# Impacts of local adaptation of forest trees on associations with herbivorous insects: implications for adaptive forest management

Frazer H. Sinclair, Graham N. Stone, James A. Nicholls, Stephen Cavers, Melanie Gibbs, Philip Butterill, Stefanie Wagner, Alexis A. Ducousso, Sophie Gerber, Rémy J. Petit, et al.

► To cite this version:

Frazer H. Sinclair, Graham N. Stone, James A. Nicholls, Stephen Cavers, Melanie Gibbs, et al. Impacts of local adaptation of forest trees on associations with herbivorous insects: implications for adaptive forest management. Evolutionary Applications, 2015, 8 (10), pp.972-987. 10.1111/eva.12329. hal-02635020

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ORIGINAL ARTICLE

# Impacts of local adaptation of forest trees on associations with herbivorous insects: implications for adaptive forest management

Frazer H. Sinclair,¹,² Graham N. Stone,¹ James A. Nicholls,¹ Stephen Cavers,³ Melanie Gibbs,² Philip Butterill,²,⁴ Stefanie Wagner,⁵,⁶ Alexis Ducousso,⁵,⁶ Sophie Gerber,⁵,⁶ Rémy J. Petit,⁵,⁶ Antoine Kremer⁵,⁶ and Karsten Schönrogge²

1. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK

2. Centre for Ecology and Hydrology, Wallingford, UK

3. Centre for Ecology and Hydrology, Edinburgh, UK

4. Faculty of Science, Biology Center, The Czech Academy of Sciences, University of South Bohemia, České Budějovice, Czech Republic

5. INRA, UMR 1202 BIOGECO, Cestas, France

6. UMR 1202 BIOGECO, University of Bordeaux, Talence, France

**Keywords**

adaptive forest management, climate matching, gallwasp, local adaptation plant-insect interactions, population nonindependence, provenance trials, *Quercus petraea*.

**Correspondence**

Graham N. Stone, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3JT, UK.
Tel.: +44 131 6507194;
fax: +44 131 6506556
e-mail: graham.stone@ed.ac.uk

Received: 17 August 2015

Accepted: 20 August 2015

doi:10.1111/eva.12329

**Abstract**

Disruption of species interactions is a key issue in climate change biology. Interactions involving forest trees may be particularly vulnerable due to evolutionary rate limitations imposed by long generation times. One mitigation strategy for such impacts is Climate matching – the augmentation of local native tree populations by input from nonlocal populations currently experiencing predicted future climates. This strategy is controversial because of potential cascading impacts on locally adapted animal communities. We explored these impacts using abundance data for local native gallwasp herbivores sampled from 20 provenances of sessile oak (*Quercus petraea*) planted in a common garden trial. We hypothesized that non-native provenances would show (i) declining growth performance with increasing distance between provenance origin and trial site, and (ii) phenological differences to local oaks that increased with latitudinal differences between origin and trial site. Under a local adaptation hypothesis, we predicted declining gallwasp abundance with increasing phenological mismatch between native and climate-matched trees. Both hypotheses for oaks were supported. Provenance explained significant variation in gallwasp abundance, but no gall type showed the relationship between abundance and phenological mismatch predicted by a local adaptation hypothesis. Our results show that climate matching would have complex and variable impacts on oak gall communities.

**Introduction**

Ecosystems across the world are experiencing profound climate change (IPCC, 2013), driving population responses whose effects can influence community structure and function (Walther et al. 2002; Parmesan and Yohe 2003). Forest ecosystems are expected to be particularly sensitive to pressures imposed by climate because the long generation times of trees result in low rates of adaptation (Lindner et al. 2010). Furthermore, because forest trees are foundation (and also often keystone) species for associated communities of plants, animals, fungi and microbes (Kennedy and

Southwood 1984; McEwan et al. 2011; Lindenmayer et al. 2014), effects on them are expected to cascade through associated trophic levels (Frelich et al. 2012). Consequently, there is considerable interest in developing adaptive forest management strategies to preserve ecosystem productivity and services in the face of climate change (Spittlehouse and Stewart 2003; Broadmeadow et al. 2005; Aitken et al. 2008; Bower and Aitken 2008; Bolte et al. 2009). Assisted migration, in the form of climate matching, has emerged as one potential strategy facilitating forest adaptation to predicted future climates (Hoegh-Guldberg et al. 2008; Dawson et al. 2011), particularly in commercial
forestry where potential for *in situ* adaptation may be lim-
ited (Broadmeadow et al. 2005; but see Cavers and Cottrell
2014). Climate matching is based on the premise that natu-
ral forest stands are locally adapted to current local climates
(Savolainen et al. 2007, 2013; Alberto et al. 2013a), and for
a given species and planting location involves the following
steps: (i) identify climate variables that predict perfor-
mance for the target tree species across its range; (ii) use
regional climate models to predict future climates at the
proposed planting site; (iii) identify locations within the
species range that currently experience the predicted condi-
tions; (iv) plant seed from tree populations in the identified
locations (termed provenances in forestry) in the expecta-
tion that resulting trees will be well matched to future cli-
mates, and contribute adaptive variation that accelerates
subsequent local adaptation (Broadmeadow et al. 2005).

As remediation strategies, climate matching and other
forms of assisted migration remain subjects of vigorous
debate (Dawson et al. 2011; Lunt et al. 2013; Cavers and
Cottrell 2014). Forests are biologically diverse ecosystems,
and the value of climate matching, or any other strategy
that modifies the genetic, physical or spatial structure of
tree populations, must be considered not only in terms of
its effectiveness in promoting forest adaptation and resili-
ence, but also its impacts on local associated biodiversity.
Community genetic research has shown that the organisms
associated with forest stands reflect the genotypes and
genetic diversity of the host trees (Dungey et al. 2000;
Wimp et al. 2005; Whitham et al. 2012), and a range of
studies have shown adaptation of plants to local herbivores
and *vice versa* (Sork et al. 1993; Hanks and Denno 1994;
Egan and Ott 2007; Tack and Roslin 2010; Bernhardsson
et al. 2013). As such, the introduction of new tree geno-
types as part of a climate matching strategy may drive
changes in the identity, abundance and genetics of associ-
ated species (Lefevre et al. 2014). However, it is difficult to
forecast the direction and magnitude of such changes.
While some studies predict increased abundance of herbi-
vores on non-native tree provenances (Bernhardsson et al.
2013), others suggest the opposite through local herbivore
adaptation to plant phenology (Van Asch and Visser 2007;
Visser 2008; Pearse and Karban 2013). The key issues are
the extent to which host plant provenances vary in pheno-
typic traits relevant to herbivore success, and the magni-
tude of herbivore population responses to such variation.

Here we explore the community effects of climate
matching by quantifying the impact of provenance on (i)
the phenotype (growth performance and phenology) of an
important and widespread European oak, *Quercus petraea*,
and (ii) the abundance of associated insect herbivores. Our
approach exploits an established INRA forestry trial at La
Petite Charnie in Northwest France, where 96 provenances
of *Q. petraea* grow surrounded by natural oak forest and

are exposed to native herbivores. Our study focuses on oak
cynipid gallwasps (Hymenoptera; Cynipidae), specialized
herbivores whose larvae develop in galls on their oak hosts
(Schönrogge et al. 2000; Stone and Schönrogge 2003; Har-
per et al. 2004). Gall induction requires access to host oak
tissues at the correct developmental stage (Harper et al.
2004), and oak cynipids are highly sensitive to host plant
phenology (Askew 1962; Crawley and Akhteruzzaman
1988). These aspects of gallwasp biology, coupled with a
partly parthenogenetic lifecycle, are associated with local
adaptation to specific host plant populations (Mopper
et al. 2000; Mopper 2005; Egan and Ott 2007; Tack et al.
2010) and individuals (Egan and Ott 2007) and make gall-
wasps an appropriate sentinel taxon for exploration of the
potential effects of climate matching.

We expect the performance of oak provenances to corre-
late positively with the extent to which they are adapted to
prevailing climatic conditions (Broadmeadow et al. 2005;
Lefevre et al. 2014). This hypothesis predicts that perfor-
mance of locally native provenances will decline over time
– and recent rapid climate change may mean that popula-
tions of long-lived trees are already somewhat mismatched
to the conditions in which they grow. The hypothesis also
predicts that provenances selected to match conditions fur-
thest in the future should be least well matched to current
conditions and so perform least well in the present. We test
this hypothesis using multiple performance measures for
20 provenances planted at the La Petite Charnie prove-
nance trial, selected to maximize the available range in
source latitude and longitude as well as the mismatch in cli-
mate between the provenance trial and the provenance ori-
gin. We then test two contrasting hypotheses linking
herbivorous insect abundance to observed variation in per-
formance among oak provenances. The plant stress
hypothesis predicts that herbivores should show improved
performance and abundance on physiologically stressed
plants, due to higher levels of mobilized nitrogen in
stressed plant tissues and an associated increase in resource
quality for herbivores (White 1969, 1974, 2009). In con-
trast, the plant vigour hypothesis (Price 1991; Cornelissen
et al. 2008) predicts increased herbivore performance and
abundance on more vigorous plants or plant organs (those
with high growth rates or large ultimate size relative to the
population mean) through elevated nutrient content and
reduced concentrations of defensive compounds. Here we
test both hypotheses using abundance data across prove-
nances for 20 cynipid gall types.

Phenological matching of key biological events is crucial
for many interactions between forest trees and associated
organisms. For example, synchronization of breeding with
high plant resource availability (budburst for leaf-eating
herbivores) is central to reproductive success in herbivores
and associated trophic levels. We thus expect associated
organisms to show local adaptation to host plant phenol-
ogy (Ducousso et al. 1996; Yukawa 2000; Van Asch and
Visser 2007; Van Asch et al. 2012; Alberto et al. 2013b).
Many plants show latitudinal clines in phenological events,
such as the timing of budburst, whose onset is often deter-
mined by photoperiodic cues (Savolainen et al. 2007; Ait-
ken et al. 2008) or the interaction between photoperiod
and temperature (Laube et al. 2014). This is relevant to
climate matching because, in a warming world, it often
involves introducing plants to higher latitudes from lower
latitudes. As a result, phenological mismatches with local
herbivores might occur by disrupting any adaptation to
the local combination of thermal and photoperiod cues,
with associated impacts on the fitness of herbivores and
trophically linked taxa such as natural enemies (Visser
2008; Thomson et al. 2010; Kerstes and De Jong 2011;
Van Asch et al. 2012). Here we quantify the phenotypic
mismatch in budburst date between the same 20 prove-
nances and assess the impact of phenological mismatch on
cynipid gall abundance. Specifically, we test the local
adaptation hypothesis, that is that herbivore abundance
will decline with increasing phenological difference
between introduced and locally native oak provenances.
We distinguish phenological effects from other potential
differences between provenances (e.g., variation in chemi-
cal defences) by incorporating provenance phenology into
our analyses.

Our analytical approach recognizes that tree populations
are linked by shared common ancestry and gene flow and
hence are unlikely to represent statistically independent
replicates (Stone et al. 2011). Here we investigate the
impact of potential nonindependence by comparing the
results of analyses that incorporate the expected variance-
covariance among populations in a generalized linear
mixed model framework (Hadfield and Nakagawa 2010).

**Materials and methods**

Provenance trial design

Provenance effects were explored using a large experimen-
tal trial of *Q. petraea* at La Petite Charnie in Northwest
France (Fig. 1; 48.086° north, 0.168° west; details on the
planting and layout of the trial are provided in Data S1).
We selected 20 provenances whose origins span 15° of lati-
tude and 34° of longitude, incorporating the greatest avail-
able geographic separation and source environmental
variation (Fig. 1, Table S1 in Data S1), and included the
closest provenance to the trial site (Forêt de Bercé, approxi-
mately 50 km from La Petite Charnie). Each sampled
provenance is planted in multiple replicate plots of 24 trees
distributed across five soil zones (see Data S1). We selected
two plots per soil zone per provenance, giving a total of
200 study plots containing 4800 trees. Previous INRA
surveys provided data for each tree for the following

<figure><img src="image_2.png" /><figcaption>**Figure 1** Location of the Petite Charnie provenance trials and the 20 selected study provenances. Sites are identified by number in Table 1. Countries contributing provenances are indicated by their two letter ISO codes.</figcaption></figure>
phenotypic traits (see Data S1 for illustrations of character states):

1. **Budburst** – spring budburst phenology was scored on a 0–5 ordinal scale, where 0 is a dormant bud and 5 is a fully open bud, in spring 1995 (this closely parallels that used in other studies on oak (Crawley and Akhteruzza-man 1988; Tack et al. 2010). A higher score on the survey date indicates earlier budburst. Although oak phenology is not known to show strong G × E interactions across years (Alberto et al. 2011), we nevertheless repeated phenology scoring in 2009 for 582 trees in a subset of eight provenances. Phenology scores for the same tree in 1995 and 2009 were significantly and positively correlated (see Data S4). We thus regard using the 1995 data for all 20 provenances as unlikely to produce misleading or contrary results.

2. **Diameter at breast height (DBH)** – tree diameter at a height of 1.3 m, measured during winter 2001–2002. A higher value is associated with more rapid growth and higher plant vigour.

3. **Form** – a measure based on tree shape, recorded on an ordinal scale from 1 to 10 during winter 2001–2002. Stressed trees with bushy, irregular growth and poor apical dominance receive a low score, while healthy trees with straight stems, even branching and strong apical dominance score highly. These phenotypic measures

showed substantial variation among provenances (discussed in more detail below), providing the basis for examination of impacts of phenological matching (*Budburst*) and performance (*DBH*, *Form*) on herbivore abundance (Table 1, Fig. 2).

## Herbivore sampling

The cynipid species surveyed in this study all have broad geographic distributions; like *Q. petraea* and closely related oaks, they span the Western Palaearctic from the Iberian Peninsula eastwards to Iran and the Caucasus. Cynipid gall abundance was sampled across the 20 selected provenances in 2008 and 2009. Gallwasps have a spring (sexual) and autumn (asexual) gall generation each year, and both were sampled, giving four surveys in total. For each of the 200 study plots, we sampled galls from 12 of the 24 available trees, a total of 2400 trees. To minimize edge effects, surveyed trees comprised the two internal rows of six trees within each plot. During each survey, using a pole pruner giving us approximately 4 m reach we selected 10 twigs on each tree, each consisting of the last 2 years' growth identified using ring scars on the bark. For each twig, we surveyed the galls on all parts (buds, leaves, stem, catkins, acorns; there are no cynipid galls on female flowers in Europe) of its terminal shoot (a module of woody growth from

<table><caption>**Table 1.** Summary of the 20 studied provenances of *Quercus petraea*, ordered by longitude from west to east showing their three digit INRA provenance codes, provenance origin (site name and country), latitude and longitude in decimal degrees, altitude (Alt), the availability of genotypic data and the mean values across all trees of each provenance for the phenotypic traits *Budburst*, *Diameter at breast height (DBH)* and *Form*.</caption><thead><tr><th>Site number<br>in Fig. 1</th><th>Code</th><th>Provenance<br>name</th><th>Country</th><th>Long (DD)</th><th>Lat (DD)</th><th>Alt (m)</th><th>Genotypic<br>data</th><th>Mean<br>Budburst</th><th>Mean<br>DBH</th><th>Mean<br>Form</th></tr></thead><tbody><tr><td>1</td><td>185</td><td>Blakeney</td><td>UK</td><td>-2.5</td><td>51.78</td><td>76</td><td>No</td><td>1.08</td><td>120</td><td>4.24</td></tr><tr><td><b>2</b></td><td><b>217</b></td><td><b>Bercé</b></td><td><b>France</b></td><td><b>0.39</b></td><td><b>47.81</b></td><td><b>155</b></td><td><b>Yes</b></td><td><b>1.59</b></td><td><b>98</b></td><td><b>4.2</b></td></tr><tr><td>3</td><td>237</td><td>Réno Valdieu</td><td>France</td><td>0.67</td><td>48.5</td><td>230</td><td>Yes</td><td>1.65</td><td>116</td><td>4.14</td></tr><tr><td>4</td><td>210</td><td>Saint Germain</td><td>France</td><td>2.08</td><td>48.9</td><td>60</td><td>Yes</td><td>1.71</td><td>113</td><td>4.13</td></tr><tr><td>5</td><td>194</td><td>Soudrain</td><td>France</td><td>2.38</td><td>46.95</td><td>178</td><td>Yes</td><td>1.24</td><td>110</td><td>4.33</td></tr><tr><td>6</td><td>211</td><td>Prémery</td><td>France</td><td>3.6</td><td>47.2</td><td>300</td><td>Yes</td><td>1.55</td><td>106</td><td>4.34</td></tr><tr><td>7</td><td>201</td><td>La Haie Renaut</td><td>France</td><td>4.95</td><td>48.67</td><td>180</td><td>Yes</td><td>1.77</td><td>109</td><td>4.24</td></tr><tr><td>8</td><td>245</td><td>Étangs</td><td>France</td><td>4.96</td><td>46.93</td><td>200</td><td>Yes</td><td>2.4</td><td>113</td><td>4.19</td></tr><tr><td>9</td><td>233</td><td>Vachères</td><td>France</td><td>5.63</td><td>43.98</td><td>650</td><td>Yes</td><td>3.69</td><td>94</td><td>4.15</td></tr><tr><td>10</td><td>230</td><td>Romersberg</td><td>France</td><td>6.73</td><td>48.82</td><td>220</td><td>No</td><td>1.06</td><td>101</td><td>4.17</td></tr><tr><td>11</td><td>250</td><td>Cochem</td><td>Germany</td><td>7.05</td><td>50.08</td><td>400</td><td>Yes</td><td>1.86</td><td>112</td><td>4.43</td></tr><tr><td>12</td><td>225</td><td>Still</td><td>France</td><td>7.25</td><td>48.58</td><td>688</td><td>Yes</td><td>1.55</td><td>105</td><td>4.56</td></tr><tr><td>13</td><td>252</td><td>Johanneskreuz</td><td>Germany</td><td>7.83</td><td>49.4</td><td>460</td><td>Yes</td><td>1.03</td><td>116</td><td>4.42</td></tr><tr><td>14</td><td>257</td><td>Wolfgang</td><td>Germany</td><td>9.05</td><td>50.15</td><td>160</td><td>Yes</td><td>1.61</td><td>96</td><td>3.92</td></tr><tr><td>15</td><td>181</td><td>Horbylunde</td><td>Denmark</td><td>9.41</td><td>56.13</td><td>80</td><td>Yes</td><td>0.88</td><td>89</td><td>3.99</td></tr><tr><td>16</td><td>255</td><td>Spakensehl</td><td>Germany</td><td>10.6</td><td>52.8</td><td>115</td><td>Yes</td><td>0.54</td><td>102</td><td>4.74</td></tr><tr><td>17</td><td>248</td><td>Kloster-marienberg</td><td>Austria</td><td>16.57</td><td>47.41</td><td>310</td><td>Yes</td><td>3.23</td><td>103</td><td>3.9</td></tr><tr><td>18</td><td>179</td><td>Sycow</td><td>Poland</td><td>17.93</td><td>51.18</td><td>210</td><td>Yes</td><td>1.42</td><td>104</td><td>4.76</td></tr><tr><td>19</td><td>249</td><td>Bolu</td><td>Turkey</td><td>31.67</td><td>40.92</td><td>1200</td><td>Yes</td><td>1.58</td><td>94</td><td>4.39</td></tr><tr><td>20</td><td>184</td><td>Telavi</td><td>Georgia</td><td>45.47</td><td>41.88</td><td>700</td><td>No</td><td>3.79</td><td>77</td><td>3.06</td></tr></tbody></table>

Values for the provenance closest to the trial site, Forêt de Bercé, are shown in bold.
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

**Figure 2** Between-provenance variation in three phenotypic variables (A) *Form*, in which higher values indicate healthier trees; (B) and (D) *Diameter at breast height (DBH)*, in which higher values indicate more vigorous growth; and (C) *Budburst* date, in which higher values indicate earlier budburst in the spring. In (A) and (B), the position of provenances on the *x*-axis relates to their genetic differentiation (estimated as *GeoFST*) from the provenance closest to the provenance trial site (Forêt de Bercé). In (C) and (D), their position relates to the latitude of their site of origin with the identity of Forêt de Bercé shown. Modelling was conducted in the MCMCglmm R package, and response variables were modelled with provenance fitted as a fixed effect and provenance, soil zone, plot and tree fitted as random effects. Models of (A) and (C) were fitted with a Poisson distribution, and (B) and (D) with a Gaussian distribution. Priors, burn-in, number of iterations and sampling frequency follow that described previously for the modelling of provenance effects on gall abundance. Vertical bars represent 95% confidence intervals for each mean. MCMC, Markov Chain Monte Carlo.

the previous year carrying the new leaves). Galls were identified to species and generation in the field using a morphological key (Redfern and Shirley 2002) and our own extensive field experience with this group (Stone et al. 2002; Csóka et al. 2005).

## Statistical analysis

*Controlling for statistical nonindependence of provenances*
One way to address potential statistical nonindependence of populations within species is to incorporate expected
similarities among populations (due to shared common
ancestry and/or gene flow) as one or more variance-covari-
ance matrices in the random effect structure of a generalized
linear mixed model (Stone et al. 2011). Covariance matrices
for nonindependence arising through each of migration and
common ancestry can be generated for small numbers of
populations using population genomic data (e.g., Hearn
et al. 2014). However, for larger numbers of populations
(as here), and where more limited genotypic data are avail-
able, an alternative is to use a measure of pairwise genetic
differentiation between populations such as F<sub>ST</sub> (Weir and
Cockerham 1984) as an estimate of their expected covari-
ance (Stone et al. 2011). Where genetic differentiation
between populations correlates strongly with geographic
distance, variance-covariance can be estimated as a function
of geographic separation between populations.

We used data from 10 putatively neutral microsatellite
markers (listed in Data S2; Guichoux et al. 2011) available
for 17 of our 20 study provenances (Table 1) to estimate
pairwise F<sub>ST</sub> as implemented in FSTAT version 2.8.3.2 (Weir
and Cockerham 1984; Goudet 1995). For comments on the
impact of using alternative measures of genetic differentia-
tion related to F<sub>ST</sub>, see Data S2. To allow us to analyse data
for all 20 study provenances, we used the highly significant
relationship between F<sub>ST</sub> and geographic distance for the
17 provenances (Mantel test in the adegenet R package,
P = 0.01 with 999 iterations) to estimate a pseudo-F<sub>ST</sub> for
the full set of 20 provenances, referred to hereafter as
GeoF<sub>ST</sub> (full details are given in Data S2). Variance-covari-
ances between provenances were then estimated as either
1-F<sub>ST</sub> or 1-GeoF<sub>ST</sub>, such that expected covariance was
greatest between provenances with lowest differentiation.

To explore the consequences of using alternative
approaches to control for provenance nonindependence,
all of the analyses described below were conducted using
the following five dataset/covariance matrix combinations:
(i) the subset of 17 provenances for which genotypic data
were available, using an identity matrix as the provenance
covariance matrix. This identity model assumes prove-
nances to be statistically entirely independent (i.e., 0
covariance between all populations); (ii) the same 17
provenances, using a covariance matrix estimated as 1-F<sub>ST</sub>;
(iii) the same 17 provenances, using a covariance matrix
estimated as 1-GeoF<sub>ST</sub>; (iv) the full set of 20 provenances
using an identity model; and (v) the full set of 20 prove-
nances where covariance is estimated as 1-GeoF<sub>ST</sub>. A
detailed discussion of this approach to addressing popula-
tion nonindependence, and values used in the covariance
matrices, are provided in the Data S2.

Statistical modelling of provenance effects on gall abundance

To investigate the influence of tree provenance on the
abundance of each individual gall type, we combined the

data for the two survey years and used generalized linear
mixed models (GLMMs) to partition the variance of gall
counts per shoot into the effects of year (as a fixed effect
with two levels) and provenance, soil zone, plot and tree
(all as random effects). When applying an identity model
for the relationship between provenances, the effect of
provenance was represented by a single random effect
with a unique independent level for each provenance. In
models that incorporated the relationship between prove-
nances, provenance co-variance was represented by two
random effects. One incorporated the specified variance-
covariance matrix (1−F<sub>ST</sub> or 1−GeoF<sub>ST</sub>) to account for
provenance nonindependence, while the second incorpo-
rated an identity matrix to capture the ‘nugget effect’
(Matheron 1963), in which variation in the response vari-
able exceeds that expected through correlation between
levels of the effect. Models were fitted in a Bayesian Mar-
kov Chain Monte Carlo (MCMC) framework using the
package MCMCglmm version 2.16 (Hadfield 2010) in R
version 2.15.1. For fixed effects, prior settings assumed a
normal distribution with a mean (μ) of 0 and a variance
(V) of 10<sup>8</sup>. For random effect variances, scaled F_{1,1}
priors (with scale 1000) were used (Gelman 2006) except
for the residual variance for which an inverse-gamma
prior was used with shape and scale equal to 0.001. Mod-
els were run for 500 000 iterations with a burn-in of
50 000 and parameter sampling every 450 iterations. As
the response variable was count data and usually con-
tained many zeros, a Poisson error distribution was
applied. Our incorporation of year as a fixed effect
allowed for variation in abundance between years, while
estimating the relationship between gall abundance and
tree phenotypic traits across both years. The proportion
of variance explained by the year fixed effect was esti-
mated as the marginal R<sup>2</sup> of the model (RsqM), following
Nakagawa and Schielzeth (2013) (R code for deriving
RsqM from MCMCglmm objects is provided in Data S3).
The variance component of each random effect was cal-
culated by dividing its estimated variance parameter by
the sum of all the random effect variance parameters,
including the residual UNIT term – a random effect with
a distinct level for each data unit that is fitted automati-
cally for Poisson distributed models in the MCMCglmm
package to account for overdispersion – and then multi-
plying each by the proportion of variance not explained
by the fixed effect (i.e., 1−RsqM). We addressed the
effects of multiple tests on false discovery rates – the
expected proportion of false discoveries among the
rejected hypotheses – following Benjamini and Hochberg
(1995) and Benjamini and Yekutieli (2001). The false dis-
covery rate is a less stringent condition than the family-
wise error rate, so this approach is more powerful than
alternatives such as Bonferroni (Nakagawa 2004).
## Effect of tree phenotype

To investigate whether tree phenotypic traits were good predictors of variation in gall abundance, GLMMs were used to model counts of individual gall types with *DBH* (growth), *Form* (tree health) or *Budburst* (spring phenology) as fixed effects. While the influences of tree growth and health on gall abundance are expected to be linear, the local adaptation hypothesis for herbivores predicts that the influence of phenology will be curvilinear, with lower gall abundance on provenances that show budburst either before or after local oaks. The fixed effect *Budburst* was therefore fitted as both untransformed and squared terms. To account for potential differences in abundance levels between the two study years, a factor *year* was fitted as a fixed effect.

For each gall type, counts per shoot were initially modelled with *year* and either *Form*, *DBH*, *Budburst* or both *Budburst* and *Budburst*² as fixed effects. The severity of co-linearity between these variables was assessed by their variance inflation factors (VIFs) using the R package *HH* version 2.3-23 (Heiberger 2013). To assess the importance of interactions between tree phenotypic traits, a model containing the fixed main effects *Form*, *DBH*, *Budburst*, and their three pairwise interactions was fitted for each gall type. All models were fitted in the MCMCglmm package with the random effect structures and prior specifications described above. The probability that the posterior distribution of the fixed effect coefficients did not include zero (pMCMC) was used to assess the predictive capacity of the fixed effects.

## Results

### Phenotypic variation among provenances

If provenances are locally adapted, and divergence in selection history between provenance origins and the trial site increases with genetic distance, then we expect more genetically divergent provenances to show higher stress (measured using *Form*) and lower vigour (measured using *DBH*). *Form* shows no linear relationship, but increases in variance in more divergent provenances (Fig. 2A). Only the most genetically divergent provenance, from Telavi in Georgia, showed a markedly lower *Form* score than other provenances. Plant vigour, measured using *DBH*, does decrease with increasing genetic divergence (Fig. 2B), consistent with local adaptation. Budburst phenology shows a strong relationship with latitude (Fig. 2C), with more southerly provenances showing earlier budburst: by the time some southern provenance trees have open leaves (scores 4 and 5), northern provenances have yet to burst their buds (scores 0 and 1). We thus expect phenological

mismatch with native oaks to correlate with differences in latitude between provenance origins and the trial site.

### Herbivore abundance

We recorded approximately 725 000 galls of 20 gall types (seven sexual generation and 13 asexual generation) during the four survey seasons, representing 15 different gallwasp species (Table 2). No acorn galls were recorded. Total counts of individual gall types varied over five orders of magnitude, from just 4 (asexual generation *Cynips longiventris*) to over 330 000 (asexual generation *Neuroterus anthracinus*). Gall abundance differed significantly between years for all gall types except *Andricus solitarius*, with five types exhibiting >10-fold differences (Table 2) and without any consistent pattern between years or seasons. Six of the 20 gall types occurred on <1% of shoots in both survey years and were excluded from further analysis.

### Oak provenance effects on gall abundance

The percentage of variance in gall abundance per shoot explained by differences between provenances (expressed as the mean variance component, ranging between 0% and 100%) varied substantially between the statistical models used. In identity models of the 20 provenance dataset (which assume provenances to be fully independent statistically), the percentage of variance in gall abundance explained by provenance ranged from 3% (in sexual generation *Neuroterus albipes*) to 33% (in asexual generation *Neuroterus quercusbaccarum*) and was >10% for seven of the 14 gall types (Fig. 3). Because provenances are related, the identity model is unlikely to be appropriate. All models incorporating provenance covariance gave higher estimates of the variance component for provenance effects (see Data S2 for comparison of results across the alternative datasets and models). Incorporation of provenance covariance using the 1-GeofST covariance matrix for the same 20 provenance dataset attributes between 38% (sexual generation *N. albipes* and 95% (asexual generation *N. quercusbaccarum*) of variance in gall abundance to provenance effects (Fig. 3). The identity models should thus be viewed as providing artificially low estimates of the variation in gall abundance attributable to differences between provenances (Table S2.2, Figure S2.1 in Data S2). On this basis, host tree provenance is an important predictor of the abundance of many gallers, explaining at least 10%, and as much as 95% of the variation in abundance of seven of the 14 gall types tested. Without more knowledge of the form of variance-covariance relationship, it is not possible to give a correct estimate. Importantly for what follows, analysis of fixed effects was not influenced by our choice of variance-covariance model.
<table><caption>**Table 2.** Prevalence, incidence and between-year variation in the abundance of the 20 gall types recorded at La Petite Charnie. Columns show, for each survey year, the total number of each gall type, the mean number of galls per shoot and incidence – the proportion of shoots bearing galls, and the ratio of mean galls per shoot between 2008 and 2009.</caption><thead><tr><th rowspan="2">Gall type (species and generation)</th><th colspan="3">2008</th><th colspan="3">2009</th><th rowspan="2">Ratio of mean galls/shoot 2008:2009</th></tr><tr><th>Total galls</th><th>Mean galls/shoot</th><th>Incidence</th><th>Total galls</th><th>Mean galls/shoot</th><th>Incidence</th></tr></thead><tbody><tr><td colspan="8"><strong>Spring sexual generation surveys</strong></td></tr><tr><td>Andricus inflator</td><td>52</td><td>0.0022</td><td>0.0018</td><td>0</td><td>0</td><td>0</td><td>—</td></tr><tr><td>Andricus testaceipes</td><td>973</td><td>0.041</td><td>0.0025</td><td>2291</td><td>0.095</td><td>0.067</td><td>1:2.3</td></tr><tr><td>Biorhiza pallida</td><td>13</td><td>0.00054</td><td>0.00046</td><td>10</td><td>0.00042</td><td>0.00042</td><td>1:3:1</td></tr><tr><td>Neuroterus albipes</td><td>822</td><td>0.034</td><td>0.03</td><td>5828</td><td>0.24</td><td>0.16</td><td>1:8</td></tr><tr><td>Neuroterus anthracinus</td><td>14 278</td><td>0.59</td><td>0.35</td><td>7411</td><td>0.31</td><td>0.23</td><td>1:9:1</td></tr><tr><td>Neuroterus numismalis</td><td>1154</td><td>0.048</td><td>0.039</td><td>17 486</td><td>0.73</td><td>0.29</td><td>1:15</td></tr><tr><td>Neuroterus quercusbaccarum</td><td>5808</td><td>0.24</td><td>0.15</td><td>7834</td><td>0.33</td><td>0.2</td><td>1:1.4</td></tr><tr><td colspan="8"><strong>Autumn asexual generation surveys</strong></td></tr><tr><td>Andricus callidoma</td><td>4</td><td>0.00021</td><td>0.00021</td><td>45</td><td>0.0019</td><td>0.0018</td><td>1:9</td></tr><tr><td>Andricus fecundatrix</td><td>334</td><td>0.017</td><td>0.013</td><td>1103</td><td>0.046</td><td>0.029</td><td>1:2.5</td></tr><tr><td>Andricus glandulae</td><td>83</td><td>0.0043</td><td>0.0037</td><td>335</td><td>0.014</td><td>0.012</td><td>1:2.3</td></tr><tr><td>Andricus inflator</td><td>44</td><td>0.0023</td><td>0.002</td><td>3</td><td>0.00013</td><td>0.00013</td><td>1:9:1</td></tr><tr><td>Andricus kollari</td><td>2</td><td>0.0001</td><td>0.0001</td><td>43</td><td>0.0018</td><td>0.0012</td><td>1:18</td></tr><tr><td>Andricus solitarius</td><td>528</td><td>0.028</td><td>0.026</td><td>726</td><td>0.03</td><td>0.028</td><td>1:1</td></tr><tr><td>Cynips divisa</td><td>1901</td><td>0.099</td><td>0.057</td><td>184</td><td>0.0077</td><td>0.0057</td><td>1:3:1</td></tr><tr><td>Cynips longiventris</td><td>1</td><td>0.000052</td><td>0.000052</td><td>3</td><td>0.00013</td><td>0.00013</td><td>1:2.5</td></tr><tr><td>Cynips quercusfolii</td><td>197</td><td>0.01</td><td>0.0085</td><td>456</td><td>0.019</td><td>0.013</td><td>1:2</td></tr><tr><td>N. albipes</td><td>45 771</td><td>2.38</td><td>0.51</td><td>27 531</td><td>1.15</td><td>0.32</td><td>2:1:1</td></tr><tr><td>N. anthracinus</td><td>313 507</td><td>16.33</td><td>0.93</td><td>23 820</td><td>0.99</td><td>0.29</td><td>16:1</td></tr><tr><td>N. numismalis</td><td>12 194</td><td>0.64</td><td>0.037</td><td>12 124</td><td>0.51</td><td>0.028</td><td>1:2:1</td></tr><tr><td>N. quercusbaccarum</td><td>123 708</td><td>6.44</td><td>0.56</td><td>94 830</td><td>3.95</td><td>0.49</td><td>1:6:1</td></tr></tbody></table>

## Tree phenotypic traits as predictors of variation in gall abundance

Significant relationships between gall abundance per shoot and at least one of the provenance phenotypic traits were identified for all but three of the 14 gall types tested (Table 3). The signs and strengths of relationships were stable across the various datasets and provenance co-vari- ance structures (see Data S2 for full comparison of results), and here, we therefore focus on the results of the 1–$GeoF_{ST}$ co-variance matrix models for the 20-provenance dataset, identifying where they differ from the identity models for the same dataset (Table 3). The phenotypic variables *Form*, *DBH* and *Budburst* were not confounded, with low VIFs (1.05, 1.02 and 1.02, respectively). Because our analyses yielded only one significant interaction term (*Budburst*: *Form* for sexual generation galls of *Neuroterus numismalis*; mean coefficient = 0.031, pMCMC = 0.014), hereafter we consider each trait in isolation.

Tree health, as measured by *Form*, significantly predicted the abundance of only one gall type (asexual generation *Cynips quercusfolii*; Table 3, Fig. 4A) for which healthier trees supported more galls per shoot, as predicted by the

plant vigour hypothesis. Tree vigour, as measured by *DBH*, was a better overall predictor of gall abundance per shoot, with significant relationships for five of the 14 gall types (two sexual and three asexual generations; Table 3, Fig. 4B). For the spring sexual generation galls (*Andricus testaceipes* and *N. numismalis*), galls were more abundant on larger trees (Fig. 4B, as predicted by the plant vigour hypothesis). Relationships varied for the three autumn asexual generation gall types: *N. albipes* were more abundant on larger trees (as predicted by the plant vigour hypothesis), while *N. numismalis* and *N. quercusbaccarum* showed the opposite pattern (as predicted by the plant stress hypothesis).

Phenology, as measured by *Budburst*, was the best overall predictor of gall abundance/shoot, with significant correlations for eight gall types (Table 3, Fig. 4C). Four gall types showed significantly nonlinear relationships, incorporating a significant *Budburst* squared term. However, no gall type showed peak abundance at an intermediate phenological stage, as expected for the local adaptation hypothesis. Instead, spring sexual generation galls were generally most abundant on provenances showing the earliest budburst (high budburst score), while the summer/autumn asexual
<figure><img src="image_7.png" /><figcaption>**Figure 3** The proportion of variation in gall abundance attributed to the effect of provenance for the 20-provenance dataset that either do not (black circles) account for population nonindependence (i.e., identity models) or incorporate a 1–$GeoF_{ST}$ variance–covariance matrix (open circles). Vertical bars represent 95% confidence intervals for each mean.</figcaption></figure>

generation galls were most abundant on trees showing the latest budburst (low budburst score, Fig. 4C).

## Discussion

Predicting future change in ecosystem functioning requires an understanding of the community impacts of both global environmental change and any associated remediation

strategies. Climate change impacts on foundation species are likely to influence the diversity, distribution and abundance of associated taxa – whether these are beneficial ecosystem service providers or potential pests. The challenge for a given system is then to estimate likely consequences of both nonintervention and remediation; and where the impacts of remediation strategies take time to develop, to understand their trajectories through time. All of these aspects apply in particular to climate matching of long-lived foundation trees, which involves introduction of non-native genotypes that take decades to establish and live for centuries. Our analyses shed light on the likely consequences for native herbivores of introducing tree provenances that, although not initially climate-matched, become increasingly so as climate changes, while native provenances become increasingly mismatched and decline in performance. A key aspect of our study is its use of a provenance trial to quantify the consequences of host plant variation in performance and phenology on multiple species in the same guild, addressing a recognized need to quantify the impacts of environmental change at guild and community levels (e.g., Forkner et al. 2008; Van Asch et al. 2012; Ohlberger et al. 2014; Petanidou et al. 2014; Vatka et al. 2014).

## Influences of oak provenance on the gallwasp guild, and incorporation of provenance covariance

Our results show that oak provenances vary significantly in phenotypic traits likely to influence associated herbivores

<table><caption>**Table 3.** Summary of GLMMs for tree phenotypic predictors, showing the significance and sign of those fixed effect coefficients that differed significantly from zero. Significance was assessed using pMCMC. Where the result of the identity models differed from the 1–$GeoF_{ST}$ models, the identity result follows the 1–$GeoF_{ST}$ result.</caption><thead><tr><th>Gallwasp species (generation)</th><th>Year</th><th>Form</th><th>DBH</th><th>Budburst</th><th>Budburst<sup>2</sup></th><th>Budburst: DBH</th><th>Budburst: Form</th><th>Form: DBH</th></tr></thead><tbody><tr><td>Andricus testaceipes (sex)</td><td>***</td><td>NS</td><td>+**/+*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Neuroterus albipes (sex)</td><td>***</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Neuroterus anthracinus (sex)</td><td>***</td><td>NS</td><td>NS</td><td>+</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Neuroterus numismalis (sex)</td><td>***</td><td>NS</td><td>+**/+***</td><td>+***</td><td>-*</td><td>NS</td><td>*</td><td>NS</td></tr><tr><td>Neuroterus quercusbaccarum (sex)</td><td>***</td><td>NS</td><td>NS</td><td>+***</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Andricus fecundatrix (asex)</td><td>***</td><td>NS</td><td>NS</td><td>-***</td><td>+**/+*</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Andricus glandulae (asex)</td><td>***</td><td>NS</td><td>NS</td><td>-*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Andricus solitarius (asex)</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Cynips divisa (asex)</td><td>***</td><td>NS</td><td>NS</td><td>-*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Cynips quercusfolii (asex)</td><td>***</td><td>+</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>N. albipes (asex)</td><td>***</td><td>NS</td><td>+</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>N. anthracinus (asex)</td><td>***</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>N. numismalis (asex)</td><td>***</td><td>NS</td><td>-*</td><td>-***</td><td>+</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>N. quercusbaccarum (asex)</td><td>***</td><td>NS</td><td>-***</td><td>-***</td><td>+</td><td>NS</td><td>NS</td><td>NS</td></tr></tbody></table>

DBH, diameter at breast height; MCMC, Markov Chain Monte Carlo.

Significance codes in the table are as follows: *P* < 0.05, **P* < 0.01, ***P* < 0.001, NS, *P* > 0.05, nonsignificant. Consideration of false-positive discovery rates for multiple testing following Benjamini and Hochberg (1995) and Benjamini and Yekutieli (2001) resulted in adjustment of *P*-values indicated as * to *P* > 0.05, while all other results indicated as ** or *** in the table below remain significant at *P* < 0.05.
<figure><img src="image_8.png" /><figcaption>**Figure 4** Plots of significant relationships between gall abundance and (A) Form, (B) Diameter at breast height (DBH) and (C) Budburst. Higher Form scores indicate healthier trees, while higher Budburst scores indicate earlier budburst. Distributions of y at intervals across the range of a phenotypic predictor (x) were derived from the 1000 stored Markov Chain Monte Carlo (MCMC) samples as: $y_i = \exp(\text{intercept}_i + x(\text{mean coefficient}_i) + 0.5x \sum \text{random effect variances}_i)$. Plots show the means (bold lines) and 95% confidence intervals from these distributions for each of the two study years. Dotted vertical lines indicate the mean value of the phenotypic trait for the most local tree provenance (Forêt de Bercé). Plots for models of the asexual generation galls of *Neuroterus numismalis* could not be produced in this way due to high random effect variances.</figcaption></figure>
and that this in turn is associated with significant variation
in the population density (abundance per oak shoot) of
multiple gallwasp species. We show that the form of these
relationships is robust across models that assume very dif-
ferent patterns of variance-covariance among oak prove-
nances, from identity models assuming total independence
to models that estimate covariance based on population
genetic and provenance origin data. Our results indicate
that models that treat provenances as statistically indepen-
dent entities, which we know not to be true, consistently
underestimate provenance effects on gall densities relative
to models in which covariance is specified based on a mea-
sure of genetic differentiation (F<sub>ST</sub> or GeoF<sub>ST</sub>). If these
models accurately describe provenance co-variance, then
the influence of tree provenance on gall abundance is sub-
stantially greater (38-95%) than would be inferred by
assuming provenances to be statistically independent.
While pairwise F<sub>ST</sub> and related measures are consistently
significantly larger than 0 (Tables S2.1 and S2.3 in
Data S2), their absolute values are small, providing little
resolution of the relationship of genetic differentiation and
geographic distance (see Data S2 for a comprehensive dis-
cussion). It is therefore difficult to estimate a 'true' value
for the impact of provenance and its explanatory power
given the nonindependence of populations (Felsenstein
2002; Stone et al. 2011).

The strong provenance effects we show are consistent
with the growing body of community genetics work linking
host plant genotypic variation to associated trophic levels
(Wimp et al. 2005; Bangert and Whitham 2007). A key
challenge in this field is to understand the importance of
host genetic effects for herbivore communities relative to
other ecological factors (Hersch-Green et al. 2011). Work-
ing near the northern limit of pedunculate oak (Quercus
robur) in Finland, Tack et al. (2010) found spatial effects to
be more important than tree genotypic effects in structur-
ing a community of herbivorous insects that included sev-
eral gallwasps. In reciprocal transplant experiments at local
and regional scales, Tack et al. (2010) found no effect of
tree genotype for any of the three gall types in their study
(asexual and sexual generations of N. quercusbaccarum are
shared with our study). The contrast between this and the
strong host plant provenance effects present at the Petite
Charnie trial is likely to reflect contrasting levels of adaptive
genetic and phenotypic diversity of the oaks in each study.
Although Finnish oak populations show substantial
intraspecific genetic diversity (Vakkari et al. 2006), they
show substantially less genetic diversity (and we expect also
phenotypic diversity) than the much wider geographic
array of provenances present at La Petite Charnie (Petit
et al. 2002). Our provenance trial study involves a much
greater array of genetic diversity than would be expected
from a single natural population, a feature shared with the

studies of genetically diverse hybrid populations (e.g., Dun-
gey et al. 2000) that often show strong plant genotype
effects on associated herbivores (Tack et al. 2010). Gener-
ally, the importance of host genetic effects relative to other
factors is likely to correlate positively with the adaptive
genetic diversity within a tree population present at a site,
and we would expect this to be increased in situations
where climate matching or other forms of assisted migra-
tion are implemented.

Impacts of between-provenance variation in phenology

The strongest predictor of between-provenance variation in
gallwasp density is variation in spring budburst phenology.
Phenological changes have long been recognized as a major
component of species responses to climate change that can
cascade through associated trophic levels. This has been
shown, for example, in work on the impact of changes in
oak budburst phenology on abundance of associated leaf-
feeding moths and breeding responses of insectivorous
birds – particularly *Parus major* (Van Asch and Visser
2007; Visser 2008; Van Asch et al. 2012; Vatka et al. 2014).
A key question in research on these systems has been the
extent to which responses of herbivores, and their natural
enemies are the result of phenotypic plasticity and local
adaptation.

It is striking that although adaptation to individual trees
has been demonstrated in oak–gallwasp interactions (Egan
and Ott 2007), none of our gall types showed declining
abundance with increasing phenological mismatch between
native oaks and planted provenances, rejecting our local
adaptation hypothesis. We also found the responses of the
spring and autumn generations in the gallwasp lifecycle can
be very different. Spring galls were more abundant on early
budbursting trees, while autumn generations of the same
species (N. numismalis and N. quercusbaccarum) were
more abundant on late budbursting trees. Similar relation-
ships to those we observed were recorded for Neuroterus
gallwasps in response to local phenological variation in
native UK Q. robur populations by Askew (1962, 1984)
and Crawley and Akhteruzzaman (1988); our results show
that the same trends can be extended to the wider pheno-
logical variation present in a provenance trial. We hypothe-
size that this contrast between the spring and autumn
generations may exist because later budbursting trees may
have higher abundance of phenologically young tissues
suitable for gall induction. Annual gallwasp phenology may
thus be the result of contrasting selection on the two lifecy-
cle generations, perhaps limiting local phenological adapta-
tion in either generation (Singer and Parmesan 2010). The
dynamics of such complex lifecycles are poorly understood,
and complex bottom-up impacts may apply to other
groups with multivoltine or more complex lifecycles, par-
ticularly where these involve alternation between genera-
tions and/or plant hosts (including many aphids; Harring-
ton et al. 2007).

Impacts of between-provenance variation in tree vigour

Tree vigour also explained significant between-provenance
variation in oak gall density. In contrast, tree shape – com-
monly used as an indicator of physiological stress – had lit-
tle impact. As with phenology, the sign of correlations
between gall density and tree vigour varied among gall gen-
erations and taxa. Significant correlations with tree vigour
were positive for spring sexual generation gall types, while
two of three were negative for asexual generation gall types.
Support was thus evenly split between the plant vigour
hypothesis (Price 1991; Cornelissen et al. 2008) and the
plant stress hypothesis (White 1969, 1974, 2009). One pos-
sibility is that the apparent contrast in patterns for spring
and autumn generation galls could be driven by the long-
known seasonal shift in oak resource investment from
growth in the spring to metabolic defence in the summer
(Feeny 1970). Highly vigorous trees could then be both best
for spring generation galls and worst (=best defended) for
autumn generation galls. Mechanisms of oak defence
against gallwasps remain poorly understood.

Predicting the impact of climate-matched planting on
associated herbivores

Climate matching necessarily requires introduction of phe-
notypically divergent tree populations. In Europe, climate
models predict warmer summers and winters (Giorgi and
Coppola 2009; Jenkins et al. 2009), and climate matching
will thus always involve introducing more southerly prove-
nances that show earlier budburst (Ducousso et al. 1996;
Broadmeadow et al. 2005). Under low and high emissions
scenarios for 2050, Broadmeadow et al. (2005) identified
climate-matched sites for five UK locations that were 0–2°
and 5–12° further south, respectively (Broadmeadow et al.
2005). Our results (Fig. 2C) suggest that while the small
latitudinal shifts required under a low emission scenario
would probably have little impact on oak phenology, the
shifts of >10° of latitude required under a high emission
scenario could lead to major phenological differences
between native and climate-matched trees, and significantly
dissimilar gallwasp assemblages.

The widespread existence of latitude-dependent pho-
toperiod cues in plant phenology (Aitken et al. 2008)
suggests that the phenological contrasts between climate-
matched and native oak populations will be shared by other
plant species. The extent to which any such differences
influence associated herbivores is likely to vary according
to specific plant–herbivore interactions, reflecting available

herbivore phenotypic plasticity and adaptive genetic diversity. We hypothesized that impacts of phenological mismatch will be greatest for interactions in which herbivore fitness is strongly correlated with successful exploitation of a narrow phenological 'window of opportunity' (Durant et al. 2007; Van Asch and Visser 2007; Forkner et al. 2008; Netherer and Schopf 2010). This includes many insects that feed on newly flushed leaves (Van Asch and Visser 2007; Forkner et al. 2008). However, we found no evidence of such a window for gallwasps: while they prefer to oviposit (or survive better on) young tissues, they show no strong preference for host trees at a single phenological stage. As discussed above, impacts on herbivores with multivoltine or multihost lifecycles may be complex.

The relationships we found between host plant vigour
and herbivore abundance are relevant to two stages in a cli-
mate matching strategy: the expected poor performance
under current climates of introduced provenances matched
to predicted future conditions, and the expected decline in
the performance of native provenances as climates move
away from any locally adapted range. The slope of the rela-
tionship between environmental variables and vigour for a
specific provenance will dictate not only the impact on
associated trophic levels, but also whether climate matching
with that provenance is feasible at all. The challenge for for-
est managers is to identify a climate-matched provenance
that is able to survive as a sapling and young tree before the
conditions justifying its selection have been reached and
become established before native provenances fail. Climate
matching is a recently developed strategy and its success at
maintaining forest productivity is untested (Broadmeadow
et al. 2005). Current guidelines in UK forestry advocate
mixed planting of both matched and local provenances,
which increases local resource heterogeneity for herbivores
(Hubert and Cottrell 2007), and creates conditions that
accelerate both environmental selection on host trees (Le-
fevre et al. 2014) and host plant-mediated selection on
associated organisms.

Assisted migration is increasingly seen as a viable strategy
for commercial and conservation settings (Broadmeadow
et al. 2005; Stone 2010; Dawson et al. 2011). Issues cited as
slowing down the implementation of climate matching as
an adaptive forestry strategy include lack of operating pro-
cedures, uncertainties over which climate models to use in
matching algorithms and risks associated with moving
plants outside their current ranges (Williams and Dum-
roese 2013). Theoretical studies nevertheless suggest that
such adaptive-, rather than resilience-targeted strategies
may be the best way to safeguard forest systems (Buma and
Wessman 2013; Lefevre et al. 2014). We would add that the
community impacts of assisted migration need to be care-
fully considered, particularly so for foundation species such
as forest trees. This requires better understanding of the
relative contributions of host plant genotype and other eco-
logical factors to the tree phenotypes exposed to herbivores
(Tack et al. 2010). Geographical patterns of local adapta-
tion and tree phenotypic variation have only been mapped
in a tiny minority of species (Savolainen et al. 2007; Roba-
kowski et al. 2012; Sork et al. 2013), and studies that target
multiple provenance trial locations are required to separate
genetic and environmental contributions to provenance
phenotypes (Tack et al. 2010). While our study was based
on a single trial, the oak phenotypic traits we analysed have
been shown to be highly heritable in the sampled prove-
nances (Sinclair 2012), so we expect the provenance effects
we found to contain a significant genetic component.
Given strong environmental effects on plant traits includ-
ing phenology, repetition of the work in an array of trials
spanning different environments is clearly desirable.
Understanding herbivore numerical and adaptive responses
then in turn requires understanding the impacts of both
plant phenotypes and other ecological processes, including
the mortality inflicted on herbivores by their natural ene-
mies (Bidart-Bouzat and Imeh-Nathaniel 2008; Thomson
et al. 2010; Van Asch et al. 2012; Rasmann et al. 2014).
Much more needs to be done before forest managers can
take informed decisions concerning likely impacts of cli-
mate matching on forest-associated biodiversity.

Acknowledgements

This research comprises work under the CLIMAFO NERC-
funded research project ‘Climate change and management
of forest biodiversity: predicting the impacts of climate
matching strategies on plant–herbivore–enemy interac-
tions’ (NE/H000038/1). We thank Jarrod Hadfield for sta-
tistical advice, Ally Phillimore, Tom Oliver and Christine
Tansey for feedback on drafts of the manuscript, and Kat
Bradley, Julja Ernst, Matt Wainhouse, Stefan Curtis and
Katherine Rushton for invaluable help in the field. We
thank Axios Review for their role in the development of
this manuscript.

Data archiving statement

Data for this study are available for download from the
NERC Environmental Information Data Centre:
http://doi.org/10/647.

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**Supporting Information**

Additional Supporting Information may be found in the online version
of this article:

**Data S1.** Petite Charnie provenance trials.

**Data S2.** Investigating the issue of non-independence of provenances.

**Data S3.** Calculating marginal $R^2$ of a MCMCglmm model.

**Data S4.** Consistency of spring bud-burst phenology between years.

**Data S5.** An exemplar .csv data frame for use with Data S3.