Modelling the Incursion and Spread of a Forestry Pest: Case Study of Monochamus Alternatus Hope (Coleoptera: Cerambycidae) in Victoria

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Article Modelling the Incursion and Spread of a Forestry Pest: Case Study of Monochamus alternatus Hope (Coleoptera: Cerambycidae) in Victoria

John Weiss 1,* , Kathryn Sheffield 1, Anna Weeks 2 and David Smith 3 1 Agriculture Victoria Research, Department of Jobs, Precincts and Resources, AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia; kathyrn.sheffi[email protected] 2 Agriculture Victoria Research, Department of Jobs, Precincts and Resources, 124 Chiltern Valley Road, Rutherglen, VIC 3685, Australia; [email protected] 3 Agriculture Victoria, Department of Jobs, Precincts and Resources, Office of the Chief Plant Health Officer, 2 Codrington Street, Cranbourne, VIC 3977, Australia; [email protected] * Correspondence: [email protected]; Tel.: +61-439-101-776

Received: 22 January 2019; Accepted: 20 February 2019; Published: 22 February 2019

Abstract: Effective and efficient systems for surveillance, eradication, containment and management of biosecurity threats require methods to predict the establishment, population growth and spread of organisms that pose a potential biosecurity risk. To support Victorian forest biosecurity operations, Agriculture Victoria has developed a landscape-scale, spatially explicit, spatio-temporal population growth and dispersal model of a generic pest pine beetle. The model can be used to simulate the incursion of a forestry pest from a nominated location(s), such as an importation business site (approved arrangement, AA), into the surrounding environment. The model provides both illustrative and quantitative data on population dynamics and spread of a forestry pest species. Flexibility built into the model design enables a range of spatial extents to be modelled, from user-defined study areas to the Victoria-wide area. The spatial resolution of the model (size of grid cells) can be altered from 100 m to greater than 1 km. The model allows core parameters to be altered by the user, enabling the spread of a variety of windborne insect species and pathogens to be investigated. We verified the model and its parameters by simulating and comparing the outputs with the 1999/2000 Melbourne incursion, but no establishment of a forestry pest beetle was believed to be Monochamus alternatus Hope (Coleoptera: Cerambycidae). The model accurately predicts the distance and direction of the historic incursion, and the subsequent failure to establish is due to low overall population density of the pest species.

Keywords: dispersal; wind dispersed; host suitability; Pinus; surveillance; insect pest; degree day; allee effect

1. Introduction Australia has a robust, dynamic and evolving plant biosecurity system. Nonetheless, pressure from increasing international trade and travel has identified capacity and capability gaps. An increasing rate of exotic forest pest establishments poses significant challenges to Australia’s forest biosecurity [1]. Quantifying the benefits of pest management actions that reduce spread of invasive species is critical for optimising pest management programs, evaluation of existing programs and to assist in determining future actions. Successful early detection of exotic forest pests significantly contributes to the effectiveness of biosecurity activities and potential outcomes. Many post-border forestry pest surveillance programs have been piloted in Eastern Australia involving port-environ surveillance, urban tree monitoring and targeted trapping [2,3]. Expansion of

Forests 2019, 10, 198; doi:10.3390/f10020198 www.mdpi.com/journal/forests Forests 2019, 10, 198 2 of 17 these activities into a nationally coordinated program, targeting high-risk sites and priority pests, will maximise the chance of early detection and possible eradication, thereby reducing the likelihood of exotic pests becoming established in Australia’s forests [1,4,5]. Pine beetles (from the families Curculionidae and Cerambycidae) including Dendroctonus and Monochamus species are among the most serious potential pests in forestry in Victoria, Australia [6]. In Australia, 9% of 20,000 crates inspected in Sydney, Brisbane and Melbourne during 2005 have “something of quarantine concern”, such as bark, fungi, live insects, or frass, including Monochamus alternatus [5]. Monochamus is a genus of longhorn beetles (Coleoptera: Cerambycidae) commonly known as sawyer beetles. Their larvae bore into dead or dying trees, especially pines. The nematode Bursaphelenchus xylophilus which causes pine wilt disease is exclusively associated with the genus Monochamus. Pine wilt diseases can cause decline and death in heavily infected trees within one growing season; crowns of infected trees turn from green to reddish brown [7] and symptoms can develop in a matter of weeks. Many Monochamus species have been recognised as Australian national priority pests [8]. The insect and associated nematode have caused extensive damage to pine forests where it has invaded countries with susceptible hosts and with native co-occurring Monochamus beetle species [5]. An Australian pest risk analysis shows that there is a moderate likelihood of pinewood nematode and its primary vector, M. alternatus (Japanese pine sawyer beetle), arriving at, establishing, spreading, and impacting Pinus plantations in Australia [5]. The species of Pinus used in Australian plantations have never encountered this pest and most likely have very little natural defense. Being able to predict how organisms that pose biosecurity threats will establish and build up population densities and spread across landscapes is essential for designing effective and efﬁcient systems for surveillance, eradication, containment and management of key biosecurity threats.

2. Materials and Methods We selected the insect pest to be modelled using the following criteria:

• A high-priority pest for the forestry industry. • An incursion of the pest would require an emergency response. • Historical data of an incursion in a similar environment to Victoria. • Sufﬁcient information available on the life history and dispersal of the pest.

Two pests that ﬁtted these criteria were pine beetles from the genus Monochamus (Coleoptera: Cerambycidae) such as the Japanese pine sawyer, M. alternatus, and pine bark beetles such as the mountain pine beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae). It was decided to focus the model on M. alternatus, as it is a vector of the pinewood nematode, Bursaphelenchus xylophilus. As noted in Akbulut and Stamps [9]: “Studies to date indicate a remarkable similarity among beetle species (Monochamus) around the globe for a variety of life-history traits, including lifespan, adult emergence numbers, ﬂight capability, nematode transmission rates and attraction to pine volatiles.” As such, with little other information available, we can assume the model is applicable across the Monochamus genus. However, it was recognised that the model should have the capacity to accommodate other species with only minor changes to either the programming code or parameters within the model.

2.1. Life Cycle of Monochamus Species and Bursaphelenchus Xylophilus The life cycles of the Monochamus species and B. xylophilus have been extensively researched, reviewed [5,10–13] and are summarily illustrated in Figure1. The emergent adults, carrying the nematode, ﬂy to healthy pine trees and feed during daylight hours on succulent young twigs for 7–12 days. During this feeding and maturation period, the nematode enters feeding wounds in the bark of the smaller branches. The nematodes feed on the lining of the tracheids, disrupting water ﬂow, and susceptible trees wilt and die as a result of the nematode damage to the phloem. Forests 2018, 9, x FOR PEER REVIEW 3 of 17

although unmated females only lay sterile eggs. The nematode will still proceed through its life cycle and kill the tree even if sterile eggs have been laid. Susceptible trees eventually wilt and die, often rapidly (within 2–3 months). Water stress and high temperatures are consistently associated with expression of pine wilt disease, especially where mean temperatures exceed 20 °C for long periods. Beetles are then attracted to these dying trees or other abiotically stressed trees and oviposit eggs under the bark, also transmitting nematodes. The beetle egg hatches and the larva feed on the inner bark, cambium and outer sapwood, progressing through 3–8 larval instars before pupation. The insect predominantly overwinters as a larva but can survive as an egg or a pupa. The insect pupates in a chamber for several days where the nematode crawls into the body of the insect via the spiracles and trachea. Adults emerge reproductively immature. Upon emergence they disperse, usually within 100 m, however, Forestslong-flight2019, 10 dispersal, 198 has been observed with flight mill experiments [12] demonstrating a maximum3 of 17 flight duration of 115 min.

Figure 1.1. LifeLife cyclecycle ofofthe theMonochamus Monochamusbeetle, beetle, the the nematode nematode (Bursaphelenchus (Bursaphelenchus xylophilus xylophilus) and) and impact impact on pineon pine trees trees (from (from Akbulut Akbu andlut and Stamps Stamps [13 ]).[13]).

2.2. ModelAfter Components maturation feeding, sexually mature beetles fly to dying or recently dead pines. Females deposit eggs below the bark and nematodes may enter the oviposition wounds. Both mated and The model was developed mechanistically by including all factors that satisfied two criteria: unmated females can produce abundant eggs and transmit nematodes through oviposition scars, although1. They unmatedwere considered females important only lay sterile influences eggs. Thein th nematodee establishment will still and proceed spread throughof pine beetles. its life cycle and2. The kill thedata tree were even available if sterile at eggsa scale have that been was suitable laid. Susceptible for inclusion trees in eventually the model. wilt and die, often rapidly (within 2–3 months). Water stress and high temperatures are consistently associated with expressionThe model of pine description wilt disease, follows especially the Overview, where mean Design temperatures concepts, Details exceed (ODD) 20 ◦C forprotocol long periods.[14,15]. BeetlesIn summary, are then the attracted modelto consists these dying of several trees or module other abioticallys that interact stressed to treessimulate and oviposit the development, eggs under thematuration bark, also and transmitting spread of a nematodes. pine beetle across a real-life landscape: (a) The Pine beetlebeetle egglife cycle hatches module and thewhich larva simulates feed on the the growth inner bark,from eggs, cambium through and the outer larval sapwood, stages, progressingpupae and then through to adult, 3–8 larvaldispersal instars of a before proportion pupation. of adult The beetles insect predominantlyto suitable nearby overwinters trees, mating as a larvaand egg but production. can survive as an egg or a pupa. The insect pupates in a chamber for several days where the nematode(b) Wind crawls spread into module the body that of disperses the insect a via proporti the spiracleson of adult and trachea. beetles Adultsacross emergethe landscape reproductively during immature.spring and Uponsummer, emergence depending they on disperse, actual or usually averaged within wind 100 direction(s) m, however, and long-flight its interaction dispersal with hasthe beendispersal observed kernel. with flight mill experiments [12] demonstrating a maximum flight duration of 115 min. (c) Host availability and suitability module that accurately describes the distribution, abundance 2.2. Model Components and health status of trees within the Pineacea family (pine, fir and spruce species), based on local, state Theand modelfederal was government developed datasets mechanistically available by across including Victoria all factors(see [16] that for satisfied a full description two criteria: of the

1. They were considered important inﬂuences in the establishment and spread of pine beetles. 2. The data were available at a scale that was suitable for inclusion in the model.

The model description follows the Overview, Design concepts, Details (ODD) protocol [14,15]. In summary, the model consists of several modules that interact to simulate the development, maturation and spread of a pine beetle across a real-life landscape:

(a) Pine beetle life cycle module which simulates the growth from eggs, through the larval stages, pupae and then to adult, dispersal of a proportion of adult beetles to suitable nearby trees, mating and egg production. Forests 2019, 10, 198 4 of 17

(b) Wind spread module that disperses a proportion of adult beetles across the landscape during spring and summer, depending on actual or averaged wind direction(s) and its interaction with the dispersal kernel. (c) Host availability and suitability module that accurately describes the distribution, abundance and health status of trees within the Pineacea family (pine, ﬁr and spruce species), based on local, state and federal government datasets available across Victoria (see [16] for a full description of the host availability and suitability dataset development). Most of the local and state government pine survey data were of high quality and spatially accurate. All data sources were curated for data quality and duplicate records from the diverse data sources were removed. Each grid cell contains a count of healthy, healthy (but infected by the nematode), stressed (but uninfected by the nematode), stressed (after being infected by the nematode) and dead pine trees.

2.3. Model Design The model operates across a landscape populated with real data, and can accommodate any spatially defined local, regional or statewide extent. The model creates an initial master grid based on a user-defined spatial resolution (>100 m × 100 m) and extent. All subsequent spatial layers are then re-projected and resampled to align to this master layer. The model is initialised with a user-defined pest infestation site, such as pre-defined assigned importation-approved arrangement (AA) site, a point selected interactively within the model interface, or user-defined co-ordinates. The model operates on a daily time-step. The time frame of the model run is defined by the user. For any point in time (day) that the model runs, each cell contains the following information:

• The number and health status of pine trees, • Average meteorological wind data, relevant to the time frame of the model run, • The presence, sex, and life stages of any beetles, • The nematode infection status of any beetles, and • Degree day information relevant for beetle development.

Based on meteorological conditions and the distribution of pine trees, the model calculates the dispersal of beetles over time, the infection of trees by the pathogen, and the beetle’s population growth (or decline). The sub-models, which describe how each module works (life cycle, host availability and suitability, and dispersal), are summarised below and in more detail in our technical report [16]. The report also details the parameter values used in each module, and the sources and rationale for the data used. We utilised MatLab® (Mathworks, Inc., Natick, MA, USA) as a modelling platform as it supports both live and static climatic data feeds.

2.4. Life Cycle The life cycle of the pinewood nematode and its vectors have been extensively researched (as reviewed by [5,9–13]). As the insect is not known to have established in Australia, we utilised the European and American research to estimate the thermal degree day thresholds for the key stages in the life cycle (i.e., from eggs to adult emergence, to sexual maturity, and to death). Daily climate data were sourced at a 5 km gridded resolution across Victoria [17], enabling statewide estimates of the likely date of occurrence of key phenological stages. Within the model construct itself, each beetle was identiﬁed as an agent with assigned properties such as; “sex”, “mated”, “location”, “degree day counter” and “phenological stage” (Table1). Agent behaviour was associated with the underlying gridded base-layers of the model, which included degree day data for phenological stages, wind data for long-distance dispersal function and tree-health information. Forests 2018, 9, x FOR PEER REVIEW 5 of 17 Forests 2019, 10, 198 5 of 17 Table 1. Agent properties for the Monochamus beetle model. Forests 2018, 9, x FOR PEER REVIEW 5 of 17 Property Description TableTable 1. 1. AgentAgent properties properties for for the the MonochamusMonochamus beetlebeetle model. model. X X-coordinate (meters) of agent PropertyPropertyY Y-coordinateDescription Description (meters) of agent PhenoStageX X Current X-coordinate phenological X-coordinate (meters) (meters)stage of agent of ofagent agent [1–5] DegDayCountY Y Current Y-coordinate cumulative Y-coordinate (meters) degree (meters) of countagent of agent for agent PhenoStagePhenoStage Current Current phenological phenological stage stageof agent of agent [1–5] [ 1–5] AdultDayCountDegDayCount Current Current number cumulative of days degree since count emergence for agent DegDayCount Current cumulative degree count for agent AdultDayCountMale CurrentMale/Female number of (true/false) days since emergence AdultDayCountMatedMale Current Whether number agent Male/Female of dayshas matedsince (true/false) emergence (true/false) MaleMated WhetherMale/Female agent (true/false) has mated (true/false) Infected Is agent infected with nematode (true/false) MatedInfected Whether Is agent agent infected has withmated nematode (true/false) (true/false) Infected Is agent infected with nematode (true/false) 2.5. Feeding, Mating and Ovipositioning 2.5. Feeding, Mating and Ovipositioning 2.5. Feeding,During Matingspring andand Ovipositioning summer, Monochamus beetles emerge from trees infected and killed the During spring and summer, Monochamus beetles emerge from trees infected and killed the previous year, carrying pinewood nematodes in their tracheal system. This component of the model previousDuring year, spring carrying and pinewoodsummer, Monochamus nematodes inbeetles their trachealemerge from system. trees This infected component and killed of the the model also updates the number and health status of any pines. The adult beetles fly to healthy pine trees alsoprevious updates year, the carrying number pinewood and health nematodes status of in any their pines. tracheal The adultsystem. beetles This component ﬂy to healthy of pinethe model trees and and feed on young shoots and twig bark (Figure 2). feedalso onupdates young the shoots number and and twig health bark status (Figure of2 ).any pines. The adult beetles fly to healthy pine trees and feed on young shoots and twig bark (Figure 2).

Figure 2. Flow diagram illustrating decisions within the model for the adult component of the beetle’s Figure 2. Flow diagram illustrating decisions within the model for the adult component of the lifeFigure cycle. 2. Flow diagram illustrating decisions within the model for the adult component of the beetle'sbeetle's lifelife cycle. In general, mating occurs around 10 days after the adults have emerged, fed, and possibly infected healthyInIn general, trees.general, Females mating then occursoccurs lay an arouarou averagendnd 10 10 ofdays days 60 toafter after 109 the eggsthe adults inadults a stressedhave have emerged, treeemerged, [10 ,11fed, ].fed, However,and and possibly possibly mating infectedcaninfected only healthyhealthy occur if trees. both malesFemalesFemales and thenthen females laylay an an are averag averag presente eof of both60 60 to spatiallyto109 109 eggs eggs andin ina temporally stresseda stressed tree (i.e., tree [10,11]. within[10,11]. a However,localisedHowever, region matingmating when can only the female occuroccur if isif both both ready males males to mate). and and females Thefemales model are are present accounts present both both for spatially this, spatially by and stipulating and temporally temporally mating (i.e.,occurring(i.e., withinwithin when aa localisedlocalised there are regionregion both whenwhen male the andthe female female is beetlesis ready ready to within tomate). mate). the The sameThe model model cell accounts after accounts dispersing for forthis, this, fromby by a stipulatingfeedingstipulating tree. matingmating At low occurring densities, whenwhen mate there there limitation are are both both reduces ma male leand theand female reproduction female beetles beetles within potential within the thesame and same decreases cell cellafter after the dispersingratedispersing of population fromfrom a growth,feeding tree. whichtree. AtAt may low low leaddensities, densities, to inbreeding mate mate limitation limitation depression reduces reduces and the lead the reproduction to reproduction failure of potential an incursionpotential and decreases the rate of population growth, which may lead to inbreeding depression and lead to andto establish decreases [18 the]. In rate addition of population to mated growth, females, whic unmatedh may females lead to caninbreeding also lay depression eggs and subsequently and lead to failure of an incursion to establish [18]. In addition to mated females, unmated females can also lay failureinfect host of an trees incursion with the to pathogen, establish which[18]. In is additi capturedon to in mated the model. females, Associated unmated with females each phenologicalcan also lay eggs and subsequently infect host trees with the pathogen, which is captured in the model. eggsstage and is a beetlesubsequently mortality infect rate. Duringhost trees oviposition, with the eggspathogen, are laid which by mated is captured females atin athe predeﬁned model. Associated with each phenological stage is a beetle mortality rate. During oviposition, eggs are laid Associateddaily rate. Allwith beetles each phenological progress through stage theiris a beetle annual mortality life cycle rate. until During they dieoviposition, and are then eggs removed are laid by mated females at a predefined daily rate. All beetles progress through their annual life cycle until byfrom mated the model.females at a predefined daily rate. All beetles progress through their annual life cycle until theythey die die and and areare then removed from from the the model. model.

Forests 2019, 10, 198 6 of 17

Forests 2018, 9, x FOR PEER REVIEW 6 of 17 2.6. Phenological Development 2.6. DegreePhenological days Development were calculated as a function of minimum and maximum temperatures and a base threshold,DegreeTbase days(Equation were calculated (1)). as a function of minimum and maximum temperatures and a base threshold, Tbase (Equation (1)). Tmax + max(Tmin, Tbase) ◦ max 𝑇𝑚𝑎𝑥 max+ (𝑇𝑚𝑖𝑛, 𝑇𝑏𝑎𝑠𝑒) − Tbase, 0 , where Tbase = 12.5 C (1) max 2 −𝑇𝑏𝑎𝑠𝑒,0,𝑤ℎ𝑒𝑟𝑒 𝑇𝑏𝑎𝑠𝑒=12.5 °C 2 PhenologicalPhenological thresholdsthresholds werewere applied to the cu cumulativemulative degree degree data. data. The The relationships relationships between between minimumminimum and and maximummaximum temperaturestemperatures and degree day are illustrated illustrated in in Figure Figure 3.3.

FigureFigure 3.3. RelationshipsRelationships between minimum and and maximum maximum temperatures temperatures and and degree degree days. days.

AnAn overview overview of of phenological phenological stagesstages forfor agentsagents inin thethe MonochamusMonochamus modelmodel is presentedpresented in Figure4 . Degree4. Degree day day thresholds thresholds for thefor phenologicalthe phenological development development stages stages were were obtained obtained from from the Unitedthe United States DepartmentStates Department of Agriculture of Agriculture Animal andAnimal Plant and Health Plant Inspection Health Inspection Service (USDA, Service APHIS) (USDA, model APHIS) [19]. modelWe [19]. determined the length and completion dates of the phenological stages across Victoria and MelbourneWe determined Region. the Aslength expected, and completion there is spatial dates of and the temporal phenological variability stages across in the averageVictoria and adult emergenceMelbourne date Region. (utilising As expected, 2010–2018 there meteorological is spatial data)and temporal across Victoria, variability with in most the beetlesaverage emerging adult betweenemergence early date December (utilising and 2010–2018 mid-March. meteorologic Median resultsal data) are presentedacross Victoria, in Table 2with. most beetles emerging between early December and mid-March. Median results are presented in Table 2.

Forests 2019, 10, 198 7 of 17 Forests 2018, 9, x FOR PEER REVIEW 7 of 17

Figure 4. Phenological stages and degree day thresholds for the Monochamus genus model based on Figure 4. Phenological stages and degree day thresholds for the Monochamus genus model based on United States Department of Agriculture, Animal and Plant Health Inspection Service (USDA, APHIS) United States Department of Agriculture, Animal and Plant Health Inspection Service (USDA, model [19]. Representative date ranges are the median value for the Melbourne region, averaged over APHIS) model [19]. Representative date ranges are the median value for the Melbourne region, the period 2010–2018, assuming egg laying in mid-winter. averaged over the period 2010–2018, assuming egg laying in mid-winter. Table 2. Degree day thresholds (DD 12.5 is the cumulative days above 12.5 ◦C) [19], typical length and completionTable 2. Degree dates day of phenological thresholds (DD stage 12.5 across is the (1) cumulati Victoria andve days (2) the above Melbourne 12.5 °C) Region. [19], typical length and completion dates of phenological stage across (1) Victoria and (2) the Melbourne Region. Victoria Melbourne Days Required to Complete Each DaysPhenological Required Stage to at DD 12.5 CompletionVictoria of Stage Length CompletionMelbourne of Stage Length Complete each Stage (Median) (Days) Stage (Median) (Days) Completion of Stage Length Completion of Stage Length PhenologicalEggs Stage at DD 80 12 September 73 25 September 86 Larvae 705Stage (median) 2 January (days) 112 Stage 18 January(median) 115(days) Pupae12.5 865 19 January 17 8 February 21 Pre-ovipositionEggs 80 965 12th September 30 January 73 11 25th 21 September February 13 86 LarvaeOviposition 705 1333 2nd January 20 March 112 49 18th 7January May 75 115 Pupae 865 19th January 17 8th February 21 2.7.Pre-oviposition Host Availability and965 Suitability 30th (Pine January Model) 11 21st February 13 OvipositionPine tree locations 1333 were based 20th on twoMarch types of spatial49 data; polygons 7th of softwoodMay plantations 75 areas and point data representing individual or groups of trees surveyed. Several government, council and departmental datasets were used to create this layer (see [16] for full details). Data for all susceptible

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Pineacea species were included, representing pines, ﬁr and spruce. The most common species were Pinus. Original data sources were used to develop a statewide pine tree density layer, calculated using a 100 m grid cell. This was used as an initial layer in the model. The host availability and suitability sub-model iteratively updates the abundance and health status of all pine trees in each of the pixels. Each grid cell contains a count of the number of pine trees within the following categories:

• Healthy: tree is growing robustly (this category is included in the initial input dataset and is based on observations recorded in source data). • Healthy and infected by the nematode: tree is not showing signs of stress but has been infected by the nematode after beetle feeding (this category is updated following beetle dispersion, feeding and egg laying events within the model). • Stressed but uninfected by the nematode: tree is showing signs of stress but has not been infected by the nematode (this category is included in the initial input dataset and is based on observations recorded in source data). • Stressed and infected by the nematode: tree is showing signs of stress and has been infected by the nematode (this category is updated following beetle dispersion, feeding and egg laying events within the model). • Dead: tree is dead but still present (this category is included in the initial input dataset and is based on observations recorded in source data).

Even with large numbers of beetles entering a stand of healthy trees, not all trees will be infected. Under the assumption that each beetle randomly selects a healthy tree, the expectation that a single tree, Ti, will be infected if B beetles enter the stand (Equation (2) and Figure5a) can be described as:

T − 1 B E(T = in f ected) = 1 − (2) i T

It follows that the total number of trees infected is the expectation of a single tree being infected multiplied by the total number of trees in the stand, T (Equation (3) and Figure5b). ! T − 1 B Infected trees = T 1 − (3) T

2.8. Dispersal The distance and direction of adult beetle dispersal is determined by the status of pine trees and the wind dispersal sub-model (Figure6). Where suitable trees are present within a cell or adjacent cell, most beetles will stay within an immediate area. A small proportion will disperse further based on wind direction and strength (wind dispersal sub-model). If there are no suitable trees within the immediate area, beetle dispersion will also be determined by the wind dispersal sub-model which is a function of the number of trees in the initial cell, wind speed and maximum beetle ﬂight time. Forests 2019, 10, 198 9 of 17 Forests 2018, 9, x FOR PEER REVIEW 9 of 17

(a)

(b)

FigureFigure 5.5.Relationship Relationship betweenbetween beetlebeetle densitydensity andandsubsequent subsequent numbernumber ofof infectedinfected trees,trees, showingshowing thethe probabilitiesprobabilities ( a(a)) and and the the number number of of infected infected trees trees ( b(b).).

2.8. DispersalTo inform wind dispersion, we utilised wind direction and speed data available from the Australian Bureau of Meteorology (BOM). Averaged hourly data for separate weather stations The distance and direction of adult beetle dispersal is determined by the status of pine trees and were calculated in eight cardinal directions: N, NE, E, SE, S, SW, W, NW. For each weather the wind dispersal sub-model (Figure 6). Where suitable trees are present within a cell or adjacent station/month/hour/direction combination, the 5th, 25th, 50th, 75th and 95th wind speed percentiles cell, most beetles will stay within an immediate area. A small proportion will disperse further based were calculated. These percentiles were then averaged by an hourly dispersion pattern, derived on wind direction and strength (wind dispersal sub-model). If there are no suitable trees within the from [20], who found that Monochamus species ﬂight activity was highest between 9 p.m. and 12 a.m. immediate area, beetle dispersion will also be determined by the wind dispersal sub-model which is a function of the number of trees in the initial cell, wind speed and maximum beetle flight time.

Forests 2019, 10, 198 10 of 17 Forests 2018, 9, x FOR PEER REVIEW 10 of 17

FigureFigure 6.6. IllustrativeIllustrative components of of a a dispersal sub-model sub-model showing showing (a (a) )calculation calculation of of beetle beetle flight flight distancedistance probabilityprobability density function (PDF) as as a a function function of of windspeed, windspeed, maximum maximum beetle beetle flight flight time time andand numbernumber ofof healthyhealthy trees in initial cell, ( b)) corresponding corresponding dispersion dispersion PDF PDF calculated calculated in in 8 8cardinal cardinal directions,directions, N,N, NE, E, SE, S, SW, W, NW, ((cc)) flight-directionflight-direction weighting weighting based based on on likelihood likelihood of of wind wind directiondirection andand ((dd)) healthyhealthy treetree weighting.weighting.

WithinTo inform the model,wind dispersion, wind dispersion we utilised occurred wind at direction emergence and or speed the end data of available the pupae from stage the of development.Australian Bureau The of dispersion Meteorology vectors (BOM). were Averaged randomly hourly sampled data from for separate a probability weather density stations function were (PDF)calculated that wasin basedeight oncardinal the density directions: of healthy N, treesNE, inE, aSE, cell, S, the SW, wind W, speed NW. and For wind each direction weather and thestation/month/hour/direction maximum flight time of the combin beetle.ation, A maximum the 5th, 25th, potential 50th, travel 75th and distance 95th waswind used speed to percentiles determine a gammawere calculated. function forThese distribution percentiles of distanceswere then travelled. averaged Thisby an distribution hourly dispersion was randomly pattern, sampled derived to determinefrom [20], thewho direction found that and Monochamus distance of beetlespecies wind flight dispersion. activity was Cumulative highest between rainfall 9 was p.m. also and generated, 12 a.m. to restrictWithin dispersal the model, during wind rain dispersion events. A fulloccurred description at emergence of this dataset or the developmentend of the pupae is given stage in ourof technicaldevelopment. report The [16 ].dispersion vectors were randomly sampled from a probability density function (PDF)The that number was based of beetles on the dispersed density of to healthy a particular trees in cell a cell, was the not wind limited. speed In and cells wind where direction beetles and were present,the maximum the status flight of time a percentage of the beetle. of healthyA maximum trees potential was changed travel progressivelydistance was used from to “healthy” determine to “healthya gamma but function infected” for distribution to “stressed of and distances infected” travelled. to “dead”. This distribution After the pre-oviposition was randomly sampled period, to the beetlesdetermine would the disperse direction to eitherand distance “stressed of but beetle not infected”wind dispersion. or “stressed Cumulati and infected”ve rainfall trees. was also generated,Numerous to restrict studies dispersal have reported during rain or modelled events. A spread full description rates for pinewood of this dataset nematodes development following is invasiongiven in ofour a technical new country report [14 [16].], which range from 2 to 15 km per year. The rate of spread is influenced by forestThe densitynumber andof beetles vector dispersed behaviour to witha particular the majority cell was of not vector limited. flight In being cells where shorter beetles distances were in contiguouspresent, the stands status and of a longer percentage distances of healthy where hoststrees arewas more changed dispersed progressively [5]. from “healthy” to “healthy but infected” to “stressed and infected” to “dead”. After the pre-oviposition period, the 2.9.beetles Study would Area disperse to either “stressed but not infected” or “stressed and infected” trees. Numerous studies have reported or modelled spread rates for pinewood nematodes following Two study area extents were defined: an area of approximately 160 km × 100 km encompassing invasion of a new country [14], which range from 2 to 15 km per year. The rate of spread is the Greater Melbourne area and the State of Victoria (an area of approximately 600 km × 300 km). influenced by forest density and vector behaviour with the majority of vector flight being shorter The Greater Melbourne study area was delineated based on the locations of AAs, as an area would distances in contiguous stands and longer distances where hosts are more dispersed [5]. initially be subject to pest incursion response. The datasets were developed statewide to allow the potential2.9. Study spread Area of a pest species to be modelled across a large region as well as smaller landscapes. This capability was tested to assess model performance using large datasets. PubliclyTwo study available area extents Class were 1 AA defined: addresses an area were of approximately downloaded from160 km http://agriculture.gov.au/ × 100 km encompassing import/arrival/arrangements/sitesthe Greater Melbourne area and the Stateand convertedof Victoria to (an a pointarea of data approximately layer for use 600 within km × the300 modelkm). asThe an Greater incursion Melbourne source point. study Toarea verify was thedelineated location base of thesed on the listed locations businesses, of AAs, their as addressesan area would were cross-referencedinitially be subject to onlineto pest data incursion sources response. such as telephoneThe datasets directories were developed and google statewide earth (see to [allow16] for the full detailspotential of thisspread dataset of a pest development). species to be modelled across a large region as well as smaller landscapes. This capability was tested to assess model performance using large datasets.

Forests 2018, 9, x FOR PEER REVIEW 11 of 17

Publicly available Class 1 AA addresses were downloaded from http://agriculture.gov.au /import/arrival/arrangements/sites and converted to a point data layer for use within the model as an incursion source point. To verify the location of these listed businesses, their addresses were Forestscross-referenced2019, 10, 198 to online data sources such as telephone directories and google earth (see [16]11 for of 17 full details of this dataset development).

2.10.2.10. VeriﬁcationVerification ofof ModelModel Historical Incursion Historical Incursion To test the model, we simulated a historical incursion using the suspected origin (Docklands) of To test the model, we simulated a historical incursion using the suspected origin (Docklands) of the nematode incursion in 1999 [21]. We compared the dispersal pattern derived from the model to the the nematode incursion in 1999 [21]. We compared the dispersal pattern derived from the model to 1999/2004 data based on the distance and direction of observed and modelled dispersion. the 1999/2004 data based on the distance and direction of observed and modelled dispersion. InIn latelate NovemberNovember 1999,1999, dying pine trees were ob observedserved near near the the docks docks in in Melbourne. Melbourne. The The cause cause waswas eventuallyeventually identified identiﬁed as as a alittle-known little-known nematode nematode BursaphelenchusBursaphelenchus humanensis, humanensis which, which for the for sake the sakeof this of thiscase-study case-study is assumed is assumed to have to have a similar a similar pathogenicity pathogenicity to tothe the more more common common pinewood pinewood nematodenematodeBursaphelenchus Bursaphelenchus xylophilus xylophilus, vectored, vectored by whatby what was assumed was assumed to be the toMonochamus be the Monochamus(Coleoptera: Cerambycidae)(Coleoptera: Cerambycidae) beetle. In January beetle. 2000, In January the death 2000, of the a mature death pineof a mature tree at thepine botanic tree atgardens the botanic near Williamstowngardens near Williamstown was reported withwas reported symptoms with similar symp totoms those similar of the to pine those wilt of diseasethe pine [22 wilt]. An disease initial sampling[22]. An initial scheme sampling focused scheme on all trees focused within on a all 1 km trees radius within of thea 1 Williamstownkm radius of the site, Williamstown with no nematodes site, detected.with no nematodes In May 2000, detected. another In single May 2000, dying anotherPinus spp. single was dying reported, Pinus 10spp. km was from reported the original, 10 km site from this time.the original The sampling site this radius time. wasThe extended.sampling radius Through was aerial extended. surveys Through and public aerial reporting, surveys aand total public of 285 treesreporting, were sampleda total of of285 which trees 31were nematode-infected sampled of which trees 31 nematode-infected were detected, the trees furthest were 60 detected, km from the the originalfurthest tree60 km (Figure from 7the). All original infected tree trees (Figure were 7). removedAll infected and trees destroyed. were removed The source and destroyed. was presumed The tosource be at was the docks.presumed Surveillance to be at wasthe docks. maintained Survei forllance a further was maintained 18 months; for however, a further no further18 months; trees werehowever, found no to further have the treesB. humanensis were foundnematode. to have Althoughthe B. humanensis 31 infected nematode. trees were Although initially 31 detected, infected no beetlestrees were were initially ever collected, detected, and no despite beetles extensive were ev surveys,er collected, no further and evidencedespite extensive of beetle establishmentsurveys, no orfurther infected evidence trees was of beetle found. establishment or infected trees was found.

FigureFigure 7.7. HistoricHistoric recordsrecords of trees sampled for nematodes (positive—solid (positive—solid yellow yellow dots dots and and negative negative —crosses)—crosses) fromfrom aa suspectedsuspected pinepine beetlebeetle incursionincursion inin MelbourneMelbourne duringduring 1999/2000 [21]. [21]. Concentric Concentric circlescircles correspondcorrespond toto distancesdistances ofof 1,1, 5, 10, 20, 40 and 60 km from the the or originaliginal infected infected tree tree (red (red cross). cross).

3. Results The ﬁgures below (Figures8 and9) illustrate the outputs of the beetle dispersal model based on the number of adults initially released during September 1999. We ran 2 scenarios using; Forests 2018, 9, x FOR PEER REVIEW 12 of 17

3. Results The figures below (Figures 8 and 9) illustrate the outputs of the beetle dispersal model based on the number of adults initially released during September 1999. We ran 2 scenarios using; (1) 10,000 simulated beetles for comparative analysis against the observed data; (2) 100 simulated beetles as an indication to what was thought to happen in 1999/2000. The probability dispersion function is presented in Figure 8, along with examples of dispersal distributions of 100 and 10,000 simulated beetles during October 1999. The Monochamus beetle dispersion probability density function is based on the density of healthy trees in a cell, the average wind speed and wind direction, and the maximum flight time of the beetle. Locations where pine tree death was positively linked with nematode presence based on observed data in Hodda et al. [21] are shown in Figure 9. The results from modelling 10,000 beetle dispersal events showed strong similarity in both distance and direction from the suspected 1999 origin (release point) (Figures 10 and 11). A Forests 2019, 10, 198 12 of 17 comparison between the modelled and observed beetle dispersal as a distance from the central AA (Figure 12) shows no significant difference between the two datasets (two-sample t-test statistic F = (1)1.74, 10,00028 degree’s simulated of freedom; beetles p for Value comparative = 0.02). Similarly, analysis againsta 2- tailed the Mann-Whitney observed data; test indicated that (2)the angle100simulated from the assumed beetles as release an indication point was to whatnot significantly was thought different to happen (U in= 124,934, 1999/2000. p Value = 0.19, significance > 0.05)).

Figure 8.8. Observed positive nematode locations (yellow dots) and modelled beetle dispersion (black dots) of 100 adults from the Melbourne Ports area,area, based on median October wind speed (2010–2018) fromfrom thethe Melbourne Melbourne (Olympic (Olympic Park) Park) weather weather station. station. The colour The colour bar illustrates bar illustrates the PDF thatthe underpinsPDF that beetleunderpins dispersal, beetle based dispersal, on the based wind on speed the wind and direction,speed and tree direction, density tree and density maximum and beetlemaximum fight beetle time. fight time. The probability dispersion function is presented in Figure8, along with examples of dispersal distributions of 100 and 10,000 simulated beetles during October 1999. The Monochamus beetle dispersion probability density function is based on the density of healthy trees in a cell, the average wind speed and wind direction, and the maximum flight time of the beetle. Locations where pine tree death was positively linked with nematode presence based on observed data in Hodda et al. [21] are shown in Figure9. The results from modelling 10,000 beetle dispersal events showed strong similarity in both distance and direction from the suspected 1999 origin (release point) (Figures 10 and 11). A comparison between the modelled and observed beetle dispersal as a distance from the central AA (Figure 12) shows no significant difference between the two datasets (two-sample t-test statistic F = 1.74, 28 degree’s of freedom; p Value = 0.02). Similarly, a 2- tailed Mann-Whitney test indicated that the angle from the assumed release point was not significantly different (U = 124,934, p Value = 0.19, significance > 0.05). ForestsForests2019 2018, 10, 9, 198x FOR PEER REVIEW 13 ofof 1717 Forests 2018, 9, x FOR PEER REVIEW 13 of 17

Figure 9. Comparison of observed positive nematode locations (yellow dots) and 10,000 modelled Figure 9. Comparison of observed positive nematode locations (yellow dots) and 10,000 modelled Figuredispersed 9. Comparison beetles (small of observedblack dots). positive nematode locations (yellow dots) and 10,000 modelled dispersed beetles (small black dots). dispersed beetles (small black dots).

FigureFigure 10. 10.Percentages Percentages of of observations observations for for both both observed observed nematode nematode records records (n = 29) (n and= 29) modelled and modelled beetle Figurerecordsbeetle 10. records (n Percentages= 10,000) (n =according 10,000) of observations according to the cardinal tofor the both direction cardinal observedfrom directio nematode then initial from records release the initial point(n =release 29) in Port and point Melbourne. modelled in Port beetleMelbourne. records (n = 10,000) according to the cardinal direction from the initial release point in Port Melbourne.

Forests 2018, 9, x FOR PEER REVIEW 14 of 17

ForestsForests 20182019, ,910, x, FOR 198 PEER REVIEW 1414 of of 17 17

Figure 11. Average distances of records from the initial release point in Port Melbourne by cardinal direction for both observed nematode records (n = 29) and modelled beetle records (n = 10,000).

Figure 11. Average distances of records from the initial release point in Port Melbourne by cardinal Figure 11. Average distances of records from the initial release point in Port Melbourne by cardinal direction for both observed nematode records (n = 29) and modelled beetle records (n = 10,000). direction for both observed nematode records (n = 29) and modelled beetle records (n = 10,000).

(a) (b)

Figure 12. Boxplot comparisons of distance (a) and angle (b) of modelled and observed Figure 12. Boxplot comparisons of distance (a) and angle (b) of modelled and observed beetles/nematodes(a) from the assumed release point. (b) beetles/nematodes from the assumed release point. 4. Discussion Figure 12. Boxplot comparisons of distance (a) and angle (b) of modelled and observed 4. DiscussionOur forest pest dispersal model accurately simulated the distance and direction of the historic beetles/nematodes from the assumed release point. 1999/2000Our forest incursion. pest dispersal In the 100 model simulated accurately beetle simula scenarios,ted the pines distance were infectedand direction with theof the nematode; historic 4.however,1999/2000 Discussion thereincursion. was noIn successfulthe 100 simulated mating ofbeetle any sc ofenarios, the females, pines andwere the infe populationcted with diedthe nematode; out. This appearshowever, to there be what was happened no successful with mating the historical of any incursion of the females, as well and (D. Smiththe populationpers. comm. diedJune out. 2018). This appearsOurOur toforest model be what pest also happeneddispersal enabled model uswith to determinethe accurately historical the simula incursion numberted the ofas adultswelldistance (D. in Smith theandinitial direction pers. releasecomm. of theJune that historic 2018). would 1999/2000be required incursion. to have aIn potential the 100 simulated mating couple beetle within scenarios, a cell pines (area: were 100 minfe×cted100 m).with For the example, nematode; for however,100 adults there dispersing was no on successful the median mating October of any wind of speed, the females, there is and less the than population a 20% chance died that out. at This least appears to be what happened with the historical incursion as well (D. Smith pers. comm. June 2018).

Forests 2018, 9, x FOR PEER REVIEW 15 of 17

Our model also enabled us to determine the number of adults in the initial release that would be required to have a potential mating couple within a cell (area: 100 m × 100 m). For example, for Forests 2019, 10, 198 15 of 17 100 adults dispersing on the median October wind speed, there is less than a 20% chance that at least one mating couple (male and female) would disperse to the same cell. If 400 beetles were released,one mating it would couple be (male likely and that female) at least would three disperse cells would to the end same up cell. with If 400at least beetles one were mating released, couple it (Figurewouldbe 13). likely that at least three cells would end up with at least one mating couple (Figure 13).

Figure 13. Relationship between the the number of adult beet beetlesles in in the the initial release and the number of cells with a least one mating couple in a 100 m × 100100 m m cell. cell.

Despite multiplemultiple interceptionsinterceptions ofofM. M. alternatus alternatusat at Australia’s Australia’s border border [6], [6], there there is no is evidenceno evidence that thatthe beetlethe beetle has establishedhas established [5]. Our[5]. Our model model not onlynot only models models the dispersalthe dispersal of M. of alternatusM. alternatus, but, but also also the thelife cycle,life cycle, population population growth, growth, and status and status of host of plants. host Althoughplants. Although in the modelled in the modelled historic incursion historic incursionno successful no successful matings occurred matings andoccurred there wasand nother futuree was generation,no future generation, the model hasthe themodel capacity has the to capacitycapture thisto capture scenario this if plausible.scenario if The plausible. model canThe be model adapted can relativelybe adapted easily relatively for other easily forestry for other pest forestryinsect species pest insect (e.g., changingspecies (e.g., the changing degree day the model, degree flight day model, times) andflight can times) account and for can multi-voltine account for multi-voltinespecies (many species generations (many per generations year). per year). Although the modelmodel utilisesutilises historic historic wind wind data data to to produce produce an an average average monthly monthly wind wind speed speed and anddirection, direction, the model the model has the has capacity the capacity to incorporate to incorpor real-timeate real-time as well as as well predictive as predictive climatic climatic data inputs. data inputs.The model The thus model has thus the capabilityhas the capa to bebility used to forbe real-lifeused for incursion real-life incursion responses. responses. The dispersal The model dispersal has modelonly been has verified only been against verified one species against and one at species one location and andat one time location in Victoria. and Intime addition, in Victoria. although In addition,the model although has used the extensivemodel has international used the extens researchive oninternational this species research to develop on the this life species cycle and to developdegree day the model,life cycle this and component degree day has model, not been this fully component verified. has not been fully verified. Although the model accurately simulated the maximum dispersal distance observed in the 1999/2000 incursion incursion (60 km), thisthis dispersaldispersal distancedistance is is excessive excessive compared compared to to the the observed observed dispersal dispersal in inEurope Europe and and North North America, America, which which range range from from 2 to 2 15 to km 15 perkm yearper year [14]. [14]. Our modelOur model appears appears to have to havea conformational a conformational bias basedbias based on the on knowledge the knowledge of the of Victorian the Victorian incursion. incursion. Further Further refinement refinement could couldinclude include evaluating evaluating the model the model against against European Euro incursionpean incursion information information and modifying and modifying the gamma the gammadispersal dispersal function function as needed. as However,needed. However, this is dependent this is dependent on the availability on the ofav suitableailability high-quality of suitable high-qualityquantitative dataquantitative on yearly data spread on ye ofarly the spread target pest.of the target pest. One componentcomponent whichwhich has has not not been been included included in thein modelthe model was upwindwas upwind movement movement of the beetlesof the beetlesduring hostduring searching host searching [23]. The [23]. lack The of quantitative lack of quantitative data of this data movement of this movement for Monochamus for Monochamuslimited our limitedabilityto our include ability it to in include the dispersal it in the sub-module. dispersal sub-module. The establishment ofof anyany newnew pestpest intointo Australia Australia is is likely likely to to add add substantial substantial costs costs to to industry, industry, as ashas has been been the casethe case for thefor sirex the woodsirex wood wasp, wasp,Sirex noctilio Sirex noctilio[24]. Thus, [24]. early Thus, detection early detection and initial and incursion initial incursionmanagement management is far more cost-effectiveis far more thancost-effecti managingve themthan oncemanaging they are them well established.once they Havingare well a established.predictive dispersal Having modela predictive in place dispersal will enhance model Victoria’s in place ability will enhance to respond Victoria’s and manage ability new to respond forestry andpest manage incursions. new forestry pest incursions. Future work and development of the model could focus on validating the life cycle and degree day sub-model, possibly against overseas data and locations. The dispersal sub-model could also be used toto illustrateillustrate the the spread spread within within a high-pine-density a high-pine-density region region (such as (such a plantation), as a plantation), or the “escape” or the of pre-mated beetles from an AA. In addition, modelling other pest species (including pathogen spread) could be investigated or developed for different regions of Australia. This model could also be used Forests 2019, 10, 198 16 of 17 to evaluate the success of any forestry pest trapping system and compare the efficiency of different trapping grids (fixed grid, random, clustering around “hot spots” or high-priority-risk areas).

5. Conclusions This modelling platform has a strong potential to enhance our ability to respond to and manage new forestry pest incursions, in this case, at a local, regional or state level. With minor modiﬁcations to parameters within the model, it can be applied to other windborne insect species and pathogens. It can support managers in planning and evaluating their responses to potential incursions of forestry pests and be used to evaluate the effectiveness and efﬁciency of different trapping systems and grids. It also has the potential to support real-time incursion management and monitoring.

Author Contributions: J.W., K.S., A.W. and D.S. all significantly developed the conceptualization and methodology of the project and wrote components of the original report; A.W. principally developed and coded the model; K.S. and D.S. produced the relevant GIS layers for the model; J.W. and K.S. analyzed the results and verification of the model; J.W. was the principal author whilst all the authors contributed significantly to the whole paper and all were involved in reviewing and editing the paper. Funding: This work was funded by DEDJTR’s Chief Plant Health Officer Unit (CPHOU). Acknowledgments: We would like to acknowledge the input from DAWR’s Ranjith Subasinghe and AVR’s Tony Dugdale who provided critical comments on the original report. We would also like to thank the two reviewers whose comments and suggestions improved the final manuscript. Conflicts of Interest: The authors declare no conflicts of interest.

References

1. DAWR. National Forest Biosecurity Surveillance Strategy; Plant Health Australia: Canberra, Australia, 2018; p. 32. 2. Mohammed, C.; Walshe, T.; Wardlaw, T.; Stone, C.; Beadle, C.; Lawson, S. An Audit of Forest Biosecurity Arrangements and Preparedness in Australia; Forest & Wood Products Australia Ltd.: Melboune, Australia, 2011; p. 109. 3. Bashford, R. The development of a port surrounds trapping system for the detection of exotic forest insect pests in Australia. In New Advances and Contributions to Forestry Research; IntechOpen: Rijeka, Croatia, 2012. 4. Carnegie, A. A decade of forest health surveillance in Australia: An overview. Aust. For. 2008, 71, 161–163. [CrossRef] 5. Carnegie, A.J.; Venn, T.; Lawson, S.; Nagel, M.; Wardlaw, T.; Cameron, N.; Last, I. An analysis of pest risk and potential economic impact of pine wilt disease to Pinus plantations in Australia. Aust. For. 2018, 81, 24–36. [CrossRef] 6. Lawson, S.A.; Carnegie, A.J.; Cameron, N.; Wardlaw, T.; Venn, T.J. Risk of exotic pests to the Australian forest industry. Aust. For. 2018, 81, 3–13. [CrossRef] 7. Fan, J.T. Invasive Species Compendium. Datasheet Report for Monochamus alternatus (Japanese pine sawyer). Available online: https://www.cabi.org/isc/datasheet/34719 (accessed on 23 February 2018). 8. Australian Government Department of Agriculture and Water Resources. National Priority Plant Pests 2016. Available online: http://www.agriculture.gov.au/pests-diseases-weeds/plant/national-priority- plant-pests-2016 (accessed on 23 February 2018). 9. Akbulut, S.; Linit, M.J. Flight performance of Monochamus carolinensis (Coleoptera: Cerambycidae) with respect to nematode phoresis and beetle characteristics. Environ. Entomol. 1999, 28, 1014–1020. [CrossRef] 10. Togashi, K.; Shigesada, N. Spread of the pinewood nematode vectored by the Japanese pine sawyer: Modeling and analytical approaches. Popul. Ecol. 2006, 48, 271–283. [CrossRef] 11. Mota, M.M.; Vieira, P. Pine Wilt Disease: A Worldwide Threat to Forest Ecosystems; Springer, Dordrecht: Tokyo, Japan, 2008. 12. Wang, Q.E. Cerambycidae of the World: Biology and Pest Management; CRC Press: Boca Raton, FL, USA, 2017. 13. Akbulut, S.; Stamps, W. Insect vectors of the pinewood nematode: A review of the biology and ecology of Monochamus species. For. Pathol. 2012, 42, 89–99. [CrossRef] Forests 2019, 10, 198 17 of 17

14. Grimm, V.; Berger, U.; Bastiansen, F.; Eliassen, S.; Ginot, V.; Giske, J.; Goss-Custard, J.; Grand, T.; Heinz, S.K.; Huse, G. A standard protocol for describing individual-based and agent-based models. Ecol. Model. 2006, 198, 115–126. [CrossRef] 15. Grimm, V.; Berger, U.; DeAngelis, D.L.; Polhill, J.G.; Giske, J.; Railsback, S.F. The ODD protocol: A review and first update. Ecol. Model. 2010, 221, 2760–2768. [CrossRef] 16. Weiss, J.; Sheffield, K.; Weeks, A.; Smith, D. Modelling the Incursion and Spread of a Forestry Pest within Victoria; Technical report, June 2018; Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources: Melbourne, Australia, 2018; p. 49. [CrossRef] 17. Jeffrey, S.J.; Carter, J.O.; Moodie, K.B.; Beswick, A.R. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Softw. 2001, 16, 309–330. [CrossRef] 18. Stephens, P.A.; Sutherland, W.J.; Freckleton, R.P. What is the allee effect? Oikos 1999, 87, 185–190. [CrossRef] 19. Coop, L. Monochamus alternatus (Japanese Pine Sawyer Beetle) Degree-Day Model Documentation. Available online: http://uspest.org/wea/Monochamus_alternatus_model.pdf (accessed on 23 February 2018). 20. Bodart, J. Flight activity, oviposition pit distribution, and emergence densities of Monochamus titillator and M. carolinensis in the Ozark-St. Francis national forest in Arkansas; University of Arkansas: Fayetteville, AR, USA, 2017. 21. Hodda, M.; Smith, D.; Smith, I.; Nambiar, L.; Pascoe, I. Incursion management in the face of multiple uncertainties: A case study of an unidentified nematode associated with dying pines near Melbourne, Australia. In Pine Wilt Disease: A Worldwide Threat to Forest Ecosystems; Mota, M.M., Vieira, P., Eds.; Springer: Dordrecht, The Netherlands, 2008; pp. 15–40. 22. Evans, H.F.; McNamara, D.G.; Braash, H.; Chadoeuf, J.; Magnusson, C. Pest risk analysis (PRA) for the territories of the European Union (as PRA area) on Bursaphelenchus xylophilus and its vectors in the genus Monochamus. EPPO Bull. 1996, 26, 199–249. [CrossRef] 23. Allison, J.D.; Borden, J.H.; Seybold, S.J. A review of the chemical ecology of the Cerambycidae (Coleoptera). Chemoecology 2004, 14, 123–150. [CrossRef] 24. Cameron, N.L.; Carnegie, A.J.; Wardlaw, T.; Lawson, S.; Venn, T. Economic appraisal of sirex wood wasp (Sirex noctilio) control in Australian pine plantations. Aust. For. 2018, 81, 37–45. [CrossRef]

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