DOI: 10.5061/dryad.3xsj3txc9
title: Small scale variability in soil moisture drives infection of vulnerable juniper populations by invasive forest pathogen

description: The oomycete plant pathogen, Phytophthora austrocedri, is an aggressive
 killer of cypress trees causing severe mortality of Chilean cedar
 (Austrocedrus chilensis) in Argentina since the 1940s and now common
 juniper (Juniperus communis s.l.) in the UK. Rapid mortality of key UK
 juniper populations was first observed in the early 2000s; the causal
 agent of mortality was confirmed as P. austrocedri in 2012 and the
 pathogen has now been widely detected - but is not ubiquitous - in juniper
 populations across Scotland and England. Although juniper has a broad
 distribution across the northern hemisphere, the UK incidence of P.
 austrocedri remains the only confirmed infection of juniper populations
 globally. Juniper is an important species for biodiversity, so it is
 imperative to understand the abiotic and biotic drivers of emergent P.
 austrocedri infection to inform detection, containment and conservation
 strategies to manage juniper populations across the full extent of its
 range. As management of UK juniper populations is primarily conducted at a
 local level, we investigated field scale drivers of disease – in three,
 geographically separate populations with different infection histories.
 Variation in the proportion of juniper showing symptoms - discoloured or
 dead foliage – was measured using stratified sampling across along key
 environmental gradients within each 100-hectare population, including
 juniper density identified from aerial imagery. Potential predictors of
 infection included altitude, slope, distance to nearest watercourse, soil
 moisture (mean percentage volumetric water content), area of red deer
 browsing damage and area of commonly associated vascular plant species. We
 assessed support in the data for alternative models explaining the spatial
 distribution of P. austrocedri symptoms using full subset covariate
 selection and Deviance Information Criteria (DIC). Despite differences in
 environmental gradients and infection histories between populations, area
 of juniper symptomatic for P. austrocedri increased with waterlogging,
 increasing with soil moisture in sites where soils had higher peat or clay
 contents, and decreasing with proximity to watercourses where sites had
 shallower, sandier soils. These results are consistent with key drivers
 identified at both local and landscape scale in Chilean cedar. Our
 approach enables identification of site-specific disease management
 strategies including prioritisation of inspections in microsites with high
 soil moisture and promoting conservation measures such as creation of
 sites for natural regeneration in drier microsites to minimise pathogen
 spread and maximise the resilience of existing juniper populations.
description: Three infected juniper populations from where
 <i>Phytophthora austrocedri</i> had previously been isolated
 (Henricot et al., 2017) were selected to best represent the diversity of
 climatic, topographic and edaphic conditions occupied by juniper in the
 UK. In all three locations, the juniper population is a component feature
 of a Special Area of Conservation designated habitat and a qualifying
 interest of a Site of Special Scientific Interest (SSSI). Two populations
 are in Scotland: one in Perthshire (P, Glenartney SSSI) and one in the
 Cairngorms (C, North Rothiemurchus Pinewood SSSI), and one population is
 situated in the Lake District (LD, Birk Fell SSSI) in the north of
 England. Juniper was sampled using 10 x 10 m quadrats
 from pre-selected locations stratified according to the area and density
 of juniper, altitude, slope and distance to watercourses. Quadrat sampling
 was carried out over five days at each location in October 2017. Quadrat
 centroids were geo-located using ArcPad v. 10.2 on a Panasonic FZ-GI
 tablet with GPS accuracy to 3 m and recorded as eastings “X” and northings
 “Y” in the British National Grid geographic reference system (datum =
 OSGB_1936). Number of sampled quadrats: P = 51, LD = 46, C = 50.
 The following predictors were measured in each
 field quadrat. “Area.of.symptoms”
 (m<sup>2</sup>) was estimated as a fraction of the
 "Area.of.juniper” (m<sup>2</sup>) present in each
 quadrat, where symptoms constituted foliage discolouration and dead
 needles (retained or dropped) that extended to a minimum of a whole branch
 and did not result from either browsing or mechanical damage.
 Where a distinctive phloem lesion typical of
 <i>P. austrocedri</i> could be found, a 500 mg tissue sample
 was collected from one representative symptomatic tree per quadrat. The
 sampled tissue was stored at - 20 °C until quantitative real-time PCR
 (qPCR) could be carried out following the protocol described in Mulholland
 et al. (2013). A positive “qPCR.result” = 1; absence of symptomatic
 lesions or “not detected” “qPCR result” = 0.  
 Area of juniper cover
 “Juniper.density” across 30 x 30 m including the 10 x 10 m quadrat was
 estimated as ≤ 20 % (0) or &gt; 21 % (1) to distinguish between
 quadrats situated in isolated or contiguous juniper
 stands. Area of “Berry.bearing”
 (m<sup>2</sup>) juniper was used to estimate the area of
 female juniper. Area of “Herbivore.damage”
 (m<sup>2</sup>) was estimated as the area of bark stripping
 plus any resulting dead branches / stems (i.e. mechanical breakage from
 wind or snow damage was excluded). Soil moisture was measured as % volumetric
 water content (VWC) using a FieldScout TDR 300 probe at 20 cm depth in
 Perthshire and the Cairngorms, and 3.8 cm depth in the Lake District.
 Measurements were collected from i) areas within each quadrat where
 juniper was absent, ii) under asymptomatic juniper and iii) under
 symptomatic juniper. An equal number of measurements (minimum four) was
 collected from each category present, resulting in eight to twelve point
 sample measurements from which the mean “Mean.soil.moisture” (% VWC) was
 calculated. Area (m<sup>2</sup>) of vascular
 plant taxa present in each quadrat was recorded according to a target list
 (Appendix B) of taxa chosen to indicate placement of microsites along soil
 moisture, nitrogen and pH gradients. Where other tree species were
 present, canopy cover was included in the estimation of area.
 “Area.of.trees” (m<sup>2</sup>) is the combined total area of
 tree species present in each quadrat. The Ellenberg F score attributed to each taxon
 was extracted from Hill, Preston, & Roy (2004).
 “Mean.Ellenberg.F.value” was calculated as a
 weighted mean of Ellenberg F scores based on the area of each taxon
 recorded in each quadrat. The 50 m digital
 rivers network (Moore et al., 2000) was clipped to the boundary of each
 study population and merged with additional watercourses mapped in the
 field using the tracking function in ARCpad. “Watercourse.proximity” (m)
 was then calculated by measuring Euclidean distance to the nearest
 watercourses from quadrat XY coordinates using the gDistance function in
 the rgeos package (Bivand and Rundel, 2017). The same methods were used to map
 deer and sheep tracks and lie-ups across each study site and calculate
 distance to nearest evidence of “Grazing.activity” (m). Activity was
 likely underestimated across all sites, but particularly in the Cairngorms
 and Lake District. The remaining covariates were
 obtained from existing GIS datasets. A digital elevation
 model (DEM) of each study site was supplied by NeXTPerspectives™ (updated
 February 2014) at 5 m resolution and averaged to 10 m using the
 <i>aggregate </i>function in the raster package (Hijmans,
 2016) implemented in R v. 3.4.0 (R Core Team, 2017). Layers
 of slope and aspect were calculated from the resampled 10 m DEM using the
 <i>terrain</i> function in the raster package (Hijmans, 2016).
 Altitude (m), slope (°) and aspect (°) were extracted to the XY coordinate
 of each sampled quadrat. “Soil.type” was extracted to
 quadrat centroids from 250 m resolution datasets, obtained from a
 digitised version of the soil map produced by Forbes (1984), the
 Soilscapes dataset (Farewell et al., 2011) and the National Soil Map of
 Scotland (James Hutton Institute, 2011) for the Perthshire, Lake District
 and Cairngorms populations respectively. National Vegetation
 Classification (NVC) community data, supplied by Scottish Natural Heritage
 (2017), was included as a covariate for the Perthshire and Cairngorms
 populations. The eight Perthshire communities were simplified to four
 broad types, amalgamated as follows: acid grasslands (U4 <i>Festuca
 ovina</i> - <i>Agrostis capillaris</i> - <i>Galium
 saxatile</i>; U20 <i>Pteridium aquilinum</i> -
 <i>Galium saxatile</i>; U24 <i>Arrhenatherum
 elatius</i> - <i>Geranium robertianum</i>), mires (M10
 <i>Carex dioica </i>– <i>Pinguicula vulgaris</i>;
 M23 <i>Juncus effusus/acutiflorus </i>– <i>Galium
 palustre</i>) and mosaic communities suggesting transition from
 drier to wetter soil (U5 <i>Nardus stricta</i> –
 <i>Galium saxatile</i>; M15 <i>Trichophorum
 germanicum</i> - <i>Erica tetralix</i> wet heath)
 (Rodwell, 1991). Topographic
 wetness index (TWI) is calculated as ln(a/tanb) where a is the specific
 catchment area and b is the local slope (Raduła, Szymura and Szymura,
 2018). There are three stages to TWI calculation: 
 Preparation of
 the DEM and slope (and river) rasters Creation of a
 flow accumulation raster from the DEM using a flow routing
 algorithm Creation of
 TWI raster using the flow accumulation and slope rasters
 As there are
 so many pre-processing and flow routing algorithm options, a validation
 step was added to the workflow in order to choose the best TWI layer
 developed for each population on the strength of the Pearson correlation
 between the TWI value and mean soil moisture (% VWC) obtained for each
 sampled quadrat. The water catchment for each study
 population was downloaded from the National River Flow Archive (https://nrfa.ceh.ac.uk/, accessed March 2018) and imported to SAGA GIS v. 2.3.2 (Conrad <i>et al.</i>, 2015). TWI calculation using the 10 m DEM performed well in the Perthshire and Lake District populations but poorly in the Cairngorms population where 25 m resolution yielded a stronger correlation. Each DEM was clipped to extent of the catchment and prepared using the “fill sinks XXL Wang/Liu” algorithm to prevent the premature termination of drainage that can occur where local elevation minima have no lower neighbours as an artefact of DEM generation and do not relate to terrain features (Reuter et al., 2009). The algorithm progressively increases the elevation values of these “sinks” until the lowest elevation value is found from which the water can “spill” out into the rest of the cell (Wang and Liu, 2006). As the DEM used for the Cairngorms population was of a coarser resolution, explicitly incorporating the river network using the “burn stream network to DEM” tool improved the calculation. This tool lowers the value of each DEM cell that interfaces with a watercourse pixel by the minimum elevation in a neighbouring cell minus one (Conrad et al<i>.</i>, 2015). The flow routing algorithm that performed best for the Cairngorms and Perthshire populations was Mass Flux, which divides each pixel into quarters and routes flow at that scale so flow can be routed in different directions where this is ambiguous (Gruber and Peckham, 2009). The recursive implementation of the deterministic eight algorithm, which apportions flow from each grid cell to a single adjacent cell through the steepest upslope gradient (and uses arbitrary assignment where flow direction is unclear), led to the strongest correlation and, therefore, best performing TWI layer for the Lake District population (O’Callaghan and Mark, 1984).
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