Predicting the likely response of data-poor ecosystems toclimate change using space-for-time substitution acrossdomainsREBECCA E . LE STER 1 , PAUL G . CLOSE 2 , JAN L . BARTON1 , ADAM J . POPE 1 and

STUART C. BROWN1

1School of Life and Environmental Sciences, Deakin University, PO Box 423, Warrnambool, Vic 3280, Australia, 2Centre of

Excellence in Natural Resource Management, The University of Western Australia, PO Box 5771, Albany, WA 6330, Australia

Abstract

Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly

applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules

of thumb in data-poor regions, making management of the relevant systems difficult. We demonstrate a method for

developing quantitative predictions of ecological response in data-poor ecosystems based on a space-for-time substi-

tution, using distant, well-studied systems across an inherent climatic gradient to predict ecological response.

Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal eco-

logical responses that are then tested in a target region. Transferability of predictions among distant locations, the

novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect

impacts of climate change on physical, chemical and ecological variables using commonly available data sources.

Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km)

ecosystems.

Quantitative forecasts of ecological change based on climate-ecosystem relationships from distant regions provides

a basis for research planning and informed management decisions, especially in the many ecosystems for which there

are few data. This application of gradient studies across domains – to investigate ecological response to climate

change – allows for the quantification of effects on potentially numerous, interacting and complex ecosystem compo-

nents and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated

long-term predictions will be of significant value to natural resource practitioners attempting to manage data-poor

ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem

types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where

no such method currently exists.

Keywords: analogy, climate change response, ecological modelling, ergodic, estuary, gradient studies

Received 9 February 2014 and accepted 8 April 2014

Introduction

Climate change represents one of the most important

contemporary risks to many ecosystems (Rustad, 2008;

Hoegh-Guildberg & Bruno, 2010) with the potential to

interact with, and exacerbate, other anthropogenic dis-

turbances (Darling & Cot�e, 2008; Genner et al., 2010;

Philippart et al., 2011). Ideally, the prediction of cli-

mate-related ecological change requires the simulta-

neous consideration of multiple climatic drivers,

population and community-level responses, and the

assessment of medium- to long-term temporal changes

to capture complex, interacting responses (Parmesan,

2006; Poloczanska et al., 2008; Wernberg et al., 2010;

Luo et al., 2011). The inherent complexity of this task

makes quantitative prediction of future ecological char-

acteristics difficult, particularly when the empirical

data upon which to base such predictions are, at best,

patchily distributed in time, space, among ecosystem

types and across biogeographical regions (Pressey et al.,

2007; Coreau et al., 2009).

Many ecosystems lack adequate biophysical and eco-

logical data sets for mechanistic assessments of possible

ecosystem responses, yet the imperative to manage

data-poor ecosystems remains. As such, decisions are

often made based on rules of thumb (e.g. setting envi-

ronmental flows; Arthington et al., 2006), expert opin-

ion or by assuming that changes and responses

observed in well-studied ecosystems will be directlyCorrespondence: Rebecca E. Lester, tel. +61 3 5563 3330,

fax +61 3 5563 3143, e-mail: rebecca.lester@deakin.edu.au

1© 2014 John Wiley & Sons Ltd

Global Change Biology (2014), doi: 10.1111/gcb.12634

Global Change Biology

applicable to the ecosystem of interest (Sutherland,

2006; Catford et al., 2013; Worthington et al., 2014).

Given the importance of the decisions that rely on

them, methods must be developed that can provide

realistic predictions of ecosystem responses based on

limited data, at spatial and temporal scales relevant to

management, and maximising the use of incomplete

and evolving scientific understanding (sensu Arthing-

ton et al., 2006; Newton et al., 2009; Worthington et al.,

2014).

Space-for-time substitutions, also known as ergodic

gradient studies, are often used to develop quantitative

predictions of ecological responses to climate change

(Fukami & Wardle, 2005; Rustad, 2008). In this context,

space-for-time substitutions use multiple sites across an

environmental gradient to predict a temporal trajectory

in ecological change that is assumed to be causally

related to the changes across the gradient (Pickett, 1989;

Fukami & Wardle, 2005; Kerr et al., 2007; Johnson &

Miyanishi, 2008). Gradient studies have been applied to

a wide range of physical, chemical and biological vari-

ables in the past, often with great success (see Table

S1).

Analogue-based methods provide a conceptual basis

for transferring predictions of future response to cli-

mate change from a well-studied region to another ty-

pologically similar but relatively data-poor region

elsewhere. Analogy is a method of scientific reasoning

by which demonstration of shared properties between

two entities can be used to infer that other properties

are also shared (Hesse, 1966; Jackson & Williams, 2004).

The criteria for using one entity as an analogue for a

second rely on there being clearly defined similarities

between the two, more positive than negative analogies

related to the variable of interest, and that causal rela-

tionships are demonstrable and/or plausible (Hesse,

1966). For example, analogue systems have been used

as the justification for applying space-for-time substitu-

tion methods to palaeo-environmental studies of pollen

(Wookey, 2008), elevational shifts in bird distributions

(Anderson et al., 2013) and changes in community com-

position on rocky shores (Hawkins et al., 2008, 2009;

Poloczanska et al., 2008).

Despite their widespread use and the common prac-

tise of retaining some data for model validation (e.g.

Kharouba et al., 2009), the ability of gradient studies to

predict ecological responses to possible future climate

in systems distant (e.g. thousands of kilometres) from

the region of the original spatial gradient does not

appear to have been quantitatively tested. This is

unfortunate as managers and researchers attempting

to predict the likely impact of climate frequently

assume that the relationships will apply in data-poor

ecosystems.

Here, we demonstrate a new application of space-for-

time substitution where knowledge derived from rela-

tively well-studied ecosystems can be quantitatively

transferred, using analogy, to distant and poorly stud-

ied ecosystems. This application effectively expands

the utility of current understanding and available data

to apply to separate ecosystems where quantitative

assessments have not been possible in the past. Our

case study uses an existing rainfall gradient to develop

predictions of ecosystem response to modelled climate

change in estuarine ecosystems over 2000 km away.

Materials and methods

Ecological predictions can be generated for data-poor systems

(a single or multiple ecosystems; the ‘target domain’) using

data from analogous systems that span a well-studied climate

gradient in another location (or set of locations; the ‘gradient

domain’). The approach requires that the climate gradient

across the gradient domain is representative of the predicted

climatic changes in the target domain and that the domains

are typologically similar. The method involves five key steps

(Fig. 1). A gradient domain with a relevant spatial gradient in

climate is identified (Step 1; Fig. 1). Using available data

across that spatial gradient, direct and indirect biophysical

relationships to climate parameters are quantified (Step 2;

Fig. 1) and used to construct hypotheses of temporal climate

response in the target domain (Step 3; Fig. 1). Evidence for the

response hypotheses are then assessed using any available

data from the target domain (Step 4; Fig. 1). If appropriate,

these newly derived relationships are used as quantitative

predictions of climate-related response to guide future

research and management decisions in the target domain

(Step 5; Fig. 1).

Example application

To demonstrate the method, we have developed predictions

of ecological response to climate change for ten relatively

small, intermittently open estuaries located along a high-

energy, microtidal coastline in Victoria, Australia (between 38°

and 39°S and 141° to 143°E; Fig. 2; Table S2). These estuaries

and their catchments constitute the target domain and are

expected to undergo substantial climate-related change (Jones

& Durack, 2005; Gillanders et al., 2011). While rainfall and

run-off predictions exist for the target domain, to our knowl-

edge, there are no quantitative predictions of future freshwa-

ter inflows, salinity or fish assemblages. Current predictions

are qualitative and have low levels of confidence (e.g. that loss

of marine connectivity may prevent marine migrant fish from

accessing feeding, spawning and nursery habitats; Gillanders

et al., 2011).

A gradient domain was identified approximately 2000 km

away, along the south coast of Western Australia, that

included 11 well-studied and functionally similar estuaries

between 33° and 35°S and 115° to 121°E where rainfall as well

as ocean-estuary connectivity tend to decrease towards the

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12634

2 R. E. LESTER et al.

east (Fig. 2; Table S2). Data for rainfall, river discharge, estua-

rine water quality and fish species richness (alpha diversity)

were collated for the ten estuaries in the target domain and for

11 estuaries in the gradient domain (Table S2).

Modelling methods for case study

Three physical and ecological relationships were identified:

rainfall:flow; flow:salinity; and salinity:fish species richness

(see Step 2 below). For each of these, we explored a range of

linear, log-linear, multiple linear and nonlinear relationships

(e.g. power models) using combinations of mean, maximum

and minimum values. Residual standard error and Akaike

Information Criterion (Akaike, 1973) were used to identify

the best-fitting, most parsimonious models for the gradient

domain, which were considered consistent with the physical

realities of the system (e.g. exponential decay for flow:salin-

ity where negative flows and salinities cannot exist). There

were no significant correlations among the independent vari-

ables used. Where significant relationships for the gradient

domain were identified, the structure of residuals was

assessed using a Shapiro–Wilk test to determine whether

residuals were normally distributed (Shapiro & Wilk, 1965)

and a runs test to determine whether their sequence was sig-

nificantly nonrandom (Zar, 2012). Analyses were conducted

in R (R Development Core Team, Vienna, Austria) using the

nlstools (Baty & Delignette-Muller, 2012), lawstat (Gastwirth

et al., 2013), mvtnorm (Genz et al., 2013) and VGAM (Yee,

2013) packages. Nonlinear threshold effects were explored

using regression trees in Salford Predictive Miner v.6.0

Fig. 1 Summary of the proposed method to predict ecological responses to climate change, illustrated using examples applicable in

Mediterranean-climate estuaries. Locations along a spatial gradient are represented by the letters A (wet end of a hypothetical rainfall

gradient) to C (dry end). Times are represented as T1 (near future, for a hypothetical drying climate prediction) to T3 (far future). The

grey arrows (Step 3) represent predicted shifts from Times 2 to 3. Bold black arrows indicate the flow between the individual steps in

the method. Narrow black arrows illustrate the links that can be made between direct, indirect and ecological effects of climate change

(e.g. in Step 3, predicted flows may be used as an independent variable to develop a relationship with salinity).

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12634

PREDICTING RESPONSES OF DATA-POOR ECOSYSTEMS 3

(Salford Systems, San Diego, USA). Possible spatial and tem-

poral autocorrelations were investigated using cross-correla-

tion analyses in the time series module of SYSTAT v. 13

(SYSTAT Software Inc., Chicago, USA). No significant

threshold effects or autocorrelations were identified, so the

results are not reported here.

The potential influence of nonclimate modifiers on the iden-

tified relationships in the gradient domain were explored as

relationships were tested and are reported for cases when they

resulted in stronger relationships (e.g. with lower residual

standard errors). The Glenelg and Hopkins estuaries were

excluded when assessing rainfall:flow as their catchment areas

are much larger than those in south-western Australia making

similar relationships less likely.

All relationships were developed using data from the gradi-

ent domain. Data from the target domain were used to test the

goodness of fit for the posited single best relationship using a

realised discrepancy assessment (sensu Gelman et al., 1996).

That is, to test the ability of the best-fitting relationship

derived from the gradient domain to describe the data from

the target domain, the parameters of the original relationship

were bootstrapped, using the mean and standard error for the

slope and intercept terms, assuming that each parameter esti-

mate was normally distributed. For each bootstrap run, an

intercept and slope value from the normal distribution and fit-

ted values for the target domain was randomly selected

(n = 10 000), and the sum of squared residuals was calculated.

The actual sum of squared residuals for the specified model

was compared to the distribution of bootstrapped sums of

squared residuals from the target domain values. The model

was considered to be a plausible fit when the actual sum of

squared residuals from the target domain fell within the 90th

percentiles of the bootstrapped distribution of the gradient

domain.

Results

Step 1. Identify a suitable gradient domain for targetsystem(s)

The target domain must be part of a broader group of

analogous systems distinguished by similarities in the

direct and indirect ways that climate-related variables

influence their ecosystems. Another subset of those sys-

tems, the gradient domain, needs to have a relevant

spatial climatic gradient that spans the current and pro-

jected climate conditions of the target domain (Fig. 1).

While such climatic gradients will often occur across

geographically contiguous regions, this approach does

not require that this always be the case.

Direct climatic effects on ecological change may be

modified by system characteristics (nonclimate modifi-

ers, e.g. soil type, catchment aspect or slope) that indi-

rectly influence other system conditions or attributes

(e.g. evapotranspiration, soil moisture content, run-off;

Poff et al., 2010). The likelihood that consistent relation-

ships exist in both domains increases where the typol-

ogy is similar (Poff et al., 2010). Nonclimate modifiers

that may alter the relationships should also be identi-

fied and, where appropriate, be accounted for.

For our target domain, projections suggest that the

run-off to associated coastal rivers will decline between

five and 40% by 2030, and to >50% by 2070 (Jones &

Durack, 2005). Functionally similar estuaries in south-

western Australia were selected as a suitable gradient

domain. They occur within a gradient across which

Fig. 2 Locations of estuaries (closed circles: gradient domain in south-western Australia; open circles: target domain in Victoria). Loca-

tions of domains are shown in the inset. Lines indicate approximate positions of current and predicted future rainfall (‘Current’ and

‘Future rainfall equivalence’, respectively) for the target domain within the gradient domain. Colours show mean total annual rainfall.

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12634

4 R. E. LESTER et al.

ambient temperature increases and rainfall declines

with increasing longitude (Hodgkin & Hesp, 1998;

Fig. 2, Table S2). Gradient domain rainfalls currently

overlap with historical and predicted rainfall in the tar-

get domain under a relatively extreme, dry 2 °C warm-

ing scenario (Post et al., 2012).

The south-western Australian estuaries are well stud-

ied (Brearley, 2005); substantial data on hydrology,

water quality and fish assemblages are available and

provide an opportunity to develop hypotheses of tem-

poral ecological response to climate change in data-

poor Victorian estuaries.

Step 2. Characterise changes across the gradient domain

The influence of climate on spatial variations in the

physical, chemical and ecological characteristics of the

gradient domain is explored, starting with the likely

direct physical effects of climate and progressing to

the examination of indirect effects that may provide

additional independent variables that are better corre-

lated with chemical or ecological response than direct

climatic variables (e.g. salinity; Fig. 1). Links between

physical, chemical and ecological responses may be

additive or interacting, and biota may respond to

physical or chemical extremes, rather than average

conditions (Denny et al., 2009). Feedbacks from eco-

logical effects on chemical and physical characteristics

are also possible (e.g. changes in dissolved oxygen

and pH related to photosynthesis), but we have

assumed that these will be less common, and/or less

pronounced, than the impact of physical and chemical

variables on ecology. When available, multiple years

of data for individual locations along the spatial

gradient can provide an estimate of the natural

variability within the systems, increasing certainty

that predicted relationships along the gradient are cli-

mate-related rather than due to a confounding effect

of small-scale variability.

For our gradient domain, we characterised changes

in flows, salinity and the species richness of estuarine

fish exploring various statistical properties of each vari-

able to identify the best relationship (e.g. including

minima and maxima to capture the impacts of extremes

in the data). We began by characterising the direct

physical effects of less rainfall on catchment-derived

estuary inflow using estuary per annum as the unit of

analysis. Mean annual catchment rainfall was signifi-

cantly related to total annual flow at the freshwater

gauge closest to the estuary when both variables were

log (x + 1) transformed (Table 1; black line Fig. 3a).

The log-linear model tended to overestimate total

annual flow at low rainfall values, consistent with sig-

nificant patterns detected in the residuals (Table 1), but

was the most parsimonious statistically-significant

model.

Next, we characterised the indirect effects of less

rainfall, via reduced flows, on estuary salinity again

using estuary per annum. Total annual freshwater

flows to an estuary, standardised by estuary basin vol-

ume (i.e. as a modifier), were significantly related to

mean annual surface salinity (Table 1; black line

Fig. 3b). The model tended to overestimate salinities at

the extremes of the relationship, particularly at high

flows and underestimate salinities in the midrange of

flows as indicated by patterns in the residuals (Table 1).

Other relationships explored (e.g. mean annual rainfall

vs. annual surface salinity, total annual freshwater

inflows vs. mean annual salinity across the water col-

umn and annual inflow volumes vs. water quality

Table 1 Significant relationships derived from the gradient domain in general form, with parameter estimates and significance

values. Statistics for the Shapiro–Wilk (W) and runs (Z) tests are also presented. Bold indicates significant values. Refer to Methods

for additional information

Variables Relationship Shapiro–Wilk Runs test

Rainfall (mm): flow (kl) y = A log(x) + B W = 0.9869 Z = �3.802

A = 19.768 � 1.322 (t = 14.96, P < 0.0001) P = 0.0422 P = 0.0001

B = �31.643 � 2.476 (t = 12.78, P < 0.0001)

Flow (kl): salinity (mg/l) y = AxB W = 0.922 Z = �2.771

A = 76440 � 1240 (t = 6.165, P < 0.0001) P = 0.0725 P = 0.0056

B = �0.3111 � 0.0462 (t = 6.736, P < 0.0001)

Salinity (mg/l): fish species richness y = Ax + B W = 0.9029 Z = �1.5275

A = �0.00001 � 0.000002 (t = �4.239, P = 0.0054) P = 0.3066 P = 0.1266

B = 0.6820 � 0.0875 (t = 7.80, P = 0.0002)

Salinity (mg/l) (x1) & basin volume

(Gl) (x2): fish species richness

y = Ax1 + Bx2 + C W = 0.947 Z = 0

A = �0.000009 � 0.000003 (t = �2.92, P = 0.0329)

B = 0.0012 � 0.0013 (t = 0.910, P = 0.404) P = 0.681 P = 1

C = 0.599 � 0.128 (t = 4.688, P = 0.0054)

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12634

PREDICTING RESPONSES OF DATA-POOR ECOSYSTEMS 5

variables other than salinity [e.g. dissolved oxygen,

pH]) were not as strong or were not significant.

Finally, we characterised the ecological effects of less

rainfall, investigating the relationships between fish

species richness and salinity. The regional richness for

each domain was calculated using all available sources

(see Table S2) and was 102 for south-western Australia

and 67 for Victoria. Total species detected in each estu-

ary as a proportion of regional richness were included

in the analysis as a single data point. A strong correla-

tion was observed between surface and depth-averaged

salinity (to a maximum depth of 4 m) in the gradient

and target domains (r = 0.99 and 0.85 respectively). As

such, depth-averaged salinity to a maximum depth of

4 m, rather than surface salinity (as for the flow:salinity

relationship above), was used as fish use the whole

water column. The maximum depth of 4 m was used to

avoid data from occasional deeper areas that commonly

exhibit significant stratification combined with periodic

hypoxia (e.g. see Brearley, 2005) which were therefore

considered to be unrepresentative of the majority of

habitat suitable for aquatic fauna.

A significant linear relationship was identified between

mean salinity and fish species richness (Table 1; black

line; Fig. 3c). No structure in the residuals (Table 1)

was detected. Code and input data sets for the rainfall

and flow component of our example case study are

available to illustrate the development of these relation-

ships (see Data S1, S2 and S3).

Step 3. Develop hypotheses for the target domain

Based on relationships identified across the gradient

domain, specific climate-related hypotheses for the tar-

get domain are developed using the climate change

predictions for the target domain itself, and knowledge

regarding the biophysical influence of any identified

modifiers. These hypotheses (e.g. the hypothesised shift

from Time 2 to Time 3 illustrated in Fig. 1) specify the

potential impact of climate change on physical, chemi-

cal and ecological attributes of the target domain. The

magnitude of change can also be estimated for a partic-

ular location, or for a particular severity of climate

change, providing a quantitative hypothesis as long as

appropriate caution is used for interpretation.

Using relationships from Step 2, we made specific

predictions relating to the direction and rate of change

in estuarine inflows, salinity and fish diversity as rain-

fall declines in the target domain. That is, we contend

that the models developed for the south-western Aus-

tralian estuaries quantitatively predict responses of Vic-

torian estuaries to climate change. Flows to estuaries

are predicted to decline exponentially as mean annual

rainfall declines (black line; Fig. 3a). Surface salinity is

(a)

(b)

(c)

Fig. 3 Derived relationships across a spatial gradient of south-

western Australian estuaries (gradient domain) and their ability

to describe temporal patterns in Victorian estuaries (target

domain). Closed circles show the gradient domain, open circles

illustrate the target domain, and the solid black line shows the

relationship derived from gradient domain. The regression

equations are based on south-western Australian estuaries,

along with the adjusted R squared and associated probability

values for linear models and achieved convergence tolerances

for nonlinear models. (a) Log-linear relationship between log

(x + 1)-transformed mean annual rainfall in a catchment (mm)

and log(x + 1)-transformed total annual flow (kl) at the gauge

closest to the estuary head. (b) Nonlinear relationship between

total annual estuarine flow standardised by estuary basin vol-

ume and mean annual surface salinity. (c) Linear relationship

between mean salinity and total fish richness as a proportion of

the maximum regional diversity.

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12634

6 R. E. LESTER et al.

predicted to increase as flows to estuaries decline (black

line; Fig. 3b), with the rate of increase accelerating as

annual flows decline below 100 times estuary volume.

This is likely to be due to declining freshwater inflows

but also associated changes in marine connectivity,

resulting in relatively high variability around the rela-

tionship (Fig. 3b). As climate change progresses, estua-

rine fish richness is predicted to decline with increasing

salinity, with reduced variability in predictions at

higher salinities (black line; Fig. 3c).

Step 4. Assess evidence for hypotheses within the targetdomain

Hypothetical relationships generated from the gradient

domain are tested using data from the target domain,

some of which are likely to exist in most cases, with

more data typically available for physical (e.g. rainfall

and flows) than biological attributes. Transferability of

relationships is assessed based on the form and

strength of relationships or a formal goodness of fit test

which should indicate where models are appropriate,

validating the analogy, or alternatively indicate differ-

ent responses in the target domain, despite similar

climatic characteristics.

For our case study, we assessed the goodness of fit of

the gradient domain models against the available data

for the target domain. The Victorian rainfall and flow

data (open circles; Fig. 3a) fell within the range of the

gradient domain, with a similar spread and slope. The

model (black line; Fig. 3a) was a highly plausible fit,

with the sum of squared residuals for the target domain

falling at approximately the 40th percentile of the distri-

bution of generated gradient domain residuals (Figure

S1a). Flow and salinity for Victorian estuaries (open cir-

cles; Fig. 3b) were consistent with predictions based on

south-western Australian data and the model fit was

plausible (black line; Fig. 2b). The degree of confidence

was lower than for the relationship between rainfall

and estuary flows (i.e. the sum of squared residuals

was more extreme compared with the bootstrapped

distribution for the south-western Australia flow and

surface salinity relationship; Figure S1b). The observed

scatter of the data around the regression line through-

out the predicted range is to be expected, given likely

differences in mouth dynamics and the timing of flows

among estuaries (Gillanders et al., 2011).

For a given salinity, target domain estuaries (open

circles; Fig. 3b) showed more variability in the propor-

tional species richness of fish than gradient domain

estuaries. Many Victorian estuaries had a smaller pro-

portion of the regional species pool present than would

be expected based on the relationship from Step 3. Estu-

aries smaller than 1 Gl in basin volume were excluded

from the comparison, as they were substantially smaller

than the estuaries in south-western Australia (mini-

mum volume = 2.4 Gl) and appeared to respond differ-

ently. For the larger Victorian estuaries, the model was

a plausible fit but there was a low degree of confidence,

with the actual sum of squared residuals at approxi-

mately the 95th percentile of the bootstrapped distribu-

tion (Figure S1c).

An alternative to excluding very small Victorian estu-

aries was to explicitly include estuary basin volume as

a second independent variable in a multiple regression.

When mean salinity and estuary basin volume were

regressed against relative regional species richness for

south-western Australia, mean salinity and the inter-

cept were significant parameters, but estuary basin vol-

ume was not (Table 1; black line; Fig. 4). The AIC for

this model (�15.64) is not significantly different from

that calculated based on the original model using only

mean salinity to calculate fish species richness (�16.42),

and there was again no structure to the residuals for

this multiple regression model (Table 1). This model

(black line; Fig. 4) was a plausible fit for the target

domain data with the model sum of squared residuals

falling at approximately the 80th percentile of the distri-

bution of bootstrapped gradient domain residuals

(Figure S1d). Thus, it was more plausible than the lin-

ear relationship including only mean salinity, and was

also able to reasonably predict fish richness for addi-

tional estuaries that had been excluded from the sim-

pler model as they were outside the range of sizes of

estuaries in the gradient domain.

Fig. 4 Multiple linear regression between mean salinity, estu-

ary basin volume and total fish richness as a proportion of the

maximum regional diversity for the south-western Australian

and Victorian regions, respectively. The figure shows estimated

vs. observed fish richness based on the model described in Step

4. Closed circles show the gradient domain, open circles illus-

trate the target domain. Refer to Fig. 3 for additional informa-

tion.

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12634

PREDICTING RESPONSES OF DATA-POOR ECOSYSTEMS 7

On the basis of the improved plausibility, we would

recommend the use of the second model, despite estu-

ary basin volume being a nonsignificant parameter in

the gradient domain. This additional nonclimate modi-

fier illustrates the potential benefits associated with

having data available for the target domain, and

increases confidence in transferring the relationship. In

the absence of those data, however, the original rela-

tionship from Step 2 is valid, but likely to yield less

accurate estimates and only for estuaries of a compara-

ble size to those in the gradient domain.

The R code illustrating this test of model fit for these

relationships is included in the Data S1, S2 and S3,

using the rainfall:flow relationship as an example.

Step 5. Application of hypotheses in target domain

Hypotheses assessed as applicable in Step 4 are applied

as predictors of expected climatic impacts in the target

domain in Step 5. The ability to quantify uncertainty

and the strength of supporting evidence will determine

the confidence (e.g., using multiple lines of evidence;

Downes et al., 2002) that can be placed in these predic-

tions. The predictions, and any causal links identified,

can then form the basis for planning and management

decisions.

The strength of evidence for particular responses in

the target domain may also not be found to be sufficient

as a basis for robust prediction. Where this is the case,

hypotheses derived from the gradient domain provide

a plausible set of trajectories for the target domain that

can be assessed through future targeted monitoring or

research at appropriate spatial and temporal scales

(Parmesan, 2006).

The relationships identified in the case study quan-

tify likely changes in flow, salinity and fish species rich-

ness in Victorian estuaries under various climate

change scenarios, in many cases for the first time.

Regional rainfall decreases from 2.6% (wet scenario,

1 °C warming, ~2030) to 16% (dry scenario, 2 °C warm-

ing, ~2050) are predicted for the target domain (Post

et al., 2012). Based on these predictions, and the rela-

tionships generated in Steps 3 and 4, mean annual

flows to target systems would be expected to decline by

30% under the wet scenario, and by 55% under the dry

scenario. Expected mean salinities would be 38% higher

under a wet scenario, and 59% under dry scenario,

while fish species richness would be expected to

decrease by 9% and 15% for the two scenarios, respec-

tively (assuming that surface salinity changes are pro-

portional to changes across the water column, given

their close correlation). These estimates provide the

basis for an assessment of the likely impacts on ecologi-

cal function and structure, as well as an understanding

of the level of management intervention required (e.g.

reductions in upstream water diversions) to protect bio-

diversity and other values of the estuaries in question.

Targeted monitoring is likely to be needed in both the

target and gradient domain (e.g. for fish assemblages)

to improve the derived relationships, and verify causal

relationships.

The salinity:fish relationship generated in this exam-

ple integrates data on fish species across multiple stud-

ies in some instances (see Table S2), and so no temporal

resolution was incorporated into that relationship.

Thus, the relationship describes long-term patterns,

rather than short-term dynamics. Given our ability to

adequately assess all relationships described here, we

suggest quantitative predictions can be generated with

similar amounts of data, including simple species lists,

provided lists are available for several systems with

similar characteristics (e.g. numerous small to medium

estuaries in Victoria in this case study).

Discussion

This research demonstrates that we can predict existing

biophysical relationships and potential climate-related

changes in ecosystems based on a spatial gradient thou-

sands of kilometres away. The method enables quanti-

tative predictions to be developed in the absence of

long-term data sets or a detailed mechanistic under-

standing of likely responses to climatic changes. By

using well-studied analogous systems in a space-

for-time substitution, the method simultaneously

assesses multiple interacting drivers across long time

scales at a community scale (Poloczanska et al., 2008;

Rustad, 2008; Sarmento et al., 2010; Luo et al., 2011;

Wernberg et al., 2012) despite the distance between

locations. This method can be applied at continental

scales, as is illustrated with our estuary case study, and

can incorporate synergistic and antagonistic effects of

climate to enable predictions of complex ecological

response, addressing some of the limitations of prior

work reviewed by Wernberg et al. (2012).

This demonstrated ability to transfer information and

understanding from a well-studied system to another

less-studied system should lead to a substantial

improvement in the accuracy and utility of future pre-

diction of ecological response to climate change with

consequent benefits for conservation and resource man-

agement (e.g. Gutzwiller et al., 2010; Worthington et al.,

2014). The method dramatically reduces the amount of

data needed to verify that predictions are likely to hold,

by identifying physical drivers of ecological change

and using readily available data sets (e.g. rainfall and

flow in our case study). It is acknowledged that there

will be some systems for which no analogue exists

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12634

8 R. E. LESTER et al.

(Jackson & Williams, 2004), but in the absence of

another basis for quantitative climate-related predic-

tions, using this approach appears justifiable as a start-

ing point.

Our method is also likely to be of substantial value

even when reasonable data sets are available for the tar-

get domain but climate change predictions lie outside

the range of these data. For example, predicted annual

rainfall and flow within our case study target domain

for scenarios of 1 °C and 2 °C of warming (approxi-

mately 2030 and 2050, respectively) are lower than his-

torical levels (Post et al., 2012). Thus, extrapolation

from existing data sets is likely to be problematic, as,

from a regional perspective, changes are likely to result

in novel systems. In this case, a well-chosen gradient

domain that spans the range of likely future climate-

related change enables quantitatively informed extrap-

olation of ecosystem response. This provides a robust

basis for predicting salinities and fish richness, where

none would exist otherwise.

Potential to apply the method across ecosystem types

Although we have provided an aquatic case study, our

method has the potential to be applied across a wide

range of ecosystem types. The range of previous appli-

cations of space-for-time substitutions (e.g. Heegaard &

Vandvik, 2004; Wernberg et al., 2012; see Table S1) sug-

gest that our method is also likely to be applicable for a

broad range of ecological and management questions.

Obvious examples exist where authors have speculated

about the ability to transfer relationships among

domains, or where data from elsewhere have been used

to verify model relationships (e.g. barnacles on rocky

shores; Poloczanska et al., 2008). New applications may

include regions with a gradient domain within a contig-

uous latitudinal or altitudinal region, but may also

include more isolated examples in similar climatic

zones across multiple continents (e.g. for desert ecosys-

tems). Although further testing across a suite of ecosys-

tem types is needed to demonstrate the general utility

of the method, its application in terrestrial systems is

likely to be of most value where there is disparity in the

quality of climate-related predictions between well-

studied systems and those of comparable typology with

few data.

Predicting climate-related changes in southernAustralian estuaries

In our case study, we identified simple relationships

that linked rainfall and flow, flow and surface salinity,

and salinity and fish species richness. These relation-

ships are highly intuitive, but can include complex

feedback loops and multiple interacting drivers, so

quantitative estimates of flow under conditions of

lower rainfall, for example, are available for few estuar-

ies.

These relationships are intended to predict long-term

(e.g. decadal) changes associated with a changing cli-

mate, rather than short-term (e.g. intra- or inter-annual)

fluctuations. For example, at short time scales, fish spe-

cies richness is likely to be affected by patterns in flow

delivery, mouth openness and connectivity with other

nearby estuaries (e.g. Harrison & Whitfield, 2006), none

of which are included in our case study. However, the

long-term pattern of fewer fish species utilising saltier

estuaries does provide a prediction of species loss

under a drier climate. It is this long-term prediction that

will be of significant value to natural resource practitio-

ners attempting to manage these ecosystems to prevent

the loss of ecological value. Species richness is only one

of many potentially important ecological responses that

could be assessed using this method, depending on

data availability. Evidence of changes in fish richness

through time, abundances and other metrics would

also be of value in formulating management responses

to possible future climate change.

For estuaries, such as those used in our case study,

there are few other methods that provide quantitative

estimates of potential future conditions under climate

change to compare our modelling results with. Model-

ling mean annual run-off is one quantitative method

that is often used to estimate changes in surface water

availability (Teng et al., 2012; Figure S2). We compared

predicted run-off, based on a Fu equation model (Teng

et al., 2012), with predicted flows from the relationships

developed in this study based directly on changes in

rainfall (Section S2). Run-off modelling and our alterna-

tive approach correlating altered rainfall with changes

in flow both attempt to characterise the impact of cli-

mate change on estuarine hydrology by quantifying

freshwater inflows to estuaries, thus enabling a com-

parison. The comparison demonstrated a similar mag-

nitude of flow although our method tended to provide

lower flow predictions overall. This comparison dem-

onstrated that our approach is generally consistent with

the findings of another simple and commonly used esti-

mate for changes in water availability. Similar quantita-

tive approaches against which to compare our salinity

and fish relationship were not available.

Two key areas of uncertainty in our predicted

responses to climate change relate to how representa-

tive the available data were of both domains, and

whether future patterns will mirror patterns observed

across the gradient domain. For example, there is the

potential for the rate of anthropogenic change to exceed

ecosystem resilience or for hysteresis, rather than

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12634

PREDICTING RESPONSES OF DATA-POOR ECOSYSTEMS 9

differences in rainfall, to have shaped the ecological

patterns observed across the spatial gradient (Pickett,

1989; Johnson & Miyanishi, 2008). Our method relies on

the assumption that the observed relationships are con-

sistent with future ecological responses (Kerr et al.,

2007). This includes the assumption that the distribu-

tion of extreme events will not change, which is unli-

kely to be true under all conditions. Ultimately though,

all predictions of future ecological response are uncer-

tain, and this must be recognised in their interpretation

and application.

We have demonstrated a methodology that extends

space-for-time substitution to enable prediction of eco-

logical response in data-poor ecosystems from distant

analogous systems. Spatial patterns in biophysical rela-

tionships derived from a well-studied gradient domain

formed the basis for simple, yet plausible quantitative

predictions of temporal change in a separate target

domain, for which there were few or patchy historical

data. Applying gradient studies takes advantage of

their ability to incorporate the many interacting and

complex elements relating climate and ecological

response, while the novel transfer of predictions to dis-

tant analogous ecosystems (i.e. the target domain)

enables robust predictions to be made where insuffi-

cient data had limited such prediction in the past. The

method is likely to be of significant value, given the

prevalence of data-poor ecosystems in terrestrial and

aquatic ecosystems worldwide, improving our ability

to generate reliable estimates of future climate-related

ecological changes.

Acknowledgements

We thank the Glenelg-Hopkins Catchment Management Author-ity, Tracy Calvert and Andrew Maughan (Western AustralianDepartment of Water), and David Tunbridge (University of Wes-tern Australia) for providing data and knowledge. We acknowl-edge Deakin University for funding to enable concept-development workshops. We thank Peter Fairweather, GerryQuinn, Angela Arthington, Graeme Hays, Robert Naiman andPeter Davies and three anonymous reviewers for helpful com-ments on draft versions of this manuscript and/or insightfulconversations regarding the concept. Finally, thanks to RalphMac Nally for assistance with testing relationships and CourtneyCummings and Damian Woodberry for assistance during thedevelopment of this method.

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Supporting Information

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

Data S1. Southwestern Australian estuary rainfall and flow.Data S2. Victorian estuary rainfall and flow.Data S3. Example of method for rainfall and flow in Australian estuaries.Figure S1. Distribution of bootstrapped sum of squared residuals for the relationship between (a) log(x + 1)-transformed meanannual rainfall and log(x + 1)-transformed total annual flow at the gauge closest to the estuary head; (b) total annual estuarine flowstandardised by estuary basin volume and mean annual surface salinity; (c) mean salinity (to 4 m depth) and estuarine fish richnessas a proportion of the total regional richness and (d) mean salinity (to 4 m depth), estuary basin volume and estuarine fish richnessas a proportion of the total regional richness.Figure S2. Comparison of predicted run-off based on run-off modelling using Fu equations (refer to Comparison with existingmethods to predict likely future climate-related change) with predicted flows based on relationships developed in Lester et al. Thesolid line indicates a 1 : 1 relationship.Table S1. Previous applications of gradient studies and their ability to be applied using the method of Lester et al.Table S2. Summary statistics for the case study data of the gradient domain, Western Australian estuaries, and the target domain,Victorian estuaries. Not all estuaries had data for every comparison. Numbers referring to data sources are given in italics and inparentheses. Physical characteristics of estuaries used for case study. Note that Oyster Harbour is maintained to be permanentlyopen.

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PREDICTING RESPONSES OF DATA-POOR ECOSYSTEMS 11