (Paap et al. 2018; van Rooyen et al. 2021; Engelbrecht et al. 2024; Townsend et al. 2024).

PSHB is an aggressive ambrosia beetle, and its spread is highly dependent on host tree availability. Therefore, understanding this beetle’s host tree preferences and movement through landscapes is important. There is a significant body of research investigating the spread, impacts, and mitigation of PSHB in agricultural and urban environments (Eskalen et al. 2013; Lynch et al. 2021; Engelbrecht et al. 2024; Roberts et al. 2024), providing a good understanding of how the beetle and its fungal symbiont interact with ornamental and agricultural trees. There is, however, a severe lack of research concerning the beetle’s spread and impacts in natural and indigenous landscapes. One study in the USA found that a close relative of PSHB, Euwallacea Kuroshio (Gomez & Hulcr), caused severe damage to native willows (Salix spp.) in the Tijuana River Valley (Boland 2016), and Bennett (2020) tracked the dispersal and expansion of PSHB in a riparian system along the Santa Clara river (California), investigating PSHB host preferences and susceptibility. In 2019 Townsend et al. (2024) established 51 permanent monitoring plots in an indigenous Afrotemperate forest complex in the Western Cape, South Africa, to monitor the spread, host range, impact, and drivers of invasion of the PSHB in indigenous forests. Over 2 years of study, it was found that PSHB invaded the forest and attacked various indigenous trees, with breeding colonies of PSHB being present in 10% of monitored tree species. Key factors influencing the invasibility of sites were identified, including the proximity of monitoring plots to PSHB source populations and the abundance of host trees within plots. It was also found that natural disturbances such as damage to trees and increased distances from surface water resulted in increased infestation of individual trees. This study offered a valuable snapshot of the PSHB populations in natural forests at an early stage of the invasion process. It also revealed important trends that warrant further investigation. However, to provide a more comprehensive understanding of the current and future dynamics of the PSHB invasion into natural forest ecosystems, it is necessary to gather extended, longer term data not only of Afrotemperate forests but of multiple forest types.

The main aims of this study were to use monitoring plot data to 1) determine the current PSHB infestation levels, 2) determine host tree preferences of the PSHB beetle in native forests, 3) determine the progression of infestations over 5 years of monitoring, and 4) determine the factors that increase invasion success by PSHB in two different climatic regions in South Africa: Southern Afrotemperate forests in the winter-rainfall dominated Western Cape Province, and coastal, mangrove, sand, and swamp forests in the summer-rainfall dominated KwaZulu-Natal province. We evaluated the factors that may affect the invasibility of these forests and the factors that may affect the susceptibility and severity of infestations of individual trees.

Methodology
Site selection
Surveys assessing the level and progression of PSHB infestations were conducted in two regions in South Africa. In the Southern Cape, 51 permanent monitoring plots were established at 11 sites in a Southern Afrotemperate forest complex spanning from George (33°56′35.2′′S 22°27′36.7′′E) to Tsitsikamma (33°57′58.5′S 23°53′45.1′E). Surveys took place between June 2019 and January 2023 (Fig. 1a). In KwaZulu-Natal, 27 permanent monitoring plots were established at 7 sites in coastal, mangrove, sand, and swamp forests from Durban North (29°49′00.3′S 31°01′01.9′E) to Ballito (29°30′43.4′S 31°13′02.0′E). These surveys occurred between September 2019 and January 2023 (Fig. 1b). Sites were selected to capture diverse natural and human impacts and ecologies, including areas frequented by humans (picnic spots with fireplaces in mature forests, areas next to roads susceptible to PSHB spread through human assistance), urban zones already invaded by PSHB, and hiking trails less likely to have PSHB introduced by human activities. The number of plots at each site varied from 1 to 6 (n = 78). Each plot measured 15 m × 10 m, with at least a 30 m separation between plots, and was chosen to represent various tree species growing under different conditions (e.g., varying distances to surface water or nearest human impact).

Data collection
Monitoring
All permanent monitoring plots were surveyed once annually during the study period (n = 5 monitoring events). Surveys comprised all living trees/shrubs found in a plot that had a diameter at breast height of ˃30 mm regardless of health condition. Following the methods of Townsend et al. (2024), individual tree data variables collected included; diameter at breast height (mm), canopy health (% as an average of estimation by two observers) and broad health category (ranging from 1 to 5, with 1 = being a tree that was close to death and 5 = tree in near perfect condition). Distance to the nearest surface water source (km) was also measured, as water availability and drought/flood stress have been shown to affect ambrosia beetle attacks on certain tree species. The degree of natural impact on trees (i.e. snapped branches or main bole, herbivory, etc.) and the degree of human impact (i.e. cut branches, bark collection, vandalism, etc.) was recorded as a percentage of the tree affected. Survey year was also noted, with the first year (2019) used as a reference point. Following Townsend et al. (2024), plot-level data variables that were recorded included; distance to surface water from the middle of each plot, distance to the border of closest known infestations (usually nearest urban border or a known infested competent host, as defined below), the abundance of trees of species that were confirmed or suspected as hosts of the PSHB beetle, discussed below (Table 2) at any time throughout the study, canopy cover (% as an average of estimation by two observers), degree of natural impact (e.g. storm damage, fallen trees) scored as the percentage of trees in a plot that had signs of damage by natural causes, degree of physical human impact scored as the percentage of trees in a plot that had signs of damage caused by humans (e.g. cutting of branches, digging up of roots), total tree species richness and abundance, tree density (number of trees/m2) (LaBau and Cunia 1990), forest type (defined by tree species composition and successional stage (Mucina and Geldenhuys 2006)), and the number of trees showing signs of PSHB colonisation, discussed below.

If an infested tree was found, the number of PSHB holes was counted (standardized from the base to the breast height of the tree (ca. 1.5 m)). The mean flight height of PSHB is ca. 1.24 m (Byers et al., 2017). A standard sampling height ensured consistency and allowed for more trees to be surveyed. This also allowed us to calculate a survey surface area for each tree using the tree radius and the survey height of 1.5 m. Tree surface area was included as an individual tree-level variable.

Infested tree confirmation
All trees were inspected for PSHB colonisation or attempted colonisation by evaluating the trunks for entry holes of the correct size (ca. 0.85 mm) or other symptoms of boring beetles such as sap flow, presence of frass, or the presence of PSHB beetles on the tree (van Rooyen et al. 2021). When holes were present, the bark was removed from the affected area using a sterile chisel to reveal any necrotic tissue in the cambium and deeper wood tissues. Boring activity/colonisation attempts were confirmed by the presence of an entry hole of the expected size for PSHB under the bark that may or may not have been accompanied by wood staining around the gallery (indicative of fungal growth). Based on the notion that F. euwallaceae is a host species-specific symbiont of E. fornicatus in South Africa and that it cannot spread without the help of its symbiotic beetle vector, confirmation of host status/successful colonisation was based on the presence of F. euwallaceae within these galleries in wood. To encourage global consistency in host tree species classification we followed the criteria of Lynch et al. (2021). The host status of species was based on the ability of PSHB to establish F. euwallaceae and to reproduce within trees. Non-host species are those trees on which no signs of PSHB attack were observed. Fusarium-colonised hosts are those in which fungal transmission from PSHB is possible but beetles do not reproduce within the tree. On these hosts, removal of the outer bark reveals necrotic tissue caused by the pathogen, but there are no signs of beetles in attempted galleries. Competent hosts are those in which the beetle can establish a natal gallery and produce offspring. These were all species that contained at least one individual with more than 10 PSHB entry holes, showed signs of extensive gallery formation when opening the wood, and in which either the beetle or F. euwallaceae could be isolated from at least one sampling site or in previous studies in South Africa (Townsend et al. 2024; van Rooyen et al. 2021). Kill-competent hosts included all tree species where at least one individual has been shown to die because of PSHB and F. euwallaceae infestations within our monitoring plots or in South Africa.

For all samples collected from potentially infested trees, wood that contained a part of the gallery and any fungal-stained wood was removed and isolated following the methods of Paap et al. (2018). For genetic analysis, DNA was extracted from mycelia using the modified cetyltrimethylammonium bromide (CTAB) extraction (Lee et al., 1988; Wu et al., 2001). Thermocycling conditions followed Na et al. (2018) and O’Donnell et al. (1998). Amplification products were purified and sequenced by Macrogen Europe, Amsterdam, Netherlands, and compared to reference sequences available on GenBank for species confirmation.

Statistical analyses
We employed generalized linear mixed models (GLMMs) to investigate patterns of PSHB infestation, host preferences, and factors influencing infestation severity at the species and family levels. All models were fitted using the glmmTMB package in R (Brooks et al. 2017), with binomial error distributions and logit link functions unless stated otherwise. Prior to analysis, all predictor variables were assessed for multicollinearity using Variance Inflation Factor (VIF) values from the ‘performance’ package (Lüdecke et al. 2021), with a threshold of VIF > 3 being used (Zuur et al. 2010). No predictor variables in any of the models used were found to exceed this threshold (Table S2, Supplementary material). To ensure sufficient variation the variability of each predictor was also assessed by calculating the Coefficient of Variation (CV), no predictor variables with a CV value below 20% (0.2) were included in our models (Table S2, Supplementary material). Model fit was assessed using a hypothesis testing approach. Residual diagnostics from the ‘DHARMa’ package (Hartig 2018), and fixed effect significance was determined using Likelihood Ratio Tests (LRT) via the ‘Anova’ function in the ‘car’ package (Fox et al. 2019). Marginal effects plots were generated using the ‘ggeffects’ package (Lüdecke 2018) to visualize key relationships between response and predictor variables.

Host preferences: host species selection and utilisation
Although PSHB has a broad host range, field observations and previous studies suggest that the beetle exhibits some degree of host species preference (Lynch et al. 2021; Townsend et al. 2024). To evaluate if PSHB is preferentially selecting specific tree species, we analysed two response variables: the proportion of infested trees per species and the proportion of PSHB entry holes per species. The number of infested trees and the number of entry holes were divided by their respective totals per plot to calculate proportions. A community-weighted relative abundance of each tree species was calculated for each plot by dividing the number of individuals of a species by the total number of trees. This was used to test whether the proportion of total infested trees and the total PSHB holes per tree species within each plot was frequency-dependent. If the number of infested trees is frequency-dependent the beetle may not be targeting specific tree species, but rather beetle attacks are directly correlated to tree abundance.

This was then used to determine the relationship between tree relative abundance and PSHB infestation rates and counts for each species allowing us to evaluate if PSHB infestations and counts are higher or lower than expected based on the relative abundance of trees. This allowed us to estimate the probability of a specific tree species being attacked and utilised relative to the species around it. The relationship between PSHB infestation (proportion of infested trees or PSHB holes) and tree species abundance was modelled as a function of the relative community-weighted abundance of each tree species using GLMMs (model 1 and 2; Table S2, Supplementary material). Both models included random slopes for ‘year’ and random intercepts for ‘plot’ nested within ‘region’ and ‘species’ within ‘plot’.

Data from both of the above analyses was used to visualise the relationship between the proportion of infested trees and the proportion of PSHB holes per tree both relative to the community-weighted relative abundance of each tree species per plot along with the sample sizes for each tree species.

Host preferences: host family selection
To evaluate if PSHB is selecting host trees at the family level and to determine if certain plant families show higher than expected infestation levels, we applied a similar approach, calculating the proportion of infested trees per family by dividing the number of infested trees per family by the total number of infested trees in each plot. As with the species-level analyses, a community-weighted relative abundance of each tree family was calculated, and the proportion of PSHB-infested trees per family was modelled against this predictor (model 3; Table S2, Supplementary material). This was then used to determine the relationship between tree relative abundance and number of PSHB-infested trees for each plant family allowing us to evaluate if infestation probability was higher or lower than expected based on the relative abundance of trees, effectively assessing the proportional relationship between the number of PSHB-infested trees per family and family abundance per plot. To account for differences in the potential infestation probability of each family a random slope for ‘family’ was included, along with a nested random intercept for ‘plot’ nested in ‘region’ and ‘region’ nested in ‘year’ (Bolker et al., 2009).

Factors that influence the severity and probability of infestations by PSHB on individual trees
To evaluate the factors associated with PSHB infestations on individual trees (n = 2313), the influence of selected variables on the number of PSHB holes on trees was tested using a GLMM with a negative binomial distribution, the model also incorporated a hurdle component (Brooks et al. 2017) (model 4; Table S2, Supplementary material). Hurdle models are designed to account for excess zeroes in the response variable (i.e. the number of PSHB holes per tree). This first component models the probability of each tree having zero (0) or non-zero (1) number of PSHB holes using a binomial distribution, while the second component models the non-zero counts using a zero-truncated negative binomial distribution (Brooks et al. 2017). Both models incorporated the same fixed effects including diameter at breast height (dbh), canopy health (ch), broad health category (bhc), distance to the nearest water source (dH2O), degree of natural impact (ni), the surface area of trees (sa) and year. Initially, the broad health category (bhc) and degree of natural impact (ni) were included as fixed effects, however, these models generated convergence issues due to the lack of variation in PSHB counts concerning each predictor. Repeated measurements taken from the same trees over time at the different sampling sites were accounted for using a subject-specific random intercept term (at the individual-tree level), while potential spatial non-independence of sites was accounted for using a site-specific random intercept term. Lastly, a correlated random slope was included to allow for each tree species to differ in the extent to which they are colonised by PSHB over time. For the binomial model component, we dropped the ‘species’ and ‘area’ random intercept terms due to a zero estimate for both terms. This estimate does not necessarily mean that the between-group variability for each term was zero, instead, it means that the variability was not sufficient to warrant inclusion in the model (Speyer et al., 2023).

Factors that influence the proportion of PSHB-infested trees in plots
To evaluate the factors associated with the prevalence of PSHB in monitoring plots (n = 78 plots), the influence of selected variables on the proportion of infested trees per plot was tested using Generalized Linear Mixed Models (GLMM’s) fitted to a binomial distribution, with plot number and year as random variables (Brooks et al. 2017) (model 5; Table S2, Supplementary material). The overall model incorporated the fixed plot effects of distance to potential source population (dsp), number of host trees (no_hosts), percentage of trees with human impact (hi), percentage of trees with natural impact (ni), overall tree species richness (spprich), canopy cover (canopy), distance to nearest surface water (dH2O) and tree density (dens, i.e. number of trees/m2). Repeated measurements taken from the different sampling sites were accounted for using a subject-specific random intercept term (at the site level).

Results
PSHB infestation levels
PSHB infestations were recorded at 15 of the 18 monitoring sites in indigenous forests over the 5-year monitoring period and within all five main forest types monitored (Table 1). Out of the 78 monitoring plots, 48 (60%) contained infested trees in 2023, an increase of 23 plots (29%) from the first monitoring event in 2019 (Table 2). These plots were spread across the survey area. A total of 2313 trees were monitored representative of 148 species. Of these, 176 (7%) trees representing 43 species (and seven unidentified species) were found to have PSHB infestations (Table 3). In 2019 at the beginning of the study, we recorded 100 infested trees across all survey sites. In 2023 we recorded 176—a mean increase of 15 (0.6%) trees per year. When comparing the two survey regions, KwaZulu-Natal (Fig. 1b) had a higher proportion of infested trees (0.11%) and all monitoring sites had PSHB present. The Western Cape (Fig. 1a) had a lower proportion of infested trees (0.06%) with eight of the eleven monitoring sites having PSHB present. In total, 18 species were recorded as competent hosts of PSHB (being able to support PSHB reproduction), 8 as kill-competent hosts (can be killed by PSHB), 24 can be colonised by Fusarium euwallaceae but not the PSHB, and 107 species showed no signs of attack by PSHB throughout the study (Table 2). Increases in the number of PSHB-infested trees were recorded in 42 out of the 78 monitoring plots, while 36 plots showed no increases in infested trees throughout the study. Table 3 shows the 11 individual trees that died as a result of PSHB infestations over the course of this study. Some tree individuals died very rapidly (within 2–5 years of first infestation) and some died with relatively few infestations (e.g. Sparmannia africana).

Source: Springer Nature

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