Abstract
Purpose of Review
Forestry in northern temperate and boreal regions relies heavily on conifers. Rapid climate change and associated increases in adverse growing conditions predispose conifers to pathogens and pests. The much longer generation time and presumably, therefore, lower adaptive capacity of conifers relative to their native or non-native biotic stressors may have devastating consequences. We provide an updated overview of conifer defences underlying pathogen and pest resistance and discuss how defence traits can be used in tree breeding and forest management to improve resistance.
Recent Findings
Breeding of more resilient and stress-resistant trees will benefit from new genomic tools, such as genotyping arrays with increased genomic coverage, which will aid in genomic and relationship-based selection strategies. However, to successfully increase the resilience of conifer forests, improved genetic materials from breeding programs must be combined with more flexible and site-specific adaptive forest management.
Summary
Successful breeding programs to improve conifer resistance to pathogens and pests provide hope as well as valuable lessons: with a coordinated and sustained effort, increased resistance can be achieved. However, mechanisms underlying resistance against one stressor, even if involving many genes, may not provide any protection against other sympatric stressors. To maintain the adaptive capacity of conifer forests, it is important to keep high genetic diversity in the tree breeding programs. Choosing forest management options that include diversification of tree-species and forest structure and are coupled with the use of genetically improved plants and assisted migration is a proactive measure to increase forest resistance and resilience to foreseen and unanticipated biotic stressors in a changing climate.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Conifers are some of the most successful lifeforms to evolve, having survived more than 300 million years of global change. The global distribution of conifers shows that they can thrive in many different ecosystems and under various stress conditions [1]. Clearly, their great ability to acclimate and adapt has contributed to their success.
As a result of past selection pressures, conifers have evolved diverse and effective defence mechanisms against insect herbivores, pathogens, and other heterotrophic organisms. In recent decades, much of the biotic and abiotic damage in conifer forests can be related to ongoing global change. Global warming is increasing tree mortality both directly, through drought and heat stress, and indirectly by increasing the susceptibility to pests and pathogens [2]. In addition, global trade of plants and plant materials introduces pathogens and pests outside their natural range, where they may have devastating impacts on evolutionary naïve tree species [3]. This may have particularly severe consequences for trees, owing to their long generation times.
Forestry has long favoured conifers over native broadleaf trees because of the high industrial value of conifer wood, coupled with the high productivity of conifer forests. Many conifer breeding programs were established after WWII to safeguard a stable supply of genetic material with improved properties for adaptation, productivity, and timber quality [4]. Most of the scientific research on conifers has focused on the north temperate and boreal zones, where conifer-dominated forests cover huge areas and form the basis of the forest bioeconomy. Here, conifers have been increasingly planted to the limits of their natural range and even beyond, into more temperate, northern, and alpine regions [5].
The aim of this article is to outline how conifer defences can be harnessed to enhance conifer resistance and resilience to native and introduced pests and pathogens (Fig. 1). We provide a short introduction of (1) conifer defence mechanisms and (2) conifer breeding for increased resistance towards biotic stressors, highlighting some of the success stories and challenges. Then, we (3) describe management practices to enhance the resilience of conifer forests to native and novel biotic stressors, and (4) outline some policy options and research needs to respond to both forecasted and unpredicted changes in biotic stressors.
Defence Mechanisms across Conifer Tissues
Conifer defence strategies involve a complex mixture of constitutive (preformed) and inducible (acquired) defences [6, 7]. These defences, and their relative importance, can vary according to the species, tissue, genotype, environment, season, and tree age [6]. Constitutive defences are always present, even in the absence of an attack. Inducible defences are mobilized in response to attack or other external stimuli and can be directly induced, prolongedly upregulated, or latently sensitized (primed) [8, 9]. This wide range of defences, executed over different temporal and spatial scales, helps to protect conifers against diverse abiotic and biotic stressors.
Leaf Defences
Most conifers have needle-like leaves that are retained for several years as an adaptation to conserve resources. Needle anatomy, with some species-specific variation, reflects adaptations to harsh environmental conditions. Conifer needles are covered by a waxy cuticle that protects needles from environmental stressors like wind and drought. Cuticle waxes and other needle surface exudates also act as antimicrobials or make the leaf surface less wettable and thus suboptimal for spore germination [10].
Needle stomata control gas exchange and are used as entry sites by many pathogens [11] (Fig. 2i). The ability to regulate stomata closure is important to control water loss, and hence for drought tolerance [12] (Fig. 2a). Constitutively produced terpenoids and phenolics, as well as physical barriers such as lignified tissues, act as antifeedant defences against herbivores (Fig. 2e) or post-infection defences against microbes. For example, white spruce (Picea glauca) needles produce antifeedant phenylpropanoids, whose accumulation depends on tree phenology and genotype [13]. Induced defence responses in needles include production of phytoalexins, antimicrobial peptides, pathogenesis-related proteins, and generation of the hypersensitive response (HR), a rapid localized cell death at the site of infection (e.g. [6, 14, 15]). HR is generally associated with race-specific resistance to biotrophic pathogens, such as rust fungi (Fig. 2g), which derive their nutrition from living tissue [16]. A more comprehensive review of needle defence mechanisms has been provided by Fraser and co-workers [10].
Bark Defences
The bark is the first barrier against pests and pathogens attacking the main stem, roots, and branches of conifers. Cells and tissues involved in bark defences are mostly laid out in concentric layers around the circumference of woody organs [7]. Thus, attackers must overcome multiple defence barriers to reach the resources of the inner bark, cambium, and sapwood. The outer bark of conifers, the periderm, is composed mostly of dead cells rich in lignin and suberin, phenolics, and calcium oxalate crystals (Fig. 2k), forming a multifunctional barrier to various stressors [7]. However, loose cell aggregates called lenticels that allow for gas exchange may serve as entry points for small insects and pathogens [17].
In the inner bark (phloem), all conifers have constitutive physical and chemical defences that include lignified sclerenchyma cells, calcium oxalate crystals, and phenolic bodies [18]. Axially oriented phloem parenchyma cells, called polyphenolic parenchyma (PP) cells, are specialized for synthesis and accumulation of phenolics (Fig. 2j) [19, 20]. PP cells and radially oriented ray parenchyma, which also serve as storage sites for starch, are involved in radial and circumferential transmission of signals that activate inducible defence responses [6]. Numerous symplastic cell junctions (plasmodesmata) facilitate transport of signal molecules and nutrients between and among PP cells and ray cells [7].
All conifers in the pine family also have radial and/or axial resin ducts, resin blisters, or resin cells [7] with epithelial cells that accumulate terpenoid resin internally or secrete it into the duct/blister lumen (Fig. 2b). Terpenoid resin is a diverse mixture of monoterpenes, sesquiterpenes, and diterpenes and serves as a chemical and physical defence against pests and pathogens [21]. When the bark is wounded by an insect, fungus, or physical damage, the pressurized resin can repel, trap, or kill the organism and seal the wound. More detailed reviews of defence mechanisms in conifer bark have been published [4, 16, 19].
The roles of terpenoids in conifer resistance to bark beetles are particularly well studied. Zhao and co-workers [22] demonstrated a dose-dependent relationship between inducibility of terpenes and tree resistance to bark beetle mass-attack. Other studies have shown a positive relationship between resin duct size and tree survival during a bark beetle outbreak [23]. However, all tree-killing bark beetles, such as the European spruce bark beetle (Ips typographus) (Fig. 2f), vector ophiostomatoid fungal symbionts that can detoxify terpenes into compounds acting as bark beetle attractants and may thus contribute to beetle attack success [24]. Additionally, drought and elevated temperatures may affect the composition and amounts of volatile terpene emissions and thus facilitate bark beetle mass attacks [25••]. Details on the complex environment-conifer-bark beetle-fungi interactions have been reviewed in earlier publications [24, 25••].
Xylem Defences
More than 90% of conifer xylem is composed of tracheids, hollow conduit cells interconnected through pits, enabling slow but robust water transport [26]. Trees with small tracheid pit apertures, high pit aperture resistance, and large valve effect are more resistant to drought- and freezing-induced embolism, i.e. blocking of water flow by an air bubble or cavity [27]. Tracheids have a thick secondary cell wall, where structural carbohydrates (cellulose and hemicellulose) are covalently linked with lignin, a complex phenolic polymer. Because tracheids undergo genetically programmed cell death soon after maturation, they have no induced defences. However, due to its molecular architecture, lignin is highly recalcitrant to degradation [28]. Microbial access to the structural carbohydrates in the tracheid cell walls requires degradation or modification of lignin, something white rot and brown rot fungi, respectively, are capable of (Fig. 2c and h).
Depending on the conifer species, up to 10% of the xylem is composed of thin-walled parenchyma primarily associated with radial rays running from the phloem and deep into the xylem. Parenchyma radial rays play a major role for storage and transport of water, nutrients, and non-structural carbohydrates, thus regulating xylem hydraulics, growth, and active defences. Many pathogenic fungi gain access to the xylem via bark wounds, which trigger the formation of traumatic resin ducts within the new annual ring in many conifers [20]. Traumatic resin duct formation can also be induced by pest attack or application of the defence hormone methyl jasmonate [29•]. In conifers with constitutive or induced xylem resin ducts, ray parenchyma and axially orientated resin ducts may form an interconnected network for defence signalling, biosynthesis, and translocation of defence metabolites [20, 30]. The extent of this network and the nature of the induced defence metabolites differ considerably between tree species [31•], probably because of adaptation to different selection pressures. Parenchyma cells in the water-conductive sapwood live for several decades, until the innermost sapwood is transformed to dead heartwood. During heartwood formation, the ray’s energy reserves are metabolized into protective heartwood extractives, such as polyphenolics and terpenes [32, 33]. The durability of heartwood towards wood decay is largely determined by the amount and antimicrobial and antioxidant properties of these extractives [34].
Wood decaying basidiomycetes, like Heterobasidion, have an optimal moisture content ranging between 40 and 80% [35]. In addition to the correct moisture, wood decaying fungi need oxygen for expression and activity of lignin and phenol degrading enzymes important in their colonization [36]. In Norway spruce, the moisture content of sapwood and heartwood is 150% and 50%, respectively, while seasonal fluctuations in oxygen healthy sapwood [37] and heartwood [38] are similar. It is challenging to determine the extent to which oxygen content varies between specific cell types (living parenchyma vs. tracheids) or within the wood microspatial, but high water content in the lumens of sapwood tracheids could be envisaged to displace oxygen at microsites in the secondary cell wall where decay processes occur. In line with this reasoning, many pathogenic wood decay fungi prefer to colonize heartwood in conifer species with non-resinous heartwood, such as species of Picea and Abies. When a heartwood-based fungal colony attempts to expand laterally, the tree forms an extractive-rich xylem defence zone at the interface between the dead heartwood and the live inner sapwood. This reaction zone is characterized by tracheids and ray cells that become filled up with phenolic compounds and terpenoids (Fig. 2c) [33, 39] and may also include dense rows of traumatic resin ducts [33]. The reaction zone forms a barrier against radial fungal spread into the sapwood, as described in the CODIT (compartmentalization of decay in trees) model [40]. More detailed descriptions of xylem defence mechanisms have been provided by, e.g. Morris and co-workers [29•] and Pearce [33].
Genetic and Epigenetic Regulation of Conifer Defences
The draft Norway spruce genome, released in 2013, was the first conifer genome to be published [41]. Conifer genomes are extremely challenging to study because of their huge size (20–30 gigabases) and long stretches of highly repetitive DNA [42]. However, rapidly improving sequencing and bioinformatics tools are now helping us unravel these giant genomes [43,44,45••].
Many defence-related genes have been identified in conifers, especially genes involved in biosynthesis of terpenes and phenylpropanoids [46, 47]. Concerning terpene production, the ability to rapidly increase gene expression can be related to resistance [48]. High heritability estimates for resistance and much intraspecific variation in resistance phenotypes indicate that terpenes and other defence compounds are under strong genetic control [49]. Like most adaptive traits, conifer defence traits have a polygenetic basis and are determined by the action of many different genes [50]. The polygenetic nature of conifer defences partly explains why defence traits generally confer partial, and not complete, resistance to pests and pathogens [51••]. Additionally, conifer defence genes are over-represented among highly polymorphic genes, indicating that they can be useful for population genomic studies of stress adaptation [52]. Recently, 30–50-kb single nucleotide polymorphism (SNP) arrays for conifer genotyping have been developed [43, 53,54,55,56]. These arrays will aid conifer breeding programs by facilitating genomic predictions and selection of new parents to create more resistant offspring.
Epigenetics is the study of changes in gene expression that are due to DNA methylation and histone methylation or acetylation but involve no alterations of the DNA sequence. Epigenetic modification regulates gene expression by determining which DNA regions are open to be transcribed. Changes in epigenetic modifications can be triggered by abiotic factors, like temperature and light, or biotic factors, like pathogens, pests, and beneficial microbes [57]. These modifications can result in stable phenotypic changes within an individual and be passed on to subsequent generations [58, 59]. Thus, epigenetic mechanisms may allow rapid adaption to the environment [60].
We are just beginning to understand the role of epigenetics in conifer adaptation. The best evidence for the importance of epigenetics in environmental adaptation is from 40-year-long studies in Norway spruce [59]. Clonal progenies produced from zygotic embryos at 18 or 30 °C show long-term differences in the timing of bud set and flushing, indicative of an epigenetic memory [61]. Under natural conditions, progenies from warm seed years become better adapted to prolonged growing seasons [62], likely without any major change in allele frequencies. Recent research also points to the involvement of DNA methylation in long-term induced resistance and defence priming of Norway spruce [8, 9]. However, more research is needed to understand what epigenetic mechanisms and machinery are important in conifers and if phenotypic plasticity resulting from epigenetic regulation shows genetic variation that can be selected for [63].
Breeding for Pest and Pathogen Resistance
Managing diseases and pests of conifers by breeding trees for increased resistance is of great interest [64]. Resistance can be defined as the ability of a tree to limit or suppress the growth and development of a pathogen or pest, or to prevent attacks altogether. Resistance is thus an observable phenotype that results from the interaction between the tree and a pest or pathogen in a given environment [51••]. Ultimately, a certain level of resistance is a prerequisite to survive long enough to reproduce and pass on genes to the next generation. The mechanisms involved in interactions between trees and pathogens/pests reflect past selection pressures, such as whether the tree population and the biotic stressor share a co-evolutionary history. The outcome of these interactions is also influenced by abiotic conditions: environmental conditions can modify the phenotypic resistance, e.g. by causing changes in the pest or pathogen population or by influencing tree defences. Tree resistance may also depend on ontogeny. For example, older trees may be able to fight off a specific attacker through a certain combination of constitutive and induced defences, whereas young seedlings may lack crucial defences and succumb. Finally, increased allocation of resources to resistance may come with a cost to growth and reproduction [65]. The strength of this allocation trade-off depends on species and environmental conditions [66]. All this complexity has important consequences for breeding programs aiming to balancing selection of resistance traits with the other important traits and maintaining genetic diversity.
Conifer Breeding
A traditional conifer breeding program usually starts with a phenotypic “plus-tree selection” of the largest and best-looking trees from a base population of naturally regenerated or planted trees (process summarized in [4]). The plus-trees are grafted into breeding arboretums or seed orchards for mating and seed production. Their progenies are evaluated under field conditions to obtain breeding values for selection of the best parents for the next breeding cycle and more advanced seed orchards. The first breeding cycle, which normally screens for variation among half-sib families with large genetic diversity (e.g. from open pollination), usually takes 10–15 years. This is estimated to be the optimal age for selection for height growth, a proxy for volume production when “genetic gain per unit-of-time” is considered [67]. Variation in resistance and response to many biotic and abiotic stressors can usually also be observed within this time. Some breeding programs choose to delay the first selection until the age of 20–30 years to be able to differentiate and evaluate traits that are expressed later, such as longer-term productivity, adaptation, health, and wood quality. Many conifer breeding programs around the world have finalized the first selection cycle [68].
Picea species are easy to propagate vegetatively. Therefore, for the second breeding cycle of a given breeding population, clonally replicated long-term field experiments have been established for the most promising 30–40 family members selected from nursery tests [69]. When the same genotypes are planted across a range of environments, such trials provide information about the phenotypic plasticity and stability of individual genotypes [4]. The drawbacks of traditional breeding strategies are that field testing is laborious and limited to a few (often four) sites, and careful selection of these sites might be critical to capture sufficient exposure to relevant stressors [4]. As climate change is already predisposing trees to various biotic stressors, important flexibility can be added to breeding programs by maintaining the initial progeny trials as genetic resources even if the breeding cycle seems completed [70]. Novel stressors might reveal new phenotypic variation and new phenotyping and genotyping methods may add new and valuable data.
Genetic diversity is a necessary resource for the long-term evolution of species, and an “insurance” of a forest’s survival and productivity, especially regarding climate uncertainties [71•]. Diversity must also be kept through the cycles of resistance breeding. It is therefore important that the breeding strategies allow selection at a sufficiently high intensity while maintaining the effective population size. There might, however, be trade-offs between the goal of improvement and diversity [64].
For resistance breeding, one can consider two different main strategies for selection (Fig. 3). In many large breeding programs, in situ selections are made of the largest, most vital, and healthiest trees, under the assumption that this will increase the total performance as an effect of the accumulation of beneficial gene variants over generations [72••]. Alternatively, selection could be trait-based, e.g. when the critical traits are known for the targeted resistance [72••]. These strategies are not mutually exclusive, but serve as a basis to orient subsequent discussion. In some situations, strategy 1 could be the initial step of a breeding program, while strategy 2 is used when the selection for the breeding population is made and more knowledge about the traits and their genetic control has been established.
Breeding for Increased Resistance in Conifers — Lessons, Challenges, and Opportunities
Screening of genetic variation for increased resistance has been conducted for many pest- and pathosystems in conifers [73], but with variable success so far. Here we present some examples of these efforts and describe the lessons learned and challenges faced. In summary, the most successful breeding efforts deal with pathosystems where genetic variation in resistance can be selected in seedlings, allowing for effective screening of many individuals. The later in development genetic variation in resistance can be screened, the more will environmental factors influence the response, and time delays in testing and phenotyping will slow down the breeding cycle. However, when to perform screening depends on the existence of knowledge about traits that correlate with resistance in the particular pathosystem or tree-pest interaction and the developmental stage of the tree species.
All North American white pines are highly susceptible to blister rust caused by Cronartium ribicola believed to originate from eastern Asia. This biotrophic fungus was accidentally introduced to North America around 1900 and is causing massive mortality that can exceed 95% in some populations [71•]. This has prompted extensive research to identify trees with natural genetic resistance to blister rust. Tree resistance to white pine blister rust can be reliably determined in young seedlings, enabling high-throughput screening methods that use spore inoculation of seedlings to mimic natural infection. The breeding process is further facilitated by the possibility to select candidate parental trees from stands suffering epidemic infection levels of this windborne and outcrossing pathogen. Major gene resistance (MGR) has been documented in four white pine species, and some level of quantitative resistance (QR) is likely present in all species [71•]. As a result of the effective breeding progress, resistant offspring are used extensively for reforestation and restoration of white pine stands [71•]. However, in sugar pine (Pinus lambertiana) and western white pine (Pinus monticola), MGR is sometimes overcome by virulent blister rust strains. Therefore, incorporation of multiple MGR- and QR-based partial resistances in elite seed orchards are needed to obtain long-term, durable resistance [73].
Another pathosystem for which increased resistance has been achieved via breeding is infection of Port-Orford cedar (Chamaecyparis lawsoniana) by the introduced water- and soilborne oomycete Phytophthora lateralis, first discovered in a North American nursery in 1923. The first testing of C. lawsoniana for genetic variation in disease resistance was carried out in the 1980s. Relatively resistant parents were crossed, and progeny resistance was screened by dipping the root system of young seedlings in a pathogen spore solution and monitoring mortality for up to 3 years. This screening revealed both MGR and QR towards P. lateralis in Port-Orford cedar. Impressively, resistant seed lots were available for seven out of 10 breeding zones within 10 years of program initiation [74]. This success story is very encouraging for those engaged in disease resistance breeding of conifers [72••].
Norway spruce (Picea abies), the dominant conifer in Northern Europe and subalpine areas of the Alps and Carpathian Mountains, is heavily affected by pathogenic white rot fungi in the genus Heterobasidion. All three Heterobasidion species indigenous to Europe can establish in roots and stem heartwood of this low-resinous conifer. Since the late 1990s, several studies have demonstrated heritable genetic variation in resistance against H. parviporum [75,76,77]. In these studies, resistance phenotyping was based on lesion lengths in the phloem and the extent of axial fungal growth in the sapwood in stems or branches of artificially inoculated seedlings and saplings. One lesson from these experiments is that the ontogenetic stage of the tree at the time of inoculation influences tree-pathogen and genotype × environment interactions [75, 77]. Some genes associated with QR in this pathosystem are involved in lignification and synthesis of catechin and lignan [76, 78]. The QR in Norway spruce towards Heterobasidion species does not confer any broad-spectrum resistance towards other necrotrophic pathogens, as Steffenrem and co-workers [79] found no correlation in phloem lesion lengths between H. parviporum and Endoconidiophora polonica, a pathogenic blue-stain fungus vectored by Ips typographus. Similarly, Capador-Barreto and co-workers [78] found that the genetic basis of QR in Norway spruce differs between H. parviporum and H. annosum even though these pathogens occur together in many regions. Selection of QR markers that are shared between such sympatric pathogens might be the best strategy for successful resistance breeding against Heterobasidion species [78].
While we are not aware of any conifer breeding programs for resistance against bark beetles, there are large differences among genotypes in resin duct production, a trait known to correlate with bark beetle resistance [66]. There is, thus, evidence suggesting that resin duct characteristics are heritable and subject to genetic control [66]. This may pave the way for resistance breeding aimed at resin-based defences. However, breeding for increased resin production might have undesirable consequences for the wood-processing industry. At a more fundamental level, breeding for tree resistance against bark beetle attack might be difficult due to the stochastic nature of how the beetles select trees for mass attack. First, beetles tend to attack trees suffering from acute stress, which may result from biotic or abiotic disturbances rather than any heritable tree defence traits. Second, the beetles’ pheromone-mediated mass attack behaviour means that trees may be attacked and overwhelmed by the beetles simply because they grow next to a susceptible tree. Finally, the stochastic occurrence of abiotic disturbances may provide ample proliferation material for the beetles, even in stands with genetically resistant trees.
An alternative strategy to increase conifer resistance or resilience to bark beetles would be to breed for increased tree resistance to abiotic stressors, such as drought, that predispose trees to bark beetle outbreaks. This would require vitality phenotyping of mature trees growing in areas prone to drought. So far, genotyping arrays including tens of thousands of SNPs have failed to find genes of major importance to drought tolerance in conifers. This may be because the sparse genomic coverage and the low linkage disequilibrium of such arrays make it difficult to capture the relevant variation in genomic data [80]. However, there is some evidence of genetic and heritable variation in drought tolerance in many conifer species, indicating that there is a potential for selective breeding [57, 74].
Breeding strategies that are under development and evaluation include the use of genomic data to predict genomic breeding values [80,81,82,83], to reconstruct a pedigree, or to calculate a genomic relationship matrix for quantitative prediction of breeding values [84, 85]. High-quality phenotypic data are necessary in all cases. However, for the genomic prediction, phenotypic data are only necessary under the development of the prediction model. For relationship-based methods, such as genomic best linear unbiased prediction (GBLUP) [84], phenotypic data is always required. These emerging strategies show particular promise in enhancing existing breeding systems by reducing the requirement for expensive controlled crosses and establishment and maintenance of trials.
Genomic prediction still needs to be sufficiently validated for conifer breeding. However, two relationship-based approaches that have already proven efficient in breeding are the “Breeding-without-breeding” [86] or “quasi-field trial” [87] strategies based on pedigree reconstruction. In short, these strategies utilize existing plantations established with material from seed orchards or breeding arboretums. Meaning, there is kinship information for these stands, but they were not necessarily established as designed trials. Spatial statistical methods are used to even-out the environmental variation observed among the phenotypes. Using phenotyping to identify the most vital and healthy trees in the population, genotyping to map the relationship structure of these select trees, and quantitative statistical methods, “ad hoc progeny trials” can be established wherever the material is planted [88]. These strategies do not require any established association between traits and genes, or genomic prediction models. They are flexible for the breeders to exploit large populations planted across environmentally variable landscapes and stress, in order to make in situ selection [89] (Fig. 3). The time from initiation of a program to selection for a new seed orchard can be as short as a few months. However, the approach requires that the stands are genetically diverse, and that information about deployed genetic materials at a site level is available.
Integrating Tree Resistance and Silvicultural Practices for Increased Resilience to Climate Change
Given the challenges posed by climate change, it is crucial that forest health perspectives are closely aligned with silvicultural practices [90]. Two approaches have been proposed to address risk management in forestry: anticipation and mitigation of risks (risk reduction) or promotion of forest resilience, i.e. the ability to absorb perturbations and maintain desired properties [91] (Fig. 4). The former involves management options that can be implemented already in forest nurseries, whereas in forest stands the applicability of each approach depends on the chosen regeneration/management regime.
Forest Nurseries
Integrating genotypes selected in breeding programs into silvicultural practices begins in the forest nurseries. Additionally, nursery production of large quantities of high-quality conifer seedlings is required to meet global forest demands [92]. Production of quality plants for reforestation entails that nurseries are aware of the genetic and physiological characteristics of each propagated species and genotype [92]. For example, nurseries must choose seed from seed sources with a level of cold adaptability appropriate for the location they supply saplings [70]. Production of quality seedlings also requires ample water, fertilizer, and pesticide as seedlings can be particularly vulnerable to drought, nutrient, and pathogen stress [93]. One way to reduce nursery reliance on fertilizer and improve plant growth and resistance to disease is to use symbiotic, epiphytic, and endophytic microbes [93]. In recent years, it has been proposed that the microbiome also increases host disease resistance through direct antibiosis, physical protection of the roots, or effects on host defences [94, 95]. The specific associations between conifers and their microbial symbionts depend on tree species, genotype, organ, age, nutrient status, and environmental conditions [96, 97]. The best-studied conifer microsymbionts are ectomycorrhizal fungi (Fig. 2d), which aid nutrient acquisition and growth [98], especially under nutrient-poor conditions [96]. Long-term studies by Gehring and co-workers on pinyon pine (Pinus edulis) showed that tree selection of ectomycorrhizal partners is genetically based and heritable, and that drought-tolerant trees have different ectomycorrhizal fungi than drought-intolerant trees [99]. The obvious place to recruit beneficial microbes in forest management is forest nurseries, where desirable microbes can be introduced to increase out-planting success and growth performance of conifers on poor sites, as has been demonstrated with the mycorrhizal fungus Pisolithus tinctorius [100]. The primary obstacle in incorporating mycorrhiza in nursery production is that the heavy usage of pesticide and fertilizer in nurseries disfavours ectomycorrhization, but ectomycorrhization is needed to reduce the dependence on these chemical products [96]. Additionally, because seedlings usually are planted in a wide range of site and soil conditions, implementing mycorrhiza in seedling production would probably require site-specific tailoring of mycorrhizae [101].
Rotation Forest Management
The forest management practice involving establishment/regeneration, thinning, and clear-felling, referred to as rotation forest management (RFM) or even-aged management, originated in the eighteenth century in Germany during a period of rapid industrialization and impending timber shortage. The transition to RFM involved clearing of native hardwood and mixed hardwood/conifer forests and planting of fast-growing monocultures of Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) [102]. RFM has been widely adopted in continental Europe [103]. In the Nordic countries, where most of the native forests are coniferous, RFM was adopted relatively late, but is today the standard silvicultural practice [104].
Irrespective of the management practice chosen, it is imperative that tree species requirements are matched with site conditions to reduce the risk that abiotic stressors will predispose trees to pathogens and pests. In the last decades, it has become painfully evident that forest susceptibility to disturbances such as windthrows, bark beetle outbreaks, and wood decay increases with forest age. In a modelling-based study, Zimova and colleagues found that simply reducing the rotation length probably will not be sufficient to manage increasing frequency and intensity of disturbance events [105]. Instead, they suggest that a combination of management measures is needed to reduce risks and enhance forest resilience. At the landscape level, increased mixing of stands with different tree species and age classes is a promising approach to improve forest resistance and resilience and avoid large-scale disturbances by biotic stressors and predisposing abiotic factors [106]. At the stand level, a meta-analysis of over 600 studies showed that when a tree species is grown in mixed conditions it is less likely to be damaged by a given specialist insect herbivore than if it is grown in a pure stand [107]. This phenomenon is known as associational resistance [108]. Even though admixtures of tree species are generally positive, certain combinations of tree species, such as those of alternate hosts of rust fungi, can be disastrous. Additionally, the optimal spatial agreement of tree species is not always clear. As a disease example of the benefits of tree species admixtures, mature Norway spruce stands in Fennoscandia are often heavily infested by Heterobasidion parviporum, which spreads via root contacts between neighbouring spruce trees. To limit infection after regeneration, it is recommended to switch tree species or establish a mixed species forest in infested stands. However, due to the lack of commercially valuable alternative tree species that are suitable for Norway spruce sites, many forest owners continue to plant Norway spruce despite the predicted increase in decay frequency in the next tree generation.
RFM comes with several options for risk reduction, whereas options for promoting forest resilience are more limited in even-aged, monoculture forests. In anticipation of a warming climate, managers can adjust tree species composition and select for individual traits, such as longer growing season and drought tolerance, in high-risk sites by planting species, migrated provenances or seedlings from breeding programs [91]. Greater tolerance to abiotic predisposing factors will likely contribute to increased biotic stress resistance.
It is well documented that endo- and epiphytes can have antagonistic effects on other microorganisms. A large proportion of endophytic fungi (80%) produce biologically active compounds with antibacterial, fungicidal, and herbicidal activities. Endophytes can also increase tree resistance to pathogen infection [109]. Most foliage endophytes have a wide host tree range, although, endophyte community composition varies with host tree [110]. Beneficial effects of increased endophyte diversity provide another mechanistic explanation for why mixed forests tend to have increased resilience to pest and pathogen attack.
Continuous Cover Forestry
Forest management practices falling under the umbrella concept of continuous cover forestry (CCF) or uneven-aged management gained momentum in the 1980s, following large-scale mortality in Central-European conifer forests, attributed to complex interactions between air pollution, soil acidification, and biotic stressors [102]. CCF usually involves the use of natural regeneration, selective harvesting, and clear-fellings with a gap size below 0.25 ha [111]. There are no solid data about the share of European forests that are managed through CCF, but Mason and co-workers [103] estimated that it could be as high as 30%. In the Nordic countries, CCF has long been treated with scepticism by forest managers [112, 113], but lately there seems to be an increased interest in the practice [114]. Although CCF is not a novel concept, there is little documented information about the resistance and resilience of CCF forests to disturbances, owing to the relatively recent adoption of CCF at a wider scale. However, CCF forests tend to be admixtures of conifers and broadleaved trees, and the benefits of tree species mixtures may also apply here. The structural diversity of CCF forests in terms of tree species and age likely increases their ability to absorb perturbations associated with storms or bark beetle outbreaks. However, uneven-aged CCF management strategies maintaining continuous Norway spruce regeneration will favour the spread of H. parviporum between trees of different size classes [115]. This negative effect is exacerbated by the difficulty to conduct forest operations without damaging the roots of the remaining trees, providing additional entry points for the root rot pathogen. If CCF is based only on natural regeneration, it has been questioned whether CCF could compromise the adaptive capacity of the forest ecosystem [116•]. However, rapid epigenetic adaptation of forest trees might provide a solution to problems such as temperature and drought stress [59, 61, 117].
Any-Aged Forest Management
An approach referred to as any-aged forest (AAF) management [118] or freestyle silviculture [119] is a kind of hybrid between RFM and CCF, as it will consider all silvicultural options at any stand state. AFF management may include prolonged periods of uneven-aged CCF, but if natural regeneration fails regeneration methods of RFM can be used [118]. Another advantage of AAF is that it allows adaptation of forest management at micro-spatial scales within a stand. This could for instance be to change the tree species composition through planting in pockets infested by pathogenic wood decay fungi with high host specificity. Additionally, the shade-response strategies of the tree species need to be considered as there is some evidence that defence-responses may be effected by shade tolerance [120]. For example, the North American white spruce (Picea glauca) thrives and is less defoliated by spruce budworm in the mixed-wood, semi-shaded conditions provided by CCF [121, 122]. Other species, like red pine (Pinus resinosa) and Douglas-fir (Pseudotsuga menziesii), require full sun and may be better adapted for RFM [123]. Thus, it is important to take into consideration the silvicultural properties of the desired conifer species when choosing the forest management strategy. Woods and co-workers concluded that proactive and flexible management options, like AAF, may be the best way to promote forest resilience to insects and diseases in the face of the uncertainties presented by climate change [90].
Conclusions and Perspectives
Rapid changes in climatic condition and associated stressors, including introductions of non-native pathogens and pests, are increasingly challenging RFM. As highlighted in several critical reviews, forest managers and policy makers must now prepare for events that lie outside the range of past experiences [90, 124].
There is obviously both regional and local variation in the nature and potential impact of different biotic and abiotic threats to forest health. Conifers that have been planted at sites they are poorly adapted to, or are growing at the latitudinal or altitudinal margins of their natural range, will be subjected to disturbances that can surpass past experiences. The magnitude of these disturbances also depends on how past management has shaped the forests at the landscape level. While milder winters contributed to enormous outbreaks of mountain pine beetle (Dendroctonus ponderosae) in British Columbia, it was pre-existing landscape conditions, involving several-fold increase in volumes of susceptible host tree over the previous century, that enabled the unprecedented scale of the outbreak [90]. In Europe, Norway spruce has clearly been following a similar trajectory in many regions.
Based on the sound principle that “prevention is better than cure”, policies and forest management practices should aim to prevent disturbances rather than to address them once they occur [124]. Diversification of forests, in terms of tree-species composition and forest structure at the landscape level, has been proposed as a proactive measure to increase forest resistance and resilience (Fig. 4). Lately, there has been a trend towards deregulation of forest policies in Europe and different countries still show a wide variation in recommended management options [125]. In order to mitigate damage caused by bark beetles, expert groups have recommended to adopt legislation that allows the implementation of a broader spectrum of forest protection measures [5]. Europe is seeing an increased interest in alternative forest management practices, but so far there are few lessons available on how these practices affect forest health. To enable active learning and adaptive forest management, we need more research on factors that affect forest resilience and resistance under different management regimes, including forest health monitoring and pathogen/pest modelling [126••].
Maintaining sufficient genetic diversity in conifer forests is a prerequisite to sustain their adaptive capacity. Preservation of genetic diversity is a risk-spreading strategy also for planted forests. Hence, it is important that conifer seed orchards produce outcrossed progenies from a sufficient number of parents with little or no relatedness [4], and that vegetatively propagated materials include a sufficient number of genotypes. Maintaining additive genetic variation is also crucial for long-term, multi-generational breeding efforts to produce genetic gains in adaptive traits and performance traits [4]. Therefore, breeding strategies usually define the size of a breeding population, after the initial selections from the first breeding cycle, to around 1000 individuals [4]. Finally, since many managed conifer stands still deliver ecosystem services close to the natural situation, forest plantations with improved genetic material must also fulfil these ecological functions (Fig. 4).
There are still only a handful of examples of successful breeding of increased resistance to biotic stressors in conifers. This may be due to several reasons, such as a lack of coordinated efforts with long-term commitment and low heritability and frequency of resistance traits in the trees [68]. Ineffective methods to detect genetic variation in resistance to a specific stressor may be an additional reason; Pike and co-workers describe how breeding for increased resistance in eastern white pine (Pinus strobus) to Cronartium ribicola initially was hampered by poor screening methods that failed to generate meaningful data because either too many or too few seedlings were killed [72••]. Refinement of the phenotyping protocol revealed significant heritability for resistance and changed the trajectory of the breeding program [127]. More collaboration in resistance breeding between countries facing similar challenges is needed, as this will allow for allocation of sufficient resources and a sustained effort over time. However, resistance breeding alone is not a silver bullet that will provide rapid solutions to future challenges. Breeding should rather be seen as part of an adaptive forest management strategy involving many approaches (Fig. 4). Breeding remains a slow process and it takes at least 10–20 years before operational planting of resistant material can start. However, with an increased understanding of the genetic basis of tree resistance and the feasibility of genome-based selection methods, there is hope that resistance breeding can be accelerated in the near future.
Data Availability
Not applicable.
References
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
Farjon A. The Kew review conifers of the world. Kew Bull. 2018;73(1):1–16.
Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manage. 2010. https://doi.org/10.1016/j.foreco.2009.09.001.
Gilbert GS, Webb CO. Phylogenetic signal in plant pathogen–host range. Proc Natl Acad Sci. 2007. https://doi.org/10.1073/pnas.0607968104.
White TL, Adams WT, Neale DB, editors. Forest genetics. UK: CABI; 2007. 704 p. https://doi.org/10.1079/9781845932855.0000
Hlásny T, Krokene P, Liebhold A, Montagné-Huck C, Müller J, Qin H, Raffa K, Schelhaas M-J, Seidl R, Svoboda M, Viiri H. Living with bark beetles: impacts, outlook and management options. From Science to Policy 8. European Forest Institute; 2019.
Krokene P. Conifer defence and resistance to bark beetles. In: Vega FE, Hofstetter RW, editors. Bark beetles: biology and ecology of native and invasive species. Elsevier, New York: Academic press; 2015. p. 177–207. https://doi.org/10.1016/B978-0-12-417156-5.00005-8
Franceschi VR, Krokene P, Christiansen E, Krekling T. Anatomical and chemical defenses of conifer bark against bark beetles and other pests. New Phytologist. 2005;167:353–76. https://doi.org/10.1111/j.1469-8137.2005.01436.x.
Mageroy MH, Wilkinson SW, Tengs T, Cross H, Almvik M, Pétriacq P, et al. Molecular underpinnings of methyl jasmonate-induced resistance in Norway spruce. Plant Cell Environ. 2020. https://doi.org/10.1111/pce.13774.
Wilkinson SW, Dalen LS, Skrautvol TO, Ton J, Krokene P, Mageroy MH. Transcriptomic changes during the establishment of long-term methyl jasmonate-induced resistance in Norway spruce. Plant Cell Environ. 2022. https://doi.org/10.1111/pce.14320.
Fraser S, Martín-García J, Perry A, Kabir MS, Owen T, Solla A, et al. A review of Pinaceae resistance mechanisms against needle and shoot pathogens with a focus on the Dothistroma - Pinus interaction. Klopfenstein NB, editor. For Pathol. 2016. https://doi.org/10.1111/efp.12201.
Zeng W, Melotto M, He SY. Plant stomata: a checkpoint of host immunity and pathogen virulence. Curr Opin Biotechnol. 2010. https://doi.org/10.1016/j.copbio.2010.05.006.
Brodribb TJ, McAdam SAM, Jordan GJ, Martins SCV. Conifer species adapt to low-rainfall climates by following one of two divergent pathways. Proc Natl Acad Sci USA. 2014. https://doi.org/10.1073/pnas.1407930111.
Mageroy MH, Jancsik S, Yuen MMS, Fischer M, Withers SG, Paetz C, et al. A conifer UDP-sugar dependent glycosyltransferase contributes to acetophenone metabolism and defense against insects. Plant Physiol. 2017. https://doi.org/10.1104/pp.17.00611.
Bullington LS, Lekberg Y, Sniezko R, Larkin B. The influence of genetics, defensive chemistry and the fungal microbiome on disease outcome in whitebark pine trees. Mol Plant Pathol. 2018. https://doi.org/10.1111/mpp.12663.
Witzell J, Martín JA. Phenolic metabolites in the resistance of northern forest trees to pathogens - past experiences and future prospects. Can J For Res. 2008;38:2711–27.
Balint-Kurti P. The plant hypersensitive response: concepts, control and consequences. Mol Plant Pathol. 2019;20(8):1163–78.
Rosner S, Führer E. The significance of lenticels for successful Pityogenes chalcographus (Coleoptera: Scolytidae) invasion of Norway spruce trees [Picea abies (Pinaceae)]. Trees. 2002;16:497–503. https://doi.org/10.1007/s00468-002-0194-2.
Hudgins JW, Christiansen E, Franceschi VR. Induction of anatomically based defense responses in stems of diverse conifers by methyl jasmonate: a phylogenetic perspective. Tree Physiol Oxf Acad. 2004;24:251–64.
Franceschi VR, Krekling T, Berryman AA, Christiansen E. Specialized phloem parenchyma cells in Norway spruce (Pinaceae) bark are an important site of defense reactions. Am J Bot. 1998. https://doi.org/10.2307/2446529.
Nagy NE, Franceschi VR, Solheim H, Krekling T, Christiansen E. Wound-induced traumatic resin duct development in stems of Norway spruce (Pinaceae): anatomy and cytochemical traits. Am J Bot. 2000;87(3):302–13.
Celedon JM, Bohlmann J. Oleoresin defenses in conifers: chemical diversity, terpene synthases and limitations of oleoresin defense under climate change. New Phytol. 2019;224:1444–63.
Zhao T, Borg-Karlson AKAK, Erbilgin N, Krokene P. Host resistance elicited by methyl jasmonate reduces emission of aggregation pheromones by the spruce bark beetle. Ips typographus Oecologia. 2011;167(3):691–9.
Zhao S, Erbilgin N. Larger resin ducts are linked to the survival of lodgepole pine trees during mountain pine beetle outbreak. Front Plant Sci. 2019;10:1459. https://doi.org/10.3389/fpls.2019.01459.
Kandasamy D, Gershenzon J, Hammerbacher A. Volatile organic compounds emitted by fungal associates of conifer bark beetles and their potential in bark beetle control. J Chem Ecol. 2016;42(9):952–69.
•• Netherer S, Kandasamy D, Jirosová A, et al. Interactions among Norway spruce, the bark beetle Ips typographus and its fungal symbionts in times of drought. J Pest Sci. 2021;94:591–614. https://doi.org/10.1007/s10340-021-01341-y. This review discusses the current knowledge on the effects of drought stress on the interactions between Norway spruce, I. typographus, and ophiostomatoid fungi.
Davis SD, Sperry JS, Hacke UG. The relationship between xylem conduit diameter and cavitation caused by freezing. Am J Bot. 1999;86(10):1367–72.
Song Y, Poorter L, Horsting A, Delzon S, Sterck F. Pit and tracheid anatomy explain hydraulic safety but not hydraulic efficiency of 28 conifer species. Geitmann A, editor. J Exp Bot. 2022;73(3):1033–48.
Ruiz-Dueñas FJ, Martínez ÁT. Microbial degradation of lignin: how a bulky recalcitrant polymer is efficiently recycled in nature and how we can take advantage of this. Microb Biotechnol. 2009;2(2 SPEC. ISS.):164–77.
• Mageroy MH, Christiansen E, Långström B, Borg‐Karlson A, Solheim H, Björklund N, et al. Priming of inducible defenses protects Norway spruce against tree‐killing bark beetles. Plant Cell Environ . 2020. https://doi.org/10.1111/pce.13661. This article identifies methyl jasmonate as a chemical priming agent of Norway spurce defenses and demonstrates its effectiveness against bark beetle attack in a field study.
Morris H, Brodersen C, Schwarze FWMR, Jansen S. The parenchyma of secondary xylem and its critical role in tree defense against fungal decay in relation to the CODIT model. Front Plant Sci. 2016;7.
• Morris H, Hietala AM, Jansen S, Ribera J, Rosner S, Salmeia KA, Schwarze FWMR. Using the CODIT model to explain secondary metabolites of xylem in defence systems of temperate trees against decay fungi. Ann Bot. 125(5):701–20. https://doi.org/10.1093/aob/mcz138. This article reviews the role of chemical defences in CODIT (compartmentalization of damage/dysfunction in trees) with a particular focus on the relationship between defense, carbon availability, and the hydraulic system.
Bamber RK. Heartwood, its function and formation. Wood Sci Technol. 1976;10:1–8. https://doi.org/10.1007/BF00376379.
Nagy NE, Norli HR, Fongen M, Østby RB, Heldal IM, Davik J, et al. Patterns and roles of lignan and terpenoid accumulation in the reaction zone compartmentalizing pathogen-infected heartwood of Norway spruce. Planta. 2022;255(3):63.
Harju AM, Venäläinen M, Beuker E, Velling P, Viitanen H. Genetic variation in the decay resistance of Scots pine wood against brown rot fungus. Can J For Res. 2001;31(7):1244–9.
Eaton RA, Hale MD. Wood: Decay, pests, and protection. London: Chapman and Hall; 1993.
Mattila HK, Mäkinen M, Lundell T. Hypoxia is regulating enzymatic wood decomposition and intracellular carbohydrate metabolism in filamentous white rot fungus. Biotechnol Biofuels. 2020;13(1).
Eklund L. Internal oxygen levels decrease during the growing season and with increasing stem height. Trees - Struct Funct. 2000;14(4).
Hietala A, Dörsch P, Kvaalen H, Solheim H. Carbon dioxide and methane formation in norway spruce stems infected by white-rot fungi Forests [Internet] 2015;6(12):3304–3325. Available from: https://doi.org/10.3390/f6093304.
PEARCE RB. Antimicrobial defences in the wood of living trees. New Phytologist. 1996;132:203–33. https://doi.org/10.1111/j.1469-8137.1996.tb01842.x.
Shigo AL. Compartmentalization: A conceptual framework for understanding how trees grow and defend themselves AL Shigo. Annu Rev Phytopathol. 1984;22(1):189–214.
Nystedt B, Street NR, Wetterbom A, Zuccolo A, Lin YCC, Scofield DG, et al. The Norway spruce genome sequence and conifer genome evolution. Nature. 2013. https://doi.org/10.1038/nature12211.
De La Torre AR, Birol I, Bousquet J, Ingvarsson PK, Jansson S, Jones SJM, et al. Insights into conifer giga-genomes. Plant Physiol. 2014;166(4).
Bernhardsson C, Zan Y, Chen Z, Ingvarsson PK, Wu HX. Development of a highly efficient 50K single nucleotide polymorphism genotyping array for the large and complex genome of Norway spruce (Picea abies L. Karst) by whole genome resequencing and its transferability to other spruce species. Mol Ecol Resour. 2021. https://doi.org/10.1111/1755-0998.13292.
Perry A, Wachowiak W, Downing A, Talbot R, Cavers S. Development of a single nucleotide polymorphism array for population genomic studies in four European pine species. Mol Ecol Resour. 2020;20:1697–705. https://doi.org/10.1111/1755-0998.13223.
•• Gagalova KK, Warren RL, Coombe L, Wong J, Nip KM, Yuen MM Saint, et al. Spruce giga-genomes: structurally similar yet distinctive with differentially expanding gene families and rapidly evolving genes. Plant J. 2022 Sep 1;111(5):1469–85. This work discusses the structural similarities between Picea species and the diversifying evolution of many key gene families. Understanding the key features and mechanism of conifer evolution will aid our monitoring of forest health and management.
Keeling CI, Weisshaar S, Ralph SG, Jancsik S, Hamberger B, Dullat HK, et al. Transcriptome mining, functional characterization, and phylogeny of a large terpene synthase gene family in spruce (Picea spp.). BMC Plant Biol. 2011. https://doi.org/10.1186/1471-2229-11-43.
Mukrimin M, Kovalchuk A, Ghimire RP, Kivimäenpää M, Sun H, Holopainen JK, et al. Evaluation of potential genetic and chemical markers for Scots pine tolerance against Heterobasidion annosum infection. Planta. 2019;250(6):1881–95. https://doi.org/10.1007/s00425-019-03270-8.
Hall DE, Robert JA, Keeling CI, Domanski D, Quesada AL, Jancsik S, et al. An integrated genomic, proteomic and biochemical analysis of (+)-3-carene biosynthesis in Sitka spruce (Picea sitchensis) genotypes that are resistant or susceptible to white pine weevil. Plant J. 2011;65(6):936–48.
Lamara M, Parent GJ, Giguère I, Beaulieu J, Bousquet J, MacKay JJ. Association genetics of acetophenone defence against spruce budworm in mature white spruce. BMC Plant Biol. 2018;18(231).
de Miguel M, Rodríguez-Quilón I, Heuertz M, Hurel A, Grivet D, Jaramillo-Correa JP, et al. Polygenic adaptation and negative selection across traits, years and environments in a long-lived plant species (Pinus pinaster Ait., Pinaceae). Mol Ecol. 2022;31(7):2089–105.
•• Whitehill JGA, Bohlmann J, Krokene P. Forest insect—plant interactions. In: Allison JD, Paine TD, Slippers B, Wingfield MJ, editors. Forest entomology and pathology. Cham: Springer; 2023. https://doi.org/10.1007/978-3-031-11553-0_7. This chapter reviews the important differences between disease and resistance and the complex interactions between a host-pest/pathogen and their environment.
Azaiez A, Pavy N, Gérardi S, Laroche J, Boyle B, Gagnon F, et al. A catalog of annotated high-confidence SNPs from exome capture and sequencing reveals highly polymorphic genes in Norway spruce (Picea abies). BMC Genomics. 2018. https://doi.org/10.1186/s12864-018-5247-z.
Caballero M, Lauer E, Bennett J, Zaman S, McEvoy S, Acosta J, Jackson C, Townsend L, Eckert A, Whetten RW, Loopstra C, Holliday J, Mandal M, Wegrzyn JL, Isik F. Toward genomic selection in Pinus taeda: Integrating resources to support array design in a complex conifer genome. Appl Plant Sci. 2021;9(6):e11439. https://doi.org/10.1002/aps3.11439.
Graham N, Telfer E, Frickey T, Slavov G, Ismael A, Klápště J, et al. Development and validation of a 36K SNP array for radiata pine (Pinus radiata D.Don). Forests. 2022;13(2).
Kastally C, Niskanen AK, Perry A, Kujala ST, Avia K, Cervantes S, et al. Taming the massive genome of Scots pine with PiSy50k, a new genotyping array for conifer research. Plant J. 2022;109(5):1337–50.
Howe GT, Jayawickrama K, Kolpak SE, et al. An Axiom SNP genotyping array for Douglas-fir. BMC Genomics. 2020;21:9. https://doi.org/10.1186/s12864-019-6383-9.
Mauch-Mani B, Baccelli I, Luna E, Flors V. Defense priming: an adaptive part of induced resistance. Annu Rev Plant Biol. 2017. https://doi.org/10.1146/annurev-arplant-042916-041132.
Bilichak A, Kovalchuk I. Transgenerational response to stress in plants and its application for breeding. J Exp Bot. 2016;67(7):2081–92. https://doi.org/10.1093/jxb/erw066.
Johnsen Ø, Kvaalen H, Yakovlev I, Dæhlen OG, Fossdal CG, Skrøppa T. An epigenetic memory from time of embryo development affects climatic adaptation in Norway spruce. In: Plant cold hardiness: from the laboratory to the field. UK: CABI; 2009. p. 99–107.
Wilkinson SW, Magerøy MH, López Sánchez A, Smith LM, Furci L, Cotton TEA, et al. Surviving in a hostile world: plant strategies to resist pests and diseases. Annu Rev Phytopathol. 2019. https://doi.org/10.1146/annurev-phyto-082718-.
Carneros E, Yakovlev I, Viejo M, et al. The epigenetic memory of temperature during embryogenesis modifies the expression of bud burst-related genes in Norway spruce epitypes. Planta. 2017;246:553–66. https://doi.org/10.1007/s00425-017-2713-9.
Solvin TM, Steffenrem A. Modelling the epigenetic response of increased temperature during reproduction on Norway spruce phenology. Scand J For Res. 2019;34(2):83–93.
Vivas M, Zas R, Sampedro L, Solla A. Environmental maternal effects mediate the resistance of maritime pine to biotic stress. PLoS One. 2013;8(7).
Sniezko RA, Koch J. Breeding trees resistant to insects and diseases: putting theory into application. Biol Invasions. 2017. https://doi.org/10.1007/s10530-017-1482-5.
Redmond MD, Davis TS, Ferrenberg S, Wion AP. Resource allocation trade-offs in a mast-seeding conifer: Piñon pine prioritizes reproduction over defence. AoB Plants 2019;11(6):plz070. https://doi.org/10.1093/aobpla/plz070.
Vazquez-Gonzalez C, Zas R, Erbilgin N, Ferrenberg S, Rozas V, Sampedro L. Resin ducts as resistance traits in conifers: linking dendrochronology and resin-based defences. Tree Physiol. 2020;40(10):1313–26.
Jansson G, Li B, Hannrup B. Time trends in genetic parameters for height and optimal age for parental selection in Scots pine. For Sci. 2003;49(5).
Woodcock P, Marzano M, Quine CP. Key lessons from resistant tree breeding programmes in the Northern Hemisphere. Ann For Sci. 2019. https://doi.org/10.1007/s13595-019-0826-y.
Rosvall O, Bradshaw RHW, Egertsdotter U, Ingvarsson PK, Mullin TJ, Wu H. Using Norway spruce clones in Swedish forestry: implications of clones for management. Scan J For Res. 2019;34(5):390–404.
Matallana-Ramirez LP, Whetten RW, Sanchez GM, Payn KG. Breeding for climate change resilience: a case study of loblolly pine (Pinus taeda L.) in North America. Front Plant Sci. 2021;12.
• Sniezko RA, Liu JJ. Genetic resistance to white pine blister rust, restoration options, and potential use of biotechnology. For Ecol Manage. 2022;520:120168. This article provides an overview of the genomic and biotechnical resources that could be used to develop white pin blister rust resistant populations.
•• Pike CC, Koch J, Nelson CD. Breeding for resistance to tree pests: successes, challenges, and a guide to the future. J For. 2021;119(1):96–105. https://doi.org/10.1093/jofore/fvaa049. This review provides a description of resistance breeding phases, examples of successful resistance breeding, an outline of challenges facing resistance breeding programs, and an overview of schemes to deploy resistant trees in forest programs.
Sniezko RA. Resistance breeding against nonnative pathogens in forest trees — current successes in North America. Can J Plant Pathol. 2006. https://doi.org/10.1080/07060660609507384.
Sniezko RA, Johnson JS, Reeser P, Kegley A, Hansen EM, Sutton W, et al. Genetic resistance to Phytophthora lateralis in Port-Orford-cedar (Chamaecyparis lawsoniana) – basic building blocks for a resistance program. Plants People Planet. 2020;2(1).
Skrøppa T, Solheim H, Steffenrem A. Genetic variation, inheritance patterns and parent–offspring relationships after artificial inoculations with Heterobasidion parviporum and Ceratocystis polonicain Norway spruce seed orchards and progeny tests. Silva Fenn. 2015;49(1).
Elfstrand M, Baison J, Lundén K, Zhou L, Vos I, Capador HD, Åslund MS, Chen Z, Chaudhary R, Olson Å, Wu HX, Karlsson B, Stenlid J, García-Gil MR. Association genetics identifies a specifically regulated Norway spruce laccase gene, PaLAC5, linked to Heterobasidion parviporum resistance. Plant Cell Environ. 2020;43(7):1779–91. https://doi.org/10.1111/pce.13768.
Swedjemark G, Stenlid J, Karlsson B. Genetic variation among clones of Picea abies in resistance to growth of Heterobasidion annosum. Silvae Genetica. 1998;46:369–73.
Capador-Barreto HD, Bernhardsson C, Milesi P, Vos I, Lundén K, Wu HX, Karlsson B, Ingvarsson PK, Stenlid J, Elfstrand M. Killing two enemies with one stone? Genomics of resistance to two sympatric pathogens in Norway spruce. Mol Ecol. 2021;30:4433–47. https://doi.org/10.1111/mec.16058.
Steffenrem A, Solheim H, Skrøppa T. Genetic parameters for wood quality traits and resistance to the pathogens Heterobasidion parviporum and Endoconidiophora polonica in a Norway spruce breeding population. Eur J For Res. 2016;135(5).
Lenz PRN, Beaulieu J, Mansfield SD, Clément S, Desponts M, Bousquet J. Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC Genomics. 2017. https://doi.org/10.1186/s12864-017-3715-5.
Isik F. Genomic selection in forest tree breeding: the concept and an outlook to the future. New Forests. 2014;45:379–401. https://doi.org/10.1007/s11056-014-9422-z.
Chen ZQ, Baison J, Pan J, Karlsson B, Andersson B, Westin J, et al. Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce. BMC Genomics. 2018. https://doi.org/10.1186/s12864-018-5256-y.
Grattapaglia D. Twelve years into genomic selection in forest trees: climbing the slope of enlightenment of marker assisted tree breeding. Forests. 2022;13(10):1554.
El-Kassaby YA, Klápště J, Guy RD. Breeding without breeding: selection using the genomic best linear unbiased predictor method (GBLUP). New For. 2012;43(5–6).
Vidal M, Plomion C, Raffin A, Harvengt L, Bouffier L. Forward selection in a maritime pine polycross progeny trial using pedigree reconstruction. Ann For Sci. 2017;74(1).
El-Kassaby YA, Lstibůrek M. Breeding without breeding. Genet Res (Camb). 2009. https://doi.org/10.1017/S001667230900007X.
Hansen OK, McKinney LV. Establishment of a quasi-field trial in Abies nordmanniana—test of a new approach to forest tree breeding. Tree Genet Genomes. 2010;6:345–55. https://doi.org/10.1007/s11295-009-0253-6.
Lstibůrek M, Hodge GR, Lachout P. Uncovering genetic information from commercial forest plantations—making up for lost time using “Breeding without Breeding.” Tree Genet Genomes. 2015. https://doi.org/10.1007/s11295-015-0881-y.
Lstibůrek M, El-Kassaby YA, Skrøppa T, Hodge GR, Sønstebø JH, Steffenrem A. Dynamic gene-resource landscape management of Norway spruce: combining utilization and conservation. Vol. 8, Frontiers in Plant Science. 2017.
Woods AJ, Heppner D, Kope HH, Burleigh J, Maclauchlan L. Forest health and climate change: A British Columbia perspective. For Chron. 2011;86(4):412–22. https://doi.org/10.5558/tfc86412-4.
Seidl R. The shape of ecosystem management to come: anticipating risks and fostering resilience. Bioscience. 2014;64(12).
Haase DL, Davis AS. Developing and supporting quality nursery facilities and staff are necessary to meet global forest and landscape restoration needs. REFOR [Internet]. 2017;0(4):69–93. Available from: https://journal.reforestationchallenges.org/index.php/REFOR/article/view/72.
Dumroese RK, Luna T, Landis TD. Nursery manual for native plants: a guide for tribal nurseries. Volumen 1: Nursery management. Agriculture Handbook. 2009;1:302.
Wang Y-H, Dai Y, Kong W-L, Zhu M-L, Wu X-Q. Improvement of sphaeropsis shoot blight disease resistance by applying the ectomycorrhizal fungus hymenochaete sp. Rl and mycorrhizal helper bacterium Bacillus pumilus HR10 to Pinus thunbergii. Phytopathology. 2022;112(6):1226–34.
Velmala SMM, Vuorinen I, Uimari A, Piri T, Pennanen T. Ectomycorrhizal fungi increase the vitality of Norway spruce seedlings under the pressure of Heterobasidion root rot in vitro but may increase susceptibility to foliar necrotrophs. Fungal Biol. 2018;122(2–3):101–9.
Vaario LM, Tervonen A, Haukioja K, Haukioja M, Pennanen T, Timonen S. The effect of nursery substrate and fertilization on the growth and ectomycorrhizal status of containerized and outplanted seedlings of Picea abies. Can J For Res. 2009. https://doi.org/10.1139/X08-156.
Romeralo C, Martín-García J, Martínez-Álvarez P, Muñoz-Adalia EJ, Gonçalves DR, Torres E, et al. Pine species determine fungal microbiome composition in a common garden experiment. Fungal Ecol. 2022;56.
Smith S, Read D. Mycorrhizal symbiosis. 3rd edition. Elsevier. 2008.
Gehring CA, Sthultz CM, Flores-Rentería L, Whipple A V., Whitham TG. Tree genetics defines fungal partner communities that may confer drought tolerance. Proc Natl Acad Sci USA. 2017;114(42).
Marx DH, Marrs LF, Cordell CE. Practical use of the mycorrhizal fungal technology in forestry, reclamation, arboriculture, agriculture, and horticulture. Dendrobiology. 2002;47.
Bai Z, Yuan ZQ, Wang DM, Fang S, Ye J, Wang XG, Yuan HS. Ectomycorrhizal fungus-associated determinants jointly reflect ecological processes in a temperature broad-leaved mixed forest. Sci Total Environ. 2020;703:135475.
Schabel HG, Palmer SL. The Dauerwald: Its role in the restoration of natural forests. J For. 1999;97(11):20–5. https://doi.org/10.1093/jof/97.11.20.
Mason WL, Diaci J, Carvalho J, Valkonen S. Continuous cover forestry in Europe: usage and the knowledge gaps and challenges to wider adoption. For Int J For Res. 2022;95(1):1–12. https://doi.org/10.1093/forestry/cpab038.
FAO and UNEP. The State of the World’s Forests 2020. In: Forests, biodiversity and people. Rome; 2020. https://doi.org/10.4060/ca8642en.
Zimová S, Dobor L, Hlásny T, Rammer W, Seidl R. Reducing rotation age to address increasing disturbances in Central Europe: potential and limitations. For Ecol Manage. 2020;475:118408.
Marini L, Økland B, Jönsson AM, Bentz B, Carroll A, Forster B, Grégoire J-C, Hurling R, Nageleisen LM, Netherer S, Ravn HP, Weed A, Schroeder M. Climate drivers of bark beetle outbreak dynamics in Norway spruce forests. Ecography. 2017;40:1426–35. https://doi.org/10.1111/ecog.02769.
Jactel H, Moreira X, Castagneyrol B. Tree diversity and forest resistance to insect pests: patterns, mechanisms, and prospects. Ann Rev Entomol. 2021;66:277–96.
Barbosa P, Hines J, Kaplan I, Martinson H, Szczepaniec A, Szendrei Z. Associational resistance and associational susceptibility: having right or wrong neighbors. Annu Rev Ecol Evol Syst. 2009;40(1):1–20.
Hardoim PR, van Overbeek LS, Berg G, Pirttilä AM, Compant S, Campisano A, et al. The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol Mol Biol Rev. 2015;79(3).
Wang L, Ren L, Li C, Gao C, Liu X, Wang M, Luo Y. Effects of endophytic fungi diversity in different coniferous species on the colonization of Sirex noctilio (Hymenoptera: Siricidae). Sci Rep. 2019;9(1):5077.
Schütz JP, Pukkala T, Donoso PJ, von Gadow K. Historical emergence and current application of CCF. In 2012.
Axelsson R, Angelstam P. Uneven-aged forest management in boreal Sweden: local forestry stakeholders’ perceptions of different sustainability dimensions. Forestry. 2011;84(5):567–79.
Valkonen S, Cheng Z. Metsäammattilaisten suhtautuminen metsän erirakenteiskasvatukseen. Metsätieteen Aikakausk. 2014;2.
Hertog IM, Brogaard S, Krause T. Barriers to expanding continuous cover forestry in Sweden for delivering multiple ecosystem services. Ecosyst Serv. 2022;53:101392.
Piri T, Valkonen S. Incidence and spread of Heterobasidion root rot in uneven-aged Norway spruce stands. Can J For Res. 2013;43(9):872–7.
• Gömöry D, Krajmerová D, Hrivnák M, Longauer R. Assisted migration vs. close-to-nature forestry: what are the prospects for tree populations under climate change? Lesnicky Casopis. 2020;66(2):63–70. This article reviews the biological mechanisms that forest population can use to cope with climate change and their limitations. The authors also discuss how different forest management practices interact with these mechanisms.
Bose AK, Moser B, Rigling A, Lehmann MM, Milcu A, Peter M, Rellstab C, Wohlgemuth T, Gessler A. Memory of environmental conditions across generations affects the acclimation potential of scots pine. Plant Cell Environ. 2020;43(5):1288–99.
Pukkala T. Improved guidelines for any-aged forestry. J For Res. 2022;33(5).
Boncina A. History, current status and future prospects of uneven-aged forest management in the Dinaric region: an overview. Forestry. 2011;84(5):467–78.
Ranade SS, Delhomme N, García-Gil MR. Transcriptome analysis of shade avoidance and shade tolerance in conifers. Planta. 2019;250:299–318.
MacKinnon WE, MacLean DA. The influence of forest and stand conditions on spruce budworm defoliation in New Brunswick, Canada. For Sci. 2003;49(5):657–67.
St-Gelais V, Béland M, Thiffault N. Exploring the potential of two-aged white spruce plantations for the production of sawlog volume with simulations using SORTIE-ND. 2020. https://doi.org/10.21750/REFO
Smith CK, Youtz J, Bradley A, Allen-Reid D, Evans Z, Johnson M, Bird B, Bada C, Fulé P. Climate change and mixed conifer/aspen systems in New Mexico: considerations for managers. In New Mexico Forest Restoration Series Working Paper 7. New Mexico Forest and Watershed Restoration Institute, Las Vegas; 2008. Available from: http://nmfwri.org/images/stories/pdfs/forestry_Restoration_Papers/wp7-mixed_conifer_final.pdf. Accessed 6 Jul 2023.
Telford A, Cavers S, Ennos RA, Cottrell JE. Can we protect forests by harnessing variation in resistance to pests and pathogens? For : Int J For Res. 2015;88(1):3–12. https://doi.org/10.1093/forestry/cpu012.
Nichiforel L, Deuffic P, Thorsen BJ, Weiss G, Hujala T, Keary K, Lawrence A, Avdibegović M, Dobšinská Z, Feliciano D, Górriz-Mifsud E. Two decades of forest-related legislation changes in European countries analysed from a property rights perspective. For Policy Econ. 2020;115:102146.
•• Huang J, Kautz M, Trowbridge AM, Hammerbacher A, Raffa KF, Adams HD, et al. Tree defence and bark beetles in a drying world: carbon partitioning, functioning and modelling. New Phytol . 2020. https://doi.org/10.1111/nph.16173. This article identifies important knowledge gaps that hinder our ability to adequately predict tree mortality in the midst of rapidly occurring global change.
Pike CC, Berrang P, Rogers S, David A, Sweeney C, Hendrickson J. Improving the resistance of eastern white pine to white pine blister rust disease. For Ecol Manage. 2018;423.
Funding
Open access funding provided by Norwegian Institute of Bioeconomy Research. M. Mageroy was supported by the project PROTECT from the Research Council of Norway (award number 324129). P. Krokene was supported by the project SPRUCEBEETLE from the Research Council of Norway (award number 325319). A. Steffenrem was supported by the project FORGENRES from the Norway Grants and Technology Agency of the Czech Republic within the KAPPA Program. N. Nagy received support from project “Frisk Skog” financed by NIBIO’s base funding from the Research Council of Norway (project number 342631/L10). A. Hietala was supported by the Precision forestry project from the Research Council of Norway (award number 281140).
Author information
Authors and Affiliations
Contributions
All authors contributed to discussions about the concepts of the manuscript. All authors contributed to writing of the manuscript. All authors contributed to discussions about the figures. Figures were prepared by M.H.M, N.E.N., and P.K. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare no competing interests.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Ethical Approval
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Mageroy, M.H., Nagy, N.E., Steffenrem, A. et al. Conifer Defences against Pathogens and Pests — Mechanisms, Breeding, and Management. Curr. For. Rep. 9, 429–443 (2023). https://doi.org/10.1007/s40725-023-00201-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40725-023-00201-5