Evaluation of Phenological Indicators for Optimizing Spring

Evaluation of Phenological Indicators for Optimizing

Spring Southern Pine Beetle (Coleoptera: Curculionidae:

Scolytinae) Trapping Surveys

Authors: Thomason, John W., Clarke, Stephen, and Riggins, John J.

Source: Florida Entomologist, 103(4) : 444-451

Published By: Florida Entomological Society

URL: https://doi.org/10.1653/024.103.00405

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Mississippi State University, Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State, Mississippi 39762, USA;

E-mail: jthomason@entomology.mssstate.edu (J. W. T.), jriggins@entomology.msstate.edu (J. J. R.)

USDA-Forest Service, Forest Health Protecon, Luin, Texas 75904, USA; E-mail: stephen.clark[email protected] (S. C.)

*Corresponding author; E-mail: jriggins@entomology.msstate.edu

444 2020 — Florida Entomologist — Volume 103, No. 4

Evaluaon of phenological indicators for opmizing

spring southern pine beetle (Coleoptera: Curculionidae:

Scolynae) trapping surveys

John W. Thomason

, Stephen Clarke

, and John J. Riggins

Abstract

Since 1987, as many as 16 southeastern US states parcipate in a 4 wk annual spring Dendroctonus frontalis (Zimmerman) (Coleoptera: Curculioni-

dae) trapping survey. The purpose of the survey is to assess the current D. frontalis outbreak potenal, and ancipate prevenon and suppression

needs for the coming yr. This predicon system relies on capturing the peak D. frontalis spring dispersal, thus ming of trap deployment is crucial.

Forest managers tradionally aempt to deploy traps at the onset of owering dogwood (Cornus orida L.; Cornaceae) bloom, which is commonly

assumed to coincide with peak D. frontalis spring dispersal. The objecve of this study is to examine the validity of dogwood bloom as an indicator

of peak D. frontalis spring dispersal. Yr-round trapping data in 2014 and 2015 from Mississippi and Florida were used to idenfy peak D. frontalis

and Thanasimus dubius (Fabricius) (Coleoptera: Cleridae) dispersal periods. Peak D. frontalis dispersal then was compared with dogwood bloom-

ing dates from the USA Naonal Phenology Network and personal records. Then, both dogwood bloom dates and peak D. frontalis dispersal were

compared with ming of actual historic state D. frontalis trapping eorts. We also compared peak D. frontalis dispersal with T. dubius peak dispersal,

because T. dubius trap captures are used in the predicon model. Last, we examined the ulity of extending the spring survey to 6 wk by comparing

the 4 wk peak D. frontalis trap captures with a corresponding 6 wk peak. On average, mean onset of dogwood bloom occurred 3 wk aer the peak

4 wk period of D. frontalis ight acvity. The average T. dubius peak dispersal occurred 1.5 wk aer peak D. frontalis dispersal. The 6 wk extension

provided only a 12% overall average increase in D. frontalis trap captures. Eastern redbud (Cercis canadensis L.; Fabaceae) also had been suggested

as a replacement trap deployment cue; therefore, eastern redbud and owering dogwood blooming dates in 2019 were monitored on a Mississippi

State University property in Okbbeha County, Mississippi, USA. On this site eastern redbud trees bloomed on average 2.3 wk before the average

bloom date of owering dogwood trees.

Key Words: bloom; Cercis canadensis; Cornus orida; Dendroctonus frontalis; monitoring

Resumen

Desde el 1987, hasta 16 de los estados del sureste de los Estados Unidos han parcipado en un sondeo anual de captura de Dendroctonus frontalis

(Zimmerman) (Coleoptera: Curculionidae) por 4 semanas en la primavera. El propósito del sondeo es evaluar el potencial actual de brote de D.

frontalis y ancipar las necesidades de prevención y supresión para el próximo año. Este sistema de predicción se basa en capturar el pico de disper-

sión de D. frontalis en la primavera, por lo que el momento del despliegue de la trampa es crucial. Los administradores forestales tradicionalmente

intentan desplegar trampas al inicio de la oración del cornejo (Cornus orida L.; Cornaceae), que comúnmente se supone que coincide con el pico

de dispersión de D. frontalis en la primavera. El objevo de este estudio es examinar la validez de la oración del cornejo como indicador del pico

de dispersión de D. frontalis en la primavera. Se ulizaron datos de captura de todo el año en el 2014 y 2015 de Mississippi y Florida para idencar

los períodos de pico de dispersión de D. frontalis y Thanasimus dubius (Fabricius) (Coleoptera: Cleridae). Luego, se comparó el pico de dispersión de

D. frontalis con las fechas de oración del cornejo de la Red Nacional de Fenología de EE.UU. y los registros personales. Luego, se compararon las

fechas de oración del cornejo y la dispersión máxima de D. frontalis con el cronometraje del estado histórico real de los esfuerzos de captura de D.

frontalis. También, comparamos el pico de dispersión de D. frontalis con el pico de dispersión de T. dubius, porque las capturas de trampa de T. dubius

se ulizan en el modelo de predicción. Por úlmo, examinamos la ulidad de extender el sondeo de la primavera a 6 semanas comparando los picos

de las capturas de trampa de D. frontalis de 4 semanas con los picos correspondientes de 6 semanas. Por general, el inicio de la oración del cornejo

empieza 3 semanas después del pico de período de 4 semanas de acvidad de vuelo de D. frontalis. El promedio del pico de dispersión de T. dubius

ocurrió 1.5 semanas después del pico de dispersión de D. frontalis. La extensión de 6 semanas proporcionó solo un aumento promedio general del

12% en las capturas de trampas de D. frontalis. También, se había sugerido el ciclamor de Canadá (Cercis canadensis L.; Fabaceae) como señal para

desplegar el reemplazo de la trampa; por lo tanto, las fechas de oración de ciclamor de Canadá y cornejo en oración en el 2019 se monitorearon

en una propiedad de la Universidad Estatal de Mississippi en el condado de Okbbeha, Mississippi, EE. UU. En este sio, los árboles de ciclamor de

Canadá orecieron en un promedio de 2.3 semanas antes de la fecha promedio de oración de los árboles de cornejo.

Palabras Clave: oración; Cercis canadensis; Cornus orida; Dendroctonus frontalis; monitoreo

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Thomason et al.: Phenological indicators of Dendroctonus frontalis trapping 445

The ability to understand and predict key life history events for insect

pests plays a vital role in their management (Ham & Hertel 1984; Clarke

et al. 2016). For instance, the pales weevil, Hylobius pales (Herbst) (Co-

leoptera: Curculionidae) is aracted to fresh cut pine stands. If a stand

is replanted too early, their brood can decimate the newly planted pine

seedlings. Forest managers may employ a silvicultural control tacc of cut-

ng early in the yr (before Jul) and planng the following winter, allowing

the pales weevil to complete its life cycle and leave the stand before the

seedlings are planted (Nord et al. 1984). The Nantucket pine p moth,

Rhyacionia frustrana (Comstock) (Lepidoptera: Tortricidae) can be a pest

of young pine and Christmas tree plantaons. Inseccide sprays may be

used to control their populaons (Berisford et al. 1984). However, eecve

control requires nursery managers to be able to predict R. frustrana egg

hatch and larval development in order to me the applicaon of insec-

cides to coincide with these life history events (Douce et al. 2002). The

aforemenoned examples express why researchers have developed pre-

dicve models (Gargiullo et al. 1985; Kumral et al. 2007; Knutson & Mueg-

ge 2010; Akotsen-Mensah et al. 2011; Haavik et al. 2013) or observaonal

cues (Mussey & Poer 1997; Herms 2004; Reding et al. 2013; Hartshorn

et al. 2016 ) to predict key life history events for many serious insect pests.

The southern pine beetle, Dendroctonus frontalis (Zimmerman) (Co-

leoptera: Curculionidae), can be a severe pest of all southern pine spe-

cies, but most notably loblolly pine (Pinus taeda L.), shortleaf pine (Pinus

echinata Mill.), slash pine (Pinus ellioi Englm.), and longleaf pine (Pinus

palustris Mill.) (all Pinaceae) (Payne 1980; Blanche et al. 1983). Approxi-

mately 84% of total pine mber losses across 10 southeastern states of

the US between 1977 and 2004 can be aributed to 4 large D. frontalis

outbreaks (Pye et al. 2004).

A regional annual D. frontalis risk assessment survey was developed

in the 1980s and became an important part of the integrated pest man-

agement strategy developed for D. frontalis (Billings 1988; Billings & Up-

ton 2010). The survey is an early warning system used to predict the D.

frontalis populaon status and infestaon trends for the current yr. This

predicon allows forest managers to appropriate adequate resources to

address potenal D. frontalis outbreaks (Billings & Upton 2010). The sur-

vey is conducted currently over a consecuve 4 wk period in the spring

using Lindgren 12-unit funnel traps (Lindgren 1983) baited with polyeth-

ylene bags containing 70% α and 30% β pinene (released at about 5 g

per d) along with D. frontalis aggregaon pheromones frontalin (released

at about 5 mg per d) and endo-brevicomin (Billings 2011; Sullivan 2016).

State and federal forest agencies deploy traps in the host pine forests

throughout each of the 16 parcipang states (Billings 2011). Trap catches

are collected weekly, and numbers of D. frontalis and their most signicant

invertebrate predator, the checkered clerid beetle, Thanasimus dubius (Fa-

bricius) (Coleoptera: Cleridae), are tallied. The mean number of D. frontalis

per trap per d and the rao of D. frontalis to T. dubius are used to derive a

predicon for a given locality. The predicons were inially obtained from

a chart developed and revised by the Texas Forest Service (Billings & Upton

2010).

The survey’s ability to accurately assess D. frontalis populaon lev-

els has been variable in recent yr. In Mississippi, outbreaks occurred on

the Homochio Naonal Forest in 2012, the Tombigbee Naonal For-

est in 2014, and the Bienville Naonal Forest in 2015 (Asaro et al. 2017);

however, the survey projected populaon trend/levels to be stac/low,

decreasing/moderate, or increasing/low, respecvely, for each outbreak

occurrence (Table 1). A variety of factors may aect the predicve power

of the survey. The chemistry of lures used was changed in 2007, because

polyethylene bags of (70% α-pinene to 30% β-pinene) replaced steam-

dislled turpenne volalized from a wicked bole as the host compound

component (Billings 2011). This change was due to a lack of commercially

available sources of turpenne (Sullivan 2016). Endo-brevicomin, which

synergizes the aracveness of the frontalin lure (Sullivan & Mori 2009), is

now also included (Billings 2017). In addion, trap placement recommen-

daons have changed, because traps must be placed 20+ m from the near-

est host pine to reduce the risk of spillover aacks on adjacent pines now

that endo-brevicomin is used (Stephen Clarke, personal communicaon).

In addion to the factors detailed above, the trap ming is important

in ensuring an accurate assessment of exisng spring D. frontalis popula-

on levels. Trap deployment must coincide with the peak of D. frontalis

spring ight acvity (Billings & Upton 2010). Spring D. frontalis ight acv-

ity generally occurs within a 3 mo me frame, usually with a 3 to 6 wk peak

period (Friedenberg et al. 2007). Predicng the peak is dicult because

all life stages of D. frontalis overwinter (Lombardero et al. 2000). Further

development or even emergence can occur during periods of favorable

winter temperatures (Moser & Dell 1979). Climate change may also aect

the ming of bark beetle spring dispersal ight (Jönsson et al. 2009; Mil-

ton & Ferrenberg 2012). Mulple emergence peaks due to variable spring

temperatures may inuence populaon levels in subsequent mo because

they may aect the ability of D. frontalis to allocate a sucient number of

beetles to mass aack pines and iniate an infestaon (Friedenberg et al.

2007). Therefore, it is crucial that forest managers have a praccal means

of predicng the peak or peaks of spring dispersal by D. frontalis.

Peak spring dispersal of D. frontalis has been anecdotally associated

with the blooming phenology of various indigenous tree species (Hopkins

1909). Flowering of eastern redbud (Cercis canadensis L.; Fabaceae) (St.

George & Beal 1929), pollen release of loblolly pine (P. taeda) (Billings

1988), and owering of owering dogwood (Cornus orida L.; Cornaceae)

(Thatcher & Barry 1982; Billings 1988) all have been suggested as indica-

tors for the onset of peak D. frontalis spring dispersal. The onset of ow-

ering dogwood bloom was the protocol for trap deployment of the an-

nual spring D. frontalis risk assessment survey for several decades (Billings

1988). Because the actual owers of owering dogwood are inconspicu-

ous, the onset of bloom refers to the white bracts that open before the

ower buds. In 2017, regional spring trapping guidelines from the USDA

Forest Service were revised to suggest the use of the bloom of redbuds

instead of dogwoods as a phenological cue for peak D. frontalis spring

dispersal (Billings 2017).

The synchronicity between peak spring D. frontalis dispersal and

the phenology of local tree species have been based solely on obser-

vaons, and analyses to assess these claims are lacking. Sub-opmal

ming of trapping may have contributed to the recent failures of the

annual survey to accurately predict local outbreaks. Therefore, we con-

ducted studies to (1) quanfy if recent survey dates were opmally

med to encompass the peak in D. frontalis spring dispersal, (2) deter-

Table 1. Dendroctonus frontalis outbreaks in Mississippi and the predicon results from the annual spring survey.

Outbreak

Dendroctonus frontalis

per trap per d

Clerids

per trap per d Predicon New spots

Homochio 2012 6 0.8 11.5 Stac/low 793

Tombigbee 2014 18 15.5 69.2 Decreasing/ moderate 180

Bienville 2015 33 2.5 5 Increasing/low 238

Spot data collected from the Southern Pine Beetle Informaon System.

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446 2020 — Florida Entomologist — Volume 103, No. 4

mine if the onset of owering dogwood bloom is a good predictor of

peak D. frontalis ight acvity, (3) compare bloom phenology of red-

bud and dogwood trees, and (4) evaluate the viability of a 4 to 6 wk

survey for describing D. frontalis spring ight acvity.

Materials and Methods

DENDROCTONUS FRONTALIS TRAPPING

We conducted yr-round D. frontalis trapping to monitor flight

activity to identify when peak spring dispersal occurred for a giv-

en location. Two Mississippi locations (Oktibbeha County and Ho-

mochitto National Forest) and 1 in Alachua County, Florida, USA,

were surveyed; henceforth these trapping locations will be referred

to as Oktibbeha, Homochitto, and Alachua. There were 2 trap sites

in 2014 and 5 trap sites in 2015 in Oktibbeha (Table 2). The number

of trapping sites remained constant throughout the study period for

both Alachua and Homochitto, with 1 and 3 sites, respectively. The

traps remained deployed for the entirety of 2014 and 2015 except

in 2014 on the Homochitto, when the traps were taken down on 6

May 2014 and redeployed 1 Jan 2015. The extended trap deploy-

ment required lures to be changed every 4 wk to ensure the baits

remained attractive to D. frontalis. All Lindgren 12-funnel traps

were baited identically to the annual spring survey traps. All lures

were purchased from Synergy Semiochemicals Corporation (Van-

couver, British Columbia, Canada). The frontalin and pinene lures

were affixed to the trap while the endo-brevicomin was attached to

a twig approximately 4 m from the trap (Sullivan & Mori 2009). All

trap locations were 20+ m from the nearest pine in hardwood bot-

tom lands adjacent to pine stands. Traps were hung so that the col-

lection cups were approximately 1 to 2 m above the ground, main-

taining uniformity with the spring survey trapping. All trap captures

were collected weekly.

For the purposes of this study, we considered the potenal spring

ight season of D. frontalis to occur between 1 Jan and 31 May of each

yr. This 5 mo range was early enough to capture the earliest late winter

ights (Moser & Dell 1979), and long enough to allow the spring dis-

persal ight to conclude. The weekly D. frontalis and T. dubius captures

from yr-round traps were tallied and recorded by locaon and trap.

Then all traps in a locaon were summed to provide the weekly total

of D. frontalis and T. dubius trap captures for each locaon. The peak D.

frontalis and T. dubius spring dispersal period was dened as the con-

nuous 4 wk period that the observed trap captures were greater than

any other connuous 4 wk period. Weekly totals also were converted

to a percentage of the 5 mo total spring trap captures to evaluate the

yearly variaon in populaon size (Akotsen-Mensah et al. 2011). The

percentage of all beetles collected during the peak period was calcu-

lated.

PEAK DISPERSAL WEEKS VS. SURVEY TRAPPING DATES

The annual 4 wk spring survey trapping dates conducted in Missis-

sippi and Florida by state and federal government agencies were com-

pared to the peak trap captures to determine if the surveys coincided

with the peak D. frontalis spring dispersal. Only surveys conducted in

the same or adjacent counes to the yr-round trapping sites were used

in the analyses.

DENDROCTONUS FRONTALIS DISPERSAL VS. FLOWERING

DOGWOOD BLOOM

Peak D. frontalis spring dispersal periods were compared also to

owering dogwood blooming dates. Because D. frontalis spring disper-

sal periods vary greatly at dierent latudes (Billings & Upton 2010),

trapping sites were compared only to owering dogwood bloom phe-

nology sites within the same plant hardiness zone. We used the 2012

USDA plant hardiness zone map (hps://planthardiness.ars.usda.

gov/), which at that me was the most current. Both Homochio and

Alachua trap sites were in zone 8B, whereas Okbbeha trap sites were

in zone 8A.

We obtained owering dogwood blooming dates from 3 sources.

One source was the USA Naonal Phenology Network (www.usanpn.

org), which provided bloom phenology across the southeastern US for

both yr of the study. Another source was the Dogwood Bloom Watch

Blog (hp://dogwoodbloomwatch.blogspot.com), which provided

me stamped photographs depicng dogwood bloom phenology

along with a wrien assessment on the progression of dogwood bloom

in the Davey Dogwood Park in Palesne, Texas, USA. We also moni-

tored and recorded dogwood blooming dates for 2015 in Okbbeha

County, Mississippi. These records consisted of tagging and monitor-

ing a patch of owering dogwoods (33.475129°N, 88.793119°W) in an

unmanaged woodlot on the periphery of the Thad Cochran Research,

Technology & Economic Development Park at Mississippi State Univer-

sity, in Starkville, Mississippi, USA. Trees were monitored from 8 Mar

to 4 Apr and checked at least twice per wk unl mostly in full bloom.

All 3 sources were used to determine the median date of the onset of

dogwood bloom, which for the purposes of this research was the earli-

est date for a tree to have at least 1 bud displaying white bracts. For

plant hardiness zone 8A there were 6 records for dogwood bloom in

2014 and 3 records in 2015. For plant hardiness zone 8B there were 3

records for dogwood bloom in 2014 and 2 records in 2015.

FLOWERING DOGWOOD BLOOM VS. EASTERN REDBUD BLOOM

Bloom dates of owering dogwood and eastern redbud were moni-

tored during spring 2019 at the same unmanaged woodlot (Okbbeha

County, Mississippi) that owering dogwood bloom was monitored

in 2015. Twenty-ve trees of each species were tagged on 1 Feb and

monitored for bloom every 2 to 3 d unl 29 Mar when the last tagged

tree had 1 or more blooms. Because the recommended use of red-

bud bloom as an indicator for D. frontalis survey ming was a recent

development, we did not have the resources to monitor yr-round D.

frontalis traps at the me. Though we could not directly compare red-

bud bloom, dogwood bloom, and peak D. frontalis dispersal, we were

able to examine the phenological relaonship between eastern redbud

bloom and owering dogwood bloom.

UTILITY OF A 4 TO 6 WEEK TIMEFRAME TO DESCRIBE DEN-

DROCTONUS FRONTALIS FLIGHT ACTIVITY IN THE SPRING

The percentage of D. frontalis captured during the 4 wk period of

peak dispersal was calculated for each site and yr, as well as the num-

Table 2. Trap site coordinates for monitoring 2014 and 2015 yr-round Dendroc-

tonus frontalis ight acvity.

Trap

Okbbeha,

Mississippi

Homochio,

Mississippi

Alachua,

Florida

1 33.367°N, 88.861°W 31.392°N, 91.054°W 29.743°N, 82.468°W

2 33.342°N, 88.880°W 31.406°N, 91.130°W

3 33.306°N, 88.906°W

31.458°N, 91.193°W

4 33.469°N, 88.905°W

5 33.606°N, 88.947°W

These traps were added in 2015.

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Thomason et al.: Phenological indicators of Dendroctonus frontalis trapping 447

ber of apparent ight peaks. Given that the ecacy of the lures can

persist for up to 6 wk, we also examined if lengthening the me frame

enabled the survey to cover mulple peaks, if present. The maximum

percentage of D. frontalis collected in any 6 wk period was calculated.

Results

PEAK DENDROCTONUS FRONTALIS DISPERSAL WEEKS VS.

SURVEY TRAPPING DATES

Compared to the consecuve 4 wk peak spring dispersal period,

annual state and federal spring surveys began an average of 3 wk aer

the start of peak D. frontalis spring dispersal (Table 3; Fig. 1). The sur-

vey and 4 wk peak ight acvity coincided only once, in 2014 on the

Homochio Naonal Forest (Fig. 1). In 2 instances, peak ight had con-

cluded prior to the survey (Okbbeha and Alachua 2015), and once the

survey iniated and concluded before peak dispersal began (Alachua

2014). Peak ight acvity began earlier in 2015 than in 2014: 1 wk on

the Homochio, 8 wk in Okbbeha, and 6 wk in Alachua.

DENDROCTONUS FRONTALIS DISPERSAL VS. FLOWERING

DOGWOOD BLOOM

Overall, only 25.7 ± 6.51 standard error (SE) of the total spring D.

frontalis trap captures coincided with a consecuve 4 wk period be-

ginning with the median onset of dogwood bloom (Table 3). Peak D.

frontalis spring dispersal oen was earlier than dogwood bloom, oset

by 1 to 9 wk at various locaons during this study. The 2014 and 2015

mean percentages of D. frontalis trap captures preceding dogwood

bloom across all locaons were 53.9 and 77.1%, respecvely (Table

3). The use of dogwood bloom as an indicator to iniate trapping was

inconsistent, as 3 of 6 of the spring surveys began at least 2 wk prior

to its onset (Fig. 1).

FLOWERING DOGWOOD BLOOM VS. EASTERN REDBUD BLOOM

Eastern redbud bloom occurred approximately 2.3 wk before ow-

ering dogwood bloom in 2019 (Fig. 2). The mean (± SE) onset of eastern

redbud bloom occurred 10.6 ± 0.13 wk aer the rst of the yr, vs. the

mean onset of owering dogwood bloom which occurred 12.9 ± 0.07

wk aer the rst of the yr. The variability of onset of bloom dates was

over 2× greater in redbuds than in dogwoods, with 2.4 wk between the

rst and last eastern redbud bloom dates, and only 1.1 wk between

rst and last bloom dates for owering dogwood trees (Fig. 2).

UTILITY OF A 4 TO 6 WK TIMEFRAME TO DESCRIBE DENDROC-

TONUS FRONTALIS FLIGHT ACTIVITY IN THE SPRING

Across all locaons and years, a 4 wk peak dispersal period ac-

counted for 45% ± 3.2 SE of the total spring D. frontalis trap captures

(Table 3). During the 4 wk in 2014 and 2015 that the D. frontalis spring

surveys were conducted, the mean captures in our yr-round traps were

only 26.8% ± 4.98 SE of the total spring D. frontalis trap captures (Table

3).

Peak spring dispersal at Okbbeha accounted for 57% of the total

spring dispersal in 2014 and 42% in 2015. On the Homochio, 43% and

35% of D. frontalis collected were captured during the peak periods

in 2014 and 2015, respecvely. Dendroctonus frontalis collecons in

Alachua followed a similar paern, with a greater percentage (51%)

captured during the peak period in 2014 than in 2015 (42%).

Collecon numbers were mulmodal in all locaons in both yr (Fig.

3). Expanding the determinaon of peak dispersal to 6 wk, the maxi-

mum eld life of the lures, increased the percent of D. frontalis col-

lected to 57% ± 3.2 SE overall, only a 12% increase (range 10–17%). In

only 2 instances (Homochio 2014 and Alachua 2015) did the use of a 6

wk period allow a marginal detecon of mulple peaks, and the overall

average peak to peak separaon was 6.5 wk (Fig. 3).

DENDROCTONUS FRONTALIS PEAK DISPERSAL VS. THANASI-

MUS DUBIUS PEAK DISPERSAL

For all yr and locaons, the average 4 wk peak dispersal of T. du-

bius occurred 1.5 wk aer peak D. frontalis dispersal. The overall mean

peak of T. dubius and D. frontalis 4 wk dispersal occurred 11 ± 0.8 SE

and 9.5 ± 1.5 SE weeks aer the rst of the yr, respecvely. Overlap

between peaks of D. frontalis and T. dubius collecons always were

present except in Okbbeha 2015. There were no substanal T. dubius

trap captures before Feb; however, in 4 instances (Homochio 2014;

Okbbeha 2015; Homochio 2015; and Alachua 2015) substanal D.

frontalis trap captures occurred in Jan (Fig. 3).

Discussion

The ability of the D. frontalis spring survey to predict outbreaks

has been unreliable in recent yr (Table 1), potenally due in part to

sub-opmal ming of trap deployment. Despite historical anecdotes

to the contrary, dogwood bloom proved to not be an eecve predic-

tor of D. frontalis spring dispersal. Within the connes of this study,

the best case scenario for using dogwood bloom as the phenological

Table 3. The ming of owering dogwood bloom (DW) peak, Dendroctonus frontalis – southern pine beetle (SPB) spring dispersal, and iniaon of annual Dendroc-

tonus frontalis spring survey along with the corresponding percentage of Dendroctonus frontalis captured in nearby yr-round traps.

Median DW bloom date

Latest date to capture

peak SPB dispersal

Iniaon of survey

trapping

%SPB trap captures

before DW

bloom date

%SPB trap

captures if DW

iniated trapping

%SPB trap captures

during actual 4 wk

survey tapping

%SPB trap

captures during

opmal 4 wk peak

12 Apr 2014 20 Mar 2014 9 Apr 2014 69 27 28 57

22 Mar 2014 11 Mar 2014 11 Mar 2014 48 41 43 43

22 Mar 2014 10 Mar 2014 17 Feb 2014 45 42 29 51

–

53.9 36.8 33.3 50.3

31 Mar 2015 21 Jan 2015 7 Apr 2015 85 12 6 42

19 Mar 2015 4 Mar 2015 23 Mar 2015 54 30 32 35

19 Mar 2015 27 Jan 2015 27 Feb 2015 92 2 23 42

–

77.1 14.7 20.3 39.7

Overall

–

65.5 25.7 26.8 45.0

Based on median of owering dogwood bloom date.

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448 2020 — Florida Entomologist — Volume 103, No. 4

indicator of trap deployment resulted in missing 45% of the total D.

frontalis spring dispersal, whereas in the worst case 92% of the spring

dispersal passed before dogwood bloom. This suggests that historical

ming of trap deployment could at least explain some of the recent

inaccuracy with the annual spring survey. This also suggests that the

survey’s predicon model is based on deated peak data, and improve-

ments to ming of the survey will require the model to be recalibrated.

The onset of eastern redbud bloom appeared a beer phenological

indicator of opmal D. frontalis spring survey ming. Our 2019 bloom-

ing survey indicated redbud bloom occurred 2.3 wk before dogwood

bloom, narrowing the average 3 wk oset between D. frontalis peak

dispersal and dogwood bloom measured in 2014 and 2015. However,

Fig. 1. Comparison of dogwood bloom dates, Dendroctonus frontalis (southern pine beetle) and clerid peak dispersal dates from yr-round traps, and dates of the

actual southern pine beetle spring survey. The black bar represents the consecuve 4 wk period beginning with the median onset of dogwood bloom by yr and

locaon. The dark gray bar represents the 4 consecuve wk that the annual southern pine beetle spring trapping survey was conducted. The light gray (southern

pine beetle) and white (clerid) bars represent the consecuve 4 wk period during which the most beetles were trapped in yr-round traps. The percentages following

the bars correspond to the percentage of southern pine beetle (black, dark gray, light gray) or clerid (white) spring trap captures from yr-round traps.

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Thomason et al.: Phenological indicators of Dendroctonus frontalis trapping 449

eastern redbud bloom was variable highly among the trees monitored

in 2019, creang the possibility of considerable asynchrony between

onset of eastern redbud bloom and onset of peak D. frontalis dispersal.

Dogwood bloom may have historically coincided with peak D. fron-

talis spring dispersal, but scienc studies are lacking. If a correlaon

did once exist, climate change may have altered the phenology of both

species (Davis et al. 2010). Dogwoods must be exposed to suciently

cold temperatures for a long enough period before growth resumpon

can be iniated within the bud (Hunter & Lechowicz 1992). Once the

chilling requirement has been met, bud burst occurs aer exposure to

a specic amount of thermal me above a temperature threshold has

accumulated (Cannell & Smith 1986), which can result in bud burst

occurring later in the spring aer atypically warm winters occur. Con-

versely, D. frontalis may have as many as 8 overlapping generaons

per yr (Hain et al. 2011). They overwinter in all life stages and connue

to develop even at 0 °C (Lombardero et al. 2000). Adults may emerge

and disperse aer a few unseasonably warm d in the winter and early

spring (Moser & Dell 1979). Cold winters should promote a synchro-

nous spring emergence, parcularly in the northern poron of the

range of D. frontalis and higher elevaon pine forests (Lombadero et al.

2018). Warmer winters result in staggered emergence as was observed

in our study in southern states. Climate change likely will increase in-

stances of mulmodal spring emergence by D. frontalis, complicang

the use of any phenological indicator for survey ming and connuing

to impact the accuracy of the predicon model.

One method to overcome increased variability in D. frontalis spring

emergence would be to extend the trapping period beyond the current

4 wk standard. Our results indicate that 6 wk of trapping, the maxi-

mum recommended life of the lures, only slightly would improve the

chances of capturing peak emergence or the presence of mulmodal

emergence. Trapping for longer than 6 wk would add addional cost

and require increased labor for trap collecon and beetle counng.

Therefore, the praccality of using longer trapping periods is minimal.

Another technique would be the development of a robust model de-

signed to determine opmal survey start dates based on weather con-

dions and thresholds of D. frontalis developmental and ight temper-

atures. Degree d models usually are good candidates for such models

because they can successfully predict key life history events for insects

depending on weather condions. The potenal winter emergence

of D. frontalis would make selecng a biologically meaningful me to

begin accumulang degree d for D. frontalis dicult. Another opon

would be to base the ming of the survey on T. dubius emergence.

Their abundance in relaon to D. frontalis numbers is an input in the

predicon model, and our results suggest less variability in emergence

paerns. The ulity of this approach requires further study.

As discussed above, using a phenological indicator to me the sur-

veys may be impraccal due to variable emergence paerns driven by

climate change. It is apparent from our results that survey trappers al-

ready are using factors other than dogwood or redbud bloom to deploy

their traps (Fig. 1). The inconsistency in survey ming could possibly be

explained by me constraints with other management dues such as

mber harvests and prescribed burns. Trappers may have used historic

trapping data for their region to help determine deployment dates.

Local knowledge of climac condions and emergence paerns of D.

frontalis may serve as the best source of determining when to begin

the spring survey in the absence of a phenological cue.

In addion to issues of survey ming, recent problems in predict-

ing D. frontalis outbreaks may be due in part to changes in the lure

combinaon previously described. A recent study has demonstrated

that the switch to the α- and β-pinene sleeve as the host component

has reduced the trap catch of D. frontalis compared to using turpenne

(unpublished data). More recently, an endo-brevicomin lure was in-

cluded to synergize the aracveness of the other 2 lures and enhance

the survey’s ability to detect low populaon levels of D. frontalis. Dif-

ferences in the responsiveness of D. frontalis to these new lure compo-

nents may explain the recent model failures parally. Recalibrang the

model for the current lure scheme also could improve the predicve

power of the survey.

The inclusion of endo-brevicomin also altered trap placement.

Trap locaons are now typically in hardwood inclusions within pine

stands. Hardwood green leaf volales have been shown to signicant-

ly decrease D. frontalis trap captures (Dickens et al. 1992; Sullivan et

al. 2007), thus the current displacement allows for more non-host

species between the trap and preferred D. frontalis habitat. Changing

trap locaons frequently may aect survey results. Ideally trappers

would examine trap catches annually and relocate traps from sites

that historically collected very few D. frontalis even when populaon

levels are moderate to high. Establishing and connuously using reli-

able trap sites would provide consistency in the survey and aid in re-

calibrang the model to improve the validity of the results. However,

maintaining the same sites from yr to yr oen is confounded due to

turnover in sta and landscape changes from management acvies,

storm events, etc.

Given the uncertainty in survey ming, revising the predicon

model to include climate data could help improve the model accuracy

back to previous standards. A forecast system using weather and stand

data, previous yr infestaon levels, and a hydrological model to pre-

dict D. frontalis levels for a county has been developed (McNulty et al.

1998, McNulty 2019). Because an operaonal version of the model is

a recent development, lile informaon has been provided to poten-

al users to date and the short- and long-term accuracy of the results

have not been thoroughly evaluated. Perhaps a combinaon of the 2

predicon methods may serve to improve the overall ability of forest

managers to ancipate and prepare for outbreaks.

The spring survey is an integral part of the integrated pest manage-

ment strategy for D. frontalis. In addion to helping predict seasonal

infestaon levels, survey results are valuable for preserving a histori-

cal record of D. frontalis populaon trends. The annual trapping also

keeps foresters aware of the impacts of D. frontalis and the ecological

and economic consequences of outbreaks. A beer predicon system

would provide addional juscaon for maintaining the survey. Our

results indicate the following could help improve the ecacy of the

predicon model: (1) shi trap deployment earlier than tradional

dates, perhaps using eastern redbud bloom as a cue; (2) include local

Fig. 2. The 2019 blooming dates of owering dogwood (DW) and eastern

redbud (RB) trees (N = 25 each) in an unmanaged woodlot on the periphery

of the Thad Cochran Research, Technology & Economic Development Park

at Mississippi State University, in Starkville, Mississippi, USA (33.475129°N,

88.793119°W).

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450 2020 — Florida Entomologist — Volume 103, No. 4

knowledge to determine when the trapping period should occur; and

(3) incorporate climac data with trap catch numbers in the model.

Acknowledgments

We appreciate John Nowak and Don Duerr with Region 8 Forest

Health Protecon for support and suggesons during this project.

Funding was provided by Region 8 Forest Health Protecon and the

Southern Pine Beetle Prevenon Program. We greatly appreciate as-

sistance from Chris Pearce, David Conser, Randy Chapin, Billy Bruce,

Jim Meeker, Brian Sullivan, Wood Johnson, Lee Dunnam, Jim Philips,

R. Whitstone, Os Fair, John Furr, James Schiller, Lane Cothren, Robby

Gill, and Keith Beay for contribung me and eort with D. frontalis

trapping. We appreciate Sam Ward for his help with R stascal so-

ware.

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