CONSERVATION ECOLOGY - ORIGINAL PAPER

Fire management to combat disease: turning interactionsbetween threats into conservation management

Helen M. Regan • David A. Keith • Tracey J. Regan •

Mark G. Tozer • Naomi Tootell

Received: 12 October 2010 / Accepted: 12 May 2011 / Published online: 5 June 2011

� Springer-Verlag 2011

Abstract As the number and intensity of threats to bio-

diversity increase, there is a critical need to investigate

interactions between threats and manage populations

accordingly. We ask whether it is possible to reduce the

effects of one threat by mitigating another. We used long-

term data for the long-lived resprouter, Xanthorrhoea res-

inosa Pers., to parameterise an individual-based population

model. This plant is currently threatened by adverse fire

regimes and the pathogen Phytophthora cinnamomi. We

tested a range of fire and disease scenarios over various

time horizons relevant to the population dynamics of the

species and the practicalities of management. While fire

does not kill the disease, it does trigger plant demographic

responses that may promote population persistence when

disease is present. Population decline is reduced with fre-

quent fires because they promote the greatest number of

germination events, but frequent fires reduce adult stages,

which is detrimental in the long term. Fire suppression is

the best action for the non-seedling stages but does not

promote recruitment. With disease, frequent fire produced

the highest total population sizes for shorter durations, but

for longer durations fire suppression gave the highest

population sizes. When seedlings were excluded, fire sup-

pression was the best action. We conclude that fire man-

agement can play an important role in mitigating threats

posed by this disease. The best approach to reducing

declines may be to manage populations across a spatial

mosaic in which the sequence of frequent fires and sup-

pression are staggered across patches depending on the

level of disease at the site.

Keywords Phytophthora cinnamomi �Xanthorrhoea resinosa � Fire � Population dynamics �Long-lived resprouter

Introduction

As threats to biodiversity increase in number and magni-

tude, there is a growing need to investigate interactions

between multiple threats and manage populations and

ecosystems accordingly. Conservation of endangered pop-

ulations typically addresses threats separately by identify-

ing the major cause of extinction, or the species trait that

makes it most vulnerable to extinction, rather than crafting

a solution that capitalizes on the interactions between

Communicated by Melinda Smith.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00442-011-2029-6) contains supplementarymaterial, which is available to authorized users.

H. M. Regan (&)

Biology Department, University of California,

900 University Ave, Riverside, CA 92521, USA

e-mail: helen.regan@ucr.edu

D. A. Keith � M. G. Tozer

NSW Office of Environment and Heritage,

Hurstville, NSW 2220, Australia

D. A. Keith

Australian Wetlands and Rivers Centre,

University of New South Wales, Kensington,

NSW 2052, Australia

T. J. Regan

School of Botany, The University of Melbourne,

Parkville, Victoria 3010, Australia

N. Tootell

Department of Mathematics and Statistics,

The University of Melbourne, Parkville,

Victoria 3010, Australia

123

Oecologia (2011) 167:873–882

DOI 10.1007/s00442-011-2029-6

threats and their effect on population dynamics (Davies

et al. 2004). However, in order to provide efficient and

effective population management strategies, it is essential

to establish a better understanding of the synergies between

threats in terms of how they influence risks of decline or

extinction. It may then be possible to reduce the effects of

one threat by choosing an appropriate option for mitigating

another.

Interactions between threats have been recognized in a

variety of conservation contexts: logging of forests creates

access routes for poachers and disease (Peres 2001), habitat

loss and fragmentation promote altered fire regimes (Syp-

hard et al. 2008), agricultural land conversion eliminates

native pollinators (Kearns et al. 1998), urbanization intro-

duces new predators to native fauna (Crooks and Soule

1999), and habitat loss and fragmentation encourage

invasive species which in turn alter natural disturbance

regimes (D’Antonio and Vitousek 1992; Richardson et al.

1996). Interactions between threats are expected to increase

in the face of global climate change and increased

anthropogenic disturbance (Rosenzweig et al. 2008).

Despite the general agreement that interactions between

threats are apparent, are likely to be exacerbated in the

future and are predicted to have critical impacts on biodi-

versity (Pimm 1996), conservation strategies are rarely

devised to address these interactions (Davies et al. 2004).

In this paper we use an individual-based population

model to investigate the cumulative effects of fire regimes

and disease on a long-lived fire-prone plant species, Xan-

thorrhoea resinosa Pers., from the eastern coast of Aus-

tralia. This species is representative of a functional group

of plants found in many fire-prone ecosystems globally:

long-lived facultative resprouters, plants that maintain their

populations by the persistence of adults with occasional

episodes of recruitment (Bond and Midgley 2001; Keith

et al. 2007). Fire is an important disturbance for many plant

species in fire-prone ecosystems such as those in Australia,

northwestern America, parts of South America, the Medi-

terranean basin, and southern Africa. Fire may stimulate

the production, release and/or germination of seeds, it

reduces competition from neighbours, and it may be a key

factor promoting long-term stability in population dynam-

ics. While some established plants may resprout, fires can

also cause varying levels of mortality. As a result, fire

presents trade-offs between recruitment and mortality.

However, alterations to fire regimes by human activity

(Keeley et al. 1999) pose threats to species that require

appropriately timed fires to complete their life cycle.

Consequently, fire management has become an essential

conservation strategy for threatened plant populations (van

Wilgen et al. 1994; Keith et al. 2002; Menges 2007).

Disease has long been recognized as a threat to biodi-

versity (Smith et al. 2006). The incidence and effects of

disease on native species are also predicted to increase with

continued human land use and global climate change

(Harvell et al. 2002). X. resinosa is susceptible to an

introduced soil-borne fungal pathogen, Phytophthora cin-

namomi, which infects roots, causing necrosis and plant

death (Weste 1974, 2003; Barker and Wardlaw 1995;

Aberton et al. 2001; Shearer et al. 2007). The spread of this

disease is of particular concern because it occurs by various

natural and anthropogenic mechanisms: the pathogen

spreads locally with the movement of soil water and hyphal

growth and disperses between sites via walkers, vehicles

and native mammals. P. cinnamomi is resistant to eradi-

cation—chemical treatments that have shown promise

under controlled conditions (e.g. Englander et al. 1980;

Guest and Grant 1991) have limited success in slowing

rates of infection and mortality in the field (Tynan et al.

2001; Shearer et al. 2004; Keith 2004). While fire has

been a successful thermosanitation soil treatment for many

other diseases (Hardison 1976), it does not kill or reduce

P. cinnamomi for a sufficient duration or to sufficient soil

depths to be an effective treatment of this pathogen in

Australasian forests (Hardison 1976; Weste and Marks

1987). Instead, fire can trigger demographic responses in

infected plant populations that may promote recruitment

and ultimately population persistence.

Long-lived plants provide important structural and

functional components of communities and are necessary

for the temporal continuity of ecosystems. Although most

current knowledge of the responses of plant population

dynamics to threats is based on relatively short-lived spe-

cies, long-lived plants are of particular conservation con-

cern because population trends may be slow and therefore

difficult to detect on timescales convenient for human

observation and intervention. Long-lived plants also have a

more limited capacity for population recovery on time-

scales relevant to threatening processes (Keith et al. 2007).

Despite recognition of their importance and potential sen-

sitivity, long-lived plants receive very little conservation

attention (Keith et al. 2007). It is therefore crucial to

understand their population dynamics in the face of

threatening processes over the long term in order to plan

for possible future declines.

The aim of this paper is to identify management strat-

egies that capitalize on apparent synergies between multi-

ple threats to reduce risks of decline and extinction in long-

lived plants. We use detailed population models, developed

from an extended demographic census, that incorporate

complex interactions to investigate the long-term effects of

both disease and altered fire regime on the risk of decline in

the long-lived fire-adapted plant species X. resinosa. Fire

does not eradicate the disease but it can promote recruit-

ment, while some established plants are retained in the

population. Fires may therefore bolster population sizes

874 Oecologia (2011) 167:873–882

123

and effectively stifle the population-level effects of disease.

On the other hand, fire provides an additional source of

mortality for all life stages which could add to disease-

induced mortality. In order to avoid unacceptable declines

in plant populations, creative management solutions need

to be devised that utilize the interaction between threats.

We address this issue by considering the population

dynamics, threatening processes and practicalities of

managing the species across a range of timescales. For

long-lived species, these timescales can reveal different

insights that ultimately support the development of alter-

native strategies for population management.

Materials and methods

Background and data sources

Xanthorrhoea resinosa Pers. is a charismatic, endemic

long-lived perennial from the family Xanthorrhoeaceae

(Order Liliales). Commonly known as a grass tree,

X. resinosa is found along the southeast coast of Australia

in heathlands and sclerophyll eucalypt woodlands. Indi-

viduals are slow growing and can live for several centu-

ries (Lamont et al. 2004).

A 20 year demographic study of X. resinosa populations

was conducted in the Royal National Park near Sydney,

Australia between 1989 and 2009. A sample of 1,162

established plants, across a range of life stages, was cens-

used approximately annually. Twelve replicate populations

were sampled over two spatial scales (100 m and several

km). These populations were each assayed for the presence

of root-rot disease, P. cinnamomi, using methods described

by Walsh et al. (2006). Only one of the 12 populations

produced no evidence of infection. A cohort of 988 seed-

lings was sown in situ after a fire at one site and censused at

decreasing frequency for 20 years. Sampling of seedlings

was stratified over three spatial scales varying from meters

to hundreds of meters. There were three major fires during

the sampling period: one in 1988 and another in 1994,

which burnt all censused populations, and another in 2002,

which burnt 6 of the 12 censused populations. The survival

of all individuals was recorded at each census. Crown size

(number of living leaves and the radius of the leaf canopy)

and caudex (stem) height were recorded for all individuals.

The number of adult plants flowering after a fire event was

also recorded. Survival rates were calculated directly from

the seedling data. Failure time analyses were applied to the

data to derive survival functions for established plants

(Keith and Tozer, unpubl.). We used these data to structure

and parameterize a spatially explicit, individual-based,

single-population model of X. resinosa.

Development, growth and survival

Seven critical life stages of X. resinosa were categorized

according to plant size (measured as crown size and caudex

height), reproductive potential and survival (Fig. 1): two

seedling stages, juveniles, subadults, and three classes of

adult plants (plants with a caudex of different heights).

Plants are generally slow growing. The rate at which

leaves are produced per plant depends on the number of

leaves in the previous year and the time since last fire; a

growth spurt in the first year after a fire decelerates with

time since last fire (Fig. 2a, Keith and Tozer, unpubl.). The

modelled number of leaves on a plant is selected from a

normal distribution, with the mean specified by the

appropriate function in Fig. 2a and a coefficient of varia-

tion of 0.1 (plants can also lose leaves from year to year).

The radius of the leaf canopy (core crown radius) is a

function of the number of leaves of the plant:

RC ¼0:0092þ 0:00032� NL � 1� 10�7ð Þ � N2

L if NL� 1000

0:23 otherwise

�

ð1Þ

where RC is the core crown radius in meters and NL is the

number of leaves per plant. The crown radius is selected

from a normal distribution bounded at 0 m with the mean

specified in Eq. 1 and a coefficient of variation of 0.1 (the

crown can decrease in size through leaf loss). The crown

radius is used to invoke competition between plants; when

the crowns of two adjacent plants start to overlap the

smaller plant dies.

When plants mature they develop a caudex that can exist

above or below the ground. The caudex is a woody stem

surrounded by tightly packed dead leaf bases. All adult

plants initially develop a belowground caudex which can

later develop into an aboveground caudex. The probability

of an adult plant developing an aboveground caudex

depends on its number of leaves:

PAC ¼0 if NL\50

5� 10�5� �

� N2L � 0:0025 if 50�NL\150

0:005 if NL� 150

8<:

ð2Þ

where PAC is the probability of an adult plant developing

an aboveground caudex and NL is the number of leaves on

a plant. Once a caudex has emerged above the ground, in

the model it grows in height at an average of 0.1 cm per

year with a coefficient of variation of 0.03 cm.

The survival of an individual plant depends on its crown

size, the presence of a caudex, the proximity of competi-

tors, disease infection status, and the time since the last fire.

The disease-free background survival rates for seedlings

aged 1–5 years were randomly selected, with replacement,

Oecologia (2011) 167:873–882 875

123

from the 40 data points presented in Fig. 2b. These were

obtained from an annual census of 40 seedling cohorts

established in 1989. For seedlings aged 6–20 years, dis-

ease-free survival rates were based on the observed mean

of 0.90 with a standard deviation of 0.055 derived from a

census of the remaining seedling cohorts that survived a

fire in 1994. For all other stages, failure time analyses,

based on a Wiebull survival model (Fox 2001), were

carried out on the data from the census of established plants

to derive background survival rates for plants in each stage

as a function of time since the last fire. Separate models

were produced for nondiseased and diseased populations,

which were identified during the Phytophthora survey

described by Walsh et al. (2006). The medians of the

resulting survival rates for each life stage were taken across

survival functions derived for the three separate fire events

3. Juveniles

4. Sub-adults 5. Adults–belowground caudex

6. Adults–aboveground caudex ≤ 10 cm

7. Adults–aboveground caudex> 10 cm

Flowering & Seeds

Predation, germination, rainfall, 1st year survival

20 yr old plants

1. Seedlings11 – 5 years

1 – 15 leaves

Plants with > 15 leaves

16 – 50 leaves > 50 leaves > 50 leaves > 50 leaves

Plants with > 50 leaves

1 – 3 years after fire

Fire

2. Seedlings26 – 20 years

5 yr old plants

Fig. 1 Life-history diagram

depicting the seven life stages

and transitions of Xanthorrhoearesinosa populations and events

causing mortality, flowering and

germination. Dashed arrowsindicate that rates of backward

transitions are very different to

forward transitions. All the

seedling age classes are

subsumed into two stages; stage

1 includes 5 ages (1–5 years)

and stage 2 includes 15 ages

(6–20 years)

0

0.05

0.1

0.15

0.2

0.25

0.3

1 2 3

Pro

babi

lity

of fl

ower

ing

Time since last fire (years)

Probability of flowering wrt time since last fire

Below ground caudex

Caudex < 10 cm

Caudex > 10 cm

0

200

400

600

800

1000

1200

1400

0 200 400 600 800 1000

Leav

es a

t tim

e t

Leaves at time t-1

Number of leaves per plant

TSLF = 1TSLF = 2TSLF = 3TSLF >= 4

0

1

2

3

4

5

6

7

8

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fre

quen

cy

Survival probability

Background 1 - 5 year old seedling survival

0.984

0.986

0.988

0.99

0.992

0.994

0.996

0.998

1

0 10 20 30 40 50

Sur

viva

l pro

babi

lity

Time since last fire (years)

Survival rates for stages 3 to 7

Stages 3 & 4Stage 5Stages 6 & 7

(a) (b)

(c) (d)

Fig. 2 Data used in model parameterization. a Number of leaves per

plant in year t as a function of the number of leaves in the previous

year (TSLF time since last fire). b Annual disease-free background

seedling survival rate. c Annual disease-free background survival

rates of all non-seedling stages as a function of the time since last fire.

d The probability of flowering with respect to time since last fire for

adult plants with belowground and aboveground caudexes

876 Oecologia (2011) 167:873–882

123

that occurred during the 20-year census; Fig. 2c displays

survival functions for nondiseased sites. Modelled adult

plants with a belowground caudex had the highest survival

rates, whereas plants with an aboveground caudex had the

lowest survival rates. The modelled survival rates increased

with time since last fire for all stages, and mortality rates

for diseased populations were approximately 14 times

greater than those for nondiseased populations across all

standing-plant stages. Survival rates were selected from a

lognormal distribution with mean values for each stage

assigned from Fig. 2c and standard deviations taken as half

the difference between the median and the value from the

other two fires that deviated the greatest from the median.

Fire events

Recruitment of X. resinosa depends on the passage of fire,

which stimulates flowering and seed production (Taylor

et al. 1998). Seeds are short-lived and are rarely produced

four or more years after a fire. The probability of a fire in

any given year depends on the number of years since the

last fire (Fig. 3a), with the shape of the curve reflecting fuel

accumulation (Conroy 1993). There are two possible types

of fire in the model, depending on available fuel levels and

weather: a ‘‘hot’’ (high severity) fire or a ‘‘cool’’ (low

severity) fire, as defined by their effects on plant survival.

If a fire occurs, then the probability of that fire being hot,

displayed in Fig. 3b, is also a function of the time since the

last fire but is higher for wildfires than prescribed fires

because the latter are usually ignited under milder weather

conditions. The probability of a cool fire (not shown) is the

difference between the curves in Fig. 3a, b. These func-

tions give an expected fire frequency that corresponds with

fire history records in the Sydney region (New South Wales

National Parks and Wildlife Service, unpublished data).

Post-fire survival

Most individuals of X. resinosa survive fire because living

tissues are insulated from heat, either by soil or by the leaf

bases that are densely packed around an aboveground

caudex. However, fire may induce an immediate pulse of

mortality in all stages of the population (Curtis 1998; Keith

and Tozer, unpubl. data). The proportion of post-fire sur-

vivors is dependent on the size of plants and the severity of

fire (hot vs. cool). Survival of immature plants and indi-

viduals with aboveground caudexes is most affected by

fire, while plants with belowground caudexes and large leaf

canopies have the highest levels of post-fire survival. In hot

fires, the survival rate of all seedlings is uniformly dis-

tributed between 0.34 and 0.44, whereas in cool fires,

seedling survival rate is the same as the background rate for

the respective seedling stages. For the remaining stages,

mortality rates under hot fires are lognormally distributed

with the following mean values (standard deviation in

parentheses): juveniles 0.038 (0.14), subadults 0.027

(0.10), adults with belowground caudex 0.0037 (0.014),

adults with aboveground caudex B10 cm 0.080 (0.080),

and adults with aboveground caudex [10 cm 0.19 (0.17).

Survival rates under cool fires are the same as hot fire rates

for juveniles, subadults and plants with a belowground

caudex, while the cool fire mortality of plants with an

aboveground caudex is one-fifth of the hot fire mortality.

Flowering

Xanthorrhoea species produce an impressive flowering

spike that grows from the top of the grassy head of the

plant. In the model, X. resinosa only flowers within 3 years

after a fire event (flowering in subsequent years is negli-

gible), and the probability of a mature individual flowering

0

0.03

0.06

0.09

0.12

0.15

0.18

0 1 2 3 4 5 6 7 8 9 10

Pro

babi

lity

of fi

re

Time since last fire (years)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 1 2 3 4 5 6 7 8 9 10

Pro

babi

lity

"hot

" fir

e

Time since last fire (years)

Wildfire

Prescribed fire

(a)

(b)

Fig. 3 Probability of a wild fire or b ‘‘hot’’ fire (conditional on a fire

occurring) for prescribed and wildfires as a function of time since last

fire. ‘‘Hot’’ fires are defined as high-intensity fires. Wildfires are three

times more likely to be ‘‘hot’’ than prescribed fires

Oecologia (2011) 167:873–882 877

123

depends on the size of the plant and the time since the last

fire (Fig. 2d). Mature plants are more likely to flower in the

second year following a fire than in the first or third years

(Keith and Tozer, unpubl. data). Seed production does not

appear to depend on the size of the plant. Similarly, La-

mont et al. (2000) found in their study of X. preissii that the

incidence of flowering was size dependent, but once the

plants flowered, the size of the spike and the number of

seeds produced was not dependent on plant size.

Seed production and germination

Seeds are nondormant, short-lived, and there is no soil seed

bank (Keith and Tozer, unpubl. data). Prior to maturation,

developing seeds are subject to predation by invertebrates

and birds. After dispersal, mature seeds may be eaten by

invertebrates and small mammals. Mature seeds that escape

predation are highly viable ([90%), but successful ger-

mination is sensitive to the availability of moisture (Keith

and Tozer, unpubl. data). In the model, germinants fail to

survive unless above-average rainfall occurs during the

year in which the seed germinates (Tootell 1998). Average

rainfall for the study area is normally distributed, with a

mean of approximately 1,000 mm and a standard deviation

of 200 mm. Accounting for pre-dispersal seed predation,

viability and post-dispersal predation in the model, the

number of germinants per flowering plant that survive their

first year was selected from a lognormal distribution with a

mean of 8.0 and standard deviation of 8.4. Wind is

apparently the only seed dispersal agent, and patterns of

seedling distribution suggest that most seeds tend to dis-

perse within a 6 m radius of the parent plant. While seeds

may disperse further under extreme winds, this was ignored

for the purpose of our single-population model.

Disease

The pathogen, Phytophthora cinnamomi Rands., was

recorded within Royal National Park more than 30 years

ago, and is now widespread across a range of different

habitats (Walsh et al. 2006). P. cinnamomi has been iso-

lated from the tissues of dying and recently dead individ-

uals of X. resinosa at eleven of the census sites (Walsh

et al. 2006). Establishment of the disease is patchy across

the landscape, allowing survival rates to be estimated for

diseased and disease-free populations. We modelled three

disease scenarios: no disease, medium disease and high

disease. Survival rates in the no disease scenario were

taken from the uninfected site described above. The high

disease scenario was based on the mean observed mortality

rates across eleven infected census populations, whereas

the medium disease scenario represents half those rates.

The high disease scenario represents the case where there is

no disease management within an infected population, the

medium disease scenario represents the case where disease

control management is 50% effective, and the no disease

scenario represents 100% effectiveness of disease control

management or absence of disease in the patch (Keith

2004). Disease control management represents measures

such as quarantine or chemical treatment (e.g. Tynan et al.

2001) that reduce disease-related mortality to varying

degrees approaching background levels. In this model,

disease is assumed to equally affect all standing-plant

stages of the plant population.

Simulations

A spatially explicit, individual-based, single-population

model was constructed using the functions and data

described above. An individual-based model was chosen

over aggregate population models in order to model com-

petition more realistically. Competition depends on the

location of plants relative to each other, so standard density

dependence functions based on population size, rather than

relative locations, are inadequate to capture mortality

through competition between clumped plants. The patch

size was set to 4 by 6 km. The initial population included

500 seedlings, 300 juveniles, 300 subadults, 400 plants

with belowground caudex, and 100 plants each in the

aboveground caudex stages, roughly based on the structure

of observed populations. The time since last fire was ini-

tialised as 10 years. Each plant was randomly assigned

coordinates in space, an initial number of leaves, a crown

radius using the function in Eq. 1, and a caudex height

where appropriate.

Along with the three disease scenarios, four fire man-

agement scenarios were implemented, each with and

without wildfire. Without wildfire, the four scenarios were

fixed fire intervals every 5, 12, 20, and 30 years, repre-

senting an ideal situation where management has full

control of the system to implement prescribed fire regimes.

In the scenarios with wildfire, prescribed fires were

implemented whenever time since fire reached thresholds

of 5, 12, 20 and 30 years, respectively, and intervals

between fires may be interrupted by unplanned wildfires, as

determined by the functions in Fig. 3b. This second group

of scenarios represents a more realistic situation where

management is responsive to unplanned wildfires. We also

tested scenarios of only wildfires and total fire exclusion,

representing a realistic case where there is no management

intervention and an ideal case where management succeeds

in preventing all fires in the system, respectively. To gauge

the impact of time horizon on management outcomes, we

ran models for 30, 75, 150 and 300 years. One thousand

simulations were run for each scenario. The risk of quasi-

extinction (Ginzburg et al. 1982) across the simulation

878 Oecologia (2011) 167:873–882

123

period was used to compare results across treatments by

calculating the expected minimum abundance (EMA) from

the risk curves (McCarthy and Thompson 2006)—this is a

conservative surrogate for population size that takes into

account variability across the duration of a simulation. This

was used to rank population persistence across all fire and

disease scenarios. In order to gauge trends in the population

central tendency, the final median abundance (FMA) was

also used to rank scenarios. EMA and FMA were calcu-

lated for the total standing population (i.e. excluding only

seeds), for the total standing population excluding seed-

lings (stages 3–7, Fig. 1), and for seedlings only (stages 1,

2). These different performance measures allowed us to

focus on persistence and regeneration as different targets

for management. Here, we cast our results, discussion and

recommendations predominantly in terms of EMA, as it is

a more conservative—and therefore risk averse—measure

than FMA.

Results

The results for the wildfires only (no burning) scenario and

the 20 and 30 year burning intervals with wildfires were

almost identical. This was because the average fire return

interval for wildfires is less than 20 years, and hence

wildfires dominate the 20 and 30 year managed fire sce-

narios. Of these three scenarios, only the results for the

wildfires scenario are shown and discussed. With the

exception of the ‘‘no fire’’ scenario, the results for fire

management scenarios without wildfires were also similar

to those with wildfires, even though the 20 and 30 year

scenarios had substantially longer fire-free intervals with-

out wildfires. Therefore, we only present and discuss

results for the management scenarios that include wildfire.

Population viability declined (as indicated by expected

minimum abundance, EMA) under all scenarios as disease

increased and duration of simulation increased, irrespective

of whether seedlings were included in (Fig. 4a–c) or

excluded from (Fig. 4d–f) the population total. EMAs for

the full disease scenarios were on average around 80, 70,

50 and 30% of the no disease scenario for the 30, 75, 150

and 300 year durations, respectively. When seedlings were

included in the population total, the EMA was highest for

the 5 year scenario, although the difference between this

and other fire scenarios diminished as disease increased.

EMA was generally lowest for the wildfire scenario across

all disease levels for all durations except 30 years (Fig. 4a–

c). For the 30 year simulation period with seedlings

included in the population total, the wildfire scenario gave

EMAs that were slightly lower than the fire-exclusion

scenario across all disease levels (Fig. 4a–c). When seed-

lings were excluded from the population total, fire

exclusion had the highest EMA across all disease levels

and management durations, while the EMAs for all other

fire scenarios were similar for each combination of disease

level and duration (Fig. 4d–f).

Fire exclusion produced no seedling recruitment. The

5 year fire scenario produced the most seedlings, but the

duration of this effect was limited (Fig. 4g–i). Without

disease, the EMA of seedlings peaked at 150 years, and

under more severe disease scenarios, it peaked at shorter

and shorter durations. At 300 years under high disease,

there was almost no additional seedling recruitment under

5 year fire intervals relative to the other fire scenarios

(Fig. 4i).

In summary, fire exclusion was the best management

strategy for persistence of established plants under all

disease levels, while the 5 year fire scenario produced the

best outcomes when seedlings were included in population

totals, except in disease-infected populations over long

management planning horizons. As disease increased, the

performance of fire exclusion improved relative to other

fire management scenarios and was the best option for the

medium disease level over 300 years and the best option

for the high disease level over 150 and 300 years. The

results for FMA corroborated the scenario rankings

observed for EMA (see the Electronic supplementary

material).

Discussion

This study highlights the complexities of designing man-

agement strategies to address multiple threats in the con-

servation of long-lived organisms. The time frames on

which conservation goals are set and how population per-

formance is measured are crucial to the outcomes achieved.

Our results suggest that management to achieve relatively

short-term goals may be in direct conflict with sustained

progress towards long-term goals. The 5 year fire scenario

promoted the greatest number of flowering and germina-

tion events, producing large numbers of seedlings and the

highest EMAs for the total population (Fig. 4a). However,

this increase in seedling densities under 5 year fire inter-

vals is unsustainable because few seedlings survive to

maturity, and the numbers of seedlings that become

established are outweighed by accelerated rates of both

background and fire-related mortality over the long term.

Moreover, frequent fires prove to be the worst management

option if sustained over longer timeframes because they

accelerate the decline in the number of established plants,

which are crucial to population persistence in long-lived

species such as Xanthorrhoea (Keith et al. 2007).

Excluding seedlings from the population assessment

showed that fire exclusion always produced the highest

Oecologia (2011) 167:873–882 879

123

EMA across all timeframes and disease levels. Even when

seedlings were included, long fire intervals surpassed

5 year intervals as the best-performing management option

when outcomes were viewed over longer durations.

In summary, frequent fires appear to bolster populations

and overcome the added mortality from disease in the short

term, but the benefits of this strategy are transient. A short

timeframe masks the longer-term effects of frequent fire in

the face of disease. The declines in population ‘‘capital’’

(sensu Harper 1977), the transient increases in seedling

abundance, and the relative merits and limitations of

alternative management options only emerge when risks of

population decline are examined over different timescales

using different measures of population performance.

Disease exacerbates population decline, especially under

frequent fire regimes, because fire keeps the population in a

state where background survival is at its lowest (Fig. 2c),

and disease reduces this relatively low survival rate further.

Therefore, under the 5 year fire interval scenario, mortality

remained at elevated levels because the population was

continually maintained at a young post-fire age with rela-

tively low background survival rates. In contrast, longer

fire intervals allowed overall mortality to subside to lower

levels for a longer period after each fire and for more years

of the simulation, slowing the rate of population decline.

Fire exclusion performed best because mortality was

always maintained at a minimal level (Fig. 2c), even when

disease was present. The observed synergism between fire

and disease mortality in X. resinosa is consistent with

patterns of disease mortality in X. preissii, which was

found to be greater in recently burnt populations than in

populations unburnt for more than 10 years (Moore et al.

2007).

The potential to reduce disease impacts by minimizing

fire frequency underscores the benefits of an integrated

management strategy rather than one that aims to mitigate

disease impacts in isolation of fire management. However,

the development of an integrated management plan that

deals with both major threats faces a number of significant

challenges. The simulations all showed a steady decline in

EMA, even in the absence of disease and irrespective of

fire frequency, indicating that survival and recruitment

rates are most likely too low to maintain stable populations

for the very long term. The decline persists, but its rate can

0

200

400

600

800

1000

1200

1400

1600E

MA

No disease, total population

0

200

400

600

800

1000

1200

EM

A

No disease, no seedlings

0

200

400

600

800

1000

EM

A

No disease, seedlings only

0

200

400

600

800

1000

1200

1400

1600

EM

A

Medium disease, total population

0

200

400

600

800

1000

EM

A

Medium disease, no seedlings

0

200

400

600

800

EM

A

Medium disease, seedlings only

0

200

400

600

800

1000

1200

1400

30 75 150 300

EM

A

Duration (years)

High disease, total population

Wildfires

5 year fires

12 year fires

no fires

0

200

400

600

800

1000

30 75 150 300

EM

A

Duration (years)

High disease, no seedlings

0

200

400

600

800

30 75 150 300

EM

A

Duration (years)

High disease, seedlings only

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Fig. 4 Expected minimum abundance (EMA) versus duration (in

years) for fire and disease management scenarios. Total standing plant

populations (stages 1–7) for a no disease, b medium disease, c high

disease. Standing plant populations excluding seedlings (stages 3–7)

for d no disease, e medium disease, and f high disease. Seedlings only

(stages 1, 2) for g no disease, h medium disease, and i high disease.

With the exception of the ‘‘no fire’’ scenario, all fire management

scenarios include wildfire

880 Oecologia (2011) 167:873–882

123

be reduced when fire appropriate management is imple-

mented. When considering the population central tendency,

FMA, the population is fairly stable under the 12 year fire

scenario, but this is not maintained in the presence of

disease (see the Electronic supplementary material). When

disease is present, fire exclusion produced the best long-

term outcome because it minimized additional mortality,

outweighing any effect caused by the lack of fire events to

trigger flowering and subsequent recruitment of seedlings

into the population. Ultimately, however, fire exclusion

would result in extirpation, because it produces no seed-

lings to replace the inevitable deaths of established plants

(Fig. 4g, h, i). In any case, a strategy aimed at long-term

fire exclusion is unlikely to succeed in the face of

unplanned fire events, which sooner or later are likely to

occur within humid temperate climate regions during

extreme weather (Bradstock 2010).

Integrated fire management planning across spatial

mosaics may be the best approach to managing declines in

X. resinosa, and in resprouters experiencing multiple

threats in general. Such a plan must be cognisant that

seedling germination may be promoted through frequent

fire, increasing population sizes for all disease levels, at

least in the shorter term. It must also recognize that by

doing so, the adult stages are reduced, and in the long term

this becomes detrimental to the population as a whole

because there will be fewer reproductive individuals until

cohorts of new recruits reach maturity. These demographic

trade-offs lead to several courses of action. First, frequency

of fire should be minimized while options are investigated

to reduce mortality of adult plants in the early post-fire

period. These options may include variations in the timing

of fires (Le Maitre 1988), enhancement of soil moisture

(Lamont et al. 1991), and the timing and application of

chemical treatments for disease (Shearer et al. 2004). This

approach also provides an opportunity to improve under-

standing about disease resistance—if disease-resistant

genotypes are present within the population, mortality rates

may be expected to subside over time as the nonresistant

genotypes succumb to selection, leaving the resistant

genotypes to dominate the population. Secondly, at an

appropriate time, a short sequence of frequent fires can be

implemented to initiate seedling recruitment, followed by

long periods of fire suppression to allow new plants to

reach the adult stages. This second action will ultimately

need to be implemented, even if experimentation fails to

produce a means of reducing post-fire mortality. Third, a

management strategy should address a spatial mosaic in

which the sequence of frequent fires and suppression are

staggered across patches depending on the level of disease

at the site. Fire mosaics have been shown to influence risks

of extinction in obligate seeding plants (Bradstock et al.

1996; Regan et al. 2003, 2010), but the outcomes depend

crucially on the spatial scale of patches that make up the

mosaic (Parr and Andersen 2006). Finally, our results

highlight the importance of considering a range of man-

agement durations for long-lived species and different life

stages as targets for management. Most management is

only considered on timescales convenient for human

intervention, rather than the most relevant timescale for the

species. For long-lived species, these timescales are

incommensurate.

Our results are likely to be applicable to other faculta-

tive resprouter species in fire-prone ecosystems that expe-

rience altered fire regimes and additional threats that

increase mortality (e.g. poaching/harvesting, degradation

or loss of habitat, increased herbivory or predation, dis-

placement by exotic species, etc.). Our results may also

have general implications for other long-lived species

whose life histories are dependent on other types of dis-

turbance regimes such as floods or storms. The work pre-

sented here shows that considering the effects of multiple

threats across a range of timescales is critical to providing

effective conservation management for the persistence of

long-lived species in the face of global change.

Acknowledgments This work was supported by a National Science

Foundation Grant NSF-DEB-0824708 awarded to HMR. We thank

Rebecca Swab and Clara Bohorquez for running simulations and

creating some of the figures, and Erin Conlisk, Rebecca Swab and

Colin Yates for comments on a previous draft of this manuscript. We

declare that the experiments comply with the current laws of the

country in which the experiments were performed.

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