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Spatial Distributions of Arthropods in Soybean and Implications Spatial Distributions of Arthropods in Soybean and Implications

for Pest Management for Pest Management

Anthony Daniel Greene Clemson University, adg2@clemson.edu

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Recommended Citation Recommended Citation Greene, Anthony Daniel, "Spatial Distributions of Arthropods in Soybean and Implications for Pest Management" (2021). All Dissertations. 2866. https://tigerprints.clemson.edu/all_dissertations/2866

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SPATIAL DISTRIBUTIONS OF ARTHROPODS IN SOYBEAN AND IMPLICATIONS FOR PEST

MANAGEMENT

A Dissertation

Presented to

the Graduate School of

Clemson University

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

Entomology

by

Anthony Daniel Greene

August 2021

Accepted by:

Dr. Jeremy Greene, Committee Chair

Dr. Francis Reay-Jones, Committee Chair

Dr. Kendall Kirk

Dr. Brandon Peoples

ii

ABSTRACT

Site-specific management of insect pests of field crops has the potential to decrease control costs

and environmental impacts associated with traditional pest management tactics, but the success of these

programs relies on the accurate characterization of arthropod distributions within a crop. Although the

expense of the fine-scale spatial sampling required for management zone identification in fields may offset

the overall reduction in costs achieved with site-specific pest management, the correlation of arthropod

counts with ground-based and remotely sensed field attribute data could help to make site-specific pest

management programs more profitable.

In this study, we chose to determine how insect pests and natural enemies in soybean were

associated with abiotic and biotic variables collected with ground-based and remote sensing technologies.

Arthropods were grid-sampled from July-October in two soybean fields at the Clemson University Edisto

Research and Education Center in Blackville, SC, in 2017 and 2018 using drop-cloth, sweep-net, and pitfall

trap sampling methods. During each sampling event, or calendar week, arthropod and soybean plant data

(Normalized Difference Vegetation Index [NDVI], plant heights, and defoliation) were collected for each

grid point for a given field. Fields were further characterized through the collection of elevation and soil

apparent electrical conductivity (soil ECa) data for all grid points. Spatial Analysis by Distance Indices

(SADIE) was used to analyze how the sweep-net collected larvae of three major lepidopteran pests

[velvetbean caterpillar, Anticarsia gemmatalis (Hübner) (Lepidoptera: Erebidae), soybean looper,

Chrysodeixis includens (Walker) (Lepidoptera: Noctuidae), and green cloverworm, Hypena scabra

(Lepidoptera: Erebidae) (Fabricius)] were spatially associated with defoliation, NDVI, and plant height in

soybean, and how the pitfall trap collected predatory Carolina metallic tiger beetle, Tetracha carolina

(Linnaeus) (Coleoptera: Carabidae), and punctured tiger beetle, Cicindelidia punctulata (Olivier)

(Coleoptera: Carabidae), were associated with abiotic (elevation and soil ECa) and biotic (Cydnidae adults

and nymphs, Elateridae adults, and Gryllotalpidae adults and nymphs) variables within the crop. Negative

binomial, zero-inflated models were used to estimate presence and drop-cloth counts of arthropod taxa

based on distance from the field edge, NDVI, soybean plant height, soil ECa, elevation, and calendar week.

iii

Although aggregations of insect taxa, as identified by SADIE, were limited for sweep-net and

pitfall-trap datasets, significant spatial overlap (42% of the total significant associations among insects and

field variables) was observed for C. punctulata and T. carolina from pitfall-trap datasets, while 14% and

6% of paired plant-insect sweep-net datasets were significantly associated or dissociated, respectively.

Cicindelines collected from pitfall traps were found to have more significant associations and dissociations

with Elateridae than any other herbivorous taxa, and more significant dissociations with soil ECa than with

elevation. NDVI was found to be more associated with sweep-net collected pest distributions than

soybean plant heights and defoliation estimates, and the majority of all plant -insect associations and

dissociations occurred in the first four weeks of sampling (late July-early August). Among all variables

from drop-cloth datasets, calendar week was the most reliable predictor of arthropod counts, as it was a

significant predictor for a majority of all taxa. Additionally, counts for a majority of drop-cloth collected

pestiferous taxa were significantly associated with distance from the field edge, elevation, soybean plant

height, and NDVI.

Given that the knowledge of the ecological interactions specific to a given species are critical to

the development of practical management applications for that species, the identification of ground-based

(e.g. soil ECa) and remotely sensed variables (e.g. NDVI) that can be associated with the in-field

distributions of important soybean pests and natural enemies represents the first step towards the

implementation of site-specific pest management in this crop. Results from this study advocate for the

relationship between distributions of pests and natural enemies and important biotic and abiotic

variables to be further investigated to better determine the strength of the correlations across years and

sites.

iv

ACKNOWLEDGMENTS

I would like to thank the Clemson Doctoral Dissertation Completion Grant, the National Institute

of Food and Agriculture/U. S. Department of Agriculture (SC-1700531 and SC-1700532), the South

Carolina Soybean Board, the W. Carl Nettles, Sr., and Ruby S. Nettles Memorial Endowment in

Entomology, and the Wade Stackhouse Graduate Fellowship for providing the funding needed for me to

complete this degree. I am also thankful to all of the individuals at Edisto Research and Education Center

who helped to collect the data for this project.

Without the support of my family, and particularly my mother, Gladys Patricia Greene, I would

never have been able to even begin an academic career, much less finish with a terminal degree. I cannot

thank you enough. I was also fortunate enough to have the support and encouragement of two fantastic

researchers and educators during my undergraduate studies at Lincoln Memorial University, Dr. John

Copeland and Dr. Agnes Vanderpool. Dr. Rebecca Trout Fryxell and Dr. Brian Hendricks introduced me to

the field of Entomology, and demonstrated how many different avenues of research could be pursued in

this field. Dr. Gideon Wasserberg furthered my interest in Entomology, and served as a fantastic advisor

during my graduate studies at the University of North Carolina at Greensboro. During my time spent

completing the Ph.D. program in Entomology at Clemson University, I experienced great personal and

professional growth due to the interactions that I had with David Bowers, Dr. Eric Benson, Dr. Julia

Kerrigan, Dr. Laura María Vásquez Vélez, Dr. Matthew Turnbull, Dr. Misbakhul Munir, Dr. Peter Adler,

Dr. Sofía Isabel Muñoz Tobar, Dr. Shelly S. Langton-Myers, and Dr. Thomas Bilbo. I am grateful to my

committee members, Dr. Brandon Peoples, Dr. Francis Reay-Jones, Dr. Jeremy Greene, and Dr. Kendall

Kirk, for their great patience and guidance, as well as the knowledge that they shared with me throughout

the completion of this degree. Finally, I would like to thank Dr. Robert Charles Holmes for everything that

he has shared with me. This accomplishment would not have been possible without his continual support

and encouragement.

v

TABLE OF CONTENTS

Page

TITLE PAGE .............................................................................................................................................. i

ABSTRACT ............................................................................................................................................... ii

ACKNOWLEDGMENTS ......................................................................................................................... iv

LIST OF TABLES ................................................................................................................................... vii

LIST OF FIGURES ................................................................................................................................. viii

CHAPTER

I. ASSOCIATING SITE CHARACTERISTICS WITH DISTRIBUTIONS

OF PESTIFEROUS AND PREDACEOUS ARTHROPODS

IN SOYBEAN ............................................................................................................... 1

Abstract .......................................................................................................................... 1

Introduction .................................................................................................................... 2

Materials and Methods ................................................................................................... 4

Results ............................................................................................................................ 7

Discussion ...................................................................................................................... 9

Acknowledgements ...................................................................................................... 14

References Cited .......................................................................................................... 15

II. SPATIAL ASSOCIATIONS OF KEY LEPIDOPTERAN PESTS

WITH DEFOLIATION, NDVI, AND PLANT HEIGHT

IN SOYBEAN ............................................................................................................. 28

Abstract ........................................................................................................................ 28

Introduction .................................................................................................................. 28

Materials and Methods ................................................................................................. 31

Results .......................................................................................................................... 34

Discussion .................................................................................................................... 36

Acknowledgements ...................................................................................................... 41

References Cited .......................................................................................................... 42

III. SPATIAL ASSOCIATIONS OF THE TIGER BEETLES (COLEOPTERA:

CICINDELINAE) Cicindela punctulata (OLIVIER) AND

Tetracha carolina (LINNAEUS) WITH BIOTIC AND

ABIOTIC VARIABLES IN SOYBEAN ..................................................................... 55

Abstract ........................................................................................................................ 55

Introduction .................................................................................................................. 56

Materials and Methods ................................................................................................. 58

Results .......................................................................................................................... 62

Discussion .................................................................................................................... 66

vi

Table of Contents (Continued)

Page

References Cited .......................................................................................................... 70

IV. CONCLUSIONS AND FUTURE WORK ......................................................................... 82

References Cited .......................................................................................................... 86

APPENDICES .......................................................................................................................................... 88

A: Publication information for Chapter I ................................................................................. 89

B: Table B1. Results of likelihood ratio tests between intercept-only

and full models for soybean arthropod taxa ................................................................. 90

vii

LIST OF TABLES

Table Page

1.1 Pestiferous taxa summary statistics and significant predictor

variables (estimates ± SE) of pestiferous taxa counts

from regression analyses of soybean drop-cloth data .................................................. 23

1.2 Predatory taxa summary statistics and significant predictor

variables (estimates ± SE) of predatory taxa counts

from regression analyses of soybean drop-cloth data. ................................................. 24

2.1 Spatial aggregation indices (Ia) from SADIE of pests and plant variables

for each sampling event (calendar week) in soybean ................................................... 47

2.2 Spatial association indices (X) from SADIE of pests and plant variables

from each sampling event (calendar week) in soybean ................................................ 48

3.1 Seasonal dynamics and spatial aggregation indices (Ia) from SADIE

of insects from each sampling event in soybean in 2017 ............................................. 74

3.2 Seasonal dynamics and spatial aggregation indices (Ia) from SADIE

of insects from each sampling event in soybean in 2018 ............................................. 76

3.3 Spatial association indices (X) from SADIE of insects and field variables

from each sampling event (calendar week) in soybean ................................................ 78

viii

LIST OF FIGURES

Figure Page

1.1 Soybean sampling locations at Edisto Research and Education

Center, Blackville, SC.................................................................................................. 25

1.2 Soybean pestiferous arthropod seasonal dynamics (average ± SE)

and associated soybean phenology across fields (A & B)

and years (2017 & 2018) .............................................................................................. 26

1.3 Soybean predatory arthropod seasonal dynamics (average ± SE)

and associated soybean phenology across fields (A & B)

and years (2017 & 2018) .............................................................................................. 27

2.1 Lepidopteran pest seasonal dynamics (average ± SE) and associated

soybean phenology across fields (A and B) ................................................................. 50

2.2 Soybean plant height and defoliation seasonal dynamics (average ± SE)

and associated soybean phenology across fields (A and B .......................................... 51

2.3 NDVI seasonal dynamics (average ± SE) and associated

soybean phenology across fields (A and B .................................................................. 52

2.4 Selected spatial interpolation maps of SADIE local aggregation indices

for datasets from the same calendar week (CW) .......................................................... 53

2.5 Selected spatial interpolation maps of SADIE local association indices

for datasets from the same calendar week (CW) .......................................................... 54

3.1 Spatial interpolation maps of local aggregation indices from significant

SADIE analyses ........................................................................................................... 80

3.2 Selected spatial interpolation maps of SADIE local association indices ............................ 81

1

CHAPTER ONE

ASSOCIATING SITE CHARACTERISTICS WITH DISTRIBUTIONS OF PESTIFEROUS

AND PREDACEOUS ARTHROPODS IN SOYBEAN1

Abstract

Although site-specific pest management has the potential to decrease the control costs and environmental

impact associated with traditional pest management tactics, the success of these programs relies on the

accurate characterization of arthropod distributions within a crop. Because potential correlation of insect

counts with remotely sensed field attribute data could help to decrease the costs associated with and need

for fine-scale spatial sampling, we chose to determine which within-field variables would be informative of

soybean arthropod counts in an attempt to move toward site-specific pest management in this crop. Two

soybean fields were grid-sampled for pestiferous and predatory arthropods, plant productivity estimates,

and abiotic variable characterization in 2017-2018. Negative binomial, zero-inflated models were used to

estimate presence and counts of soybean arthropod taxa based on normalized difference vegetation index

(NDVI), soybean plant height, soil electrical conductivity (ECa), elevation, and calendar week. Among all

variables, calendar week was the most reliable predictor of arthropod counts, as it was a significant

predictor for a majority of all taxa. Additionally, counts for a majority of pestiferous taxa were significantly

associated with distance from the field edge, elevation, soybean plant height, and NDVI. Although site-

specific pest management has the potential for reduced management inputs and increased profitability over

conventional management (i.e. whole-field) practices, management zones must first be clearly defined

based on the within-field variability for the variables of interest. If site-specific pest management practices

are to be applied in soybean, calendar week (and associated soybean phenology), soybean plant height (and

associated elevation), and NDVI may be useful for describing the distributions of pests, such as kudzu bug,

Megacopta cribraria (Hemiptera: Plataspidae) (Fabricius), green cloverworm, Hypena scabra

1 This article has been accepted for publication in Environmental Entomology published by Oxford

University Press

2

(Lepidoptera: Erebidae) (Fabricius), velvetbean caterpillar, Anticarsia gemmatalis (Lepidoptera: Erebidae)

(Hübner), and soybean looper, Chrysodeixis includens (Lepidoptera: Noctuidae) (Walker).

KEY WORDS NDVI, site-specific pest management, predator, phenology, plant height

Introduction

In the US, more than 700 species of herbivorous insects have been reported from soybean, Glycine

max (L.) Merrill (Way 1994). Of those, 50 species or species complexes have been categorized as either

significant economic pests, occasional/sporadic pests, or infrequent pests (Kogan and Turnipseed 1987,

Higley and Boethel 1994, Steffey 2015). Additionally, more than 150 natural enemies have been identified

in soybean fields (Deitz 1976). Turnipseed and Kogan (1983) highlighted 13 taxa as important indigenous

predators, based on their abundance (Turnipseed 1973, LeSar and Unzicker 1978, McCarty et al. 1980) or

their ability to prey upon the eggs or early instars of major soybean pests, thereby limiting pest populations

from reaching potentially economically injurious levels.

Although multiple pests inhabit soybean fields, it is likely that these fields are not occupied

uniformly, as many pests are known to have spatially aggregated within-field distributions (Davis 1994),

including kudzu bug, Megacopta cribraria (Hemiptera: Plataspidae) (Fabricius) (Seiter et al. 2013),

soybean aphid, Aphis glycines (Hemiptera: Aphididae) (Matsumura) (Hodgson et al. 2004), Neotropical

brown stink bug, Euschistus heros (Hemiptera: Pentatomidae) (Fabricius) (da Fonseca et al. 2014), and

green cloverworm, Hypena scabra (Lepidoptera: Erebidae) (Fabricius) (Bechinski et al. 1983).

Furthermore, soil and crop characteristics that affect crop production (Gebbers and Adamchuk 2010) are

often spatiotemporally variable within fields (Zhang et al. 2002). By characterizing the variability of these

biotic and abiotic components, field areas with similar values for a given variable (i.e. management zones)

can be created (Doerge 1999). These management zones can then be managed independently of one

another, including for insects, which is known as site-specific pest management. An accurate

characterization of the variability of the biotic and abiotic components of interest is therefore fundamental

to site-specific management, and ground-based and remote sensing technologies provide methods of

assessing the variability.

3

Remote sensing, or the non-contact collection of data concerning an object, has been used in

agriculture since the 1970s (Bauer and Cipra 1973, Jewell 1989, Doraiswamy et al. 2003) to evaluate crop

condition, predict yield, detect crop pests and diseases, and more (Liaghat and Balasundram 2010, Mulla

2013). Remotely sensed plant reflectance can provide information on plant condition, as reflectance data of

stressed plants typically differ from that of non-stressed plants (Hatfield and Pinter Jr 1993, Prabhakar et al.

2012). Vegetation indices can be calculated from remotely sensed plant reflectance data, and Alves et al.

(2015) was able to correlate normalized difference vegetation index (NDVI) values with cumulative

soybean aphid days in soybean. Ground-based sensing technologies, such as apparent soil electrical

conductivity (ECa), have been used in agriculture to correlate a soil’s ability to conduct an electrical current

to its texture, cation exchange capacity, salinity, and crop yield (Grisso et al. 2005). Because soil ECa has

also been associated with a soil’s water-holding capacity (Grisso et al. 2005), this measurement may be

informative for arthropod management, as Dauber et al. (2005) found a negative association between soil

humidity and carabid species richness across various land use types. Arthropod distributions are also

affected by factors associated with plant quality, biomass, and complexity (Joern et al. 2012). Taller plants

have been shown to modify microclimatic conditions (e.g. increased humidity) and attract hydrophilic taxa

(Desender 1982, Dennis et al. 1998), while vegetation structure has been shown to affect spider activity,

density, and diversity (Rypstra et al. 1999).

Through the combination of knowledge of an organism’s interactions with its environment and

remotely sensed characterization of that environment, pest management strategies can be optimized. For

example, Willers et al. (1999) found higher densities of tarnished plant bug, Lygus lineolaris (Hemiptera:

Miridae) (Palisot de Beauvois), in field regions with tall, vigorous cotton, Gossypium hirsutum L., when

compared with regions with shorter, less vigorous plants; these healthy field areas were easily

distinguishable from other field areas using vegetation indices calculated from remotely sensed data.

Consequently, Campanella (2000) made insecticide applications targeting L. lineolaris in only those field

areas with high NDVI values, resulting in a 60 percent reduction in insecticide usage across a 405 ha field.

Field data revealed that L. lineolaris abundance did not increase in response to the reduced insecticide

usage, and yield levels also remained at acceptable levels (Campanella 2000). By applying insecticides to

4

only those management zones with high pest pressure, site-specific pest management techniques have the

potential to increase profitability, reduce the environmental impact of insecticide applications, conserve

natural enemies through the creation of unsprayed refuges within fields, and decrease the rate of insecticide

resistance in local pest populations (Midgarden et al. 1997, Park and Krell 2005, Park et al. 2007).

Although a reduction in control costs may be associated with site-specific pest management, the

extent by which those costs are reduced may be offset by the increased cost of the intensive, fine-scale

arthropod sampling that is required for management zone identification within fields. Krell et al. (2003)

suggested that the correlation of insect presence with field attributes detected via remote sensing could

decrease sampling costs, thereby increasing the return of site-specific pest management programs. With the

goal of improving pest management in soybean, this study sought to determine which within-field factors

would be informative of soybean arthropod counts. Our objectives were 1) to estimate populations of

pestiferous and predatory arthropod taxa in soybean using the following within-field factors: distance to the

field edge, elevation, NDVI, soil ECa, and soybean plant height, and 2) to determine if count estimation

patterns exist for groupings of taxa for a given set of factors and vice versa.

Materials and Methods

Field Trials

Fields “A” (8.9 ha) and “B” (5.7 ha) were planted with soybeans (A: Asgrow® AG75X6 Roundup

Ready 2 Xtend® in 2017 and AG69X6 in 2018; B: Bayer Credenz® LibertyLink® 7007LL in 2017 and

Pioneer P67T90R2 in 2018) using 96.5 cm row spacing at the Edisto Research and Education Center (REC)

in Blackville, SC on 9 June (A) and 12 June (B) in 2017 and on 16 June (A) and 12 June (B) in 2018. Prior

to planting, Extension recommendations for plant populations and herbicide and fertilizer applications were

followed (Marshall et al. 2020). On 27 June 2017, Hinder® Deer and Rabbit Repellent was sprayed (39.2

ml/l) in field B to deter deer feeding. No insecticides were applied to the crop.

5

Sampling

Sampling grids were set up by placing fiberglass flags ≈ 40 m apart (starting from the field edge)

for a total of 66 flags in field A and 54 flags in field B (Figure 1.1). Grid points were selected based on

GPS location and were identical across years. Near each flag (within 10 m), arthropod samples, soybean

plant heights, and NDVI data were collected during calendar weeks (CW) 29-32, 34, 36, and 40 [21 July-

02 Oct.; V6-R7 growth stages] for field A in 2017; CW 29-32, 33, 35, and 39 [20 July- 27 Sept..; V7-R7

growth stages] for field B in 2017; CW 30-32, 34, 36, 39, and 41 [24 July- 09 Oct.; V5-R8 growth stages]

for field A in 2018; and CW 30, 31, 33, 35, 38, 40, and 42 [27 July- 17 Oct.; V3-R7 growth stages] for field

B in 2018 (Fehr and Caviness 1977) (Figures 2 & 3). Arthropod samples, soybean plant heights, and NDVI

data collected from all flags within a field during the same calendar week were considered as part of the

same sampling event.

Arthropod samples were collected by forcefully shaking all soybean plants from two parallel 1.83

m sections of row (3.66 m total row sampled per flag) over a 0.91 x 0.91 m white canvas cloth placed on

the ground between rows and underneath the soybean canopy. Pestiferous and predatory arthropods on beat

cloths were identified in the field to at least family level, with the exception of spiders, which were

identified as Araneae. Additionally, five soybean plants that were representative of the plants in the area

around each sampling flag were randomly pulled, and their total height (ground to terminal length) was

measured during each sampling event, with the average height across the five plants used for the analyses.

NDVI was measured during all sampling events (except for CW38 for field B) by measuring the

reflectance of all plants within a 6 m section of row with a Trimble® GreenSeeker® Handheld Crop

Sensor. Care was taken to sample areas that had not been sampled in the previous sampling event to ensure

that samples were indeed representative of undisturbed plants around each flag. The distance from the field

edge was calculated for each flag using Trimble® Farm Works ™ software. A Veris 3100 EC meter (Veris

Technologies, Salina, KS) was used to measure shallow (0.0-0.3 m) and deep (0.0-0.9 m) soil ECa data

along the length of each field on 22 March 2019, with separate lengthwise runs occurring every ≈ 7.6 m

across the width of a field. Soil ECa measurements were first averaged across shallow and deep portions of

the soil profile, and then averaged within a 12-m radius circle around each sampling location. Although

6

temporal changes in factors such as soil moisture can alter the values of soil ECa measurements themselves,

ECa values are generally stable over time (Sudduth et al. 2005); the soil ECa measurement taken on 22

March 2019 was therefore used in all analyses. Elevation for each flag was acquired from the Veris 3100

log data, which used a Trimble® AgGPS 332 receiver with beacon DGPS for positioning. Singular

elevation values for each flag were produced by averaging all elevation values within a 12-m radius circle

around each sampling location; these elevations were used for the analysis of the data from both years.

Data Analyses

Spatial autocorrelation arising from grid-sampling was accounted for by subjecting a matrix of

geographic distances between each flag to principal components of neighborhood matrices (PCNM)

analysis using the pcnm function in the vegan package (Oksanen et al. 2019) in R version 3.5.3 (R Core

Team 2019). This approach produces spatial eigenvectors equal to the number of sampling locations (120),

and each eigenvector was tested for significance using Moran’s I. One eigenvector was found to account

for 99% of the spatial variation in sampling sites. This eigenvector was named “spatial EV”, extracted, and

used in regression modeling to account for the effect of space on arthropod counts.

For those arthropods that exceeded 1% of the total counts (among all collected arthropods) for

their trophic level (i.e. pest or predator; Tables 1.1 and 1.2), counts were individually estimated using

generalized linear mixed models. Because abundances were measured in counts, and were overdispersed

(means were much lower than variances), a negative binomial distribution was used for the error structure.

Because counts had high proportions of zeroes that may be related to a separate process of nondetection

(i.e., structural zeroes), an additional level in each model predicting presence/absence was also created

(zero-inflated level). Within models, the zero-inflated level estimated effects of independent variables on

whether or not an organism was detected, and given that information, the second level (negative binomial)

described effects of independent variables on organismal counts. All GLMMs contained random intercepts

of year (2017, 2018) and flag nested within field (fields A and B) to account for nonindependence among

years and subsampling within fields and sampling points. GLMMs were fit using the glmmTMB package

(Brooks et al. 2017) in R (R Core Team 2019). All within-field variables (distance from the field edge,

7

elevation, NDVI, soil ECa, and soybean plant height) were used as independent variables in both the zero-

inflated and negative binomial portions of models. Models also contained the fixed effects of spatial EV

and CW to account for the effects of spatial and temporal autocorrelation on arthropod counts. Prior to

analysis, all predictor variables were scaled and centered (μ = 0, σ2 = 1). Predictor variables were screened

for collinearity based on Pearson correlations and variance inflation factor (VIF). Because correlation

coefficients and VIF values did not exceed thresholds of 0.70 and 5, respectively, no variables were

excluded. For each model, fit was assessed by X2 comparison of the log-likelihood values of the assembled

and intercept-only models.

Results

Models estimating arthropod counts were created for 23 life stages belonging to 15 taxa, as the

counts for these taxa exceeded 1% of the total counts (among all collected arthropods) for their trophic

level (i.e. pest or predator) (Tables 1.1 and 1.2). Of those, models were created for 12 life stages (8 taxa) of

pests and 11 life stages (7 taxa) of predators. Summary statistics (e.g. percentages, totals. etc.) herein are

based on only those arthropods used in analyses. Out of a total of 109,208 arthropods from drop-cloth

sampling for which models were created, 84% (91,584) were pests while 16% (17,444) were predators.

Larvae of Anticarsia gemmatalis (Lepidoptera: Erebidae) (Hübner) (21%; 18,853) and adults (35%;

31,817) and nymphs (19%; 17,605) of M. cribraria made up 75% (68,278) of the total pest counts used in

analyses. With the exceptions of larvae of Chrysodeixis includens (Lepidoptera: Noctuidae) (Walker)

(10%; 8,725) and H. scabra (5%; 4,522), the remaining life stages made up < 5% of the total modeled pest

counts each. Adult Formicidae (57%; 9,965), Araneae (14%; 2,503), and adult Anthicidae (9%; 1,489)

made up 80% (13,957) of the total modeled predator counts. With the exceptions of Nabidae (5%; 888) and

Geocoridae adults (5%; 790), the remaining life stages made up < 5% of the total modeled predator counts

each. Models estimating arthropod counts were successful for all taxa, except for adults of Chinavia hilaris

(Hemiptera: Pentatomidae) (Say), Reduviidae, and Geocoridae (Tables 1.1 and 1.2). All assembled models

provided a significantly better fit than intercept-only models (p <0.001; Table B1).

8

In the zero-inflated portions of models, NDVI and soybean plant height had a significant effect on

whether or not the majority of pestiferous taxa were observed (Table 1.1). NDVI and soybean plant height

had the largest positive effect on the presence of larvae of A. gemmatalis and Cicadellidae, respectively,

and the largest negative effect on the presence of larvae of H. scabra (NDVI) and M. cribraria, Spissitilus

festinus (Hemiptera: Membracidae) (Say), larvae of C. includens, and nymphs of C. hilaris and Nezara

viridula (Hemiptera: Pentatomidae) (Linnaeus) (soybean plant height). All significant associations between

soil E ECa and whether or not a pestiferous taxa was detected were positive, with the exception of larvae of

H. scabra and adults of S. festinus. No variables were significantly associated with the presence of adults of

N. viridula, while each variable was significantly associated with the presence of nymphs of M. cribraria

(Table 1.1).

Among zero-inflated portions of predatory models, soybean plant height had a significant,

negative effect on whether or not the majority of taxa were observed, as well as the largest negative effect

on the presence of Nabidae, adults of Anthicidae, and nymphs of Geocoridae and Reduviidae (Table 1.2).

All significant associations between the presence of predatory taxa and distance from the field edge and

NDVI were positive. No variables were significantly associated with the presence of Araneae and adults of

Podisus maculiventris (Hemiptera: Pentatomidae) (Say).

Calendar week, soybean plant height, NDVI, distance from the field edge, and elevation were

significant predictors of counts (negative binomial level) for the majority of pestiferous taxa (Table 1.1).

Calendar week had the largest effect on pest counts for Cicadellidae and N. viridula, adults of M. cribraria,

larvae of A. gemmatalis and C. includens, and nymphs of C. hilaris, while the largest effect on counts of

adults of S. festinus, larvae of H. scabra, and nymphs of M. cribraria was soybean plant heights. All

significant associations for elevation and soybean plant height were positive, with the exception of nymphs

of N. viridula for plant height and adults of M. cribraria for both variables. Counts of N. viridula, adults of

M. cribraria and S. festinus, larvae of A. gemmatalis, and nymphs of C. hilaris increased significantly with

calendar week, whereas the opposite effect was observed for Cicadellidae, larvae of C. includens and H.

scabra, and nymphs of M. cribraria. All significant associations between pest counts and NDVI were

9

positive, while distance from the field edge and soil ECa had only significant negative associations with

counts for pestiferous taxa (Table 1.1).

Calendar week significantly affected counts (negative binomial level) for the majority of predatory

taxa, and had the largest positive effect on predator counts for P. maculiventris and Nabidae, adults of

Formicidae, and nymphs of Geocoridae (Table 1.2). NDVI had the largest positive effect on predator

counts for Araneae, and was a significant positive predictor for adults of Formicidae and Nabidae, and

nymphs of Geocoridae. As distance from the field edge increased, counts of Araneae and adults of

Formicidae significantly increased while counts of nymphs of P. maculiventris significantly decreased. All

significant associations between predator counts and elevation, NDVI, and calendar week were positive,

while soil ECa and spatial EV had only significant negative associations with counts for predatory taxa. No

variables were significantly associated with counts of nymphs of Reduviidae (Table 1.2).

Discussion

Given the high disturbance levels (harvest, crop rotation, etc.) present within agricultural systems

(Holland et al. 2005), count data collected in these environments may include zeroes because the organism

was simply missed during sampling (i.e. random zeroes) or because conditions were unfavorable for its

presence (i.e structural zeroes) (Cunningham and Lindenmayer 2005). In this study, the zero-inflated

portion of models was used to determine which variables were associated with structural zeroes in our data,

while the negative binomial portion was used to identify which variables were associated with arthropod

counts. Results from these models demonstrated the capacity of each of the measured variables to impact

the arthropod structural zeroes and count data differently. Given that our main objective was to identify

which within-field factors would be informative of soybean arthropod counts in an attempt to move toward

site-specific pest management, the results from the negative binomial portions of models were considered

to be generally more applicable to the overall goal of this study.

Calendar week was the most reliable predictor of arthropod counts, as it was a significant predictor

for the majority of all taxa. The strong link between calendar week and counts of predatory and pestiferous

taxa in this study is likely associated with soybean phenology for many taxa. Calendar week 41 (Oct.) was

10

associated with soybeans in the R6-R7 growth stages across both fields, and the peak of M. cribraria adults

that coincided with this calendar week and developmental stages (Figure 1.2A) is similar to the population

trends reported by Seiter et al. (2013). In our study, populations of C. hilaris nymphs peaked during

calendar weeks 39 (Sept.) and 41 (Oct.), while peaks of N. viridula adults and nymphs occurred during

week 42 (Oct.); these peaks corresponded to R6-R8 growth stages (Figure 1.2B). A similar trend was

reported in Arkansas soybean, as Smith et al. (2009) found that the abundance of stink bugs (Hemiptera:

Pentatomidae), including N. viridula, C. hilaris, Euschistus servus (Say), Piezodorous guildinii

(Westwood), and Thyanta (Stål) spp., peaked during the R7 growth stage. Among lepidopteran species,

significant associations with calendar week were positive for larvae of A. gemmatalis and negative for

larvae of C. includens and H. scabra. We observed that a decline in counts of C. inlcudens and H. scabra

coincided with an increase in counts of A. gemmatalis (Figure 1.2A). The inverse relationship could have

been due to competition among these species. Previous research at the same location also suggested that

competition from abundant A. gemmatalis was responsible for limiting the larvae of other lepidopteran

taxa, including Helicoverpa spp., loopers (mostly C. includens), and H. scabra (Shepard et al. 1977). The

positive effect of calendar week on densities of predatory taxa observed in this study is also supported by

the literature for several taxa (Figure 1.3). Shepard et al. (1974) and Baur et al. (2000) found increased

abundance of Araneae in mid- to late-season soybean, while Kharboutli and Mack (1991) reported that

numbers of Solenopsis invicta (Hymenoptera: Formicidae) (Buren) in peanut increased throughout the

season and then sharply declined near the end of the season. Additionally, the highest levels of abundance

for Nabis spp. (Hemiptera: Nabidae) (Latreille) and Geocoris spp. (Hemiptera: Geocoridae) (Fallén) were

found in mid- to late-September in South Carolina soybean (Shepard et al. 1974).

Soybean plant height was also an important predictor in this study, as this variable was

significantly associated with whether or not an arthropod was observed for a majority of taxa (pestiferous

and predatory). Although counts of predatory taxa were not significantly associated with soybean plant

height, counts for a majority of pestiferous taxa were significantly associated with this variable.

Additionally, the majority of significant associations between soybean plant height and pest counts were

positive. The significant, positive associations between plant height and the counts of pestiferous taxa

11

found in this study are supported by the plant size hypothesis. Posited by Lawton (1983), this hypothesis

states that more insect species can be supported by larger plants when compared with smaller plants, due to

the fact that the larger hosts are more likely to be discovered (Marques et al. 2000). Additionally, as plants

increase in biomass, they are predicted to be able to support a greater abundance of herbivorous insects

than smaller hosts (Basset 1991, Marques et al. 2000, Whitfeld et al. 2012). This effect was demonstrated

for heather, Calluna vulgaris (L.), as increased abundance of lepidopteran larvae was correlated with

increased plant height in England and Scotland (Haysom and Coulson 1998). The interaction between

herbivores and plant height may also help explain the associations between elevation and insect counts in

this study. In a similar manner by which taller plants are more likely to be discovered by herbivores

(Marques et al. 2000), plants at higher elevations within a field may also be more easily detected. This is

supported by our data, as counts for a majority of pestiferous taxa significantly increased as elevation

increased.

The weak association between soybean plant height and predator counts in this study suggests that

these taxa selected their in-field habitat based upon traits other than plant height. For example, floral

resources have been previously shown to be beneficial to a wide variety of arthropod predators (Wäckers

2005). In a study involving 48 plant species, the authors found that the association between floral traits and

abundance was stronger for natural enemies when compared with herbivorous arthropods (Fiedler and

Landis 2007). Furthermore, resource-related traits may not always be the dominant drivers of predatory

distributions. Structure-mediated effects, such as microclimate modification via vegetation cover, have

been shown to be at least as important as resource-mediated effects in governing the population dynamics

(e.g. abundance, activity density, diversity, etc.) of predatory arthropods, such as spiders and ground beetles

in crop systems (Diehl et al. 2012, Balzan et al. 2016, Gardarin et al. 2018).

In our study, NDVI had a significant effect on whether or not the majority of pestiferous taxa were

observed, and each significant association for this variable and the presence of predatory taxa was positive.

NDVI was also a significant, positive predictor of counts for a majority of pestiferous taxa, while all

significant associations between this variable and counts of predatory taxa were also positive. Contrary to

our findings, previous studies on the associations of NDVI and pests have frequently reported inverse

12

relationships between the two. Beet armyworm, Spodoptera exigua (Lepidoptera: Noctuidae) (Hübner),

was found to correspond to low NDVI values in cotton (Sudbrink et al. 2003), and cumulative soybean

aphid days were associated with decreased NDVI values in soybean (Alves et al. 2015). However,

significant positive associations were reported for stink bug-related boll injury and NDVI values in cotton

(Reay-Jones et al. 2016). The authors suggested that this relationship might be due to either stink bugs

possessing a greater propensity to damage the bolls of vigorous cotton or the attraction of stink bugs to the

vigorous plants themselves. Proposed by Price (1991), the plant vigor hypothesis states that herbivores will

prefer and function better on more vigorous plants or plant modules, which may help to explain the positive

relationships between pest counts and NDVI in this study. Additionally, red reflectance (one half of the

NDVI equation) is known to be indicative of photosynthesis and chlorophyll content, while near-infrared

reflectance (the other half of the NDVI equation) can be related to leaf structural components along with

canopy cover and biomass (Hatfield et al. 2008, Marston et al. 2019). While some studies have shown that

the increased nutrient availability in stressed host plants can be beneficial for herbivores (e.g., the plant

stress hypothesis (White 1984), an increase in herbivorous insect fitness has also been associated with

vigorous plant growth (Prada et al. 1995). Given that a majority of all counts (pestiferous + predatory) were

significantly, positively associated with NDVI, our results support the plant vigor hypothesis.

Distance from the field edge was a significant predictor of counts for a majority of pestiferous

taxa, while Araneae, adults of Formicidae, and nymphs of P. maculiventris were the only predatory taxa

whose counts were significantly associated with the variable. For those pestiferous taxa that were

significantly associated with distance from the field edge, each association was negative (i.e. as distance

from the field edge increased, counts decreased). The negative association reported herein for M. cribraria

was previously shown by Seiter et al. (2013). The negative associations between distance from the field

edge and the counts of A. gemmatalis and C. includens differs from previous reports, however, as these

species have been described as randomly distributed in soybean fields (Hammond and Pedigo 1976,

Shepard and Carner 1976, Strayer et al. 1977). The in-field distribution of H. scabra has been found to vary

in soybean, as Pedigo et al. (1972) described a random distribution, while Bechinski et al. (1983) reported

an aggregated distribution. Indeed, it is likely that many herbivorous species exhibit clumped, or

13

aggregated distributions (Sabelis et al. 1999). Drivers of such aggregations may include herbivore

aggregation on plants whose defenses have been previously overwhelmed or otherwise weakened, the

oviposition of large egg clutches in a single location, and the generational overlap in a single location of

insects requiring minimal nutrition with relatively high reproductive ability, brief generation times, and low

dispersal (e.g. scales, aphids) (Sabelis et al. 1999).

Soil ECa measurements have been associated with many soil properties, including salinity, cation

exchange capacity, texture, and a soil’s water holding capacity (Grisso et al. 2005). Although thrips

presence and injury have been associated with soil ECa data in cotton (Reay-Jones et al. 2019), this variable

was a relatively poor predictor of arthropod counts in this study. Soil moisture is known to affect the

behavior (Villani and Wright 1988) and mortality (Hulthen and Clarke 2006) of soil arthropods, and soil

texture has been associated with variable densities of carabids in sugar beet, (Beta vulgaris (L.), fields

(Baker and Dunning 1975). The effects of soil moisture and texture on canopy-dwelling arthropods, such as

those collected through drop-cloth sampling in this study, may be minimal, however, given the infrequent

direct contact between these organisms and the soil.

Significant within-field variable associations varied widely among models for taxa within a

trophic level. Two of the most consistent predictors in this study (NDVI and plant height) were associated

with plant health and size (Ma et al. 2001, Hatfield et al. 2008, Marston et al. 2019), and some herbivorous

insects are known to respond to these host plant traits (Lawton 1983, Basset 1991, Price 1991, Haysom and

Coulson 1998, Marques et al. 2000, Poorter et al. 2004, Cornelissen and Stiling 2006, Whitfeld et al. 2012).

As previously mentioned, the plant traits that natural enemies (including predators) can respond to, such as

floral resources (Wäckers 2005, Fiedler and Landis 2007) and structure-mediated effects (Diehl et al. 2012,

Balzan et al. 2016, Gardarin et al. 2018), may differ from those that impact herbivorous insects. The overall

better estimation of pestiferous taxa (vs. predatory taxa) in this study is therefore likely due to the strength

of the relationship between herbivorous insects and their host plants.

Knowledge of the ecological interactions specific to a given species are critical to the development

of practical management applications for that species (Holland et al. 2005). Although multiple taxa may

share the same habitat, inherent biological differences among those species can play a role in how they

14

interact with their environment. These variable interactions may therefore require them to be managed

according to different criteria. Zaller et al. (2008) found that pollen beetle, Brassicogethes aeneus

(Coleoptera: Nitidulidae) (Fabricius), cabbage and rape stem weevil, Ceutorhynchus pallidactylus

(Coleoptera: Curculionidae) (Marsham), and pod midge, Dasineura brassicae (Diptera: Cecidomyiidae)

(Winnertz), which differed in feeding patterns, overwintering strategies, generation cycles, and mobility,

were associated with different within-field and landscape factors within oilseed rape, (Brassica napus (L.).

The different associations observed for these species led the authors to recommend management strategies

specific to each pest. In our study, calendar week (and associated soybean phenology), soybean plant height

(and associated elevation), and NDVI were the most consistent (positive or negative) significant predictors

of the presence and counts of arthropods across all models. Site-specific pest management has the potential

to improve the accuracy of farm records, reduce management inputs, increase profit, and reduce the

pollution associated with conventional management (i.e. whole-field) practices (Park and Krell 2005). In

order for site-specific practices to be implemented, however, management zones must be clearly defined

based on the within-field variability for the variables of interest. In this study, we identified which variables

were most effective at predicting the presence and counts of arthropods, as well as which taxa were most

associated with the within-field variables measured in soybean in South Carolina. If site-specific pest

management practices are to be applied in soybean, calendar week (and associated soybean phenology),

plant height (and associated elevation), and NDVI may be useful for describing the distributions of pests

such as M. cribraria, H. scabra, Cicadellidae, A. gemmatalis, and C. includens.

Acknowledgements

The authors thank everyone who assisted with data collection at the Clemson University Research

and Education Center, as well as the W. Carl Nettles, Sr., and Ruby S. Nettles Memorial Endowment in

Entomology, and the South Carolina Soybean Board for providing funding for this project. This is technical

contribution No. 6919 of the Clemson University Experiment Station. This manuscript is based upon the

work supported by the National Institute of Food and Agriculture/U. S. Department of Agriculture, under

project numbers SC-1700531 and SC-1700532.

15

References Cited

Alves, T. M., I. V. Macrae, and R. L. Koch. 2015. Soybean aphid (Hemiptera: Aphididae) affects

soybean spectral reflectance. J. Econ. Entomol. 108: 2655–2664.

Azrag, A. G., C. W. Pirk, A. A. Yusuf, F. Pinard, S. Niassy, G. Mosomtai, and R. Babin. 2018.

Prediction of insect pest distribution as influenced by elevation: Combining field observations and

temperature-dependent development models for the coffee stink bug, Antestiopsis thunbergii (Gmelin).

PloS One. 13(6): e0199569.

Baker, A. N., and R. A. Dunning. 1975. Some effects of soil type and crop density on the activity and

abundance of the epigeic fauna, particularly Carabidae, in sugar-beet fields. J. Appl. Ecol. 809–818.

Balzan, M. V., G. Bocci, and A.C. Moonen. 2016. Utilisation of plant functional diversity in wildflower

strips for the delivery of multiple agroecosystem services. Entomol. Exp. Appl. 158: 304–319.

Basset, Y. 1991. Influence of leaf traits on the spatial distribution of insect herbivores associated with an

overstorey rainforest tree. Oecologia. 87: 388–393.

Bauer, M. E., and J. E. Cipra. 1973. Identification of agricultural crops by computer processing of ERTS

MSS data, pp. 205–212. In Proc Symp Signif. Results ERTS-1, 5-9 March 1973, New Carollton, IN.

Purdue University, W. Lafayette, IN, USA.

Baur, M. E., D. J. Boethel, M. L. Boyd, G. R. Bowers, M. O. Way, L. G. Heatherly, J. Rabb, and L.

Ashlock. 2000. Arthropod populations in early soybean production systems in the mid-south. Environ.

Entomol. 29: 312–328.

Bechinski, E. J., G. D. Buntin, L. P. Pedigo, and H. G. Thorvilson. 1983. Sequential count and decision

plans for sampling green cloverworm (Lepidoptera: Noctuidae) larvae in soybean. J. Econ. Entomol. 76:

806–812.

Brooks, M. E., K. Kristensen, K. J. van Benthem, A. Magnusson, C. W. Berg, A. Nielsen, H. J. Skaug,

M. Maechler, and B. M. Bolker. 2017. glmmTMB Balances Speed and Flexibility Among Packages for

Zero-inflated Generalized Linear Mixed Modeling. R J. 9: 378–400.

Campanella, R. 2000. Testing components toward a remote-sensing-based decision support system for

cotton production. Photogramm. Eng. Remote Sens. 66: 1219–1228.

16

Cornelissen, T., and P. Stiling. 2006. Responses of different herbivore guilds to nutrient addition and

natural enemy exclusion. Ecoscience. 13: 66–74.

Cunningham, R. B., and D. B. Lindenmayer. 2005. Modeling count data of rare species: some statistical

issues. Ecology. 86: 1135–1142.

Dauber, J., T. Purtauf, A. Allspach, J. Frisch, K. Voigtländer, and V. Wolters. 2005. Local vs.

landscape controls on diversity: a test using surface‐dwelling soil macroinvertebrates of differing mobility.

Glob. Ecol. Biogeogr. 14: 213–221.

Davis, P. M. 1994. Statistics for describing populations. Handb. Sampl. Methods Arthropods Agric. 33–54.

Deitz, L. L., J. W. Van Duyn, J. R. Bradley Jr., R. L. Rabb, W. M. Brooks, and R. E. Stinner. 1976. A

guide to the identification and biology of soybean arthropods in North Carolina. Tech. Bull No. 238. North

Carolina Agricultural Research Service, Raleigh, NC.

Dennis, P., M. R. Young, and I. J. Gordon. 1998. Distribution and abundance of small insects and

arachnids in relation to structural heterogeneity of grazed, indigenous grasslands. Ecol. Entomol. 23: 253–

264.

Desender, K. 1982. Ecological and faunal studies on Coleoptera in agricultural land. II. Hibernation of

Carabidae in agroecosystems. Pedobiologia. 22:295-303.

Diehl, E., V. Wolters, and K. Birkhofer. 2012. Arable weeds in organically managed wheat fields foster

carabid beetles by resource-and structure-mediated effects. Arthropod-Plant Interact. 6: 75–82.

Doerge, T. 1999. Defining management zones for precision farming. Crop Insights. 8: 1–5.

Doraiswamy, P. C., S. Moulin, P. W. Cook, and A. Stern. 2003. Crop yield assessment from remote

sensing. Photogramm. Eng. Remote Sens. 69: 665–674.

Fehr, W. R., and C. E. Caviness. 1977. Stages of soybean development. Iowa Agricultural and Home

Economics Experiment Station Special Report, pp. 3-11. Iowa State University, Ames, IA.

Fiedler, A. K., and D. A. Landis. 2007. Plant characteristics associated with natural enemy abundance at

Michigan native plants. Environ. Entomol. 36: 878–886.

17

da Fonseca, P. R. B., M. G. Fernandes, W. Justiniano, L. H. Cavada, and J. A. N. da Silva. 2014.

Spatial distribution of adults and nymphs of Euschistus heros (F.)(Hemiptera: Pentatomidae) on Bt and

non-Bt soybean. J. Agric. Sci. 6: 131.

Gardarin, A., M. Plantegenest, A. Bischoff, and M. Valantin-Morison. 2018. Understanding plant–

arthropod interactions in multitrophic communities to improve conservation biological control: useful traits

and metrics. J. Pest Sci. 91: 943–955.

Gebbers, R., and V. I. Adamchuk. 2010. Precision agriculture and food security. Science. 327: 828–831.

Grisso, R.B., M. A. W. G., Wysor, D. Holshouser, and W. Thomason. 2009. Precision Farming Tools,

Soil Electrical Conductivity. Virginia Cooperative Extension: 442–508.

Hammond, R. B., and L. P. Pedigo. 1976. Sequential sampling plans for the green cloverworm in Iowa

soybeans. J. Econ. Entomol. 69: 181–185.

Hatfield, J. L., A. A. Gitelson, J. S. Schepers, and C. L. Walthall. 2008. Application of spectral remote

sensing for agronomic decisions. Agron. J. 100: S-117-S-131.

Hatfield, P. L., and P. J. Pinter Jr. 1993. Remote sensing for crop protection. Crop Prot. 12: 403–413.

Haysom, K. A., and J. C. Coulson. 1998. The Lepidoptera fauna associated with Calluna vulgaris: effects

of plant architecture on abundance and diversity. Ecol. Entomol. 23: 377–385.

Higley, L. G., and D. J. Boethel. 1994. Handbook of soybean insect pests. Entomological Society of

America, Lanham, MD.

Hodgson, E. W., E. C. Burkness, W. D. Hutchison, and D. W. Ragsdale. 2004. Enumerative and

binomial sequential sampling plans for soybean aphid (Homoptera: Aphididae) in soybean. J. Econ.

Entomol. 97: 2127–2136.

Holland, J., C. F. G. Thomas, T. Birkett, S. Southway, and H. Oaten. 2005. Farm‐scale spatiotemporal

dynamics of predatory beetles in arable crops. J. Appl. Ecol. 42: 1140–1152.

Hulthen, A. D., and A. R. Clarke. 2006. The influence of soil type and moisture on pupal survival of

Bactrocera tryoni (Froggatt) (Diptera: Tephritidae). Aust. J. Entomol. 45: 16–19.

Jewell, N. 1989. An evaluation of multi-date SPOT data for agriculture and land use mapping in the United

Kingdom. Int. J. Remote Sens. 10: 939–951.

18

Joern, A., T. Provin, and S. T. Behmer. 2012. Not just the usual suspects: insect herbivore populations

and communities are associated with multiple plant nutrients. Ecology. 93: 1002–1015.

Kahle, D., and H. Wickham. 2013. ggmap: Spatial Visualization with ggplot2. R J. 5: 144–161.

Kharboutli, M. S., and T. P. Mack. 1991. Relative and seasonal abundance of predaceous arthropods in

Alabama peanut fields as indexed by pitfall traps. J. Econ. Entomol. 84: 1015–1023.

Kogan, M., and S. G. Turnipseed. 1987. Ecology and management of soybean arthropods. Annu. Rev.

Entomol. 32: 507–538.

Krell, R. K., L. P. Pedigo, and B. A. Babcock. 2003. Comparison of estimated costs and benefits of site-

specific versus uniform management for the bean leaf beetle in soybean. Precis. Agric. 4: 401–411.

Lawton, J. H. 1983. Plant architecture and the diversity of phytophagous insects. Annu. Rev. Entomol. 28:

23–39.

LeSar, C. D., and J. D. Unzicker. 1978. Soybean spiders: species composition, population densities and

vertical distribution. Ill. Nat. Hist. Surv. Biol. Notes 107: 1-14.Liaghat, S., and S. K. Balasundram. 2010.

A review: The role of remote sensing in precision agriculture. Am. J. Agric. Biol. Sci. 5: 50–55.

Ma, B. L., L. M. Dwyer, C. Costa, E. R. Cober, and M. J. Morrison. 2001. Early prediction of soybean

yield from canopy reflectance measurements. Agron. J. 93: 1227–1234.

Marques, E. S. D. A., P. W. Price, and N. S. Cobb. 2000. Resource abundance and insect herbivore

diversity on woody fabaceous desert plants. Environ. Entomol. 29: 696–703.

Marshall, M., J. Greene, D. Gunter, F. Reay-Jones, J. Mueller, D. Anco, K. Moore, P. Peterson, B.

Powell, C. Heaton, J. Crouch, and B. Beer. 2020. 2020 South Carolina Pest Management Handbook.

Clemson University Extension.

Marston, Z. P., T. M. Cira, E. W. Hodgson, J. F. Knight, I. V. Macrae, and R. L. Koch. 2019.

Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from

Unmanned Aerial Vehicles. J. Econ. Entomol. 113: 779–786.

Maunsell, S. C., R. L. Kitching, C. J. Burwell, and R. J. Morris. 2015. Changes in host–parasitoid food

web structure with elevation. J. Anim. Ecol. 84: 353–363.

19

McCarty, M. T., M. Shepard, and S. G. Turnipseed. 1980. Identification of predaceous arthropods in

soybeans by using autoradiography. Environ. Entomol. 9: 199–203.Midgarden, D., S. J. Fleischer, R.

Weisz, and Z. Smilowitz. 1997. Site-specific integrated pest management impact on development of

esfenvalerate resistance in Colorado potato beetle (Coleoptera: Chrysomelidae) and on densities of natural

enemies. J. Econ. Entomol. 90: 855–867.

Mulla, D. J. 2013. Twenty five years of remote sensing in precision agriculture: Key advances and

remaining knowledge gaps. Biosyst. Eng. 114: 358–371.

Oksanen, J., F. G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P. R. Minchin, R. B.

O’Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens, E. Szoecs, and H. Wagner. 2019. vegan:

Community Ecology Package.

Park, Y.-L., and R. K. Krell. 2005. Generation of prescription maps for curative and preventative site-

specific management of bean leaf beetles (Coleoptera: Chrysomelidae). J. Asia-Pac. Entomol. 8: 375–380.

Park, Y.-L., R. K. Krell, and M. Carroll. 2007. Theory, technology, and practice of site-specific insect

pest management. J. Asia-Pac. Entomol. 10: 89–101.

Peck, R. W., P. C. Banko, M. Schwarzfeld, M. Euaparadorn, and K. W. Brinck. 2008. Alien

dominance of the parasitoid wasp community along an elevation gradient on Hawai’i Island. Biol.

Invasions. 10: 1441–1455.

Pedigo, L. P., G. L. Lentz, J. D. Stone, and D. F. Cox. 1972. Green cloverworm populations in Iowa

soybean with special reference to sampling procedure. J. Econ. Entomol. 65: 414–421.

Poorter, L., M. Van de Plassche, S. Willems, and R. G. A. Boot. 2004. Leaf traits and herbivory rates of

tropical tree species differing in successional status. Plant Biol. 6: 746–754.

Prabhakar, M., Y. G. Prasad, and M. N. Rao. 2012. Remote sensing of biotic stress in crop plants and its

applications for pest management, pp. 517– 545. In B. Venkateswarlu, A. K. Shanker, C. Shanker and M.

Maheswari (eds.), Crop stress and its management: perspectives and strategies. Springer, Dordrecht,

Netherlands.

20

Prada, M., O. J. Marini-Filho, and P. W. Price. 1995. Insects in flower heads of Aspilia foliacea

(Asteraceae) after a fire in a central Brazilian savanna: evidence for the plant vigor hypothesis. Biotropica.

27: 513–518.

Price, P. W. 1991. The plant vigor hypothesis and herbivore attack. Oikos. 244–251.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. R Foundation for

Statistical Computing, Vienna, Austria.

Reay-Jones, F. P., J. K. Greene, and P. J. Bauer. 2016. Stability of spatial distributions of stink bugs,

boll injury, and NDVI in cotton. Environ. Entomol. 45: 1243–1254.

Reay-Jones, F. P., J. K. Greene, and P. J. Bauer. 2019. Spatial Distributions of Thrips (Thysanoptera:

Thripidae) in Cotton. J. Insect Sci. 19: 3.

Rypstra, A. L., P. E. Carter, R. A. Balfour, and S. D. Marshall. 1999. Architectural features of

agricultural habitats and their impact on the spider inhabitants. J. Arachnol. 371–377.

Sabelis, M. W., M. Van Baalen, F. M. Bakker, J. Bruin, B. Drukker, C. J. M. Egas, A. Janssen, I. K.

A. Lesna, S. H. Pels, and P. C. J. Van Rijn. 1999. The evolution of direct and indirect plant defence

against herbivorous arthropods. Plants Herbiv. Predat. 109–166.

Seiter, N. J., F. P. F. Reay-Jones, and J. K. Greene. 2013. Within-Field Spatial Distribution of

Megacopta cribraria (Hemiptera: Plataspidae) in Soybean (Fabales: Fabaceae). Environ. Entomol. 42:

1363–1374.

Shepard, M., and G. R. Carner. 1976. Distribution of insects in soybean fields. Can. Entomol. 108: 767–

771.

Shepard, M., G. R. Carner, and S. G. Turnipseed. 1974. Seasonal abundance of predaceous arthropods

in soybeans. Environ. Entomol. 3: 985–988.

Shepard, M., G. R. Carner, and S. G. Turnipseed. 1977. Colonization and resurgence of insect pests of

soybean in response to insecticides and field isolation. Environ. Entomol. 6: 501–506.

Smith, J. F., R. G. Luttrell, and J. K. Greene. 2009. Seasonal abundance, species composition, and

population dynamics of stink bugs in production fields of early and late soybean in south Arkansas. J.

Econ. Entomol. 102: 229–236.

21

Steffey, K. L. 2015. Insects and their management, pp. 136–137. In G. L. Hartman, J. C. Rupe, E. J.

Sikora, L. L. Domier, J. A. Davis, and K. L. Steffey (eds.), Compendium of soybean diseases and pests, 5th

ed., APS Press, St. Paul, MN.

Strayer, J., M. Shepard, and S. G. Turnipseed. 1977. Sequential sampling for management decisions on

the velvetbean caterpillar on soybeans. J. Ga. Entomol. Soc. 12: 220-227.

Sudbrink, D. L., F. A. Harris, J. T. Robbins, P. J. English, and J. L. Willers. 2003. Evaluation of

remote sensing to identify variability in cotton plant growth and correlation with larval densities of beet

armyworm and cabbage looper (Lepidoptera: Noctuidae). Fla. Entomol. 86: 290–294.

Sudduth, K. A., N. R. Kitchen, W. J. Wiebold, W. D. Batchelor, G. A. Bollero, D. G. Bullock, D. E.

Clay, H. L. Palm, F. J. Pierce, and R. T. Schuler. 2005. Relating apparent electrical conductivity to soil

properties across the north-central USA. Comput. Electron. Agric. 46: 263–283.

Turnipseed, S. G. 1973. Insects, pp. 545–572. In Caldwell, E. (ed.), Soybeans Improv. Prod. Uses. Amer.

Soc. Agron., Madison, WI.

Turnipseed, S., and M. Kogan. 1983. Soybean Pests and Indigenous Natural Enemies, pp. 1–6. In Pitre,

H.N. (ed.), Nat. Enemies Arthropod Pests Soybean, So. Coop. Ser. Bull. 285:1-90.

Villani, M. G., and R. J. Wright. 1988. Use of radiography in behavioral studies of turfgrass-infesting

scarab grub species (Coleoptera: Scarabaeidae). Bull. ESA. 34: 132–144.

Wäckers, F. L. 2005. Suitability of (extra-) floral nectar, pollen, and honeydew as insect food sources. In:

Wackers, F.L., van Rijn, P.C.J., Bruin, J. (Eds.), Plant Provided Food for Carnivorous Insects. Cambridge

University Press, Cambridge, UK, pp. 17–74.

Way, M. O. 1994. Status of soybean insect pests in the United States, pp. 15Ð16. In L. G. Higley and D. J.

Boethel [eds.], Handbook of soybean insect pests. Entomological Society of America, Lanham, MD.

White, T. t. 1984. The abundance of invertebrate herbivores in relation to the availability of nitrogen in

stressed food plants. Oecologia. 63: 90–105.

Whitfeld, T. J., V. Novotny, S. E. Miller, J. Hrcek, P. Klimes, and G. D. Weiblen. 2012. Predicting

tropical insect herbivore abundance from host plant traits and phylogeny. Ecology. 93: S211–S222.

22

Zaller, J. G., D. Moser, T. Drapela, C. Schmöger, and T. Frank. 2008. Effect of within-field and

landscape factors on insect damage in winter oilseed rape. Agric. Ecosyst. Environ. 123: 233–238.

Zhang, N., M. Wang, and N. Wang. 2002. Precision agriculture—a worldwide overview. Comput.

Electron. Agric. 36: 113–132.

23

Table 1.1. Pestiferous taxa summary statistics and significant predictor variables (estimates ± SE) of pestiferous taxa counts from regression

analyses of soybean drop-cloth data

Total = total number of observed arthropods; Average = average number of observed arthropods per 3.7 m of row sampled

NB = Negative Binomial portion of model; ZI = Zero-Inflated portion of model; White cells = variable was not significant

Light Gray cells = variable was significantly positively associated with arthropod counts

Dark Gray cells = variable was significantly negatively associated with arthropod counts

Analysis was unsuccessful for adults of C.hilaris

Life Stage(s) Larva Nymph Larva Both Larva Adult Nymph Adult Nymph Adult Nymph

Taxa

Anticarsia

gemmatalis

(Hübner)

Chinavia

hilaris (Say)

Chrysodeixis

includens

(Walker)

Cicadellidae

Hypena

scabra

(Fabricius)

Megacopta

cribraria

(Fabricius)

Megacopta

cribraria

Nezara

viridula

(Linnaeus)

Nezara

viridula

Spissistilus

festinus (Say)

Spissistilus

festinus

Total 18853 700 8725 1923 4522 31817 17605 687 2361 1102 2950

Average 11.22 0.42 5.19 1.14 2.69 18.94 10.48 0.41 1.41 0.66 1.76

SEM 0.73 0.03 0.22 0.07 0.17 0.87 0.53 0.03 0.08 0.03 0.05

Distance

from the

field edge

ZI 0.11 ± 0.27 1.08 ± 0.31 0.12 ± 0.15 0.52 ± 0.15 -0.2 ± 0.1 0.66 ± 0.37 0.34 ± 0.08 -0.67 ± 0.54 0.16 ± 0.17 -0.78 ± 0.47 -0.39 ± 0.18

NB -0.16 ± 0.06 -0.01 ± 0.09 -0.19 ± 0.04 -0.21 ± 0.08 -0.25 ± 0.09 -0.21 ± 0.04 -0.27 ± 0.05 0.1 ± 0.08 0.09 ± 0.06 -0.06 ± 0.04 0.03 ± 0.03

Elevation

ZI -0.06 ± 0.19 0.67 ± 0.31 -0.39 ± 0.15 -0.24 ± 0.19 -0.1 ± 0.1 0.47 ± 0.39 -0.59 ± 0.09 0.68 ± 0.46 -0.21 ± 0.17 0.43 ± 0.37 0.05 ± 0.17

NB 0.27 ± 0.1 0.18 ± 0.12 -0.1 ± 0.06 0.26 ± 0.09 0.26 ± 0.13 0.27 ± 0.06 0.33 ± 0.07 0.18 ± 0.13 0.26 ± 0.09 0.07 ± 0.07 -0.02 ± 0.05

NDVI

ZI 2.38 ± 0.66 1.85 ± 0.64 0.33 ± 0.18 -0.45 ± 0.16 -0.72 ± 0.12 -0.91 ± 0.23 -0.59 ± 0.09 -0.18 ± 0.3 2.06 ± 0.35 0.09 ± 0.3 -0.03 ± 0.15

NB 0.09 ± 0.05 0.54 ± 0.09 0.16 ± 0.06 0.04 ± 0.07 0.27 ± 0.07 0.03 ± 0.04 0.08 ± 0.07 0.36 ± 0.1 0.44 ± 0.08 0.13 ± 0.06 0.17 ± 0.04

Soil ECa

ZI 0.89 ± 0.27 0.76 ± 0.3 0.46 ± 0.15 -0.42 ± 0.21 -0.42 ± 0.14 0.13 ± 0.34 0.64 ± 0.09 -3.23 ± 1.94 -0.1 ± 0.19 -1.16 ± 0.64 -0.5 ± 0.34

NB -0.22 ± 0.09 0 ± 0.12 -0.11 ± 0.06 0.06 ± 0.08 -0.14 ± 0.12 0.03 ± 0.06 -0.07 ± 0.08 -0.08 ± 0.11 0.08 ± 0.08 -0.04 ± 0.07 -0.07 ± 0.05

Soybean

plant height

ZI 0.3 ± 0.63 -2.63 ± 0.52 -2.98 ± 0.28 0.86 ± 0.28 0.71 ± 0.14 -3.12 ± 1.05 -1.82 ± 0.13 -1.32 ± 0.69 -2.72 ± 0.35 0.09 ± 0.3 -2.3 ± 0.55

NB 1.05 ± 0.08 -0.17 ± 0.16 1.04 ± 0.07 0.6 ± 0.12 1.1 ± 0.09 -0.24 ± 0.04 0.52 ± 0.08 -0.22 ± 0.12 -0.28 ± 0.09 0.18 ± 0.07 0.01 ± 0.05

Calendar

week NB 1.62 ± 0.08 0.83 ± 0.11 -1.83 ± 0.08 -1.54 ± 0.14 -1.06 ± 0.11 1.05 ± 0.04 -0.35 ± 0.14 1.69 ± 0.11 1.09 ± 0.14 0.24 ± 0.06 0.03 ± 0.06

Spatial EV NB 0.05 ± 0.12 0.17 ± 0.14 -0.06 ± 0.07 -0.27 ± 0.11 -0.2 ± 0.16 0.37 ± 0.08 0.36 ± 0.1 -0.1 ± 0.15 -0.19 ± 0.11 0.1 ± 0.08 0.21 ± 0.06

24

Table 1.2. Predatory taxa summary statistics and significant predictor variables (estimates ± SE) of predatory taxa counts from regression

analyses of soybean drop-cloth data

Life Stage(s) Adult Both Adult Nymph Adult Nymph Adult Nymph Nymph

Taxa Anthicidae Araneae Formicidae Geocoridae Nabidae Nabidae

Podisus

maculiventris

(Say)

Podisus

maculiventris Reduviidae

Total 1489 2503 9965 651 888 451 142 249 196

Average 1.03 1.49 5.93 0.39 0.53 0.27 0.08 0.15 0.12

SEM 0.05 0.05 0.25 0.02 0.03 0.03 0.01 0.01 0.01

Distance

from the

field edge

ZI 0.4 ± 0.31 0.21 ± 0.11 0.22 ± 0.07 0.84 ± 0.29 0.08 ± 0.11 0.84 ± 0.32 0.18 ± 0.38 -0.51 ± 0.58 0.3 ± 0.35

NB 0.03 ± 0.08 -0.16 ± 0.04 -0.17 ± 0.04 0.06 ± 0.07 0.08 ± 0.08 0.07 ± 0.13 0.01 ± 0.14 0.31 ± 0.09 0.05 ± 0.12

Elevation

ZI -1.12 ± 0.37 -0.07 ± 0.14 0.29 ± 0.08 -0.1 ± 0.33 -0.03 ± 0.14 -0.34 ± 0.28 0.06 ± 1.59 0.93 ± 0.76 -0.18 ± 0.44

NB 0.21 ± 0.12 0 ± 0.06 0.06 ± 0.06 -0.17 ± 0.1 0.39 ± 0.13 -0.18 ± 0.15 -0.14 ± 0.37 0.45 ± 0.14 0.11 ± 0.17

NDVI

ZI -0.45 ± 0.25 0.03 ± 0.14 -0.13 ± 0.07 0.75 ± 0.32 -0.14 ± 0.14 1.14 ± 0.4 -0.83 ± 0.55 3.16 ± 1.35 0.47 ± 0.51

NB 0.05 ± 0.09 0.23 ± 0.04 0.21 ± 0.04 0.2 ± 0.08 0.32 ± 0.12 0.31 ± 0.17 0.02 ± 0.21 0 ± 0.09 0.12 ± 0.15

Soil ECa

ZI 0.16 ± 0.31 -0.25 ± 0.18 -0.01 ± 0.08 0.26 ± 0.3 -0.09 ± 0.13 -0.86 ± 0.44 1.72 ± 1.16 -5.14 ± 2.57 -0.03 ± 0.44

NB -0.29 ± 0.11 0.04 ± 0.05 0.01 ± 0.05 -0.1 ± 0.1 -0.23 ± 0.11 -0.15 ± 0.14 0.36 ± 0.46 -0.12 ± 0.14 -0.31 ± 0.18

Soybean

plant

height

ZI -2.7 ± 0.87 0.14 ± 0.22 -0.09 ± 0.08 -3.12 ± 0.68 -0.67 ± 0.21 -2.2 ± 0.78 1.04 ± 1.76 -3.69 ± 1.62 -3.07 ± 0.64

NB -0.15 ± 0.13 0.05 ± 0.06 -0.09 ± 0.05 -0.19 ± 0.13 0.09 ± 0.15 -0.09 ± 0.2 0.22 ± 0.34 0.19 ± 0.13 -0.31 ± 0.24

Calendar

week NB -0.03 ± 0.11 0.13 ± 0.04 0.42 ± 0.04 0.53 ± 0.1 0.45 ± 0.1 0.41 ± 0.16 1.06 ± 0.13 1 ± 0.11 0.1 ± 0.17

Spatial

EV NB -0.62 ± 0.15 0.09 ± 0.07 -0.25 ± 0.07 -0.07 ± 0.12 -0.46 ± 0.13 -0.47 ± 0.17 0.08 ± 0.22 0.15 ± 0.17 0.12 ± 0.2

Total = total number of observed arthropods; Average = average number of observed arthropods per 3.7 m of row sampled

NB = Negative Binomial portion of model; ZI = Zero-Inflated portion of model; White cells = variable was not significant

Light Gray cells = variable was significantly positively associated with arthropod counts

Dark Gray cells = variable was significantly negatively associated with arthropod counts

Analyses were unsuccessful for adults of Geocoridae and Reduviidae

25

Figure 1.1. Soybean sampling locations at Edisto Research and Education Center, Blackville, SC. Each

numbered point represents a sampling location marked with a fiberglass flag. A) Field A: sampling points

1-66. B) Field B: sampling points 67-120. Maps were constructed using the ggmap package (Kahle and

Wickham 2013) in R version 3.5.3 (R Core Team 2019).

26

Figure 1.2. Soybean pestiferous arthropod seasonal dynamics (average ± SE) and associated soybean

phenology across fields (A & B) and years (2017 & 2018). A) taxa with high counts B) taxa with low

counts. Average = average of all drop-cloth samples taken from all locations during a particular calendar

week across fields (A & B) and years (2017 & 2018). Soybean Growth Stage = range of soybean growth

stages across all sampled locations across fields (A & B) and years (2017 & 2018). No samples were

collected during calendar week 37.

27

Figure 1.3. Soybean predatory arthropod seasonal dynamics (average ± SE) and associated soybean

phenology across fields (A & B) and years (2017 & 2018). A) taxa with high counts B) taxa with low

counts. Average = average of all drop-cloth samples taken from all locations during a particular calendar

week across fields (A & B) and years (2017 & 2018). Soybean Growth Stage = range of soybean growth

stages across all sampled locations across fields (A & B) and years (2017 & 2018). No samples were

collected during calendar week 37.

28

CHAPTER TWO

SPATIAL ASSOICATIONS OF KEY LEPIDOPTERAN PESTS WITH DEFOLIATION,

NDVI, AND PLANT HEIGHTS IN SOYBEAN

Abstract

In soybean, Glycine max (L.) Merrill, production, losses to and control costs for insect pests can be

significant limiting factors. Although the heterogeneity of pests has typically been ignored in traditional

field management practices, technological advancements have allowed for site-specific pest management

systems to be developed for the precise control of pests within a field. In this study, we chose to determine

how the in-field distributions of the larvae of three major lepidopteran pests [velvetbean caterpillar

Anticarsia gemmatalis (Hübner) (Lepidoptera: Erebidae), soybean looper Chrysodeixis includens (Walker)

(Lepidoptera: Noctuidae), and green cloverworm Hypena scabra (Lepidoptera: Erebidae) (Fabricius)] were

spatially associated with defoliation, Normalized Difference Vegetation Index (NDVI), and plant height in

soybean. Spatial analysis by distance indices (SADIE) of data from two South Carolina soybean fields in

2017 and 2018 revealed a limited number of spatial aggregations for insect datasets. However, 14% and 6%

of paired plant-insect datasets were significantly associated or dissociated, respectively. NDVI was found

to be more associated with pest distributions than soybean plant heights and defoliation estimates, and the

majority of all plant-insect associations and dissociations occurred in the first four weeks of sampling (late

July-early August). If changes are to be implemented regarding how a pest is managed, critical factors

explaining the spatial distribution of pests must be identified. Results from this study advocate for the

relationship between early-season distributions of pests and important plant variables such as NDVI to be

further investigated to better determine the strength of the correlations across years and sites.

KEY WORDS NDVI, site-specific pest management, Lepidoptera, SADIE, plant height

Introduction

With over 118 million metric tons harvested on 37 million hectares in 2017, soybean, Glycine max

(L.) Merrill, is the second-largest field crop in the United States (USA) (USDA-NASS 2019). There are

29

many challenges to profitable production of soybean, and losses to and control costs for pests can be

significant limiting factors. Although weeds (Oerke 2006), pathogens (Hartman et al. 1991, Hoffman et al.

1998), and nematodes (Hartman et al. 2011) can be costly pests, the short generation time, rapid dispersal,

and fecundity of many insect pests (MacArthur and Wilson 1967) can result in such substantial population

growth that equilibrium is never reached in seasonal monocultures such as soybean (Horn 2000).

Additionally, numerous components of soybean plants are fed upon by insects, as representatives of the

defoliating [e.g. velvetbean caterpillar, Anticarsia gemmatalis (Hübner) (Lepidoptera: Erebidae) and

soybean looper, Chrysodeixis includens (Walker) (Lepidoptera: Noctuidae)], phloem-feeding [e.g. soybean

aphid, Aphis glycines Matsumura (Hemiptera: Aphididae) and kudzu bug, Megacopta cribraria (Fabricius)

(Hemiptera: Plataspidae) (Stubbins et al. 2017)], and seed-feeding guilds [e.g. green stink bug, Chinavia

hilaris (Say) (Hemiptera: Pentatomidae) and southern green stink bug, Nezara viridula (Linnaeus)

(Hemiptera: Pentatomidae)] are known pests in the crop (Sinclair et al. 1997, O’Neal and Johnson 2010).

Control of insect pests in soybean is further complicated by the fact that these organisms are

frequently unevenly distributed across space and time (Oerke et al. 2010). Despite such heterogeneous

distributions, decisions regarding field management are typically applied to an entire field (Park et al. 2007,

Merrill et al. 2015). Within the last 40 years, however, technological advancements in global positioning

systems (GPS) and geographical information systems (GIS) (Krell et al. 2003), combined with variable-rate

technology (Pedigo 2002) and proximal and remote sensing (Gebbers and Adamchuk 2010), have allowed

for the heterogeneous regions within fields to be managed independently. Taken together, these

components comprise the production practice known as precision agriculture (Seelan et al. 2003), while

site-specific management is the utilization of these techniques to apply the appropriate management

practice at the correct place and time (Oerke et al. 2010). While site-specific management tools such as

GPS soil maps, yield maps, and GPS-guided operating systems have been adopted on 30, 40, and 50% of

USA corn and soybean hectares, respectively (Schimmelpfennig 2016), the paucity of information

regarding how arthropods are distributed in crop systems presents a challenge for the adoption of site-

specific pest management programs (Oerke et al. 2010). Given that site-specific pest management is most

useful for those pests that display spatially aggregated distributions and cannot readily disperse (Krell et al.

30

2003, Park and Krell 2005), gaining a better understanding of how arthropods are distributed within crops

is of paramount importance to increasing the adoption rate of site-specific pest management programs.

In soybean, although pests such as M. cribraria (Seiter et al. 2013), Neotropical brown stink bug,

Euschistus heros (Fabricius) (da Fonseca et al. 2014), A. glycines (Hodgson et al. 2004), and bean leaf

beetle, Cerotoma trifurcata (Forster) (Coleoptera: Chrysomelidae) (Park and Krell 2005) have been found

to have aggregated in-field distributions, site-specific pest management has only been explored for the

latter species in this crop. In doing so, Krell et al. (2003) found that site-specific management of bean leaf

beetle could produce marginally greater returns, but only when sampling costs were not included in the

estimation. The authors further stressed the importance of the use of technology to decrease the sampling

costs of site-specific management programs through the association of remotely-sensed within-field

variables with pest presence.

In this study, we chose to examine the spatial aggregation patterns of the larvae of three major

lepidopteran pests (Higley and Boethel 1994, Funderburk et al. 1999, Guillebeau et al. 2008): A.

gemmatalis, C. includens, and green cloverworm, Hypena scabra (Lepidoptera: Erebidae) (Fabricius),

along with the plant variables: defoliation, Normalized Difference Vegetation Index (NDVI), and plant

height in soybean. Although these pests were found to be randomly distributed in previous research in

soybean (Pedigo et al. 1972, Hammond and Pedigo 1976, Shepard and Carner 1976, Strayer et al. 1977),

with an additional report of an aggregated distribution for H. scabra (Bechinski et al. 1983), those analyses

focused on the relationships between the mean densities and variance of the respective pests in an effort to

identify the appropriate frequency distribution describing an organism’s in-field population dynamics. The

location of samples within a field was not considered in those analyses. Furthermore, we were interested in

determining how the spatial distributions of the lepidopteran pests and plant variables in soybean were

spatially associated. Vegetation indices such as NDVI have previously been correlated with cumulative

soybean aphid days in soybean (Alves et al. 2015), while simulated insect defoliation was found to

significantly reduce plant heights in soybean plots also challenged by weed competition (Gustafson et al.

2006). Greene et al. (2021) sought to understand how the presence and counts of pestiferous and

predaceous soybean arthropods collected via drop-cloth sampling were associated with the soybean plant

31

height and NDVI data reported herein. In our previous study, the effect of space was not of specific

interest, but was instead accounted for using principal components of neighborhood matrices (PCNM)

analysis. In this study, we focused on examining the spatial distributions and associations of plant variables

and sweep-net collected lepidopteran pests through spatial analysis by distance indices (SADIE). Given the

high costs associated with the spatially-intensive arthropod sampling required for site-specific management

decision-making, the correlation between the spatial distributions of the plant variables defoliation, NDVI,

and plant height and the spatial distributions of key lepidopteran pests is important for developing future

site-specific management of these pests in soybean that is more cost-effective than uniform management

tactics.

Materials and Methods

Field Trials

At the Clemson University Edisto Research and Education Center (REC) in Blackville, SC, fields

‘A’ (8.9 ha) and ‘B’ (5.7 ha) were planted on 9 June (A) and 12 June (B) in 2017 and 16 June (A) and 12

June (B) in 2018 using a 96.5 cm row spacing and soybean varieties Asgrow AG75X6 Roundup Ready 2

Xtend and Bayer Credenz LibertyLink 7007LL in 2017 and AG69X6 and Pioneer P67T90R2 in 2018,

respectively. Extension recommendations were followed for plant populations and herbicide and fertilizer

applications (Marshall et al. 2020). No insecticides were applied during trials. Data from these two fields

were previously used to determine how soybean arthropods observed via drop-cloth sampling were

associated with site characteristics, including the soybean plant height and NDVI data used in this study

(Greene et al. 2021).

Sampling

Sampling grids consisted of placing fiberglass flags ≈ 40 m apart, totaling 66 flags in field A and

54 flags in field B, with identical grids across years in each field. Within a 5 m radius of each flag, insect

samples, defoliation estimates, soybean plant heights, and NDVI data were collected during calendar weeks

32

(CW) 29–31, 34, 36, and 40 (21 July to 2 October; V6-R7 growth stages) for field A in 2017; CW 29–33,

35, and 39 [20 July to 27 September.; V7-R7 growth stages) for field B in 2017; CW 29–32, 34, 36, 39, and

41 (19 July to 9 October; V4-R8 growth stages) for field A in 2018; and CW 30, 31, 33, 35, 38, 40, and 42

(27 July to 17 October; V3-R7 growth stages) for field B in 2018 (Fehr and Caviness 1977). All variables

collected within a field during the same calendar week were considered part of the same sampling event.

Insect samples were collected at each flag by performing 20 sweeps across two soybean rows with a 38 cm

diameter sweep net. Samples were then transferred to HDPE produce bags and frozen at -28°C for > 24h,

after which the number of A. gemmatalis, C. includens, and H. scabra were counted. Additionally, five

soybean plants that were typical of the plants in the sampling area around each flag were randomly pulled

by hand, and total height (ground to terminal length) was measured. Visual estimates of defoliation (%)

were recorded during all sampling events (except for CW 33 for Field B in 2017). Average height (cm) and

defoliation percentage across the five plants were used for the analyses. A Trimble GreenSeeker Handheld

Crop Sensor was used to collect NDVI data during all sampling events (except for CW 29 and 34 for field

A, and CW 38 for field B in 2018) by assessing the reflectance of all plants in a 6-m section of row within

the 5 m sampling radius of each flag.

Data Analyses

The spatial distributions of insect densities and crop measurements from each sampling event were

each separately analyzed using SADIE (Perry 1998) [sadie function in the epiphy package (Gigot 2018) in

R version 3.5.3 (R Core Team 2019)]. SADIE analyzes count data with associated locations (e.g. grid

points in our sampling grid) expressed as absolute positions, and was created to handle patchy ecological

data (Winder et al. 2019). Values of all variables were expressed as integers prior to analysis. Because

NDVI values range from -1 to 1 (Myneni et al. 1995), all NDVI values were multiplied by 100 before being

expressed as integers (Reay-Jones et al. 2016).

Four main steps were used in this approach for each field: 1) the occurrence and amount of

clustering was determined through the calculation of an overall index of aggregation (Ia); 2) local

aggregation indices that quantify the occurrence of areas of comparatively high or low counts (patches [Vi]

33

and gaps [Vj], respectively, were created; 3) clustering in the dataset was visually represented by plotting

patches and gaps; and 4) an association index (X) was calculated to quantify how two datasets that share the

same sampling locations may be associated or dissociated; this association or dissociation was also visually

represented by plotting local association indices (Winder et al. 2019).

The minimum distance (D) required to achieve the most uniform distribution possible of counts

was determined in order to calculate Ia. Randomly distributed counts had Ia values = 1, while counts

aggregated into clusters had Ia values of > 1, and uniformly distributed counts had Ia values < 1. Associated

Pa values < 0.025 or > 0.975 indicated significant aggregation or regularity in the observed data,

respectively, and were derived from 5,967 randomizations of the data. Locations exceeding the average

count value (m) were assigned positive cluster indices (vi) and considered patch locations, while gap

locations (count values < m) were assigned negative cluster indices (vj). Individual cluster indices were

used to calculate the overall patch (Vi) and gap (Vj) cluster indices. Linearly interpolated [interp function,

akima package (Akima and Gebhardt 2016), R (R Core Team 2019)] locations, whose individual cluster

index values were greater than 1.5 (patches) and less than -1.5 (gaps), were displayed as clusters in plots

mapped on Google satellite imagery [get_googlemap function, ggmap package (Kahle and Wickham

2013), R (R Core Team 2019)].

Spatial associations were completed among plant and insect variables from the same sampling

event using N_AShell (Version 1.0 © 2008 Kelvin F. Conrad). For each association analysis, location

association indices (Xk) for each grid point were calculated based on the similarity between the individual

cluster index (vi and vj) values in that location for the two datasets. Positive Xk values were indicative of the

presence of a patch or gap in both datasets, while negative Xk values signified a patch in one dataset and a

gap in the other. The overall association index (X) was calculated as the average of all Xk values, and

significance of the index was determined via randomization (5,967 randomizations) of the data. Associated

p values < 0.025 or > 0.975 indicated significant association or dissociation in the data, respectively. Using

the same methodology applied to cluster index plot construction, association plots featured linearly

interpolated [interp function, akima package (Akima and Gebhardt 2016), R (R Core Team 2019)] Xk

34

values, with positive and negative Xk values displayed as clusters in plots on Google satellite imagery

[get_googlemap function, ggmap package (Kahle and Wickham 2013), R (R Core Team 2019)].

Results

Summary Statistics

Across both years and fields, A. gemmatalis was the most abundant of the three species considered

(total of 7,596; average per 20 sweeps: 4.2 ± 0.5 [SEM]), followed by C. includens (total of 1,421; 0.8 ±

0.05), and H. scabra (total of 842; 0.5 ± 0.05). In 2017, population trends for C. includens and H. scabra

were inconsistent across fields (Figure 2.1A). The highest average for C. includens (2.0 ± 0.24; 20 sweeps)

in Field A occurred at the beginning of sampling in CW 29, while populations peaked (2.6 ± 0.7) at the end

of September (CW 39) in Field B. Populations of H. scabra reached a high (0.5 ± 0.22) of at the end of

August (CW 35) in Field B, while numbers remained relatively low (< 0.1 per 20 sweeps) throughout the

season in Field A. By the end of August (CW 34-35) in 2017, A. gemmatalis was more abundant in both

fields than either C. includens or H. scabra at their peaks. Thereafter, A. gemmatalis numbers continued to

increase, reaching highs of (61.6 ± 11.1) and (22 ± 2.61) in Field A (CW 40) and Field B (CW 39),

respectively.

Overall trends for all three species were similar across fields in 2018, with population peaks

occurring during CW 32-34 and CW 38-39 (Figure 2.1B). During the first peak, C. includens (3.8 ± 0.74)

and H. scabra (2.8 ± 0.42) were the most abundant of the three species in Field A and Field B, respectively.

Populations of A. gemmatalis exceeded those of C. includens and H. scabra in both fields (Field A: 4.1 ±

0.82; Field B: 5.4 ± 0.71) during the second peak.

By the end of each season, defoliation estimates were > 12% for each field, with highs of 70.5 (±

4.59) for Field A in 2017 and 24.2 (± 1.77) for Field B in 2018 (Figure 2.2 A, B). In both fields, the high

values for defoliation occurred during or after the A. gemmatalis peaks. As reported in Greene et al. (2021),

NDVI patterns for each season were similar, as highs reached 0.79-0.84 (± 0.002-0.01) during late July-

early August (CW 30-32) for all year-field combinations, except for Field B, with a high of 0.84 (± 0.004)

35

recorded during CW 35 of 2018 (Figure 2.3 A, B). Low NDVI values occurred at the end of each season,

and were all between 0.68-0.73 (0.005-0.01), with the exception of Field A in 2017 (0.58 ± 0.02). The low

NDVI value for Field A in CW 40 of 2017 corresponded with the >70% defoliation estimate from the same

calendar week (Figure 2.3A).

SADIE Aggregation Analyses

For all pest species and plant variables across all sampling events, 31 out of 166 (19%)

aggregation indices were significant (p < 0.025) (Table 2.1, Figure 2.4). Across years, 16 out of 85 (19%)

aggregation indices were significant for pests and plant variables in Field A (Field B = 15/81 [19%]).

Across fields, 10 out of 81 aggregation indices (12%) were significant for pests and plant variables in 2017

(2018 = 21/85 [25%]). Populations of A. gemmatalis were significantly aggregated in two out of 29

analyses (7%) over both years, while populations of C. includens and H. scabra were not significantly

aggregated for any sampling event. Significantly aggregated A. gemmatalis populations were both found in

Field A, with one from 2017-CW 36 and the other from 2018-CW 39 (Table 2.1).

Defoliation estimates were significantly aggregated in 9 out of 27 analyses (33%) over both years

and fields. Of those significant aggregations, 22% (2/9) were from 2017, 78% (7/9) were from 2018 across

fields, 67% (6/9) were from Field A, and 33% (3/9) were from field B across years (Table 2.1). NDVI

values were significantly aggregated in 9 out of 25 analyses (36%) over both years and fields, with 56%

(5/9) from 2017 and 4/9 (44%) from 2018 across fields. Four out of nine (44%) significant aggregations

were from Field A and 56% (5/9) from Field B across years. Soybean plant heights were significantly

aggregated in 11 out of 27 analyses (41%) over both years and fields. Of those significant aggregations,

18% (2/11) were from 2017, and 82% (9/11) were from 2018 across fields. Four out of eleven (36%) were

from Field A, and 64% were from Field B (7/11) across years (Table 2.1).

SADIE Association Analyses

Across both years and fields, 14% (28/198) of SADIE association analyses among pests and plant

variables within the same sampling event were significantly associated (p < 0.025), while another 6%

36

(11/198) were significantly dissociated (p > 0.975) (Table 2.2, Figure 2.5). Across fields, the percentage of

significant associations was similar in 2017 (12/96; 13%) and 2018 (16/102; 16%), while the percentage of

significant associations in Field A (20/99; 20%) was 12% greater than those from Field B (8/99; 8%) across

years. The percentage of significant dissociations was consistent across fields and years (5-6%). Seventy-

five percent of all significant associations (21/28) and fifty-five percent of all significant dissociations

(6/11) occurred during the first four sampling events (CW 29-32) across fields and years. Significant

associations in CW 29 occurred in both years (2017 = 3; 2018 = 2), but only in Field A, while the six

significant associations in CW 30 occurred in both years and fields. Calendar weeks 31 and 32 had

significant associations only in 2018; these significant associations occurred in both fields in week 31 and

only in Field A in week 32 (Table 2.2).

NDVI was more associated with pest species than any other plant variable (12 significant

associations), while defoliation had more significant dissociations (6) than any other variable (Table 2.2).

Across years, 83% (10/12) and 67% (6/9) of the significant associations for NDVI and soybean plant

height, respectively, occurred in Field A, while significant associations for defoliation were similar for both

fields (Field A = 4; Field B = 3). Across fields, 86% (6/7) of the significant associations for defoliation

were in 2018, while significant associations for NDVI (2017 = 7; 2018 = 5) and soybean plant height (2017

= 4; 2018 = 5) were similar for both years. H. scabra had more significant associations (13) with plant

variables than A. gemmatalis (8) or C. includens (7), and the majority of these associations were found in

Field A for all three species (H. scabra = 9/13; A. gemmatalis = 6/8; C. includens =5/7) across years.

Across fields, the majority of significant associations occurred in 2018 for H. scabra (10/13), in 2017 for A.

gemmatalis (6/8), and was split more evenly for C. includens (2017 = 3; 2018 = 4). Furthermore, H. scabra

was not significantly dissociated with NDVI or plant heights during any sampling event (Table 2.2).

Discussion

Anticarsia gemmatalis was the most abundant of the three lepidopteran species in this study, and it

is thought that this species may be able to outcompete other defoliating species in soybean. Shepard et al.

(1977) proposed that populations of H. scabra, loopers (including C. includens), Helicoverpa (= Heliothis)

37

spp. (Hardwick) (Lepidoptera: Noctuidae), and other lepidopteran pest species were hampered by the

competitive advantage of large numbers of A. gemmatalis in plots of soybean in the same location as this

study. In laboratory tests involving intra- and interspecific confinement of A. gemmatalis and C. includens

larvae in cups (100 mL) with soybean leaves, the highest rate of predatory behavior was exhibited by A.

gemmatalis larvae in interspecific scenarios (Ongaratto et al. 2021). The aggressive behaviors displayed by

A. gemmatalis larvae led the authors to conclude that this species would likely have a competitive

advantage over C. includens under field conditions.

The numbers of each lepidopteran species varied across seasons and fields (Figure 2.1 A, B).

Although A. gemmatalis and C. includens are considered mid-season pests, this categorization spans a wide

range of soybean growth stages (V1-R5) (Sinclair et al. 1997, O’Neal and Johnson 2010). As common

pests in soybean, populations of H. scabra are known to peak in mid-to-late August, but in years in which

numbers reach epiphytotic-levels, populations can peak two to three weeks earlier (Pedigo et al. 1972,

Pedigo 1980). Various factors are known to impact the timing and severity of insect numbers in crop

systems. The seasonal occurrence of A. gemmatalis moths migrating from tropical overwintering sites is

contingent on the availability of appropriate crop and wild host plants (Herzog and Todd 1980), while the

availability of cotton nectar for C. includens adults can result in outbreaks of larvae in cotton-soybean

agroecosystems (Burleigh 1972, Jensen et al. 1974). Additionally, Mascarenhas and Pitre (1997) found that

C. includens moths exhibited an ovipositional preference for mature soybeans when given the choice

between plants in the vegetative and reproductive growth stages. In both years, the growth stage of soybean

plants within both fields varied early in the season (late July-early August; CW 29-32) before becoming

more uniform thereafter (Figure 2.1 A, B). The seasonal variability observed for the three species in this

study is consistent with previous reports, as these pests are known to be associated with soybean in various

growth stages and portions of the growing season (Pedigo et al. 1972, Pedigo 1980, Sinclair et al. 1997,

O’Neal and Johnson 2010). The observed differences in the abundance levels of these three pests across

fields and years were also likely influenced in part by a combination of competitive differences among

species and the observed spatiotemporal differences in soybean phenology.

38

In all fields and years, low values of NDVI occurred at the end of each season (Figure 2.3 A, B),

coinciding with the highest values of defoliation (Figure 2.2 A, B). This result is consistent with Board et

al. (2007), in which the authors observed strong relationships (r2 = 0.93-0.97) in linear regression models

between NDVI and both leaf area index (LAI) and light interception data collected from soybean plots.

Defoliation has previously been shown to be related to LAI and light interception, as Haile et al. (1998)

found these criteria to be associated with the degree of yield loss that soybean plants sustain following

defoliation. Stunting in soybean has also been previously shown to occur due to defoliation. Using

simulated defoliation techniques designed to mimic both painted lady, Vanessa cardui (Linnaeus)

(Lepidoptera: Nymphalidae) and H. scabra feeding, Hammond and Pedigo (1982) found significantly

smaller soybean plant heights in plots with defoliation when compared with those in control plots.

Aggregation of lepidopteran pests was limited, as only A. gemmatalis was found to be aggregated in 2017-

CW 36 and 2018-CW 39 in Field A (Table 2.1, Figure 2.4). This result is in agreement with previous

studies that found random distributions for A. gemmatalis (Shepard and Carner 1976, Strayer et al. 1977),

C. includens (Shepard and Carner 1976), and H. scabra (Pedigo et al. 1972, Hammond and Pedigo 1976,

Shepard and Carner 1976) in soybean. Although Bechinski et al. (1983) found H. scabra populations to be

aggregated in soybean, the authors stipulated that this distribution was likely only appropriate for

describing larvae at high densities, and that a random distribution pattern would be more accurate for larvae

at low and intermediate population levels.

Despite the limited number of aggregated insect datasets, 14% and 6% of paired plant-insect

datasets were significantly associated or dissociated, respectively. The majority of defoliation and soybean

plant height associations with pests, along with the majority of defoliation aggregations, occurred in 2018

in Field A (Tables 2.1 and 2.2). These patterns may be explained in part by the seasonal variability of the

lepidopteran pests among years and fields, as the population peaks of C. includens and H. scabra that

occurred in mid-to-late August (CW 32-34) in Field A in 2018 (Figure 1B) were much higher than the

levels reached by these species in the same field in the previous year during any sampling event (Figure

2.1A). Furthermore, larvae of all three species have preferred feeding strata within plants at some point in

their development (Pedigo et al. 1973, Herzog 1980, Herzog and Todd 1980). Taller plants may have

39

provided more surface area to oviposit and/or feed on when compared with shorter plants, and were,

therefore, preferentially selected as hosts. The levels of defoliation observed in this study are considered to

be mainly due to A. gemmatalis, C. includens, and H. scabra feeding, as grasshoppers were the only other

defoliating taxa that were regularly observed (adults: total of 755; average per 20 sweeps: 0.42 ± 0.03

[SEM]; nymphs: 1,176; 0.66 ± 0.04). Grasshopper feeding likely did not significantly contribute to the

observed defoliation and soybean plant aggregation and association patterns, as this taxa is rarely

considered to be a pest in soybean (DeGooyer and Browde 1994); a recent survey of 16 soybean-producing

states in the USA found that grasshoppers were below the economic threshold for > 99% of all surveyed

hectares (Musser et al. 2017). Variability in plant growth within fields may be related to variability in soil

characteristics such as water availability (van Helden 2010), as soil types within the southeastern U.S.

Coastal Plain have been found to vary in texture, water content, and plant available water (Duffera et al.

2007). Associations between insects and plant variables such as plant height and NDVI may therefore be

linked to variability in soil quality within and between fields.

Previous studies in soybean have documented that each of these three species display oviposition

site selection preferences. Eggs were found to be significantly more abundant in the middle and middle-to-

upper portions of soybean canopies for H. scabra (Pedigo et al. 1973) and C. includens (Mascarenhas and

Pitre 1997), respectively, while more eggs were deposited on abaxial surface of soybean leaves than any

other part of the plant for A. gemmatalis (Herzog and Todd 1980) and C. includens (Mascarenhas and Pitre

1997, Hamadain and Pitre 2002). Additionally, insects are able to not only differentiate plant species via

the detection of plant volatiles, but the nutritional quality of the host plant can also be assessed (Bruce and

Pickett 2011). Given that oviposition site selection preferences have already been observed for each of

these three species, it is possible that gravid moths selected vigorously growing plants with greater NDVI

values to oviposit. Another potential explanation for the number of pest associations with NDVI is that

plants that were subjected to larval feeding became a lower-quality resource after being fed upon when

compared with nearby plants yet to be defoliated. In some species, individuals have been known to disperse

to higher quality areas following the reduction in host quality as a result of feeding intensity (van Helden

2010). Because plant damage is often known to lag behind the time of the initial pest attack (van Helden

40

2010), this may help to explain the significant dissociations between the distributions of pests and

defoliation estimates from the same CW. The dissociations between NDVI and pests are also likely a

function of the lag between attack and observable damage. Pest abundance was lower in the CWs in which

the dissociations between NDVI and pest species occurred than in the previous 1-2 CWs (Figure 2.1 A, B).

Hypena scabra had the most associations with plant variables out of three species in this study, with the

majority of these associations in 2018 (Table 2.2, Figure 2.5). This is somewhat surprising, given that this

species also had the lowest overall abundance out of the three species over years and fields. However, H.

scabra numbers were much higher in both fields in 2018 (Figure 2.1B) when compared with 2017 for the

majority of the growing season (Figure 2.1A). A similar trend was observed for A. gemmatalis in 2017, as

the majority of its associations with plant variables were likely a function of the immense population

growth that was observed in both fields.

The majority of all associations and dissociations occurred in the first four weeks of sampling (late

July-early August; CW 29-32) (Table 2.2, Figure 2.5). Although each of these pests can feed in soybean at

various growth stages, management recommendations have stressed the importance of early and frequent

monitoring given their explosive capacity for population growth (Herzog 1980, Herzog and Todd 1980,

Pedigo 1980). The results from this study demonstrate that associations between plant variables and pests

can be made early in the growing season, even when pest numbers are relatively low. Although these

results represent the initial stages of the development of site-specific pest management plans for

lepidopteran pests in soybean, the identification of consistent associations among plant variables and pests

provides support for developing tools to predict how in-field pest numbers change across space and time. In

the case of the spatial distribution of green leafhopper, Empoasca vitis (Goethe) (Hemiptera: Cicadellidae),

in vineyards, Decante et al. (2009) were able to associate plant vigor (leaf chlorophyll concentration and

leaf density) with aggregations of this pest that had previously demonstrated stability across years (Decante

and Van Helden 2008). The association of this plant variable with the persistent E. vitis patterns allowed

the authors to recommend that future monitoring efforts be concentrated in field areas with vigorous

growth.

The prediction of a pest's distribution in a crop is considered to be dependent on ample

41

knowledge of the biology and eco-ethology of the pest itself (van Helden 2010). If changes are to be

implemented regarding how a pest is managed, critical factors explaining the spatial distribution of pests

must be identified (Daane and Williams 2003). In this study, NDVI was found to be more associated with

pest distributions than soybean plant heights and defoliation estimates, and most of the significant

associations and dissociations between pests and plant variables occurred early in the growing season.

Greene et al. (2021) also emphasized the importance of NDVI in describing pest distributions, as

significant associations between this variable and counts of pestiferous taxa were found in soybean.

However, as pest aggregations were rare in this study, it is currently unclear as to whether site-specific pest

management practices may be more efficient than conventional management practices applied to whole

fields. Nevertheless, the relationship between early-season distributions of pests and important plant

variables such as NDVI needs to be further investigated to better determine the strength of the correlations

across years and sites. The incorporation of eco-ethological data into future studies, such as oviposition site

selection by gravid A. gemmatalis, C. includens, and H. scabra moths may help explain how these

important defoliators of soybean are distributed in the crop, thereby leading to improved management

practices.

Acknowledgements

This project was made possible through funding from the South Carolina Soybean Board and the

W. Carl Nettles, Sr., and Ruby S. Nettles Memorial Endowment in Entomology. We would like to express

our appreciation for everyone who contributed to the data collection for this project at the Clemson

University Edisto Research and Education Center. This is technical contribution No. 6990 of the Clemson

University Experiment Station. This manuscript is based upon the work supported by the National Institute

of Food and Agriculture/U.S. Department of Agriculture, under project numbers SC-1700531 and SC-

1700532.

42

References Cited

Akima, H., and A. Gebhardt. 2016. akima: Interpolation of Irregularly and Regularly Spaced Data.

Alves, T. M., I. V. Macrae, and R. L. Koch. 2015. Soybean aphid (Hemiptera: Aphididae) affects

soybean spectral reflectance. J. Econ. Entomol. 108: 2655–2664.

Bechinski, E. J., G. D. Buntin, L. P. Pedigo, and H. G. Thorvilson. 1983. Sequential count and decision

plans for sampling green cloverworm (Lepidoptera: Noctuidae) larvae in soybean. J. Econ. Entomol. 76:

806–812.

Bruce, T. J., and J. A. Pickett. 2011. Perception of plant volatile blends by herbivorous insects–finding

the right mix. Phytochemistry. 72: 1605–1611.

Burleigh, J. G. 1972. Population dynamics and biotic controls of the soybean looper in Louisiana. Environ.

Entomol. 1: 290–294.

Daane, K. M., and L. E. Williams. 2003. Manipulating vineyard irrigation amounts to reduce insect pest

damage. Ecol. Appl. 13: 1650–1666.

Decante, D., and M. Van Helden. 2008. Spatial and temporal distribution of Empoasca vitis within a

vineyard. Agric. For. Entomol. 10: 111–118.

Decante, D., C. Van Leeuwen, and M. Van Helden. 2009. Influence of plot characteristics and

surrounding vegetation on the intra‐plot spatial distribution of Empoasca vitis. Agric. For. Entomol. 11:

377–388.

Duffera, M., J. G. White, and R. Weisz. 2007. Spatial variability of Southeastern US Coastal Plain soil

physical properties: Implications for site-specific management. Geoderma. 137: 327–339.

Fehr, W. R., and C. E. Caviness. 1977. Stages of soybean development. Iowa Agricultural and Home

Economics Experiment Station Special Report, pp. 3-11. Iowa State University, Ames, IA.

da Fonseca, P. R. B., M. G. Fernandes, W. Justiniano, L. H. Cavada, and J. A. N. da Silva. 2014.

Spatial distribution of adults and nymphs of Euschistus heros (F.) (Hemiptera: Pentatomidae) on Bt and

non-Bt soybean. J. Agric. Sci. 6: 131.

Funderburk, J., R. McPherson, and D. Buntin. 1999. Soybean insect management, pp. 273-290. In L. G.

Heatherly and H. F. Hodges (Eds.), Soybean production in the Midsouth. CRC Press, Boca Raton, FL.

43

Gebbers, R., and V. I. Adamchuk. 2010. Precision agriculture and food security. Science. 327: 828–831.

Gigot, C. 2018. epiphy: Analysis of Plant Disease Epidemics.

Greene, A. D., F. P. Reay-Jones, K. R. Kirk, B. K. Peoples, and J. K. Greene. 2021. Associating site

characteristics with distributions of pestiferous and predaceous arthropods in soybean. Environ. Entomol.

50: 477–488.

Guillebeau, L. P., N. Hinkle, and P. Roberts, [eds.]. 2008. Summary of losses from insect damage and

cost of control in Georgia, 2006. Univ. Ga. Coll. Agric. Environ. Sci. Misc. Publ. 106. University of

Georgia, Athens, GA.

Gustafson, T. C., S. Z. Knezevic, T. E. Hunt, and J. L. Lindquist. 2006. Simulated insect defoliation

and duration of weed interference affected soybean growth. Weed Sci. 54: 735–742.

Hamadain, E. I., and H. N. Pitre. 2002. Oviposition and larval behavior of soybean looper, Pseudoplusia

includens (Lepidoptera: Noctuidae), on soybean with different row spacings and plant growth stages. J

Agric Urban Entomol. 19: 29–44.

Hammond, R. B., and L. P. Pedigo. 1976. Sequential sampling plans for the green cloverworm in Iowa

soybeans. J. Econ. Entomol. 69: 181–185.

Hartman, G. L., T. C. Wang, and A. T. Tschanz. 1991. Soybean rust development and the quantitative

relationship between rust severity and soybean yield. Plant Dis. 75: 596–600.

Hartman, G. L., E. D. West, and T. K. Herman. 2011. Crops that feed the World 2. Soybean—

worldwide production, use, and constraints caused by pathogens and pests. Food Secur. 3: 5–17.

van Helden, M. 2010. Spatial and temporal dynamics of arthropods in arable fields, pp. 51–64. In E.C.

Oerke, R. Gerhards, G. Menz, and R.A. Sikora (Eds.), A. Precision crop protection – the challenge and use

of heterogeneity. Springer, Netherlands.

Herzog, D. C. 1980. Sampling soybean looper on soybean, pp. 141–168. In M. Kogan and D.C. Herzog

(Eds.), Sampling methods in soybean entomology. Springer-Verlag, New York.

Herzog, D. C., and J. W. Todd. 1980. Sampling velvetbean caterpillar on soybean, pp. 107–140. In M.

Kogan and D.C. Herzog (Eds.), Sampling methods in soybean entomology. Springer-Verlag, New York.

44

Higley, L. G., and D. J. Boethel. 1994. Handbook of soybean insect pests. Entomological Society of

America, Lanham, MD.

Hodgson, E. W., E. C. Burkness, W. D. Hutchison, and D. W. Ragsdale. 2004. Enumerative and

binomial sequential sampling plans for soybean aphid (Homoptera: Aphididae) in soybean. J. Econ.

Entomol. 97: 2127–2136.

Hoffman, D. D., G. L. Hartman, D. S. Mueller, R. A. Leitz, C. D. Nickell, and W. L. Pedersen. 1998.

Yield and seed quality of soybean cultivars infected with Sclerotinia sclerotiorum. Plant Dis. 82: 826–829.

Horn, D. J. 2000. Ecological control of insects, pp. 3–21. In J.E. Rechcigl and N. A. Rechcigl (Eds.),

Insect pest management: Techniques for environmental protection. Agriculture and environment series.

CRC Press/ Lewis Publishers, Chelsea, Michigan.

Jensen, R. L., L. D. Newsom, and J. Gibbens. 1974. The soybean looper: effects of adult nutrition on

oviposition, mating frequency, and longevity. J. Econ. Entomol. 67: 467–470.

Kahle, D., and H. Wickham. 2013. ggmap: Spatial Visualization with ggplot2. R J. 5: 144–161.

Krell, R. K., L. P. Pedigo, and B. A. Babcock. 2003. Comparison of estimated costs and benefits of site-

specific versus uniform management for the bean leaf beetle in soybean. Precis. Agric. 4: 401–411.

MacArthur, R. H., and E. O. Wilson. 1967. The theory of island biogeography. Princeton University

Press. Princeton, NJ.

Marshall, M., J. Greene, D. Gunter, F. Reay-Jones, J. Mueller, D. Anco, K. Moore, P. Peterson, B.

Powell, C. Heaton, J. Crouch, and B. Beer. 2020. 2020 South Carolina Pest Management Handbook.

Clemson University Extension, Clemson, SC.

Mascarenhas, R. N., and H. N. Pitre. 1997. Oviposition responses of soybean looper (Lepidoptera:

Noctuidae) to varieties and growth stages of soybean. Environ. Entomol. 26: 76–83.

Merrill, S. C., T. O. Holtzer, F. B. Peairs, and P. J. Lester. 2015. Validating spatiotemporal predictions

of an important pest of small grains. Pest Manag. Sci. 71: 131–138.

Myneni, R. B., F. G. Hall, P. J. Sellers, and A. L. Marshak. 1995. The interpretation of spectral

vegetation indexes. IEEE Trans. Geosci. Remote Sens. 33: 481–486.

Oerke, E.-C. 2006. Crop losses to pests. J. Agric. Sci. 144: 31–43.

45

Oerke, E.-C., R. Gerhards, G. Menz, and R. A. Sikora. 2010. Precision crop protection-the challenge

and use of heterogeneity. Springer, Netherlands.

O’Neal, M. E., and K. D. Johnson. 2010. Insect pests of soybean and their management, pp. 300–324. In

Singh, G. (ed.), The soybean – Botany, production and uses. CABI, Cambridge, MA.

Ongaratto, S., E. L. Baldin, T. E. Hunt, D. G. Montezano, E. A. Robinson, and M. C. Dos Santos.

2021. Effects of intraguild interactions on Anticarsia gemmatalis and Chrysodeixis includens larval fitness

and behavior in soybean. Pest Manag. Sci. 77: 2939-2947.

Park, Y.-L., and R. K. Krell. 2005. Generation of prescription maps for curative and preventative site-

specific management of bean leaf beetles (Coleoptera: Chrysomelidae). J. Asia-Pac. Entomol. 8: 375–380.

Park, Y.-L., R. K. Krell, and M. Carroll. 2007. Theory, technology, and practice of site-specific insect

pest management. J. Asia-Pac. Entomol. 10: 89–101.

Pedigo, L. P. 1980. Sampling green cloverworm on soybean, pp. 169–186. In M. Kogan and D.C. Herzog

(Eds.), Sampling methods in soybean entomology. Springer-Verlag, New York.

Pedigo, L. P. 2002. Entomology and pest management, 4th ed. Prentice Hall, Upper Saddle River, NJ.

Pedigo, L. P., G. L. Lentz, J. D. Stone, and D. F. Cox. 1972. Green cloverworm populations in Iowa

soybean with special reference to sampling procedure. J. Econ. Entomol. 65: 414–421.

Pedigo, L. P., J. D. Stone, and G. L. Lentz. 1973. Biological synopsis of the green cloverworm in central

Iowa. J. Econ. Entomol. 66: 665–673.

Perry, J. N. 1998. Measures of spatial pattern for counts. Ecology. 79: 1008–1017.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. R Foundation for

Statistical Computing, Vienna, Austria.

Reay-Jones, F. P., J. K. Greene, and P. J. Bauer. 2016. Stability of spatial distributions of stink bugs,

boll injury, and NDVI in cotton. Environ. Entomol. 45: 1243–1254.

Schimmelpfennig, D. 2016. Farm profits and adoption of precision agriculture (Economic Research Report

No. 217). United States Department of Agriculture, Economic Research Service.

Seelan, S. K., S. Laguette, G. M. Casady, and G. A. Seielstad. 2003. Remote sensing applications for

precision agriculture: A learning community approach. Remote Sens. Environ. 88: 157–169.

46

Seiter, N. J., J. K. Greene, and F. P. F. Reay-Jones. 2013. Reduction of soybean yield components by

Megacopta cribraria (Hemiptera: Plataspidae). J. Econ. Entomol. 106: 1676–1683.

Shepard, M., and G. R. Carner. 1976. Distribution of insects in soybean fields. Can. Entomol. 108: 767–

771.

Shepard, M., G. R. Carner, and S. G. Turnipseed. 1977. Colonization and resurgence of insect pests of

soybean in response to insecticides and field isolation. Environ. Entomol. 6: 501–506.

Sinclair, J. B., M. Kogan, and M. D. McGlamery. 1997. Guidelines for the integrated management of

soybean pests. National Soybean research Laboratory Publication, Urbana-Champaign, IL.

Stewart, S., A. Thompson, and R. Patrick. 2009. Soybean Insects- Loopers (No. W199). The Univeristy

of Tennessee Institute of Agriculture, UT Extension.

Strayer, J., M. Shepard, and S. G. Turnipseed. 1977. Sequential sampling for management decisions on

the velvetbean caterpillar on soybeans. J. Ga. Entomol. Soc. 12: 220-227.

Stubbins, F. L., P. L. Mitchell, M. W. Turnbull, F. P. Reay‐Jones, and J. K. Greene. 2017. Mouthpart

morphology and feeding behavior of the invasive kudzu bug, Megacopta cribraria (Hemiptera:

Plataspidae). Invertebr. Biol. 136: 309–320.

USDA-NASS. 2019. 2017 Census of Agriculture, United States Summary and State Data (No. Volume 1,

Part 51, AC-17-A-51), Geographic Area Series. United States Department of Agriculture, National

Agricultural Statistics Service.

Winder, L., C. Alexander, G. Griffiths, J. Holland, C. Woolley, and J. Perry. 2019. Twenty years and

counting with SADIE: Spatial Analysis by Distance Indices software and review of its adoption and use.

Rethink. Ecol. 4: 1.

47

Table 2.1. Spatial aggregation indices (Ia) from SADIE of pests and plant variables for each sampling event (calendar week) in soybean Calendar Week

Year Field Variable 29 30 31 32 33 34 35 36 38 39 40 41 42

2017

A

Hypena scabra 0.93 N/A N/A — — 0.79 — 0.78 — — N/A — —

Chrsyodeixis

includens 1.02 0.89 0.90 — — 0.95 — 0.93 — — N/A — —

Anticarsia

gemmatalis 1.14 N/A 1.08 — — 1.46 — 1.60 — — 0.73 — —

Defoliation 1.14 1.04 1.40 — — 1.00 — 1.86 — — 1.76 — —

NDVI 1.29 1.52 1.18 — — 1.66 — 1.27 — — 1.52 — —

Plant Height 1.38 1.21 1.53 — — 1.25 — 1.27 — — 1.15 — —

B

Hypena scabra 1.02 0.96 0.91 1.20 0.97 — 0.92 — — N/A — — —

Chrsyodeixis

includens 1.09 1.08 1.06 0.81 1.14 — 1.38 — — 1.00 — — —

Anticarsia

gemmatalis 0.84 1.03 N/A 1.12 0.88 — 1.25 — — 0.88 — — —

Defoliation 1.02 0.95 1.40 1.38 1.08 — 1.27 — — 1.29 — — —

NDVI 1.41 1.35 1.56 0.90 1.13 — 1.31 — — 1.53 — —

Plant Height 1.24 1.15 1.08 0.97 0.94 — 1.19 — — 1.42 — — —

2018

A

Hypena scabra 0.98 0.92 1.03 0.91 — 1.15 — N/A — 0.89 — N/A —

Chrsyodeixis

includens 0.94 1.35 0.86 1.21 — 0.94 — 0.73 — 1.09 — 0.98 —

Anticarsia

gemmatalis 0.83 1.15 0.92 0.91 — 1.12 — 1.48 — 1.60 — 1.04 —

Defoliation 1.76 1.42 1.00 2.11 — 1.54 — 1.24 — 1.59 — 1.53 —

NDVI — 1.21 0.99 1.07 — — — 1.54 — 0.91 — 1.01 —

Plant Height 1.32 1.17 1.23 1.29 — 1.64 — 1.71 — 1.54 — 1.43 —

B

Hypena scabra — 1.09 1.03 — 0.93 — 0.98 — 1.13 — N/A — N/A

Chrsyodeixis

includens — 0.95 0.93 — 0.94 — 0.91 — 1.05 — 0.95 — N/A

Anticarsia

gemmatalis — N/A 0.84 — 0.95 — 1.34 — 0.95 — 1.04 — 0.93

Defoliation — 1.21 1.53 — — — 1.24 — 1.42 — 1.13 — 1.96

NDVI — 1.59 1.70 — 1.44 — 1.07 — — — 1.18 — 1.09

Plant Height — 1.78 1.52 — — — 1.56 — 1.62 — 1.90 — 1.71

Bolded values indicate signification aggregation for Ia values (p < 0.025).

N/A = All counts were 0

— = Data was not collected during this calendar week

No samples were collected during calendar week 37

48

Table 2.1. Spatial association indices (X) from SADIE of pests and plant variables from each

sampling event (calendar week) in soybean 2017 2018

Field A Field B Field A Field B

Def

oli

atio

n

ND

VI

Pla

nt

Hei

gh

t

Def

oli

atio

n

ND

VI

Pla

nt

Hei

gh

t

Def

oli

atio

n

ND

VI

Pla

nt

Hei

gh

t

Def

oli

atio

n

ND

VI

Pla

nt

Hei

gh

t

Week Species

29

Hypena scabra -0.24 0.29 0.48 -0.05 -0.33 -0.11 0.45 — -0.13 — — —

Chrsyodeixis

includens 0.25 0.39 0.12 0.22 0.05 0.20 -0.11 — 0.33 — — —

Anticarsia

gemmatalis -0.03 0.54 0.35 0.29 0.01 0.33 0.53 — -0.22 — — —

30

Hypena scabra N/A N/A N/A 0.17 -0.14 -0.20 -0.30 0.55 0.34 0.48 -0.22 0.19

Chrsyodeixis

includens 0.21 0.32 0.30 -0.10 0.24 0.31 0.18 -0.11 0.01 -0.03 -0.13 0.01

Anticarsia

gemmatalis N/A N/A N/A -0.32 0.37 0.28 -0.09 -0.06 0.05 N/A N/A N/A

31

Hypena scabra N/A N/A N/A -0.43 -0.38 -0.18 -0.27 0.21 0.40 0.33 0.32 0.42

Chrsyodeixis

includens 0.06 0.13 0.14 0.09 -0.39 -0.12 0.24 0.28 0.25 -0.03 0.19 -0.07

Anticarsia

gemmatalis -0.11 0.08 0.21 N/A N/A N/A 0.29 -0.02 0.14 -0.10 0.11 0.00

32

Hypena scabra — — — -0.23 0.04 -0.17 -0.07 0.45 0.43 — — —

Chrsyodeixis

includens — — — 0.25 0.00 0.30 -0.01 0.26 0.30 — — —

Anticarsia

gemmatalis — — — 0.38 -0.02 0.20 -0.32 0.20 0.22 — — —

33

Hypena scabra — — — -0.09 0.12 -0.20 — — — — 0.11 —

Chrsyodeixis

includens — — — -0.05 0.25 -0.13 — — — — -0.12 —

Anticarsia

gemmatalis — — — 0.02 0.19 0.33 — — — — 0.02 —

34

Hypena scabra 0.20 0.44 0.31 — — — -0.22 — 0.14 — — —

Chrsyodeixis

includens -0.04 0.01 -0.19 — — — -0.06 — 0.21 — — —

Anticarsia

gemmatalis 0.16 0.42 0.27 — — — 0.02 — 0.28 — — —

35

Hypena scabra — — — 0.05 -0.08 0.12 — — — 0.10 0.00 -0.24

Chrsyodeixis

includens — — — 0.36 0.02 0.00 — — — -0.48 0.02 -0.22

Anticarsia

gemmatalis — — — 0.19 -0.33 0.31 — — — 0.19 -0.21 -0.37

36

Hypena scabra -0.08 0.34 0.23 — — — N/A N/A N/A — — —

Chrsyodeixis

includens -0.59 0.19 -0.41 — — — -0.21 -0.37 0.04 — — —

Anticarsia

gemmatalis 0.73 -0.10 0.19 — — — 0.01 0.00 0.03 — — —

38

Hypena scabra — — — — — — — — — 0.25 — 0.01

Chrsyodeixis

includens — — — — — — — — — 0.36 — 0.18

Anticarsia

gemmatalis — — — — — — — — — 0.27 — -0.07

49

Bolded values indicate significant associations for X > 0 (p < 0.025) or significant dissociations for X < 0

(p > 0.975)

N/A = all counts were 0

— = Data was not collected during this calendar week

39

Hypena scabra — — — N/A N/A N/A 0.01 -0.19 0.05 — — —

Chrsyodeixis

includens — — — -0.17 0.12 -0.11 -0.01 -0.12 0.17 — — —

Anticarsia

gemmatalis — — — 0.05 0.04 0.10 0.05 -0.24 -0.11 — — —

40

Hypena scabra N/A N/A N/A — — — — — — N/A N/A N/A

Chrsyodeixis

includens N/A N/A N/A — — — — — — 0.13 -0.10 0.33

Anticarsia

gemmatalis 0.09 -0.03 0.12 — — — — — — 0.29 -0.06 -0.16

41

Hypena scabra — — — — — — N/A N/A N/A — — —

Chrsyodeixis

includens — — — — — — -0.27 -0.12 -0.18 — — —

Anticarsia

gemmatalis — — — — — — 0.08 0.04 0.02 — — —

42

Hypena scabra — — — — — — — — — N/A N/A N/A

Chrsyodeixis

includens — — — — — — — — — N/A N/A N/A

Anticarsia

gemmatalis — — — — — — — — — -0.11 0.03 0.07

50

Figure 2.1. Lepidopteran pest seasonal dynamics (average ± SE) and associated soybean phenology across

fields (A and B). (A) 2017; (B) 2018. Average = average of all sweep-net samples taken from all locations

in a field during a particular calendar week. Soybean growth stage = range of soybean growth stages across

all sampled locations across fields (A and B). No samples were collected during calendar week 37.

51

Figure 2.2. Soybean plant height and defoliation seasonal dynamics (average ± SE) and associated soybean

phenology across fields (A and B). (A) 2017; (B) 2018. Average = average of all samples taken from all

locations in a field during a particular calendar week. Soybean growth stage = range of soybean growth

stages across all sampled locations across fields (A and B). No samples were collected during calendar

week 37.

52

Figure 2.3. NDVI seasonal dynamics (average ± SE) and associated soybean phenology across fields (A

and B). (A) 2017; (B) 2018. Average = average of all samples taken from all locations in a field during a

particular calendar week. Soybean growth stage = range of soybean growth stages across all sampled

locations across fields (A and B). No samples were collected during calendar week 37.

53

Figure 2.4. Selected spatial interpolation maps of SADIE local aggregation indices for datasets from the

same calendar week (CW). Clusters depict aggregation index values of < -1.5 and > 1.5 as gaps and

patches, respectively. A-G: 2017, Field A. H-J: 2017, Field B. K-S: 2018, Field A. T-DD: 2018, Field B.

54

Figure 2.5. Selected spatial interpolation maps of SADIE local association indices for datasets from the

same calendar week (CW). Black letters indicate significant associations (p < 0.025) between the datasets,

while white letters indicate significant dissociations (p > 0.975). A-H: 2017, Field A. I-N: 2017, Field B. O-

W: 2018, Field A. X-DD: 2018, Field B.

55

CHAPTER THREE

SPATIAL ASSOCIATIONS OF THE TIGER BEETLES (COLEOPTERA: CICINDELINAE) Cicindela

punctulata (OLIVIER) AND Tetracha carolina (LINNAEUS) WITH BIOTIC

AND ABIOTIC VARIABLES IN SOYBEAN

Abstract

In the southeastern U.S., the epigeal, predatory Carolina metallic tiger beetle, Tetracha carolina (Linnaeus)

(Coleoptera: Carabidae), and punctured tiger beetle, Cicindela punctulata (Olivier) (Coleoptera:

Carabidae), commonly occur in a variety of habitats, including crop systems, and these predators generally

differ in size, diel patterns, and habitat usage. In this study, we sought to determine how C. punctulata and

T. carolina were distributed within two soybean fields in South Carolina (SC) in 2017 and 2018, as well as

the associations that these predators might have with the distributions of abiotic (elevation and soil apparent

electrical conductivity [soil ECa]) and biotic (Cydnidae adults and nymphs, Elateridae adults, and

Gryllotalpidae adults and nymphs) variables within the crop. Although aggregations of insect taxa, as

identified by Spatial Analysis of Distance Indices (SADIE), were limited, significant spatial overlap (42%

of the total significant associations among insects and field variables) was observed for C. punctulata and

T. carolina. Given the potential for overlap in the diel patterns of these cicindelines, our results suggest that

the larger T. carolina may act as an intraguild predator on the smaller C. punctulata. Cicindelines also had

more significant associations and dissociations with Elateridae than any other herbivorous taxa, and more

significant dissociations with soil ECa than with elevation. Further research on how potential intraguild

predation of T. carolina on C. punctulata may modify the biological control effect that these predators

exert in this crop, as well as how these predators respond to other, more economically important pests, is

warranted.

KEY WORDS SADIE, intraguild predation, tiger beetle, soil electrical conductivity, biological control,

pest management

56

Introduction

In the United States (U.S.) alone, it has been estimated that the service provided by beneficial

insects in regulating native herbivorous pests reaches nearly $4.5 billion annually (Losey and Vaughan

2006). Furthermore, natural enemies have been estimated to contribute at least half of all pest control that

occurs in crop systems (Pimentel 2005). However, the control that natural enemies exert in managed

systems is dependent on the diversity of the community in which they exist. In diverse consumer

communities, prey consumption and parasitism can be increased by way of the sampling effect and species

complementarity (Loreau et al. 2001, Tylianakis et al. 2006). In the former, environments with increased

diversity possess a greater likelihood of harboring a highly influential species (e.g. an effective consumer)

(Hooper et al. 2005, Straub and Snyder 2006). Species complementarity can be further separated into

resource partitioning and facilitation effects. Resource partitioning occurs when higher consumer diversity

in an environment allows for increased niche occupation and resource utilization, resulting in enhanced

consumption when compared with less diverse environments (Loreau et al. 2001, Hooper et al. 2005).

Facilitation effects can be described as the synergistic effects that occur when the presence of one

consumer positively influences another (Loreau et al. 2001).

Interactions among natural enemies may not always enhance the effect of biological control,

however. Intraguild predation occurs when omnivorous (i.e. feeding across various trophic levels)

predatory species consume other natural enemies, which may result in a reduction in biological control of

pests (Polis et al. 1989, Straub et al. 2008). Resource partitioning may occur in environments in which the

functionality of natural enemies differs based on the density of prey species (Straub et al. 2008). The

abundance and effectiveness of generalist natural enemies might be increased when pest numbers are low,

as these organisms can feed on alternative prey sources. Consequently, because the population of generalist

natural enemies may not be closely linked to any particular prey species, these organisms may not display a

numerical response to pest outbreaks (Straub et al. 2008). A decrease in the consumption of the target pest

species (e.g. species causing economic loss) by a generalist predator may also occur in the presence of

alternative prey (Murdoch 1969).

57

In the southeastern U.S., the epigeal (i.e. ground-dwelling), predatory Carolina metallic tiger

beetle, Tetracha carolina (Linnaeus) (Coleoptera: Carabidae), and punctured tiger beetle, Cicindela

punctulata (Olivier) (Coleoptera: Carabidae), commonly occur in a variety of habitats, including crop

systems (Knisley and Schultz 1997, Pearson et al. 2006). Whereas T. carolina is typically associated with

habitats near water sources (Knisley and Schultz 1997), C. punctulata is thought to have fewer ecological

restrictions than any other tiger beetle in North America (Graves and Pearson 1973). Furthermore, while T.

carolina is nocturnal and gregarious (Graves and Pearson 1973), C. punctulata is a diurnal predator that

occurs in scattered to moderately-dense distributions (Knisley and Schultz 1997).

As the second largest field crop in the U.S. (USDA-NASS 2019), soybean harbors over 700 (Way

1994) and 150 (Deitz 1976) species of herbivorous insects and natural enemies, respectively. Although

members of both the canopy- and ground-dwelling natural enemy communities are considered to play

significant roles in preventing pests from reaching economically injurious levels (Turnipseed and Kogan

1983), the service provided by members of the latter group in the suppression of soybean pests is not well

understood (Price and Shepard 1980). In this study, we sought to determine how T. carolina and C.

punctulata were distributed within soybean fields in South Carolina (SC), as well as the associations that

these predators might have with the distributions of abiotic (elevation and soil apparent electrical

conductivity [soil ECa]) and biotic (Cydnidae adults and nymphs, Elateridae adults, and Gryllotalpidae

adults and nymphs) variables within the crop. Soil ECa measurements can be useful in agricultural settings

due to the association of this variable with soil texture, organic matter, salinity, and drainage conditions

(Grisso et al. 2005). This measurement may also be useful for habitat characterization of arthropods, as low

and high ECa values are known to be correlated with low and high moisture-holding capacities within soils,

respectively (Grisso et al. 2005). The elevation and soil ECa data reported in this study was previously used

to explain counts of pestiferous and predaceous soybean arthropods from drop-cloth sampling (Greene et

al. 2021). The effect of space was not of primary interest in our previous study, and was incorporated into

analyses via principal components of neighborhood matrices (PCNM). In this study, spatial analysis by

distance indices (SADIE) was used to determine how elevation and soil ECa were associated with

herbivorous and predatory insect taxa collected from pitfall traps. With respect to their eco-ethological

58

differences, we hypothesized that T. carolina will be more aggregated than C. punctulata and more

associated with lower field elevations and higher soil ECa values within soybean fields. Although both

species can be considered generalist predators, we were also interested in determining whether the

distributions of each species were more associated with the distributions of the most abundant herbivorous

taxa (Cydnidae adults and nymphs, Elateridae adults, and Gryllotalpidae adults and nymphs) observed in

pitfall trap samples. Although Cydnidae, Elateridae, and Gryllotalpidae can be considered crop pests in

various systems, they are typically not considered to be major pests in soybean (Ulagaraj 1975, Chapin and

Thomas 2003, Hodgson et al. 2012). However, given the abundance of these organisms and their ground

habitat co-occupation with T. carolina and C. punctulata, it is possible that these minor pest taxa serve as

alternative prey in soybean, particularly if spatiotemporal associations exist between these trophic levels.

Because T. carolina and C. punctulata exhibit different diel patterns, we hypothesized that these species

would not be significantly dissociated in field areas with similar elevation and soil ECa values; similar in-

field distributions for T. carolina and C. punctulata would support the existence of temporal resource

partitioning of prey. Given the relative paucity of information on pest suppression by epigeal predators in

soybean, a better understanding of which factors are involved in the spatiotemporal distributions of T.

carolina and C. punctulata will help to conserve and promote the effect of biological control in this crop.

Materials and Methods

Field Trials

Soybeans were planted in fields ‘A’ (8.9 ha) and ‘B’ (5.7 ha) at the Clemson University Edisto

Research and Education Center (REC) in Blackville, SC using 96.5 cm row spacing on 9 June (Field A:

Asgrow AG75X6 Roundup Ready 2 Xtend variety) and 12 June (B: Bayer Credenz LibertyLink 7007LL)

in 2017 and 16 June (A: AG69X6) and 12 June (B: Pioneer P67T90R2) in 2018. Extension

recommendations were used for decisions regarding plant populations and herbicide and fertilizer

applications (Marshall et al. 2020). Insecticides were not applied during trials. Elevation and soil ECa data

59

from the two fields reported in this study were previously used to associate field characteristics with

soybean arthropod counts from drop-clothing sampling (Greene et al. 2021).

Sampling

Fiberglass flags spaced ≈ 40 m apart were used to define sampling grids in fields A (66 flags) and

B (54 flags). Grid points within a field were identical across years. Near each grid point (within 4 m),

pitfall trap data was collected during calendar weeks (CW) 28, 32-34, 36 and 41 (14 July – 10 October) for

field A in 2017, CW 28, 29, 31-33, 35, 37, and 40 (13 July – 3 October) for field B in 2017, CW 30-36, 39,

and 40 (24 July – 3 October) for field A in 2018, and CW 30-36, and 39-42 (24 July –17 October) for field

B in 2018. The CW corresponding to each sampling event represents the point in time in which pitfall trap

data was collected (after 7-12 days of field exposure).

Pitfall traps were constructed using two (bottom and top), 207 ml plastic cups (6.4 cm diameter x

8.9 cm depth). The top cup featured 4 equally spaced 0.75 cm drainage holes (covered with mesh) around 3

cm below the rim to allow for overflow in case of rainfall. The bottom cup had a centrally placed 0.75 cm

hole in the bottom, and was filled with gravel to a depth of around 2 cm to facilitate drainage from the top

cup. The entire pitfall trap consisting of both cups was placed in the soil in such a way that the rim of the

top cup was just below the soil surface. The top cup was filled up to the drainage holes with an arthropod

preservation and retention solution composed of propylene glycol and water (50:50 mixture). Green food

coloring was added to the propylene glycol mixture to help determine if rainfall had diluted the solution,

and more preservation fluid was added when necessary. During each sampling event, the top cup and all its

contents (preservative and arthropods) were removed after 7-12 days of field exposure. The top cup

(containing preservative) was then replaced if sampling was to be conducted during the next CW.

Otherwise, replacements occurred 7-12 days before the next sampling event. After the preservative solution

was drained from collected samples, pitfall trap contents were stored at -28°C for > 24h. All arthropods

from pitfall traps were later identified to at least family, with identifications to the genus and species level

made when possible. Identifications to and within Carabidae, Scarabaeoidea, and all other taxa (Araneae,

Dermaptera, Hemiptera, and Orthoptera) were made using Ciegler (2000), Harpootlian (2001), and

60

Triplehorn et al. (2005), respectively. The sum of the adults and nymphs within each pitfall trap were used

for analyses for Cydnidae and Gryllotalpidae.

Using a Veris 3100 EC meter (Veris Technologies, Salina, KS), lengthwise runs were made across

each field’s width (one run per ≈7.6 m of width) to collect shallow (0.0–0.3 m) and deep (0.0–0.9 m) soil

ECa data from both fields on 22 March 2019. The average soil ECa values (for each grid point) used in all

analyses were created by averaging the shallow and deep values first, and then by averaging these values

within a 24-m diameter circle encompassing each grid point. Although soil ECa values within a field may

change by 5-10% (except for pure sand) within a season, the soil properties, and therefore the soil

management zone, for a given location will not (Grisso et al. 2005). For this reason, the soil ECa

measurement taken for both fields on 22 March 2019 was used in all analyses. A Trimble AgGPS 332

receiver with beacon DGPS for positioning within the Veris 3100 EC meter was used to obtain elevation

data for each grid point. The average elevation values (for each grid point) used in all analyses were created

by averaging all values within a 24-m diameter circle encompassing each grid point.

Data Analyses

Spatial analysis by distance indices (SADIE) (Perry 1998) [sadie function in the epiphy package

(Gigot 2018) in R version 3.5.3 (R Core Team 2019)] was used to analyze the spatial distributions of insect

taxa for each sampling event, as well as soil ECa and elevation data for both fields from 22 March 2019.

Count and accompanying location data (e.g. flags within our fields) are analyzed by SADIE through the

expression of location data as absolute positions, and analyses are capable of handling “patchy” data.

Before data were analyzed, the values of all insect and field variables were converted to integers.

For each field, four steps were involved in SADIE analyses: 1) an overall index of aggregation (Ia) was

calculated based on the existence and magnitude of clustering in a dataset; 2) patches [Vi] and gaps [Vj]

were created based on field areas with higher or lower count values within the dataset, respectively, as

identified by local aggregation indices; 3) patches and gaps were plotted to visually depict how the dataset

was clustered (Figure 3.1); and 4) the amount of dissociation or association between two datasets featuring

61

identical location data (as identified through the calculation of an overall association index [X]) was also

visually depicted in plots of local association indices (Figure 3.2) (Winder et al. 2019).

The calculation of Ia was based on the minimum distance (D) needed for counts to uniformly

distributed. An Ia value of 1 indicated that counts that were randomly distributed, while Ia values that were

greater and less than one represented counts that were aggregated and uniformly distributed, respectively.

Significant aggregation or regularity in datasets was assessed with data randomizations (5,967) and had

associated p values of < 0.025 or > 0.975, respectively. Positive cluster indices (vi) were assigned to patch

locations, as they exceeded the average count value (m), while negative cluster indices (vj) were assigned to

gap locations, as they possessed values below m. The overall patch (Vi) and gap (Vj) cluster indices were

calculated based on individual cluster indices. Linearly interpolated [interp function, akima package

(Akima and Gebhardt 2016), R (R Core Team 2019)] patch (vi > 1.5) and gap (vj < -1.5) locations were

visually depicted as clusters on Google satellite imagery [get_googlemap function, ggmap package (Kahle

and Wickham 2013), R (R Core Team 2019)] (Figure 3.1).

Spatial association analyses between tiger beetle species, and between the combination of each

herbivorous insect taxa and each cicindelid from the same sampling event were completed using N_AShell

(Version 1.0 © 2008 Kelvin F. Conrad). N_AShell was also used to carry out spatial association analyses

between cicindelid datasets from each sampling event and soil ECa and elevation datasets for both fields.

The relationship between individual cluster index values (vi and vj) for each sampling location in the two

datasets in each association analysis was used to calculate the local association index (Xk) for those

locations. Positive Xk values indicated that a patch or a gap was present in both datasets, while negative Xk

values were indicative of a patch in one dataset and a gap in the other. Randomizations (5,967) were used

to test the significance of the overall association index (X) (the mean of all Xk values). Datasets that were

significantly associated or dissociated had p values < 0.025 or > 0.975, respectively. Linearly interpolated

[interp function, akima package (Akima and Gebhardt 2016), R (R Core Team 2019)] positive and negative

Xk values were visually depicted as clusters on Google satellite imagery [get_googlemap function, ggmap

package (Kahle and Wickham 2013), R (R Core Team 2019)] (Figure 3.2).

62

Results

Summary statistics

Across fields and years, C. punctulata (total of 4,208; average per trap per sampling date: 2.2 ±

0.12 [SEM]) and T. carolina (3,375; 1.8 ± 0.14) were the most abundant predatory species collected from

pitfall traps. Cydnidae (2,355; 1.2 ± 0.08), Elateridae (1,034; 0.5 ± 0.05), and Gryllotalpidae (553; 0.3 ±

0.03) were the most abundant herbivorous taxa among pitfall trap captures. The abundance of each taxa

varied across years and fields. While the seasonal average (i.e. average of all samples taken from each

sampling point in a field for all sampling events within a year) of C. punctulata was never < 1 per trap for

any field-year combination, the highest seasonal average for T. carolina in Field A was 0.11 ± 0.04 per trap

in 2017 (Table 3.1), while the lowest seasonal average for this species in Field B was 2.88 ± 0.28 per trap

in 2018 (Table 3.2). The lowest seasonal average for Cydnidae (0.35 ± 0.04) coincided with the highest

seasonal average for Elateridae (1.35 ± 0.14) in Field B in 2018, while the seasonal averages were higher in

Field B (2017: [0.35 ± 0.1]; 2018: [0.48 ± 0.04]) than in Field A (2017: [0.21 ± 0.04]; 2018: [0.1 ± 0.02])

for both years for Gryllotalpidae.

Intraseasonal abundance patterns also differed among taxa. The peak for each taxa (except T.

carolina) occurred in the first week of sampling (CW 28) in Field A in 2017 (Table 3.1). Thereafter,

populations fell to an average of < 0.4 per trap during CWs 34, 36, and 41 in Field A in 2017 for C.

punctulata, Elateridae, and Gryllotalpidae. Population peaks for Cydnidae (CW 35: 5.81 ± 1.15) and

Gryllotalpidae (CW 36: 0.33 ± 0.08) occurred in the latter half of the sampling season for Field A in 2018

(Table 3.2). The population peaks that occurred in 2018 in Field A for C. punctulata (CW 30: 8.57 ± 1.12)

and Cydnidae (CW 35: 5.81 ± 1.15) reflect the highest recorded abundance for these taxa in this study.

Cicindela punctulata averages remained above 3 per trap throughout the first half of the sampling season

(CW 30-34) in Field A in 2018, while C. punctulata populations had already fallen to an average of 0.47 ±

0.19 per trap by the third sampling event (CW 33) in this field in 2017. After Elateridae populations peaked

at 0.75 ± 0.35 per trap during the first week of sampling (CW 30), populations never exceeded an average

63

of 0.30 per trap in Field A in 2018. The average per trap never exceeded 0.36 for T. carolina in Field A in

either year.

Populations of all taxa peaked within the first two calendar weeks (28-29) of sampling in Field B

in 2017 (Table 3.1). These population peaks represent the highest densities of Elateridae (CW 29: 4.13 ±

0.56), Gryllotalpidae (CW 29: 1.31 ± 0.71), and T. carolina (CW 28: 20.98 ± 2.04) observed during this

study. Populations for all taxa except Cydnidae peaked within the first four weeks of sampling (CW 30-33)

in Field B in 2018 (Table 3.2). Population peaks for all taxa were lower in 2018 than in 2017 in Field B,

with the exception of Cydnidae. Cydnidae averaged at least 1 per trap in 5 out of 11 sampling events in

Field B in 2018, with the highest per trap average occurring in CW 39 (3.23 ± 0.96). Taxa averages in Field

B never exceeded one per trap during the latter half of the sampling season in both years for all taxa, with

the exception of Cydnidae in 2018.

As reported in Greene et al. (2021), soil ECa and elevation values differed between fields. The

average soil ECa value in Field B (3.01 ± 0.16) exceeded that of Field A (1.04 ± 0.09), while the opposite

was true for average elevation values (Field A: 94.4 m ± 0.22; Field B: 90.41 m ± 0.21). Soil ECa values

ranged from 0.26-3.78 in Field A and from 0.66-6.56 in Field B. Elevation values ranged from 91.33-97.83

m in Field A and from 86.72-92.76 m in Field B.

SADIE Aggregation Analyses

For all insect taxa across all sampling events, 8 out of 170 (5%) SADIE aggregation indices were

significantly aggregated (p < 0.025) (Tables 3.1 and 3.2, Figure 3.1). Aggregations were split evenly across

years, but 75% (6/8) of aggregations occurred in Field A. Half (4/8) of all significant aggregations were for

Cydnidae; these aggregations were split evenly across years, but 75% (3/4) occurred in Field A. The

significant aggregations for Cydnidae in Field A, CW 28, in 2017 and for T. carolina in Field B, CW 28, in

2017 coincided with their respective population peaks in these fields. Each taxa was significantly

aggregated in at least one CW, with the exception of Gryllotalpidae. No significant aggregations occurred

after CW 36 in either year or field. The single, significant aggregation index indicating uniformity (p >

0.975) among counts occurred for, and coincided with the population peak of, Elateridae in Field A, CW

64

28, in 2017. Elevation values were significantly aggregated in both fields (Field A: Ia = 2.15, p < 0.001;

Field B: Ia = 2.55, p < 0.001), while soil ECa values were not significantly aggregated in either field (p >

0.025).

SADIE Association Analyses.

Across all sampling events, 9% (31/344) of SADIE association analyses among insects and field

variables within the same calendar week were significantly associated (p < 0.025), while 11% (39/344)

were significantly dissociated (p > 0.975) (Table 3.3, Figure 3.2). Across fields, the percentage of

significant associations was similar in 2017 (52%: 16/31 significant associations) and 2018 (48%: 15/31),

while the percentage of significant dissociations in 2018 (64%: 25/39 significant dissociations) was 28%

greater than those in 2017 (36%: 14/39). Across years, Field B (58%: 18/31) had more significant

associations than Field A (42%: 13/31), while the opposite was true for significant dissociations (Field A:

56% [22/39]; Field B: 44% [17/39]). Across years and fields, more significant associations and

dissociations occurred in Field B in 2017 (32%: 10/31) and in Field A in 2018 (38%: 15/39) than in any

other field-year combination, respectively. Association analyses between soil ECa and elevation were not

significant for Field A (X = -0.01, p = 0.529) or Field B (X = 0.35, p = 0.026).

Significant associations between C. punctulata and T. carolina accounted for 42% (13/31) of the

total significant associations among insects and field variables (Table 3.3, Figure 3.2). The percentage of

significant associations was similar across years (2017: 46% [6/13]; 2018: 54% [7/13]), but 77% (10/13) of

these associations occurred in Field B. Cicindela punctulata and T. carolina were significantly associated

when T. carolina was significantly aggregated (Field B, CW 28, 2017), but not when C. punctulata was

significantly aggregated (Field A, CW 34, 2018). Cicindela punctulata and T. carolina were not

significantly associated with any other variable for 93% (12/13) of the calendar weeks in which they were

significantly associated with each other. For 46% (6/13) of the calendar weeks in which C. punctulata and

T. carolina were significantly associated, one cicindelid was significantly dissociated with a particular

herbivorous taxa while the other was not. The significant association between C. punctulata and T. carolina

65

corresponded to the seasonal population peak for the former in Field B, CW 32, 2018 and the latter in Field

A, CW 31, 2017. Cicindela punctulata and T. carolina were not significantly dissociated in any CW.

The 7 (23%) significant associations between C. punctulata and herbivorous pests and field

variables, along with the 11 (35%) significant associations between T. carolina and herbivorous pests and

field variables accounted for the remaining 58% (18/31) of total significant associations across all sampling

events (Table 3.3, Figure 3.2). Seventy-five percent (3/4) of the significant associations between C.

punctulata and herbivorous pests and field variables that occurred in 2017 were in Field B, while 100%

(3/3) of the significant associations in 2018 were in Field A. The significant association between C.

punctulata and Elateridae in Field A, CW 28, in 2017 occurred during the latter’s seasonal population peak

when it was significantly uniformly distributed. Sixty-seven percent (4/6) of the significant associations

between T. carolina and herbivorous pests and field variables that occurred in 2017 were in Field A, while

60% (3/5) of the significant associations in 2018 were in Field B. The significant association between T.

carolina and Gryllotalpidae in Field A, CW 28, in 2017 occurred during the latter’s seasonal population

peak.

The percentage of significant dissociations across all sampling events was similar for C.

punctulata (49%: 19/39) and T. carolina (51%: 20/39) (Table 3.3, Figure 3.2). Fifty-seven percent (4/7) of

the significant dissociations between C. punctulata and herbivorous pests and field variables that occurred

in 2017 were in Field A, while 58% (7/12) of the significant dissociations in 2018 were in Field A. Fifty-

seven percent (4/7) of the significant dissociations between T. carolina and herbivorous pests and field

variables that occurred in 2017 were in Field B, while 62% (8/13) of the significant dissociations in 2018

were in Field A.

Cicindelines had more significant associations and dissociations with Elateridae than any other

herbivorous taxa (7 associations and 7 dissociations) (Table 3.3, Figure 3.2). Seventy-five percent (3/4) of

the significant associations between T. carolina and Elateridae were in Field B in 2018. Eighty percent

(4/5) of the significant associations for Gryllotalpidae were with T. carolina. Cicindelines had more

significant dissociations with soil ECa (16 dissociations) than with elevation (10 dissociations). Cicindela

punctulata (10 dissociations) had more significant dissociations with soil ECa than T. carolina (6

66

dissociations) did, while the opposite was true for elevation (C. punctulata: 3 dissociations; T. carolina: 7

dissociations). Sixty percent (6/10) of the significant dissociations between C. punctulata and soil ECa were

from Field A in 2018, while 86% (6/7) of the significant dissociations between T. carolina and elevation

were from 2018.

Discussion

Per-trap averages for the two most abundant predatory species in this study, C. punctulata and T.

carolina, varied considerably between fields, years, and within seasons (Tables 3.1 and 3.2). Previous

reports on epigeal predatory assemblages have documented the co-occurrence and variable abundance of C.

punctulata and T. carolina in soybean (Goyer et al. 1983), corn (Lesiewicz et al. 1983), cotton (Torres and

Ruberson 2007), and multi-year fallow field habitat adjacent to a cotton field (Young 2011) in Louisiana,

North Carolina, Georgia, and Mississippi, respectively. While C. punctulata was the more abundant of the

two species in corn (Lesiewicz et al. 1983), T. carolina was the most abundant predatory species in multi-

year fallow field habitat (Young 2011) and the most abundant species in cotton (Torres and Ruberson

2007). The temporal patterns displayed by C. punctulata and T. carolina in this study are similar to those

reported by previous studies. A frequent occurrence of cicindelines in June, followed by a sharp decrease in

numbers thereafter was reported in cotton (Torres and Ruberson 2007), while C. punctulata and T. carolina

numbers were highest from mid-to-late July through mid-to-late August in multi-year fallow field habitat

(Young 2011).

Per-trap averages of insects in this study also varied among fields and years (Tables 3.1 and 3.2).

The variability of seasonal per-trap averages of Elateridae was more pronounced between years than

between fields, while the opposite was true for T. carolina and Gryllotalpidae. The difference between

years in Elateridae averages may be associated with rainfall. Kozina et al. (2015) found that adults of

Agriotes sputator (Linnaeus) (Coleoptera: Elateridae) were more prevalent in pheromone traps in various

arable crop (including soybean) and cereal fields when yearly rainfall totals were < 740 mm. In this study,

rainfall totals in Blackville, SC, were lower for each month in which sampling was conducted in 2017 than

in 2018 (NOAA-NESDIS 2021); Elaterid seasonal averages were higher in 2017 than in 2018 for both

67

fields. The difference in T. carolina and Gryllotalpidae field averages may be associated with soil moisture.

Tetracha carolina occurs most often in habitats associated with water sources (Knisley and Schultz 1997),

while soil moisture is correlated with the depth of Gryllotalpidae tunnels (Capinera and Leppla 2018).

Given the tendency for water to accumulate in low-lying areas, along with the positive association between

soil ECa values and a soil’s moisture-holding capacity (Grisso et al. 2005), the higher seasonal averages for

Gryllotalpidae and T. carolina in Field B (soil ECa x̄: 3.01 ± 0.16, elevation x̄: 90.41 m ± 0.21) when

compared with Field A (soil ECa x̄: 1.04 ± 0.09, elevation x̄: 94.4 m ± 0.22) for both years may be related

to the differences in soil moisture levels due to differential elevation and soil ECa values between these

fields.

Aggregations were limited for insect taxa in this study, as only 5% (8/170) of SADIE aggregation

analyses were significant (Tables 3.1 and 3.2, Figure 3.1). Cydnidae was more aggregated than any other

taxa with 4 significant aggregations. This result is in agreement with Lis et al. (2000), as the authors state

that burrower bugs are reported as having a patchy distribution in field crops. Tetracha carolina was

significantly aggregated at its population peak in Field B, CW 28, in 2017, and was also significantly

associated with C. punctulata during this sampling event. During the same sampling event, C. punctulata

was significantly dissociated with Elateridae. The significant dissociation of one tiger beetle species with

an herbivorous taxa during sampling events in which tiger beetles were significantly co-associated was a

common occurrence in this study, as this event occurred for nearly half (6/13) of all calendar weeks in

which cicindelines were co-associated (Table 3.3). Cicindela punctulata and T. carolina were more

associated with each other than with any other insect or field variable, and were not significantly associated

with any other variable, except for one of the sampling events in which they were significantly associated

with each other. Due to the number of significant associations between C. punctulata and T. carolina, along

with the number of dissociations with herbivorous taxa during the sampling events in which these

cicindelines were associated, the potential occurrence of intraguild predation between these two tiger beetle

species should not be dismissed. Although T. carolina is considered to be principally nocturnal, diurnal

activity can occur during warm, overcast days for this species (Pearson et al. 2006). Furthermore, C.

punctulata is frequently attracted to lights at night (Pearson et al. 2006) and is capable of prey capture in

68

complete darkness (Riggins and Hoback 2005). Temporal overlap of the field distributions of C. punctulata

and T. carolina in this study is therefore plausible. Intraguild predation of the larger Cicindela circumpicta

(LaFerte) (Coleoptera: Cicindelinae) on the smaller C. togata (LaFerte) (Coleoptera: Cicindelinae occurred

in laboratory settings, which helped to explain the discovery of C. togata exoskeletal remains from field

observations for the spatiotemporally co-occurring species (Hoback et al. 2001). Given the degree of

spatial, and the potential for temporal, co-occurrence between C. punctulata and T. carolina in this study,

the larger T. carolina (12- 20 mm body length) may act as an intraguild predator on the smaller C.

punctulata (10-13 mm) (Knisley and Schultz 1997).

As hypothesized, T. carolina was more associated with lower elevations (more significant

dissociations) than C. punctulata (Table 3.3). This result is consistent with the propensity of T. carolina to

occur near water sources (Knisley and Schultz 1997), as water is more likely to accumulate in low-lying

field areas. Although there is a tendency for low-lying areas, in which water accumulates, to have higher

soil ECa values than higher areas with better drainage (USDA-NRCS 2014), elevation and soil ECa were

not significantly dissociated with each other for either field in this study. Soil ECa is not only associated

with soil moisture holding capacity, however, but with soil cation exchange capacity, organic matter,

salinity, and texture (Grisso et al. 2005). Higher soil ECa values have also been associated with higher

nutrient availability in nonsaline soils (USDA-NRCS 2014), which can impact plant productivity. Low soil

ECa values may have been associated with field areas with lower nutrient availability in this study, which

may have affected soybean biomass. The majority of dissociations for soil ECa were with C. punctulata,

which is known to occur in open, sparsely vegetated areas (Knisley and Schultz 1997, Pearson et al. 2006).

Rather than soil moisture, the in-field habitat of C. punctulata may be associated with soil ECa due to its

correlation with soil properties such as nutrient availability.

Although Cydnidae, Elateridae, and Gryllotalpidae are not significant pests in soybean (Ulagaraj

1975, Chapin and Thomas 2003, Hodgson et al. 2012), an understanding of the relationship between these

abundant and spatiotemporally co-occurring “alternative prey” and the predatory C. punctulata and T.

carolina is important from a pest management perspective, as this relationship may be informative of how

cicindelines interact with more economically important crop pests. In this study, C. punctulata and T.

69

carolina were found to be significantly associated with different herbivorous taxa within the same sampling

event (Table 3.3), which may suggest the occurrence of resource partitioning of prey between these species.

Variable reactions to herbivorous taxa have also been previously reported for T. carolina, as altered

searching behavior in laboratory cage experiments led to a reduced functional response of T. carolina to

adult twolined spittlebug, Propasia bicincta (Say) (Hemiptera: Cercopidae), when this species was offered

as prey alongside larval fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae),

when compared with experiments in which only one species was available (Nachappa et al. 2006). Despite

the reduction in functional response, the authors noted that P. bicincta was still killed by T. carolina when

offered alongside S. frugiperda, and that distinct prey switching behaviors (Flinn et al. 1985), or the

consumption of preferred prey over that of another when prey densities are high, were not observed

(Nachappa et al. 2006). Furthermore, the diet breadth of T. carolina has been shown to include over 40

different arthropod taxa in laboratory experiments (Young 2012), with at least one (pupae of velvetbean

caterpillar, Anticarsia gemmatalis [Hübner] [Lepidoptera: Erebidae] [Lee et al. 1990]) considered to be an

important pest in soybean (Herzog and Todd 1980). Cicindelines and T. carolina, in particular, should

therefore be given consideration as important predators in soybean due to their behavioral propensity to

attack multiple pest species, and the preservation of this behavior even when different prey species are

available. Although cicindelines were associated with minor pests (Cydnidae, Elateridae, and

Gryllotalpidae) in this study, the behavior of these predators could allow for them to play a role in the

regulation of more economically important pests in this crop, such as A. gemmatalis.

In this study, we found that the field variables soil ECa and elevation could be informative of the

spatial distributions of the abundant soybean predators C. punctulata and T. carolina. Furthermore, the

discovery of associations of C. punctulata and T. carolina with each other and with abundant herbivorous

epigeal taxa (Cydnidae, Elateridae, and Gryllotalpidae) (Table 3.3, Figure 3.2) is important for developing

a better understanding of the role that these predators play in the regulation of soybean pests. Further

research on how potential intraguild predation of T. carolina on C. punctulata may modify the biological

control effect that these predators exert in this crop, as well as how these predators respond to other, more

economically important pests, is warranted.

70

References Cited

Akima, H., and A. Gebhardt. 2016. akima: Interpolation of Irregularly and Regularly Spaced Data.

Capinera, J., and N. Leppla. 2018. Shortwinged Mole Cricket, Neoscapteriscus abbreviatus (Scudder);

Southern Mole Cricket, Neoscapteriscus borellii (Giglio-Tos); and Tawny Mole Cricket, Neoscapteriscus

vicinus (Scudder) (Insecta: Orthoptera: Gryllotalpidae) (No. EENY-235). Department of Entomology and

Nematology, University of Florida Institute of Food and Agricultural Services.

Chapin, J. W., and J. S. Thomas. 2003. Burrower bugs (Heteroptera: Cydnidae) in peanut: seasonal

species abundance, tillage effects, grade reduction effects, insecticide efficacy, and management. J. Econ.

Entomol. 96: 1142–1152.

Ciegler, J. C. 2000. Ground beetles and wrinkled bark beetles of South Carolina (Coleoptera: Geadephaga:

Carabidae and Rhysodidae). Biota of South Carolina, v. 1. Clemson University, Clemson, SC.

Flinn, P. W., A. A. Hower, and R. A. J. Taylor. 1985. Preference of Reduviolus americoferus

(Hemiptera: Nabidae) for potato leafhopper nymphs and pea aphids. Can. Entomol. 117: 1503–1508.

Gigot, C. 2018. epiphy: Analysis of Plant Disease Epidemics.

Goyer, R. A., D. W. Brown, and J. B. Chapin. 1983. Predaceous arthropods found in soybeans in

Louisiana. Proc La. Acad Sci. 46: 29-33.

Graves, R. C., and D. L. Pearson. 1973. The tiger beetles of Arkansas, Louisiana, and Mississippi

(Coleoptera: Cicindelidae). Trans. Am. Entomol. Soc. 99: 157–203.

Greene, A. D., F. P. Reay-Jones, K. R. Kirk, B. K. Peoples, and J. K. Greene. 2021. Associating Site

Characteristics With Distributions of Pestiferous and Predaceous Arthropods in Soybean. Environ.

Entomol. 50: 477–488.

Grisso, R. D., M. M. Alley, D. L. Holshouser, and W. E. Thomason. 2005. Precision Farming Tools.

Soil Electrical Conductivity (No. 442–508). Virigina Cooperative Extension.

Harpootlian, P. J. 2001. Scarab beetles (Coleoptera: Scarabaeidae) of South Carolina. Biota of South

Carolina, v. 2. Clemson University, Clemson, SC.

Herzog, D. C., and J. W. Todd. 1980. Sampling velvetbean caterpillar on soybean, pp. 107–140. In M.

Kogan and D.C. Herzog (Eds.), Sampling methods in soybean entomology. Springer-Verlag, New York.

71

Hoback, W. W., L. G. Higley, and D. W. Stanley. 2001. Tigers eating tigers: evidence of intraguild

predation operating in an assemblage of tiger beetles. Ecol. Entomol. 26: 367–375.

Hodgson, E. A., A. Sisson, D. Mueller, L. Jesse, E. Saalau-Rojas, and A. Duster. 2012. Field Crop

Insects (No. CS10014). Iowa State University and Iowa Soybean Association, Ames, IA.

Hooper, D. U., F. S. Chapin, J. J. Ewel, A. Hector, P. Inchausti, S. Lavorel, J. H. Lawton, D. M.

Lodge, M. Loreau, and S. Naeem. 2005. Effects of biodiversity on ecosystem functioning: a consensus of

current knowledge. Ecol. Monogr. 75: 3–35.

Kahle, D., and H. Wickham. 2013. ggmap: Spatial Visualization with ggplot2. R J. 5: 144–161.

Knisley, C. B., and T. D. Schultz. 1997. The biology of tiger beetles and a guide to the species of the

south Atlantic states. Virginia Museum of Natural History, Martinsville, VA.

Kozina, A., D. Lemic, R. Bazok, K. M. Mikac, C. M. McLean, M. Ivezić, and J. Igrc Barčić. 2015.

Climatic, edaphic factors and cropping history help predict click beetle (Coleoptera: Elateridae) (Agriotes

spp.) abundance. J. Insect Sci. 15: 100.

Lee, J.-H., S. J. Johnson, and V. L. Wright. 1990. Quantitative survivorship analysis of the velvetbean

caterpillar (Lepidoptera: Noctuidae) pupae in soybean fields in Louisiana. Environ. Entomol. 19: 978–986.

Lesiewicz, D. S., J. W. Van Duyn, and J. R. Bradley Jr. 1983. Determinations on cornfield carabid

populations in northeastern North Carolina. Environ. Entomol. 12: 1636–1640.

Lis, J. A., M. Becker, and C. W. Schaefer. 2000. Burrower bugs (Cydnidae), pp. 405-419. In C. W.

Schaefer and A. R. Panizzi [eds.] Heteroptera of economic importance. CRC, Boca Raton, FL.

Loreau, M., S. Naeem, P. Inchausti, J. Bengtsson, J. P. Grime, A. Hector, D. U. Hooper, M. A.

Huston, D. Raffaelli, and B. Schmid. 2001. Biodiversity and ecosystem functioning: current knowledge

and future challenges. Science. 294: 804–808.

Losey, J. E., and M. Vaughan. 2006. The economic value of ecological services provided by insects.

Bioscience. 56: 311–323.

Marshall, M., J. Greene, D. Gunter, F. Reay-Jones, J. Mueller, D. Anco, K. Moore, P. Peterson, B.

Powell, C. Heaton, J. Crouch, and B. Beer. 2020. 2020 South Carolina Pest Management Handbook.

Clemson University Extension, Clemson, SC.

72

Murdoch, W. W. 1969. Switching in general predators: experiments on predator specificity and stability of

prey populations. Ecol. Monogr. 39: 335–354.

Nachappa, P., S. K. Braman, L. P. Guillebeau, and J. N. All. 2006. Functional response of the tiger

beetle Megacephala carolina carolina (Coleoptera: Carabidae) on twolined spittlebug (Hemiptera:

Cercopidae) and fall armyworm (Lepidoptera: Noctuidae). J. Econ. Entomol. 99: 1583–1589.

NOAA-NESDIS. 2021. Global Summary of the Month. National Environmental Satellite, Data, and

Information Service, National Oceanic & Atmospheric Administration, U.S. Department of Commerce,

National Centers for Environmental Information, Asheville, NC.

Pearson, D. L., C. B. Knisley, and C. J. Kazilek. 2006. A Field Guide to the Tiger Beetles of the United

States and Canada: Identification, Natural History, and Distribution of the Cicindelidae. Oxford University

Press, New York.

Perry, J. N. 1998. Measures of spatial pattern for counts. Ecology. 79: 1008–1017.

Pimentel, D. 2005. Environmental and Economic Costs of the Application of Pesticides Primarily in the

United States. Environ. Dev. Sustain. 7: 229–252.

Polis, G. A., C. A. Myers, and R. D. Holt. 1989. The ecology and evolution of intraguild predation:

potential competitors that eat each other. Annu. Rev. Ecol. Syst. 20: 297–330.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. R Foundation for

Statistical Computing, Vienna, Austria.

Riggins, J. J., and W. W. Hoback. 2005. Diurnal tiger beetles (Coleoptera: Cicindelidae) capture prey

without sight. J. Insect Behav. 18: 305–312.

Straub, C. S., D. L. Finke, and W. E. Snyder. 2008. Are the conservation of natural enemy biodiversity

and biological control compatible goals? Biol. Control. 45: 225–237.

Straub, C. S., and W. E. Snyder. 2006. Species identity dominates the relationship between predator

biodiversity and herbivore suppression. Ecology. 87: 277–282.

Torres, J. B., and J. R. Ruberson. 2007. Abundance and diversity of ground‐dwelling arthropods of pest

management importance in commercial Bt and non‐Bt cotton fields. Ann. Appl. Biol. 150: 27–39.

73

Triplehorn, C. A., N. F. Johnson, and D. J. Borror. 2005. Borror and DeLong’s introduction to the study

of insects. Thompson Brooks/Cole, Belmont, CA.

Tylianakis, J. M., T. Tscharntke, and A.-M. Klein. 2006. Diversity, ecosystem function, and stability of

parasitoid–host interactions across a tropical habitat gradient. Ecology. 87: 3047–3057.

Ulagaraj, S. M. 1975. Mole crickets: ecology, behavior, and dispersal flight (Orthoptera: Gryllotalpidae:

Scapteriscus). Environ. Entomol. 4: 265–273.

USDA-NRCS. 2014. Soil Electrical Conductivity-Inherent Factors Affecting Soil EC, Guides for

Educators. United States Department of Agriculture, Natural Resources Conservation Service.

Winder, L., C. Alexander, G. Griffiths, J. Holland, C. Woolley, and J. Perry. 2019. Twenty years and

counting with SADIE: Spatial Analysis by Distance Indices software and review of its adoption and use.

Rethink. Ecol. 4: 1.

Young, O. P. 2011. Ground-surface arthropods of an old-field habitat in the Delta of Mississippi, with

emphasis on the Cicindelidae (Coleoptera). J. Entomol. Sci. 46: 292–307.

Young, O. P. 2012. Laboratory evaluation of Tetracha carolina (Coleoptera: Carabidae: Cicindelinae) as a

predator of ground-surface arthropods in an old-field habitat. Entomol. News. 122: 192–197.

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Table 3.1. Seasonal dynamics and spatial aggregation indices (Ia) from SADIE of insects from each sampling event in soybean in 2017 July August September October Grand Total/

Seasonal

Mean ± SE

Calendar Week

Field Variable Metric 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

A

Cydnidae

Ia 1.93 — — — 1.29 0.91 1.65 — 0.96 — — — — 0.90 — 1.27 ± 0.18

Total 204 — — — 48 47 48 — 59 — — — — 17 — 423

Mean

± SE

3.19 ±

0.46 — — —

0.73

±

0.16

0.71

±

0.23

1.02

±

0.28

—

1.09

±

0.32

— — — —

0.29

±

0.12

— 1.19 ± 0.13

Elateridae

Ia 0.75 — — — 1.72 1.20 N/A — 0.82 — — — — N/A — 1.12 ± 0.22

Total 173 — — — 2 100 0 — 5 — — — — 0 — 280

Mean

± SE

2.7 ±

0.61 — — —

0.03

±

0.02

1.52

±

0.47

0 ± 0 —

0.09

±

0.05

— — — — 0 ± 0 — 0.79 ± 0.15

Gryllotalpidae

Ia 1.09 — — — 0.95 0.87 0.77 — 1.03 — — — — 1.05 — 0.96 ± 0.05

Total 37 — — — 10 3 1 — 13 — — — — 10 — 74

Mean

± SE

0.58 ±

0.19 — — —

0.15

±

0.05

0.05

±

0.03

0.02

±

0.02

—

0.24

±

0.08

— — — —

0.17

±

0.09

— 0.21 ± 0.04

Cicindela

punctulata

Ia 1.4 — — — 0.95 1.02 0.92 — 1.39 — — — — 1.13 — 1.14 ± 0.09

Total 461 — — — 161 31 9 — 14 — — — — 21 — 697

Mean

± SE

7.2 ±

0.87 — — —

2.44

± 0.8

0.47

±

0.19

0.19

±

0.07

—

0.26

±

0.11

— — — —

0.36

±

0.15

— 1.96 ± 0.26

Tetracha

carolina

Ia 0.87 — — — 1.33 1.2 — N/A — — — — N/A — 1.11 ± 0.1

Total 12 — — — 1 24 0 — 1 — — — — 0 — 38

Mean

± SE

0.19 ±

0.06 — — —

0.02

±

0.02

0.36

±

0.22

0 ± 0 —

0.02

±

0.02

— — — — 0 ± 0 — 0.11 ± 0.04

B

Cydnidae

Ia 0.99 0.88 — 0.82 1.02 1.37 — 0.93 — 0.95 — — 0.98 — — 0.99 ± 0.06

Total 42 26 — 18 14 32 — 6 — 2 — — 4 — — 144

Mean

± SE

0.78 ±

0.14

0.48

±

0.18

—

0.33

±

0.09

0.26

±

0.07

0.62

±

0.17

— 0.16

± 0.1 —

0.04

±

0.03

— —

0.08

±

0.05

— — 0.35 ± 0.04

Elateridae

Ia 0.95 0.85 — 0.96 1.16 1.27 — 1.05 — 0.87 — — 0.82 — — 0.99 ± 0.06

Total 156 223 — 38 62 41 — 6 — 13 — — 15 — — 554

Mean

± SE

2.89 ±

0.6

4.13

±

0.56

— 0.7 ±

0.16

1.15

±

0.37

0.79

±

0.23

—

0.16

±

0.06

— 0.24

± 0.1 — —

0.29

±

0.11

— — 1.35 ± 0.14

75

Gryllotalpidae

Ia 0.83 1.12 — 1.02 1.29 1 — 1.09 — 1 — — 1.02 — — 1.05 ± 0.05

Total 23 71 — 4 13 10 — 5 — 11 — — 6 — — 143

Mean

± SE

0.43 ±

0.14

1.31

±

0.71

—

0.07

±

0.04

0.24

±

0.08

0.19

±

0.09

—

0.14

±

0.08

— 0.2 ±

0.11 — —

0.12

±

0.05

— — 0.35 ± 0.1

Cicindela

punctulata

Ia 1.15 1.08 — 1.13 0.94 0.93 — 1.05 — 0.86 — — 0.79 — — 0.99 ± 0.05

Total 491 474 — 67 42 18 — 6 — 2 — — 2 — — 1102

Mean

± SE

9.09 ±

1.11

8.78

±

1.14

—

1.24

±

0.42

0.78

±

0.25

0.35

±

0.13

—

0.16

±

0.06

—

0.04

±

0.03

— —

0.04

±

0.04

— — 2.69 ± 0.29

Tetracha

carolina

Ia 2.22 1.07 — 0.98 1.28 1.26 — 1.04 — 1.33 — — 1 — — 1.27 ± 0.14

Total 1133 387 — 15 54 18 — 2 — 2 — — 1 — — 1612

Mean

± SE

20.98

± 2.04

7.17

±

0.94

— 0.28

± 0.1

1 ±

0.3

0.35

±

0.11

—

0.05

±

0.05

—

0.04

±

0.03

— —

0.02

±

0.02

— — 3.93 ± 0.46

Bolded values indicate signification aggregation for Ia > 1 (p < 0.025) or significant regularity for Ia < 1 (p > 0.975); mean/total = mean/total of all samples collected

from all sampled locations in a field during a particular calendar week; seasonal mean/grand total = mean/total of all samples collected from all sampled locations for

each field-year combination

N/A = all counts were 0; — = Data was not collected during this calendar week

76

Table 3.2. Seasonal dynamics and spatial aggregation indices (Ia) from SADIE of insects from each sampling event in soybean in 2018 July August September October Grand Total/

Seasonal

Mean ± SE

Calendar Week

Field Variable Metric 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

A

Cydnidae

Ia — — 1.05 1.22 1.13 1.34 1.65 1.4 1.53 — — 0.82 0.93 — — 1.23 ± 0.09

Total — — 35 12 14 90 168 372 245 — — 50 28 — — 1014

Mean

± SE — —

0.54

±

0.12

0.2 ±

0.07

0.22

±

0.09

1.36

±

0.46

2.55

±

0.76

5.81

±

1.15

4.08

±

0.79

— —

0.82

±

0.27

0.51

±

0.28

— — 1.81 ± 0.21

Elateridae

Ia — — 0.93 0.99 0.92 1.08 1.21 1.1 1.82 — — 0.94 0.83 — — 1.09 ± 0.1

Total — — 49 13 16 8 9 19 4 — — 4 2 — — 124

Mean

± SE — —

0.75

±

0.35

0.22 ±

0.11

0.25

± 0.1

0.12

±

0.08

0.14

± 0.1

0.3 ±

0.2

0.07

±

0.04

— —

0.07

±

0.05

0.04

±

0.03

— — 0.22 ± 0.05

Gryllotalpidae

Ia — — 1.06 0.78 1.47 0.81 0.84 0.79 0.82 — — 0.89 0.88 — — 0.93 ± 0.07

Total — — 2 1 3 4 3 5 20 — — 12 5 — — 55

Mean

± SE — —

0.03

±

0.02

0.02 ±

0.02

0.05

±

0.03

0.06

±

0.04

0.05

±

0.03

0.08

±

0.04

0.33

±

0.08

— — 0.2 ±

0.1

0.09

±

0.04

— — 0.1 ± 0.02

Cicindela

punctulata

Ia — — 1.07 1.08 1.18 0.98 1.53 0.82 1.02 — — 1.07 1.12 — — 1.1 ± 0.06

Total — — 557 229 423 209 214 40 29 — — 72 28 — — 1801

Mean

± SE — —

8.57

±

1.12

3.88 ±

0.86

6.61

±

1.41

3.17

±

0.69

3.24

±

0.88

0.63

±

0.17

0.48

±

0.15

— —

1.18

±

0.26

0.51

± 0.2 — — 3.22 ± 0.29

Tetracha

carolina

Ia — — 1.15 1.03 1.16 0.89 0.91 N/A N/A — — 1.02 0.98 — — 1.02 ± 0.04

Total — — 10 10 9 5 5 0 0 — — 3 2 — — 44

Mean

± SE — —

0.15

±

0.14

0.17 ±

0.08

0.14

± 0.1

0.08

±

0.03

0.08

±

0.04

0 ± 0 0 ± 0 — —

0.05

±

0.04

0.04

±

0.03

— — 0.08 ± 0.02

B

Cydnidae

Ia — — 1.03 0.77 0.8 1.12 1.68 1.26 1.02 — — 0.89 1.23 1.08 0.81 1.06 ± 0.08

Total — — 5 7 39 47 54 164 79 — — 155 51 136 37 774

Mean

± SE — —

0.09

±

0.05

0.13 ±

0.07

0.72

± 0.2

0.87

± 0.2

1 ±

0.2

3.04

±

0.64

1.52

±

0.47

— —

3.23

±

0.96

0.98

±

0.31

2.52

±

0.92

0.69

±

0.25

1.33 ± 0.15

Elateridae

Ia — — 0.9 N/A 1.22 0.98 0.9 0.97 0.81 — — 1.19 1.29 N/A 1.15 1.05 ± 0.06

Total — — 7 0 11 25 2 7 6 — — 6 10 0 2 76

Mean

± SE — —

0.13

±

0.07

0 ± 0 0.2 ±

0.09

0.46

±

0.16

0.04

±

0.03

0.13

±

0.07

0.12

±

0.07

— —

0.13

±

0.06

0.19

±

0.12

0 ± 0

0.04

±

0.03

0.13 ± 0.02

77

Gryllotalpidae

Ia — — 1.04 0.93 1.07 1.09 1.1 1.13 0.99 — — 1.08 1.1 1.12 1.16 1.07 ± 0.02

Total — — 7 6 50 47 35 16 31 — — 40 4 33 12 281

Mean

± SE — —

0.13

±

0.05

0.11 ±

0.06

0.93

±

0.15

0.87

±

0.15

0.65

±

0.17

0.3 ±

0.1

0.6 ±

0.15 — —

0.83

±

0.16

0.08

±

0.04

0.61

±

0.16

0.22

±

0.09

0.48 ± 0.04

Cicindela

punctulata

Ia — — 1.03 1.12 1.3 0.99 0.96 1.12 1.06 — — 1.16 0.94 0.97 1.08 1.07 ± 0.03

Total — — 95 50 223 123 40 18 32 — — 16 6 4 1 608

Mean

± SE — —

1.76

±

0.46

0.93 ±

0.3

4.13

±

0.98

2.28

±

0.61

0.74

±

0.35

0.33

±

0.11

0.62

±

0.21

— —

0.33

±

0.15

0.12

±

0.05

0.07

±

0.04

0.02

±

0.02

1.04 ± 0.13

Tetracha

carolina

Ia — — 0.95 1.02 0.96 0.96 1.07 1.2 1.06 — — 1.17 1 1.16 1.26 1.07 ± 0.03

Total — — 388 573 493 149 34 19 11 — — 3 5 4 2 1681

Mean

± SE — —

7.19

±

1.23

10.61

± 1.52

9.13

±

1.48

2.76

±

0.63

0.63

±

0.28

0.35

±

0.17

0.21

±

0.08

— —

0.06

±

0.04

0.1 ±

0.05

0.07

±

0.04

0.04

±

0.03

2.88 ± 0.28

Bolded values indicate signification aggregation for Ia > 1 (p < 0.025) or significant regularity for Ia < 1 (p > 0.975); mean/total = mean/total of all samples collected

from all sampled locations in a field during a particular calendar week; seasonal mean/grand total = mean/total of all samples collected from all sampled locations for

each field-year combination

N/A = all counts were 0; — = Data was not collected during this calendar week

78

Table 3.3. Spatial association indices (X) from SADIE of insects and field variables from each sampling event (calendar week) in soybean

Cicindela punctulata

Calendar Week Year Field Variable 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

2017

A

Soil ECa 0.05 — — — -0.56 -0.13 -0.42 — -0.50 — — — — -0.17 — Elevation 0.09 — — — 0.06 0.08 0.03 — 0.04 — — — — -0.16 — Cydnidae 0.10 — — — -0.20 0.06 -0.27 — -0.15 — — — — 0.12 — Elateridae 0.34 — — — -0.16 -0.20 N/Ae

— 0.10 — — — — N/Ae

— Gryllotalpidae 0.23 — — — 0.05 0.04 -0.34 — -0.08 — — — — -0.33 —

T. carolina 0.23

— — — -0.05

0.01

N/At — 0.57

— — — — N/At —

B

Soil ECa 0.19 0.15 — -0.09 -0.06 0.14 — -0.17 — 0.14 — — -0.25 — —

Elevation 0.32 -0.04 — 0.13 -0.05 0.02 — -0.18 — 0.66 — — -0.72 — — Cydnidae -0.22 0.09 — -0.14 0.13 -0.06 — -0.28 — 0.51 — — 0.63 — — Elateridae -0.39 -0.18 — -0.23 0.20 -0.05 — -0.44 — 0.14 — — 0.06 — —

Gryllotalpidae 0.16 -0.02 — -0.32 0.08 0.42 — -0.12 — 0.37 — — -0.27 — — T. carolina 0.45

0.40

— 0.45

0.20

0.03

— 0.37

— 0.35

— — -0.05

— —

2018

A

Soil ECa — — -0.26 -0.47 -0.49 -0.39 -0.35 -0.21 -0.30 — — -0.46 0.18 — —

Elevation — — -0.23 -0.14 0.22 0.30 -0.19 0.20 0.05 — — 0.04 0.21 — — Cydnidae — — 0.21 0.04 0.09 -0.26 0.03 -0.04 0.00 — — 0.15 0.32 — — Elateridae — — -0.24 0.09 0.39 0.39 -0.03 0.20 -0.19 — — -0.40 0.27 — —

Gryllotalpidae — — 0.27 -0.21 -0.07 0.36 0.05 -0.05 0.00 — — 0.11 -0.08 — — T. carolina — — 0.21

0.48

0.09

0.26

0.02

N/At N/At — — 0.29

-0.04

— —

B

Soil ECa — — 0.10 -0.03 0.01 0.05 -0.13 -0.03 0.01 — — -0.12 -0.08 -0.12 -0.57

Elevation — — 0.06 -0.05 -0.42 0.24 -0.05 -0.03 0.00 — — -0.18 0.11 -0.28 -0.58 Cydnidae — — -0.13 0.23 0.09 -0.01 -0.04 -0.44 -0.08 — — -0.27 -0.06 -0.34 -0.42 Elateridae — — 0.23 N/Ae

0.02 0.01 -0.18 -0.32 0.06 — — 0.35 0.01 N/Ae

-0.77

Gryllotalpidae — — 0.11 0.11 0.11 -0.08 0.02 0.10 0.17 — — 0.12 0.30 0.20 -0.07 T. carolina — — 0.20

0.05

0.39

0.49

0.35

0.22

0.30

— — 0.41

0.08

0.57

0.66

Tetracha carolina

2017

A

Soil ECa -0.12 — — — 0.16 -0.35 N/Aet — -0.33 — — — — N/Aet —

Elevation -0.13 — — — 0.41 -0.25 N/Aet — 0.07 — — — — N/Aet — Cydnidae -0.03 — — — 0.14 -0.10 N/Aet — -0.24 — — — — N/Aet — Elateridae 0.04 — — — 0.96 0.15 N/Aet — -0.30 — — — — N/Aet —

Gryllotalpidae 0.27 — — — -0.11 0.31 N/Aet — -0.37 — — — — N/Aet —

B

Soil ECa -0.01 0.05 — -0.15 -0.17 0.24 — -0.59 — 0.34 — — 0.23 — —

Elevation 0.25 -0.23 — -0.28 0.01 0.43 — -0.51 — 0.07 — — 0.12 — — Cydnidae -0.30 0.04 — -0.33 -0.19 -0.16 — 0.23 — 0.49 — — -0.08 — — Elateridae -0.23 -0.17 — 0.12 -0.16 0.20 — -0.41 — -0.13 — — -0.18 — —

Gryllotalpidae -0.06 -0.13 — 0.08 -0.22 -0.13 — -0.16 — 0.17 — — 0.24 — —

2018 A Soil ECa — — -0.36 -0.39 0.12 -0.33 -0.26 N/At N/At — — -0.14 0.23 — —

Elevation — — -0.41 -0.18 0.08 0.06 -0.26 N/At N/At — — -0.78 -0.55 — — Cydnidae — — -0.22 -0.02 0.26 -0.22 -0.07 N/At N/At — — -0.22 -0.10 — —

79

Elateridae — — -0.53 -0.10 -0.03 0.01 -0.09 N/At N/At — — 0.07 -0.08 — — Gryllotalpidae — — 0.47 -0.18 0.50 0.23 -0.35 N/At N/At — — 0.04 -0.12 — —

B

Soil ECa — — -0.08 0.05 0.27 0.23 -0.18 0.04 0.22 — — -0.17 0.04 -0.25 -0.24

Elevation — — 0.10 -0.02 0.13 0.26 -0.51 -0.47 -0.06 — — 0.30 0.26 -0.61 -0.80 Cydnidae — — -0.22 -0.03 0.07 0.14 -0.28 -0.09 0.13 — — -0.17 -0.05 -0.42 -0.23 Elateridae — — 0.04 N/Ae

0.26 0.01 -0.12 0.35 0.42 — — 0.36 0.21 N/Ae

-0.63

Gryllotalpidae — — -0.16 0.07 -0.14 0.09 0.10 -0.16 0.03 — — 0.27 0.17 0.16 -0.08

Bolded values indicate significant associations for X > 0 (p < 0.025) or significant dissociations for X < 0 (p > 0.975)

N/Ae, N/At, and N/Aet = all counts were 0 for Elateridae, Tetracha carolina, and both, respectively

— = Data was not collected during this calendar week

80

Figure 3.1. Spatial interpolation maps of local aggregation indices from significant SADIE analyses.

Clusters depict aggregation index values of < -1.5 and > 1.5 as gaps and patches, respectively. A-C: 2017,

Field A. D: 2017, Field B. E-G: 2018, Field A. H: 2018, Field B. Elevation data used in SADIE analyses

was collected on 22 March 2019 for Field A (I) and Field B (J). CW = Calendar Week.

81

Figure 3.2. Selected spatial interpolation maps of SADIE local association indices. Associations and

dissociations between insect taxa were from the same calendar week (CW). For associations and

dissociations between cicindelines and soil ECa and elevation, the CW affiliated with each subfigure is

associated with cicindelid data, while soil ECa and elevation data were collected on 22 March 2019. Black

letters indicate significant associations (p < 0.025) between the datasets, while white letters indicate

significant dissociations (p > 0.975). A-F: 2017, Field A. G-L: 2017, Field B. M-W: 2018, Field A. X-DD:

2018, Field B.

82

CHAPTER FOUR

CONCLUSIONS AND FUTURE WORK

The widespread agricultural intensification (e.g. increase in field size and mechanization, decrease

in non-crop habitat) that occurred during the latter half of the 20th century was characterized by a

significant increase in farming efficiency (Stafford 2000, Plant 2001, Zhang et al. 2002, Tscharntke et al.

2005). Prior to this period of intensification, smaller fields were delineated due to natural (e.g. bodies of

water, forests, soil type) or unnatural (e.g. roads, infrastructure) features, and growers managed these fields

in a site-specific manner fundamentally (Stafford 2000, Plant 2001, Zhang et al. 2002). Once the scale by

which crops were produced changed, management inputs no longer targeted the variability of important

factors related to crop production that existed within the amalgamated fields (Stafford 2000). Instead,

uniform applications of management inputs were distributed across entire fields or farms. In doing so,

certain field areas received more or less inputs than necessary, resulting in a management approach

characterized by environmental contamination, inefficiency, and improvidence (Oerke et al. 2010). By the

last decade of the 20th century, however, environmental mandates requiring greater efficiency and safety

where agricultural chemicals were concerned, along with growing concerns from producers with respect to

reducing inputs, maximizing profits, and the production of agricultural commodities with higher nutritional

value and quality, resulted in a significant interest in returning to agricultural management in a site-specific

manner (Stafford 2000, Plant 2001, Pinter Jr et al. 2003).

The development of the global positioning system (GPS) by the U.S. Department of Defense in

the 1970s represented the fundamental advancement that allowed for site-specific management to occur in

modern agriculture, as farm machinery could now access to the positional data required for site-specific

applications to be made (Stafford 2000). Today, precision agriculture (including site-specific management)

practices rely on various tools, including GPS, geographic information systems (GIS), variable-rate

technology, and proximal and remote sensing (Pedigo 2002, Krell et al. 2003, Gebbers and Adamchuk

2010). Given that arthropods are also frequently heterogeneously distributed in crops (Oerke et al. 2010),

site-specific management of arthropods (i.e. site-specific pest management) can potentially improve

83

traditional pest management tactics applied to entire fields through the generation of more precise

information for decision-making, a reduction in management inputs, an increase in profitability, and a

reduction in the environmental impact associated with whole-field applications of chemicals and fertilizers

(Park and Krell 2005). However, the high cost of the fine-scale sampling of bean leaf beetle, Cerotoma

trifurcata (Forster) (Coleoptera: Chrysomelidae), from multiple georeferenced points within soybean fields

was found to prohibit site-specific pest management from being more profitable than uniform management

scenarios in which a minimum number of samples were collected to calculate the field mean for C.

trifurcata (Krell et al. 2003). To increase the profitability of site-specific pest management tactics, Krell et

al. (2003) advocated for the correlation of insects with field attributes that can be detected with

technologies such as remote sensing. An understanding of the associations of agricultural arthropods with

variables that can be measured by technologies with comparatively low sampling costs is critical for cost-

effective implementation of site-specific pest management. Therefore, we chose to determine how insect

pests and natural enemies in soybean were associated with abiotic and biotic variables collected with

ground-based and remote sensing technologies.

Canopy-dwelling and epigeal arthropod communities were grid-sampled from two soybean fields

during 2017 and 2018 at the Edisto Research and Education Center in Blackville, SC, using drop-cloth,

sweep-net, and pitfall trap sampling methods. During each sampling event, or calendar week, arthropod and

soybean plant data (Normalized Difference Vegetation Index [NDVI], plant heights, and defoliation) were

collected for each grid point for a given field. Fields were further characterized through the collection of

elevation and soil apparent electrical conductivity (soil ECa) data for all grid points. Negative binomial,

zero-inflated models were used to estimate presence and drop-cloth counts of arthropod taxa based on

distance from the field edge, NDVI, soybean plant height, soil ECa, elevation, and calendar week. Spatial

Analysis by Distance Indices (SADIE) were used to analyze how sweep-net-sampled larvae of velvetbean

caterpillar, Anticarsia gemmatalis (Hübner) (Lepidoptera: Erebidae), soybean looper, Chrysodeixis

includens (Walker) (Lepidoptera: Noctuidae), and green cloverworm, Hypena scabra (Lepidoptera:

Erebidae) (Fabricius), were spatially associated with defoliation, NDVI, and plant height in soybean, and

how the pitfall-trap-collected Carolina metallic tiger beetle, Tetracha carolina (Linnaeus) (Coleoptera:

84

Carabidae) and punctured tiger beetle, Cicindelidia punctulata (Olivier) (Coleoptera: Carabidae), were

associated with abiotic (elevation and soil ECa) and biotic (Cydnidae adults and nymphs, Elateridae adults,

and Gryllotalpidae adults and nymphs) variables within the crop.

Among all variables from drop-cloth datasets, calendar week was the most reliable predictor of

arthropod counts, as it was a significant predictor for a majority of all taxa. Additionally, counts for a

majority of drop-cloth collected pestiferous taxa were significantly associated with distance from the field

edge, elevation, soybean plant height, and NDVI. Although aggregations of insect taxa, as identified by

SADIE, were limited for sweep-net and pitfall-trap datasets, significant spatial overlap (42% of the total

significant associations among insects and field variables) was observed for C. punctulata and T. carolina

from pitfall-trap datasets, while 14% and 6% of paired plant-insect sweep-net datasets were significantly

associated or dissociated, respectively. Cicindelines collected from pitfall traps were found to have more

significant associations and dissociations with Elateridae than any other herbivorous taxa and more

significant dissociations with soil ECa than with elevation. NDVI was found to be more associated with

sweep-net collected pest distributions than soybean plant heights and defoliation estimates, and the

majority of all plant-insect associations and dissociations occurred in the first four weeks of sampling (late

July-early August).

Site-specific pest management is considered to be advantageous over whole-field management

when pests are aggregated and possess a low dispersal ability (Krell et al. 2003, Park and Krell 2005). The

limited number of aggregations for significant pests in sweep-net datasets and predators in pitfall trap

datasets suggests that site-specific pest management may not be a practical alternative to traditional pest

management tactics for the locations sampled in this study. However, arthropod dynamics within an

individual field may be influenced by a number of intrinsic (within-field level) and extrinsic

(agroecosystem level) factors. Previous research has shown that differences in field size, climatic

conditions, and non-crop habitat are associated with differential arthropod dynamics within agricultural

fields (Lesiewicz et al. 1983, Holland et al. 2005, Kozina et al. 2015). In this study, only those variables

that could be measured within individual fields were used to determine their associations with soybean

arthropods; the effect of the habitats surrounding each field (e.g. field boundaries) were not considered.

85

Different field boundary types (e.g. fragmented forests, uncultivated grassland strips, hedgerows, etc.) may

differ in their permeability for arthropod populations, and this effect may also vary with an organism’s

ability to disperse (Sawyer and Haynes 1985). The differential permeability of variable field boundaries is

thought to influence how arthropods are spatially distributed, as well as how the larger metapopulation is

structured within the agroecosystem (Holland et al. 2005).

The development of enhanced management strategies for a species is dependent upon a detailed

understanding of its eco-ethology and the successful identification of significant factors associated with its

spatial distribution (Daane and Williams 2003, Holland et al. 2005, van Helden 2010). Although most taxa

were not found to be significantly aggregated in this study, the multiple associations that were found for

soybean arthropod distributions with abiotic and biotic within-field variables are of great value from a pest

management perspective. Given that the in-field population dynamics of arthropods can be affected by

various factors at the field and landscape levels, the application of site-specific pest management may be

appropriate for the control of soybean arthropods in some locations, but not in others. Future research

should be conducted to determine which within-field factors are most consistently associated with soybean

arthropods across fields and seasons. The effect of landscape level factors should also be addressed by

testing the effect of field boundaries on soybean arthropod populations. In doing so, we will gain a better

understanding of which pests and associated field and landscape variables may be exploited to develop site-

specific pest management strategies in soybean.

86

References Cited

Daane, K. M., and L. E. Williams. 2003. Manipulating vineyard irrigation amounts to reduce insect pest

damage. Ecol. Appl. 13: 1650–1666.

Gebbers, R., and V. I. Adamchuk. 2010. Precision agriculture and food security. Science. 327: 828–831.

van Helden, M. 2010. Spatial and temporal dynamics of arthropods in arable fields, pp. 51–64. In E.C.

Oerke, R. Gerhards, G. Menz, and R.A. Sikora (Eds.), A. Precision crop protection – the challenge and use

of heterogeneity. Springer, Netherlands.

Holland, J., C. F. G. Thomas, T. Birkett, S. Southway, and H. Oaten. 2005. Farm‐scale spatiotemporal

dynamics of predatory beetles in arable crops. J. Appl. Ecol. 42: 1140–1152.

Kozina, A., D. Lemic, R. Bazok, K. M. Mikac, C. M. McLean, M. Ivezić, and J. Igrc Barčić. 2015.

Climatic, edaphic factors and cropping history help predict click beetle (Coleoptera: Elateridae) (Agriotes

spp.) abundance. J. Insect Sci. 15: 100.

Krell, R. K., L. P. Pedigo, and B. A. Babcock. 2003. Comparison of estimated costs and benefits of site-

specific versus uniform management for the bean leaf beetle in soybean. Precis. Agric. 4: 401–411.

Lesiewicz, D. S., J. W. Van Duyn, and J. R. Bradley Jr. 1983. Determinations on cornfield carabid

populations in northeastern North Carolina. Environ. Entomol. 12: 1636–1640.

Oerke, E.-C., R. Gerhards, G. Menz, and R. A. Sikora. 2010. Precision crop protection-the challenge

and use of heterogeneity. Springer, Netherlands.

Park, Y.-L., and R. K. Krell. 2005. Generation of prescription maps for curative and preventative site-

specific management of bean leaf beetles (Coleoptera: Chrysomelidae). J. Asia-Pac. Entomol. 8: 375–380.

Pedigo, L. P. 2002. Entomology and pest management, 4th ed. Prentice Hall, Upper Saddle River, NJ.

Pinter Jr, P. J., J. L. Hatfield, J. S. Schepers, E. M. Barnes, M. S. Moran, C. S. Daughtry, and D. R.

Upchurch. 2003. Remote sensing for crop management. Photogramm. Eng. Remote Sens. 69: 647–664.

Plant, R. E. 2001. Site-specific management: the application of information technology to crop production.

Comput. Electron. Agric. 30: 9–29.

Sawyer, A. J., and D. L. Haynes. 1985. Spatial analysis of cereal leaf beetle abundance in relation to

regional habitat features. Environ. Entomol. 14: 92–99.

87

Stafford, J. V. 2000. Implementing precision agriculture in the 21st century. J. Agric. Eng. Res. 76: 267–

275.

Tscharntke, T., A. M. Klein, A. Kruess, I. Steffan‐Dewenter, and C. Thies. 2005. Landscape

perspectives on agricultural intensification and biodiversity–ecosystem service management. Ecol. Lett. 8:

857–874.

Zhang, N., M. Wang, and N. Wang. 2002. Precision agriculture—a worldwide overview. Comput.

Electron. Agric. 36: 113–132.

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APPENDICES

89

Appendix A

Chapter I has been previously published in Environmental Entomology. It has been reproduced exactly as it

appears in print, with the exception of the formatting changes required by the dissertation guidelines of

Clemson University.

Greene, A. D., F. P. Reay-Jones, K. R. Kirk, B. K. Peoples, and J. K. Greene. 2021. Associating Site

Characteristics With Distributions of Pestiferous and Predaceous Arthropods in Soybean. Environ.

Entomol. 50: 477–488.

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Appendix B

Table B1. Results of likelihood ratio tests between intercept-only and full models for soybean

arthropod taxa

Taxa Intercept-only Model

Log-likelihood

Full Model

Log-likelihood

X2 test

statistic p

Anticarsia gemmatalis larvae -8092.79 -3226.51 9732.55 <0.001

Chinavia hilaris nymphs -2813.01 -942.31 3741.39 <0.001

Chrysodeixis includens larvae -6079.23 -3351.12 5456.22 <0.001

Cicadellidae adults and nymphs -4244.45 -1337 5814.9 <0.001

Hypena scabra larvae -5636.19 -2341.07 6590.24 <0.001

Megacopta cribraria adults -8379.15 -5239.47 6279.37 <0.001

M. cribraria nymphs -7562.38 -3376.13 8372.51 <0.001

Nezara viridula adults -2790.43 -922.83 3735.21 <0.001

N. viridula nymphs -4405.69 -1876.13 5059.13 <0.001

Spissistilus festinus adults -2577.29 -1548 2058.58 <0.001

S. festinus nymphs -3644.79 -2622.28 2045.01 <0.001

Anthicidae adults -3032.52 -1578.19 2908.64 <0.001

Araneae -3585.69 -2372.32 2426.74 <0.001

Formicidae adults -6271.18 -3775.11 4992.14 <0.001

Geocoridae nymphs -2189.31 -1030.55 2317.52 <0.001

Nabidae adults -2895.32 -1196.33 3397.97 <0.001

Podisus maculiventris adults -535.132 -388.54 293.19 <0.001

P. maculiventris nymphs -1379.28 -513.91 1730.73 <0.001

Reduviidae nymphs -1059.27 -494.34 1129.85 <0.001

Full model = model for each taxa from Tables 1.1 and 1.2

Intercept-only model df =2; Full model df = 18

X2 test statistic = 2*(Full Model Log-likelihood - Intercept-only Model Log-likelihood)