Comparison of methods to prioritise plant diseases and their hosts for surveillance

doi:10.21203/rs.3.rs-2587588/v1

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Comparison of methods to prioritise plant diseases and their hosts for surveillance

https://doi.org/10.21203/rs.3.rs-2587588/v1

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published 11 Jan, 2024

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Non-native plant pests/pathogens are a mostly overlooked threat to biodiversity. Surveillance for plant diseases is key to early detection yet is rarely undertaken in semi-natural habitats. Currently there is no standard methodology available to help managers prioritise where surveillance should occur. This study compared five potential methods (M). Prioritisation of: pests/pathogens most likely to establish (M1); plant genera known to host the pests/pathogens most likely to establish (M2); habitats known to host the greatest number of pests/pathogens most likely to establish (M3); plants classed as foundation species (those that drive ecosystem functioning and support populations of dependent biodiversity) (M4); habitats with low plant species diversity and hence low resilience (M5). Twelve habitats and 22 heathland vegetation communities in the UK were used as a case-study.

M1 gave 91 pests/pathogens to monitor and relied on having up-to-date lists of pests/pathogens relevant to plant species in semi-natural habitats. M2 gave 121 plant genera to monitor across all habitats and 14 within heathlands. M3 and M5 prioritised different habitats because M3 is based on existing lists of pests which are biased towards those of commercial importance. M4 gave 272 foundation species for surveillance across all habitats and 14 within heathlands.

Surveillance of habitats and plants prioritised on potential ecological impact (M4-5) is recommended rather than known pests/pathogens (M2-3) as this avoids biases within existing lists of pests/pathogens, removes the need for the prioritisation to be regularly updated as new pests/pathogens are identified and takes account of impacts on associated biodiversity and ecosystem functions.

invasive

monitoring

pathogen

pest

plant health risk register

wildlife disease monitoring

Due to the close links between wildlife diseases and human health (Guberti et al. 2014) there has been considerable work on establishing strategies for wildlife disease surveillance (e.g. Boadella et al. 2011; Ciliberti et al. 2015; Guberti et al. 2014; Hartley, Lysons 2011; Holmes et al. 2019; Kuiken et al. 2011; Lawson et al. 2021; Maas et al. 2016). In comparison there has been little equivalent work for plant diseases in semi-natural habitats outside of forests. Plant diseases are defined here as the plant health consequences of biotic agents, i.e. pests and pathogens, referred to as pests throughout. Plant diseases caused by non-native pests have been described as an insidious, mostly overlooked threat to biodiversity (Jonsson, Thor 2012) and the cause of extinction cascades (Hultberg et al. 2020). The invasion of alien species, which includes non-native pests, are one of the five direct drivers of global biodiversity loss (IPBES 2019). Plant diseases are therefore a serious threat to the conservation of our biodiversity, yet we lack appropriate surveillance in most semi-natural habitats. This contrasts with agriculture, horticulture and forestry where the risks associated with plant pests are well known, and these industries have clear guidance for surveillance, monitoring and reporting in many countries.

Plant pests can have a devastating impact on local plant species (e.g. Herms, McCullough 2014; Jacobs 2007; Potter et al. 2011; Wingfield et al. 2008). For example, the chestnut blight pathogen Cryphonectria parasitica killed billions of American chestnut trees following its introduction into North America in the early 20th century. Dutch elm disease, Ophiostoma novo-ulmi, killed around 28 million elm trees in the UK between 1970 and 1990 (Brasier 2008), and is still impacting trees in the north of UK where the last natural populations of elm remain. In Australia, South Africa and Europe the invasive pathogen Phytophthora cinnamomi continues to cause enormous damage to native woody ecosystems (Brasier 2008). Most recently, the ash dieback epidemic, caused by the invasive fungal pathogen Hymenoscyphus fraxineus, is estimated to have cost the UK around £15 billion due to associated loss of numerous ecosystem services (Hill et al. 2019). Large-scale declines in native plant species caused by plant diseases can lead to a range of cascading effects on associated biodiversity (species that use the host plant for feeding, breeding and shelter) and ecosystem functioning, (Ellis et al. 2012; Gandhi, Herms 2010a; Gandhi, Herms 2010b; Hultberg et al. 2020; Lõhmus, Runnel 2014; Lubek et al. 2020; Mitchell et al. 2014; Mitchell et al. 2019).

Surveillance is fundamental to the early detection of disease, allowing time for disease control measures to be implemented. Despite the well documented impacts that plant diseases can have on biodiversity and ecosystem function, surveillance of plant diseases in semi-natural habitats, outside of forests, is rare. Developing a plant health surveillance programme is challenging as it involves considering a wide range of pests and hosts. Currently there is no advice or methodology available to help managers of semi-natural habitats prioritise where any surveillance should occur. This study explored five methods for prioritisation of surveillance (Table 1). Prioritisation of: 1) those pests most likely to establish; 2) those plant species or genera known to host the pest that are most likely to establish; 3) those habitats known to host the greatest number of pest that are most likely to establish; 4) those species classed as foundation species and whose decline in abundance would drive changes in ecosystem functioning and cascading changes in the populations of dependent biodiversity; 5) those habitats with low species diversity and hence likely to have low resilience. Surveillance approaches may either use direct surveillance for the pest (Method 1) or indirect surveillance (Methods 2–5), monitoring the health of either plant host species or habitats. Prioritization of plant species and habitats for surveillance can either be based on the known risks (existing lists or risk registers of plant pests, Methods 2–3) or based on potential ecological impact (Methods 4–5).

Table 1

The five methods (M) for prioritisation of surveillance for plant diseases in semi-natural habitats.
Type of monitoring	What to monitor	Type of method
Type of monitoring	What to monitor	Known risks	Potential ecological impact
Direct monitoring	Pests	The pests most likely to establish prioritised (M1).	N/A
Indirect monitoring	Plant species/genera	Plants known to host the greatest number of known pests that are most likely to establish prioritised (M2).	Foundation species whose loss or decline would drive changes in populations of associated species and ecosystem services prioritised (M4).
Indirect monitoring	Habitat	Habitats that host the greatest number of known pests that are most likely to establish prioritised (M3).	Habitats with low species diversity which therefore might be lest resilient due to lack of functional redundancy prioritised (M5).

The use of existing lists or risk registers of plant pests to prioritise pests and plant species or habitats for surveillance may result in the analysis having to be redone regularly as: i) new pests that are not included on a current risk register are identified; ii) the severity of existing pests changes due to climate change; iii) new hosts for a given pest are identified or a pest jumps between hosts. An alternative is to identify plant species or habitats where pests would have the greatest ecological impact using ecological theories about a) foundation species and b) resilience and diversity. Foundation species are “a single species that defines much of the structure of a community by creating locally stable conditions for other species, and by modulating and stabilizing fundamental ecosystem processes” (Dayton 1972). If a foundation species is lost or declines in abundance due to a pest than it will have a greater effect on the ecosystem than if non-foundation species are impacted. One can therefore argue that if resources are limited, surveillance should be prioritised for foundation species. There is no list of foundation species on which to draw but, given the above definition, a simplistic assumption is that those species that occur at high abundance are most likely to be foundation species (Method 4). Diverse communities are generally considered more stable and more resilient than less diverse communities (Dovciak, Halpern 2010; Naeem, Li 1997; Tilman et al. 2006) (Method 5). In part, this is because they are likely to have high functional redundancy with species able to substitute for each other if species are lost due to pests, because other species are present within the system that fulfil similar functions (Laliberte et al. 2010; Pillar et al. 2013; Rosenfeld 2002). Plant pests are likely to have a greater ecological impact on habitats with low resilience meaning that those habitats with low diversity should be prioritised.

To assess the potential advantages and dis-advantages of the different methods this work used the UK as a case study. Using the UK Plant Health Risk Register (PHRR) (Defra 2021b) and the UK’s National Vegetation Classification (NVC) system the study aim to provide a prioritised list for 12 habitats across the UK and for 22 heathland vegetation communities to show how the methods might work at different scales.

The analysis used two datasets: The UK Plant Health Risk Register (PHRR) (Defra 2021b) and the UK National Vegetation Classification (Rodwell 1992; Rodwell 1991a, b, 1995, 2000).

2.1 The UK Plant Health Risk Register

The UK PHRR (Defra 2021b) provides information (e.g., host range, distribution and regulatory status) for more than 1000 plant pests. The pests include bacteria, fungi, insects, mites, nematodes, oomycetes, phytoplasma, viruses and viroids, which are all collectively referred to throughout as pests. The PHRR includes a likelihood (of occurrence) score, ranging from 1-low to 5-high. The calculation of the likelihood score differs depending on whether the pest is already present in parts of the UK (Defra 2021a). If the pest is absent from all parts of the UK then the likelihood score is composed of two sub-scores, those of entry into and establishment within the UK. The PHRR uses the lower of the two scores of entry and establishment. This is because both entry and establishment are necessary for a pest to be introduced. The limiting step for introduction of a pest is therefore whichever component is least likely. The UK PHRR usually only includes pests which are present in limited areas of the UK, not those that are present nationwide. For those pests already present in the UK the likelihood score is based on how likely the pest is to spread to maximum extent in the next five years. The PHRR provides likelihood scores both with and without mitigation. This study used the likelihood scores with mitigation assuming that all mitigations, such as import prohibition on the key hosts, had been implemented. The impact assessment from the PHRR were not used as this is largely based on the impact on commercial operations not the natural environment (Defra 2021a).

The PHRR allows one to search by pest and provides a list of hosts (Baker et al. 2014). For each pest the list of hosts is contained as one data entry point (cell) in the spreadsheet. The data was manipulated to provide a separate record for each pest/host combination, allowing one to search by host and obtain a list of all pests listed on that host. The host name was further sub-divided into host genera and host species to allow searches to be made at either genera or species level.

2.2 The UK National Vegetation Classification

The vegetation of the UK is classified by the National Vegetation Classification system (NVC). This lists 12 habitat types: Aquatic communities; Calcicolous grasslands; Heathlands; Mires; Maritime cliff communities; Mesotrophic grasslands; Open habitats; Swamps and tall-herb fens; Shingle, strandline and sand-dune communities; Salt Marsh; Calcifugous grasslands and montane communities; Woodlands. Within each habitat are vegetation communities. In some instances, there are further sub-communities, but this study only used the habitats and the community level data. For each community, the NVC lists the frequency and abundance (percentage cover presented as Domin scores) of the plant species present. In this study the abundance data was used and, where a range was given the higher value was used.

2.3 Linking the datasets

The two datasets were linked together in an MS Access database at the plant genera level since a) it is unlikely that many native UK plant species will be included as hosts in the PHRR, rather their commercial varieties are more likely to be included (Defra pers. Comm.) and b) this takes account of pests establishing on new hosts within the same genera. For all the methods below bryophytes, lichens and algae were removed from the analyses as the PHRR did not provide pests for these species.

For each of the methods below the analysis was conducted at two levels; a) at the habitat scale; b) at the community scale. The study focused on the 22 communities within the heathland habitat as an example of how the methods may provide information at this scale. Unless otherwise stated, the study focused on plant species occurring at 25% cover or more within the communities, as they were thought to form a significant part of the community and would cause ecosystem change if loss or a decline in abundance resulted from a pest.

2.4 Surveillance of pests (M1)

The PHRR was searched for any pest hosted by genera occurring in semi-natural habitats at more than 25% cover in the UK. The list was then refined by mitigated likelihood of the pests occurring, selecting those pests with a likelihood of 4 or 5, the highest two categories. This provided a list of all the pests that are most likely to establish in the UK within these habitats and thus for which surveillance should be put in place. The list of pests was then refined for heathlands to show which pests were hosted within different heathland vegetation communities. This provides a list of pests for each community for which surveillance should be put in place.

2.5 Surveillance of hosts (M2)

All the plant genera occurring at more than 25% cover and hosting at least one pest with a mitigated likelihood of 4 or 5 were extracted. Those genera that host the greatest number of pests are suggested as being prioritised for surveillance. Lists of genera are provided for each habitat and then for each heathland vegetation community.

2.6 Surveillance of habitats known to host the greatest number of pests (M3)

Pests with a mitigated likelihood of 4 or 5 hosted by genera occurring at over 25% cover in each habitat or heathland community were extracted. Those habitats or heathland vegetation communities that host the greatest number of pests were prioritise for surveillance.

2.7 Surveillance of foundation species (M4)

Plant species occurring at more than 75% cover in any plant community in the NVC were extracted. The assumption was made that those species occurring at high abundance are likely to be foundation species. It is acknowledge this is an oversimplification. However, a) loss or decline of species occurring at more than 75% cover will have a major impact on community composition, even if they may not strictly be defined as foundation species; and b) this provided a pragmatic approach to help managers identify what might be considered foundation species. A list of plant species categorised as foundation species, and thus suggested for surveillance, was produced for each habitat and for each heathland vegetation community.

2.8 Surveillance of habitats with low resilience (M5)

The number of species in each vegetation community (excluding algae, lichens and bryophytes) that occur at more than 25% cover were extracted. To asses whether habitats differed in their species richness (surrogate measure for resilience), generalized linear effect models with a Poisson distribution using the glm function within lme4 (Bates et al. 2015) were used in R version 3.6.2 (R Core Team 2018). Tukey’s pair-wise comparisons were used to determine differences between pairs of habitats, and P values were adjusted using the Tukey correction method for multiple tests (Lenth 2019). For each habitat the average number of species per a community was calculated, and those habitats with the lowest average species diversity are suggested as being prioritised for surveillance. For the vegetation communities within the heathland habitat, those communities with the lowest species richness were suggested as being prioritised for surveillance.

3.1 Surveillance of pests (M1)

The PHRR lists 916 pests which could be hosted by genera that occur in semi-natural habitats at more than 25% cover in the UK. If this list is refined by the mitigated likelihood of the pests establishing with a likelihood of 4 or 5 (the highest two likelihood categories), then the list reduces to 91. Fourteen of these have a likelihood of 5 and 77 have a likelihood of 4 (Supplementary material Table S1). The number of pests for surveillance ranges between nine in aquatic habitats and 87 in woodland habitats (Fig. 1a, Table S1).

For heathland communities, there are 31 pests with a likelihood of 4 and four with a likelihood of 5 (Table S1). When broken down by communities, the number of pests prioritised for surveillance ranged from 18 pests in the Calluna vulgaris-Scilla verna heath community (H7) to only one pest in three of twenty-two communities (Fig. 2a, Table S2).

3.2 Surveillance of hosts (M2)

In total, across the 12 habitats, there are 121 plant genera occurring at more than 25% cover that host pests with a mitigated likelihood of establishment of 4 or 5 (the highest two categories) (Table S3). In declining order of the number of pests hosted, the genera Prunus, Solanum, Rosa, Fragaria, Acer, Salix, Ulmus, Lactuca. Rubus, Fraxinus, Pinus, Quercus, Betula, Viburnum, Allium, Brassica, Corylus, Iris, Juniperus could all host 6 or more pests with a mitigated likelihood of 4 or 5. Prunus and Solanum host more than 20 pests. Thus, those genera with the potential to host the greatest number of pests would be prioritised for surveillance.

Across the 22 heathlands communities there are 14 plant genera that can host pests with a likelihood of 4 or 5 (Table 2) and should therefore be surveyed. The number of genera to survey per a heathland community was between one and seven (Table S4). There are consistent patterns across the different heathland communities with Calluna, Erica, Festuca and Vaccinium all highlighted for surveillance in ten or more of the 22 communities and the other ten genera only suggested for surveillance in five or less of the communities.

Table 2

Comparison of the genera/species suggested for surveillance for heathland communities in the UK under Method 2 (genera known to host the greatest number of pests most likely to establish) and Method 4 (foundation species, those occurring at > 75% cover).
Genera	Species	Method 2	Method 4
Agrostis	Agrostis capillaris Agrostis curtisii		Yes
Arctostaphylos		Yes
Calluna	Calluna vulgaris	Yes	Yes
Carex	Carex arenaria, Carex bigelowii
Dactylis		Yes
Empetrum	Empetrum nigrum		Yes
Erica	Erica ciliaris, Erica cinerea,	Yes	Yes
Festuca	Festuca ovina	Yes	Yes
Genista		Yes
Juncus		Yes
Juniperus		Yes
Molinia	Molinia caerulea		Yes
Nardus	Nardus stricta		Yes
Plantago		Yes
Pteridium		Yes
Rubus		Yes
Salix		Yes
Teucrium		Yes
Ulex	Ulex europaeus Ulex gallii		Yes
Vaccinium	Vaccinium myrtillus	Yes	Yes

3.3 Surveillance of habitats known to host the greatest number of pests (M3)

When compared across all the habitat types, woodlands (87 pests) and open habitats (54 pests) have the potential to host the greatest number of pests listed in the PHRR with a likelihood (mitigated) of 4 or 5. Aquatic habitats and salt marshes, on the other hand, have the least at 9 and 14, respectively. This approach would therefore suggest prioritising surveillance towards woodlands and open habitats (Fig. 1a).

If the analysis is refined to the 22 heathland vegetation communities, then it is possible to target communities at risk. If those communities able to host the greatest number of pests with a likelihood of establishment (mitigated) of 4 or 5 are prioritised, then the four communities Calluna vulgaris-Scilla verna heath (H7), Vaccinium myrtillus-Cladonia arbuscula heath (H19), Calluna vulgaris-Ulex gallii heath (H8) and Vaccinium myrtillus-Racomitrium lanuginosum heath (H20) (Fig. 2a) should be prioritised.

3.4 Surveillance of foundation species (M4)

Two hundred and seventy-two plant species were identified that occur at more than 75% cover in plant communities across the UK (Table S5). However, the number of species suggested for surveillance for any one habitat was considerably lower, ranging from 71 in woodlands to 11 in Calcicolous grasslands (Table S5).

The 14 species (Table 2) suggested as foundation species for surveillance in the heathland habitat may be further sub-divided within the 22 heathland vegetation communities (Table S6). Two communities, Ulex minor-Agrostis curtisii heath (H3) and Calluna vulgaris-Ulex gallii heath (H8), have five species for surveillance. One community, Vaccinium myrtillus-Cladonia arbuscula heath H19, has four species for surveillance and two communities (Vaccinium myrtillus-Deschampsia flexuosa heath (H18) and Ulex gallii-Agrostis curtisii heath (H4) have three species. This approach gives two communities with no foundation species for surveillance. Calluna vulgaris-Racomitrium lanuginosum heath (H14) only has bryophyte species occurring at more than 75% cover and this approach is focussed on surveillance of vascular plants. Vaccinium myrtillus-Rubus chamaemorus heath (H22) has no species that occur at more than 75% cover.

3.5 Surveillance of habitats with low resilience (M5)

There was a significant difference between habitats in their species richness (χ2(11, N = 265) = 1705, p < 0.0001) (Fig. 1b). Under this methodology those habitats with lower species richness would be prioritised for surveillance in declining order of priority: Salt Marsh, Swamps and tall-herb fens, Heathlands, Mires, Aquatic communities, Calcifugous grasslands and montane communities, Open habitats, Maritime cliff communities, Calcicolous grasslands, Shingle, strandline and sand-dune communities, Mesotrophic grasslands and Woodlands. However, there can be large variation in the species richness between communities within a habitat and therefore how resilient that community might be. Within the heathlands habitat (Fig. 2b) H21: Calluna vulgaris-Vaccinium myrtillus-Sphagnum capillifolium heath is suggested as the least resilient, with only two species occurring at more than 25% cover and H8: Calluna vulgaris-Ulex gallii heath as the most resilient with 21 species occurring at over 25% cover.

This work compares methods to prioritise plant pests and their hosts for surveillance in semi-natural habitats to provide early detection and rapid eradication where possible. There are many examples of the ranking of wildlife diseases for surveillance (e.g. Boadella et al. 2011; Ciliberti et al. 2015) but there have been few attempts to rank their hosts (Cardoso et al. 2022). To the author’s knowledge this is the first time an attempt at prioritisation has been made for plant diseases and their hosts in semi-natural habitats outside of forests. The results suggest that prioritisation of plant species or habitats for surveillance based on potential ecological impact (as assessed using ecological theory), rather than risk (known pests) provides a less biased (towards plants of commercial importance) list and allows for the detection of new diseases, the known unknowns.

4.1 Surveillance based on current known risks or potential impact

Surveillance based on known risks (M1-3) assumes that a complete list of pests is available and that their risk of establishment has been correctly assessed. A literature review by Mitchell (2023) identified a further 142 pests hosted by plant genera present on UK moorlands that are not listed on the PHRR. These additional pests may include species that are already present and widespread in the UK, and hence not included in the risk register, e.g. heather beetle Lochmaea suturalis Thomson, but whose severity or distribution could change with climate change (Scherber et al. 2013). A further example is that one of the most concerning current plant health threats, the bacterial plant pathogen Xylella fastidiosa which has a host range causing multiple disease outbreaks across a wide range of plant species (Rapicavoli et al. 2018), is only listed in the PHRR as a threat for a limited range of host species. Xylella fastidiosa is known to be hosted by Calluna vulgaris (L.) (Chapman et al. 2022) but C. vulgaris is not included as a host in the PHRR. As shown for wildlife disease surveillance (Boadella et al. 2011), any surveillance method needs to be flexible enough to incorporate new diseases. Thus, the problem of prioritising surveillance based on current known risks is that the prioritisation process must be repeated every time a new pest or host is identified.

The four indirect surveillance methods (M2-5) gave very different sets of species/genera or habitats to prioritise, depending on whether the prioritisation was based on known risks or potential impact (Table 2, Figs. 1–2). Methods M2 and M4 for heathland communities gave a combined list of 24 genera for surveillance. However, only three genera are common to both lists (Calluna, Erica, Vaccinium) (Table 2). The differences between M2 and M4 are due to the PHRR being biased towards pests of commercial importance, with the hosts listed being predominantly relevant to agriculture, horticulture or forestry (Mitchell 2023). For example, M2 for heathlands lists Dactylis, Juncus, Plantago, Salix and Teucrium, which are all genera of some commercial importance; none of these genera are listed under M4. M2 at the habitat scale also has a predominance of genera that have commercial value, e.g., Prunus, Solanum, Rosa. The basis towards plants of commercial importance then influences which habitats or communities are prioritised in M3 and M5, with M3 prioritizing habitats or communities with plants related to commercial species. Deciding which parameters to target for surveillance is key to improving surveillance of wildlife diseases (Boadella et al. 2011) and this study shows how the targets for surveillance change depending on how the prioritisation is carried out.

Direct monitoring of known pests often requires specialist skills in their identification, which are often in short supply. Indirect monitoring via the health of plants allows for a wider participation. Once unhealthy plants are identified, then the specialists can be used to identify whether the disease is of concern. However, we currently lack a baseline of what “healthy” looks like for many plants, or at least what the contemporary disease burden is. The indirect monitoring approaches enable baselines to be established to provide comparisons for changes in the future in plant health in semi-natural habitats. While this study has focused on declines in plant health caused by pests, the use of indirect monitoring via the health of plants would also allow the monitoring of damage caused by abiotic agents, such as climate change, e.g. heat or water stress.

Prioritisation based on potential ecological impact rather than known risk has many advantages. The ranking of hosts according to whether they are foundation species (M4), allows surveillance to be targeted on those plants whose decline would have the greatest impact on wider biodiversity, ecosystem function and service delivery. The ranking therefore takes account of the wider risks to the whole ecosystem rather than just the risk to a specific plant species. Ranking according to which habitats are least resilient (M5) also takes account the wider ecosystem level impacts, targeting those habitats or communities that may recover less quickly. Both approaches allow for the detection of new pests and pathogens, and do not require a reprioritisation of the list every time a new pest is found.

This work used the UK as a case-study, but the difficulties identified here in basing priorities for plant health surveillance in semi-natural habitats on existing risk registers are valid globally. The methods trailed here for prioritisation of foundation species or habitats of low species richness (assumed to have low resilience) could be implemented in any country.

4.2 Way forward

Plant disease surveillance is a key, but often missing tool, within the conversation toolkit to help alleviate the biodiversity crisis and ‘bend the curve’ (Leclere et al. 2020; Mace et al. 2018) of biodiversity losses. If plant disease surveillance was implemented in semi-natural habitats it could reduce further biodiversity declines in not only host species but associated biodiversity dependent on the host.

Wildlife disease surveillance is more advanced than plant health disease surveillance in semi-natural habitats. Many of the challenges and recommendations around implementing wildlife disease surveillance (e.g. Cardoso et al. 2022) are relevant to plant disease surveillance. One of the challenges for wildlife disease monitoring is the split in responsibilities for wildlife-related issues among administrations and departments (Cardoso et al. 2022). Plant disease surveillance in semi-natural habitats crosses the conservation-disease management sector boundaries. The condition of habitats, M3 and M5 in this study, is usually viewed as the remit of conservation organisations, while disease surveillance (M1) or plant health (M2 and M4) is usually viewed as the remit of forestry, agriculture and horticulture. As in wildlife disease surveillance (Lawson et al. 2021), plant health surveillance needs to join up across these different sectors.

Resources are always a limiting factor, but experience in wildlife disease surveillance (Cardoso et al. 2022) shows that combined, cross-collaborative efforts allow establishing acceptable schemes with a low enough cost to be sustainable over time. The incorporation of citizen science into wildlife health surveillance can be beneficial, particularly where resources are limited, and cost-effectiveness is paramount. Citizen science facilitates large-scale surveillance, both in time and space, which would otherwise be financially infeasible, and raises awareness of incidents occurring on privately owned land (Lawson et al. 2015). In the UK, there are citizen science schemes for tree health (Tree alert) but no equivalent alert scheme for non-tree plants. The need to adopt a collaborative and multidisciplinary approach to plant health surveillance needs to be recognised and the general public can make a significant contribution through citizen science.

This study has focused on identifying which pests, hosts or habitats to prioritise for surveillance. To be a fully functional plant disease surveillance scheme, further work is required around establishing a reasonable sampling effort and a suitable sampling stratification to ensure detection of changes over time. Details of how the surveillance is conducted, whether through statutory agencies or with support of citizen science, need to be agreed and the appropriate resources put in place. Any plant surveillance schemes should ideally have a joined-up approach across borders and should certainly communicate its findings to enable appropriate action in neighbouring countries.

Acknowledgements

The work was funded through the Plant Health Centre (Scotland’s Centre of Expertise for Plant Health) and Scottish Government’s Rural and Environment Research and Analysis Directorate 2022-2027 strategic research programme project JHI-D4-2. The author thanks Duncan Stone and Pete Hollingsworth for useful discussions, which formulated many of the ideas in this manuscript and for their, and Robin Pakeman and Ian Toth’s, comments on earlier drafts.

Conflict of interest

The author declares no conflicts of interest and has no relevant financial or non-financial interests to disclose

Author contribution statement

RJM conceived the idea, did the analysis and wrote the manuscript

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Comparison of methods to prioritise plant diseases and their hosts for surveillance

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Abstract

Figures

1 Introduction

2 Method

2.1 The UK Plant Health Risk Register

2.2 The UK National Vegetation Classification

2.3 Linking the datasets

2.4 Surveillance of pests (M1)

2.5 Surveillance of hosts (M2)

2.6 Surveillance of habitats known to host the greatest number of pests (M3)

2.7 Surveillance of foundation species (M4)

2.8 Surveillance of habitats with low resilience (M5)

3 Results

3.1 Surveillance of pests (M1)

3.2 Surveillance of hosts (M2)

3.3 Surveillance of habitats known to host the greatest number of pests (M3)

3.4 Surveillance of foundation species (M4)

3.5 Surveillance of habitats with low resilience (M5)

4 Discussion

4.1 Surveillance based on current known risks or potential impact

4.2 Way forward

Declarations

Acknowledgements

Conflict of interest

Author contribution statement

References

Supplementary Files

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