DOI: 10.5061/dryad.ngf1vhhxg
title: Neighbours matter and the weak succumb: chalara ash dieback infection is more severe in trees with fewer conspecific neighbours and lower prior growth rate

description: The epidemiology and severity of ash dieback (ADB), the disease caused by
 the ascomycete fungus, Hymenoscyphus fraxineus, has been linked to a
 variety of site conditions, however, there has been a lack of analysis at
 an individual-tree scale.  Symptoms of ADB were scored
 on ca. 400 trees of Fraxinus excelsior (ash) in permanent sample plots
 during two successive years in a UK natural woodland reserve. Using
 comprehensive plot records maintained since 1945, and detailed spatial
 records updated since 1977, we assembled an array of potential explanatory
 variables, including site environment factors, ash tree density, previous
 and present tree condition and near neighbourhood summary statistics
 (NNSS), such as species mingling and size dominance. Their impact on the
 severity of ADB of focal ash trees was tested with generalised linear
 mixed effects models (GLMM). The severity of ADB was much greater
 in the lower slope parts of the site with moister soils and least in a
 managed area subject to tree thinning in the previous 35 years. Severity
 of ADB had a negative association with focal ash tree prior relative
 growth rate over a period of a decade immediately before the disease was
 detected at the site. Greater ADB severity was also significantly
 associated with smaller diameter-at-breast-height of ash trees.
 Additionally, ADB was significantly positively associated with a greater
 proportion of heterospecific trees amongst the six nearest neighbours of
 the focal tree.  Synthesis. The relationship of the
 severity of ADB disease with site environment, tree condition and
 neighbourhood is complex but nevertheless important in the progression of
 the disease. The findings suggest some silvicultural interventions, such
 as thinning to increase the vigour of retained ash trees, might reduce the
 impact of ADB.
description: There is a long history of monitoring at the site with rare
 longitudinal records from repeated censuses of all trees in ten long thin
 permanent sample plots, termed “transects”. Hand-drawn maps of the
 locations of the centre of each individual woody plant ≥1.3 m height in
 transects I-VI were recorded on a Cartesian coordinate grid by reference
 to transect edge markers with a regular 30.5-m spacing in 1977. Transects
 VII-X were similarly mapped in 1984–85. These records were updated and
 checked for accuracy at each successive enumeration of each transect,
 including in 2013 before ADB was first observed in the woodland and in
 anticipation of its arrival. For the present study, these maps were
 scanned digitally using Esri ArcGIS Pro® 2.7.3 (2021) software onto
 Ordnance Survey digital maps and allocated their British National Grid
 coordinate values. The geographical location of the centre of each tree
 was normalised for slope. After the initial enumeration of each transect,
 all woody plants ≥ 1.3 m height (with no minimum diameter limit) in
 transects I-IX were identified to species, recorded as live or dead and
 measured for diameter at breast height (1.3 m, dbh) in 1955, 1977 and
 1983, and in all ten transects in 1986, 1992, 2000, 2002, 2010, 2013 and
 2018. In the case of the many multiple-stemmed coppice stools, an
 equivalent single diameter was assigned, calculated from the sum of their
 stem cross-sectional areas.  A total of 464
 <em>F. excelsior</em> trees across the ten permanent
 transects, representing all live individuals ≥1.3 m height at the start of
 the study in 2019, were assessed for ADB symptoms in July and August 2019,
 and 381 during August 2020. The lower number in the second year is due to
 the increased restriction on field-work due to risk assessment and
 restriction of available time under that constraint. Therefore, our main
 statistical models were only applied to the more complete data set
 recorded in 2019. We have used the 2020 data solely to assess the overall
 rate of progression of the disease at the time of the study.
 The severity of crown dieback was scored between 0 and 100% based
 on a visual estimation of proportionate level of defoliation, similar to
 the assessment methods used in other studies of ADB (Turczanksi, 2020;
 Grosdidier et al., 2020; Lenz et al., 2012). Two observers surveyed each
 focal tree according to an agreed scale of crown defoliation in scoring
 classes of 0–10%, 10–20%, 20–30%, 30–40%, 40–50%, 50–60%, 60–70%, 70–80%,
 80–90%, 90–100%, with their assessments calibrated at regular intervals
 against photographs of previously agreed percentage defoliation scores to
 ensure scoring did not drift over the study. Independent scoring of the
 same tree by each of the observers was conducted periodically to guard
 against observer bias and the scoring of the transects was carried out in
 random order to avoid confounding with topographic/stand type. Ash trees
 with a score of 0% crown dieback and no trunk sprouting or other visible
 sign of infection were classified as uninfected. Two
 periods of recorded prior growth rates were selected: 1977 to 2013, and
 2000–2002 to 2013. Basal area of each tree was also calculated from the
 dbh measurements as an explanatory variable in its own right for focal ash
 trees, but also to calculate further explanatory variables, such as the
 total basal area of the stand. Basal area of larger trees (bal), an
 indicator of the level of competition for light on the focal tree at the
 stand level (Pommerening & Grabarnik, 2019; Wykoff, 1990), was
 calculated as the total basal area of all trees larger or equal to the
 basal area of the focal tree <em>i</em> in the same sub-plot
 at a particular time t (see Table 2, formula 5 in the MS).  By “sub-plot”
 here we mean the regular subdivisions of the transect plots permanently
 marked by stakes at 400-ft (121.9-m) intervals down the slope, which were
 used for ease and accuracy of the successive enumerations of the transects
 but were also, it transpired during our study, the most ecologically
 appropriate for near-neighbour spatial analysis. The size of each sub-plot
 of the transects was 19.8 m x 121.9 m (2414 m<sup>2</sup>) and
 they were arranged so that each lay entirely within either the "old
 growth" or "young growth" stands". An alternative
 measure of a focal tree’s crown dominance relative to its neighbours was
 provided by the recording, in previous enumerations, of each tree’s crown
 position in four categories: ground, understorey, sub-canopy, and
 canopy. Spatial point-process methods were used to
 compute the Euclidean distances between nearest-neighbour points within
 each 2414 m<sup>2</sup> sub-plot (as illustrated in Figure 2
 in the MS). The ‘spatstat’ package in R was used to estimate near
 neighbour summary statistics (NNSS). Using ‘spatstat’, a number of
 established individual-tree neighbourhood indices were calculated
 (Baddeley, Rubak & Turner, 2015; Illian et al., 2008; Pommerening
 & Grabarnik, 2019) for use as explanatory variables, as outlined
 below. We used the minus-sampling “NN1” nearest neighbour edge-correction
 method outlined by Pommerening & Grabarnik (2019), in turn, based
 on Hanisch (1984), for all these NNSS.  (i) Species
 mingling (mi) and weighted species mingling (wmi) Both
 species mingling (mi) and richness-weighted species mingling (wmi) are
 spatially-explicit diversity indices. Species mingling (mi) is a
 calculated value between zero and one representing the proportion of
 heterospecific (rather than conspecific) trees in the near neighbourhood
 of the focal tree (see Table 2, formula 1 in the MS). So, if there are
 four neighbours counted and three are heterospecific, the focal tree is
 given a species mingling value of 0.75. The total number of nearest
 neighbours (k) can be varied according to the ecological context. We used
 a version of the formula which incorporates weighting by the number of
 species in the k nearest neighbours, known as “richness-weighted species
 mingling” (wmi) (Hui et al., 2011), with the weighting effect limited by
 the number of species present in the focal tree’s sub-plot (which sets the
 maximum possible number of species in k + 1 trees at that location) (Wang
 et al. 2021) (Table 2, formula 2). The wmi index is thus a more
 sophisticated version of the mingling index, which encompasses the
 component of species richness in a tree’s heterospecific near
 neighbourhood, producing a greater variation in index values between
 trees. However, to test the use of wmi for assessing the dominance of ash
 trees’ neighbourhoods by heterospecific trees, we examined the linear
 correlation of wmi with mi for the 464 ash trees included in the study;
 the Pearson correlation co-efficient (r) was 0.89 giving us full
 confidence in selecting wmi for this purpose.  (ii)
 Size dominance (ui) The size dominance index (ui)
 measures the size of a focal tree relative to its k nearest neighbours
 (see Table 2, formula 3 in the MS). We used stem diameter (dbh) to
 describe size, but other size characteristics are also possible. The
 calculation of this index is similar to that of the species mingling
 index, with a value between zero and one, and the potential to vary k (Hui
 et al., 1998; Aguirre et al., 2003; Pommerening & Grabarnik,
 2019).  (iii) Local tree density (l)
 Local tree density around a focal tree (l) was calculated using a
 kernel smoothing function in R (Baddeley, Rubak & Turner, 2015).
 It was originated by Diggle (1985) as “a method for estimating the local
 intensity” as a point process, and it includes edge correction within the
 formula (see Table 2, formula 4 in the MS). 
description: R, Spatstat, Excel