Epidemiology of Acute Oak Decline in Britain
19/03/2014
Nathan Brown
Imperial College London Department of Life Sciences
PhD Thesis
Table of contents
ABSTRACT ...... 7 1 INTRODUCTION...... 8 2 BACKGROUND INFORMATION ...... 14 2.1 INTRODUCTION: ...... 14 2.2 LITERATURE REVIEW: OAK DECLINE ...... 17 2.3 FIELD OBSERVATIONS: SYMPTOMS OF AOD ...... 23 2.4 LITERATURE REVIEW: INTRODUCTION TO AGRILUS ...... 26 2.5 LITERATURE REVIEW: CAUSES OF NECROSIS AND STEM BLEEDS ...... 27 2.6 LITERATURE REVIEW: BACTERIAL DISEASES OF TREES ...... 29 2.7 THE AOD SYSTEM: BEETLE AND BACTERIA INTERACTIONS...... 33 3 DEVELOPMENT OF AOD SYMPTOMS WITHIN SITE ...... 37 3.1 INTRODUCTION ...... 37 3.2 METHODS: ...... 40 3.2.1 Mapping and data collection ...... 40 3.2.2 Disease development and spatial patterns ...... 43 3.2.3 Ripley’s k: the L function and the O-ring statistic ...... 44 3.2.4 Statistical Analysis: Clustering ...... 47 3.2.5 Aspect ...... 49 3.2.6 Canopy condition ...... 49 3.2.7 Mortality ...... 50 3.2.8 Remission ...... 50 3.3 RESULTS ...... 51 3.3.1 Mapping and data collection ...... 51 3.3.2 Disease development ...... 66 3.3.3 Aspect ...... 77 3.3.4 Canopy ...... 81 3.3.5 Mortality ...... 82 3.3.6 Remission ...... 83 3.4 DISCUSSION ...... 84 4 SPREAD OF STEM BLEEDING SYMPTOMS OVER TIME ...... 89 4.1 INTRODUCTION ...... 89 4.2 METHODS ...... 91 4.2.1 Assessing spread using Ripleys k ...... 91 4.2.2 Site comparisons ...... 93 4.2.3 Analysis at multiple temporal scales ...... 93 4.2.4 Winding Wood final spatial pattern ...... 95 4.2.5 Distance to paths ...... 95 4.3 RESULTS ...... 96 4.3.1 Site comparisons ...... 96 4.3.2 Analysis at multiple temporal scales ...... 98 4.3.3 Winding Wood ...... 103 4.4 DISCUSSION ...... 104 5 LITERATURE REVIEW: AGRILUS BIGUTTATUS ...... 108 5.1 LITERATURE REVIEW: IDENTIFICATION AND LIFE CYCLE OF A. BIGUTTATUS...... 114 5.2 LITERATURE REVIEW: SYMPTOMS AND SIGNS OF A. BIGUTTATUS ATTACK...... 117 5.3 LITERATURE REVIEW: AGRILUS BEHAVIOUR AND HOST PREFERENCES...... 121 5.4 LITERATURE REVIEW: BEETLE AND BACTERIA INTERACTIONS? ...... 126 5.5 LITERATURE REVIEW: CONTROL OF A. BIGUTTATUS ...... 129 5.6 LITERATURE REVIEW: SUMMARY OF A. BIGUTTATUS ...... 132 6 EXTERNAL SYMPTOM CO-OCCURRENCE: STEM BLEEDING AND D-SHAPED EXIT HOLES ...... 134
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6.1 INTRODUCTION ...... 134 6.2 METHODS ...... 137 6.2.1 Co-occurrence of stem symptoms in the annual surveys ...... 137 6.2.2 Sequential development of external symptoms and signs ...... 137 6.2.3 Spatial dynamics of exit holes and stem bleeds ...... 137 6.2.4 Seasonality ...... 139 6.3 RESULTS ...... 141 6.3.1 Co-occurrence of stem symptoms in the annual surveys ...... 141 6.3.2 Sequential development ...... 142 6.3.3 Spatial dynamics of exit holes and stem bleeds ...... 143 6.3.4 Seasonality ...... 147 6.4 DISCUSSION ...... 151 7 AGRILUS TRAPPING ...... 154 7.1 INTRODUCTION ...... 154 7.2 METHODS ...... 157 7.2.1 Colour experiment ...... 157 7.2.2 Lure experiment ...... 159 7.2.3 Identification ...... 160 7.2.4 Statistical Analysis ...... 160 7.3 RESULTS ...... 161 7.3.1 Species composition ...... 161 7.3.2 Flight period for A. biguttatus ...... 162 7.3.3 Colour experiment ...... 163 7.3.4 Lure experiment ...... 166 7.4 DISCUSSION ...... 167 8 IDENTIFICATION OF BEETLES, LARVAE AND ASSOCIATED BACTERIA ...... 171 8.1 INTRODUCTION ...... 171 8.2 METHODS ...... 173 8.2.1 Sample acquisition...... 173 8.2.2 Molecular identification of beetles...... 174 8.2.3 Isolation and Identification of gut bacteria: ...... 176 8.3 RESULTS ...... 178 8.3.1 Larval identification: ...... 178 8.3.2 Bacterial Identification:...... 179 8.4 DISCUSSION ...... 182 9 NATIONAL DISTRIBUTION ...... 183 9.1 INTRODUCTION ...... 183 9.2 METHODS ...... 188 9.2.1 Data sources ...... 188 9.2.2 CLIMEX estimates of species suitability...... 191 9.2.3 Distributions in Great Britain ...... 193 9.2.4 Spatial clustering of AOD sites in relation to NBN Agrilus sightings ...... 193 9.2.5 Spatial models of AOD distribution ...... 195 9.3 RESULTS ...... 197 9.3.1 Distributions ...... 197 9.3.2 European CLIMEX Model ...... 199 9.3.3 North American Models ...... 202 9.3.4 Distributions in Great Britain ...... 204 9.3.5 Spatial clustering of AOD sites in relation to NBN Agrilus sightings ...... 207 9.4 DISCUSSION ...... 210 9.5 ACKNOWLEDGEMENTS: ...... 215 10 GENERAL DISCUSSIONS ...... 216 ACKNOWLEDGEMENTS ...... 225 REFERENCES ...... 226 APPENDIX 1: DISTRIBUTION RECORDS FOR AGRILUS BIGUTTATUS ...... 259
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Table of figures
Figure 1: Symptoms and signs of AOD ...... 15 Figure 2: Bleeding occurs from a crack between bark plates ...... 25 Figure 3: Cavity behind bleed exposed by removing bark ...... 25 Figure 4: Schematic diagram showing bacteria and Agrilus interactions ...... 32
Figure 5: Location of monitoring sites ...... 42 Figure 6: Circular vs. O-ring sampling ...... 46 Figure 7: Maps of symptom development at the Hatchlands study site ...... 58 Figure 8: Maps of symptom development at the Langdale Wood study ...... 59 Figure 9: Maps of symptom development at the Sandpit Wood study site ...... 60 Figure 10: Maps of symptom development at the Winding Wood study site ...... 61 Figure 11: Maps of symptom development at the Beecham Spinney study site ...... 62 Figure 12: Maps of symptom development at the Great Monks Wood study site ...... 63 Figure 13: Maps of symptom development at the Rookery Wood study site ...... 64 Figure 14: Maps of symptom development at the Sheen Wood study site ...... 65 Figure 15: Assessment of clustering in the first year distribution of oak with stem bleeds at the four 2009 study sites ...... 67 Figure 16: Frequency of occurrence of stem symptoms across years at Hatchlands ..... 68 Figure 17: Frequency of occurrence of stem symptoms across years at Langdale Wood ...... 69 Figure 18: Frequency of occurrence of stem symptoms across years at Sandpit Wood ...... 70 Figure 19: Frequency of occurrence of stem symptoms across years at Winding Wood ...... 71 Figure 20: Assessment of clustering in the first year distribution of oak with stem bleeds at the four 2010 study sites ...... 72 Figure 21: Frequency of occurrence of stem symptoms across years at Beecham Spinney ...... 73 Figure 22: Frequency of occurrence of stem symptoms across years at Great Monks Wood ...... 75 Figure 23: Frequency of occurrence of stem symptoms across years at Rookery Wood ...... 76 Figure 24: Frequency of occurrence of stem symptoms across years at Sheen Wood ... 77 Figure 25: Incidence of bleeding symptoms occurring on each aspect of study trees, presented by study site ...... 80 Figure 26: Total counts of symptom free oak and symptomatic oak in each canopy condition category and year ...... 81 Figure 27: Proportions of oak in each condition category at each site ...... 82
Figure 28: Assessment of clustering of all newly symptomatic oak around the initially observed distribution of oak with stem bleeds ...... 97 Figure 29: Assessment of clustering at Langdale wood between consecutive years ...... 99 Figure 30: Assessment of clustering at Langdale wood at two and three year intervals ...... 101 Figure 31: Assessment of clustering at Great Monks wood at one and two year
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intervals ...... 102 Figure 32: Assessment of clustering at Winding wood considering all oak observed with stem bleeds during all four years ...... 103 Figure 33: Distances of affected trees from paths in Winding Wood ...... 104
Figure 34: Historical sightings of Agrilus bigutattus ...... 112 Figure 35: Agrilus biguttatus adult ...... 115 Figure 36: Agrilus cf biguttatus larvae ...... 117 Figure 37: Agrilus biguttatus galleries ...... 119 Figure 38: D-shaped exit holes ...... 121 Figure 39: An adult A. biguttatus emerges from an oak tree ...... 125 Figure 40: Spathius curvicaudis ...... 132
Figure 41: Co-occurrence of exit holes and stem bleeds ...... 142 Figure 42: Assessment of clustering of all oak symptomatic for stem bleeds around all oak with exit holes at the 2009 study sites ...... 145 Figure 43: Assessment of clustering of all oak symptomatic for stem bleeds around all oak with exit holes at the 2010 study sites ...... 146 Figure 44: Summary data of the monthly monitoring ...... 147 Figure 45: Retained interactions in the total stem bleed model ...... 150
Figure 46: Location of trapping sites ...... 158 Figure 47: Bi-weekly catch totals for A. biguttatus in 2011 ...... 163 Figure 48: Main effects of colour experiment for A. biguttatus, A. laticornis and A. sulcicollis ...... 165 Figure 49: Main effects in the lure experiment for A. biguttatus and A. sulcicollis ...... 167
Figure 50: Phylogeny reconstruction for Agrilus species ...... 179 Figure 51: Summary of the bacterial population isolated from the gut of adult A. biguttatus ...... 180 Figure 52: Summary of the bacterial population isolated from swabs of adult A. biguttatus faecal matter ...... 181 Figure 53: Summary of the bacterial population isolated from the gut of larval A. biguttatus ...... 181
Figure 54: Distribution records for all records of Agrilus biguttatus in Europe ...... 197 Figure 55: European distribution of Quercus robur and Quercus petraea ...... 198 Figure 56: Climex model for A. biguttatus with distribution records super-imposed .... 201 Figure 57: Distribution of Agrilus biguttatus in the Alps ...... 202 Figure 58: CLIMEX model projected onto North America ...... 203 Figure 59: USDA biome based prediction of the potential distribution of A. biguttatus ...... 203 Figure 60: NAPPFAST Climate model for A. biguttatus ...... 203 Figure 61: Distribution of A. biguttatus (left) and AOD (right) in the UK superimposed over STRM altitude maps...... 204
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Figure 62: Oak distribution for Great Britain ...... 205 Figure 63: CLIMEX prediction for the establishment of A. biguttatus in Great Britain using 5 km2 climate data ...... 206 Figure 64: Clustering of AOD reports around A. biguttatus sightings ...... 208 Figure 65: Clustering of all NBN sightings within all report locations ...... 208 Figure 65: Predicted conditional intensity of AOD sightings based on oak class, A. biguttatus EI ...... 209
Table of tables
Table 1: Chapter summary ...... 13 Table 2: Overview of site characteristics ...... 57 Table 3: Symptoms and signs observed on oak that died during monitoring ...... 83 Table 4: Summary of remission rates at each of the study sites ...... 84 Table 5: Summary of sequential development of symptoms ...... 143 Table 6: Total trap catch at each site for all Agrilus species ...... 162 Table 7: CLIMEX parameters used to model the distribution of Agrilus biguttatus ...... 200 Table 8: Parameter estimates for the final AOD PPM ...... 209
Declaration of originality
The work presented in this thesis was completed by Nathan Brown during the course of a three year PhD funded by the Forestry Commission. It builds upon work completed as an Msc project (Imperial College) by the author. Where projects were completed as collaborations citations are offered in the text and a full list of acknowledgements is presented at the end of the document.
“The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work”
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Abstract
Oak has long been affected by episodes of decline, an interaction of multiple factors that reduces host vigour. A wide range of abiotic, insect and pathogenic agents may play causal roles in an outbreak; acting independently, together, or consecutively in a complex process.
In Britain a novel form of Acute Oak Decline (AOD) is increasingly reported. Typified by symptoms of stem “bleeding”, bark-splits that weep dark exudate, the stem symptoms overlay patches of necrotic cambial tissue concealed beneath the bark. An initial investigation by Forest Research identified a suite of bacterial species as putative causal agents. Signs of insect activity are frequently found in association with the stem lesions. Galleries are usually present in the conductive tissue and some of the affected trees have distinctively D-shaped exit holes created by the buprestid beetle Agrilus biguttatus. Although declines are typified by complex causes, the dominance of these two organisms (bacteria and beetle) makes understanding their interaction an important step in defining the epidemiology of the syndrome.
The current study aims to provide initial investigations into the epidemiology of AOD. By mapping and monitoring trees within eight study sites symptom development was accurately recorded from a reliable baseline. These data give insights into disease development in terms of rate of spread, tree mortality and disease distribution. Repeated monitoring at multiple time points documents the progression of symptoms, within and among trees.
Site monitoring is complemented by preliminary studies investigating the role of the buprestid beetle Agrilus biguttatus in the decline complex. By developing trapping methods populations of Agrilus beetles can be monitored in terms of flight period and species composition. The focus on A. biguttatus seeks to confirm whether the larvae present in galleries are A. biguttatus and to investigate the link between beetle, larvae and bacteria.
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1 Introduction
Both natural environments and commercial crops are facing an increased threat from plant pests and pathogens due to global change (Brasier, 2008; Pautasso et al., 2010;
Wilkinson et al., 2011). In Britain alone forests are facing a wide range of emerging problems. In addition to Acute Oak Decline (AOD) (Denman & Webber, 2010; Denman et al., 2010; Denman et al., 2013), the most high profile pathogens include: Chalara fraxinea on Ash (Sansford, 2013; Pautasso et al., 2013); Phytophthora ramorum on larch
(Brasier & Webber, 2010; Webber et al., 2010); red band needle blight on pine (Brown &
Webber, 2008); and Pseudomonas syringae pv aesculi on horsechestnut (Webber et al.,
2008). The recent addition of Phytophthora lateralis on Lawson’s cypress (Robin et al.,
2011; Appleby, 2011; Green et al., 2013) and P. austrocedrae on juniper (Green et al.,
2012) to this list, as well as the forest pests oak processionary moth, Thaumetopoea processionea (Townsend, 2006) and horse chestnut leaf miner, Cameraria ohridella
(Tilbury & Evans, 2002; Straw & Tilbury, 2006) and Asian Long Horn Beetle
Anoplophora glabripennis (Anonymous, 2013) only serve to emphasise the growing threat faced by British forests. The present upsurge in pest and disease threats can be attributed to two root causes: globalisation causing increased trade, with increased pest and disease introduction (Brasier, 2008; MacLeod et al., 2010); and climate change altering viable ranges and host susceptibility (Garrett et al., 2006; Dukes et al., 2009;
Sturrock et al., 2011).
In order to limit the negative impact of pests and pathogens it is important to quickly understand their damage potential, so that control can be implemented in an expedient manor (Potter et al., 2011). Spatial modelling has been used to assess the risk posed by
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invasive plant pathogens in many situations, for example: in relation to potential climatic range (Watt et al., 2009); the impact of human populations (Cushman & Meentemeyer,
2008); and forest diversity (Haas et al., 2011).
As part of the ongoing effort to detail the AOD outbreak in Britain, research described in this thesis aims to provide initial investigations into the epidemiology of this newly defined decline complex. By mapping and monitoring all trees within eight study sites symptom development can be accurately recorded from a reliable baseline. This data gives insights into disease development in terms of rate of spread, tree mortality and disease distribution. Repeated monitoring at multiple time points documents the progression of symptoms, within and among trees. The location of newly affected trees can be examined in relation to previously affected trees using statistical analysis of the data that will allow inferences to be made about the spread of AOD (Holdenrieder et al.,
2004; Reich & Lundquist, 2005; Plantegenest et al., 2007).
The site monitoring is complemented by preliminary studies investigating the role of the buprestid beetle Agrilus biguttatus within the decline complex. By developing trapping methods populations of Agrilus beetles can be monitored in terms of flight period and species composition. In addition trapping data records the effectiveness of prism traps in different local environments (e.g. ride edge vs. closed canopy). This information will supplement the site monitoring an begin to investigate the beetles prefences. The study on
A. biguttatus seeks to confirm whether the larvae present in galleries are indeed A. biguttatus and the link between beetle, larvae and bacteria is investigated in relation to the symptomatology of AOD.
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The main aims provided by the Forrestry Commision, who funded this work, were to establish a network of monitoring plots in order to document the sympotom development and threat posed by AOD, a newly defined bacterial condition affecting oak. In addition, a further aim was to assess the relationship between the visable symptoms and the buprestid beetle A. biguttatus, which was frequently linked to the condition by landowners. To achieve this the following objectives were defined:
Objective 1: Review current opinion on oak decline. Establish whether symptoms of
AOD have been described previously.
Objective 2: Record the within site impact of AOD. Estimate risk from observed
patterns.
Objective 3: Move beyond exitholes to study the beetle itself. Develop trapping
methods for Agrilus species in order to gain specimens and examine
species composition present at oak decline sites.
Objective 4: Investigate the relation of Agrilus biguttatus to stem bleeding.
Objective 5: Assess the national distribution data for AOD and Agrilus biguttatus.
Establish if within site trends are present at greater spatial scales.
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This report will address each of the above objectives, using the following research questions:
Objective 1:
1. Review literature for previous descriptions of necrosis and stem
bleeding.
2. Review literature to find descriptions of Agrilus damage.
Objective 2:
1. Record whether AOD symptoms are increasing in incidence. Are new
trees affected?
2. Record whether AOD symptoms result in mortality. If so calculate rate
of mortality.
3. Is there evidence of healing?
4. Examine data to determine spatial patterns; are affected trees
aggregated within the study site?
5. Is there evidence of localised spread, do newly affected trees occur in
close proximity to already symptomatic trees?
6. Are the symptoms seasonal?
Objective 3:
1. Are prism traps developed for use with the Emerald Ash Borer
effective at monitoring British Agrilus species? Does effectiveness
depend on colour? And will lures be effective?
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2. Are Agrilus beetles spatially aggregated; in terms of height and open
vs. closed environments?
3. Is A. biguttatus the only Agrilus species present? Or the most
abundant?
Objective 4:
1. Are external symptoms and signs, stem bleeding and exit holes, found
on the same trees?
2. Is there an order to symptom development?
3. Are exitholes caused by A. biguttatus?
4. Are AOD bacteria associated with A. biguttatus?
Objective 5:
1. Does national distribution data suggest that A. biguttatus and AOD
occupy similar climatic niches?
2. Is there evidence that these niches are climate limited?
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Chapter summaries
An overview of chapters is included in Table 1, to highlight their content and relation to the overall discussion of AOD.
Table 1: Chapter summary.
Chapter Summary
1. Introduction The current section outlines the context and scope of the current project. The reseach questions are described and finally each chapter is summarised.
2. Background information This chapter sets the scene for the thesis. It presents a review of the relevant literature and a description of AOD.
3. Site monitoring The bulk of the chapters in the thesis discuss within site symptom development at 8 monitoring sites. This chapter introduces the sites together with a description of the dynamics of stem bleeding symptoms.
4. Spread of stem bleeds Following on from the previous chapter in terms of topic this chapter introduces a spatial element to the analysis and assesses the occurrence of newly symptomatic trees in relation to those previously observed with stem symptoms.
5. Agrilus literature review A review of the literature relating to A. biguttatus shifts the focus and sets the scene for the later chapters that consider the interactions of the beetle and bacteria.
6. Co-occurrence of exit holes Using data from the monitoring sites the relationship between and stem bleeds symptoms of stem bleeding and exit holes is examined. Both standard and spatial analyses are presented. 7. Agrilus trapping In order to further investigate A. biguttatus populations and its role as a potential vector trapping methodologies were trialled. This allowed the seasonality of flight periods to be documented and specimens were gained for isolations.
8. Molecular identification of The various life stages of A. biguttatus were identified using beetles and bacteria molecular techniques, confirming the link between larvae within oak and specimens of adult beetles. Both life stages were then examined for bacterial associations.
9. National distribution The data presented in this final chapter represents analysis at a greater spatial scale. Here the national distributions of beetle sightings and affected sites are compared and related to the underlying distribution of oak and the climatic suitability for the beetle.
10. General discussion This chapter draws together the various stands discussed throughout the thesis.
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2 Background information
2.1 Introduction:
Britain has only two native species of oak, Quercus robur and Q. petraea (Gardiner,
1974), but they are the dominant components of most native woodlands (Savill, 1991) and as such they are the most common broadleaf species in Britain (Smith & Gilbert,
2003). On a wider scale these species are also the most important deciduous species in
Central Europe in terms of forest area (Thomas, 2008). In parallel with their physical presence oaks play a prominent cultural role, having many associations in place names, church carvings and folk law (Jones, 1974; Tyler, 2008). Ancient oak trees and woodlands retain a special significance in the landscape and are regarded as heritage sites
(Read, 2000; Forestry Commission, 2005). Their iconic status is thus deeply embedded in the natural ecology and culture in Britain.
In continental Europe oak health has long been affected by decline, which is caused by a complex interaction of many factors that reduce host vigour (Thomas, 2008). In Britain and continental Europe various episodes of decline have been reported throughout the last century (Falck, 1918; Day, 1927; Starchenko, 1931; Jacquiot, 1949; Greig, 1992; Gibbs
& Greig, 1997; Denman & Webber, 2010). A wide range of abiotic, insect and pathogenic factors have been associated with various outbreaks of decline. They can act independently, together, or consecutively in a complex process (Gibbs, 1999; Thomas et al., 2002; Thomas, 2008).
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Oak decline is typically a slow process gradually affecting vigour over many years and even decades (Thomas, 2008), although attempts have been made to subdivide the process so research can be focused on to smaller groups of causal agents. Denman and
Webber (2010) suggest that the rate of decline allows this traditional, slow or “Chronic”, oak decline to be distinguished from faster acting “Acute” oak decline. In this system chronic decline often involves factors that affect the root system; such as fungal pathogens like Collybia fusipes and Phytophthora spp. and even the climatic effect of drought. These limit the oaks access to both water and nutrients, reducing overall tree condition (Gibbs, 1999; Denman et al., 2010). In many cases oak decline may be considered part of the natural aging process (Rackham, 2006); however certain agents can hasten this process and cause a more widespread problem. For example: in southern
Britain in the 1920s the combination of defoliation by Tortrix viridana on the first flush of leaves followed by mildew attack, Erysiphe alphitoides, on later re-growth caused a rapid, or “Acute” Oak Decline, when attacks occurred over successive years (Day, 1927;
Gibbs & Greig, 1997).
Figure 1: Symptoms and signs of AOD a) stem bleeding b) Agrilus biguttatus exit holes Pictures: NB 2010
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In Britain a novel form of Acute Oak Decline (AOD) has been increasingly reported since
2008 (Denman & Webber, 2010), examination of historical data suggests this set of symptoms was first observed in the UK in the 1990’s (S. Denman, personal communication). AOD is typified by symptoms of stem “bleeding” (Figure 1), dark exudate that weeps out between the bark plates. These symptoms are located above patches of necrotic cambial tissue which are concealed beneath the bark. As symptoms become more developed the bark may split above the decayed and desiccated tissue leaving a visible crack between the bark plates (Denman et al., 2013). The obvious and striking nature of stem “bleeding” along with mortality of affected trees caused public alarm at a number of sites in Loughborough (Copping, 2008). Affected sites are now increasingly reported and are distributed across the south of England from the Welsh border across to East Anglia. An initial investigation by Forest Research has consistently isolated a suite of bacteria from symptomatic trees (Brady et al., 2010; Denman et al.,
2010; Brady et al., 2011; Denman et al., 2011) and the role of this assemblage in the necrosis of cambial tissue is currently being investigated (Denman et al., 2013).
In Britain the bleeding symptoms are found in conjunction with distinctive and characteristic D-shaped exit holes created during the emergence of the two spotted oak buprestid, Agrilus biguttatus Fabricus, with its larval galleries a common sight in the cambial layer (Denman et al., 2013). This combination of symptoms and signs has also been described for A. biguttatus attack in continental Europe (Moraal & Hilszczanski,
2000; Vansteenkiste et al., 2004) ; see Figure 1). This raises the possibility of an interaction between beetle and bacteria. However, Agrilus is considered a secondary pest
(Moraal & Hilszczanski, 2000; Vansteenkiste et al., 2004), and may, in an opportunist
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manner, merely select compromised trees due to their reduced vigour and thus the co- occurrence may be circumstantial rather than causal of AOD.
The rest of this chapter will provide background information relevant to the main body of the thesis. In order to compartmentalise the discussion the chapter is divided into discrete sections. The following section sets the scene by offering a concise review of the oak decline literature. This is then followed by a description of AOD symptoms as observed in the field, which serves to outline the focus and scope of the current research efforts.
Field observations are then contrasted with observations from continental Europe: firstly via an introduction to the A. biguttatus literature; then through a history of stem bleeding symptoms on oak that culminates with a discussion of the microorganisms implicated in
AOD. This is followed with a more general discussion of bacterial pathogens on plants and trees. These strands of discussion are drawn together in a final section that presents a discussion of the AOD system in terms of potential interactions between the beetle and bacteria.
2.2 Literature review: Oak decline
The process of decline needs to be considered differently to the action of a specific pathogen or pest, as by definition it has no single causal agent (Thomas et al., 2002;
Thomas, 2008). Forest and tree decline represent the actions of multiple agents that have a combined effect reducing the health of host trees. In the case of Q. robur and Q. petraea decline is an often cited malady with a long list of proposed causal agents (Thomas et al.,
2002; Thomas, 2008). During an individual episode of decline a selection of the causal agents will affect oak health, either acting together or sequentially.
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Various attempts have been made to subdivide and simplify the complexity of the decline process, with the following three trends recurring in the literature:
1) Decline factors can be split between the stages of decline at which they become involved: Primary agents, or stresses, are involved in the initial weakening of trees; whereas secondary agents only affect weakened trees playing a role in the further decline and mortality of hosts (Delatour, 1983; Wargo, 1996). The consecutive and cumulative nature of the decline process makes it difficult to attribute blame to any individual factors.
This however does not stop academic argument, with some authors considering primary causes as key, triggering an inevitable process of decline (Marcais et al., 2011) and others viewing primary stresses as causes of sub-optimal growth from which recovery is possible in the absence of secondary agents that ultimately cause the death of host trees
(Desprez-Loustau et al., 2006; McDowell et al., 2008).
2) Types of decline can be distinguished based on the speed and extent of host damage.
The recent outbreak of decline in Great Britain has led to two forms being distinguished as chronic and acute (Denman & Webber, 2010). These types of decline are proposed to involve separate groups of factors. Chronic decline is often thought to be caused by root problems, with acute forms damaging the above ground parts of the host; either through a combination of mildew/defoliation or the stem form which is the focus of this study.
Given these divisions an individual host could be found being attacked by both chronic and acute decline factors. Similar divisions based on the rate of decline were applied by
Petrescu (1966) when describing oak decline in Romania (cited in Delatour, 1983).
Although in this context, it was found that trees showing rapid decline often had declining increment growth over an extended period (20-30 years) before the decline was noticed.
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With these findings in mind it is possible to consider the similarities between primary agents / chronic decline along with secondary factors /acute decline.
3) Finally, divisions can be made based on the scale of the decline, with wider landscape level effects contrasted with those that effect individual trees. Forest decline is by definition decline on a large scale which may be caused under a range of conditions.
Rarely this can represent the impact of a vigorous primary pathogen such as Ophiostoma novo-ulmi, Dutch elm disease or Phytophthora ramorum on larch. More often forest decline can be triggered by predisposing factors such as drought or nutrient deficiencies in the soil. A further factor that may influence stand level dieback is the age of the trees themselves. Often forests consist of similar aged cohorts of trees, either due to re-growth after natural disruptions or through planting and management operations. This leaves stands prone to cohort senescence where the population of trees reaches, and passes, its mature life stage in synchrony creating a monoculture of susceptible hosts (Mueller-
Dembois, 1986).
In practice it is useful to hold all three systems in mind, and to consider that they are not mutually exclusive, rather they are aids to placing the actions of individual agents within the wider decline complex. These simplified systems allow the multitude of decline factors to be grouped and investigated before the exact causal order is known; and further allow comparisons between locations and episodes of decline where the combinations of causal factors may vary.
The full complexity of oak decline in Europe has been extensively reviewed (Gibbs &
Greig, 1997; Thomas et al., 2002; Thomas, 2008; Denman & Webber, 2010) and involves
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a long list of causal agents. Rather than present a comprehensive summary the remainder of this section will instead provide information on recurring themes and agents progressing through the stages of decline where they are typically found.
Environmental factors are frequently cited as early stage primary stresses for example hard late winter frost causing large areas of bark death (Hartmann & Blank, 1992), waterlogging and most commonly drought (Falck, 1918; Jacquiot, 1949; Hartmann &
Blank, 1992). Waterlogged soil causes conditions similar to drought restricting uptake by tree roots. Water displaces air in the soil creating hypoxic conditions, where roots cannot respire. This leads to the death of fine roots and thus limits the uptake of water and nutrients. Both species of native oak are affected by this process although Q. robur is more tolerant of waterlogged conditions (Vincke & Delvaux, 2005; Parelle et al., 2006).
Drought affects the health of trees via two distinct pathways: (a) Carbon starvation which is a gradual process that occurs when stomata close, restricting transpiration and limiting photosynthesis; (b) xylem embolisms, which are formed by air pockets in the xylem and these occur under extreme drought conditions triggering hydraulic failure so that no further water can be drawn up the vessel (Bréda et al., 2006; Hoffmann et al., 2011).
These processes affect both oak species although Q. petraea is more tolerant of drought conditions (Levy et al., 1992). The effects of both waterlogging and drought are dependent not only on climatic conditions but also soil type as it strongly influences water availability with clay soil proving especially unfavourable for oak, as it is most susceptible to the unfavourable conditions created under both extremes of water availability (Vincke & Delvaux, 2005). These factors are likely to act at scales greater than the individual trees and may predispose the forest stand to subsequent decline agents that affect individual hosts.
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Water and nutrient uptake can be further hindered by damage to the roots. A number of fungal pathogens can infect oak roots, with Phytophthora spp., Collybia fusipes and
Armillaria spp. found most commonly. Three species of Phytophthora occur frequently
(P. quercina, P. cambivora and P. citricola), associated with decay of the fine roots (Jung et al., 1999; Jung et al., 2000). By contrast C. fusipes and some Armillaria spp. are primary pathogens that attack the larger roots and buttresses of oak (Marcais et al., 2011).
Across Europe Armillaria spp. vary in their ecology and distribution and will act as primary or secondary agents (Guillaumin et al., 1993). On oak the two most aggressive pathogenic species documented are Armillaria mellea and A. gallica, although young trees have the ability to recover from attack (Sicoli et al., 2002). Armillaria mellea has a broad host range affecting many tree species and has clones of varying virulence with some capable of having a very destructive primary role, although pedunculate oak, Q. robur, is more resistant than other tree species (Redfern, 1978). Armillaria gallica is unable to damage healthy oak, but it does contribute to the later stages of decline where it limits a trees ability to overcome more acute stresses (Marcais & Breda, 2006). The distribution of A. gallica in a woodland is not dictated by soil type, waterlogging or the dominance of host trees, but rather local clustering indicates infection foci where mycelium, often in the form of Rhizomorphs, have spread from neighbouring trees
(Marcais & Cael, 2006). Collybia fusipes affects both oak species although it is more often associated with Q. robur (Piou et al., 2002). Of all the root decay fungi it is considered to be most damaging primary root pathogen (Marcais et al., 2011) although attack takes a long time to weaken trees (Piou et al., 2002). Infections are associated with coarse textured soil (Camy et al., 2003), possibly caused by the fact that its mycelium is intolerant of hypoxic conditions (Camy et al., 2003). The multi-factorial effect of decline can thus be seen in microcosm when considering root pathogens; there is a strong
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cumulative effect causing larger lesions when C. fusipes, A. mellea and Phytophthora spp. infect the same host (Marcais et al., 2011).
Oak health may be further reduced by defoliation events. On native oak insect defoliators are found in the greatest numbers during the spring soon after trees flush (Southwood et al., 2004). The two most commonly cited pest species, Tortrix viridana and Operophtera brumata, both synchronise egg hatching with bud burst in a process controlled by temperature (Visser & Holleman, 2001; Ivashov et al., 2002). The impact of this early year (spring) defoliation is increased if mildew, caused by Erysiphe alphitoides, affects the same individual trees later in the season (Day, 1927). The mycelium of this fungus grows epiphytically on oak foliage impairing its photosynthetic capability, but it also plunges specially adapted nutrient absorbing structures called haustoria, into the tissues damaging the leaves. The magnitude of this effect has been measured and while the reduction in stomatal conductance and net CO2 assimilation rates are small in infected leaves, the reduced life span of heavily infected leaves decreases carbon uptake over the growing season (Hajji et al., 2009). At present E. alphitoides is the only mildew described in Britain, although studies in France have shown it to be one of four species present
(Feau et al., 2012). The extent of oak mildew infection has been shown to affect the available resource for late summer insect communities altering their composition (Tack et al., 2012).
The final demise of oak is often associated with secondary bark boring insects with
Agrilus biguttatus the most frequently cited agent (Hartmann & Blank, 1992). This role is one that is frequently played by various insects in decline situations (McDowell et al.,
2008; Jactel et al., 2012). In even later stages of decline oak may be exploited by further species of bark beetle (along with longhorn beetles, and pinhole borers deeper in the
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wood) although these are not thought to play a role in the tree’s demise (Vansteenkiste et al., 2004; Tilbury, 2010).
2.3 Field observations: Symptoms of AOD
The most obvious identifying symptom associated with AOD is stem bleeding. Most characteristically this occurs at multiple points on the main stem of an affected tree. It is not uncommon for a heavily affected individual tree to weep from more than 30 point locations, although within affected woodland there are frequently also lightly affected trees with only one or two bleed points. Bleeding points have a tendency to occur in a vertical plane affecting some sides more than others. Heavily affected sides can occur on any aspect, but often face open areas. Bleeds frequently occur less than 2m from the ground, but can be present high in the canopy.
Typically stem bleeding is associated with 5-10 cm longitudinal cracks that are positioned between the bark plates (Figure 2). Dark exudations seep from the cracks and when active bleeding stops a dry black crust remains in streaks down the stem. These distinctive symptoms allow for mapping to take place in the absence of a clinical test or even a confirmed cause (pathogen or pathogens), although ideally bacterial isolations are required to confirm the agents involved.
The exact progression of symptoms is uncertain although the following description fits with the observed symptoms. In what appears to be an early stage of externally visible symptoms, liquid can be seen flowing from between the bark plates in the absence of a bark crack. This flow begins from the central point between plates, where the two plates readily separate during growth. The liquid arises from an area of necrosis that can be seen
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developing in the phloem tissue when the outer bark is removed. As the lesion develops it extends affecting the full depth of the inner bark eventually breeching the cambium and affecting the outermost rings of sapwood (Denman et al., 2013). As the inner bark is degraded a cavity is formed and fills with fluid. These can be found hidden below the surface in the absence of external symptoms. As the lesion and cavity develop, a split appears between the bark plates and the external symptoms become most characteristic.
Typically investigations below a bark crack reveal a lesion (Figure 3). The size of the necrotic area can vary greatly and adjacent areas may merge, but 5 cm × 10 cm would be most typical. It is likely that the presence of cavities disrupts the vascular system causing decline, however the specific effects of tissue necrosis have not been investigated.
An apparent later stage of symptom development can be seen on some of the affected trees. This involves the cession of the exudate flow, and the formation of callus, raising the possibility that individual points and perhaps whole trees may enter remission.
Externally cracks often remain visible between bark plates, although these occasionally heal leaving a linear callus. Even when the external wound is not visibly callused there are often signs of this regenerative growth below the bark. Callus forms around the edges of lesions and begins to overgrow the necrotic region; this process may eventually lead to further rings of functional vascular tissue forming over the damaged area (Pearce, 1996).
Occasionally lesions are found deeper within the sapwood, these are surrounded by callus growth and presumed to be the sites of earlier infection rather than ongoing necrosis penetrating into the sapwood.
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Figure 2: Bleeding occurs from a crack between Figure 3: Cavity behind bleed exposed by bark plates. removing bark. Yellow arrow highlights cavity that penetrates into the sapwood. Picture: NB 7/2009 Picture: NB 7/2009
Signs of insect activity are frequently found in association with the stem lesions. At AOD
sites a proportion of the affected trees have distinctively D-shaped exit holes created by
the buprestid beetle A. biguttatus (Denman & Webber, 2010). Investigations below the
bark reveal a network of insect galleries in close proximity to the necrotic lesions.
Galleries can be seen in a range of sizes, often they are 2 to 5 mm wide, winding and in
close proximity, these are assumed to belong to the larvae of A. biguttatus. In the most
heavily declined, almost moribund, trees additional galleries further complicate this
picture as bark beetles, pinhole borer and longhorn beetle larvae begin to colonise the
tree. The galleries presumed to belong to A. biguttatus occur in the phloem tissue and are
most frequent close to the cambial layer (Denman et al., 2013) and similarly to the lesions
those found deeper in the sapwood are associated with callus tissue and are presumed to
have been created in previous years.
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2.4 Literature review: Introduction to Agrilus
Stem bleeding on Q. robur and Q. petraea, often termed weeps or slime flux, has frequently been reported in association with attacks by the Two Spotted Oak Buprestid,
A. biguttatus (Syn. A. pannonicus) (Falck, 1918; Jacquiot, 1949; Hartmann & Blank,
1992; Vansteenkiste et al., 2004). The larvae of this beetle develop within the inner bark forming galleries in live phloem tissue (Vansteenkiste et al., 2004). In the literature this bleeding from stem cracks has been reported as a first sign of infestation (Hartmann &
Blank, 1992; Hartmann & Kontzog, 1994; Moraal & Hilszczanski, 2000; Habermann &
Preller, 2003; Vansteenkiste et al., 2004), although the link between areas of necrosis and
A. biguttatus action has not been investigated. Given the recurring discussion of stem bleeding in relation to A. biguttatus in the literature and the frequent co-occurrence found on AOD sites a full review of its role is presented as a later chapter, and only a brief summary included below.
Agrilus biguttatus is a beetle thought to be native to Britain and one that was considered a rarity as recently as the 1980’s (Allen, 1973; Shirt, 1987), more recently however it has been frequently recorded across an expanding range that now covers large areas of southern England (Foster, 1987; Allen, 1988; Hackett, 1995; Alexander, 2003). This species is considered a secondary pest, affecting weakened trees, with attacks often thought to follow periods of drought, defoliation or root decay (Hartmann & Blank, 1992;
Gibbs & Greig, 1997; Moraal & Hilszczanski, 2000). In this context A. biguttatus has been proposed as an agent that plays an important role in the final decline and mortality of host trees (Hartmann & Blank, 1992).
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Larval feeding takes place in live vascular tissue (Starchenko, 1931) and acts to reduce tree health. Feeding occurs in the inner bark, through the phloem tissue occasionally breaching the cambium (Vansteenkiste et al., 2004). This process will act to reduce the flow of nutrients and photosynthetic product around the tree. The vascular system is further compromised by the tree’s own defence system when tyloses form in adjacent xylem vessels blocking the flow of water within the tree (Jacquiot, 1976; Vansteenkiste et al., 2004).The impact of this process is amplified in heavy infestations where the density of larvae can be very high, in extreme more than 50 m of gallery was recorded in a square meter of bark (Starchenko, 1931). This process may girdle the host tree (completely severing the vascular system). The impact of larval damage is likely to be accentuated when it is found in combination with areas of phloem necrosis. The process of girdling will begin long before the externally visible exit holes appear, making it hard to establish what role Agrilus beetles play in the decline itself. In many cases there is no external sign of exit holes, but old galleries can be found beneath the bark (Vansteenkiste et al., 2004).
This is indicative that not all attempts at colonisation are successful, with host defences overcoming larval attack (Jacquiot, 1976; Hartmann & Blank, 1992; Vansteenkiste et al.,
2004).
2.5 Literature review: Causes of necrosis and stem bleeds
In Britain extensive stem bleeding on oak is a recent problem, it first rose to prominence in the 1990’s in association with a general decline of oak that often included Agrilus attack (Gibbs & Greig, 1997). Similar symptoms have been reported as a more general malady associated with a wide range of factors, including the progression of Armillaria mycelium into the stem (S. Denman personal communication; Kowalski, 1991; Kaus et al., 1996). Lesions on stems of younger trees have been reported in association with gall
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midge Resseliella sp. and the fungi Ceratocystis piceae and Fusarium solani which were presumed to be secondary colonisers (Gibbs, 1982). These reports indicate that localised stem bleeding could be a more general problem following stem damage.
In Europe stem bleeding has been more commonly reported. Attempts to isolate fungi from stem lesions have been made on multiple occasions but no study has found the same species consistently (Kowalski, 1991; Kehr & Wulf, 1993; Kaus et al., 1996), with a single study finding 25 species associated with stem necrosis (Kowalski, 1991). The most frequently mentioned species in these studies are: Ceratocystis sp.; Cytospora sp.; and
Fusarium sp. all of which have questionable pathogenicity on oak. Similar symptoms have been reported on Mediterranean oak species in Spain where investigations have identified the following fungal associations: Botryosphaeria stevensii; Botryosphaeria dothidea and Diplodia sarmentorum (Sánchez et al., 2003), although other studies indicate bacterial involvement (Biosca et al., 2003; Poza-Carrion et al., 2008).
Recent studies at AOD sites have investigated micro-organisms associated with the necrotic tissue found behind bark cracks. A number of fungi have been isolated intermittently, but the predominant component of the microbial assemblage is bacterial
(Denman & Webber, 2010; Denman et al., 2010). As may be expected these regions of dead and decaying tissue contain many bacterial species, although the isolations, which focus on the dead-live junction of cavities and gallery margins, yield some species consistently (Brady et al., 2013; Sapp et al., 2013). The most frequently isolated species is Gibbsiella quercinecans gen. nov. sp. nov. (Brady et al., 2013), which had not previously been described (Brady et al., 2010). Further analysis showed Gibbsiella quercinecans matched with bacterial isolates from Q. ilex and Q. pyrenaica in Spain,
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where it was isolated from bark cankers. Originally described as a species of Serratia these isolates have been shown to have pathogenic action on saplings (Biosca et al., 2003;
Poza-Carrion et al., 2008). In addition cankers in Britain and Spain both yielded isolates of Lonsdalea quercina gen. nov. (Brady et al., 2011): Lonsdalea quercina ssp. quercina formally Brenneria quercina the causal agent of Drippy Nut disease in California
(Hildebrand & Schroth, 1967) not present in Britain; Lonsdalea quercina ssp. britanica gen. nov. sp. nov. from British oaks; and Lonsdalea quercina ssp. iberica gen. nov. sp. nov. from Spanish oaks only
A further new species Brenneria goodwinii sp. nov. has also been frequently isolated from the necrotic lesions (Denman et al., 2011), along with two further novel species that reside in the genus Rahnella (S. Denman, personal communication). One or a combination of the isolated bacteria may act to cause necrosis and investigations are underway to investigate the necrogenic ability of this assemblage (Denman et al., 2013).
2.6 Literature review: Bacterial diseases of trees
Plants contain rich sources of nutrition for microbes, but relatively few pathogenic bacteria have been shown to exploit this niche. The majority of known pathogens fall within a small group of Gram-negative bacteria; within the Enterobacteriaceae and
Pseudomonadaceae (Alfano & Collmer, 1996). Tree diseases caused by bacteria produce a range of symptoms but are often typified by localised areas of necrosis in host tissue.
Notable examples include: (1) shoot canker and gummosis for example as caused by
Pseudomonas syringae pv. morsprunorum on cherry (Jones, 1971; Latorre & Jones,
1979) and Erwinia amylovora on rosaceous plants (Johnson & Stockwell, 1998;
Hildebrand et al., 2000); and (2) stem cankers such as those caused by Brenneria
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nigrifluens (shallow canker), B. rubrifaciens (deep bark canker) on walnut (McClean et al., 2008; Roshangar & Harighi, 2009) and Pseudomonas syringae pv. aesculi (Pae) on horse chestnut (Webber et al., 2008; Green et al., 2009). Additional types of disease caused by bacteria include bacterial wilt, although this is often less obvious in trees For example: Brenneria salicis disrupts the xylem tissue of willow causing leaves to brown and wilt (Maes et al., 2009; Huvenne et al., 2009); leaf scorch on oak caused by Xylella fastidiosa (Chatterjee, Almeida & Lindow, 2008, McElrone, Jackson & Habdas, 2008); and finally, gall formation, as in the case of Pseudomonas savastanoi in olive branches
(Marchi et al., 2006).
Pathogenic bacteria exploit and often kill plant cells without detection by the host. Action of some bacterial species appears to be host specific as plants have a plethora of resistance mechanisms (e.g. many pathogens can be subdivided into separate pathovars that are host specific). Early studies of pathogenicity identified genes involved in this process that were able to induce a hypersensitive response (HR) in non-host plants. HR involves a rapid and localised area of cell death that occurs around pathogenic bacteria that are dependent on living tissue. Host-pathogen interactions were shown to involve elicitor proteins, such as harpin (Wei et al., 1992) that are delivered directly into host tissue via the type III secretion system (Alfano & Collmer, 1997). Subsequent work has identified a wide range of protein effectors that infiltrate hosts and block signal transduction pathways that trigger host response. In this way pathogenic strains release a wide ranging set of biochemical compounds that are specifically tailored to the host species as part of an evolutionary battle to avoid detection (Mudgett, 2005; Tampakaki et al., 2010). Bacterial species that achieve this can be regarded as successful primary
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pathogens, they possess within their genome the full molecular toolkit to side-step defences and exploit host cells as nutrient sources.
It is now considered that the primary pathogen model presents only one extreme of plant- bacteria interactions that also rely on type III secretion, as non-pathogenic bacteria also use the Type III secretion system to establish relationships with plants. For example the mutualistic bacteria, Rhizobium spp., interact with plant roots in this way in order to avoid attack by host plant defences (Soto et al., 2009). In some cases plant-bacteria interactions are not clearly categorised as pathogenic or mutualistic, but as an individual species may switch between roles depending on host life stage, fitness or environmental cues (Newton et al., 2010). Even B. salicis, traditionally cited as a primary pathogen, has been found living as an endophyte in non-symptomatic hosts, but switches to pathogenic action when population density is high (Huvenne et al., 2009; Maes et al., 2009). Bacteria can assess local population density using a mechanism known as quorum sensing. Individual bacteria release chemical signals that other members of the species can detect via external receptors, as the population builds so does the concentration of signal (von Bodman et al.,
2003). More complex interactions take place when multiple species are involved. Seventy percent of olive tree galls, caused by Ps. savastanoi, also contain Pantoea agglomerans; colonisation by P. agglomerans is aided by the presence of Ps. savastanoi which it then outcompetes (Marchi et al., 2006). In this way bacterial species can act sequentially during symptom development and more complex interactions may also occur.
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Figure 4: Schematic diagram showing bacteria and Agrilus interactions. The AOD system involves three componets the host, the phloem necrosis and the Agrilus beetle. The direct actions of each component are indicated in the above diagramwhile more detailed consideration of potential interactions in the text below. The two putative causal agents are placed centrally and linked to the cryptic damage caused below the bark which may eventually progress to cause the visable external symptoms (shown near the outer edge of the diagram). The negative impacts of the agents on oak health are shown in red and the hosts ability to repel and recover from attack (summarised as “callus”) is shown in green. The types of interactions between each of the agents and the host are likely to be influenced by the health staus of the host, as well as the resistance mechanisms coded in its geneome. Nathan Brown 32
2.7 The AOD system: Beetle and Bacteria Interactions.
When in an advanced stage AOD can be distinguished by a clear set of external symptoms, namely declining oak with bark cracks, or stem bleeding, and possibly Agrilus exit holes. These symptoms indicate three important factors in the AOD system (Figure
4):
1) The host oak forms the central hub on which both Agrilus and bacteria can act.
When considering the host, the key element is likely to be its health status at the
time of attack. In a decline situation many environmental and biotic factors are
likely to be influencing tree health. The health of the host will influence its
susceptibility to the other components of the system, as well as its ability to callus
and recover from attack. Thus both the bacteria and beetle are likely to have a
window of opportunity when they can successfully utilise the host. Within this
general mechanism it is worth considering that some hosts are likely to have a
greater genetic potential to repel attacks.
2) Bark cracks are found above localised areas of necrosis in the phloem tissue.
Bacteria are thought to cause the necrosis, with bark cracks a probable
consequence of the damaged and desiccated tissue.
3) Exit holes are found in conjunction with gallery networks below the bark and
represent the successful completion of the Agrilus larvae’s development to an
adult beetle.
In this system, damage to the host oak is caused by bacteria via the formation of phloem necrosis and by Agrilus larvae as their galleries disrupt the phloem and tyloses form in the xylem. Additional impacts on the health of the host may occur when adult beetles feed on
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foliage (although this is likely to be of limited significance) as well as via any additional decline factors that may be present.
If the host oak is sufficiently healthy it may be able to repel an attack by one or both of the AOD agents, halting the further progress of necrosis and galleries; for example liquid exuded from the host may act to drown young larvae. Callusing is often seen in conjunction with both necrotic cavities and galleries, indicating that with sufficient resources a host may recover and overgrow damaged tissue. The lignification of this tissue may also help to restrict future incursions. The observation of deeper callused damage below more recent attack suggests that this process may not be permanent. Later damage may result either from a recurrence of organisms within the host or new incursions from external sources. Recurrent attempts to infest or infect an individual host are likely to have a health impact and may reduce the health of the host sufficiently for the beetle and/or bacteria to fully establish.
There is some evidence that biochemical compounds associated with larval frass promote callus growth in oak (Jacquiot, 1950; Jacquiot, 1976), and there remains the possibility that these chemicals are produced by a microbial component; so one of the first interactions between causal agents and host may in fact trigger a beneficial recovery response.
The above interaction would rely on there being an association between beetle and bacteria. This could be found in the form of larvae interacting with bacteria within the host tree, either by creating sites for infection through their galleries, or galleries may simply create an area of local damage where endophytes can bloom. In a further role,
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larvae may act to spread bacteria within the tree, picking up cells as they pass areas of necrosis and transporting them to fresh areas of tissue. In a final process, adult beetles could transport bacteria between trees, either incidentally or via a more complex association (although this is made less likely as adult beetles lay eggs externally; discussed more fully in Chapter 5).
Bacterial infection may aid the beetle indirectly by influencing the health status of the host. Damage caused by necrotic cavities will limit the ability of the vascular system to transport water and sugars within the tree, reducing its overall health and increasing its attractiveness to a secondary pest like Agrilus. In this way both beetle and bacteria may benefit from a combined attack. Conversely a microbial component in callus formation may also benefit a beetle by prolonging the limited window of opportunity it exploits before the host dies. If the microbial component is beneficial to the bacteria it is possible that females may preferentially select infected trees as egg laying sites. Bark cracks may allow volatiles specific to the decay to be released into the atmosphere and act as attractant cues.
Most of the onsite observations fit with a model where the beetle and bacteria have strong interactions; however occasionally cases do occur where one component is seen in the absence of the other. Agrilus galleries have been observed in the absence of phloem necrosis, although it may be that this may simply take longer to develop or the bacterial component has not yet colonised available infection courts. Bacterial infection in the absence of clear galleries has also been observed, although these sites are often strongly callused around the edges. In these situations an initial colonisation attempt may have
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been over come by the host’s defences but still managed to create a site for infection, or infection has occurred at an independently created wound or damage site.
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3 Development of AOD symptoms within site
3.1 Introduction
This study descibes the within site development of Acute Oak Decline (AOD) and forms part of the ongoing effort to document the current outbreak. The research involved the establishment of long term monitoring plots. These allowed the accurate monitoring of annual variations in AOD symptoms throughout the project, and enabled the within site development to be analysed. The inclusion of time as a factor allows sequential patterns to be investigated. Year on year changes in symptom development gives vital information regarding the epidemiology of the decline. Initial analysis will focus of the symptoms of stem bleeding as these are the most identifiable visual symptom of AOD and closely linked to the newly characterised bacterial species. Analysis of the monitoring data may reveal trends at the affected sites. Epidemiological theory suggests development at a population level follows a trend predicted by a sigmoid curve: the number of symptomatic trees increases as the epidemic intensifies (often following an initial establishment period) and eventually stabilise as all susceptible hosts are affected. An additional level of complexity may be added to this picture if host trees can overcome the damage and symptoms caused by the decline agents and return to the healthy condition.
Interpretation of this process from external symptoms and across a short time window is difficult and as such trees exhibiting this pattern will be considered to be in remission, showing apparent healing in the short term but where the long term outcome remains uncertain. Perhaps most importantly in this context records of tree (canopy) health in relation to symptom expression together with the relative rates of mortality indicate the seriousness of the treat posed by AOD.
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Landscape analysis is one of the most rapidly growing fields in ecology (Fortin & Dale,
2005) due to the increasing availability of accurate GPS (Global Positioning Systems) and
GIS (Geographical Information Systems) aiding analysis (Gatrell et al., 1996; Stoyan &
Penttinen, 2000; Plantegenest et al., 2007). Landscape pathology involves the study of disease at a landscape scale and considers the wider scale impacts of pathogens and their dependence on underlying landscape features (Holdenrieder et al., 2004). The pattern of visible symptoms can offer valuable information about the distribution and dispersal of a pathogen (Reich & Lundquist, 2005; Plantegenest et al., 2007). Understanding the effects of landscape features on dispersal is important in modelling risk and designing protection measures (Plantegenest et al., 2007).
Spatial analysis relies on attaching a geographical location to attributes such as infection by a disease (Fortin & Dale, 2005). A commonly cited example from the field of human health is that of an 1854 cholera outbreak in London. John Snow located the contaminated water source by examining the distribution of cases (Gatrell et al., 1996;
Plantegenest et al., 2007). To conduct this type of analysis the prevalence and severity of attributes need to be defined spatially. This can occur either as frequencies per unit area or as a number of individual points. Once the form of data available and the type of question asked are decided, scale becomes a key consideration. The scale of analysis must match the questions asked. Subtle distinctions may become aggregated at larger scales and conversely large scale trends can easily remain undetected amongst smaller scale, random, variation (Reich & Lundquist, 2005; Fortin & Dale, 2005). Analysis must therefore try to have a biological process in mind. Analysis at larger scales often assesses the differences between homogenous areas (Reich & Lundquist, 2005). In a recent example large scale quadrat sampling was used to monitor the development of a Sudden
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Oak Death (Phytophthora ramorum) outbreak across a Californian state park (Kelly et al.,
2008). Another Sudden Oak Death study compared analyses at a range of scales, ranging from local to state wide, in order to examine the impact of human populations. Each stage of the study reinforced the finding that human activity increased the occurrence of
Sudden Oak Death (Cushman & Meentemeyer, 2008). Smaller scale, within stand, analysis can be achieved using point patterns (Ripley, 1981; Diggle, 2003; Reich &
Lundquist, 2005). These are commonly used in forestry where trees can be assigned an
(x, y) co-ordinate along with the relevant attributes. The pattern formed by individuals with a particular attribute type can be simply defined as random, regular or clustered
(Fortin & Dale, 2005).
A specific aim of this study is to evaluate the distribution of oaks affected by Acute Oak
Decline by assessing them for localised clustering. A clustered pattern represents the occurance of points at local densites that exceed the expected range predicted by the overall density of ponts. In these studies the level of clustering displayed by a pattern will consider the distance from a central point without any bias in direction, such that localised areas form circles centered on each of the points that form the pattern. A non- random distribution, where symptoms occur more frequently in close proximity, is suggestive of a localised cause. This clustering may be due to variations in the underlying distribution of hosts, as a first order effect. Variation in host distribution may be due to environmental factors including: soils, competition, climate, and seed dispersal. The clustering of disease symptoms could however be independent of this underlying pattern instead due to an external, second order, cause related to a pathogen (Law et al., 2009). A formal test is required to separate any second order effects in the distribution of symptoms from the natural pattern of trees, which is often clustered (Stoyan & Penttinen,
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2000). Analysis of second order clustering in a two-dimensional plane is possible using
Ripley’s k function and its variants (Ripley, 1981; Diggle, 2003; Wiegand & Moloney,
2004). These test statistics give a formal measure of neighbourhood density.
The current study will use completely mapped site data to assess any clustering of Acute
Oak Decline symptoms from eight sites across Britain. Baseline data will be analysed to establish the pattern of affected trees. As all monitoring is spatially defined, with symptoms linked to trees whose positions are known, spread can also be analysed spatially. Patterns of symptomatic trees will be analysed to test if their distribution is non-random, with any localised clustering indicative of a localised cause. At present the description of the impact of AOD relies on anecdotal evidence collected from site owners and managers, but the current study seeks to quantify this impact empirically. Addressing the issues of spread, remission, and mortally allows for future risk to be estimated.
3.2 Methods:
3.2.1 Mapping and data collection
Site selection
Eight study sites (Figure 5) were selected from oak decline enquiries received by Forest
Research from 2006 to 2008, as such they cannot be said to be a random sample of all potential sites. An essential factor influencing site choice was the presence of AOD symptoms on site and the willingness of landowners to allow access to the site. The sites chosen in 2009 were selected for initial visits based on the above criteria, leading to eleven initial site visits. The final four were selected due to the presence of existing data
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regarding the distribution of symptomatic trees. At each of the four sites the study area to be monitored was selected based on its size (approximately 100-200 trees). The boundaries of the study areas were designed to follow natural boundaries so that monitoring sites included either the whole of a small wood land or a distinct block of land.
An additional four study sites were included in 2010, again all sites had trees with AOD symptoms. One site was included as it had a long history of monitoring and severe symptoms with the final three sites being in an early stage of infection. Because of these criteria, the additional plots offered the opportunity to monitor sites at both extremes of the infection process with the sites in early stages offering the best opportunity to record spread and new infection with the site in later stages allowing the monitoring of mortality and recovery.
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Figure 5: Location of monitoring sites. Blue squares show original 2009 sites (Hatchlands, Langdale Wood, Sandpit Wood and Winding Wood) and green circles show 2010 additions (Beecham Spinney, Great Monks Wood, Rookery Wood and Sheen Wood).
Global Positioning System (GPS)
Tree locations were measured using GPS: Trimble Pathfinder ProXT; an external antenna
and Geo-beacon were used to maximise accuracy in forest environments that hamper
clear signal reception. Accurate GPS positioning requires signals to be received from
more than four satellites (Trimble, 2009; Wing, 2008) . This can be an issue in forest
conditions due to the small windows of sky that are visible (Sigrist et al., 1999). In the
forest environment additional multipath errors are caused by canopy interference. This
source of error is greatest when the trees are in full leaf (Sigrist et al., 1999). The GPS
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unit used in this study is designed to give accuracies of less than 1m. This is achieved using differential correction. An FM signal broadcast from a base station corrects for atmospheric interference and satellite position errors (Trimble, 2008; Wing, 2008).
Map Creation
Maps were generated using ArcGIS 10.0 (ESRI). The base plan was obtained from OS
Master Map (Ordinance Survey). Additional features, such as paths and understory were manually recorded in the field with a best fit method, where they were placed relative to the known tree positions.
3.2.2 Disease development and spatial patterns
Data Collection
Site visits were made between May and August in 2009, 2010, 2011 and 2012. At each site trees were numbered and assessed under a number of criteria. All data were linked directly to the spatial location of trees using a Trimble Recon handset running ArcPAD 8
(ESRI). In this way survey information was collected and saved directly as shapefiles
(.shp) that could be imported directly into ArcGIS 10.0 (ESRI).
The dataset included all trees greater than 15cm in diameter at 1.3m. For all trees, the species was recorded, with additional attributes collected for all native oaks. The presence or absence of bleeding symptoms was noted along with the presence of Agrilus exit holes.
Site area and area of understory were calculated using ArcGIS 10.0 (ESRI) from polygon shapefiles (converted to the OSGB36 projection). Densities of oak and all tree species were calculated using these site areas.
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3.2.3 Ripley’s k: the L function and the O-ring statistic
Ripley’s k (Ripley, 1981) is a function that assesses the cluster of points in a plane. It generates a statistic based on all inter-point distances and assesses clustering at a range of scales (Wiegand & Moloney, 2004). This offers clear benefits over traditional nearest neighbour techniques that consider only the shortest distance between points (Perry et al.,
2006). Ripley’s k statistic offers a summary of the pattern describing the degree of clustering relative to overall pattern intensity. The resulting description is not unique to a given pattern, two patterns may generate the same result, instead the degree of clustering is summarised over various distances (Dixon, 2002).
The use of Ripley’s k requires completely mapped data for a given study region (Ripley,
1981), where all possible locations (for example: of affected trees and potential hosts) have been recorded. The method looks at clustering around a random point in a pattern.
Clustering is assumed to be isotropic, without directional bias (Dixon, 2002).
Local density is calculated by counting the number of points within a local area, given by a circle radius t centred on a random point. This local density is divided by the overall density of the study area (λ) to give k (t); a score of clustering. As k (t) is a function of t it assesses clustering at multiple distance scales (at different values of t). The numerical value of k (t) increases with the degree of clustering (values above 1 indicate that clustering within distance t is greater than expected given λ).
-1 k (t) = λ Expected[number of extra events within distance t of a randomly chosen event]
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A modified version of k was used in this study. L expresses clustering due to distance, whereas k is expressed in terms of area. The L function is a square root transformation that reduces scale dependence and stabilises variance. A positive value of L is indicative of a clustered pattern (Wiegand & Moloney, 2004).
kii Lii t
A second function that will be used in the current study is the O-ring statistic. This replaces the circular assessment area generated by the radius t with a hollow ring.
Whereas k is cumulative and thus confounds effects at large distances, O-rings have the ability to isolate specific distance classes as shown in Figure 6 (Wiegand & Moloney,
2004). Both O-ring and L functions will be used in the current study; k uses more points so is more powerful in rejecting a null model, but O-rings show neighbourhood densities more effectively (Xu et al., 2009). With the O-ring function clustering can be shown to occur at a specific distance from other points. kii is the accumulative version of the pair correlation function gii, as such gii describes the rate of change in kii (Xu et al., 2009). In this chapter where only the singular pattern of diseased trees is considered O-rings are analysed using gii.
dkii (t) gii (t) 2 t dt
when, replacing circles of radius t with rings of radius t
The numerical implementation of this statistic used in the current study allows for a choice of ring widths to be considered adding width consideration to all analyses. If the
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ring is narrow gii (t) will be close to its theoretical value, but may also vary erratically with distance. The width should be large enough that you would expect to find another point of the pattern within the area. But if the width is too large then resolution is lost, as gii (t) begins to approximate kii (Wiegand & Moloney, 2004).
Figure 6: Circular vs. O-ring sampling. Both rows show sampling around an arbitrary point (red). Radius t increases from left to right. A summary graph (far right) shows number of additional points included at each stage. Circular selection is illustrated top with O- rings below. Diagram: NB 8/2009
Significance
A common assessment of the degree of clustering is the comparison with Complete
Spatial Randomness (CSR) as predicted by a Poisson distribution (Fortin & Dale, 2005).
This can be tested empirically or, as is more often the case, by Monte Carlo simulation based on the density of points λ (Dixon, 2002). This is a useful method in some cases, but is less relevant when the underlying pattern is itself clustered. In the present study the distribution of oak trees is likely to be non-uniform so any clustering of bleeding oaks needs to take this into account. As the positions are known for all oaks, all potential hosts have been identified (assuming oak is the only host). In this situation random labelling can be used to test the clustering of one pattern (symptoms) upon another (host trees).
Monte Carlo simulations of the data randomly label all oaks as healthy or bleeding
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weighted by the proportion of symptomatic trees in the study area. Values of L(t) and Oij (t) are generated for the simulated data with the extreme values used to generate simulation envelopes that are therefore constrained by the observed host positions (Dixon, 2002;
Wiegand & Moloney, 2004). Simulation envelopes generated in this way show the amount of clustering expected when points are randomly distributed across the underlying pattern. When the simulation envelopes are exceeded clustering is due to a process other than the underlying pattern (Wiegand & Moloney, 2004). Simulation envelopes generated in this way cannot be interpreted as confidence intervals for formal hypothesis testing.
Type I error inflation may occur due to simultaneous inference (testing at multiple spatial scales). This is less of an issue with O-ring analysis as at each distance a different set of points is used (Xu et al., 2009).
3.2.4 Statistical Analysis: Clustering
Programita is a software programme designed by Thorsten Wiegand (2004) to analyse point patterns. The software estimates L and O functions using a grid based system that divides the study site into equal sized squares. Circles and rings are approximated using grid squares. The program generates tests statistics numerically by counting the number of type j points within a set number of squares of a given point of pattern i. This value is calculated for each point in the pattern. The total number of available squares is also calculated. The sum of the number of points is divided by the sum of the number of squares to give a value proportional to λKij. λKij is then used to generate L and O using the formulas presented above. For univariate analysis the same pattern is used as both i and j, so that clustering is assessed between points of the same pattern (Wiegand, 2004).
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An issue arises with this form of analysis when points are close to the edge of the study area. Part of a circle or ring may then fall outside the study site, so the exact number of points in its area is unknown. Programita deals with this issue by simply adjusting the area part of the calculation to include only squares inside the study site. This adjustment allows site boundaries to be accurately included in the analysis no matter what shape they form.
Analysis
The clustering of bleeding oaks was assessed using a univariate analysis. For each site
GPS position data in longitude and latitude (WGS86) was transformed to OSGB36 in
ArcGIS 10.0. The resulting ‘easting’ and ‘northing’ data was used to generate raster images where the symptomatic oak and asymptomatic oak were assigned separate categories. Irregular edges were included in the analysis of the sites. This ensured λ was estimated accurately and edge effects were controlled. To achieve edge correction site maps for analysis were generated in ascII file format using ArcGIS 10.0 (ESRI). These showed the extent of the study site and the locations of both symptomatic and asymptomatic trees. For all sites cell size was fixed to 1m.
Using Programita the pattern of symptomatic oak was analysed with random labelling used across the positions of all living oaks used to generate simulation envelopes. Both L and O functions were used for all sites. All analyses used a 1m grid resolution. O-rings
5m in width were used. Univariate analysis used 99 simulations, as this was the maximum available, to give a 1-2% simulation envelope (Xu et al., 2009).
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3.2.5 Aspect
Severity of AOD symptoms was recorded by counting bleeding symptoms below 3m on the trunk; the circumference of the stem was divided in relation to four aspects with counts conducted separately. Observations were repeated in each of three summers.
Statistical analysis was conducted using R 15.2 (R Development Core Team, 2011) and the package lme4. Individual sites were analysed separately as trends were not consistent across all sites. Generalised mixed models included random effects to control for variation between individual trees and year of observation (repeated measures). A Poisson error distribution was assumed as data involved counts. For each analysis the inclusion of the fixed effect aspect was justified following comparison with a null model containing only the random effects. The significance of contrasts is reported using z values, which are approximate due to ongoing uncertainty regarding degrees of freedom.
3.2.6 Canopy condition
Canopy condition was assessed for all oak present in each study site between 2010 and
2012 using the categorical scale outlined below: 1 = Dead; 2 = 80-95% canopy missing;
3a = Stag headed; 3b = Moderate decline (Leaves yellowing, canopy thinning 55%, gaps in canopy) including minor dead wood (100mm diameter in outer crown); 4 = Minor reduction in canopy health (10%-30% missing); 5 = Healthy canopy.
For analysis, categories 3a and 3b were combined as there were a limited number of stag headed trees. Once trees had died they retained a category 1 canopy even if they were subsequently felled. All dead trees retained the symptoms they expressed before death, the single tree in remission that died at Winding Wood was classed as symptomatic. All
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live trees that were felled were excluded from subsequent years of data (cumulative number removed: 2010 = 1, 2011= 5, 2012 = 10). Statistical analysis was conducted using
R 15.2 (R Development Core Team, 2011): The polr function of the package MASS was used to conduct proportional hazard analysis, including factors to show symptom expression and site and a continuous variable for study year.
3.2.7 Mortality
Data for all trees that died during the study period were selected from the database using
ArcGIS 10.0 (ESRI). This selection was split by study site with the four 2009 sites grouped together and the four 2010 sites grouped separately. The initial health and symptoms observed on these stems was summarised, along with any changes that occurred during the observation period.
3.2.8 Remission
Remission data analyses the final, 2012, status of all oak observed with stem bleeds during the study period. Trees are considered to be in remission when they have initially been observed with stem bleeds that are no longer present in following years. Data are grouped by site and shows the number of trees in remission at that time and indicates the length of the remission period. Trees are considered to be relapsed when further symptoms are observed after at least a year of remission.
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3.3 Results
3.3.1 Mapping and data collection
In 2009 an initial four sites were selected for the study of within site symptom development. These sites were all selected from a list of all reported and confirmed AOD sites at the time. The Key attribute for selection was the existence of prior records of the location of diseased trees, this data is not used in the analyses below, which are based solely on recorded survey data. The four sites covered a range of woodland types, management and tree densities (summarised in Table 2).
Hatchlands
Hatchlands is a Repton inspired National Trust parkland. An 18th century house sits within a 170 ha park. The landscape is formed from rolling hills mostly covered with open wood pasture which is still grazed. Most of the trees in the parkland are oak Q. robur with occasional Q. petraea and specimen plantings. Many of the mature specimen oak have diameters exceeding 1 m. In addition to the open grown trees there are a number of small woodlands. The woodlands are dominated by mature oak and young hornbeam
(Carpinus betulus) and are not included in the survey area. The soils are mostly clay with some chalk to the south.
Affected trees are scattered throughout the pasture and within the small woods. A concentration of bleeds can be found in a low lying shelterbelt to the north of an area of raised ground. This area flooded in 2001, before the bleeding symptom was noticed. Dead oak have been left standing. Many of the oak show signs of chronic decline and a number had already died. Canopies are dying back and there are obvious signs of root damage and
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decay. A Detailed 2006 survey, by Tree Works Environmental Practice, made the site ideal for inclusion in this study.
Langdale Wood
The site forms an overflow car park for a show ground. The site itself is similar to a very open high forest, the trees have been thinned to almost regular 10 to 20 m spacing and all understory has been cleared. For most of the year the site is isolated visited by only a few dog walkers. This changes drastically when an event is on at the showground and cars are parked beneath the trees. Most of the trees within the site are mature oak, Q. robur. The edges of the site are more densely populated with oak and ash (Fraxinus excelsior).
Additional areas of woodland lay to the south and the east.
The site is almost level with only a gradual easterly slope. The soil is from a parent material of marl stone. The site manager reports water logging in the winter, which was also present in summer 2012. Twenty six Symptomatic trees were felled in 2007, since the monitoring plot was established only dead trees have been removed.
Sandpit Wood
This small wood lays in an urban setting. The wood is adjacent to a school with many children travelling through the wood on their school routes. The site is crossed by many small worn footpaths. The wood is on a gradual south east slope within a concave dip, formed by sand quarrying. The ground is very uneven. Areas of the site have become overgrown. Oak, Q. robur, dominates the canopy and holly (Ilex aquifolium) fills understory especially to the west of the site. Where the holly is not present brambles thrive. When bleeding symptoms were first noticed in 2008 (early in the autumn) the
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most obviously affected trees were mapped. Symptomatic trees were mostly within the open areas at the centre site. During the study period this site has undergone intense management, for health and safety and aesthetic improvements. Some understory in the east of the site was cleared and the levels were smoothed including work in the rooting zones of study oak. Trees showing die back had extensive reductions to their remaining crown with some left as little more than trunks and lower branches. Dead trees were felled along with two oak diagnosed as hazards (one before monitoring in 2011 and a further one before 2012).
Winding Wood
Winding Wood is a small wood that is isolated in arable fields. The site has been managed for timber production. The trees have been periodically thinned and are of a reasonably even age. The majority of the overstorey is oak, Q. robur, although field maple (Acer campestre), ash and wild cherry (Prunus avium) are also present, especially at the edges of the site. Areas within the site have dense understory. This is predominantly hazel (Corylus avellana) although many young field maple, crab apple
(Malus sylvestris) and elm (Ulmus sp.) are also present. The soils are clays and there is no sign of waterlogging. The site has recently been managed to fulfil an additional amenity role. In 2006 areas of understory were cleared and a number of paths were introduced.
The site is adjacent to a public footpath and is visited infrequently by dog walkers. The management of this site was mostly none intervention, dead trees were left standing and the brambles were gradually overgrowing paths. Before the final summers monitoring four oak trees were removed for timber production, these were all live and displayed a variety of symptoms. They were selected for timber and silvicultural reasons.
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To the south of the wood approximately 500 m away is Mill Wood. Bleeding patches were first noticed in Mill Wood in 1997, they are now widespread throughout the site.
Bleeding symptoms were not noticed in Winding Wood until after the 2006 clearance. In the autumn of 2007 symptomatic trees were marked with an orange dot. These marks are concentrated in the south west corner of the site.
2010 study sites
Research funding was secured for continued monitoring of the original study sites under the condition that the number of sites was increased. Four additional sites were selected in
2010 again from the list of all recorded sites records. These sites were selected to document a wider range of conditions than were present at the first four sites. A focus was for sites in early stages of decline where a limited number of trees were symptomatic, although one site was selected where the disease was long established and had been part of a Forestry Commission monitoring project in the 1990’s.
Beecham Spinney
This small copse is positioned on top of a small hill and forms part of a wider estate small woodlands are managed and adjacent fields are mixed agriculture. On the north and easterly slopes oak, Q. robur, were mature, and dominated the overstory whereas on the southerly slope trees were younger post war plantings of oak and ash with areas of turkey oak, Q. cerris. A track runs east to west across the spinney following the brow of the hill.
The study area consists of approximately half the spinney it is located to the west of a road (on the northerly slope) and wire fencing (on the southerly slope) and contains all
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symptomatic trees. AOD symptoms were mostly found on the mature oak on the northerly slope, although there were also a few trees at the bottom of the southerly slope.
Prior to the commencement of the study some of the mature symptomatic trees were felled for timber and parts of the southerly slope were thinned and extraction routes introduced. These open areas were used to rear pheasants for a shoot on the estate. No further forest operations were conducted during the monitoring period.
Great Monks Wood
Forming part of a 47 ha site this large woodland has predominantly oak, Q. robur, overstory and small leaved lime coppice understory. Across the wood coppicing finished over 30 years ago and the lime is now grown up to the crowns of the oak. Recent management has begun to re-coppice and open up areas of the wood. The study site consists of one block coppiced in the winter of 2008-2009 and 2009-2010. A final set of forest operations were conducted in the winter of 2010-2011 when dead trees and four heavily declined oak trees were removed. Symptomatic trees are present through out the wood and were noticed in the study site after it was opened. This was the only one of the eight sites where no trees had exit holes at the beginning of the study, although they were present in the other areas of the wood. The wood is prone to water logging and the study site was especially wet in the summer of 2012.
Rookery Wood
This site falls within a 190 ha parkland associated with a country house and high visitor numbers. The study site is a small amenity plantation between the house and stable block.
The overstory is predominantly mature oak, Q. robur; understory is patchy and mostly consists of more recent plantings of oak and beech. The site falls on a gradual south- facing slope. Paths run from the main visitor entrance in the stable block through to the
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house and park. At the beginning of 2011 all access through the main area of symptomatic trees was restricted with the area fenced off. The only management to take place in this area involved the felling of two dead trees before the 2012 monitoring period.
Sheen Wood
The wider site consists of 847 ha of deer park containing a number of boundary belts and woodlands. The study site is wood on the boundary of the park adjacent to car parking area. It has very high visitor numbers and is crossed by many footpaths. The overstory is predominantly oak, Q. robur, and below the ground is level, grassed and free from understory. Scattered young oak have been planted where older trees have died and been felled. Oak decline has been recorded at Sheen Wood for a long time and a Forestry
Commission monitoring plot was established here in 1990. The site was monitored until
1994. Photographs from that period clearly show stem bleeding thought to be caused by
Agrilus biguttatus larval damage. This site differs from other monitoring sites as oak processionary moth has recently arrived adding to the list of oak decline factors. An active program of nest removal has so far limited the extent of damage caused by caterpillar feeding. Due to the high visitor numbers the trees are managed for health and safety with dead wood removed and dead trees reduced.
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Table 2: Overview of site characteristics. Summary of site information collected during site visits and discussions with land owners and agents. Areas were calculated from polygons of site extent using ArcGIS 10.0. Densities were calculated using these figures. Isolations of bacterial species from symptomatic tissue were carried out at Alice Holt by Susan Kirk, Jonathan Partridge, Sarah Plumer and Gavin Hunter; basic summaries of species present are included with their permission.
Bacteria isolated
(cm)
present
Present 3m SD)
.
- by owner (all stems / ha) Year established study site Site Bleeds Agrilus Historic Data First Noticed Site description Mean oak diameter at 1 (+/ Site Area (ha) Number study oak * of live Density (stems / ha) of oak Density of tree cover Percentage understory Gibbsiella Brenneria Lonsdalea
2009 Hatchlands Yes Yes Survey After Parkland, with 84.63 Y N N 22.82 140 XX6.14 XX8.02 x0.xx (2006) 2001 shelterbelts (+/-26.59) Langdale Yes Yes Stumps Before Open high forest, 66.00 Y N Y Wood remain 2006 mowed grass below (+/- 10.58) X8.40 260 X30.95 X31.79 x0.xx (2007) Sandpit Yes Yes Severe Lots of Oak dominated urban 56.68 Y Y N Wood mapped trees by woodland (+/- 17.84) X1.42 162 114.39 132.75 13.19 (2008) 2008
Winding Yes Yes Marked trees 2006 Oak dominated 56.21 44.05 Y Y N wood (2007) woodland managed for (+/- 13.54) X2.31 201 X87.12 141.74 timber 2010 Beecham Yes Yes No 2009 Oak dominated 41.19 - - - Spinney woodland, areas of (+/- 18.13) X2.20 186 X84.48 239.37 33.12 Ash and Turkey oak. 2009 thinning. Great Monks Yes Not in No 2009 Oak dominated 60.91 Y Y Y Wood study woodland managed for (+/- 16.08) X3.31 145 X43.85 X58.98 x0.xx area timber. Lapsed lime coppice. Rookery Yes Yes Mapped 2007 Oak dominated 55.91 Y N N Wood (2009) amenity woodland, (+/- 38.33) X1.46 115 X78.50 133.11 14.79 parkland plantation Sheen Wood Yes Yes 1990’s Oak 1997 (?) Oak dominated urban 76.96 - - - Monitoring woodland, boundary (+/- 19.26) X3.97 152 X38.32 X40.84 x0.xx Plot belt of park
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Figure 7: Maps of symptom development at the Hatchlands study site. Windows show conditions in each of the four monitoring years and are presented successively from the earliest (2009) on the left through to the most recent (2012) on the right. The locations of all study oak are show in one of four categories: Green circles = no stem bleeds; Red triangles = stem bleeds; Blue squares = in remission (previously observed symptoms are absent); black stars = dead oak. The locations of any dead oak present at the start of the study are also included. In addition the locations of oak with Agrilus exit holes are superimposed as open purple circles with a cross. The limits of the study area are denoted by a black line and shaded areas indicate the presence of unmapped woodland blocks. Maps were generated using ArcGIS 10.0 (ESRI). Nathan Brown 58
2009 2010 2011 2012
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0 50 100 200 Meters
Figure 8: Maps of symptom development at the Langdale Wood study site. Windows show conditions in each of the four monitoring years and are presented successively from the earliest (2009) on the left through to the most recent (2012) on the right. The locations of all study oak are show in one of four categories: Green circles = no stem bleeds; Red triangles = stem bleeds; Blue squares = in remission (previously observed symptoms are absent); black stars = dead oak. The locations of any dead oak present at the start of the study are also included. In addition the locations of oak with Agrilus exit holes are superimposed as open purple circles with a cross. The limits of the study area are denoted by a black line. Maps were generated using ArcGIS 10.0 (ESRI).
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Figure 9: Maps of 2009 symptom development at the Sandpit Wood study site. Windows show conditions in each of the four monitoring years and are presented successively from the earliest (2009) at the top through to the most recent (2012) at the 2010 bottom. The locations of all study oak are show in one of four categories: Dark green circles = no stem bleeds; Red triangles = stem bleeds; Blue squares = in remission (previously observed symptoms are absent); black stars = dead oak. The locations of any 2011 dead oak present at the start of the study are also included. In addition the locations of oak with Agrilus exit holes are superimposed as open purple circles with a cross. The limits of the study area are denoted by a black line. Areas of understory are 2012 shaded light green and footpaths are shown with brown lines. Maps were generated using ArcGIS 10.0 (ESRI).
0 50 100 200 Meters
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Figure 10: Maps of symptom development at the Winding Wood study site. Windows show conditions in each of the four monitoring years and are presented successively from the earliest (2009) at the top through to the most recent (2012) at the bottom. The locations of all study oak are show in one of four categories: Dark green circles = no stem bleeds; Red triangles = stem bleeds; Blue squares = in remission (previously observed symptoms are absent); black stars = dead oak. The locations of any dead oak present at the start of the study are also included. In addition the locations of oak with Agrilus exit holes are superimposed as open purple circles with a cross. The limits of the study area are denoted by a black line. Areas of understory are shaded light green and footpaths are shown with brown lines. Two small ponds are shown as shaded blue areas. Maps were generated using ArcGIS 10.0 (ESRI).
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Figure 11: Maps of symptom development at the Beecham Spinney study site. Windows show conditions in each of the three monitoring years and are presented successively from the earliest (2010) on the left through to the most recent (2012) on the right. The locations of all study oak are show in one of four categories: Dark green circles = no stem bleeds; Red triangles = stem bleeds; Blue squares = in remission (previously observed symptoms are absent); black stars = dead oak. The locations of any dead oak present at the start of the study are also included, along with the locations of turkey oak (orange circles). In addition the locations of oak with Agrilus exit holes are superimposed as open purple circles with a cross. The limits of the study area are denoted by a black line. Areas of understory are shaded light green and footpaths are shown with brown lines. Maps were generated using ArcGIS 10.0 (ESRI). Nathan Brown 62
Figure 12: Maps of symptom development at the Great Monks Wood study site. Windows show conditions in each of the three monitoring years and are presented successively from the earliest (2010) on the left through to the most recent (2012) on the right. The locations of all study oak are show in one of four categories: Dark green circles = no stem bleeds; Red triangles = stem bleeds; Blue squares = in remission (previously observed symptoms are absent); black stars = dead oak. The locations of any dead oak present at the start of the study are also included. In addition the locations of oak with Agrilus exit holes are superimposed as open purple circles with a cross. The limits of the study area are denoted by a black line. Areas of understory are shaded light green and footpaths are shown with brown lines. Maps were generated using ArcGIS 10.0 (ESRI).
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Figure 13: Maps of symptom development at the Rookery Wood study site. Windows show conditions in each of the three monitoring years and are presented successively from the earliest (2010) on the left through to the most recent (2012) on the right. The locations of all study oak are show in one of four categories: Dark green circles = no stem bleeds; Red triangles = stem bleeds; Blue squares = in remission (previously observed symptoms are absent); black stars = dead oak. The locations of any dead oak present at the start of the study are also included. In addition the locations of oak with Agrilus exit holes are superimposed as open purple circles with a cross. The limits of the study area are denoted by a black line. Areas of understory are shaded light green and footpaths are shown with brown lines. Maps were generated using ArcGIS 10.0 (ESRI). Nathan Brown 64
Figure 14: Maps of symptom development at the Sheen Wood study site. Windows show conditions in each of the three monitoring years and are presented successively from the earliest (2010) at the top through to the most recent (2012) on the right. The locations of all study oak are show in one of four categories: Green circles = no stem bleeds; Red triangles = stem bleeds; Blue squares = in remission (previously observed symptoms are absent); black 2010 stars = dead oak. The locations of any dead oak present at the start of the study are also included. In addition the locations of oak with Agrilus exit holes are superimposed as open purple circles with a cross. The limits of the study area are denoted by a black line and footpaths are shown with brown lines. Maps were generated using ArcGIS 10.0 (ESRI).
2011
2012
0 50 100 200 Meters
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3.3.2 Disease development
Hatchlands
The distribution of symptomatic trees at Hatchlands appears scattered across the open parkland trees but symptoms are concentrated where oak are found at higher densities in the shelterbelts, this pattern remains constant across the study period (Figure 7). The number of trees with stems bleeds dropped after the first year but then the number of symptomatic trees stabilises, so there is little development over time. However the number of trees with Agrilus exit holes continues to increase (Figure 16). Analysis of the initial pattern of trees with bleeds backs up the observation that they are clustered together (Figure 15). The L function far exceeds the simulation envelopes and the O ring plot suggests clustering is at distances less than 20 m, so clustered within the shelterbelts.
The values of this analysis are very large as the underlying pattern is also clustered, at the location of symptomatic trees, so comparisons of local density far exceed the overall density of trees on site.
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A: Hatchlands B: Langdale wood
50 100 6 10
80 5 60 5 0 40 L11 L11 40 20 4 -5 30 0 -10
0 10 20 30 40 50 3 0 10 20 30 40 50 g11 g11 20 Scale (m) Scale (m) 2
10 1
0 0
0 10 20 30 40 50 0 10 20 30 40 50
Scale (m) Scale (m)
C: Sandpit wood D: Winding Wood
4 8 4 10 6 5 4
L11 2 L11 3 3 0 0
-2 -5
2 0 10 20 30 40 50 2 0 10 20 30 40 50 g11 Scale (m) g11 Scale (m)
1 1
0 0
0 10 20 30 40 50 0 10 20 30 40 50
Scale (m) Scale (m)
Figure 15: Assessment of clustering in the first year distribution of oak with stem bleeds at the four 2009 study sites. For each plot the main axis shows outputs of 5m width O-ring analysis. Insets graphs show the outputs of L-function analysis of the same pattern. Test statistics are shown with filled red circles and red lines and simulations envelopes are shown with open blue circles. Simulation envelopes were generated using 99 simulations and random labelling across the locations of all oak. Significant clustering can be detected when the red line falls above the blue dots generated from simulations.
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35 30
30 25
25 20
20 15
15 Count of study oak study of Count 10 10 Number of oak with Agrilus exit holesAgriluswithoakexit of Number
5 5
0 0
2009 2010 2011 2012 2009 2010 2011 2012
YEAR YEAR
Figure 16: Frequency of occurrence of stem symptoms across years at Hatchlands. Total counts of stem bleeds (red triangles) and trees in remission (Blue squares) are shown on the left window. Total counts of oak with Agrilus exit holes (open purple circles with a cross) are shown in the right window. Trees that died or were felled during the monitoring period were included in exit hole counts. Total study oak on site = 140.
Langdale Wood
At Langdale wood symptomatic trees were found in two areas, one at the north of the site and one at the south. At the start of the study Agrilus exit holes were only present on one recently dead tree in the southern area but were more common in the north (Figure 8).
The number of infected trees has increased after an initial drop in 2010 (Figure 17), with the majority of newly symptomatic trees at the northern end of the site. The number of trees with Agrilus exit holes also shows a large increase across the study period. Analysis of the initial distribution of bleeds, recorded after the removal of 26 affected trees, shows
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marginally significant clustering of affected trees (Figure 15). The L function was close to the upper simulation envelope exceeding it intermittently between 7 m and 22 m. The O function shows a similar trend only exceeding the simulation envelopes at 5 m, 8 m and
41 m.
70 25
60 20
50
15 40
30 10 Count of study oak study of Count
20 Number of oak with Agrilus exit holesAgriluswithoakexit of Number 5 10
0 0
2009 2010 2011 2012 2009 2010 2011 2012
YEAR YEAR
Figure 17: Frequency of occurrence of stem symptoms across years at Langdale Wood. Total counts of stem bleeds (red triangles) and trees in remission (Blue squares) are shown on the left window. Total counts of oak with Agrilus exit holes (open purple circles with a cross) are shown in the right window. Trees that died or were felled during the monitoring period were included in exit hole counts. Total study oak on site = 260.
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Sandpit Wood
60 30
50 25
40 20
30 15 Count of study oak study of Count 20 10 Number of oak with Agrilus exit holesAgriluswithoakexit of Number
10 5
0 0
2009 2010 2011 2012 2009 2010 2011 2012
YEAR YEAR
Figure 18: Frequency of occurrence of stem symptoms across years at Sandpit Wood. Total counts of stem bleeds (red triangles) and trees in remission (Blue squares) are shown on the left window. Total counts of oak with Agrilus exit holes (open purple circles with a cross) are shown in the right window. Trees that died or were felled during the monitoring period were included in exit hole counts. Total study oak on site = 162.
The symptomatic trees at Sandpit wood were located within the centre of the site and there has been little evidence of spread to new hosts (Figure 9). The predominant trend at this site is of increasing numbers of trees in remission (Figure 18). Interestingly this has matched with an increase in the number of trees with exit holes. The initial distribution of symptomatic trees shows a strongly clustered pattern (Figure 15). The L function shows strong clustering for all distances above 4 m, with the O ring analysis suggesting that clustering is most prominent between 2 m and 21m.
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Winding Wood
At Winding wood the symptomatic trees were initially located to the west of the site
(Figure 10). During the monitoring period newly affected trees were observed in the west of the site. Despite this the overall totals show a drop in the number of affected trees with trees entering remission at a steady rate throughout the monitoring period (Figure 19).
The trees in remission are mostly located in the western edge of the site. Analysis of the initial distribution of symptomatic trees reveals a clustered pattern (Figure 15). The L function only shows significant clustering at distances greater than 25 m and the O ring analysis identifies peaks of clustering at 23 m to 25 m, 31 to 34m and 43 to 44 m.
60 25
50 20
40 15
30
10 Count of study oak study of Count 20 Number of oak with Agrilus exit holesAgriluswithoakexit of Number 5 10
0 0
2009 2010 2011 2012 2009 2010 2011 2012
YEAR YEAR
Figure 19: Frequency of occurrence of stem symptoms across years at Winding Wood. Total counts of stem bleeds (red triangles) and trees in remission (Blue squares) are shown on the left window. Total counts of oak with Agrilus exit holes (open purple circles with a cross) are shown in the right window. Trees that died or were felled during the monitoring period were included in exit hole counts. Total study oak on site = 201.
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A: Beecham spinney B: Great Monks wood
8 25 8 20 20 15 10 10
L11 5 L11 6 6 0 0 -5 -10 -10 4 0 10 20 30 40 50 4 0 10 20 30 40 50 g11 Scale (m) g11 Scale (m)
2 2
0 0
0 10 20 30 40 50 0 10 20 30 40 50
Scale (m) Scale (m)
C: Rookery wood D: Sheen Wood
8 30 4 6 25 4 20 2 15 6 L11 10 3 L11 0 5 -2 0 -4 -5 4 0 10 20 30 40 50 2 0 10 20 30 40 50 g11 Scale (m) g11 Scale (m)
2 1
0 0
0 10 20 30 40 50 0 10 20 30 40 50
Scale (m) Scale (m)
Figure 20: Assessment of clustering in the first year distribution of oak with stem bleeds at the four 2010 study sites. For each plot the main axis shows outputs of 5m width O-ring analysis. Insets graphs show the outputs of L-function analysis of the same pattern. Test statistics are shown with filled red circles and red lines and simulations envelopes are shown with open blue circles. Simulation envelopes were generated using 99 simulations and random labelling across the locations of all oak. Significant clustering can be detected when the red line falls above the blue dots generated from simulations.
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Beecham Spinney
25 5
20 4
15 3
10 2 Count of study oak study of Count Number of oak with Agrilus exit holesAgriluswithoakexit of Number 5 1
0 0
2010 2011 2012 2010 2011 2012
YEAR YEAR
Figure 21: Frequency of occurrence of stem symptoms across years at Beecham Spinney. Total counts of stem bleeds (red triangles) and trees in remission (Blue squares) are shown on the left window. Total counts of oak with Agrilus exit holes (open purple circles with a cross) are shown in the right window. Trees that died or were felled during the monitoring period were included in exit hole counts. Total study oak on site = 186.
At Beecham spinney the symptomatic trees occur in two distinct areas, most are found on the northerly slope on mature oak with a smaller group found low on the southerly slope
(Figure 11). During the monitoring period there has been little change to the overall pattern, although by 2012 a small number of oak were in remission (Figure 21). Three additional trees showed Agrilus exit holes by the end of the monitoring period, although the final total of four was the lowest found across all monitoring sites. Across most distances symptomatic trees were randomly distributed with marginally significant
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clusters detected at 18 to 20 m using the L function and 16 to 18 m using the O ring analysis (Figure 20).
Great Monks Wood
Symptomatic trees were initially only observed in the north and the east regions of the study plot at Great Monks Wood. During the monitoring period newly symptomatic trees were observed further to the southwest of the site (Figure 12). This pattern resulted in increasing numbers of symptomatic trees, while very few trees showed signs of remission
(Figure 22). During the initial 2010 survey no Agrilus exit holes were observed, although this changed in 2011 with increasing numbers in 2012. Analysis of the initial pattern of symptomatic trees showed significant clustering at distances greater than 19m using the L function, with the O ring analysis indicating that clustering was greatest at distances of
19-28m (Figure 20).
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30 10
25 8
20 6
15
4 Count of study oak study of Count 10 Number of oak with Agrilus exit holesAgriluswithoakexit of Number 2 5
0 0
2010 2011 2012 2010 2011 2012
YEAR YEAR
Figure 22: Frequency of occurrence of stem symptoms across years at Great Monks Wood. Total counts of stem bleeds (red triangles) and trees in remission (Blue squares) are shown on the left window. Total counts of oak with Agrilus exit holes (open purple circles with a cross) are shown in the right window. Trees that died or were felled during the monitoring period were included in exit hole counts. Total study oak on site = 145.
Rookery Wood
The symptomatic oak in Rookery wood are located at the southern tip of the wood, this pattern varied little across the monitoring period although newly affected trees were observed further north in later monitoring (Figure 13). This pattern is reflected in a slight increase in the total number of symptomatic trees (Figure 23). Remission was only observed in 2012 when three trees stopped displaying stem bleeds. The number of trees with Agrilus exit holes increased slightly across the monitoring periods. The L function
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detected clustering at all scales greater than 11m with the O ring function indicating the pattern was most clustered at 10 to 18 m, 25 to 29 m and marginally at 35 to 36 m (Figure
20).
25 15
20
10 15
10 Count of study oak study of Count 5 Number of oak with Agrilus exit holesAgriluswithoakexit of Number 5
0 0
2010 2011 2012 2010 2011 2012
YEAR YEAR
Figure 23: Frequency of occurrence of stem symptoms across years at Rookery Wood. Total counts of stem bleeds (red triangles) and trees in remission (Blue squares) are shown on the left window. Total counts of oak with Agrilus exit holes (open purple circles with a cross) are shown in the right window. Trees that died or were felled during the monitoring period were included in exit hole counts. Total study oak on site = 115.
Sheen Wood
Symptomatic trees were found throughout Sheen Wood in all monitoring periods (Figure
14). There was little change to the pattern although the total number of symptomatic trees fell (Figure 24). There were more trees with Agrilus exit holes at this site than at any
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other monitoring plot with the total number increasing slightly. L function analysis showed intermittent and marginally significant clustering at distances greater than 17 m and O ring analysis only exceeded simulation envelopes at 18 m (Figure 20).
80 50
40 60
30
40
20 Count of study oak study of Count
20 Number of oak with Agrilus exit holesAgriluswithoakexit of Number 10
0 0
2010 2011 2012 2010 2011 2012
YEAR YEAR
Figure 24: Frequency of occurrence of stem symptoms across years at Sheen Wood. Total counts of stem bleeds (red triangles) and trees in remission (Blue squares) are shown on the left window. Total counts of oak with Agrilus exit holes (open purple circles with a cross) are shown in the right window. Trees that died or were felled during the monitoring period were included in exit hole counts. Total study oak on site = 152.
3.3.3 Aspect
The frequency of occurrence of bleeding symptoms varied between aspects and although oak at the majority of sites exhibited more symptoms on the southern aspect this pattern was not consistent varying between sites, so results and analysis are presented separately for each site.
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For the Hatchlands data the addition of aspect as a factor significantly improved the model (χ2 = 53.654, d.f. = 3, p < 0.001). The observed counts (Figure 25) were significantly greater in the east (z = 3.931, p < 0.001) and lower in the west (z = 2.971, p
= 0.002) when compared to the counts on the northern aspect.
For the Langdale Wood data the addition of aspect as a factor significantly improved the model (χ2 = 211.77, d.f. = 3, p < 0.001). The observed counts (Figure 25) were significantly greater in the east (z = 4.641, p < 0.001) the south (z = 12.692, p <0.001) and west (z = 4.303, p < 0.001) when compared to the counts on the northern aspect.
For the Sandpit Wood data the addition of aspect as a factor significantly improved the model (χ2 = 29.791, d.f. = 3, p < 0.001). The observed counts (Figure 25) were significantly greater in the east (z = 3.013, p = 0.003) and the south (z = 4.655, p <0.001) when compared to the counts on the northern aspect.
For the Winding Wood data the addition of aspect as a factor significantly improved the model (χ2 = 12.496, d.f. = 3, p = 0.006). The observed counts (Figure 25) were significantly greater in the east (z = 2.066, p = 0.039) and the south (z = 3.079, p = 0.002) when compared to the counts on the northern aspect.
For the Beecham Spinney data the addition of aspect as a factor significantly improved the model (χ2 = 50.466, d.f. = 3, p < 0.001). The observed counts (Figure 25) were significantly lower in the east (z = 3.8848, p < 0.001) the south (z = 3.608, p <0.001) and west (z = 6.372, p < 0.001) when compared to the counts on the northern aspect.
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For the Great Monks Wood data the addition of aspect as a factor significantly improved the model (χ2 = 81.553, d.f. = 3, p < 0.001). The observed counts (Figure 25) were significantly greater in the east (z = 3.784, p = 0.039) and the south (z = 7.755, p < 0.001) when compared to the counts on the northern aspect.
For the Rookery Wood data the addition of aspect as a factor significantly improved the model (χ2 = 26.547, d.f. = 3, p < 0.001). The observed counts (Figure 25) were significantly lower in the east (z = 3.713, p < 0.001) when compared to the counts on the northern aspect.
For the Sheen Wood data the addition of aspect as a factor significantly improved the model (χ2 = 73.08, d.f. = 3, p < 0.001). The observed counts (Figure 25) were significantly greater in the south (z = 6.529, p < 0.001) when compared to the counts on the northern aspect.
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Hatchlands Langdale 2010 2011 2012 2.5 5 2.0 4 1.5 3 1.0 2 0.5 1 0.0 0 NORTH EAST SOUTH WEST NORTH EAST SOUTH WEST Mean stem bleeds +/- 1 S.E. 1 +/- stem bleeds Mean S.E. 1 +/- stem bleeds Mean
Sandpit Winding
4 2.0 3 1.5 2 1.0 1 0.5 0 0.0 NORTH EAST SOUTH WEST NORTH EAST SOUTH WEST Mean stem bleeds +/- 1 S.E. 1 +/- stem bleeds Mean S.E. 1 +/- stem bleeds Mean
Beecham Great Monks
4 5 3 4 3 2 2 1 1 0 0 NORTH EAST SOUTH WEST NORTH EAST SOUTH WEST Mean stem bleeds +/- 1 S.E. 1 +/- stem bleeds Mean S.E. 1 +/- stem bleeds Mean
Rookery Sheen
6 2.5 5 2.0 4 1.5 3 2 1.0 1 0.5 0 0.0 NORTH EAST SOUTH WEST NORTH EAST SOUTH WEST Mean stem bleeds +/- 1 S.E. 1 +/- stem bleeds Mean S.E. 1 +/- stem bleeds Mean
Figure 25: Incidence of bleeding symptoms occurring on each aspect of study trees, presented by study site. Columns show mean counts for each aspect in each year from 2010 to 2012. Shading is successive with lightest green bars showing 2010 values and darkest green bars showing 2012 values. Error bars show standard error of the mean of individual columns. Each mean is calculated from counts on all symptomatic trees at a given site in a given year. Number of symptomatic trees (2010, 2011, 2012): Hatchlands = 24, 24, 24; Langdale = 37, 42, 66; Sandpit = 47, 43, 32; Winding = 42, 43, 38; Beecham = 17, 19 17 ; Great Monks = 20, 21, 27 ; Rookery = 17, 19, 19 ; Sheen = 77, 69, 61.
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3.3.4 Canopy
The canopy condition of oak in the monitoring sites did not vary significantly between years, but there were differences between sites and an overall trend for less healthy crowns when oak displayed stem bleeds (Figures 26 & 27).
600 500 400 300 200 100
Asymptomatic oak 0 1 2 3 4 5
150 2010 100 2011 2012 50
0 Oak with stem bleeds 1 2 3 4 5 Canopy condition category
Figure 26: Total counts of symptom free oak (top panel) and symptomatic oak (lower panel) in each canopy condition category and year. Data is combined for all sites. Columns show total counts for each crown category in each year from 2010 to 2012. Shading is successive with lightest green bars showing 2010 values and darkest green bars showing 2012 values. Category 5 is defined by 100% healthy crowns with health deteriorating successively across categories, with category 1 oak comprising dead trees. Full category definitions are presented in the methods section. Trees that died during the monitoring period are retained as category 1 even if they are subsequently felled. Any live trees that were felled were removed from analysis. Number of symptomatic study oak 2010 = 283, 2011 = 292 and 2012 = 301 and symptom free oak 2010 = 1077, 2011 = 1064 and 2012 = 1050.
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Canopy condition key 5 4 3 2 1
AOD : Bleeds AOD : None
0.8
0.6
0.4
0.2 Prob(CanopyCondition)
B GM H L R SN SP W B GM H L R SN SP W Site
Figure 27: Proportions of oak in each condition category at each site. Left hand panel shows symptomatic trees and the right hand panel shows symptom free trees. Proportional hazard analysis shows that canopy conditions do vary by site (χ2 = 220.40, d.f. = 7, p < 0.001) but there is also a difference between symptomatic and symptom free populations (χ2 = 455.19, d.f. = 1, p < 0.001). Variation between years was not significant (χ2 = 2.24, d.f.=1, p = 0.134).
3.3.5 Mortality
For both sets of study sites the rate of mortality was greatest when both bleeding symptoms and exit holes had been observed in the first year’s survey. Trees with only bleeds died at a higher rate than symptom free trees. No trees that developed stem bleeds during the monitoring period went on to die, so initial symptom development to death is greater than 2 years. Exit holes did appear on trees that died during the monitoring period, linking there occurrence to the late stage of decline.
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The number of tree deaths varied between study sites. Sandpit wood had the greatest
number with deaths (n = 9) almost evenly distributed between symptomatic (n=4) and
asymptomatic oak (n = 5). This ratio differs from the overall total with 55.56% of all
asymptomatic tree deaths occurring at this one site.
Table 3: Symptoms and signs observed on oak that died during monitoring. Data is presented as totals for each year that monitoring sites were established (4 in 2009, 4 in 2010). Aside from the total count of trees in each category (used to calculate percentages that died) all other information relates solely to the trees that died. Initial canopy condition describes the canopy status in the first year trees were monitored. For symptoms and signs that developed during the study counts of the number of affected trees are shown. Initial canopy condition Bleeds Exit holes Year Number began appeared study Initial stem Total trees of trees % during during began symptoms in category that died died 2 3 4 5 study study Bleeds and 2009 exit holes X17 4 23.53 4 - -
Bleeds Only 171 6 X3.51 6 - 2
X0.X Exit holes only XX6 0 X 0 -
No symptoms 569 7 X1.23 6 1 0 1
Bleeds and 2010 exit holes X30 4 13.33 5 - -
Bleeds Only 101 3 X2.97 2 - 2
Exit holes only X10 0 X0.X 0 -
No symptoms 457 2 X0.44 1 1 0 2
3.3.6 Remission
All study sites contained trees that stopped expressing bleeding symptoms during the
monitoring period. This apparent remission occurred at a higher rate on sites monitored
for four years rather than three (Table 4). In general the relapse rate was low with few
trees subsequently showing stem bleeds. The rate of relapse was greatest at Langdale
Wood and Great Monks Wood, sites where the overall trend was for increasing numbers
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of symptomatic trees. In one instance an oak died the year after symptoms stopped so this
pattern of symptoms may not always indicate healing.
Table 4: Summary of remission rates at each of the study sites. Remission status shows the 2012 observation for all trees that have entered remission (total remission) and is divided into the length of remission period ( 3, 2, or 1 year), the number that have relapsed and show symptoms again in 2012 and the number that have died. Percentage remission is calculated individually by site by dividing the total remission by the number of trees that have shown symptoms. The percentage relapse is calculated by dividing number of 2012 relapsed oak by the total remission for each study site.
Remission Status
number of
number of
Site Total oak that have shown stem bleeds 3 years 2 years 1 year Relapse Died Total oak havethat shown remission % remission % relapse
Hatchlands 43 11 3 3 2 0 19 44.19 10.53 Langdale 88 9 3 9 9 0 30 34.09 30.00 Send 63 10 6 11 0 0 27 42.86 00.00 Winding 72 12 6 12 1 1 32 44.44 03.13
Beecham 21 - 1 4 0 0 5 23.81 00.00 Great Monks 32 - 1 2 2 0 5 15.63 40.00 Rookery 24 - 0 3 0 0 3 12.50 00.00 Sheen 87 - 9 13 4 0 26 29.89 15.38
3.4 Discussion
The overwhelming trend across all monitoring sites is that each has is own set of
considerations and patterns should be considered with the local environment in mind.
Broadly speaking the sites can be grouped into three categories depending on the stage of
progression of the decline cycle. At four sites symptoms incidence is showing little
change. The overall totals of symptomatic trees are remaining constant indicating that the
epidemic curve has reached a late stage plateau, with remaining trees either recovering or
not suitable for disease development. At three sites the symptoms did appear to be in an
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earlier stage where incidence was increasing: Langdale Wood had an exponential year on year increase, Great Monks showed early signs of this pattern, and Winding Wood showed both spread to new areas and remission in the initial infections. One site Beecham
Spinney showed a different pattern. There was an established outbreak area, in late stage plateau, but also a new outbreak area showing little change. This was still in the early stages of development and symptoms were at a point where establishment was not guaranteed.
At most of the study sites symptoms occurred in localised clusters, with neighbouring trees also likely to be expressing symptoms. The only exception to this rule was Beecham
Spinney where affected trees were randomly distributed in a pattern influenced by both the felling of infected trees (before the monitoring began) and the early stage of the new break out. Across all other sites affected trees were locally aggregated although the scale and extent of clustering varied between sites. At Langdale Wood there was also felling of affected trees before the monitoring period and in line with this the pattern here is only weakly clustered. The trend is also weak at Sheen Wood where the numbers of affected trees was greatest. With high numbers of affected trees the simulations predict a high occurrence of symptomatic trees in close proximity by chance alone, although the observed pattern still exceeds the simulation envelopes. At Winding Wood clustering is only detected when larger distances are considered. This could be due to the presence of understory and other tree species disrupting very local spread, although a similar pattern is also seen at Great Monks Wood which has been opened up to limit the effect of these factors on future spread. At both Hatchlands and Sandpit Wood clustering is detected from the shortest distances, but across the other study sites patterns are most clustered at distances of 10 to 25 m indicating that susceptible trees maybe more widely spaced and
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dispersal is not simply a proximity based movement as would be typical will a disease spread by rain splash or ground water. Spread of symptoms over larger distances may suggest other forms of movement, such as vectoring, although if infection and symptom expression is affected by the local environment around hosts or variations in host susceptibility this may also explain an intermittent pattern.
There is an overall tendency for symptoms to occur most frequently on the warmer southerly aspect (data not shown), although there is a wide degree of variation between sites with three of the eight sites showing different trends. At Hatchlands and Beecham
Spinney the majority of affected trees occur on the northerly aspect of a slope. This is likely to restrict the amount of sunlight reaching the generally warmer southern aspect. At
Rookery wood the only trend was for lesser symptoms in the east, although this was based on a small sample of affected trees that was skewed by two heavily infected trees in
2010. The occurrence of symptoms on southerly aspects was greatest at more open sites
(Langdale, Great Monks and Sheen) where sunlight is more likely to reach the tree trunks and any warming affect is likely to be most pronounced. Sites with denser tree planting and understory showed weaker trends (Winding Wood, Sandpit Wood and Rookery
Wood). This pattern would fit well with the above clustering results which suggest affected trees may not be in the densest areas of the site.
The open nature of clusters of affected trees is likely to be increased by their reduced canopy condition. Although symptomatic trees do occur in all canopy condition categories they are generally in a worse condition than asymptomatic trees. In this way the specific AOD symptoms of stem bleeding correlate with the generic decline symptoms expressed through poor canopy growth and die back. This pattern can also be
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seen in the records of mortality where symptomatic trees died at a higher rate than asymptomatic trees (especially when considering the rates outside Sandpit Wood). With additional years of data it would be useful to formally test this difference in a survival analysis. The highest rates of mortality were observed on trees with both bleeding symptoms and exit holes indicating that the beetle can complete its life cycle most successfully on trees in the final stages of decline. This fact contrasts with observations of galleries, which are found in conjunction with bleeding symptoms consistently across most sampled trees (Denman et al., 2013). Should Agrilus play a role in the development and transmission of AOD, the distinction between hosts that are attacked and hosts where adults emerge to attack further hosts will be a key factor. The monitoring data shows that exit holes may only appear on host trees in the final years before death, whereas bleeding symptoms appear earlier. From the observed time window it is possible to infer that symptom development to death takes longer than two years; no healthy tree in year 1 developed symptoms in year 2 and died in years 3 or 4. As such the current study has not documented the full progression of symptoms; more years of data are required to complete the cycle.
The cycle of AOD development on an individual host is likely to be further complicated by the fact that trees can enter remission and progression may not simply be a linear progression from initial symptoms to death. The monitoring data suggest that rates of affected trees entering remission can be high, up to 44% over four years, and rate of relapse are generally low but monitoring over a longer period is required to assess this process. When affected trees have been felled and examined there is occasionally evidence of successive outbreaks with callused over galleries and cavities found deeper in the wood and more active symptoms above them in the inner bark layer. Relapse rates are
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greatest at sites with increasing numbers of symptomatic trees, raising the possibility that they are re-infected rather than relapsing, although individual bleeding points have been observed to become active after periods of apparent healing. Overcoming an episode of
AOD is likely to be costly in terms of the host’s energy reserves, as both defensive compounds and callus represent an investment of resources. With successive outbreaks of
AOD the effect on host health is likely to be cumulative with oak trees less able to respond and repel later incursions. This model would fit well in the context of a decline agent wearing down a resilient host.
In summary there is great variation between the study sites but certain trends do appear:
Affected trees are locally clustered, although this is not always with the closest adjacent trees; generally speaking symptoms are found most frequently on the warmer southern aspect, although local environment may cause this to vary; trees with symptoms tend to have worse canopy condition and die more frequently than asymptomatic trees; although there may be high rates of remission.
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4 Spread of stem bleeding symptoms over time
4.1 Introduction
The establishment of long term monitoring plots has enabled the spatial dynamics of symptom development to be documented and studied across years. From the baseline survey (Chapter 3) further spread to newly affected trees can be documented and analysed spatially. Within year occurrences indicate that there is a pattern to the location of AOD affected trees; a non-random spatial distribution is indicative of a localised cause. This study will examine the data to establish if there is evidence of localised spread, and at what distance newly affected trees are expected to occur around those already showing symptoms. The degree and type of clustering in the distribution of infected trees can give important information about pathogen dispersal, especially when patterns are compared between time points (Plantegenest et al., 2007). Further analysis will assess the importance of spatial characteristics on the occurrence and incidence of AOD and detect trends that may exist within the distribution of bleeding trees.
Three sites were adjudged to show changing patterns of affected oak. At two sites
Langdale Wood and Great Monks Wood (Chapter 3, Figure 5) the number of symptomatic trees has been shown to be increasing. These sites are flat, consist almost solely of mature oak and have almost no understory or other features to interfere with dispersal. To assess changes in pattern based on directionless, isotopic, localised clustering a variation of Ripley’s k function can be used (Ripley, 1981). These assess clustering of the newly affected trees based on areas that are centred on trees affected in previous years (Wiegand & Moloney, 2004). This process documents the distribution of one pattern around another to document whether between years spread is randomly
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distributed or clustered around older infections. Significance is assessed in a similar way to the clustering of individual patterns with simulations distributing affected trees randomly across the known positions of host trees (Wiegand, 2004). These methods have been used to study both forest pathogen epidemics (Xu et al., 2009) and forest pest outbreaks (Colombari et al., 2013) at larger spatial scales, and have been applied to within stand dispersal in terms of tree species regeneration (Martinez et al., 2013;
Navarro-Cerrillo et al., 2013). This study uses these methods to assess the within-stand spread of AOD stem bleeding symptoms. Analyses of data also addresses the temporal scale of clustering using comparisons between observations in successive years, as well as at two, and where possible three year separations.
A further site Winding wood (Chapter 3, Figure 5) showed newly affected trees developing within the wood and appearing to be associated with paths. This observation raises the possibility of human or animal vectoring of AOD bacteria, assuming the bacteria plays a causal role. Similar mechanisms of dispersal have been documented for the fungal plant pathogen Phytophthora ramorum (Webber & Rose, 2007; Cushman &
Meentemeyer, 2008) and bacterial pathogens such as Erwinia amylovora (Johnson &
Stockwell, 1998). However it should also be considered that these more open routes, through an otherwise dense woodland understory, are also likely to have increased exposure to wind (Dupont & Brunet, 2006; Dupont & Brunet, 2008; Belcher et al., 2012) and therefore movement of bacteria via rain splash, or wind blown rain (e.g. for spore movement; Gibson et al., 1964).
This chapter answers key questions about the potential dispersal of AOD stem bleeds.
Questions are addressed regarding the development of spatial patterns of stem bleeding,
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as the pattern of symptom expression changes between years. Specifically the location of trees that become newly affected during the study are investigated to see if they occur close to those that are already infected, which would be indicative of localised spread.
Thus how the locations of stem bleeding symptoms are related to each other is assessed.
4.2 Methods
4.2.1 Assessing spread using Ripleys k
A bivariate form of k (t) can be used to compare points of two patterns. This assesses how one pattern j clusters around another pattern i. Such analysis proves useful when studying the development of a pattern over time (Wiegand & Moloney, 2004).
-1 kij = λ E[number of type j events within distance t of a randomly chosen type i event]
For each analysis the Lij Function and O-ring function, gij or Oij, are presented (for definitions see Chapter 3). Oij is related to the bivariate pair correlation function gij, giving the expected number of points of pattern j at distance t (Wiegand & Moloney, 2004).
Oij (t) j gij (t)
when, replacing circles of radius t with rings of radius t.
In this chapter where only a single pattern of diseased trees is considered (analyses with out temporal movements) O-rings are analysed using gii and its modified form Oij is used when the relationships between years (involving spread to newly affected trees) are
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considered. In these later analyses Oij replaces gij due to the restrictions placed on site area while generating the simulated patterns, discussed below.
Statistical analysis
The clustering of bleeding oaks around trees symptomatic in previous years was assessed in Programmita using a trivariate analysis, or “random labelling under antecedent conditions”. Symptomatic trees in the initial year were classed as pattern i and newly affected oak as pattern j. An irregular study area was defined to include only the coordinates of living oaks and the locations of previously affected trees was fixed, enabling random simulations of the newly affected trees to be constrained by the positions of available hosts.
Monte Carlo simulations of the data generate patterns of newly affected trees across the available host location weighted by the proportion of symptomatic trees in the study area.
Values of Lij (t) and Oij (t) are generated for the simulated data with the extreme values used to generate simulation envelopes that are therefore constrained by the observed host positions (Dixon, 2002; Wiegand & Moloney, 2004). Simulation envelopes generated in this way show the amount of clustering expected when points are randomly distributed across the underlying pattern. When the simulation envelopes are exceeded clustering is due to a process other than the underlying pattern (Wiegand & Moloney, 2004).
The dispersal of AOD symptoms was assessed for clustering using a trivariate analysis.
For each site GPS position data in longitude and latitude (WGS86) was transformed to
OSGB36 in ArcGIS 10.0. The resulting easting and northing data was used to generate raster images where each host location was classed as initially symptomatic, newly
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symptomatic or asymptomatic oak. No edge correction was included as the analysis was limited to cells containing host oak and this pattern was used to constrain simulations.
Site maps for analysis were generated in ascII file format using ArcGIS 10.0 (ESRI). For all sites and time periods cell size was fixed to 1 m.
For simulations, random labelling was used to randomly distribute pattern j (newly affected trees) across a limited study area that considered only the locations of trees not symptomatic in the initial time period (pattern i). At each site both L and O functions were used. All analyses used a 1 m grid resolution. O-rings 5 m in width were used.
Trivariate analysis used 99 simulations to give 1-2% simulation envelopes (Xu et al.,
2009).
4.2.2 Site comparisons
The two sites showing an increase in total numbers of affected oak were assessed for dispersal patterns, specifically clustering around the initially affected oak. All trees were considered in this analysis as potential hosts and with stem symptoms in the first year used as pattern i and all trees that became newly affected during the study period as pattern j. This gives an indication of the overall pattern of change.
4.2.3 Analysis at multiple temporal scales
Clustering at each site was then assessed using year to year changes. This set of analyses began by considering spread around the preceding years affected trees and went on to consider longer year gaps. Each analysis considered the status of trees at two time points: at Langdale wood the four years of data allowed three analyses based on preceding years
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(with one year gaps), two with gaps of two years and one across three years; at Great
Monks wood the three years of data allowed two one year gap analyses and one two year analyses.
Each analysis included, point patterns generated using the following rules:
1) All potential (live) hosts on site were used to generate the locations of the
restricted study area.
2) Patterns i and j only included trees that were expressing symptoms in the
respective two years.
3) Any trees that were in remission at the outset were considered asymptomatic and
available for new infection.
4) Oak that relapsed in the second year were considered new infections (in pattern
j).
5) For temporal gaps greater than one year any oak that became symptomatic in the
interim years were excluded from the analysis.
6) All trees that died before the first year were excluded from the analysis.
7) Asymptomatic trees that died before the second year were excluded from the
analysis.
8) When a symptomatic tree died before the second year it retained in the analysis in
pattern i.
At Langdale Wood two trees died in 2011: one was asymptomatic for stem bleeds and was excluded as a potential host for all analyses to 2011 and 2012; the dead symptomatic tree was also excluded from pattern i in the analysis between 2011 and 2012. Analysis of
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non-consecutive years excluded infections first observed in the intervening years: 4 for
2009-2011; 9 for 2010-2012; and 11 for 2009-2012.
At Great Monks Wood three healthy trees were felled in 2011 these were excluded as potential hosts for all analyses, one symptomatic tree was also felled and this was excluded from pattern i in the 2011-2012 analysis. In 2012 2 trees asymptomatic for stem bleeds died and were excluded as hosts in this year. Analysis of non-consecutive years
2010-2012 excluded 5 trees that were first observed with symptoms in 2011.
4.2.4 Winding Wood final spatial pattern
The final pattern, that considers all affected trees across all four monitoring periods, was assessed for spatial aggregation using Lii and gii. In this analysis all trees present in the first survey were considered as potential hosts.
4.2.5 Distance to paths
Distances to paths were calculated using ESRI ArcGIS 10.0, using the spatial join feature to link tree locations to the path locations and in the process assign the shortest distance from the path to each host tree.
Statistical analysis
As all possible path distances were known statistical analysis was conducted as a permutation test using the R package lmPerm. In this process p-values are not obtained using theoretical distributions, rather by comparison to simulated data where observed counts are randomised across the treatments. This method has similarities with the
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random labelling approach used to assess clustering. In this process p-values are generated by comparing the variation for the observed treatment means (sum of squares) with the values generated by the simulated data, such that p values indicate the proportion of times the simulated data (sum of squares) exceed the observed treatments (sum of squares). Due to the large number of possible permutations (in this experiment [ !200]) only a random sample are used. After a minimum of 50 permutations the lmPerm package stops resampling when the estimated standard deviation of the p values falls below 0.1 of the estimated p value (Wheeler, 2010). For all results the sum of squares and p-values are reported along with the number of simulations used to generate them (p = x, iterations = x).
The permutation test was conducted on the following model, which considered the presence or absence of stem symptoms:
Distance ~ Stem symptoms
4.3 Results
4.3.1 Site comparisons
An initial comparison of the dynamics of symptom development at the three study sites indicates that different processes may be at work. At Langdale Wood new symptoms are found in close proximity to those that were affected in the baseline survey (Figure 28).
The L function detects this pattern when considering radii above 23 m and the O-ring statistic detects significant clustering between 17 and 25 m. This positive result contrasts with the two other sites, where no trend based on proximity could be detected.
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A: Langdale 2009-All new symptoms 10 1.0 5
L12 0 0.8 -5 0.6 -10 0 10 20 30 40 50 O12 0.4 Scale (m) 0.2 0.0
0 10 20 30 40 50
Scale (m) B: Great Monks 2010-All new symptoms 1.0 20 10 0
0.8 L12 -10 0.6 -20 0 10 20 30 40 50 O12 0.4 Scale (m) 0.2 0.0
0 10 20 30 40 50
Scale (m) C: Winding Wood 2009-All new symptoms 1.0 10 5 0 0.8 L12 -5 0.6 -10 0 10 20 30 40 50 O12 0.4 Scale (m) 0.2 0.0
0 10 20 30 40 50
Scale (m)
Figure 28: Assessment of clustering of all newly symptomatic oak around the initially observed distribution of oak with stem bleeds. The top panel shows results for Langdale Wood (n (2009) = 46, n (new) = 39, n (asymptomatic) = 175). The middle panel shows results for Great Monks Wood (n (2010) = 20 , n(new) = 12 , n (asymptomatic) = 113). The lower panel shows results for Winding Wood (n (2009 ) = 52, n (new) = 20 , n (asymptomatic) = 129). For each plot the main axis shows outputs of 5m width O-ring analysis. Insets graphs show the outputs of L-function analysis of the same pattern. Test statistics are shown with filled red circles and red lines and simulations envelopes are shown with open blue circles. Simulation envelopes were generated using 99 simulations and random labelling across the locations of all oak that were asymptomatic at the outset. Significant clustering can be detected when the red line falls above the blue dots generated from simulations.
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4.3.2 Analysis at multiple temporal scales
Langdale
At Langdale wood a clustering pattern can be detected, with newly affected trees located in close proximity to those that were symptomatic in the previous season (Figure 29).
This effect becomes increasingly strong as the time periods progress: Between 2009 and
2010 there was no significant trend; between 2010 and 2011 both L and O-ring functions detect clustering at shorter distances (12 to 20 m and 10 to 14 m respectively); finally between 2011 and 2012 the pattern becomes clear, the L function shows a clustered pattern that becomes significant above 20 m and the O-ring suggests clustering at distances greater than 12 m. The increasing significance of the clustering pattern matches with increasing numbers of affected trees. This will cause the power of the statistical analysis to increase.
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A: Langdale 2009-2010 1.0 40 20 0.8 L12 0 -20 0.6 0 10 20 30 40 50 O12 0.4 Scale (m) 0.2 0.0
0 10 20 30 40 50
Scale (m) B: Langdale 2010-2011 0.7 20 0.6 10
L12 0 0.5 -10 0.4 -20 0 10 20 30 40 50 O12 0.3 Scale (m) 0.2 0.1 0.0
0 10 20 30 40 50
Scale (m) C: Langdale 2011-2012 0.7 15 10 0.6 5 L12 0 0.5 -5 0.4 -10 0 10 20 30 40 50 O12 0.3 Scale (m) 0.2 0.1 0.0
0 10 20 30 40 50
Scale (m)
Figure 29: Assessment of clustering at Langdale wood between consecutive years. The top panel presents analysis of spread between 2009-2010 (n (2009) = 46 , n(new 2010) = 5 , n(asymptomatic) = 209), the middle panel 2010-2011(n (2010) = 37 , n (new 2011) = 11 , n (asymptomatic) = 211 ) and the lower panel 2011-2012 (n (2011) = 42 , n (new 2012) = 32 , n (asymptomatic) = 184 ). For each plot the main axis shows outputs of 5m width O-ring analysis. Insets graphs show the outputs of L-function analysis of the same pattern. Test statistics are shown with filled red circles and red lines and simulations envelopes are shown with open blue circles. Simulation envelopes were generated using 99 simulations and random labelling across the locations of all oak that were asymptomatic at the outset. Significant clustering can be detected when the red line falls above the blue dots generated from simulations.
All analyses between non consecutive years show clustered patterns, although not all of these are significant (Figure 30). When the time gap is two years a significant trend can be detected between 2010 and 2012, but this is not clear between 2009 and 2011. The
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2010 to 2012 result detected clustering above 20m with the L function and between 19 m to 26 m with the O-ring. This pattern is very similar to the single year result to 2012, when the number of newly affected hosts is comparable. It is difficult to directly compare the models, especially as the patterns for one years spread and that over two years are so similar. The patterns for spread over a single year seem to exceed the simulation envelopes more consistently and to a greater extent. Finally the 2009 to 2012 analysis again shows clustering, although this barely exceeds the simulation envelopes and is the least convincing of the spread to 2012 analyses.
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A: Langdale 2009-2011
0.5 20 10
0.4 L12 0 -10 0.3 -20 0 10 20 30 40 50 O12 0.2 Scale (m) 0.1 0.0
0 10 20 30 40 50
Scale (m) B: Langdale 2010-2012 15 0.7 10 5 0.6 0 L12 -5 0.5 -10 0.4 -15 0 10 20 30 40 50
O12 0.3 Scale (m) 0.2 0.1 0.0
0 10 20 30 40 50
Scale (m) C: Langdale 2009-2012 1.0 15 10 5
0.8 L12 0 -5 0.6 -10 0 10 20 30 40 50 O12 0.4 Scale (m) 0.2 0.0
0 10 20 30 40 50
Scale (m)
Figure 30: Assessment of clustering at Langdale wood at two and three year intervals. The top panel presents analysis of spread between 2009-2011 (n (2009) = 46 , n (new 2011) = 8 , n (asymptomatic) = 201), the middle panel 2010-2012 (n (2010) = 37 , n (new 2012) = 27, n (asymptomatic) = 186 ) and the lower panel 2009-2012 (n (2009) = 46 , n (new 2012) = 26, n (asymptomatic) =176 ).For each plot the main axis shows outputs of 5m width O-ring analysis. Insets graphs show the outputs of L-function analysis of the same pattern. Test statistics are shown with filled red circles and red lines and simulations envelopes are shown with open blue circles. Simulation envelopes were generated using 99 simulations and random labelling across the locations of all oak that were asymptomatic at the outset. Significant clustering can be detected when the red line falls above the blue dots generated from simulations.
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Great Monks
At Great Monks Wood no significant pattern of clustering could be detected in the newly symptomatic trees between any of the time periods (Figure 31).
A: Great Monks 2010-2011 30 20 1.0 10 0 0.8 L12 -10 -20 0.6 -30 0 10 20 30 40 50 O12 0.4 Scale (m) 0.2 0.0
0 10 20 30 40 50
Scale (m) B: Great Monks 2011-2012 1.0 20 10 0 0.8 L12 -10 0.6 -20 0 10 20 30 40 50 O12 0.4 Scale (m) 0.2 0.0
0 10 20 30 40 50
Scale (m) C: Great Monks 2010-2012 1.0 20 10
0.8 L12 0 -10 0.6 -20 0 10 20 30 40 50 O12 0.4 Scale (m) 0.2 0.0
0 10 20 30 40 50
Scale (m)
Figure 31: Assessment of clustering at Great Monks wood at one and two year intervals. The top panel presents analysis of spread between 2010-2011 (n (2010) = 20, n (new 2011) = 5, n (asymptomatic) = 120), the middle panel 2011-2012(n (2011) = 21 , n (new 2012) = 9 , n (asymptomatic) = 109 ) and the lower panel 2010-2012 (n (2010) = 20 , n (new 2012) = 7, n (asymptomatic) = 108). For each plot the main axis shows outputs of 5m width O-ring analysis. Insets graphs show the outputs of L-function analysis of the same pattern. Test statistics are shown with filled red circles and red lines and simulations envelopes are shown with open blue circles. Simulation envelopes were generated using 99 simulations and random labelling across the locations of all oak that were asymptomatic at the outset. Significant clustering can be detected when the red line falls above the blue dots generated from simulations.
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4.3.3 Winding Wood
In addition to the random pattern recorded for new stem bleeds at Winding wood (Figure
28) the overall pattern of affected trees changed from the beginning of the monitoring period, when it was clustered over distances greater than 25 m (Chapter 3, Figure 15).
The pattern observed at the end of the monitoring period shows that affected trees are randomly distributed across the site (Figure 32) when considering all trees that have expressed symptoms.
The observed pattern of stem bleeds appears to show a relationship with the network of paths through the site (Chapter 4, Figure 10). When comparing the distance to the closest path for affected trees appear to be closer than asymptomatic trees (Figure 33). When this relationship is modelled using permutation tests it is shown to be non-significant (p =
0.061, iterations = 1553). The pattern falls apart further when affected trees are split in to two groups, with the hosts initially observed to be symptomatic closer than newly affected trees. Again distances showed no significant differences when analysed as a permutation test (p = 0.152, iterations = 2042).
A: Winding Wood all affected oak
6 4 5 2 L11 0 4 -2
3 0 10 20 30 40 50 g11 2 Scale (m) 1 0
0 10 20 30 40 50
Scale (m)
Figure 32: Assessment of clustering at Winding wood considering all oak observed with stem bleeds during all four years. The main axis shows outputs of 5m width O-ring analysis. Insets graphs show the outputs of L-function analysis of the same pattern. Test statistics are shown with filled red circles and red lines and simulations envelopes are shown with open blue circles. Simulation envelopes were generated using 99 simulations and random labelling across the locations of all oak. Significant clustering can be detected when the red line falls above the blue dots generated from simulations.
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All affected oak Symptoms divided
10 10
8 8
6 6
4 4 Distance from nearest path (m) path nearest Distancefrom (m) path nearest Distancefrom
2 2
0 0 Asymptomatic AOD Asymptomatic Old New
Figure 33: Distances of affected trees from paths in Winding Wood. Left graph divides into two groups one that includes all asymptomatic trees (n = 129) and one that includes all trees that expressed stem bleeds (n = 72). The right graph divides trees with stem bleeds into those that were symptomatic when monitoring began, old (n = 52) and those that began to express symptoms during the monitoring period, new (n = 20).
4.4 Discussion
At the three sites where AOD incidence was considered to be increasing the patterns of symptom dispersal varied. At one of the sites Langdale wood there was evidence of localised spread from old infections to new hosts. This site had the largest numbers of affected trees which increases the statistical power of both the L function and O-ring statistic; so that any clustered patterns would be easier to detect. The presence of large
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numbers of new infections may not be the sole reason that this site was unique in exhibiting localised spread. The uniform nature of the underlying distribution of oak may also have contributed. Trees were evenly spaced and even aged, there was no understory or other site variables and so available hosts were very similar in terms of condition and local environment. This makes the contrast in pattern observed at Great Monks wood surprising as it had very similar conditions, although there was slightly more variation in the size and distribution of oak.
At Great Monks wood the newly affected hosts were found to be randomly distributed and occurred in an area of the site further to the south than the initial infections (Chapter
4, Figure 12). The open environment was only created at this site in 2009 when the lime understory was coppiced and so the development of the epidemic can be said to be at an earlier stage than at Langdale wood. So the development of symptoms to the south of the site may represent the beginning of a secondary cluster, making the prolonged monitoring at this site especially interesting. If clusters begin with an element of host choice and spread is not solely distance-based the likelihood of a vector for the bacterial pathogen is increased. Of course more random dispersal may still be occurring due to chance events involving wind and rain, while variations in the susceptibility of hosts may influence the observed pattern of symptom expression so that it does not simply follow an inoculum gradient.
The pattern of newly affected hosts is also randomly distributed across all asymptomatic oak at Winding Wood, although at this site there are many factors that may influence the spread of symptoms. The environments around potential hosts are heterogeneous with areas of understory, openings and a network of paths creating a more complex
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environment than is present at either of the other two sites. In this complex environment the clustering observed in the initial symptoms was only significant when considering larger diameter areas, above 25 m, a fact that indicates that spread may be affected by additional factors and not only dependent on proximity to infected trees. Over the course of the monitoring period the degree of clustering decreased, with the final distribution of all affected trees giving no indication of clustering and symptomatic trees randomly distributed. This makes Winding wood a unique case and is a further suggestion that other factors may be influencing the spread of symptoms. On first impressions, oak with stem bleeds appear to occur more frequently along the forest paths. When the mean distances away from paths are calculated symptomatic oak are a little closer than asymptomatic oak, however this association is not significant. It is possible that both proximity to other affected trees and distances to paths could both need to be considered together in order to explain the observed pattern of symptom development. A further possibility is that the apparent association with paths is due to these being within open spaces inside the wood, meaning that openness is the important factor and proximity of paths is merely coincidental.
With these between site variations in observed patterns, the mechanisms behind the spread of stem bleeding symptoms are far from clear. There is some evidence that newly affected trees occur in close proximity to older infections, although this is not the case at all sites. The relationship between observed symptoms may be complicated by variations in the susceptibility of host trees, via genetic predisposition, underlying health or local environment. The issues of latency of symptom expression, or worse still asymptomatic infection further blur the issue and may contribute to the apparently inconsistent results.
When localised spread of symptoms was demonstrated at Langdale Wood the degree of
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association was similar at intervals of one and two years, although the strongest trend appeared to be between adjacent years. Associations were weakest when in the three year gap analysis. A final set of possibilities that should be considered surround A. biguttatus and its role in the AOD complex. If the beetle plays a role in the dispersal of AOD bacteria the proximity of other stem bleeds may not be as relevant as the proximity of exit holes. These issues are developed more fully in subsequent chapters.
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5 Literature review: Agrilus biguttatus
The status of Agrilus biguttatus in Britain and its role in oak decline.
Oak decline is defined as having a complex of causal agents, with multiple factors interacting and having a cumulative negative effect on oak health (Thomas, 2008).
Traditionally this process is described as slow with trees taking many years, or even decades to die. In recent years the number of reports of oak decline in Britain have increased dramatically. Reported cases occur with both native oak species, Quercus robur
L. and Q. petraea, and describe a prominent set of stem symptoms and fast rate of mortality, so the syndrome has been termed Acute Oak Decline (AOD) (Denman &
Webber, 2010). Affected trees show signs of stem damage in the form of bark cracking with black exudate flowing down the stem. Below the bark cracks an area of necrosis can be found in the vascular tissue from which a suite of bacteria is consistently isolated
(Brady et al., 2008; Brady et al., 2011; Denman et al., 2011). This area of decay is frequently coupled with signs of Agrilus biguttatus activity (Denman et al., 2010). This species of buprestid beetle has been reported as a pest of oak in the UK (Gibbs & Greig,
1997) and across its European range (Moraal & Hilszczanski, 2000; Moraal &
Hilszczanski, 2000; Vansteenkiste et al., 2004; Evans et al., 2004). Globally members of this genus are increasingly reported as economic pests of forest trees; examples occur when they are in their native range (Dunn et al., 1990; Nielsen et al., 2011) but are most striking when they arrive as invasive pests (Poland & McCullough, 2006; Coleman et al.,
2011). Despite the growing emphasis on A. biguttatus as a pest species it has been little studied in Britain. This review brings together the existing literature in terms of its
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historical role, biology and damage caused to host species and speculates on the role this insect may play within the oak decline complex.
The genus Agrilus falls within the family Buprestidae, or Jewel beetles, so called because they are often bright metallic colours. Adult beetles of this genus have a body shape that is elongated and tapering from a flat front end, so they appear bullet shaped. The head is retracted into the prothorax and the mouth has a protruding lower jaw (hypognathous), but its appearance is dominated by prominent oval eyes (Bily, 1982).
In Britain five Agrilus species were documented as native (Levey, 1977), although a further three have now been discovered in, or introduced to, Great Britain (James, 1994;
Hodge, 2010; Sage, 2010). The larval stages of all eight species develop within the live woody tissue of their hosts. For four species the larval host species is oak: the larvae of the largest species, A. biguttatus develop within the bark of the main stem, while the larvae of A. angustulus and A. laticornis develop within the branches (Bily, 1982). The niche exploited by the recently arrived A. sulcicollis is less clearly defined; its larvae are said to develop higher in the canopy, on smaller diameter hosts in weakened states
(Moraal & Hilszczanski, 2000), although it has been observed laying eggs on the main stem of mature oak (Personal observation). While A. sulcicollis is present at some oak decline sites its distribution appears to be localised; First discovered in Hertfordshire in
1994 it can now be found across East Anglia (See chapter 7: Agrilus Trapping). The timing of A. sulcicollis arrival coincides with early reports of AOD, although it has only been caught within a limited region and A. biguttatus was caught at all AOD sites.
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Agrilus biguttatus can be found across most of Europe. Parts of Great Britain, Norway and Sweden form the northern boundary of an area that extends east to the Ukraine and south to the Middle-East and Northern Africa (Bily, 1982). In Britain the distribution of
A. biguttatus is limited the southern England (Alexander, 2003).
The status of A. biguttatus in Britain appears to have changed greatly over the last 25 years (Alexander, 2003). At the beginning of this period the species was listed in the Red
Data Book as vulnerable (Shirt, 1987). At that time it was reported at a few historic refuges (including Sherwood, Windsor and the New Forest) and considered a species of conservation concern; to the extent that a sighting was of note to entomologists (Allen,
1973). Its marginal status perhaps reflected a species at the edge of its climatic range.
Since the 1980’s A. biguttatus has been more frequently recorded, the number of sightings has increased as has the geographical range they cover (Foster, 1987; Allen,
1988). By 1994 signs of its presence had been recorded from 33 sites in London alone
(Hackett, 1995; Hackett, 1995). The current distribution, as recorded by the National
Biodiversity Network (NBN) and observed on oak decline sites, covers most of southern
England with sightings particularly abundant in London, the Home Counties and East
Anglia (see Figure 34). Viewing the records chronologically there appears to be clear evidence of a significant increase in range and abundance, however during this period there is also likely to have been an increase in both the number of recorders and the awareness of this species. It has been noted that the link between the beetle and its distinctively shaped exit hole was not mentioned in older literature, a fact that would hinder any attempt to survey for the species (Hackett, 1995). Further evidence of range expansion can be found in continental Europe: In the Netherlands a long term study of insect populations shows A. biguttatus numbers have risen since 1985 (Moraal &
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Akkerhuis, 2011). The species was extremely rare until 1996 when it started being reported in large numbers. After a few years numbers of new reports had fallen but still arrive at a steady rate (L. Moraal, Personal Communication); Agrilus biguttatus was also recently discovered in Denmark for the first time (Pedersen & Jørum, 2009). The changing climate has been proposed as a cause of the increasing British population
(Alexander, 2003; Broadmeadow & Ray, 2005) and it is likely that warmer summers will favour thermophilic species such as those in the genus Agrilus (Habermann & Preller,
2003; Domingue et al., 2011), however more explicit climate events should also be considered. The storms of 1987 left an abundance of damaged oak in South East England
(Alexander, 2003) and importantly this was followed by periods of drought in the 1990’s
(Marsh et al., 2007) leaving host trees in a susceptible state.
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Pre 1990 Legend "" " pre 1929 " " 1930 - 1980
1981 " 1982 1984 1985 1986
1987 " " 1988 " " 1989 " " " "
1990 - present Legend
1990 2002 1991 2003 1992 2004 1993 2005 # # # # # # # 1994 2006 ### ## # # # # ## # # 1995 2007 # # ## ## # # # # # # ## # # # # # # # # 1996 2008 # ### # # # # # # # # # # # # # ## 1997 2009 # # # # # # # 1998 2010 # 1999 2011 ## ## 2000 2012
2001 # DDAS Reports # 2006 # 2010 # 2007 # 2011 # 2008 # 2012 Reports of Agrilus biguttatus # # 2009 2013 The information used here was sourced through the NBN Gateway website and included the following resources: Biological Records Centre, National Trust, Natural England, Countryside Council for Wales, Royal Horticultural Society, Leicestershire & Rutland Coleoptera, Suffolk Biological Records Centre, Kent & FigureMedway 34 Biological: Historical Records Centre, sightings Norfolk Biodiversity of Agrilus Records bigutattus Centre , Worcester. Top panelBiological shows Records sightingsCentre, Herefordshire before Biological 1990 Records and the Centre, lower Tullie House Museum and the personal records of Kieth Alexander (Accessed November 2012). The NBN and its data contributors bear no responsibility for the further panelanalysis shows or interpretation earlier offrom this material, 1990 dataonwards and/or information.. When multiple Copyright © sightingsCrown Copyright. occur All rights at areserved site only NERC the 100017897 earliest 2004. record Additional in points each are panelincluded is shown.from Forest Data Research obtained Disease Diagnosisvia NBN and AdvisoryGateway Service and (DDAS) courtesy . © Crown of copyright. data contributors All rights reserved. (Accessed Forestry Commission. March 100025498. 2011). The [2012]. dataset includes information from a number of sources the detail of which can be found in the references at the back of this publication. The NBN and its data contributors bear no responsibility for the further analysis or interpretation of this material, data and/or information. Copyright © Crown Copyright. All rights reserved NERC 100017897 2004. Additional points are included from Forest Research Disease Diagnosis and Advisory Service (DDAS). © Crown Copyright. All rights reserved. Forestry Commission. 100025498. [2013]
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At the same time as the British population increased A. biguttatus began to be considered as an agent involved in oak decline (Greig, 1992) and is now frequently found at oak decline sites. The species is considered to be a secondary pest, affecting trees already in decline, but its role is considered as an important one. Damage is caused as the larvae of
A. biguttatus develop beneath the bark of mature oak trees. Feeding takes place on live tissue within the phloem causing galleries that sever the vascular system. The actions of individual or a few larvae could easily be tolerated by a mature oak, however larval feeding often involves many individuals. Heavily infested trees have been recorded with up to 38 exit holes per 0.5 m2 bark (Moraal & Hilszczanski, 2000). Studies in Germany suggest that damage caused by A. biguttatus larval feeding is one of the most important factors causing oak mortality (Hartmann & Blank, 1992) and has been noted as a key factor in oak decline in Poland (Hilszczanski & Sierpinski, 2006) and across Europe
(Moraal & Hilszczanski, 2000). This description matches with generalised observations and predictions that point to the key role of secondary insects in influencing mortality of weakened hosts, particularly following periods of drought (McDowell et al., 2008; Jactel et al., 2012).
The emerging role of A. biguttatus as a significant forest pest caused the USDA to rank it as a potentially invasive pest that would have a high economic and environmental impact if it became established (Davis et al., 2005). The Canadian authorities are also watchful for this exotic pest (Kimoto & Duthie-Holt, 2006). These assessments conducted in
North America are likely to take a cautionary stance due to the devastating impact of the
Emerald Ash Borer, A. planipennis Fairmaire. First detected in Michigan in 2002 (Haack et al., 2002) this Asian species of Agrilus has become established in the United States causing widespread damage (Poland & McCullough, 2006). As a direct impact of larval
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feeding tens of millions of ash trees have already died and the predicted economic impact has been estimated at $10.7 billion for the period 2009-2019 (Kovacs et al., 2010). The massive scale of damaged caused by A. planipennis in the United States is due to the native ash species being highly susceptible hosts with little resistance to attack (Rebek et al., 2005; Rebek et al., 2008). This is a clear contrast to the situation in Britain where both host and pest species have co-evolved in their native environment. As such A. biguttatus is described as a secondary pest only attacking weakened hosts. However, events in Michigan highlight the growing prominence of the genus as forestry pests. To further illustrate this point A. auroguttatus Schaeffer (Hespenheide et al., 2011), the Gold
Spotted Oak Borer, has recently been described as a pest of the American oaks Q. agrifolia, Q. chrysolepis and Q. kelloggii in San Diego County (Coleman & Seybold,
2008; Coleman & Seybold, 2008). Its arrival most likely due to an introduction in fire wood from Arizona (Coleman & Seybold, 2011) and it has found a niche on new host species with devastating effect (Coleman et al., 2011). Given the growing role of Agrilus species as economic pests of forest trees and the frequency with which A. biguttatus is found on oak decline sites in the UK it has become important to review the species in more detail.
5.1 Literature review: Identification and life cycle of A.
biguttatus.
Adult A. biguttatus are metallic green/blue in colour. They can be distinguished from the other British native Agrilus species by their two white spots (see Figure 35), formed by a patch of downy pubescence located at the base of each wing case (elytron). Additional
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distinguishing features include an evenly rounded fifth abdominal sternite and their overall size, which at 10-13mm makes A. biguttatus generally larger than all other members of the genus present in the UK, all are included in an identification key (Vorst,
2009). Size alone is not a reliable identifier as there is overlap with other species, especially with males as they are smaller and narrower than females (Bily, 1982). The larvae are more difficult to identify to species level. As with all buprestids they are apodous, dorso-ventrally flattened and almost hairless and the prothorax is enlarged, almost engulfing a smaller submerged head. Larvae of the genus Agrilus can be distinguished by their long abdomen and the presence of two sclerotised spines on the anal segment (Bily, 1982; see Figure 7).
Figure 35: Agrilus biguttatus adult. Pictures: Forest Research.
The development of A. biguttatus has been summarised recently (Moraal & Hilszczanski,
2000; Vansteenkiste et al., 2004; Evans et al., 2004). Most of the life cycle takes place within the vascular tissue of mature oaks. Adult beetles are generally only observed during a short period in the summer months. A study in Germany recorded adult
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emergence between the May 30th and July 8th (Habermann & Preller, 2003), although exact dates are likely to vary between years dependent on variations in seasonal climate.
After the adult beetle has emerged it feeds on oak leaves (chewing in from the edge)
(Wachtendorf, 1955) and searches for a mate (Domingue et al., 2011). After mating females use their long ovipositor to deposit eggs within cracks and crevices in the bark of suitable host trees, often between the bark plates (Personal Observation). In artificial conditions females have been observed to lay small clusters of lenticular shaped eggs
(Wachtendorf, 1955). By laying eggs deep within the crack between bark plates females ensure that newly hatched larvae have a minimal amount of outer bark to chew through before reaching the nutritious inner bark. These eggs hatch after one to two weeks and soon afterwards the young larvae burrow into the inner bark (phloem), where they are well protected from predation. They develop within this thin layer of living tissue next to the cambium, feeding in both the sapwood and inner bark (Vansteenkiste et al., 2004).
Complete development takes 1-2 years (Starchenko, 1931), and during this time larvae progress through five instars. When development is almost complete larvae are between
25-43mm in length (Figure 36). During their feeding they may have excavated galleries up to 1.5m in length (Moraal & Hilszczanski, 2000). The galleries zig-zag within the inner bark becoming steadily wider as the larvae develop (Figure 37), gallery width ranges from 0.5mm to 5mm (Vansteenkiste et al., 2004). Individual galleries can be hard to distinguish as successful colonisation often involves many larvae whose galleries progress erratically crossing many times (personal observation). Before pupation to adulthood larvae move into the bark plates, where they overwinter in a folded or doubled back position (Habermann & Preller, 2003). Pupation then occurs in the spring before adult beetles emerge through characteristic D-shaped exit holes (approximately 5mm wide, see Figures 38 and 39).
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Figure 36: Agrilus cf biguttatus larvae. Picture: Forest Research.
5.2 Literature review: Symptoms and signs of A. biguttatus
attack.
One of the earliest symptoms of an A. biguttatus attack has been reported as bark cracking combined with exudates, dark liquid running down the stem, which are associated with areas of necrosis beneath the bark (Falck, 1918; Jacquiot, 1949; Jacquiot, 1976; Hartmann
& Blank, 1992; Hartmann & Kontzog, 1994; Gibbs & Greig, 1997; Moraal &
Hilszczanski, 2000; Vansteenkiste et al., 2004). These symptoms match closely with those described for AOD (Denman & Webber, 2010). Beneath the bark splits a fluid filled cavity and/or area of spongy necrotic (dead) tissue can be found (Denman et al., 2010).
This region of necrotic tissue consistently yields a number of bacteria (Brady et al., 2010;
Brady et al., 2011; Denman et al., 2011) that may act to degrade host tissue either independently or as part of an assemblage. Preliminary site surveys at AOD sites in
Britain show A. biguttatus exit holes occur more often on trees with these symptoms (See
Chapter 6; (Denman et al., 2010). The above study examined external symptoms and signs and showed many more trees to have cracks and exudate than exit holes but activity
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beneath the bark suggests a stronger link with A. biguttatus. Surveys at AOD sites in
Britain show A. biguttatus activity to occur on more than 90% of trees investigated for bacterial isolations (Denman et al., in prep). Complementing this observation are results from a study that showed bark cracking was always linked to the presence of A. biguttatus galleries, although exit holes were only present on some of the trees (Vansteenkiste et al.,
2004).
At present, the interaction between A. biguttatus and the microbial component of AOD is unclear. It has been reported that buprestid attack precedes the development of the lesions, with wound sites enabling infection by microorganisms (Vansteenkiste et al.,
2004). Such a scenario would involve the bacterium exploiting areas of damage created by larval feeding, where insect frass would provide a substrate where a bacterium could bloom. The following chapters begin to elaborate on the relationship between beetle and bacteria, and a potential role as a vector. An association between affected trees and the beetle alone is not sufficient evidence as A. biguttatus may be acting as an opportunist on trees already affected by a bacterially instigated decline; or both factors may independently prefer similarly weakened hosts. This latter scenario would in fact then imply that both beetle and pathogen are secondary pests, and that host condition is the critical factor in determining which trees suffer from AOD.
Analysis of crown condition shows that bleeding symptoms can occur on relatively healthy trees whereas exit holes only appear when decline is more progressed
(Vansteenkiste et al., 2004). This pattern could infer the cause of the bleeds is present before A. biguttatus moves in as a secondary agent or simply that bleeding is a response to an earlier stage of infestation and infection. Deterioration in crown condition does
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progress in parallel with A. biguttatus infestation, condition is worse when the number of larvae is greater (Habermann & Preller, 2003) and ranges from 20-30% declined when there are signs of galleries to more than 50% declined if there are exit holes
(Vansteenkiste et al., 2004). Successful colonisation therefore requires hosts in a poor state of health, but attempts may begin after a smaller amount of initial weakening. Larval feeding takes place mostly in the phloem tissue of the host where damage will directly impact the flow of nutrients and photosynthetic product around the tree. This impact will be compounded by a reduction in xylem conductance caused by the trees defence mechanisms, as tyloses form in response to larval galleries (Jacquiot, 1949; Jacquiot,
1976; Vansteenkiste et al., 2004). The combined action of these processes can only serve to further reduce the health of infested trees.
Figure 37: Agrilus biguttatus galleries. Picture: NB 2011
A further symptom that has been described during later stages of A. biguttatus attack is callusing (Jacquiot, 1976; Vansteenkiste et al., 2004). Callus tissue develops over 1 to 2
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years after initial attack and result in swellings on the stem and abnormal wood structure.
Tunnels are overgrown by an expanded ring containing mostly parenchyma cells and few vessels (Jacquiot, 1976). This process is very similar to the healing process undergone when oaks are wounded and/or infected with a pathogen. Initially reaction wood forms covering the damaged area with a thick toughened layer; and only after a minimum of 21 weeks does a normal ring structure resume on its outer layers (Pearce, 1996; Yamada,
2001). Jacquiot (1976) also reports that extracts from the gut of A. biguttatus can be added to fresh oak tissue and induce a similar set of symptoms. The study however does not identify the chemical compounds involved, instead crude extracts were applied and these originated not from the gut of A. biguttatus but from the decaying frass found in its galleries. So questions remain about the source of the chemicals; are they released from the larvae, from colonising microbes or even part of oak chemical defences? Regardless of cause, callusing offers hope of recovery. Host trees have the ability to heal their wounds and continue growth after Agrilus attack. The callusing response may also explain some of the differences between external symptoms and observations beneath the bark, as trees may be able to repel Agrilus attack. In this situation not every attempted colonisation would result in the presence of exit holes. A similar set of factors have been shown to affect an American Agrilus species, the two lined chestnut borer, A. bilineatus
Weber; adult beetles prefer trees with reduced health (with reduced winter starch stores) as egg laying sites but these attempts do not always result in successful larval development. The success of colonisation attempts was influenced by the hosts defence
(callusing) response (Dunn et al., 1990).
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Figure 38: D-shaped exit holes. Pictures: NB 2010
5.3 Literature review: Agrilus behaviour and host preferences.
Little is known about the behaviour of adult A. biguttatus. They have been observed most often on the bark of oak trees and logs, especially where they can bask in full sunlight
(Foster, 1987; Allen, 1988), as such they have been observed to be most active late in the afternoon (Domingue et al., 2011). In addition adult beetles have been beaten from the oak foliage (Allen, 1973; Domingue et al., 2011). Studies of the related species A. planipennis, show that beetles spend most time either resting or walking on ash foliage
(Rodriguez-Saona et al., 2007). During their time feeding on oak foliage A. biguttatus are well camouflaged, this cryptic behaviour perhaps explains why the adult is rarely recorded.
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Studies of A. biguttatus have focused on documenting the symptoms and signs that are visible on host trees. Across its range A. biguttatus larvae are commonly documented in association with both native species of oak, Quercus robur L. and Q. petraea. Other hosts are occasionally mentioned in the literature, including Q. pubescens, Q. ilex, Q. suber, Q. pyrenaica and Q. cerris along with Fagus sylvatica and Castanea sativa and very rarely
Q. rubra (Summarised in: (Bily, 1982; Moraal & Hilszczanski, 2000; Davis et al., 2005).
Symptoms are most common on trees with reduced health and thinning crowns
(Vansteenkiste et al., 2004) leading A. biguttatus to be described as a secondary pest, although further research is required to define what makes a suitable host. In particular it would be interesting to know what cues direct females to host trees and how this relates to underlying tree health. When numbers of exit holes are low (<5 on a tree) they are most common on the host trees southern aspect (Vansteenkiste et al., 2004). In keeping with this preference 67% of all beetles and larvae were present on the southern half of the trunk (Habermann & Preller, 2003). South facing trunks are likely to be warmer and sunnier spots, especially as poor canopy health is also likely to reduce shading. This trend has also been reported for other Agrilus species. Agrilus planipennis galeries are found most often on the south-west aspect of host ash trees (Timms et al., 2006) and adults land more often on south facing stems (Lyons et al., 2009).
As well as variations due to aspect, exit holes are also more common at certain heights and diameters of trunks. They show a tendency to be lower on the main trunk
(Starchenko, 1931; Vansteenkiste et al., 2004), although the greatest densities have been recorded between 5 and 10m and they can be found up to 16m (Habermann & Preller,
2003). With this distribution exit holes are not always visible from the ground; in one study that examined recently dead trees, exit holes were often only found above two
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metres (Oosterbaan et al., 2001). Suitable hosts have been reported as being mature trees
(DBH 30 to 40 cm) (Moraal & Hilszczanski, 2000) where bark thickness is greater than
10.2 mm Q. robur and 13.3 mm Q. petraea (Vansteenkiste et al., 2004). The presence of a sufficient thickness of inner bark is thought to be important as development occurs in this layer, with larvae feeding closely following the cambium. As such larvae feed on living bark tissue only (Vansteenkiste et al., 2004). Occasionally on declining oaks galleries are found deeper in the sapwood, although these are more likely to be caused by the larvae of various species of Longhorn (Cerambycidae) beetle as the galleries are wider and lack distinct edges (Vansteenkiste et al., 2004).
Of the few behaviours described for A. biguttatus the simplest to explain is a defensive behaviour where individuals will drop from the trunk when approached. In an attempt to evade capture beetles will either fall to the ground and stay still or take flight as they fall
(Foster, 1987). A second behaviour involves opening the elytra and wings, while resting.
This has been proposed as a sexual behaviour with females attempting to attract a mate, however it could simply be the beetle preparing to take-off (Foster, 1987). A similar behaviour has been observed in studies of A. planipennis (Rodriguez-Saona et al., 2007).
Attempts to investigate this behaviour have shown that decoys with open elytra and wings do not attract mates whereas those with closed elytra do (Lelito et al., 2007). These observations make it hard to attribute a sexual signalling role to the behaviour, although movement may be an important part of the signalling.
Studies show that Agrilus species have a well developed visual system. Agrilus biguttatus males conduct mate choice on the wing; they fly around the tree then quickly descend on females who are waiting on the bark (Domingue et al., 2011). This behaviour has also
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been observed for A. planipennis and suggests Agrilus species have an effective visual system; detecting mates and making decisions in flight (Rodriguez-Saona et al., 2007;
Lelito et al., 2007). Further evidence of the use of visual cues comes from trapping experiments that show beetles express colour preferences, landing preferentially on purple traps (Francese et al., 2008; Crook et al., 2009). As well as a strong visual system,
A. planipennis behaviour is also affected by olfactory cues. Adult A. planipennis are caught most frequently on trees that have been freshly girdled by removing a ring of bark.
These weakened trees will release chemical signals associated with a tree in decline.
Examination of the bark shows that girdled trees contain higher densities of larvae than undamaged trees and are therefore preferred as hosts (McCullough et al., 2009). Further investigation has shown chemicals released from declining Manchurian ash, Fraxinus mandshurica include a number of sesquiterpene compounds. These compounds generate large electrophysiological responses in the antenna of A. planipennis (Rodriguez-Saona et al., 2006) and increase trap catch when used as lures (Crook et al., 2008; Crook &
Mastro, 2010).
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Phictures: 2011NB
emerges from an oak tree. Successive pictures run left to right. A. A. biguttatus
gure 39: An adult Fi
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5.4 Literature review: Beetle and Bacteria interactions?
The simplest type of association between A. biguttatus and a bacterial pathogen would rely on chance co-occurrence. This type of interaction could easily occur if the same host were susceptible to both the decline factors. Agrilus biguttatus has been described as a secondary pest so trees already weakened by a bacterial pathogen are likely to be favoured as egg laying sites.
It is also possible that A. biguttatus may create wound sites (entry and exit holes as well as feeding sites on leaves) where a pathogen could enter the host (Vansteenkiste et al.,
2004). Dispersal of bacteria to these locations would rely on independent mechanisms, although as discussed below A. biguttatus could have a more direct role as a vector. The action of A. biguttatus larvae could further favour a bacterial pathogen as feeding would damage host tissue create an environment where a bacterium would a nourishing substrate with limited defences where it could bloom. In this environment endophytic bacteria could alter their behaviour from symbiotic to a more exploitative role. Signals either released by the larvae or by the host in response to damage could trigger this change to a pathogenic lifestyle (Newton et al., 2010; Tampakaki et al., 2010).
A more direct interaction is also possible. Given their attraction to declining oaks adult beetles are likely to visit trees with bleeds, so a more direct role as a vector is also possible. The sticky exudates could cause the beetles to pick up bacteria and act as an incidental vector. Transmission of disease would be possible for a short period, depending on how long the bacteria were viable and remained with the beetle. This type of transmission would not be limited to A. biguttatus and raises the possibility of many insect species acting as non-specific vectors, as is the case with fireblight, Erwinia
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amylovora, which is transmitted by a range of insects including bees (Hildebrand et al.,
2000). The most likely times of infection would be during feeding and during egg laying, although as so little is known about the life cycle and strategies of both beetle and bacteria this is at best speculative. Feeding wounds in the leaves present a possible route for infection, but they are located far from the observed symptoms. Egg laying takes place near the observed symptoms, but there is no maternal gallery so transmitting the bacteria would be more difficult. Transmission in this form is more complex than incidental spread and is likely to involve a more permanent association.
To date, many Agrilus species have been investigated as potential vectors of plant pathogens, although so far no study has clearly demonstrated transmission. Agrilus bilineatus has been investigated as a potential vector for the fungus Ceratocystis fagacearum, which causes oak wilt in the USA, and although a small percentage of adults emerging from infected trees were found to be carrying spores of the fungus, no experimental transmission was shown (Rexrode, 1968). The buprestid Buprestis aurulenta was examined as a possible vector for the wood-degrading fungi found in association with damage to Douglas fir trees, although no consistent association could be found (Garcia & Morrell, 1999). Agrilus biguttatus was itself investigated for associations with various fungi: It was one of many bark boring beetles discussed in association with
Ceratocystis kubanicum in Russia, where spores were isolated from adults (Kryukova,
1976); Studies in Italy concluded that although an association with Fusarium eumartii was presumed it could not be demonstrated (Tiberi & Ragazzi, 1998).
In one exception to this rule transmission of the bacterium Pseudomonas pseudoalcaligenes by Agrilus nubeculosus has been reported, interestingly in a parallel to
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stem bleeding this process is thought to increase gum arabic production by the host tree
Acacia senegal (El Atta et al., 2011). Prolonged associations with bacterial species have been identified for A. planipennis and studies of gut fauna have shown that some bacterial types are present throughout the life cycle. Identification was based on the 16S rRNA sequence and showed that there is a degree of continuity of the gut bacteria between the beetle life stages (larvae, pre-pupa, pupa and adult). In addition three groups of isolates showed cellulolytic enzyme activity indicating a potential role for symbionts in aiding digestion (Vasanthakumar et al., 2008).
Regardless of the type of association between beetle and bacteria the impact on dispersal of any vectoring should now be considered. Movement via a vector would be subject to different constraints compared to independent movement of a bacterium. Beetle movements are likely to be possible across longer distances than simple rain splash and be less constrained than movement relying on standing water. The flight behaviour of
Agrilus species has been studied in the case of A. planipennis. Studies using an artificial arm to record circular flights showed that 20% of beetles travelled more than 3,000 m in
24 hours, with 1% travelling more than 6,000 m in the same period. Interestingly females that had already mated flew the furthest, around 2.5 times the distance of virgin females.
Long distance flights took place in short bursts with an average of 2 to 4 minutes of flying time then followed by 4 to 7 minutes resting (Taylor et al., 2010). Movement over such large distances would have serious implications in terms of the rate of spread, however the distances travelled in the natural environment may not be so great. In field studies
88% of observed flights resulted in the beetle landing in the canopy of the same tree it had taken off from (Rodriguez-Saona et al., 2007).
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5.5 Literature review: Control of A. biguttatus
Management advice to reduce populations of A. biguttatus is a complex issue. The beetle is native to the UK and recently described as at risk so any management program cannot be widespread in effect and must consider its conservation impact. In woodlands managed purely for conservation, a low level of tree death may be tolerated as part of a natural process, allowing future regeneration. However if the infestation is widespread or the trees have high value, as a commercial crop or as part of a historic or amenity landscape, it may be desirable to limit the localised effect of A. biguttatus. As a first consideration good silvicultural management and care for underlying tree health may be sufficient to reduce the impact of this secondary pest. By removing primary stress such as competition, drought and waterlogging and considering the management possibilities discussed below it may be possible to reduce the impact of this beetle without the need for direct control.
Control of a beetle that spends most of its life cycle enclosed within the trunk of a tree is not straightforward. Development within the bark takes place within a highly sheltered environment where the larvae are hard to detect and harder to influence. Despite this pyrethroid insecticides have been used experimentally with some effect (Habermann &
Preller, 2003). Effective use of these sprays requires application just before adults emerge in order to affect beetles. One drawback of spraying is that larvae at earlier life stages, deeper within the wood, will be unaffected by the treatment. Systemic insecticides have been successfully trialled in North American ash trees against A. plannipennis. A variety of application methods are available such as soil drenches, trunk injections and spraying of the lower stem. If the tree is not too far declined, control is often effective using these
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treatments, although success on larger trees was more inconsistent (Herms et al., 2009).
The use of insecticidal sprays on a keystone species such as oak is likely to have far reaching impacts on the wider insect community and will greatly reduce the ecological value of the resource. In addition to ethical considerations logistical difficulties and costs associated with spraying mature trees may make this treatment impractical while hosts are alive, unless targeted at particularly important individual trees. However similar applications could be used to limit emergence from felled timber. Agrilus biguttatus is described as requiring living hosts, but adult beetles have been observed exiting from recently dead trees and from felled material (Allen, 1988; Hackett, 1995).
Agrilus biguttatus populations are held in check by a number of natural processes; however, harsh winters are unlikely to play a role as pupae are likely to be cold tolerant, with A. plannipennis pupae accumulating high concentrations of glycerol and antifreeze chemicals that allow them to survive harsh winters (Crosthwaite et al., 2011). Predation by woodpeckers is a commonly cited example (Moraal & van Achterberg, 2001) and the bark beetle predator Thanasimus formicarius L. (Coleoptea: Cleridae) is also mentioned
(Kenis & Hilszczanski, 2004). The larvae are thought to be targeted by a number of parasitic wasps (Moraal & van Achterberg, 2001) although only two species have been conclusively reared from larvae: the ichneumonid Deuteroxorides elevator Panzer (Kenis
& Hilszczanski, 2004); and the braconid Spathius curvicaudis Ratzeburg (Shaw, 1988;
Moraal & van Achterberg, 2001).
Spathius curvicaudis is a native species that has been shown to parasitize A. biguttatus larvae before they pupate. The adult female (Figure 40) locates larvae within the bark before ovipositing a number of eggs (up to 14) on or close to it. Larval development is
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quick so there is potential for multiple generations per year (a multivoltine life cycle). In common with other members of the genus Spathius overwintering takes place as pupae
(Shaw, 1988; Moraal & van Achterberg, 2001). This species is of interest as a similar species S. agrili Yang has been shown to have large impacts on A. planipennis populations in China. Early in the season less than 10% of observed A. planipennis larvae were parasitized but by the end of the summer this had risen to an average of over 40% with some locations reaching 90% (Yang et al., 2010). Spathius agrili has been reared in laboratory conditions and is currently being investigated as a potential biocontrol agent in the USA (Gould et al., 2011), but as yet this avenue remains unexplored in the case of A. biguttatus and S. curvicaudis.
While biocontrol may have future potential, current methods of controlling A. biguttatus populations rely on silvicultural management. This applies both in general terms, as healthy trees are less susceptible to attack, and in specific terms in the case of sanitation felling. In Britain management options have not been studied, so there is a need to develop research-driven solutions, including an investigation of methods presented in current European guidance (Hartmann & Kontzog, 1994; Oosterbaan et al., 2001; Evans et al., 2004). This suggests that felling should take place only after careful consideration as it could create an open environment full of sunlit stems which would be suitable egg laying sites for any remaining females. As part of the long term planning process the encouragement of understorey and mixed species edge planting can be used to increase shading to oak stems. If carefully planned the removal of trees can be beneficial in reducing the number of beetles that emerge in the following season. Action should be taken during winter before the beetles emerge. Exit holes often appear in large numbers on a few trees allowing the Agrilus population to be controlled by careful targeting of
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trees to be removed. Initial surveys should identify trees with heavily declined canopies
(more than 60% declined) where there are signs of exit holes. These trees should be removed before the May to August emergence period to maximise effectiveness of the procedure. It is worth noting that bark from suspect trees should be processed to stop further more widespread dispersal. In the United States this type of movement, especially as firewood, has been identified as an important pathway in the dispersal of many xylophagous insects (Haack et al., 2010).
Figure 40: Spathius curvicaudis (Ratzeberg). Although there is some question as to whether this species is different from S. erythrocephalus (Wesmael) (Mark Shaw, Personal communication). Pictures: NB 2011
5.6 Literature review: Summary of A. biguttatus
At present Agrilus biguttatus is thriving in British woodland and represents an up-turn of fortune for a beetle once thought to be under threat. It is found in large numbers at oak decline sites where it can be considered a pest species, although defining its role within the AOD complex needs further research to address some key questions including: its interaction with the bacterial assemblage, host oaks suitability as egg laying sites and
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susceptibility to colonisation, as well as the effectiveness of management techniques in limiting the impact of infestation.
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6 External symptom co-occurrence: stem bleeding and
D-shaped exit holes
6.1 Introduction
At AOD sites affected trees can be identified by externally visible symptoms of bark cracks that weep dark exudate. In addition to these symptoms there are frequently signs of the presence of Agrilus sp. in the form of distinctive D-shaped exit holes. The focus of this chapter is the relationship between these two sets of external symptoms, with the potential co-occurrence raising questions about the beetle vectoring putative AOD bacterial pathogens.
There are many examples of insects transmitting plant pathogens between hosts, this may be via an incidental association as is the case with fireblight, Erwinia amylovora, which is transmitted by a range of insects (Hildebrand et al., 2000) or via more permanent interactions for example: bacterial wilt of cucurbits caused by Erwinia tracheiphila and the striped cucumber beetle Acalymma vittatum (F.) (Mitchell & Hanks, 2009); Xylella fastidiosa causes a range of wilt diseases, such as Pierce’s disease of grape, and has been shown to be vectored by a group of xylem sap-feeding Hemipteran insects known as sharpshooter leafhoppers (Chatterjee et al., 2008); leaf blight of maize, caused by
Pantoea stewartii and the flea beetle, Chaetocnema pulicaria (Melsh) (Correa et al.,
2012). Insect vectors have been little studied with bacterial tree pathogens, but an association between Pseudomonas pseudoalcaligenes and Agrilus nubeculosus has been reported to increase Gum Arabic production by the host tree Acacia senegal (El Atta et al., 2011). Insects do however act as vectors for a number of important fungal tree
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pathogens including: Dutch elm disease, Ophiostoma novo-ulmi associated with Scolytus scolytus and other bark beetles (Webber & Brasier, 1984); and oak wilt Ceratocystis fagacearum associated with various Scolytid and Nitidulid beetles (Juzwik & French,
1983). Similar fungal associations have been investigated for A. biguttatus, but there is little evidence in favour of this hypothesis (Kryukova, 1976; Tiberi & Ragazzi, 1998), although it has also been suggested that damage caused by larval feeding creates an infection court, where necrotic agents may enter the host and establish (Vansteenkiste et al., 2004).
Systems involving insects and pathogen interactions have been discussed at length by
Leach (1940), who documented known examples of interactions, considered the types of systems where both agents could be involved and defined the steps that should be taken to prove an insect has a role as a vector (Leach, 1940). These have been recently summarised as Leach’s principles (Al Adawi et al., 2013), a four step series of proofs that will confirm an insect’s role in the transmission of disease:
1) Close association of the insect with diseased plants.
2) Regular visits by insects to healthy plants under conditions suitable for the
transmission of the disease.
3) Presence of the pathogen in or on insects in nature or following visitation to
diseased plants.
4) The experimental reproduction of disease by insect visitation under controlled
conditions.
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This chapter will address the first step in this process and consider whether and how the
AOD symptoms co-occur. As both bacterial necrosis and Agrilus biguttatus have been implicated in the final stages of decline, shortly before the death of the host
(Vansteenkiste et al., 2004; Denman & Webber, 2010) it is possible that both agents are simply taking advantage of hosts already at an early stage of decline. Certainly A. biguttatus has been described as a secondary pest (Moraal & Hilszczanski, 2000) and an attraction to weakened trees has been documented for other Agrilus species (Dunn et al.,
1990) responding to volatiles released by the host (Crook et al., 2008; Crook & Mastro,
2010). The potential for this type of chance co-occurrence does not exclude the possibility of a vectoring role for the beetle, especially as specific attraction to stem bleeds can also not be ruled out. Bark and ambrosia beetle have been shown to be attracted to
Phytophthora ramorum (Sudden Oak Death) lesions boring directly into the bleeding cankers accelerating the disease and providing entry to pathogens and decay fungi
(McPherson et al., 2008). Further, manipulations of insect behaviour can be seen in the case of Ceratocystis fagacearum, where fungal mats produce volatiles that attract
Nitidulid beetles which pick up spores while they feed (Lin & Phelan, 1992). Regardless of cause the first step in assessing a potential insect-pathogen interaction is to establish the insects association with the diseased plants. In this chapter the relationship will be assessed on both the annual and seasonal scales with further analyses considering spatial patterns and temporal order of symptom development. This set of analyses should establish not only whether a co-occurrence can be found, but also begin to define the relationship and causal sequence of symptom development.
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6.2 Methods
6.2.1 Co-occurrence of stem symptoms in the annual surveys
For each year of survey, data from all monitoring sites was combined and divided into one of four categories depending on the symptoms expressed; trees were either asymptomatic, with stem bleeds only, with exit holes only or with both stem bleeds and exit holes. For each year a two by two contingency table was generated and a χ2 test was used to test the null hypothesis that the two sets of symptoms were distributed independently.
6.2.2 Sequential development of external symptoms and signs
Trees that first expressed stem bleeds during the monitoring period were counted for each year of monitoring. The percentage of these trees that had pre-existing exit holes was calculated. In addition, the percentage of these trees that developed both sets of symptoms in the same monitoring period was also calculated. Similar calculations were made for all trees that first expressed exit holes during the monitoring period with regard to the co- occurence of stem bleeds.
6.2.3 Spatial dynamics of exit holes and stem bleeds
The clustering of bleeding oaks around trees with exit holes was assessed in programmita, using a trivariate analysis or “random labelling under antecedent conditions”. Trees with exit holes were classed as pattern i and those with stem bleeds as pattern j. In this chapter all analyses assess the clustering of one pattern, stem bleeds, around a second pattern, exit holes, so the modified form of O-ring Oij was used (See
Chapters 4 and 5 for discussion). In these analyses Oij replaces gij due to the restrictions
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placed on site area while generating the simulated patterns, discussed below. An irregular study area was defined to include only the coordinates of living oaks and the locations of previously affected trees was fixed, enabling random simulations of the newly affected trees to be constrained by the positions of available hosts.
Monte Carlo simulations of the data generated patterns of newly affected trees across the available host location weighted by the proportion of symptomatic trees in the study area.
Values of Lij (t) and Oij (t) were generated for the simulated data with the extreme values used to generate simulation envelopes that are therefore constrained by the observed host positions (Dixon, 2002; Wiegand & Moloney, 2004). Simulation envelopes generated in this way show the amount of clustering expected when points were randomly distributed across the underlying pattern of trees. When the simulation envelopes are exceeded clustering is due to a process other than the underlying pattern (Wiegand & Moloney,
2004).
Analysis
The relationship between exit holes and AOD symptoms was assessed for clustering using a trivariate analysis. For each site GPS position data in longitude and latitude
(WGS86) was transformed to ordinance survey Eastings and Northings (OSGB36) in
ArcGIS 10.0 (ESRI), as this projection scales in meters on both axes. The resulting
Easting and Northing data was used to generate an ordered list of all tree locations. Each location had two data columns to show presence/absence for exit holes in column one and stem bleeds in column two. In this way both sets of symptoms could co-occur on the same hosts, which is not possible with the ascII method used in previous chapters. No edge correction was included as the analysis was limited to cells containing host oak and
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this pattern was used to constrain simulations. For all sites and time periods cell size was fixed to 1m2.
Analysis was conducted for all eight study sites and considered all initially observed locations of live host oak. Patterns of stem bleeds and exit holes considered all time points during the monitoring to give the cumulative effect of the relationship.
For simulations random labelling was used to distribute pattern j across the limited study area. At each site both L and O functions were used. All analyses used a 1m grid resolution. O-rings 5 m in width were used. Trivariate analysis used 99 simulations to give 1-2% simulation envelopes (Xu et al., 2009).
6.2.4 Seasonality
A stratified random sample of 20 trees was selected at each of 2 sites: Sandpit Wood and
Winding Wood. Trees were placed into four categories based on 2010 survey data; asymptomatic, callused (2009 bleeds had healed), low incidence of bleeds (5 or less) and high incidence of bleeds (more than 5). At each site, 5 trees were selected from each category. A proportion of the trees in each category were selected to also have Agrilus exit holes as a co-occurrence had been observed, so 1 asymptomatic, 1 callused, 2 high incidence and 2 low incidence trees also had exit holes. All data selection and randomisation of samples was conducted in R.2.13 (R Development Core Team, 2011).
The selected trees were monitored every four weeks between 5/4/2011 and 9/1/2013. In this way monitoring points were distributed evenly, with 13 falling in a calendar year.
Each tree was assessed for visible symptoms, with counts of the number of active and
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inactive bleeds conducted on the lower 3 m of the trunk. Exit holes were counted on the lower 2 m of trunk.
One study tree at Winding Wood was felled during the summer of 2011, during a forest management. Data from this tree were excluded from the analysis leaving Winding Wood with 19 study trees. The missing data was from a high incidence tree without exit holes. A further tree failed to flush in the spring of 2012, monitoring of this dead (low incidence with exit holes) tree continued throughout the study period. At Sandpit wood all trees were alive and present through the study although one tree had a major crown reduction to meet health and safety requirements.
Statistical analysis was conducted using R 15.2 (R Development Core Team, 2011) and the package lme4. Generalised mixed models included random effects to control for variation between sites and individual trees within sites. A Poisson error distribution was assumed as data involved counts. For each analysis the inclusion of fixed effects was justified following simplification from a maximal model containing only the random effects. Fixed effects included: the within year monitoring period (1-13); a squared monitoring period term to account for the post peak declining counts; a factor designating the year of monitoring (year one or year two); the stratification categories (high, low, no symptoms or callused); the prior presence of exit holes; and the number of new exit holes present across the whole of the relevant site in the corresponding monitoring period. The fixed effects for the maximal model are outlined below:
Stem bleed count ~ Monitoring period * Year * Host category * Prior exit holes *
Count of new Exit holes + I(Monitoring period ^ 2)
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The significance of retained factors and interactions are reported using model comparison. Contrasts from the final model will be reported using z values, which are approximate due to on-going uncertainty regarding degrees of freedom in mixed effects models.
6.3 Results
6.3.1 Co-occurrence of stem symptoms in the annual surveys
A consistent trend can be seen in all four years, with exit holes and stem bleeds occurring on the same trees more often than expected if they were independently distributed (Figure
41). Many more trees were observed with stem bleeds than exit holes, but exit holes were observed more often on trees with stem bleeds than on those without them.
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1000 2009 2010 2011
800 2012 600 400 Number of oak study Number 200 0
Asymptomatic Bleeds Agrilus Both
Figure 41: Co-occurrence of exit holes and stem bleeds. For each year of monitoring total counts of live oak are presented in four categories: asymptomatic trees (no bleeds and no exit holes), trees with stem bleeds only, trees with Agrilus exit holes only and trees with both stem bleeds and exit holes. Chi squared analysis shows a significant co-occurrence is present in each year’s data: 2009 χ2 = 28.27, df = 1, p < 0.001, n= 762 ; 2010 χ2 = 157.29, df = 1, p < 0.001, n= 1360 ; 2011 χ2 = 183.10, df = 1, p < 0.001, n= 1356 ; 2012 χ2 = 166.42, df = 1, p < 0.001, n= 1351).
6.3.2 Sequential development
Given that stem bleeds and exit holes occur on the same host trees it is important to see if there is an order to AOD symptom development. By comparing the history of trees that develop stem bleeds and exit holes for the first time during the monitoring periods, it is possible to infer an order to symptom development (Table 5). Of the trees that develop stem bleeds for the first time only a small proportion (4.55%) already had exit holes,
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whereas trees that develop exit holes were often already symptomatic for stem bleeds
(66.67%). A further 14.10% of exit holes appeared on trees that developed stem bleeds in the same year. This pattern suggests that although all sequential orders of symptom development can occur, exit holes are most likely to appear after stem bleeds.
Table 5: Summary of sequential development of symptoms. The number of trees that first expressed stem bleeds during the monitoring period were counted for each year of monitoring. The percentage of these trees that had pre-existing exit holes was calculated. In addition, the percentage of these trees that developed both sets of symptoms in the same monitoring period was also calculated (shown in parentheses). Similar calculations were made for all trees that first expressed exit holes during the monitoring period with regard to the co-occurence of stem bleeds.
Count of Percentage of trees with new Count of Percentage of trees with new Year trees with bleeds that already had exit trees with exit holes that already had new bleeds holes* new exit bleeds* holes
2010 15 13.33 ( 0.00) 26 80.77 ( 0.00)
2011 43 6.98 (11.63) 23 56.52 (21.74)
2012 52 0.00 (11.76) 29 62.07 (20.69)
Total 110 4.55 (10.00) 78 66.67 (14.10)
* all trees observed with exit holes first had exit holes at the start of monitoring period
6.3.3 Spatial dynamics of exit holes and stem bleeds
Across the eight sites there was a general trend for stem bleeds to be clustered around
Agrilus exit holes. This pattern is likely to be representative of exit holes developing on the trees that had been affected longest, although as there is no temporal element to the pattern it could be interpreted as either exit holes being the centres of spread or simply occurring on those trees infected for the longest.
Figure 42 shows the four 2009 monitoring sites: At Hatchlands the L function detects significant clustering from 11 m and the O-ring shows the pattern to be most clustered between 10 and 15 m; The L function at Langdale wood shows significantly clustering at
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16 m and above, with the O-ring analysis showing it is most clustered between 13 and 21 m; Clustering is detected from 4 m with the L function at Sandpit wood and the O-ring shows it is most clustered below distances of 23 m; Winding wood is an exception to the general trend with the pattern not showing strong clustering. Here the only occasions simulation envelopes are exceeded are at 10m with the L function and at 8m with the O- ring. Figure 43 shows the four 2010 study sites: Beecham spinney is similar to Winding wood and shows no significant clustering, here the simulation envelopes are never exceeded with either function; The clustered pattern can be seen again at Great Monks wood where the L function detects clustering at distances of 13 m and greater, with the O- ring analysis suggesting that this is most prominent between 12 to 15 m and 23 to 28 m;
Rookery wood shows significant clustering at distances greater than 10m with the L function analysis and is most clustered between 8 and 20 m; At Sheen wood clustering only becomes significant at 25m and above and is most clustered from 25 to 26 m.
In addition all sites other than Beecham spinney exceeded the simulation envelopes at a distance of 0 m, offering further evidence that the two sets of symptoms co-occur significantly on the same trees.
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A: Hatchlands B: Langdale wood
1.5 10 1.5 10 5 5 0 L12 0 L12 -5 -5 1.0 1.0 -10 -10 0 10 20 30 40 50 0 10 20 30 40 50 Scale (m) Scale (m) O12 O12 0.5 0.5
0.0 0.0
0 10 20 30 40 50 0 10 20 30 40 50
Scale (m) Scale (m)
C: Sandpit wood D: Winding Wood
6 1.5 1.5 4 4 2 2 0 L12 L12 0 -2 -4 1.0 -2 1.0 -6 0 10 20 30 40 50 0 10 20 30 40 50 Scale (m) Scale (m) O12 O12 0.5 0.5
0.0 0.0
0 10 20 30 40 50 0 10 20 30 40 50
Scale (m) Scale (m)
Figure 42: Assessment of clustering of all oak symptomatic for stem bleeds around all oak with exit holes at the 2009 study sites. Panel A shows results for Hatchlands (n (exit) = 15, n (bleeds) = 43). Panel B shows results for Langdale wood (n (exit) = 18, n (bleeds) = 88). Panel C shows results for Sandpit wood (n (exit) = 25, n (bleeds) = 65). Panel D shows results for Winding wood (n (exit) = 17, n (bleeds) = 72). For each plot the main axis shows outputs of 5m width O-ring analysis. Insets graphs show the outputs of L-function analysis of the same pattern. Test statistics are shown with filled red circles and red lines and simulations envelopes are shown with open blue circles. Simulation envelopes were generated using 99 simulations and random labelling across the locations of all oak at the outset. Significant clustering can be detected when the red line falls above the blue dots generated from simulations.
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A: Beecham spinney B: Great Monks wood
1.5 15 1.5 15 10 10 5 5 0 L12 0 L12 -5 1.0 -5 1.0 -10 -10 -15 0 10 20 30 40 50 0 10 20 30 40 50
O12 Scale (m) O12 Scale (m) 0.5 0.5
0.0 0.0
0 10 20 30 40 50 0 10 20 30 40 50
Scale (m) Scale (m)
C: Rookery wood D: Sheen Wood
3 1.5 15 2.0 2 10 1 5 L12 L12 0 0 1.5 -1 1.0 -5 -2 0 10 20 30 40 50 1.0 0 10 20 30 40 50
O12 Scale (m) O12 Scale (m) 0.5 0.5
0.0 0.0
0 10 20 30 40 50 0 10 20 30 40 50
Scale (m) Scale (m)
Figure 43: Assessment of clustering of all oak symptomatic for stem bleeds around all oak with exit holes at the 2010 study sites. Panel A shows results for Beecham spinney (n (exit) = 4, n (bleeds) = 21). Panel B shows results for Great monks wood (n (exit) = 9, n (bleeds) = 32). Panel C shows results for Rookery wood (n (exit) = 11, n (bleeds) = 24). Panel D shows results for Sheen wood (n (exit) = 41, n (bleeds) = 87). For each plot the main axis shows outputs of 5m width O-ring analysis. Insets graphs show the outputs of L-function analysis of the same pattern. Test statistics are shown with filled red circles and red lines and simulations envelopes are shown with open blue circles. Simulation envelopes were generated using 99 simulations and random labelling across the locations of all oak at the outset. Significant clustering can be detected when the red line falls above the blue dots generated from simulations.
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6.3.4 Seasonality
15 15 10 10 5 5 0 0 Mean number of stembleeds of number Mean stembleeds of number Mean May Aug Nov Feb May Aug Nov Feb
Monitoring period 2011 Monitoring period 2012 10 10 5 5 0 0
Mean number of active stembleeds of number Mean May Aug Nov Feb active stembleeds of number Mean May Aug Nov Feb
Monitoring period 2011 Monitoring period 2012 52 10 50 8 48 6 6 4 4 2 2 Total new exit holes exit new Total holes exit new Total 0 0
May Aug Nov Feb May Aug Nov Feb
Monitoring period 2011 Monitoring period 2012
Figure 44: Summary data of the monthly monitoring. The first annual cycle (beginning in 2011) is presented in the left hand column with the second annual cycle (beginning in 2012) presented in the right hand column. The top row shows the mean number of all stem bleeds counted at each site visit and the second row show the mean number of actively weeping bleeds counted in each visit: for all four graphs data are presented as means for each symptom category: high incidence in a red line, low incidence is as an orange line, callused as a blue line and asymptomatic as a green line. Error bars show the standard error of the mean. The bottom row contains barcharts displaying the total number of newly formed exit holes observed in each monitoring period: Dark grey bars show data from Sandpit Wood; light grey bars show data from Winding Wood. The exit hole counts at Winding Wood in monitoring periods 5 and 6 of the second annual cycle were extremely high (53 and 49 respectively, with all these exit holes observed on a single stem).
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Across both years of observation newly formed D-shaped exit holes were observed in the same 6 monitoring periods (periods 2-7; Figure 4) that fell between May and September.
The numbers emerging in each four week period on was generally low (7 or less), but in two periods during the second (2012) season 102 exit holes appeared on a single heavily declined tree in Winding Wood, indicating that emergence patterns may be highly clustered toward individual hosts. For analysis monitoring periods the total number of newly formed exit holes at each site in each monitoring period was used as a co-factor to explain the occurrence of stem bleeds.
The total counts of all stem bleeds show a more pronounced seasonality for the highly symptomatic trees, with symptoms most abundant in the summer months. The patterns peak at different points in each of the seasons; in 2011 the peak occurs in the second monitoring period (May) whereas in 2012 the peak occurs much later in period 6
(August). The observations of new exit holes follow a similar trend (Figure 44). These patterns can be seen in the results of the mixed effects models. All high level interactions can be removed from the maximal model without causing significant differences in the residual deviance. Four three-way interactions need to be retained (Figure 45):
Monitoring period: Year: Category (χ2= 14.35, d.f. = 3, p = 0.002); Monitoring period:
Category: Exit holes (χ2= 14.35, d.f. = 3, p = 0.002); Monitoring period: Year: Exit holes
(χ2= 13.89, d.f. = 1, p < 0.001); and Year: Category: Exit holes (χ2= 40.10, d.f. = 3, p <
0.001). This makes a complicated final model but simply states that all stratification was necessary. A separate mean and slope needs to be fitted for each category (of stem bleeds and exit holes) in each year. All interactions with the Agrilus emergence counts can be removed, but the variable itself needs to be retained (χ2 = 12.91, d.f. = 1, p < 0.001). This result is the most important aspect of the model and shows that Agrilus emergence and
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total stem bleed expression co-occur. In the final model the rate of emergence positively correlates with the number of stem bleeds (z = 3.69, p < 0.001). The squared Monitoring period term to account for curvature also needs to be retained in the final model (χ2 =
51.44, d.f. = 1, p < 0.001).
Similar trends can be seen with the counts of active stem bleeding. The peaks occur early in 2011 and later in 2012, where there is a noticeably larger peak. Again these trends can be seen most clearly in the highly symptomatic trees, although the corresponding peak in lightly affected trees is also clear in 2012 (Figure 44). The minimum adequate model also retains interaction terms between the stratification categories, although only a single three way interaction is retained: Monitoring period: Category: Year (χ2 = 22.95, d.f. = 3, p <
0.001), so rates of increase vary between categories and years. In addition the two-way interaction Year: Exit holes also has a significant effect (χ2 = 6.45, d.f. = 1, p = 0.011).
The number of new exit holes is significant as a variable (χ2 = 78.49, d.f. = 1, p < 0.001) and none of it’s interaction terms were retained in the final model. Exit hole count has a positive correlation with the number of active bleeds in the final model (z = 8.97, p
<0.001). In addition curvature in the response variable requires a squared monitoring period term to be retained in the model (χ2 = 111.11, d.f. = 1, p < 0.001).
Both the total number of stem bleeds and, more strongly, the number of active bleeds present on a host tree can be seen to vary seasonally with peaks in symptom expression correlating with peak emergence periods of the Agrilus biguttatus as observed in counts of new exit holes across the site.
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Figure 45: Retained interactions in the total stem bleed model. Three way interactions are shown in the following four graphs. Top left (Year Class and Monitoring period) : For high incidence trees the number of stem bleeds has a greater rate of change across the season in year two. Top Right (Exit holes Class and Monitoring period): For low incidence trees with exit holes there is no August peak in stem bleed activity. Bottom Left (Year Exit holes and Montioring period): In year one trees with no exit holes show decreasing numbers of stem bleeds across the season. Bottom right ( Class Exit holes and Year): Trees in both the high and low incidence categories show fewer stem bleeds in year two when exit holes are present and greater numbers when they are absent.
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6.4 Discussion
Data from the monitoring sites shows that external symptoms do co-occur on the same host trees. These results match with observations taken when removing panels of bark for bacterial isolation (Denman et al., 2013), where larval galleries were found in more than
90% of symptomatic trees (S. Denman, Personal Communication). So while both external and internal symptoms show a co-occurrence the degree of association varies. Below the bark galleries and necrotic lesions are almost always found together, whereas externally stem bleeding is a more common sight than exit holes (for example: in 2012 30.23% of trees with stem bleeds also had exit holes). One explanation for this pattern is that stem bleeding, which can be seen high into the canopy, is more noticeable than exit holes, which are only visible lower on the stem. When beetle emergence has been monitored the majority of exit holes (approximately 85%) were below 7 m with approximately 40% on the lower 4m (Habermann & Preller, 2003), where they may start to become visible to ground based surveys. With this distribution it is possible that exit holes on more lightly affected trees could be missed, although examination of felled trees confirms that affected trees do not always have exit holes (Vansteenkiste et al., 2004). Further the sampling for bacterial isolations was conducted from the ground on standing trees, so galleries were present at a height where exit holes would be clearly visible. This suggests that while there may have been a degree of sampling error, missing lightly affected trees, signs of beetle are more common below the bark than externally. This implies that not all larvae successfully complete their life cycles and emerge as adult beetles. Additional evidence in support of this can be found in the frequent observation of galleries below the bark when contrasted with the rare sightings of larvae in AOD trees. The processes of callusing and flooding of galleries have been described as defence mechanisms against A. biguttatus
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attack. (Falck, 1918; Jacquiot, 1949; Hartmann & Blank, 1992), although questions surrounding host resistance have been little studied.
If colonisation success is related to active host defence A. biguttatus would be more likely to complete its life cycle on the most weakened hosts. The observed patterns support the theory that exit holes appear at a later decline stage, after stem bleeds and sometimes in the final seasons before the death of the host (Chapter 3, Table 3). Over time galleries, and the large areas of necrosis in the inner bark (Denman et al., 2013) are likely to weaken the tree and increase the likelihood of a larvae successfully completing its life cycle. Certainly the degree of success the beetles can have in the final stages of decline can be dramatic, as highlighted by the emergence of 102 beetles in an eight week period.
Observations of seasonal changes also show that the number of visible, and active, stem bleeds on the tree stems correlates with the number of new exit holes at the site level.
This is the period of time when adult beetles will be present in the canopies and laying eggs on oak stems. Of course, it is possible both symptoms are simply influenced by the same climatic factors, such as temperature.
Analysis of the spatial distribution of stem bleeding symptoms in relation to exit holes provides further support in favour of an order to symptom development. Trees with stem bleeds were found around those with exit holes, often at distances of around 10 to 20m.
This pattern matches with a process where beetles exit from more heavily affected trees that are found in the centres of groups of oak with stem bleeding, with galleries below the bark, resulting from failed colonisation attempts, found more widely. There were two sites that did not show this trend. Firstly at Beecham spinney, a site where low numbers
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of affected trees had shown little sign of increase across the monitoring periods, these low numbers would make the detection of any pattern less likely. Secondly at Winding Wood, where there should have been large enough numbers of affected trees to detect patterns if they were present. Both of these sites had areas of dense understory and this diversity may have disrupted the pattern of symptom development. In addition the understory would shade the lower stem and may limit the ability of larvae to complete there life cycles, which can progress higher on the stem. Sandpit Wood and Rookery Wood had a similar mix of environments but both had larger more open areas whereas at Beecham
Spinney and Winding Wood understory and open areas were more closely mixed.
The analyses presented in this chapter show a correlation between the beetle and trees with stem necrosis. Satisfying the first step in Leach’s principles (Leach, 1940), and raising the possibility of a role as a vector of AOD bacteria. Further steps remain to establish whether the form of the co-occurrence is direct as a vector, with the beetle is aiding the spread of bacterial agents, or simply acting as an opportunist exploiting the most weakened of hosts.
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7 Agrilus trapping
Monitoring Agrilus (Coleoptera: Buprestidae) species at oak
decline sites in Britain; first trials of prism traps.
7.1 Introduction
The two spot oak buprestid Agrilus biguttatus is a pest of Britains’ native oak (Gibbs &
Greig, 1997). Larval feeding reduces the health of host trees and causes mortality across its European range (Jacquiot, 1976; Hartmann & Blank, 1992; Moraal & Hilszczanski,
2000; Vansteenkiste et al., 2004; Evans et al., 2004) as part of a wider oak decline complex involving multiple agents (Thomas et al., 2002; Thomas, 2008). Globally species of the genus Agrilus are increasingly reported as economic pests of forest trees both in their native ranges (Dunn et al., 1990; Nielsen et al., 2011) and when they arrive as introductions (Poland & McCullough, 2006; Jendek & Grebennikov, 2009; Haack et al., 2009; Coleman et al., 2011). In Britain A. biguttatus is increasingly reported in connection with Acute Oak Decline (AOD) (Denman et al., 2010), but little is know about its lifecycle and behaviour.
An important first step in developing our understanding of Agrilus biguttatus is to develop techniques that allow populations of the beetle to be monitored directly rather than rely on signs of insect damage, which are both cryptic and long lasting. Exit holes and galleries will remain in the bark for many years after they are formed, and although the D-shaped hole is characteristic it could be caused by a variety of species in the genus.
In Britain four species of Agrilus are known to use oak as a host (Levey, 1977; James,
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1994). Agrilus biguttatus is the largest of these species, at 8.3 to 13 mm in length (Bily,
1982) and the one most commonly associated with oak decline (Vansteenkiste et al.,
2004; Domingue et al., 2011). Its larvae develop along the length of the main stem where damge to the vascular system will have wide reaching effects (Habermann & Preller,
2003). The two smallest species A. angustulus (3.7 to 6.5 mm) and A. laticornis (4.3 to
6.2 mm) develop only within the branches (Bily, 1982). A fourth species, A. sulcicollis was first discovered in Britain in 1992 (James, 1994) and is of intermediate size (6 to 8.5 mm) (Bily, 1982). Agrilus sulcicollis can also use the main stem for larval development but is described as preferring to be higher in the crown or on smaller and weakened hosts
(Moraal & Hilszczanski, 2000). Interest in this species is growing following its introduction to North America (Jendek & Grebennikov, 2009; Haack et al., 2009).
Monitoring of Agrilus populations would allow not only the quantity of beetles to be estimated, but also give an insight into flight periods and the Agrilus species present at oak decline sites.
Specific trapping methods for Agrilus beetles have not been trialled in Europe but are used extensively in North America (Francese et al., 2008). The introduction of an Asian species Agrilus planipennis, the Emerald Ash Borer (EAB) (Haack et al., 2002), has resulted in the infestation and deaths of tens of millions of native ash trees (Poland &
McCullough, 2006) that have little resistance to colonisation (Rebek et al., 2008). Due to the scale of this invasion trapping methods have been extensively studied and this study aims to test similar methodology to monitor Agrilus populations in Britain.
EAB monitoring programs rely on “prism traps”, three sided plastic traps that present a vertically aligned rectangle (30 cm × 60 cm) on each face, which mimics a large stem.
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Insects are captured on glue coated surfaces when they land. Surface colour has been shown to influence trap catch with purple traps catching more EAB beetles than red or white traps (Francese et al., 2008). Subsequent studies suggest green is even more effective than purple when traps are placed high in the canopy (Crook et al., 2009;
Francese et al., 2010), although this may only be true when population densities are high
(Marshall et al., 2010). Purple traps have also proved effective in the capture of the Gold
Spotted Oak Borer, A. auroguttatus (Coleman & Seybold, 2008) and A. sulcicollis was found as by-catch in EAB surveys (Haack et al., 2009). To test whether similar preferences exist in the case of A. biguttatus an initial study tested Purple, Red, Blue and
Green traps against Black “silhouette” traps. Trap catch for EAB is greater at canopy level (Francese et al., 2008; Crook et al., 2009; Francese et al., 2010) and a similar trend has been shown for European buprestids (Wermelinger et al., 2007), so traps were placed at both 3 m and 10 m.
The antenna of EAB are sensitive to a number of volatiles released from Manchurian ash,
Fraxinus mandshurica after adult feeding (Rodriguez-Saona et al., 2006), as well as bark volatiles including sesquiterpenes (Crook et al., 2008; Crook & Mastro, 2010). These compounds are present in Manuka oil which has been shown to increase trap catch for
EAB (Crook et al., 2008). Studies have shown that sesquiterpenes are also released from declining oak (Vrkocova et al., 2000) and Manuka oil is attractive to the bark beetle
Xyleborus glabratus (Hanula & Sullivan, 2008), so a second experiment was established to examine whether Manuka oil lures would increase trap catch in the case of A. biguttatus. This experiment also examined the effect of trap location on catch. European
Agrilus species are most common at the forest mantle (Wermelinger et al., 2007), where
EAB are caught in greater numbers (Francese et al., 2008), so traps were placed both on
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ride edges and 30 m back within closed forest. Agrilus biguttatus larval activity is most common between 5 m and 10 m (Habermann & Preller, 2003) so traps were placed at both 3 m and 6 m to test if adult’s preferences are similar.
7.2 Methods
7.2.1 Colour experiment
Experimental Design
The experiment was conducted in Essex, within a single privately owned woodland
(Figure 46). Each of four blocks was set-up in a localised area of ride edge. The four trapping locations were chosen using the following criteria of an open area and enough accessible high branches for all traps.
For each block five ropes were positioned between 4 m and 10 m apart and suspended from tree branches (> 10 m high). Each rope was used to hold two trap hangers one above the other, at 10 m and 3 m. To ensure all traps hung at the correct heights pre-measured lengths of sisal were used to space the higher and lower traps and to fix their distance to the ground where they were fixed to a concrete weight. A prism trap was then attached to each hanger. Pre –cut boards were folded and secured with cable ties.
Prism trap templates were cut from Correx board (www.theplasticshop.co.uk) to the design as specified in Francese et al. (2008), with three surfaces of 300 × 600 mm. Red,
Blue and Black plastic was used as it came whereas green and purple traps were coloured using green (Plastikote, Apple Green 2113) and purple (Plastikote, Sumptuous Purple
2120) spray paint to colour white templates. Each block contained 10 coloured sticky
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traps; five colours (Black, Blue, Green, Purple, Red) placed at each of two heights (10 m and 3 m). The positions of the colours within the block were randomised (using dice rolls) across the five ropes once they were in position. The exterior surfaces of all prism traps were covered with Oecotac glue (www.oecos.co.uk).
In addition two flight intercept window traps were placed next to each block (one at 10m and one at 3m). The window traps were hung independently from high strength fishing line. Traps were checked bi-weekly between 1st June and 2nd September 2010, with two blocks assessed each week.
Figure 46: Location of trapping sites. Green dot represents 2010 trial (Essex) and three blue dots represent the five 2011 blocks (1 in Essex, and 2 in each of Suffolk and Shropshire). Map generated using ArcGIS 9.1 (ESRI).
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7.2.2 Lure experiment
Experimental Design
Each of five blocks was set-up in a localised area of ride edge, across four wood land sites. Two blocks were located on adjacent rides in a Shropshire forest. Three further blocks were located in East Anglia in two adjacent woods in Suffolk and one wood in
Essex (Figure 46). All sites were privately owned. The trapping locations were chosen using the following criteria: open areas with accessible high branches for traps.
For each block eight ropes were positioned (a minimum of 25 m apart) with four on the ride edge and four 30 m into the forest. Ropes were hung from tree branches (> 6 m high).
Each rope was used to hold a trap hanger. To ensure all traps hung at the correct heights pre-measured lengths of rope were used to fix their distance to the ground. A prism trap was then attached to each hanger. Pre–cut and pre-glued (Oecotac glue, www.oecos.co.uk), boards were folded and secured with cable ties.
Purple prism trap templates were made as described above with white Correx Board
(www.theplasticshop.co.uk) and spray paint (Plastikote, Sumptuous Purple 2120). Each block contained 8 purple sticky traps; 4 were placed on the ride edge, 2 at each of two heights (6 m and 3 m), with a Manuka oil lure (Synergy Semiochemicals Corp.) placed in one trap at each height. The remaining four traps were placed 30 m back into the forest with the same arrangement of heights and lures. The positions of the heights within the block were randomised (using a coin toss) across the four ropes once they were in position, the lures were then randomised across the heights (using a coin toss).
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Traps were checked bi-weekly between the 16th May and the 2nd of September 2011, with sites in East Anglia and those in the West Midlands checked in alternating weeks. Traps and Lures were replaced twice during the monitoring period to ensure maximum efficacy.
Monitoring of the two blocks in the West Midlands finished early on the 1st of August as the ride edge traps were vandalised.
7.2.3 Identification
All beetles in the genus Agrilus were identified using the Bily’s key (1981).
7.2.4 Statistical Analysis
All statistics were conducted using R 2.13.1 (R Development Core Team, 2011). Analysis was conducted separately for each species in each experiment, using the total trap catch for the monitoring period. Generalised linear models were used with Poisson errors to account for count data. Factors were added sequentially to a minimal model including only block effects and retained when they explained significant amounts of deviance. The contrasts presented are taken from the final “minimum adequate” model, including only significant factors. The following analyses required quasi-poisson errors due to overdispersion of the data: colour experiment for A. laticornis, lure experiment with A. biguttatus.
Due to the low total catch for A. biguttatus in the 2010 colour experiment statistical analysis was repeated as a permutation test using the R package lmPerm. In this process p values are not obtained using theoretical distributions, but rather by comparison to simulated data where observed counts are randomised across the treatments. In this process p values indicate the number of times the means of the simulated data (sum of
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squares) exceeds the observed treatment means (sum of squares). Due to the large number of possible permutations (in this experiment [ !40]) only a random sample was used. After a minimum of 50 permutations the lmPerm package stops resampling when the estimated standard deviation of the p values falls below 0.1 of the estimated p value (Wheeler,
2010). For all results p-values will be reported along with the number of simulations used to generate them (p = x, iterations = x).
The permutation test was conducted on the following model, which considered interactions between colour and height:
Catch ~ Colour * Height + Block
7.3 Results
7.3.1 Species composition
Agrilus biguttatus was caught across all monitoring sites (Table 6), although numbers varied greatly between sites and it was never the only Agrilus species trapped at a site.
Perhaps the most striking result was the frequency that A. sulcicollis was present; it was found at all sites in East Anglia, sometimes in large numbers. Of the two smaller species
A. laticornis was the most common with numbers much greater in 2010 most probably due to the experimental design (see below). Due to the extremely low catch window trap data were not included in any further analysis. Agrilus angustulus were not caught in sufficient numbers to analyse trap preference in either experiment and A. laticornis could only be analysed in the 2010 colour experiment.
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Table 6: Total trap catch at each site for all Agrilus species. In 2010 all prism trap data is combined (n=40) where as in 2011 it is divided by site (n = 8). 2010 window traps are reported separately (n = 8).
Site A. biguttatus A. sulcicollis A. laticornis A. angustulus Essex (site 1 ) 2010 - Prism traps 24 51 123 5 -Window 00 01 000 0 traps
Essex (site 2) 2011 04 07 001 0 Shropshire (site 1) 2011 01 00 001 0 Shropshire (site 2) 2011 46 00 001 1 Suffolk (site 1) 2011 04 023 003 0 Suffolk (site 2) 2011 36 6 000 0
7.3.2 Flight period for A. biguttatus
In 2010 A. biguttatus adults were collected from traps between the 7th June and 9th
August, with the peak catch (6 individuals) occurring in the 28th June and 12th of July collections. A very similar pattern can be seen in 2011 (Figure 47) beetles were collected between 30th May and 22nd August with the peak catch (21 individuals) on the 4th July.
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Figure 47: Bi-weekly catch totals for A. biguttatus in 2011. White bars indicate East Anglian collections and grey bars indicate West Midlands collections. West Midlands collections finish on the 1st August.
7.3.3 Colour experiment
Agrilus biguttatus
Analysis using generalised linear models indicates that trap colour significantly affected total catch (deviance = 19.305, d.f. = 4, p > 0.001). Purple was the only colour (Figure
48) to catch significantly more than black controls (z = 2.346, p = 0.019). The model was
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not significantly improved by including height (deviance = 0.66979, d.f. = 1, p = 0.413) or the interaction between colour and height (deviance = 2.8404, d.f. = 4, p = 0.585).
These results are confirmed when the data were analysed using a permutation test. There was no significant difference between blocks (p = 0.56, iterations = 534). Trap colour significantly influenced trap catch (p = 0.008, iterations = 5000), where as both height (p
= 0.54, iterations = 87) and the interaction between colour and height did not (p = 0.92, iterations = 72). Analysis of contrasts showed that green traps caught significantly fewer beetles (p = 0.03, iterations = 3189) and purple traps significantly more (p = 0.005, iterations = 5000).
Agrilus laticornis
Trap colour significantly affected total catch (F32,4=8.2233, p > 0.001).Green was the only colour (Figure 48) to catch significantly more than black controls (t = 4.678, p > 0.001).
Including height significantly improved the model (F31,1 = 45.616, p > 0.001), with traps at 10 m catching significantly more than those at 3 m (t = 5.349, p > 0.001). There was no significant interaction between colour and height (F27,2 = 2.2801, p = 0.879).
Agrilus sulcicolis
In this experiment trap catch varied between blocks with one block catching significantly more beetles. Trap colour significantly affected total catch (deviance = 61.731, d.f. = 4, p
> 0.001). Purple caught significantly more than black controls (z = 3.542, p > 0.001) as did blue (z = 3.064, p = 0.002) (Figure 48). Height significantly affected catch (deviance
= 26.389, d.f. = 1, p > 0.001) with traps at 10 m catching significantly more (Figure 18)
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beetles than those at 3 m (z = 4.368, p > 0.001). There was no significant interaction between colour and height (deviance = 3.2219, d.f. = 4, p = 0.521).
Figure 48: Main effects of colour experiment for A. biguttatus, A. laticornis and A. sulcicollis.
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7.3.4 Lure experiment
Agrilus biguttatus
In this experiment there was significant variation in trap catch between blocks (Table 6);
Shropshire 1 caught significantly less than Shropshire 2 (t = 2.390, p = 0.023) and Suffolk
2 (t = 2.230, p = 0.032). For A. biguttatus trap location significantly affected trap catch
(Figure 49; F34,1 = 17.6805, p > 0.001) with traps on the ride edges catching significantly more than those within the wood (Figure 19; t = -3.774, p > 0.001). Trap height (F33,1 =
1.6784, p = 0.206) and Manuka lures (F32,1 = 0.0038, p = 0.951) had no effect on trap catch (Figure 19). None of the interactions improved the model.
Agrilus sulcicollis
Trap location significantly affected trap catch (deviance = 40.768, d.f. = 1, p > 0.001) with traps on the ride edges catching significantly more (Figure 49) than those with the wood (z = -3.506, p > 0.001). Trap height significantly affected trap catch (deviance =
17.466, d.f. = 1, p > 0.001) with traps at 3 m catching significantly more than those at 6m
(z =-3.599 p > 0.001) and the effect of Manuka lures was marginally significant (deviance
= 4.078, d.f. = 1, p = 0.043) with more beetles caught without lures (Figure 49; z = 1.961 p = 0.0499). None of the interactions improved the model.
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Figure 49: Main effects in the lure experiment for A. biguttatus and A. sulcicollis.
7.4 Discussion
This study shows prism traps can catch not only A. biguttatus but all four Agrilus species associated with native oak in Britain. Although catch numbers remain low in these initial trails, the limited assessment of window traps showed even less success, so the advantage of a specifically tailored trapping method is clear.
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Agrilus biguttatus were caught at all study sites although the numbers collected varied significantly between sites. This combined with overdispersion of the analysed counts suggest that Agrilus populations may be locally clustered. In addition the diversity of other Agrilus species also varied. Agrilus laticornis was collected at the most sites, with only one set of traps failing to catch any. Numbers of this species were significantly reduced in 2011 when only purple traps were used. The other small species A. angustulus was caught in the lowest numbers, although whether this species is rare or simply not attracted to the available traps it is impossible to tell. The biggest surprise of this experiment was the presence of A. sulcicollis at so many sites (across East Anglia). Given the numbers caught at some of the sites this species has clearly established itself in
Britain since its introduction (James, 1994), although it’s current distribution is unknown.
The effectiveness of prism traps is influenced by a number of factors with preferences varying between species. Agrilus biguttatus was caught most often on purple traps. This is similar to EAB (Francese et al., 2008), although the species differed in their preference for green traps (Crook et al., 2009; Francese et al., 2010) which caught no A. biguttatus.
This contrasts with recent studies of canopy based traps that caught most (of the 14) A. biguttatus when they were green (Domingue, et al., 2013). Across both trials A. biguttatus was caught in similar numbers at all heights tested. The range of height matches with studies of damage that have shown A. biguttatus to be present along the full length of the main stem, but the even distribution is surprising as most larval activity is between 5 to 7 m (Habermann & Preller, 2003).
In keeping with its presence in EAB by-catch (Haack et al., 2009) A. sulcicollis was caught on purple traps, with this being the most effective of the colours tested. Purple
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traps had the highest numbers of both A. biguttatus and A. sulcicollis indicating they are attracted to similar visual cues, and perhaps egg laying sites, although A. sulcicollis was not found in similar numbers across all heights. The two trials produced conflicting results with regard to A. sulcicollis and height in the colour trial the beetle was caught in significantly greater numbers at 10m, but in the lure trial it was caught more frequently at
3m. This result is hard to explain with beetles found most often at canopy level and low on the trunk, but not in between. This result could be due to the small sample size, and it will take further study to resolve this discrepancy.
In contrast to the other two species A. laticornis was caught in the greatest numbers on green traps near the canopy. This behaviour fits with a species that feeds on foliage and lays eggs on small branches (Bily, 1982), as it would have no need to land on large stems or traps that mimic them.
In the lure trial only two species, A. biguttatus and A. sulcicollis, were caught in sufficient numbers for analysis and both showed preferences for ride edges. Similar behaviour has been shown for other buprestids (Wermelinger et al., 2007; Francese et al., 2008) and A. biguttatus has been found more often in warmer locations (Allen, 1988; Habermann &
Preller, 2003; Domingue et al., 2011). The effect of aspect was not investigated experimentally in these trials, but the variation in catch between sites can perhaps be explained by this factor. Within a single Shropshire forest the greatest catch of any of the lure sites occurred on a ride adjacent to the lowest catch. The high numbers were caught on a south facing ride edge that caught the sun throughout the afternoon, whereas the adjacent block was on an east facing ride edge that was shaded from the mid afternoon
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onward. Agrilus biguttatus has been reported to be most active in the late afternoon
(Domingue et al., 2011), but further study is required to see if aspect affects trap catch.
The Manuka oil lures did not increase trap catch for either A. biguttatus or A. sulcicollis, with the latter caught in lower numbers on baited traps. In contrast to the results presented in this paper, studies using canopy traps have suggested that odours may affect trap catch for some European Agrlus species, including A. sulcicollis (Domingue, et al., 2013), although larger scale studies may be required to detect significant differences between individual treatments. It is worth considering that the composition of volatiles that are attractive to European Agrilus species has not been studied directly and may not match with A. plannipennis lures such as Manuka oil. However given the attraction of these species to declining trees it seems likely that a host-based lure is attainable with further study. A further consideration is that the effectiveness of a host-based lure may have been reduced by the proximity of traps to stressed and declining oaks that released their own volatiles.
These first trials with prism traps provide a proof of concept indicating that European
Agrilus species can be monitored in this way. The experiments suggest that trapping methods can be tailored to individual species, through the choice of colour and placement. Further work is required to refine these methods, but the template used for
EAB monitoring provides a good starting point. One further advance that should be investigated would involve the use of reusable multi-funnel traps (Francese et al., 2011) instead of the single use sticky traps. These would be reusable and therefore reduce the cost of long term monitoring.
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8 Identification of beetles, larvae and associated bacteria
8.1 Introduction This chapter combines molecular investigations of Agrilus populations and their bacterial fauna. Larvae found within oak were identified to species level using gene sequences recorded from the more easily identified adult beetles. Species of bacteria associated with both life stages were then isolated and identified. These steps aim to establish whether a link exisits between A. biguttatus and AOD affected trees. A comparision between bacteria associated with phloem necrosis and those associated with the beetle is made in order to test the A. biguttatus’ potential role as a vector. Beetles were assessed at various life stages in order to address questions of adults vectoring bacteria between trees and larvae aiding there movement within tree.
Monitoring of insect populations at AOD sites (Chapter 7) revealed that A. biguttatus was one of four species of Agrilus beetles present in the trap catch, raising questions regarding the species associated with damaged trees. Keys for the larval identification are available, but have been less well developed than those for adults (Bily, 1982) meaning questions remain regarding the species found within oak stems. As A. biguttatus is much greater in size than the other species, reported to develop on the main stem and the one most strongly linked to oak decline it seemed the most likely candidate for causing the observed damage (Moraal & Hilszczanski, 2000; Vansteenkiste et al., 2004), although A. sulcicollis may overlap into this niche (Bily, 1982; Haack et al., 2009; Jendek &
Grebennikov, 2009). To investigate the species involved in causing the damage larvae were collected from oak stems and matched to adult beetles collected during the trapping experiments using molecular techniques.
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Samples from each of the four Agrilus species found at AOD sites were used for molecular analysis. The molecular taxonomy of the genus Agrilus has been investigated using 12S rDNA and a segment that spanned regions of NADH dehydrogenase I (ND1), leucine tRNA gene, and 16S rDNA (Bernhard et al., 2005), although these gene regions are less frequently used for insect identification with many studies favouring the cytochrome c oxidase I (COI) region (Hebert et al., 2003). All three gene regions were initially trialled, but as COI gave good species separation of adults based on a single gene region it was used to establish a database from which larvae could be accurately identified
(without having to successfully rear larvae through to adulthood). This method allowed the adult beetles to be directly linked to larval stages associated with damage in the phloem.
Bacterial isolations were made from A. biguttatus at different life stages in order to test for the presence of the putative causal agents of AOD (Brady et al., 2010; Denman et al.,
2011; Brady et al., 2011; Denman et al., 2013; Brady et al., 2013). This process aimed to further progress the fulfilment of Leach’s principles (Leach, 1940; Al Adawi et al., 2013).
Experiments in previous chapters have shown an association between the beetle and affected trees (Chapter 6), and then shown that beetles are likely to visit unaffected trees as they land on un-baited traps (Chapter 7). This chapter will specifically assess whether the bacterial species are found on or in adult beetles. The bacterial assemblages associated with A. planipennis have been investigated (Vasanthakumar et al., 2008), with studies focusing on the gut bacteria and symbionts that are present across the various life stages of the beetle. Isolations and molecular identification demonstrated that some species are constantly found regardless of the stage of development. This type of permanent association would increase the ability of A. biguttatus to act as a vector. A number of
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plant diseases are transported via the gut of insects including: Erwinia tracheiphila
(Mitchell & Hanks, 2009) and Xylella fastidiosa (Chatterjee et al., 2008). This chapter aims to assess the potential for A. biguttatus to directly vector necrotic agents between oak trees by isolating bacteria from adult beetles. Finally the potential to aid spread within the host will be assessed through isolations from larvae.
8.2 Methods
8.2.1 Sample acquisition. Adult Agrilus beetles were gained from trapping experiments and from field capture of live beetles. For the molecular identification of Agrilus species: 7 A. biguttatus samples were from Traps in Essex (site 1), along with 2 A. angustulus and 8 A. laticornis; 1 A. biguttatus and 4 A. sulcicollis specimens were caught by hand at Essex (site 2). For the
Bacterial isolations 10 A. biguttatus were taken from traps: 1 at Essex (site 1), 4 at
Shropshie (site 2), 4 at Suffolk (site 1) and 1 at Suffolk (site 2). A further 16 were caught by hand (11 at Langdale Wood, 4 at futher site in Suffolk and 1 at Winding Wood).
All Larvae were found on trees that had Armilaria infections and had declined rapidly, without signs of stem bleeding. These trees had limited host responces, in terms of visable callusing, and larvae survived in much greater numbers than in those with clear signs of
AOD. Those used for molecular identification (5) came from a site in Sussex and all (8) used for bacterial isolation came from Chestnuts Wood, Forest of Dean. Ceramycid larvae were found in a sample from a site in Oxfordshire.
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8.2.2 Molecular identification of beetles. Adult Agrilus beetles were identified morphologically (Bily, 1982) and found to belong to four species: A. biguttatus, A. sulcicollis, A. angustulus and A. laticornis.
For DNA extraction: A. biguttatus flight muscle was dissected and macerated mechanically with flame sterilised forceps; smaller species i.e. A. angustulatus, .A. laticornis, A. sulcicollis were macerated whole; early instar larvae were homogenised whole, but for later instars heads were removed and used as the sole source of DNA.
DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer’s instructions. Digestion was left to take place over night (approx 16 hours at 56oC). Following DNA extraction, any residual RNA in the sample was digested by the addition of 4L RNaseA (10mg/mL) and incubated at room temperature for 30 minutes.
Extracted DNA served as templates for Polymerase Chain Reactions (PCR) amplification of a 658bp fragment of the mitochondrial gene Cytochrome Oxidase I (COI) as specifed by (Hebert et al., 2003). Primers used in PCR reactions were:
LCO1490 (5’-GGTCAACAAATCATAAAGATATTGG-3’)
HCO2198 (5’-TAAACTTCAGGGTGACCAAAAAATCA-3’)
PCR reactions were carried out in a total volume of 25l and and contained 2.5l 5
GoTaq Flexi Buffer (Promega, WI, USA), 2.0 mM MgCl2 (Promega, WI, USA), 2.5mM of each dNTP (dATP, dTTP, dCTP, dGTP) (Promega, WI, USA), 0.2M each of primers
LCO1490 and HCO2198 (IDT Technologies, Belgium), 0.75U GoTaq Polymerase
(Promega, WI, USA), 2.5l extracted insect DNA suspension and sterile distilled water to a total volume of 25l .
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PCR reaction conditions consisted of an initial denaturation temperature of 94 C for 1 min. followed by 5 cycles of: denaturation at 94 C for 1 min, primer annealing at 45 C for 90 s and strand extension at 72 C for 90 s followed by 35 cycles of: denaturation at
94 C for 1 min, primer annealing at 50 C for 90 s. and strand extension at 72 C for
90 s. The reaction was completed with a final elongation step at 72 C for 5 min. PCR products were visualised in 1% agarose gels (wt/v) stained with GelRed (Biotium, CA,
USA) and viewed under ultra-violet light. PCR products were subsequently purified using the DNA Clean 7 Concentrator TM (Zymo Research, CA, USA) as directed by the manufacturer.
Purified PCR products served as templates for subsequent DNA sequencing. Sequencing reaction mixtures consisted of 5l purified PCR product and 1l primer (3.5pmol/l).
These reaction mixturess were sent to Genepool (Edinburgh, UK) for sequencing using
ABI 3730 capillary sequencing instruments following a BigDye reaction. The resulting sequence chromatograms were examined trimmed and joined manually using Sequencher version 5.0 (Genecodes, MI, USA). Sequences were aligned using a multiple alignment programme for nucleotide sequences (MAFFT) (Katoh, 2011); and a web based multiple alignment program. Molecular Evolutionary Genetic Analysis (MEGA) version 5.0
(Tamura et al., 2011) was used for phylogenetic reconstructions. Here, aligned sequences were subjected to genetic distance analyses using the Neighbour Joining statistical method with p-distances employed as the evolutionary model, a 500 replicate bootstrap test of phylogeny, all nucleotide substitutions were included, rates among sites were uniform and the patterns among lineages was the same while gaps and missing data were completely deleted. Two cerambycid larvae were employed as outgroups in order to root the resulting phylogeny.
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8.2.3 Isolation and Identification of gut bacteria:
Beetle dissection:
Dissection of adult beetles followed methods similar to those used by (Vasanthakumar et al., 2008). Beetles were surface sterilised in 70% ethanol for 30 s before being washed in autoclaved deionised water. Forceps and razor blades were sterilised by flaming before use. The head was removed using forceps revealing the crop. Beetles were cut on both sides of the abdomen and opened before approximately 100l of 0.01M Phosphate Buffer
Solution pH 7.4 (PBS) was added to allow the digestive tract to be removed intact. The crop and digestive tract were placed in 150l of PBS, before they were macerated using the tip of the forceps and vortexed to suspend the gut contents. Larvae were cut to remove their heads and terminal abdominal segment, before the length of the abdomen was cut open allowing the digestive tract to be removed. PBS solution was added to allow the digestive tract to be seen and aid removal. In addition to dissections swabs were taken of faecal matter of live specimens in the lab.
Isolation of bacteria:
The gut suspensions were spread onto PYGA (0.5% Yeast extract, 0.5% Proteose peptone, 1% Glucose, and 1.5% Agar (wt/v)) plates in serial dilutions (neat, 1:10, 1:100,
1:1000) that were incubated at 25oC for a week. Individual colonies were picked off the dilution plates as they appeared, starting at approximately 48 hours, with at least two examples of each type of colony morphology being removed for further examination.
Each isolate was then quarter streaked on Nutrient Agar and again incubated at 25oC.
Single colonies for analysis could then be selected from the re-streaked plates.
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Identification of bacteria:
A small amount of each single colony bacterium was gathered on the tip of an inoculation loop and suspended in 150l autoclaved deionised water, for freezer storage awaiting molecular identification. A secound sample was gathered and tested using 3% KOH
(wt/v) to identify gram-positive and gram-negative species (Halebian et al., 1981).
Further DNA-based molecular analysis was conducted on all gram-negative bacteria, a group which contained both putative causal agents. DNA was not extracted from isolated bacterial strains. Instead whole cells were used as starting material when conducting PCR reactions.
For PCR reactions, primers gryB01F (5’-TAARTTYGAYGAYAACTCYTAYAAAGT-
3’) and gryB02R (5’-CMCCYTCCACCARGTAMAGTTC-3’) were used to amplify a section of the housekeeping gene DNA gyrase B (GyrB) (Brady et al., 2008). PCR reactions were undertaken in a total volume of 25 l and contained 2.5l 5 GoTaq Flexi
Buffer (Promega, WI, USA), 2.0 mM MgCl2 (Promega, WI, USA), 2.5mM of each dNTP
(dATP, dTTP, dCTP, dGTP) (Promega, WI, USA ), 0.2M each of primers gyrB01F and gyrB02R (IDT Technologies, Belgium), 0.75U GoTaq Polymerase (Promega, WI, USA),
2.5l bacterial cell suspension and sterile distilled water to a total volume of 25l . PCR reaction conditions included an initial denaturation at 95 C for ten minutes, followed by
3 cycles of: denaturation at 95 C for 1 min, primer annealing at 46 C for 2 min, strand elongation at 72 C for 1 min and then a further 35 cycles of: denaturation at 95 C for
35 s, primer annealing at 46 C for 75 s, strand elongation at 72 C for 75 s and the reaction was terminated with a final elongation at 72 C for 7 min. PCR products were visualised in 1% agarose gels (wt/v) stained with GelRed (Biotium, CA, USA) and
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viewed under ultra-violet light. PCR amplicons were subsequently purified using the
DNA Clean 7 Concentrator TM (Zymo Research, CA, USA) or the MinElute 96 UF
PCR purification Kit (Qiagen) following the manufacturer’s instructions.
Purified PCR products served as templates for subsequent DNA sequencing. Sequencing reactions consisted of 5l purified PCR product and 1l primer (3.5pmol/l). These reactions were sent to Genepool (Edinburgh, UK) for sequencing using ABI 3730 capillary sequencing instruments following a BigDye reaction. The resulting sequence chromatograms were examined trimmed and joined using Sequencher version 5.0
(Genecodes, MI, USA).
Sequences were provisionally identified using NCBI nucleotide BLAST. When sequences showed similarities with the bacteria isolated from oak they were then aligned within a database containing oak lesion isolates using Molecular Evolutionary Genetic Analysis
(MEGA) version 5.0 (Tamura et al., 2011) by colleagues at Forest Research
8.3 Results
8.3.1 Larval identification: Sequences for the COI region were amplified and sequenced for three of the Agrilus species, the fourth species A. latticornis did not amplify with the primer set used in this study. The available sequences were used to separate A. biguttatus, A. angustulus and A. sulcicollis reliably to species level (Figure 50). All larval samples matched to A. biguttatus.
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Figure 50: Phylogeny reconstruction for Agrilus species. COI sequence data (686bp) was compiled using Neighbour joining (NJ) method, with a 500 replicate Bootstrap test of phylogeny. A nucleotide substitution type with a p-distance model was employed with uniform rates among sites and complete deletion of gaps and missing data. All larvae sequences match that of A. biguttatus.
BOLD sequence ID (sequences not available for inclusion): biguttatus (VVGC387-09), sulcicollis (FBCOC009-10 ; FBCOC032-10 ), angustulus (FBCOC597-10) and Phymatodes testaceus (CERPA212-08 ; CERPA211-08 ; COLAT040-08 ; COLAT042-08 ; FBCOC006-10 ; COLAT041-08 ; COLAT039-08 ; FBCOC133-10 )
8.3.2 Bacterial Identification: The species composition of the adult beetles (Figure 51) was broadly similar to that isolated from faecal swabs (Figure 52) and found in larvae (Figure 53). In all three cases the dominant component of the population was formed of Gram positive bacteria and various species of Pseudomonas, of which a species similar to P. putida was commonly found in adults and faecal swabs. Two species of Erwinia were also isolated with relative frequency, although only E. billingiae was found in larvae.
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The species of interest, the putative AOD causal agents, were not isolated in abundance.
Brennanaria goodwinii and the Londsdalea species were not found at all. Gibbsiella quercinecans was isolated only once from a beetle that had been caught by hand and was alive in the lab, so contamination cannot be fully ruled out. At best the frequency of isolation would imply a relationship as an incidental vector rather than an obligate dependency. Several species of Rahnella were isolated and these are often found in affected oak, but most frequently in galleries. They are not currently thought to be important agents in phloem necrosis.
Figure 51: Summary of the bacterial population isolated from the gut of adult A. biguttatus. Species names represent the closest match found on NCBIs nucleotide BLAST.
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Figure 52: Summary of the bacterial population isolated from swabs of adult A. biguttatus faecal matter. Species names represent the closest match found on NCBIs nucleotide BLAST.
Figure 53: Summary of the bacterial population isolated from the gut of larval A. biguttatus. Species names represent the closest match found on NCBIs nucleotide BLAST.
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8.4 Discussion
Confirmation of a link to between larvae found in galleries and the beetles in the wider environment allowed the role as a vector to be investigated with full confidence that the correct species was being assessed.
The isolations of gut bacteria suggest that A. biguttatus does not play a direct role as a vector, as the bacterial assemblage did not contain most of the species associated with
AOD. Gibbsiella quercinecans was isolated once from a single adult beetle, so there is potential for the beetle to act as an incidental vector. Further investigation of this topic may be justified due to the importance of the question to AOD epidemiology and the limited scope and budget of the current investigation.
These isolations do not rule out the possibility that the AOD bacteria are transported via another mechanism, perhaps externally or in an internal gland, but the evidence gathered to date is not supportive of a role as a vector.
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9 National Distribution
The distribution of A. biguttatus and its relation to AOD reports in Great Britain.
9.1 Introduction
A link has been shown between symptoms of stem bleeding and Agrilus exit holes at the within site level. The national distribution records for Agrilus biguttatus and AOD sites appear similar, with both found in the south east of Great Britain in a region that extends north to the Midlands and west to the Welsh border. This indicates a correlation at a greater spatial scale. This chapter will investigate the co-occurrence of Agrilus sightings and AOD across Great Britain and assess the effects of host abundance and climate on the distribution of AOD reports.
The process of assessing pest and pathogen distributions is frequently conducted as part of pest risk assessments (PRAs), which aim to define potential risk areas for invasive species. These are used as guidance in legislative process and to direct practical intervention. In order to implement PRAs in a consistent manner the European Union established the PRATIQUE project. This included a work package focused on climatic mapping that included a decision support system (DSS) for rating the suitability of climate in the PRA area and the appropriateness of implementing predictive modelling
(Eyre et al., 2012). Using the DSS the appropriateness of climatic mapping is defined based on: the likelihood of climate influencing the distribution; the availability of reliable
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data recording the effect of climatic variable on development; the type and quality of location data for the species (Eyre et al., 2012).
Both AOD and A. biguttatus scored poorly on these criteria, with the process highlighting key knowledge gaps that would ideally be filled before a reliable climate model could be produced. A provisional assessment of AOD concluded that mapping could not be achieved reliably given the limited information on the decline: recorded locations were all self-reported including no absence data and were limited to England; the effect of climate could not be judged due to remaining uncertainty surrounding the identity of causal agents and the novel nature of the species under investigation.
When A. biguttatus was assessed under the same criteria, the available data was also shown to be limited. The suitability for climatic modelling was judged less than optimal, although enough data were available for a tentative attempt. Factors influencing this judgement include:
1) Agrilus biguttatus is documented as a native species, indicating that is should be
well adapted to its niche and occupy its potential range. This would make the
process of modelling potential range redundant; however its range, in terms of
reported sightings, appears to be increasing at an exponential rate. This species
was initially reported as having a very limited range, to be found in a single wood
(Fowler, 1888) and despite being found in other locations it remained a rarity and
a sighting of note to entomologists (Allen, 1973; Shirt, 1987), until sightings
began increasing in the 1980 and 1990s (Foster, 1987; Allen, 1988; Hackett,
1995). The rapid increase in sightings raises questions about range expansion as a
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result of a changing climate and even the potential for the species to have arrived
by introduction, which is a possibility as its native status has not always been
discussed as a certainty (Allen, 1973). This pattern may however be a result of
reporter bias, as discussed below.
2) The pattern of observations is restricted to self-reported cases, so limited by the
number of recorders and their awareness of this species. Starting in the late 1970’s
a number of publications acted to raise awareness of A. biguttatus and highlighted
the link to distinctively shaped exit holes (Allen, 1973; Foster, 1987; Allen, 1988;
Hackett, 1995). Its inclusion in the NCC red data book (Shirt, 1987) can only have
increased the interest in recording its presence. Even after considering these
potential biases the data were judged to offer a more reliable estimate than the
AOD reports because of the availability of sighting data across Europe, covering
the beetle’s wider range.
3) In Great Britain the beetle is at the edge of its range, where climate maybe
limiting and climate change allowing range expansion. Warmer summers have
been discussed in the literature as a driver of its population increase.
4) No data was found in the literature regarding A. biguttatus development thresholds
however some data were available from field observations of other Agrilus
species: A. plannipennis, A. anxius and A. bilineatus (Haack et al., 1981; Haack &
Benjamin, 1982; Akers & Nielsen, 1984; Crosthwaite et al., 2011; Sobek et al.,
2011).
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5) The life cycle of A. biguttatus involves a long larval stage within the host tissue
where it is insulated from climatic effects. These have been shown to reduce
temperature fluctuations (Vermunt et al., 2012; Vermunt et al., 2012).
6) The need to understand the relation between the newly observed AOD symptoms
and the “native” beetle. This need is heightened due to the potential for a severe
impact, particularly because oak ecosystems are an integral feature of the native
environment in Britain.
The available records used to generate national distributions are not based on targeted surveys and rely on self-reported information from entomologists and woodland owners.
As such they cannot be regarded as a complete description of occurrences, or even a truly random sample, but examination of trends within this type of data is possible using the bioclimatic niche model CLIMEX (Sutherst et al., 2007). This approach has been used for a wide range of forest pests and diseases including: Asian longhorn beetle (MacLeod et al., 2002), Ips bark beetles (Vanhanen et al., 2008), Phytophthora cinammomi on oak
(Brasier & Scott, 1994), Dothistroma needle blight (Watt et al., 2009), and P. ramorum
(Ireland et al., 2013). The CLIMEX model for A. biguttatus was developed using distribution data for continental Europe. This allowed the regional level trends to be compared independently with the distribution of A. biguttatus in Great Britain.
Model outputs were also compared with outputs from PRA assessments developed to assess the threat A. biguttatus poses as a potential invasive to North America. These were developed by initially matching biome types (Davis et al., 2005) and then further examined using the climatic modelling program NAPPFAST (Chanelli et al., 2010).
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A second aim of the chapter is to compare the distributions of AOD sightings and A. biguttatus across Great Britain using the following steps:
1) CLIMEX model parameters were re-run for Great Britain using a high resolution
(5km) climate dataset, to assess the fit of the Agrilus distribution and for use in
later steps.
2) Variations of Ripley’s k function (Ripley, 1981) were used to assess relations
between the spatial patterns in entomologist’s reports of the beetle and AOD sites
(Baddeley, 2010).
3) Point process models were used to fit the recorded distribution of AOD sites using
host abundance and the predicted climatic suitability for A. biguttatus as
covariates (Baddeley & Turner, 2006).
These three steps will address the relations between the entomological records and reports of decline, assess the similarities between the spatial patterns, and finally attempt to explain the distribution of affected woodland based on the CLIMEX model. The Ripley’s k analyses will test the null hypothesis that that A. biguttatus sightings and AOD reports are independently distributed against a pattern where they are locally clustered, before further addressing whether they are in fact part of the same pattern. The point process model assesses how the distribution of AOD sites is influenced by the climatic suitability for A. biguttatus and the underlying distribution of oak in order to further define the relationship between the spatial patterns.
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9.2 Methods
9.2.1 Data sources
AOD distribution:
The records from the Disease Diagnostic Advisory Service (DDAS), provided by Forest
Research, were examined for the period of 2006 to the present. The relevant oak decline records were selected from the database of self-reported enquires and examined to see if described symptoms involved stem bleeding. Where possible site owners were contacted and asked for photographic evidence. Records were collated in a database linked to
ArcGIS 10.0 (ESRI) so the spatial distribution could be analysed. Sites included in this dataset had the presence of AOD confirmed through either: bacterial isolation; site visits by FR staff; or unquestionable photographic evidence of symptoms. The final list included 140 sites, 85 of which also had confirmed evidence of A. biguttatus presence
(exit holes were also documented).
Agrilus biguttatus distribution:
Distribution data was used in three forms:
1) For CLIMEX model development the full European range was considered. Wider
distribution data was gathered via the Global Biodiversity Information Facility
(www.gbif.org/). Information from these data bases was supplemented with
additional records following a literature search using: CAB abstracts; Web of
Knowledge; Google search.
2) For the UK a “NBN data set” was used to compare entomologist’s sightings of
the beetle with the distribution of AOD sites. National Data for Great Britain was
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accessed via the National Biodiversity Network website (www.nbn.org.uk/). From
this data set individual data providers were contacted for additional records that
were not freely available, or are more recent than 2004. Once duplicate records
and repeated sightings at the same locations had been excluded 229 sites remained
and were included in analysis. A full list of data providers is presented in the
acknowledgements section. The NBN and its data contributors bear no
responsibility for the further analysis or interpretation of this material, data and/or
information. Copyright © Crown Copyright. All rights reserved NERC
100017897 2004.
3) Both data sets were combined, along with all the confirmed sightings of A.
biguttatus in the DDAS database to give a “All Agrilus sightings” dataset to
display on maps of CLIMEX outputs and host distribution. Full data sources are
summarised in Appendix 1.
Oak distributions:
European distribution data of Quercus robur and Quercus petraea was obtained from the
European Forestry Institute and show relative abundance as well as distribution using combined data for both oak species (Brus et al. 2012), additional maps are from
EUFORGEN (http://www.euforgen.org/distribution_maps.html).
Data for native oak in Great Britain were obtained from the Forestry Commissions
National Inventory of Woodlands and Trees (Smith & Gilbert, 2003). This combines records for both species. The original survey was a stratified random sample of all woodland greater than 2ha and was designed to sample:
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• 2.0 ha – <100 ha: every fifth wood
•100 ha – <500 ha: two woods in five
• 500 ha and larger: all woods
The data used in analyses was summarised for 10 km grid squares, which were coded depending on “maximum oak class” where: 0 = no survey points containing oak; 1 = oak woods with 10% or less oak in the upper canopy; 2 = oak woods with 10 to 30% oak in the upper canopy; 3 = oak woods with greater than 30% oak in the upper canopy. These categories showed similar trends to the total number of survey points containing oak in each square. There were significant differences in the number of sites surveyed in each grid square between the maximum oak class categories (data not shown).
Elevations:
The NASA Shuttle Radar Topographic Mission (SRTM) has provided digital elevation data (DEMs) for over 80% of the globe. This data is currently distributed free of charge at
3 arc s (approx. 90 m at the equator) resolution. The vertical error of the DEM’s is reported to be less than 16 m. The raw data has been post processed by Consultative
Group for International Agricultural Research (CGIAR) Consortium for Spatial
Information (CGIAR-CSI) with no data regions filled through interpolation
(http://www.cgiar-csi.org/data/elevation/item/45-srtm-90m-digital-elevation-database- v41) accessed Feb. 2012.
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Climate data:
Global data: The 10 arc minute interpolated climate surface CliMond
CM10_1975H_V1.1 (https://www.climond.org/) was used to develop the A. biguttatus model. It is a dataset of long-term monthly climate means (across 50 years and centered on 1975) for: precipitation; maximum temperature; minimum temperature; and relative humidity at both 9 am and 3 pm. CLIMEX uses these monthly means to calculate weekly values for use in its modelling.
Great Britain data: a grid at 5 km resolution was obtained from Richard Baker at FERA.
This contained the same parameters for use in CLIMEX.
9.2.2 CLIMEX estimates of species suitability
CLIMEX (Sutherst & Maywald, 1985; Sutherst et al., 2007) includes the function
“compare locations” which calculates an annual index of climatic suitability, the
Ecoclimatic Index (EI). This indicates the potential for a species to undergo population growth at locations depending on the climatic suitability. EI is calculated using an annual potential for growth (GIA) which may be limited by the need to survive stressful periods triggered by individual aspects of climate (SI) or by interacting climate effects (SX).
GIA describes the potential for growth of the host and pest as a function of moisture
(Moisture Index; MI) and temperature (Temperature Index; TI) both of which accumulate during the growing season while conditions are favourable. Both indices are calculated
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weekly across the year, dependent of climate variables at each location and the growth thresholds entered into the model. The weekly potential for growth is then calculated by combining the two indices to give the weekly thermo-hydrological growth Index (TGIW).
∑
where,
To calculate EI the potential for growth is then limited by climatic stress indices: SI describing, cold stress (CS), wet stress (WS), heat stress (HS) and dry stress (DS); and SX describing interacting climatic effects, Cold-Dry Stress ( CDX) , Cold-Wet Stress
(CWX), Hot-Dry Stress (HDX) and = Hot-Wet Stress (HWX). These indices penalize EI accumulating during weeks when climate values are extreme.
( ) ( ) ( ) ( )
( ) ( ) ( ) ( )
The resulting EI value covers the range 0 to 100, where 0 describes locations that the species cannot persist and 100 defines locations that are optimal. For all maps showing potential for establishment at the regional scale and using the 10 arc min climate data EI is presented using the following arbitrary categorical scale: 0; 1-9; 10-23; 24-38; 39-54.
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These groupings were selected based on the European regional output and “natural breaks” divisions implemented by ArcGIS 10.0.
9.2.3 Distributions in Great Britain
The CLIMEX parameters developed for the European Agrilus dataset were run using a 5 km grid for Great Britain. This was then used as a co-factor in later spatial models (see below).
9.2.4 Spatial clustering of AOD sites in relation to NBN Agrilus sightings
All analysis was conducted in R 3.0.1 (R Development Core Team, 2011), using the package spatstat (Baddeley & Turner, 2005). This implements the Ripley’s k function
(Ripley, 1981) as described in previous chapters. To ensure consistency of interpretation,
Figures show the transformed L-function using the same formula.
kij Lij t
Clustering was assessed using a combined dataset of the NBN sightings and DDAS reports. The clustering of AOD reports (pattern j) was assessed around the NBN Agrilus sightings (pattern i). significance was tested using 99 simulations each of which generates an underlying point pattern based on complete spatial randomness across mainland Great
Britain and then randomly assigned marks i and j. Edge corrections took the form
“border” which reduces the sample area by the amount within the plot area (Baddeley,
2010). This matches with the form of correction used previously (Chapters 3, 4, and 6).
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The relationship between the locations of the entomological NBN data and the reported
AOD was investigated to further assess the similarities of the patterns. In order to assess whether each patterns was distributed evenly across the locations of all sites, the function
Kdiff (t) was used. This compared the K function centred only on Agrilus sightings, but considering all other points, Kidot(t), with the k function of the overall pattern of locations, K(t). This shows a clustered trend if, and when, Agrilus sightings are in the denser parts of the pattern of all locations (Baddeley, 2010).
-1 Kidot (t) = λ Expected [number of extra events of both pattern i and pattern j within distance t of a randomly chosen event of pattern i]
Significance was assessed using random labelling simulations. These resample across all the locations of sightings, with each location relabelled as NBN or AOD at random. Kdiff was then calculated for each of 99 simulations, with the extreme values used to draw simulation envelopes. When observed values of Kdiff fall within the simulation envelopes it indicates the two patterns were in fact part of the same pattern, with each randomly distributed across all locations. If the simulation envelopes are exceeded there is evidence that the intensities of the patterns vary and they are not part of the same pattern
(Baddeley, 2010).
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9.2.5 Spatial models of AOD distribution
The spatstat package also offers functions to fit point process models (PPMs). These look to explain localised variation through covariates, spatial trends, dependence on cofactors and in more complicated cases can include inter-point interactions. Under complete spatial randomness (CSR) points in a pattern are distributed independently explained by a random Poisson distribution based on λ, the overall density of points. This is homogenous across the whole of the area under consideration, so that the conditional intensity (β) is constant for a pattern with number of points (x) and locations (μ).
Poisson PPMs consider that points remain independent and explain variations from CSR by using an inhomogeneous λ that is dependent on covariates. In this situation local conditional intensity varies dependent on the location of points within the pattern.
PPMs test for significant trends in the conditional intensity of point patterns, as predicted by covariates, using the constant intensity predicted by CSR as a null model (Baddeley &
Turner, 2006) . As a first step PPMs generate a series of quadrature points: these include all data points as well as a series of regularly distributed dummy points that are placed across the study area. Conditional intensity is then calculated discretely across the range of each covariate, S(μ) using the quadrature points.
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PPMs require the intensity function β (u) to be log-linear in the parameter θ:
Where, S (u) is a real-valued or vector-valued function of location u. Although as S (μ) could be a function of the spatial coordinates of μ, or an observed covariate, or a mixture of both, there is little restriction. Maximum pseudo-likelihood estimates for θ are then calculated using Poisson log-linear regression models (Baddeley & Turner, 2006).
Analyses were based on the locations of DDAS AOD reports, using these to generate conditional intensities and included the covariates: 10km NIWT maximum oak class and
5km predictions of the A. biguttatus CLIMEX model, as well as the geographical influences of Eastings and Northings. Model selection was conducted using the function anova.ppm which performs model comparison and reports χ2 p-values (Baddeley &
Turner, 2006). Covariates were added to a null model of CSR in the following order
(CLIMEX EI, oak class, Northings and Eastings) and retained in the model when they explained significant amounts of deviance. The co-ordinate based covariates (Eastings and Northings) were added independently and as polynomials in order to assess curvature in CI, which occurs when clustering of points is centered away from the edges of the country.
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9.3 Results
9.3.1 Distributions
The records for Agrilus biguttatus show a distribution that stretches across temperate
Europe (Figure 54). The distribution of sightings is clustered in northern Europe, although there is great variation in the numbers of sightings in each country so this could easily be an artefact reflecting recorder effort. Across most of its range sightings of A. biguttatus closely match with the distribution of host oak (Figure 55), although this is not the case in the Great Britain and Ireland.
Figure 54: Distribution records for all records of Agrilus biguttatus in Europe. Points indicated record where individual sightings could be located and the areas marked with hatching are where only the region containing the sighting was mentioned.
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Figure 55: European distribution of Quercus robur and Quercus petraea. The top map is from European Forestry Institute data and shows relative abundance as well as distribution using combined data for both oak species (Brus et al. 2012), the middle and bottom maps are from EUFORGEN.
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9.3.2 European CLIMEX Model
Development of the CLIMEX model for Agrilus biguttatus used the Mediterranean template as a starting point, as the species is described as thermophilic and drought stress is thought to increase the availability of susceptible host trees. Due to the limited available information on developmental requirements for A. biguttatus the model was primarily built using the available distribution data. As A. biguttatus occupies a much wider range than that described by the template the first steps in building the model involved the removal of the hot-wet and cold stresses. Much of the documented range of the species has wet summers and the related species, the Emerald Ash Borer (A. plannipennis) has been recorded overwintering at temperatures far below zero (Croswaite et al., 2011; Vermunt et al, 2012a). Next, adjustments were made to the development temperatures. The threshold for heat accumulation and development has not been documented for any Agrilus species in laboratory conditions but estimates are available from studies of emergence and pupation of members of the Agrilus genus (not active feeding and development). They predicted a threshold between 8 and 10 oC air temperature for Agrilus anxius emergence from hosts and 17.8 oC when Agrilus bilineatus pupae were incubated in the laboratory (Akers & Nielsen DG, 1984; Haack et al. 1981).
The lower estimate is likely to be a reasonable approximation as heat accumulation is likely to occur faster within the sheltered environment of the bark than in the open
(Vermunt et al, 2012b). For the model the threshold was lowered to 13 degrees and day degrees adjusted to 550 in order to fit the northern boundary of the recorded distribution
(in Norway and Sweden). The fit of the model in the Mediterranean was improved by adding heat stress at a rate of 0.01 above 29 oC. Finally to adjust the model to favour areas with drought stressed hosts, moisture index maximum was reduced to 1.2.
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Table 7: CLIMEX parameters used to model the distribution of Agrilus biguttatus.
Index Parameter Value
Temperature DV0 = lower threshold 10 °C
DV1 = lower optimum temperature 13 °C
DV2 = upper optimum temperature 24 °C
DV3 = upper threshold 28 °C
Moisture SM0 = lower soil moisture threshold 0.2
SM1 = lower optimum soil moisture 0.4
SM2 = upper optimum soil moisture 0.7
SM3 = upper soil moisture threshold 1.2
Heat stress TTHS = heat stress threshold 29
THHS = stress accumulation rate 0.01
Wet stress SMWS = wet stress threshold 1.6
HWS = wet stress rate 0.0015
Dry stress SMDS = soil moisture dry stress threshold 0.02
HDS = stress accumulation rate -0.005 week -1
Threshold heat PDD = number of degree-days above DV0 550 °C days sum needed to complete one generation
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Figure 56: Climex model for A. biguttatus with distribution records super-imposed. EI is presented with 0 values in green, then using the following arbitrary categorical scale progressing from light yellow to dark brown respectively: 1-9; 10-23; 24-38; 39-54. These groupings were selected based on the European regional output and “natural breaks” divisions implemented by ArcGIS 10.0.
The CLIMEX model for A. biguttatus predicts high EI values across large areas of
Europe (Table 7; Figure 56). Recorded sightings for the beetles are mostly found in areas where EI is above 0 (see below for discussion of exceptions). Importantly the model predicts establishment will be possible for sightings at the edge of the documented range for the beetle. The darkest shaded area, where EI is greatest, falls in a band across central
Europe that includes the greatest concentration of sightings. The model does not predict establishment at the locations of all beetle sightings, this is especially noticeable in the
Alps.
The deficiencies of the A. biguttatus CLIMEX model in the area of the Alps were further investigated by plotting the distribution of A. biguttatus on an elevation map for this area
(Fig. 57), this shows that the distribution records are from the alpine valleys. The climatic data used for modelling does not pick up on the differences between the valleys and mountains and gives an averaged and accurate prediction.
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Figure 57: Distribution of Agrilus biguttatus in the Alps. The main plot covers an area of the Alps where EI is 0 but sightings are recorded. The blue grid indicates the resolution of climate data used in the regional scale CLIMEX model. Grey scale shading shows the fine resolution elevation data (darkest regions are highest, see scale). An inset map shows the location of the main plot. In the inset EI is presented with 0 values in green, then using the following arbitrary categorical scale progressing from light yellow to dark brown respectively: 1-9; 10-23; 24-38; 39-54. These groupings were selected based on the European regional output and “natural breaks” divisions implemented by ArcGIS 10.0.
9.3.3 North American Models
The CLIMEX model we developed (Table 7; Figure 58) can be compared with models for the climatic suitability of North America for A. biguttatus. A biome based attempt (Figure
59) predicted a very different potential range, but a model developed using NAPPFAST
(Figure 60) indicated a potential range that was generally similar. The NAPPFAST model does however predict high climatic suitability for areas of Europe where Agrilus is not found, notably northern Britain and Scandinavia. It is also notable that the CLIMEX predictions bear a striking resemblance to the area of the current EAB outbreak.
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Figure 58: CLIMEX model projected onto North America. EI is presented with 0 values in green, then using the following arbitrary categorical scale progressing from light yellow to dark brown respectively: 1- 9; 10-23; 24-38; 39-54. These groupings were selected based on the European regional output and “natural breaks” divisions implemented by ArcGIS 10.0.
Figure 59: USDA biome based prediction of the potential distribution of A. biguttatus (Davis et al, 2005). Yellow areas predict establishment.
Figure 60: NAPPFAST Climate model for A. biguttatus. Red areas have most favourable climates, blue areas are less suitable and white areas least favourable climatically (Chanelli et al., 2010).
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9.3.4 Distributions in Great Britain
Legend
! AOD
! NBN Elevation (m) < = 50 50 - 100 100 - 150 150 - 200 200 - 250 250 - 500 500 - 750 750 - 1,000 1000 - 1335
! ! ! !! ! !! !! !! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! !! ! ! !!! ! ! ! ! ! ! !! ! ! !! !! ! ! !! ! ! ! !! ! !! ! !!! ! ! ! ! !! !! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! !! ! !!! ! ! ! ! ! ! ! ! ! ! ! ! !! ! !! ! ! ! ! !!! ! !!!! ! ! ! ! ! ! ! ! !! ! ! ! ! ! !!!! ! ! ! ! ! !!!!!!!!!!! !! ! ! ! !! ! ! !!! !!!! ! ! !! ! ! !!!! !!!!!!!!!!! !! ! ! !!!! ! !! ! !!!!!! ! ! ! ! !!!!!!! ! ! ! !! ! !!! ! !! !! !! !!!!!!! !! ! ! ! !!! !! ! ! !!!! ! ! ! ! ! ! ! ! !
Figure 61: Distribution of A. biguttatus (left) and AOD (right) in the UK superimposed over STRM altitude maps.
The distribution of both A. biguttatus sightings and AOD reports are located on southern
England and the Welsh borders. Both distributions are found in lowland areas (Figure
61), although this pattern is likely to be influenced by the distribution of host trees (Figure
62) and climate (EI for A. biguttatus shown in Figure 63). Neither distribution is solely influenced by host abundance as there are areas with high oak class that lack any sightings, but within their ranges they tend to occur in squares with a higher oak class.
These associations will be examined using PPM.
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Legend
! All A. biguttatus sightings Maximum Oak Class 0 1 2 3
! !! !! !!!!! !
!! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! !! ! !! ! ! ! ! ! !! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! !! ! ! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! !! !! !! !! !!!!!!! !! ! ! ! ! !!!!! ! ! ! ! ! ! ! !!! !!! ! ! ! ! ! !! ! !!!!!! !! ! ! !! ! ! ! ! !! ! ! ! !!!!!!!!! !!! ! ! ! ! ! ! ! !!! !! ! ! !!! ! ! ! !! ! ! ! ! ! ! ! ! ! !! !! ! !!! ! ! ! ! !
Figure 62: Oak distribution for Great Britain. Each 10 km grid square is shaded depending on oak class, darker squares have more oak present (see key). Pink data points include all Agrilus sightings.
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Legend
! All A. biguttatus sightings Value
Low : 0 High : 48
! !! !! !!!!! !
!! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! !! ! !! ! ! ! ! ! !! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! !! ! ! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! !! !! !! !! !!!!!!! !! ! ! ! ! !!!!! ! ! ! ! ! ! ! !!! !!! ! ! ! ! ! !! ! !!!!!! !! ! ! !! ! ! ! ! !! ! ! ! !!!!!!!!! !!! ! ! ! ! ! ! ! !!! !! ! ! !!! ! ! ! !! ! ! ! ! ! ! ! ! ! !! !! ! !!! ! ! ! ! !
Figure 63: CLIMEX prediction for the establishment of A. biguttatus in Great Britain using 5 km2 climate data. Darker shaded areas predict a greater chance of establishment (EI). Pink data points include all Agrilus sightings.
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9.3.5 Spatial clustering of AOD sites in relation to NBN Agrilus sightings
AOD reports are located in close proximity to NBN A. biguttatus sightings (Figure 64).
This localised clustering greatly exceeds all simulated patterns where all locations and their types are assigned randomly. The two distributions cannot however be said to be the realisations of the same pattern. NBN sightings are locally clustered across the pattern of all observed locations, occurring in the area where the density of points is greatest (Figure
65). This result indicates that the AOD reports are more evenly distributed across the country. Interpretation is difficult but perhaps implies a bias in the NBN records which are clustered close to London, or if these data represent the true relationship between beetle and decline the likelihood of an AOD outbreak is not influenced by the density of the A. biguttatus population.
The distribution of AOD sites can be explained using a point process model (Figure 66).
The conditional intensity generated by the observed data and quadrature points shows a significant correlation with both CLIMEX EI for A. biguttatus (deviance = 299.95, p <
0.001) and maximum oak class (deviance = 55.26, p < 0.001). The addition of Northings did not improve the model (deviance = 1.58, p = 0.21), but it was retained due to the significant effect of its associated polynomial (deviance = 62.46, p < 0.001). The marginally significant Easting (deviance = 3.90, p = 0.048) was retained in the final model but its polynomial was not (deviance = 0.04, p = 0.85). Parameters in the final model (Table 8) show that Conditional intensity has a positive correlation with CLIMEX
EI and mean values are significantly greater for oak classes 2 and 3. Conditional intensity positively correlates with Northings but the significant polynomial term means that the correlation becomes negative. These two Northings covariates act to narrow the range and focus predicted conditional intensity of AOD into a band across the midlands. The
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positive correlation with Easting is slight and accounts for the higher density of reports from East Anglia.
Figure 64: Clustering of AOD reports around A. biguttatus sightings. The black line shows the L function clustering of AOD reports around NBN Agrilus sightings. The grey shaded region contains all L function results from 99 simulations of CSR.
Figure 65: Clustering of all NBN sightings within all report locations. The black line shows the result for the Kdiff function (Kdot – K) for NBN A. biguttatus sightings. The grey shaded region contains all Kdiff results from 99 randomly labelled permutations across the locations of all AOD and NBN reports.
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Spatial models of AOD distribution
D D
D D D D D D D D D D D D D D D D D D DD DDD DD DDD D D DD DD D D DD DD DD DD DDDDDD DD DD DDDDDD DDD DDDD DD D DDDDDD DDD DDDD DD D DDDDDD D D D D D DDD D D D D D D D DDD D D D D DD DDDDDD D D DD DDDDDD DD D DDDD D DD D DDDD D D DD DDD D DD DDD D D D D DD D D D D DD D D D D D DDD D DDD DD DD D DD D DD DD D DD D D D D D D D
CLIMEX EI and Oak Class only Final Model
Legend Conditional Intensity Conditional Intensity
0 - 0.000332864
0.0000039 - 0.00008484 0.00008484 - 0.000327654 0.0003276540.000610936 - 0.0006109360.001015625 - 0.0010156250.001460783 - 0.0014607830.001986879 - 0.0019868790.002917663 - 0.0029176630.004253137 - 0.0042531370.006236113 - 0.006236113 - 0.010323472 0.0003328640.001093695 - 0.0010936950.001949631 - 0.0019496310.002853118 - 0.0028531180.003946813 - 0.0039468130.005135612 - 0.0051356120.006467067 - 0.0064670670.007703418 - 0.0077034180.008939769 - 0.008939769 - 0.012125751
Figure 66: Predicted conditional intensity of AOD sightings based on oak class, A. biguttatus EI. Shading of cells darkens as predicted conditional intensity increases. X marks the locations of AOD sites used in the model. The final model includes Northings and Eastings to account for the localised distribution AOD sites across the potential range predicted by CLIMEX EI and oak class alone.
Table 8: Parameter estimates for the final AOD PPM.
Parameter Parameter estimate S.E slope mean (Intercept) -19.929 1.590 e-0 Northings a0.075 e-0 0.013 e-0 I(Northings2) -1.551 e-4 2.673 e-5 Eastings a0.002 e-0 0.001 CLIMEX EI a0.072 e-0 0.015 e-0 Oak class 1 aa0.390 0.523 e-0 Oak class 2 aa1.141 0.409 e-0 Oak class 3 aa1.172 0.412 e-0
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9.4 Discussion The PRATIQUE assessment indicated a number of limitations and considerations to the climatic modelling process. Agrilus biguttatus is described as a native species and as such is less suitable for Risk Assessment modelling than an introduced organism; however evidence of a recent expansion in range and abundance and questions about the origins of
Agrilus are good reason for applying CLIMEX to model potential range.
Further complications to the modelling process were identified, notably: reliable data on environmental factors that affect development could not be sourced for this species, although some data could be found for related species; furthermore, development within the sheltered environment of a tree trunk added further complications when considering temperature accumulation; sightings were reported by the public and not as part of a designed survey; and the lack of negative (absence) data weakened the reliability of the model. However, modelling proceeded tentatively with these shortfalls in mind due to the pressing need to understand this newly-defined decline syndrome.
Initial research and data analysis described the distribution of A. biguttatus, as this covers large areas of Europe includes isolated reports from North Africa the climatic range can be said to be broad. Much of its range is similar to that of host oak species, but in the
Great Britain this is not the case (A. biguttatus has been observed only in the Midlands and South East, whereas oak can be found across the country). Therefore other factors, such as climate, are likely to be influencing the distribution in Great Britain. The following climatic variables were considered in the development of the CLIMEX model described the potential range of A. biguttatus:
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a) The northern limit is likely to be influenced by summer temperature in terms of
Day Degrees.
b) Overwintering survival is likely to occur in cold regions. Members of the genus
Agrilus have been shown to have high tolerance of cold conditions. This has not
been investigated for A. biguttatus although as development takes place within the
sheltered environment of the host tree cold is less likely to affect development.
c) Low rainfall areas are more likely to contain suitable hosts. Drought has been
reported as an important predisposing factor to A. biguttatus attack.
The resulting model matched with most of the observed locations across Europe, although it did not predict presence in the Alps where a number of sightings have been recorded.
Fine scale analysis of elevation placed all these records within valleys, in a micro climate that is not accounted for in the large scale (10 arc minute) CLIMEX parameters.
Preliminary investigations of the UK sightings and elevation also showed a match with low lying areas, although much of this variation is explained by the fine scale (5 km) climate data.
By using data from continental Europe to build the model the British sightings can be compared to the predicted EI across Britain. Generally the distribution of AOD sites matched with the CLIMEX model for A. biguttatus, falling in the south east of the United
Kingdom. The record observations of A. biguttatus fall within areas with greater EI, although validation of the model is difficult without any known negatives. The CLIMEX model predicts a wider range than is observed in the location data.
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The projections of the CLIMEX model in North America and the Pareto Risk Map do not agree with the areas at risk. The methodologies used to generate these maps differ greatly, with the initial North American risk map created simply by selecting areas with similar environments or biomes. A recent modelling project has generated results similar to the Pareto Risk Map (Yemshanov, 2013). However this model included no climate parameters that would influence the beetle itself, instead focusing on point of introduction, putative host distribution, and drought stress that would make the trees suceptable. The resulting model predicted highest risks in the south of the USA, and area that is predicted to be climatically less suitable using the CLIMEX parameters developed in this model. The NAPPFAST climate model achieves a close match to the CLIMEX model, although it does predict a wider range that is notably different in Europe.
Analysis of the spatial patterns of AOD records shows that they occur in close proximity to NBN A. biguttatus sightings, although they do not form part of the same pattern as the latter are clustered across the locations of all available records. This clustering in the
London area is likely an artefact of reporter bias although it may represent either the population density of beetles or their historic range. More sightings are likely if the species have been present longer and these do, generally, represent older sightings although with the bias in mind it becomes difficult to differentiate the possibility of proliferation of entomological awareness and a proliferation of the beetle itself. 1987 was both the year of an extreme climatic event that has been suggested as a trigger for population increase, and the year the red data book for insects was published. The fact that the CLIMEX model predicts a greater potential range for the beetle than has been observed could be indicative of a changing distribution where room for future expansion
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remains, although there are other explanations for this as discussed below, notably limitations in the available data used to develop the model.
The distinctions between the predicted ranges and observed reports are highlighted in the
Point Process Model for AOD reports. This indicates a significant correlation between the conditional intensity of AOD sightings and both the abundance of host oak and the climatic suitability for A. biguttatus. These covariates on their own still leave much of the distribution unexplained and the model is has a poor fit of residuals. Within the oak and
EI only model points are still locally clustered, so the inclusion of Northings terms is required not only to explain more of the variation but also to make the final model predict a Poisson process (and remove the need for interaction terms). The polynomial term for
Northings focuses the positive model predictions into a band across the Midlands, which fits with the observed distribution. The inclusion of a non-biological covariate is indicative of the discrepancy between the CLIMEX predictions of EI and the location of both AOD records and A. biguttatus sightings, as both fall within a more restricted area of the country. Reasons for these discrepancies include: the CLIMEX model overestimates the potential for establishment; recorded locations do not reliably represent what is happening on the ground; and Agrilus biguttatus has not yet occupied the available climatic niche. If this final point was proved true it would raise questions about the native status of the beetle, although given the available data it is difficult to make conclusive statements either way.
The process of data gathering and modelling has identified factors that may influence the lifecycle and distribution of A. biguttatus. Given the preliminary nature of this investigation, many information gaps have been identified, however this process should
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enable future work to address present shortfalls in a systematic fashion. The modelling process has identified potential factors that limit the distribution of the beetle, which could be tested in future studies. Key information is required in order to developing an improved model namely:
a) Designed randomised surveys for A. biguttatus noting presence and absence.
b) Laboratory based studies investigating development thresholds for all life stages.
c) Improved knowledge of host susceptibility.
With this information the climatic suitability for A. biguttatus could be more accurately predicted, which would enable the co-occurance with AOD sites to be considered less subjectively and may begin to answer questions surrounding the native status of the beetle.
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9.5 Acknowledgements: The analyses presented in this chapter were initiated under the following project:
Framework for the future deployment of modelling in support of Plant Health, sub- contract PH0441 WP11: Climex mapping of AOD and Agrilus biguttatus in the UK
Nathan Brown, Dominic Eyre, Richard Baker, Mike Jeger, Sandra Denman
A contract between FERA and FR was agreed in October 2011, initiating a pilot study which aimed to investigate environmental parameters that influence the distribution of
Agrilus biguttatus in Britain using climate based modelling. This process involved training Nathan Brown (PhD student working on AOD) to develop models using the
CLIMEX software. A final end point is aimed at developing a model that can be used to investigate relationships and correlations between AOD and Agrilus biguttatus.
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10 General discussions Acute Oak Decline involves a complex of factors that act to reduce the health of both species of native oak, Q. robur and Q. petraea, in Great Britain. Affected trees can be clearly distinguished by distinctive external symptoms which aid diagnosis while causal agents are investigated. Galleries and exit holes of the buprestid beetle A. biguttatus are found in conjunction with patches of necrotic phloem. Isolations from the necrosis frequently yield bacteria of which two species are found consistently and considered the putative causal agents, Gibbsiella quercinecans and Brenneria goodwinii. This study has shown that the two sets of agents, beetle and bacteria, co-occur and suggests that understanding the interaction between them is of paramount importance to further the current knowledge regarding the nature of the decline.
The research questions raised in the introduction to this thesis have been addressed, with the studies providing the following answers:
Objective 1.1: The historical literature describes various bouts of stem bleeding on oak,
although no causal agent has been consistently isolated among many
studies that focus on fungal pathogens.
Objective 1.2: Agrilus biguttatus has been described as a pest of oak that most often
appears in the later stages of decline. Some authors present the beetles role
as opportunistic once oak have reached the point of no return whereas
other authors present a causal role in tree mortality. Callusing and healing
of larval galleries has also been documented. Affected trees are often
observed to have stem bleeding.
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Objective 2.1: At all eight monitoring sites symptoms were observed to develop on trees
that were previously unaffected. The rate of this occurrence varied greatly
between sites.
Objective 2.2: AOD symptomatic trees died during the study period. The rate of
mortality was greatest among trees that had both stem bleeds and exit
holes when the site was first mapped. No tree was observed to develop
symptoms and then die within the study period, so this process it though to
take more than 2 years.
Objective 2.3: Symptomatic trees were observed to enter remission. The cession of
symptoms was seen in up to 44% of symptomatic trees
Objective 2.4: Affected trees were spatially aggregated within most study sites.
Objective 2.5: Localised spread from older symptomatic trees was observed at one of the
study sites. This site showed the largest number of newly infected trees
and was a close to homogenous environment; the stand was open with no
understorey.
Objective 2.6: Stem bleeding is most prevalent in the summer months at the same time
that new exit holes appear on oak trees.
Objective 3.1: Prism traps caught all four species of Agrilus that are associated with oak
in Great Britain. Catch numbers tended to be low and locally clustered.
Purple traps caught more beetles than other colours, but the tested lures did
not increase trap catch.
Objective 3.2: Prism traps caught most beetles on the ride edges, with no difference
found between the tested heights.
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Objective 3.3: Agrilus biguttatus was the only species caught at all trapping sites,
although never the only species present. The smaller canopy dwelling
species A. latticornis and A. angustulus were the most abundant, but the
larvae of these species do not affect the main stems of oak.
Objective 4.1: Stem bleeding and exit holes were observed to co-occur on the same host
trees. Across all years approximately 30% of trees with stem bleeding also
had exit holes.
Objective 4.2: Exit holes were more likely to appear on trees that already had stem
bleeding. The bleeds themselves developed without a particular
association with pre-existing exit holes.
Objective 4.3: All larvae found in the main stem of oak trees were A. biguttatus, although
A. sulcicollis was found at some sites and observed laying eggs on the
main stem.
Objective 4.4: Gibbsiella quercinecans was isolated from an adult A. biguttatus, although
only in a single instance. None of the other bacterial species associated
with the stem necrosis were isolated.
Objective 5.1: The distributions of A. biguttatus and AOD are found in the same areas of
Great Britain.
Objective 5.2: A CLIMEX model was developed to describe the A. biguttatus
distribution. This model was mostly influence by temperature
accumulation (day degrees) and seasonal rainfall. The model can also be
used to fit the locations of AOD sites.
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Insects and plant pathogens frequently affect the same host so a framework was presented by Leach (1940) to define the type of interaction and examine possible roles of insects in transmission of disease. The first stage is to examine the co-occurrence of the two agents, which has been investigated at multiple spatial resolutions in this study.
At the most local scale, the level of the individual tree, necrotic symptoms and signs of beetle activity co-occur. Externally almost one third of trees with stem bleeds were also found with exit holes (Chapter 6). The degree of association greatly increases when the outer bark is removed and examinations focus on the phloem. Here more than 90% of symptomatic trees investigated for bacterial isolations also showed larval galleries. A sequential order can be seen in the development of external symptoms with exit holes most likely to appear on trees that already had stem bleeds; however the symptoms below the bark are not so clear. In the phloem, galleries are found more consistently on trees with stem bleeding and are found almost always when stem bleeds are investigated by bark removal. This finding would suggest that beetle attack occurs before the stem bleeds, but their relation to necrosis below the bark remains unclear. Given uncertainty about the latency of infection and the time delay between necrosis in the phloem and externally visible stem bleeding, it remains possible that larvae are introduced to the system after bacterial infection. What is clear is that not all colonisation (Chapter 3) attempts by A. biguttatus are successful, and that host defences may play a role in limiting emergence.
More generally the high proportion of trees in remission from stem bleeding symptoms
(as high as 44% over 3 years) is indicative that the relationship with the host is not a singular progression to death, but balanced by other host factors. This finding provides some optimism regarding the long term outcome of AOD infected trees.
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At the level of the site (Chapter 3), stem bleeding symptoms were observed most frequently on the southerly aspects of trees and the sites that showed the greatest increases in the number of affected trees were the most open. These open environments are likely to receive the most sunlight and be ideal for both egg laying and bacterial growth. Stem bleeds were associated with hosts with more heavily declined crowns, indicating that necrosis is detrimental to tree health (or arise due to poor health).
Although this observation does not rule out the need for hosts to be weakened before infection by the bacteria can occur, in the literature A. biguttatus is often associated with such hosts. Trees with stem bleeding were shown to be found in local aggregations
(Chapters 3 & 4) and further to cluster around trees with exit holes (Chapter 6). From this pattern it is possible to present a scenario where exit holes represent the source of future infections; however, it is also worth noting that trees in the centre of a group are likely to have been infected for the longest period. Exit holes have been shown to be a later stage of infection, often only appearing in the final years before death, one in four trees initially observed with both symptoms and signs died over 3 years (Chapter 3).
When considering the national distributions (Chapter 9), it is clear that reports of trees with AOD symptoms occur in the same areas of the country as records of A. biguttatus.
The two patterns however are not the same as they vary in intensity, with AOD sites most common in coastal East Anglia and entomological records centred close to London.
These variations may simply represent variations in the presence and awareness of the two sets of recorders, but without a more detailed, systematic survey it is impossible to tease these factors apart. The entomological records have existed over a far longer time period and when separated chronologically appear to show an expansion in the range of sightings from the early 1990’s. This trend has been attributed to increased host
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abundance and a changing climate: it is, however, uncharacteristic for a native species to expand its range on such a large scale. When a species is well established and there are no physical barriers to dispersal it can be expected to fill its niche in the environment. A fast expansion in the range occupied by a species would be more characteristic of an introduction, but could also arise in the data where the species had been under-recorded in the past. The distribution of AOD sites is concentrated in areas where oak is more abundant and where CLIMEX modelling predicts A. biguttatus is climatically suited.
AOD sites were however not found across the full range predicted by these two covariates. This could be an artefact of the modelling process, given the limited data available to build the model a wide degree of error is expected. Although it does raise further questions about range expansion, either the beetle has not fully filled its range, or the bacteria are a new component to the system.
The later chapters in this thesis study aimed to take the initial steps in assessing the potential for A. biguttatus to directly transmit the putative causal agents between hosts. A first step is to assess whether the association between hosts and insects extends to healthy plants in conditions suitable for the transmission of disease. This was not documented directly, although trapping of adult beetles (Chapter 7) was possible using prism traps that are designed to mimic the main trunk of a tree. It is therefore likely that females visit stems without the need for disease symptoms to be present as traps contained no AOD specific cues. A further step in Leache’s principles is to look for the pathogen in or on the insect. While the adult beetles were matched with larvae found within oak trees, the causal agents were not found frequently at any life stage (Chapter 8). Only one of the bacterial species was isolated on a single occasion from a single beetle, so there is little evidence for a direct role as a vector. So while the beetle and bacteria have both been
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shown to be strongly linked with each other and with declining trees, their exact relationship requires further study. As a link to the causal agents could not be shown no attempt was made to reproduce the disease experimentally, so this remains as a future research question.
Future research should focus on defining the interactions between the causal agents, in this respect a number of approaches could be used to address the key issues:
1) Continued monitoring of the monitoring sites established during this study will
enable further spread of symptoms to be documented and importantly allow
more accurate estimates of mortality rates and remission.
2) Further analysis of the monitoring site data could develop the spatial models to
include dynamics beyond isotropic spread. For example poisson regression
analysis could be used to assess correlations between disease incidence and
environmental factors, although the large number of healthy trees would have
to be accounted for (perhaps using zero-inflated models). The spatial data
could also be considered a network, and links between trees could be defined,
perhaps to account for openness and understory.
3) Data documenting the within tree symptom development could be used to
inform a system of ordered differential equations that would explore the
relationship between symptoms, mortality and remission.
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4) There is little evidence for transmission of the bacteria by the beetle, but
cannot be fully ruled out. A physiological examination of the beetle for
structures that may harbour bacteria beyond the gut may prove useful, along
with a meta-genomic assessment of the microbial fauna associated with the
beetle.
5) Defining the relationship between the beetle and host trees would also provide
useful insights. Cues, such as host volatiles, may attract females to egg-laying
sites, either directing them to weakened trees or even to AOD affected trees if
there are stem-bleed-specific cues.
6) Understanding the defence mechanisms of oak, whether modulated through
genetic predisposition or the health status of the host, would allow remission
to be investigated and potentially allow for the selection of resistant trees.
7) The impact of site management on beetle populations, could manipulation of
the local environment affect breeding success? For example, making the
environment less open through the promotion of understory and working in
smaller areas when coppicing and felling.
8) Assessment of the national distribution of AOD and A. biguttatus by collecting
accurate survey data would allow the scale of the problem to be defined and
establish if range expansion is occurring.
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In summary: there are many unknowns surrounding Acute Oak Decline but it appears to involve two manifest symptoms, caused by a group of bacteria and a buprestid beetle, which have been shown to significantly co-occur. An understanding of the relationship and interactions between these two sets of agents is the key to developing further the current knowledge of AOD. Importantly, the host tree also appears to influence the roles taken by the two agents in the decline syndrome, and in some instances appears able to overcome their combined attempts to colonise the host. Data obtained during these studies suggests that affected trees are found in open areas within woodland. A finding that can be used to direct survey and monitoring effeorts and influence future stand management in order to limit the future spread of the condition. The current study has also set out a framework for the further examination of this decline complex informing the continuing research projects that aim to safeguard the future of woodland trees: a vital component of Great Britain’s environment and culture.
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Acknowledgements
This work was funded by the Forestry Commission. I am grateful for the help and supervision provided by Mike Jeger (Imperial College) and Sandra Denman (Forest
Research), as well as the helpful insights that many others have offered along the way. At
Imperial College, Jon Knight and Simon Archer helped in numerous aspects of supervision.
Collegues at Forest Research provided expertise in numerous aspects of woodland ecology and epidemiology. Entomological discussions with Daegan Inward, Dave
Williams, Nigel Straw and Dave Wainhouse helped to develop the topic of Agrilus beetles. The molecular lab component was supported by the work of Gavin Hunter, Sarah
Plummer and Jonathan Partridge. The construction of prism traps was guided by Clive
Muller in the Alice Holt workshop. Finally my “Buddy”, Susan Kirk, ensured that the many aspects of the project stayed on track, the lab work was documented and that I was not lost in the woods.
Additional funding was provided by DEFRA which allowed the CLIMEX mapping work to be undertaken jointly with Dominic Eyre and Richard Baker at FERA in York. This process was incredibly helpful in developing the understanding of AOD across Britain.
The final thank you is perhaps the most important and it goes to all the site managers, owners, charities, and organisations that have been supportive of AOD research. In particular those who have enabled the monitoring and experiments conducted during the last four years.
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Data sources for Agrilus map:
Biological Records Centre, Countryside Council for Wales, Herefordshire Biological
Records Centre, Kent & Medway Biological Records Centre, Leicestershire & Rutland
Coleoptera, National Trust, Natural England, Norfolk Biodiversity Records Centre,
Royal Horticultural Society, Suffolk Biological Records Centre, Tullie House Museum,
Worcester Biological Records Centre, and the personal records of Dr Kieth Alexander.
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Appendix 1: Distribution records for Agrilus biguttatus
COUNTRY LOCATION SOURCE
United NBN DATA: http://www.nbn.org.uk/ (accessed March. 2011) Kingdom United Forest Research DDAS database Kingdom United Selected references: Alexander, K. A. (2003) Changing distributions of Cantharidae and Kingdom Buprestidae within Great Britain (Coleoptera). In: Reemer, M., van Helsdingen, P. J. & Kleukers, R. M. J. C. (eds.) Proc. 13th Colloquium of the European Invertebrate Survey. Leiden, Nederland, EIS. pp.87- 91. [Online] [Accessed 28/1/11].
Allen, A. A. (1973) Agrilus biguttatus F. (Col., Buprestidae) at Windsor; with some account of its history in Britain. Entomologist's Record and Journal of Variation. 85, 12-14.
Allen, A. A. (1988) Notes on Agrilus pannonicus (Pill. and Mitt.) (Coleoptera: Buprestidae) in 1985. Entomologist's Record and Journal of Variation. 100, 25-28.
Foster, A. P. (1987) Agrilus pannonicus (Piller and Mitterpacher 1783) (Col.: Buprestidae) and other noteworthy insects recorded from Hampstead Heath in 1984. Entomologist's Record and Journal of Variation. 99, 153-155.
Gibbs, J. N. & Greig, B. J. W. (1997) Biotic and abiotic factors affecting the dying back of pedunculate oak Quercus robur L. Forestry. 70 (4), 399-406.
Hackett, D. S. (1995) The jewel beetle Agrilus pannonicus in the London Area. London Naturalist. 74, 161-164. Europe GBIF: www.gbif.org/ (accessed Dec. 2011) Algeria Davis, E. E., French, S. & Venette, R. C. (2005) Mini risk assessment: Metallic beetle Agrilus biguttatus Fabricus [Coleoptera: Buprestidae]. [Online]. United States Azerbaijan Davis, E. E., French, S. & Venette, R. C. (2005) Mini risk assessment: Metallic beetle Agrilus biguttatus Fabricus [Coleoptera: Buprestidae]. [Online]. United States Belarus Davis, E. E., French, S. & Venette, R. C. (2005) Mini risk assessment: Metallic beetle Agrilus biguttatus Fabricus [Coleoptera: Buprestidae]. [Online]. United States Belgium Buggenhout forest and Vansteenkiste, D., Tirry, L., Van Acker, J. & Stevens, M. (2004) Soignes forest Predispositions and symptoms of Agrilus borer attack in declining oak trees. Annals of Forest Science. 61 (8), 815-823. Denmark Funen and Jaegersborg Pedersen, H. Jorum, P. (2009) The jewel beetle Agrilus bituttatus Hegn (Fabricius, 1777) found in Denmark (Coleoptera, Buprestidae). [Danish] Entomologiske Meddelelser. 77: 1, 19-26 France Paris, Loire valley, Jacquiot, C. (1976) Tumors caused by Agrilus biguttatus Fab. attacks Vosges, Normandy on the stems of oak trees. Marcellia. 39 (1), 61-67. Germany Revierförsterei, Trochel Habermann, M. & Preller, J. (2003) Studies on the biology and control des Niedersächsische, of two-spotted lichen buprestid (Agrilus biguttatus Fabr.). Forst Und Rotenburg, Germany Holz. 58 (8), 215-220. Germany Baden-Wurttemberg, Moraal, L. G. & Hilszczanski, J. (2000) The oak buprestid beetle, Rheinland-Pfalz Agrilus biguttatus (F.) (Col., Buprestidae), a recent factor in oak decline in Europe. Journal of Pest Science. 73 (5), 134-138.
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Germany Northern Hartmann, G. & Blank, R. (1992) Winter frost, insect defoliation and attack by Agrilus biguttatus as causal factors in the complex of oak decline in Northern Germany. Forst Und Holz. 47 (15), 443-452. Italy Venetia, Tuscany, Riziero, T. Ragazzi A., A. Marianelli, L. Sabbatini, P. Roversi, P. F. Marche and Latium (2002) Insects and fungi involved in oak decline in Italy. Bulletin (Italy) OILB/SROP. 25: 5, 67-74. Latvia http://leb.daba.lv/Coleoptera.htm (Accessed Jan. 2012) Morocco Davis, E. E., French, S. & Venette, R. C. (2005) Mini risk assessment: Metallic beetle Agrilus biguttatus Fabricus [Coleoptera: Buprestidae]. [Online]. United States Poland Niepolomice forest Moraal, L. G. & Hilszczanski, J. (2000) The oak buprestid beetle, district, near Krakow. Agrilus biguttatus (F.) (Col., Buprestidae), a recent factor in oak Also lower silesia. decline in Europe. Journal of Pest Science. 73 (5), 134-138. Poland Southwestern part of Hilszczanski, J. & Sierpinski, A. (2006) Agrilus spp. the main factor of Poland (Odra valley, oak decline in Poland. IUFRO Working Party 7.03.10 Proceedings of Krotoszyn lowland) the Workshop 2006. Gmunden/Austria. Poland Bialowieza Primeval Davis, E. E., French, S. & Venette, R. C. (2005) Mini risk assessment: Forest, Wroclaw, Metallic beetle Agrilus biguttatus Fabricus [Coleoptera: Buprestidae]. Radom, Poznan, and [Online]. United States Department of Agriculture. [Accessed 28/1/11]. Zielona Gora Russia Voronej region Starchenko, I. I. (1931) Agrilus biguttatus Fab. in Shipov Forest, Voronezh province [Southern Russia] (A paper from Shipov Research Forest) Zashchita Rastenii ot Vreditelei [Plant Protection] 7(4-6): 303- 306. Spain Madrid Davis, E. E., French, S. & Venette, R. C. (2005) Mini risk assessment: Metallic beetle Agrilus biguttatus Fabricus [Coleoptera: Buprestidae]. [Online]. United States Department of Agriculture. [Accessed 28/1/11]. USSR Volgograd and Rostov Kryukova, E. A. (1976) Insects and vascular mycosis of oak. regions [Russian] Zashchita Rastenii. 5, 42-43.
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