Journal of Applied Ecology 2012, 49, 1296–1305
doi: 10.1111/j.1365-2664.2012.02219.x
Impacts of an invasive herbivore on indigenous
forests
Andrew M. Gormley1*, E. Penelope Holland1, Roger P. Pech1,2, Caroline Thomson1 and
Ben Reddiex3
1
Landcare Research, PO Box 40, Lincoln, 7640, New Zealand; 2Joint Graduate School in Biodiversity and
Biosecurity, School of Biological Sciences, University of Auckland, Auckland, New Zealand; and 3Department of
Conservation, PO Box 10420, Wellington, 6143, New Zealand
Summary
1. Invasive herbivores can have large negative impacts on natural ecosystems. Management
of invasive populations often requires frequent, broadscale, expensive control, which must be
justified by demonstrating progress in achieving conservation objectives. This study evaluates
benefits of regular extensive control of an invasive herbivore and develops an alternative
strategy based on damage thresholds.
2. We carried out replicated experimental management of brushtail possums, Trichosurus vulpecula, in three areas of native forest in New Zealand. Each area included a site that had
extensive possum control for 10 years, prior to and during the 5-year study, and a paired site
with no control. We measured indices of possum browse on c.2400 possum ‘preferred’ and
c.1200 ‘non-preferred’ trees, and an index of possum abundance, at the beginning and end of
the experiment.
3. Extensive control was effective in reducing possum browse on preferred tree species.
Reductions in browse led to increased foliage cover and decreased probability of tree mortality. The probability of browse on an individual tree decreased with increasing amounts of
possum-preferred foliage on nearby trees but increased with tree size and with increasing levels of browse on nearby trees. At one site where possum control ceased prematurely, foliage
cover decreased, reducing benefits from earlier control.
4. Synthesis and applications. Our study provides evidence that sustained, extensive control
of invasive herbivores can result in significant conservation benefits to susceptible tree species,
and that both impacts and benefits can be measured using data typically collected in herbivore impact studies. Furthermore, it shows how local factors such as forest composition can
influence the impact of herbivory, how this can be included in large-scale assessments of the
benefits of pest control and how site- and species-specific damage thresholds can be derived
for improving pest management.
Key-words: experimental management, invasive species, native forest, New Zealand, pest
management, plant-herbivore interactions, tree mortality, Trichosurus vulpecula
Introduction
Invasive mammals are a key driver of global change
through impacts on native biodiversity, agriculture and
human health (Mack et al. 2000). In many countries, their
control is conducted over large areas, often frequently,
*Corresponding author. E-mail [email protected]
and at high cost. Management agencies usually monitor
the effect of control on the target species (e.g. Reddiex
et al. 2006; Clayton & Cowan 2010). However, there is
limited knowledge of the relationships between pest abundance and resulting damage (e.g. Hone 2007), making it
difficult to estimate the potential value of intervention or
to use damage thresholds for deciding when to control
pests. Often the actual benefits of control are not measured directly or at all (e.g. Reddiex & Forsyth 2006;
© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society
Conserving forests from invasion impacts
Clayton & Cowan 2010). Impacts on native biota can be
difficult to quantify, especially for generalist foragers
affecting many species, and for impacts that accumulate
slowly over time. Nevertheless, some forest impacts with
these characteristics have been quantified for invasive herbivores ranging from deer (e.g. Tanentzap et al. 2009) to
invertebrates (e.g. Gandhi & Herms 2010). Our aim was
to assess benefits for native forests from large-scale control of invasive brushtail possums Trichosurus vulpecula in
New Zealand, using data typical of that collected in herbivore impact studies worldwide (e.g. Makhabu, Skarpe &
Hytteborn 2006; Kamler et al. 2010; Nugent et al. 2010).
Brushtail possums were introduced to New Zealand in
1837 to establish an export fur trade (Clout & Ericksen
2000). They became widespread, colonizing most forested
areas, and are highly abundant, with a recent estimate of
c.30 million possums (Warburton, Cowan & Shepherd
2009). High population densities in New Zealand are due
to a combination of suitable habitat and a lack of competitors, predators and parasites (Clout & Ericksen 2000).
Possums are nocturnal, predominantly arboreal folivores,
supplementing their diet with high-energy, non-foliar
foods when available (Nugent et al. 2000). Selective
browsing by possums has been shown to alter forest composition, with tree species preferred by possums becoming
locally extinct and less preferred species becoming relatively more abundant (Campbell 1990; Owen & Norton
1995). This change can be rapid with defoliation of forest
canopies and subsequent canopy collapse occurring within
20 years of colonization by possums (Payton 2000).
1297
Current management of possums on public conservation lands ranges from localized, intensive and continuous
suppression to very low densities, to large-scale (mean,
7178 ha; Veltman & Westbrooke 2011) aerial poisoning
operations at intervals varying from three to 4 years
(Parkes & Murphy 2003). There are published case studies
of the effectiveness of possum control for reducing
impacts on vegetation (e.g. Nugent et al. 2002; Urlich &
Brady 2006); however, there are few examples assessing
the effectiveness of extensive possum control with a controlled experimental design including replicated treatments
(but see Nugent et al. 2010; Duncan et al. 2011).
In 2004, the New Zealand Department of Conservation
(DOC) set up a management experiment to determine
whether survival of tree species browsed by possums
improved at sites with a history of extensive control every
4–5 years. The experiment was designed to test three
hypotheses (Fig. 1): (1) There is a direct relationship
between possum abundance and resulting damage: lower
possum density results in (a) reduced levels of browse, (b)
increased tree foliage and (c) lower mortality of possumpreferred species. (2) Impacts are heterogeneous because
possums browse (a) more in patches with a large number
(or biomass) of preferred tree species or conversely (b) less
in patches with more non-preferred species (i.e. patch selection). There is (c) a higher likelihood of browse on larger
trees that are more likely to have den sites and new foliage
exposed to high levels of sunlight (i.e. tree selection). (3)
Trees require a minimum amount of foliage to survive;
trees browsed to below this threshold will not survive.
Fig. 1. Management actions and outcomes, processes relating to browse on ‘indicator’ trees, and available data. Trap-Catch Index (TCI)
is a measure of possum abundance, Foliage Cover Index (FCI) is a measure of canopy biomass, Foliar Browse Index (FBI) is the proportion of leaves showing evidence of browse by possums. Tree size is indexed by diameter at breast height (DBH).
© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society, Journal of Applied Ecology, 49, 1296–1305
1298 A. M. Gormley et al.
We analysed the data to test these hypotheses and to
determine whether there are damage thresholds that could
be used to specify when control of possums is required
for environmental management.
Materials and methods
FIELD METHODS
Paired sites, one subject to extensive control (treatment) and one
with no control (non-treatment), were located at Coromandel
(treatment: 2300 ha, 36°56′S, 175°37′E; non-treatment: 1200 ha,
36°58′S, 175°39′E) and Urewera (850 ha, 38°17′S, 177°11′E;
850 ha, 38°14′S, 177°11′E) in the North Island, and Haast in
the South Island (1300 ha, 44°03′S, 168°47′E; 1300 ha, 44°06′S,
168°39′E). Each pair of sites had similar topography, altitude
and forest composition (assessed using the Recce method; Hurst
& Allen 2007). Extensive possum control was carried out at:
Coromandel, 1995, 1999 and 2003 prior to the study and during
the study in 2006; Haast, 1995–96 and 2000–01 prior to the
study, but the next control operation was delayed until after
the study period; and Urewera, 1993–94, partially in 1996–97,
1998–99 and 2002–03 prior to the study, and during the study
in 2006–07.
Experimental protocols were based on techniques used by
DOC to assess the abundance of possums (Trap-Catch Index
(TCI); National Possum Control Agency 2010) and their impacts
on tree canopies (Foliar Browse Index (FBI) and Foliage Cover
Index (FCI); Payton, Pekelharing & Frampton 1999) and survival. Additional data were collected to characterize foraging patterns at a local scale because previous studies found significant
site differences in possum impacts (Duncan et al. 2011). The
experimental design included measurements of tree size using
diameter at breast height (DBH), local availability of other food
sources and possum browse at the patch scale (Fig. 1).
In each area, two tree species with foliage preferred by possums and one with foliage not preferred (Owen & Norton 1995;
Sweetapple 2003; Sweetapple, Fraser & Knightbridge 2004) were
selected as ‘indicators’ (Table 1) to assess the outcomes of extensive possum control. A criterion for selecting species was that
they were relatively common and widespread throughout the
area. Non-preferred species acted as experimental controls for
non-selective disturbances such as storms, drought and earthquakes. Smaller numbers of a third highly preferred species were
selected from sparse populations at Coromandel (Dysoxylum
spectabile) and Haast (Fuchsia excorticata).
Preliminary power analysis indicated that 200 trees of each species per site would be required to have an 80% chance of detecting a treatment effect in annual tree survival where the difference
in annual mortality is 2% and the interval between measurements is 4 years (B. Reddiex unpublished data). Initial measurements (Time 1) and remeasurements (Time 2) were carried
out in 2004 and 2009, respectively, at Coromandel and Haast,
and in 2006 and 2010 at Urewera. Sampling transects, consisting
of 5–15 plots at 50-m intervals, were located randomly in each
site with randomly selected bearings. Transects and plot centres
were marked and GPS coordinates recorded. Within 20 m of
each plot centre, up to four trees of each indicator species at least
10 m apart where possible were permanently marked using
numbered tags. Trees were included if they measured 10 cm
DBH (14 m above the ground), and if the canopy was visible
from the ground.
Browse damage was recorded using Foliar Browse Index (FBI)
in five categories, 0–4, indicating that 0%, 1–25%, 26–50%,
51–75% or >75% of leaves, respectively, showed evidence of possum browse. Foliage cover was recorded using Foliage Cover
Index (FCI), the fraction of sky occluded by leaves, observed
from beneath the centre of the tree crown looking up. Occlusion
was recorded in 10% categories, that is, 005 = 0–10%, 015 = 10
–20%, etc. For each indicator tree, DBH, FCI and FBI were
recorded at Time 1, and again at Time 2 for those trees still alive.
At Time 2 the status (alive or dead) of each tree was recorded.
At each plot, the closest indicator tree to the plot centre was
selected as the ‘focal tree’. The DBH, Alive/Dead status and FBI
of all trees (with DBH 10 cm) within 5 m of the focal tree
were measured (irrespective of species). These ‘neighbourhood
trees’ were used to characterize a patch of forest that could be
selected by foraging possums.
Possum Trap-Catch Index (TCI) was obtained at each site as
near to Times 1 and 2 as possible following the standard protocol
of randomly located lines within each area, each line consisting
Table 1. Preferred and non-preferred tree species at each site, annual tree mortality at non-treatment and treatment sites [sample size n
of trees that were tagged at the start of the study (Time1) and relocated during the remeasurement period (Time 2)]. Trees with unrecorded browse data (FCI and/or FBI), or that could not be relocated, at Time 2 were not used for analysis
Non-treatment sites
Area
Species
Coromandel
Weinmannia silvicola
Olearia rani
Dysoxylum spectabile
Knightia excelsa
Weinmannia racemosa
Schefflera digitata
Fuchsia excorticata
Nothofagus menziesii
Weinmannia racemosa
Beilschmiedia tawa
Knightia excelsa
Haast
Urewera
Abbreviation
Possum
preferred
Mortality (%)
WEISIL
OLERAN
DYSSPE
KNIEXC
WEIRAC
SCHDIG
FUCEXC
NOTMEN
WEIRAC
BEITAW
KNIEXC
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
No
018
217
620
025
00
1553
2120
060
687
033
011
n
225
221
84
237
229
200
102
202
218
229
222
Treatment sites
Mortality (%)
028
094
00
009
041
1216
1973
057
011
040
012
n
213
238
21
206
247
197
3
212
237
249
211
P†
0525
0014
0008
0359
0957
0040
05
05
<0001
0621
05
†Bold text indicates statistical significance (at the 5% level).
© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society, Journal of Applied Ecology, 49, 1296–1305
Conserving forests from invasion impacts
of 10 or 20 traps set for three nights (National Possum Control
Agency 2010). TCI lines were located independently of the transects for measuring browse impacts. DOC measured TCI on
treatment sites after each control operation; we used DOC trap
lines that overlapped our study area to calculate mean TCI values
(6–18 lines per site). In the non-treatment areas, we measured
TCI using 7–10 lines per site, with some additional DOC measurements at Haast.
ANALYTICAL METHODS
Although there was no possum control at the Haast treatment
site between Times 1 and 2, we assumed it to be notionally ‘treated’ when testing for treatment effects. This is just within the
likely period of reduced possum abundance, taking into account
their expected recovery rate (Morgan & Hickling 2000).
Treatment effects on possum browse (FBI), canopy
biomass (FCI) and tree mortality
Comparisons were carried out between treatment and non-treatment sites in each of the three areas, at Time 1 and Time 2 separately. Distributions of FBI values for each indicator species at
each site and time were compared using Fisher’s exact test for
count data (Hypothesis 1a). To account for the low frequency of
higher FBI values, we also compared the proportion of trees in
the non-zero browse categories (‘any browse’) and the proportion
of trees in FBI category 2 or higher (‘moderate-to-severe
browse’). A one-sided binomial proportions test was used to
determine whether the proportion of trees in each category was
greater at the non-treatment site.
Distributions of FCI values for each indicator species at each
site and time were compared using a one-sided t-test (Hypothesis
1b). The relationship between FBI and FCI was assessed using
data pooled across treatment and area (where applicable) for
each preferred species at Time 1.
Annual mortality of each tree species was compared between
treatment and non-treatment sites using a one-sided t-test for the
difference of two proportions (Hypothesis 1c). The observed
annual mortality (m) for each tree species (by area and treatment)
was calculated using N1(1 m)yrs=N2 where Nt is the number of
trees alive in the sample at time t and yrs is the number of years
between observations.
We examined whether tree mortality differed depending on
FCI at Time 1 (Hypothesis 3). Due to the low number of
recorded tree deaths, FCI values were grouped into three categories (low = <04; moderate = 041–07; high = 071–10) and
pooled across treatment and non-treatment sites. Distributions of
the FCI groups were compared, by species, between trees alive
and dead at Time 2, using Fisher’s exact test for count data.
Hierarchical models of possum browse and tree
mortality
We used hierarchical models to generalize the relationships
between browse and/or tree mortality and a range of covariates
across tree species and sites. First, we modelled no browse,
FBI = 0, vs. any browse, FBI > 0, on the possum-preferred ‘focal
trees’ at Time 2 against the covariates: (1) Treatment/non-treatment, (2) PrefBio: sum of the biomass of preferred neighbour-
1299
hood trees, excluding the focal tree (Hypothesis 2a), (3)
NPrefBio: sum of the biomass of non-preferred neighbourhood
trees surrounding the focal tree (Hypothesis 2b), (4) PrefEat:
index of level of use of the forest patch by possums, calculated as
the amount of preferred biomass consumed at each plot, that is,
FBI 9 Biomass for each neighbourhood tree, summed across all
neighbourhood trees in the plot (Hypothesis 2a), (5) DBH: index
of size of focal tree (Hypothesis 2c) and (6) TCI: Trap-Catch
Index at Time 2.
A complete set of covariate data were available from Time 2,
and browse levels on the focal tree at Time 2 were assumed to
depend on the current state, not the state 5 years ago. Each tree’s
biomass was calculated using an allometric relationship between
DBH and biomass for New Zealand trees, where Biomass = 00406 9 DBH153 (Richardson et al. 2009). Tree species
additional to the indicator species were defined as being either
preferred or non-preferred by possums based on published results
(Owen & Norton 1995; Sweetapple 2003; Sweetapple, Fraser &
Knightbridge 2004), possum browse from this study, and expert
opinion (P. Sweetapple, pers. comm.).
For the seven main indicator species, both preferred and nonpreferred, we investigated the relationship of annual mortality for
the ‘focal trees’ against the covariates for their initial state and
subsequent cumulative impacts: FCI at Time 1 (Hypothesis 3),
DBH at Time 1 (Hypothesis 2c), treatment (Hypothesis 1) and
average TCI from Times 1 and 2 (Hypothesis 1).
Browse and mortality were modelled using a Bayesian hierarchical approach, where for each covariate the coefficients for each tree
species are distributed with a mean and variance from a higherlevel ‘hyper-distribution’ (Royle & Dorazio 2008). This enabled us
to model all species together, yet was flexible enough to allow the
relationship between the covariates and FBI to differ between species. A range of models were specified separately for browse and
mortality with various combinations of covariates mentioned
above. The natural log of all continuous covariates (PrefBio,
NPrefBio and PrefEat) was taken after adding a small value to
accommodate zero values where applicable (1 for PrefBio and
NPrefBio, 01 for PrefEat). To assist with convergence, covariates
were standardized to have mean = 0 and SD = 1. Models were fitted using OpenBUGS 31 (Lunn et al. 2009), and compared using
Deviance Information Criterion (DIC; Spiegelhalter et al. 2002).
Results
ABUNDANCE OF POSSUMS
Mean Trap-Catch Index (TCI) was generally higher on
the non-treatment sites in all years, the exception being
Urewera in 2010 (Table S1, Supporting Information).
There was large variation in TCI between sites, reflecting
the differences in forest composition (and hence food
availability and carrying capacity) between pairs of sites,
as well as treatment effects within pairs and variability in
TCI between years within sites.
TREATMENT EFFECTS ON POSSUM BROWSE, CANOPY
BIOMASS AND TREE MORTALITY
For Hypothesis 1a, non-preferred species consistently had
zero or near zero probabilities of suffering any browse in
© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society, Journal of Applied Ecology, 49, 1296–1305
1300 A. M. Gormley et al.
all sites (Fig. 2) and were never recorded as having ‘moderate-to-severe browse’. There was no evidence of a difference in the distributions of Foliar Browse Index (FBI) for
all non-preferred species between treatments at initial
measurement (Time 1) or at remeasurement (Time 2)
(Fisher’s P; Table S2, Supporting Information).
For all preferred tree species at Coromandel and Urewera, there was strong evidence for a difference in the distributions of FBI between treatment and non-treatment
sites (Fig. 2 and Fisher’s P; Table S2, Supporting Information), as well as differences in the probability of ‘any
browse’ and ‘moderate-to-severe browse’ at both Times 1
and 2 (Table S2, Supporting Information). For Haast,
there was moderate evidence of a difference in the distributions of FBI between treatment and non-treatment sites
for Weinmannia racemosa and F. excorticata at Time 1,
but no evidence of a difference for any preferred species
at Time 2. There was moderate evidence that all preferred
species in the non-treatment site had a higher probability
of ‘any browse’ at Time 1, but weak evidence for W. racemosa only at Time 2, and no evidence for a higher probability of moderate-to-severe browse for any species at
Time 1 or Time 2.
For Hypothesis 1b, most possum-preferred species had
higher mean Foliage Cover Index (FCI) values at Times 1
and 2 on treatment sites compared with non-treatment
sites (Fig. 3). Despite strong evidence for a positive effect
of treatment on FCI for many species and/or sites, the
magnitudes of the differences with non-treatment sites
were often small (Table S3, Supporting Information).
There was a negative relationship between FCI and
FBI for nearly all the possum-preferred species, indicating
a negative impact of browse on canopy health at Times 1
and 2: P< 0001 for all species, except Beilschmiedia tawa
(P = 0756, Time 1; P = 0018, Time 2) and Schefflera
digitata (P = 0411, Time 2; Fig. S1, Supporting Information). There was no detectable relationship for the
non-preferred species (Knightia excelsa, Nothofagus menziesii) due to negligible levels of possum browse.
For non-preferred species, overall tree mortality was
low ( 06%), with no evidence of a treatment effect. For
preferred species, the results were mixed (Hypothesis 1c;
Table 1). At Coromandel, mortality of Olearia rani (and
the additional preferred species, D. spectabile) was significantly higher in the untreated area, yet nearly all
Weinmannia silvicola trees survived in both areas. At
Haast, there was a strong treatment effect on mortality of
S. digitata, but no difference for W. racemosa (low mortality) or F. excorticata (high mortality). At Urewera,
there was a strong treatment effect for W. racemosa, but
no difference for B. tawa.
When pooled across treatment and non-treatment sites,
there was strong evidence of a negative relationship
between mortality and categorical values of FCI at Time
1 for all preferred species except W. silvicola at Coromandel and W. racemosa at Haast (Hypothesis 3; Table 2). At
Haast, there was moderate evidence of a negative relationship between canopy biomass and mortality for the nonpreferred species N. menziesii, although the number of
trees with highest mortality (‘Low FCI’) was very small
(n = 7). (Mortality rates, by FBI, are shown in Table S4,
Supporting Information)
At the species+site level, there was evidence of a threshold effect on annual mortality for browse and tree canopy
cover (Fig. 4). A rapid shift to increased mortality
occurred when the proportion of trees with moderate-tosevere browse was above 005, and when the mean FCI
was below 05 (Fig. 4).
HIERARCHICAL MODELS OF POSSUM BROWSE AND
TREE MORTALITY
The mean number of neighbourhood trees in each 5 m
radius plot was 89 (95% CI = 2–187), with a mean of
Fig. 2. Distribution of the Foliar Browse Index (FBI) for possum-preferred species (rows 1 and 2) and non-preferred species (row 3) at
the paired non-treatment (light bars) and treatment (dark bars) sites at Coromandel, Haast and Urewera at Time 1 (2004 or 2006) and
Time 2 (2009 or 2010). FBI categories: 0, 1, 2, 3 and 4 correspond to 0%, 1–25%, 26–50%, 51–75% or more than 75% of leaves
showing evidence of possum browse. See Table 1 for full species names.
© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society, Journal of Applied Ecology, 49, 1296–1305
Conserving forests from invasion impacts
1301
Fig. 3. Distribution of the Foliage Cover Index (FCI) for possum-preferred species (rows 1 and 2) and non-preferred species (row 3) at
paired non-treatment (light bars) and treatment (dark bars) sites at Coromandel, Haast and Urewera at Time 2 (2009 or 2010). FCI,
which is the fraction of sky occluded by leaves, was recorded in 10% categories, i.e. 005 = 0–10% occlusion, 015 = 10–20% occlusion,
etc. See Table 1 for full species names.
Table 2. Annual tree mortality by FCI at Time 1, pooled across treatments. Number of trees shown in parentheses
Area
Species*
Coromandel
WEISIL
OLERAN
DYSSPE
KNIEXC
WEIRAC
SCHDIG
FUCEXC
NOTMEN
WEIRAC
BEITAW
KNIEXC
Haast
Urewera
Low FCI (0–04)
Moderate FCI (041–07)
High FCI (071–10)
26%
47%
163%
00%
00%
185%
349%
65%
90%
39%
00%
02%
13%
12%
03%
02%
107%
39%
05%
08%
02%
02%
00%
25%
00%
01%
03%
39%
NA
04%
00%
02%
01%
(8)
(28)
(34)
(9)
(2)
(189)
(77)
(7)
(114)
(11)
(2)
(392)
(406)
(51)
(199)
(337)
(197)
(28)
(317)
(311)
(279)
(133)
(38)
(25)
(20)
(235)
(137)
(11)
(0)
(90)
(30)
(188)
(298)
P†
0147
0008
<0001
0389
0637
<0001
<0001
0023
<0001
0011
0526
*Non-preferred species are italicized. See Table 1 for full species names.
†Bold text indicates statistical significance (at the 5% level).
43 (95% CI = 0–12) possum-preferred trees and 45 (95%
CI = 0–13) non-preferred trees. The mean number of preferred trees per plot was similar, irrespective of the focal
tree species; however, plots with S. digitata and O. rani as
the focal tree had higher numbers of non-preferred neighbourhood trees (73 and 62, respectively), and subsequently a lower proportion of preferred trees (Fig. S2,
Supporting Information).
The browse model with the lowest DIC included PrefBio, PrefEat, DBH and Treatment as predictors of pos-
sum browse on focal trees (Table S5, Supporting
Information), consistent with Hypotheses 2a and c. Inclusion of Treatment and PrefEat gave the greatest reductions in DIC, followed by PrefBio and DBH. Including
NPrefBio resulted in a worse model (as measured by
DIC) than comparable models without it, contrary to
Hypothesis 2b. The model with TCI at Time 2 performed
much worse than the model with Treatment.
The probability of browse on focal trees increased with
decreasing PrefBio, increasing PrefEat and increasing
© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society, Journal of Applied Ecology, 49, 1296–1305
1302 A. M. Gormley et al.
Fig. 4. Relationship between the annual mortality for indicator tree species and the proportion of trees with moderate-to-severe browse
(FBI>1) at Time 1 (left) and mean foliage cover (FCI) at Time 1 (right) for all species+site combinations. The dotted lines indicate
observed thresholds in levels of FBI and FCI where there is a rapid shift from near zero to high annual tree mortality.
Fig. 5. Mean coefficients (Beta) and 95% CIs for the hierarchical browse model with the lowest DIC value (Table S6), for each preferred
species, and the average across species (mu). Beta < 0 indicates a negative relationship; beta > 0 indicates a positive relationship. See
Table 1 for full species names.
DBH (Fig. 5).There was lower probability of browse on
focal trees at treatment sites for all species except S. digitata, consistent with the treatment effect observed for
focal trees (Table S6, Supporting Information) but not
with the observations for the complete sample of indicator
trees (Table S2, Supporting Information).
The best model for annual mortality included FCI and
Treatment, consistent with Hypotheses 1c and 3 (Table
S7, Supporting Information). A strong negative relationship between mortality and FCI was apparent for all preferred and non-preferred species (Fig. 6). The relationship
was weakest for S. digitata, which had the highest overall
mortality, with deaths occurring over a wide range of FCI
values. There was generally lower mortality on treatment
areas (Fig. 6), but no evidence of a treatment effect for
S. digitata when FCI was included in the model despite
the significant difference observed with most indicator
trees (Table 2).
The best model where DBH was included (‘Mortality =
FCI+logDBH’) showed that larger trees with relatively
lower FCI had a higher probability of mortality, which is
consistent with Hypothesis 2c. However, the results were
equivocal, including weak evidence for a negative relationship between mortality and size for S. digitata (Fig. S3,
Supporting Information).
Discussion
OUTCOMES OF POSSUM CONTROL AND IMPACTS OF
POSSUMS ON TREE SPECIES
A management experiment was used to test hypotheses
derived from a process model for the impacts of possum
browse in native forests in New Zealand (Fig. 1). The
results show that extensive possum control at intervals of
4–5 years was typically effective in lowering possum
© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society, Journal of Applied Ecology, 49, 1296–1305
Conserving forests from invasion impacts
1303
Fig. 6. Mean coefficients (beta) and 95% CIs for the best hierarchical tree mortality model (Table S8), for each species and the overall
mean (mu). See Table 1 for full species names.
impacts on preferred tree species. In general, in treated
areas, there was less browse on preferred tree species
(Hypothesis 1a), higher foliage biomass (Hypothesis 1b)
and decreased mortality of at least one preferred tree species per area (Hypothesis 1c).
The negative relationship between the probability of
any browse on a preferred tree and the amount of preferred biomass within a 5-m radius (Fig. 5) was contrary
to Hypothesis 2a that possums will browse more in
patches dominated by preferred tree species. The absence
of a relationship between the probability of browse on
preferred trees and the amount of nearby non-preferred
biomass (Hypothesis 2b; Table S5, Supporting Information) suggests possums had no difficulty finding isolated
preferred trees. The positive relationship between the
probability of browse and tree size indicates that large
trees may be important for possums as a food source
(Hypothesis 2c; Fig. 5) and may contribute to the higher
mortality rates of larger trees.
The annual mortality rates measured for W. racemosa
spanned a similar range to the values reported by Bellingham, Stewart & Allen (1999) for sites with a long-term
presence of possums. Their highest rate of 66% p.a. was
recorded at Pureora, which matches the maximum rate
recorded for W. racemosa in this study at the non-treatment site at Urewera (687% p.a.; Table 1). For all
preferred tree species, there was strong evidence of a
threshold in foliage biomass below, which trees have much
reduced survival (Hypothesis 3; Fig. 4). As expected, mortality of non-preferred tree species was low at all sites, and
the observed rates of 060% and 057% p.a. for N. menziesii (Table 1) were within the range of values reported for
this species by Bellingham, Stewart & Allen (1999).
ADDITIONAL FACTORS AFFECTING ASSESSMENTS OF
POSSUM IMPACTS ON SUSCEPTIBLE TREES
Levels of possum browse on all trees in the immediate
neighbourhood of ‘focal trees’ provided data for the hierarchical browse model used to evaluate hypothesis 2 on
patch and tree selection by possums (Table S6, Supporting Information). Unlike the hierarchical model of tree
mortality (Table S7, Supporting Information), the hierar-
chical browse model was conditional on trees surviving to
the end of the study. Consequently, where there was very
high mortality, for example S. digitata at Haast, it is possible that the probability of browse was lower on the
treatment site, but because a large proportion of browsed
trees on the non-treatment site had died (Table 1), it
appeared that browse was higher on the treatment site
(Fig. 2).
Possum control did not take place as planned during
the study period at the Haast treatment site. Increased
possum abundance at this site is likely to be why treatment differences in browse at the initial measurement
(Time 1) were not maintained at remeasurement (Time 2)
for any preferred species (Table S2, Supporting Information), although we note the measured increase was small
(Table S1, Supporting Information). Depending on control effectiveness and demographics at the site (i.e. single
vs. double breeding within a season and/or compensatory
breeding), possum populations may recover to their precontrol densities within 10 years (Veltman & Pinder
2001). Therefore, partial recovery would be expected with
the 5-year period of this study. The discontinuation of
possum control at Haast was unfortunate; however, difficulties with maintaining large-scale management experiments are not uncommon (Walters 2007). Had this also
occurred at one of the other two areas, it is unlikely the
effectiveness of extensive possum control could have been
assessed adequately. Nevertheless, the results from Haast
suggest that, to be effective, control operations need to be
maintained so that possum populations remain suppressed
to low densities.
Some unofficial possum control, for example fur harvesting, probably occurred at some sites. This might have
contributed to the observed declines in average possum
abundance on the non-treatment sites, and to increased
variability of possum abundance and impacts within sites
where public access was localized. Patchiness in the distribution of possums post-control can also occur at the scale
of several hectares (Fraser & Coleman 2005), either
through inadvertent gaps in coverage by aerial baiting or
through reaggregation of surviving possums. It is possible
that localized areas of high possum abundance may
coincide with transects for monitoring trees, which can
© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society, Journal of Applied Ecology, 49, 1296–1305
1304 A. M. Gormley et al.
result in estimates of possum density being higher than
the area-wide estimate.
The effect of treatment appears to be a better predictor of browse and mortality than possum abundance as
measured by the Trap-Catch Index (TCI). Measurements
of TCI are subject to sampling variation, especially when
small numbers of lines are used. In addition, possum
abundance is likely to vary with or without possum control, while variation in abundance will exist between sites
due to forest composition (Efford 2000), so that equal
TCI measurements from different sites may correspond
to different levels of possum browse impacts (Payton
2000). As a result, using possum TCI to indicate when
to carry out control may not be appropriate unless the
appropriate TCI threshold has been tailored for a specific site.
MANAGEMENT APPLICATIONS
Herbivore impacts on forests and woodlands have been
assessed using indices of browse damage and canopy condition in many studies worldwide (e.g. Nugent et al. 2002;
Makhabu, Skarpe & Hytteborn 2006), and a reduction in
herbivore density usually results in reduced impacts
(Kamler et al. 2010). This study supports the view that
browse by an invasive herbivore can decrease canopy biomass and increase tree mortality rates and that herbivore
control can have measurable benefits for susceptible tree
species.
That we were able to measure differences in tree mortality between treatment and non-treatment sites over an
interval of only 5 years shows that the effect size can be
large. The results suggest that regular control of invasive
herbivores is effective in decreasing tree mortality. However, effects may not persist long term (as evidenced by
the reduced benefits at Haast where possum control did
not continue as planned), and control at regular intervals
is required.
A key finding is that such differences could be inferred
using only the type of data typically collected during routine herbivore impact monitoring (e.g. browse damage
and canopy cover), at a broadscale. These metrics can be
measured relatively quickly, robustly and inexpensively to
identify areas where high levels of tree mortality are
expected to occur, negating the need for direct observations of mortality over long time frames.
CONCLUSION
Heterogeneity of herbivore densities and their impacts
among sites and years is a characteristic pattern of plant–
herbivore systems (Hone 2007). Our results support previous findings that equal herbivore densities may result in
different impacts on the same species (Bee et al. 2009;
Duncan et al. 2011). As a result, site and species differences must be considered when planning for herbivore
control. Management actions can also be based on
damage thresholds: tree browse and canopy cover can be
measured relatively quickly and robustly using simple
measures such as Foliar Browse Index (FBI) and Foliage
Canopy Index (FCI), respectively. These metrics (FCI
especially) can be used to rapidly identify sites where high
levels of tree mortality are likely to occur, negating the
need for direct observations of mortality over long time
frames. However, managers need to be aware that
observed mortality is a result of multiple interacting processes, and not just browse by invasive herbivores. Siteand species-specific information such as those collected in
this study could be used in conjunction with a mechanistic
browse model to estimate the proportion of mortality
attributable to herbivores, and hence which sites will
benefit most from herbivore control (e.g. Holland et al.,
in press).
Clearly, tree mortality is only one component of forest
dynamics. Forest management also needs to consider the
balance between mortality and recruitment, which can
also be impacted by invasive herbivores (Tanentzap et al.
2009), and between the chronic impacts of herbivory and
acute impacts such as abiotic disturbances (e.g. Hurst
et al. 2011).
Acknowledgements
This work was funded by the New Zealand Department of Conservation
(DOC) and the Animal Health Board. We thank DOC, Te Waimana Kaaku Tribal Executive and Ngati Koura Tuhoe iwi for permission to access
the sites and for help with the study. We thank the considerable efforts of
all those that carried out the field work (N. Fea, D. Hurst, M. Bridge, R.
Heyward, C. Brausch, K. Pullen, C. Stowe, S. Hough, S. Whitford and W.
Chin, R. Clayton, K. Ladley, K. Borkin, P. Horton, F. Thomson, T.
Thurley, J. Pari, T. Rochford, A. Perfect and D. Ruth), M. Robinson and
H. De méringo for data entry, and C. Veltman and P. Cowan for valuable
discussions and comments on earlier drafts.
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Supporting Information
Additional Supporting Information may be found in the online version
of this article.
Fig. S1. Distribution of FCI and FBI for all indicator species.
Fig. S2. Summary of possum-preferred neighbourhood trees.
Fig. S3. Coefficients for hierarchical mortality model.
Table S1. Mean TCI values for each site.
Table S2. Probability of browse by site, species and treatment.
Table S3. Mean FCI of trees by site, species and treatment.
Table S4. Annual mortality as a function of browse at Time 1.
Table S5. DIC values for competing models of browse.
Table S6. Proportion of focal trees with browse by treatment and
species.
Table S7. DIC values for competing models of mortality.
As a service to our authors and readers, this journal provides
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may be re-organized for online delivery, but are not copy-edited
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© 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society, Journal of Applied Ecology, 49, 1296–1305