1 INTRODUCTION
Biotic invaders are a major threat, causing great destruction to the
environment, economies, and biological safety, and with the development
of transportation and commerce under globalization, few, if any, areas
have escaped these invasions (Mack et al., 2000; Sun, Yuan, & Ou, 2002;
Walker & Steffen, 1996). Pine wilt disease (PWD) is a major disease
that invades forests and is highly destructive to its host pines. The
disease spreads quickly, causes disease over a wide range, and is
difficult to control and cure; thereby, PWD is also known as the
“cancer” of pines (Naves, Camacho, Sousa, & Quartau, 2007; Wu et al.,
2013). The disease, native to North America, was introduced into Japan
in 1905, and then outbreaks occurred successively in China, South Korea,
Mexico, Portugal, and Spain (Mota & Vieira, 2008; Zhao, Mota, Vieira,
Butcher, & Sun, 2013). To date, PWD has caused serious negative effects
on the economy and the environment of many countries. From 2001 to 2012,
the European Union spent thirty million euros on the surveillance and
control of the disease, with projected losses of up to twenty billion
euros in Europe by 2030 (Soliman et al., 2012). In Japan, at least
700,000 m3 of pine trees are lost every year (Mamiya
& Shoji, 2009). In China, the direct and indirect economic losses due
to PWD have reached 100 billion yuan, and the area of Pinus
massoniana , its main host, decreased by 8.07 million ha from 1994 to
2013, with 60 million ha of pine forests that remain greatly threatened
by this disease (Ye, 2019). In addition, according to the hypothesis of
an insect–fungus complex (Lu, Wingfield, Gillette, & Sun, 2011), the
native ecosystems are at risk of reinvasion because pine wilt disease
acquired greater adaptability in the invasion of new habitats (Maehara
& Futai, 1997; Zhao et al., 2013).
The causal agent of PWD is the pinewood nematode (PWN,Bursaphelenchus xylophilus ), which is spread by vector beetles of
the genus Monochamus (Mota & Vieira, 2008). The main
vector beetles in North America include M. carolinensis , M.
scutellatus , and M. mutator (Akbulut & Stamps, 2011). M.
galloprovincialis is the only insect vector in Portugal and other
European countries (Sousa et al., 2001); whereas in several Asian
countries, including China, the main vector is M. alternatus .
Therefore, the effective control of M. alternatus plays an
important role in controlling the continued spread of PWD in Asia (Zhao
et al., 2008). In addition to self-migration, human-mediated and
environmental factors are also important in the spread of M.
alternatus . Moreover, because of the host dominance and ecosystem
dominance of forest pests (Lian & Zhang, 2005), it is necessary to
better understand the influence of host and nonhost landscapes on the
dispersal behavior of this species.
The landscape plays an important role in the ecological processes
(Turner, 1989). Landscape diversity can have major effects on the
dispersal of forest pests (Thibaud et al., 2014; Foley et al., 2005), by
either promoting or inhibiting the gene flow of dispersing insects and
their movement between different landscape types (Fontaine, Bergerot, Le
Viol, & Julliard, 2016; Yadav, Stow, & Dudaniec, 2019). The abundance
of insects generally increases with the density of host plants, and
insects have a tendency to migrate to patches of host plants (Underwood,
Inouye, & Hambäck, 2014). The evidence is also increasing that with
high tree diversity associational resistance can form against pests and
reduce their damage (Damien et al., 2016; Harri, Koricheva, & Kai,
2007; Jactel, Goulard, Menassieu, & Goujon, 2002; Jactel &
Brockerhoff, 2007; Jacte et al., 2015). Especially in mixed stands with
hosts and nonhosts, the nonhosts can reduce the proportion of hosts
available to the pest, and the natural enemies of pests are also
typically more abundant, both of which increase the resistance to forest
pests (Barbosa et al., 2009; Quayle, Regniere, Cappuccino, & Dupont,
2003). In addition to the effects of tree species in the landscape,
other landscape types (e.g., urban areas, farmlands, and roads) can also
have different effects on the gene flow and dispersal of insects
(Keller, Strien, & Holderegger, 2012; Ortego, Bonal, & Muñoz, 2010;
Yadav et al., 2019). Therefore, in a heterogeneous landscape, it is
particularly important to understand the relationships between landscape
types and pest dispersal behavior, population genetic structure, and
gene flow. With such an understanding, the mechanisms to explain pest
occurrence can be revealed, and scientific and effective ecological
control methods can be established.
Although the relationship between landscape and some longhorn beetles
has been described, most studies used microsatellite or mitochondrial
DNA markers to study the invasion history and the influence of
environmental factors such as climate and altitude (David, Giffard,
Piou, Roques, & Jactel, 2016; Haran, Roques, Barnard, Robinet, & Roux,
2015; Haran et al., 2017; Javal et al., 2017; Javal et al., 2019;
Koutroumpa, Rougon, Bertheau, Lieutier, & Roux, 2013; Tsykun et al.,
2019). However, because of the limited number of polymorphic sites and
the lack of detailed information on tree species, the dispersal behavior
and the genetic model of M. alternatus at a fine scale has not
been studied. In particular, a comprehensive description of the
relationships between this species and finely classified tree species
and nonhost landscapes such as urban areas, farmlands, and roads is
lacking. This information is particularly important to clarify the
dispersal behavior of M. alternatus in a heterogeneous landscape,
as well as to efficiently manage and control PWD. With the emergence of
next-generation sequencing, millions of single nucleotide polymorphisms
(SNPs) can be detected and compared with microsatellite markers. In
addition, fewer samples are needed to obtain more accurate and better
information on population structure. Thus, next-generation sequencing
not only facilitates the development of landscape genetics but also is
expected to become the standard in environmental association analysis in
coming years (Rellstab et al., 2015; Jeffries et al., 2016). Currently,
SNP analysis is becoming more widely accepted and applied in landscape
research (Bay et al., 2018; Nancy et al., 2020; Zhen et al., 2017).
Moreover, with the large numbers of SNPs, the analysis is more powerful,
and the differences between populations can be detected at a fine scale
(Liu et al., 2019; Rašić, Filipović, Weeks, & Hoffmann, 2014).
In this study, the whole genome of M. alternatus was resequenced
at different landscape scales (intermediate and fine scales), and the
tree species in the forested landscape were classified in detail based
on a forest inventory of China. To analyze the influence of multiple
scales, first, the population structure at intermediate and fine scales
was compared, and then, the relations between gene flow and genetic
diversity and forests with different tree species, urban areas,
farmlands, roads, and water sources were studied at a fine scale. At the
fine scale, the isolation-by-distance (IBD) and the least-cost distance
(LCP) models(Adriaensen et al., 2003) were used, and the distances
calculated by the models were correlated with genetic distance. To
define the resistance surface more objectively and further study the
effects of different landscape types on gene flow and dispersal ofM. alternatus , the least-cost transect analysis (LCTA) developed
by Strien, Keller, & Holderegger. (2012) and maximum-likelihood
population effects (MLPE) developed by Clarke, Rothery, & Raybould.
(2002) were adopted. The distance-based redundancy analysis (db-RDA)
(Legendre & Anderson, 1999) and the generalized additive model (GAM)
(Lengyel & Podani, 2015) were also used to test the influence of the
landscape composition around each sample point on genetic diversity.