Global Change Biology Global Change Biology (2014) 20, 3471–3481, doi: 10.1111/gcb.12634

Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains R E B E C C A E . L E S T E R 1 , P A U L G . C L O S E 2 , J A N L . B A R T O N 1 , A D A M J . P O P E 1 and STUART C. BROWN1 1 School 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 substitution, 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 ecological 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 components 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 disturbances (Darling & C^ ote, 2008; Genner et al., 2010; Philippart et al., 2011). Ideally, the prediction of climate-related ecological change requires the simultaneous 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, Correspondence: Rebecca E. Lester, tel. +61 3 5563 3330, fax +61 3 5563 3143, e-mail: [email protected]

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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 characteristics 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 ecological 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 environmental flows; Arthington et al., 2006), expert opinion or by assuming that changes and responses observed in well-studied ecosystems will be directly 3471

3472 R . E . L E S T E R et al. 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 Arthington 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 variables in the past, often with great success (see Table S1). Analogue-based methods provide a conceptual basis for transferring predictions of future response to climate change from a well-studied region to another typologically 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 relationships are demonstrable and/or plausible (Hesse, 1966). For example, analogue systems have been used as the justification for applying space-for-time substitution methods to palaeo-environmental studies of pollen (Wookey, 2008), elevational shifts in bird distributions (Anderson et al., 2013) and changes in community composition on rocky shores (Hawkins et al., 2008, 2009; Poloczanska et al., 2008). Despite their widespread use and the common practise 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-fortime substitution where knowledge derived from relatively well-studied ecosystems can be quantitatively transferred, using analogy, to distant and poorly studied 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 highenergy, 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 knowledge, there are no quantitative predictions of future freshwater 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

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P R E D I C T I N G R E S P O N S E S O F D A T A - P O O R E C O S Y S T E M S 3473

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).

east (Fig. 2; Table S2). Data for rainfall, river discharge, estuarine 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 © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3471–3481

domain, which were considered consistent with the physical realities of the system (e.g. exponential decay for flow:salinity where negative flows and salinities cannot exist). There were no significant correlations among the independent variables 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 significantly 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

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

(Salford Systems, San Diego, USA). Possible spatial and temporal autocorrelations were investigated using cross-correlation 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 identified 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 gradient 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 estimate was normally distributed. For each bootstrap run, an intercept and slope value from the normal distribution and fitted 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.


Step 1. Identify a suitable gradient domain for target system(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 systems, the gradient domain, needs to have a relevant spatial climatic gradient that spans the current and projected 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 modifiers, e.g. soil type, catchment aspect or slope) that indirectly influence other system conditions or attributes (e.g. evapotranspiration, soil moisture content, run-off; Poff et al., 2010). The likelihood that consistent relationships exist in both domains increases where the typology is similar (Poff et al., 2010). Nonclimate modifiers that may alter the relationships should also be identified 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 southwestern Australia were selected as a suitable gradient domain. They occur within a gradient across which © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3471–3481

P R E D I C T I N G R E S P O N S E S O F D A T A - P O O R E C O S Y S T E M S 3475 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 target domain under a relatively extreme, dry 2 °C warming scenario (Post et al., 2012). The south-western Australian estuaries are well studied (Brearley, 2005); substantial data on hydrology, water quality and fish assemblages are available and provide an opportunity to develop hypotheses of temporal ecological response to climate change in datapoor 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 correlated 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 ecological 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 climate-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 variable 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 significantly 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 significant 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 volume (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 column 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



Runs test

Rainfall (mm): flow (kl)

y = A log(x) + B A = 19.768  1.322 (t = 14.96, P < 0.0001) B = 31.643  2.476 (t = 12.78, P < 0.0001) y = AxB A = 76440  1240 (t = 6.165, P < 0.0001) B = 0.3111  0.0462 (t = 6.736, P < 0.0001) y = Ax + B A = 0.00001  0.000002 (t = 4.239, P = 0.0054) B = 0.6820  0.0875 (t = 7.80, P = 0.0002) y = Ax1 + Bx2 + C A = 0.000009  0.000003 (t = 2.92, P = 0.0329) B = 0.0012  0.0013 (t = 0.910, P = 0.404) C = 0.599  0.128 (t = 4.688, P = 0.0054)

W = 0.9869 P = 0.0422

Z = 3.802 P = 0.0001

W = 0.922 P = 0.0725

Z = 2.771 P = 0.0056

W = 0.9029 P = 0.3066

Z = 1.5275 P = 0.1266

W = 0.947


P = 0.681


Flow (kl): salinity (mg/l)

Salinity (mg/l): fish species richness

Salinity (mg/l) (x1) & basin volume (Gl) (x2): fish species richness

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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 estuary as a proportion of regional richness were included in the analysis as a single data point. A strong correlation 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 relationships (see Data S1, S2 and S3).

Step 3. Develop hypotheses for the target domain

Fig. 3 Derived relationships across a spatial gradient of southwestern 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 volume and mean annual surface salinity. (c) Linear relationship between mean salinity and total fish richness as a proportion of the maximum regional diversity.

Based on relationships identified across the gradient domain, specific climate-related hypotheses for the target 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, chemical and ecological attributes of the target domain. The magnitude of change can also be estimated for a particular 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 rainfall declines in the target domain. That is, we contend that the models developed for the south-western Australian estuaries quantitatively predict responses of Victorian estuaries to climate change. Flows to estuaries are predicted to decline exponentially as mean annual rainfall declines (black line; Fig. 3a). Surface salinity is © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3471–3481

P R E D I C T I N G R E S P O N S E S O F D A T A - P O O R E C O S Y S T E M S 3477 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 relationship (Fig. 3b). As climate change progresses, estuarine 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 target domain 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 different 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 distribution of generated gradient domain residuals (Figure S1a). Flow and salinity for Victorian estuaries (open circles; 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 throughout 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 proportional species richness of fish than gradient domain estuaries. Many Victorian estuaries had a smaller proportion of the regional species pool present than would be expected based on the relationship from Step 3. Estuaries smaller than 1 Gl in basin volume were excluded © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3471–3481

from the comparison, as they were substantially smaller than the estuaries in south-western Australia (minimum volume = 2.4 Gl) and appeared to respond differently. 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 approximately the 95th percentile of the bootstrapped distribution (Figure S1c). An alternative to excluding very small Victorian estuaries 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 intercept were significant parameters, but estuary basin volume 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 distribution of bootstrapped gradient domain residuals (Figure S1d). Thus, it was more plausible than the linear relationship including only mean salinity, and was also able to reasonably predict fish richness for additional estuaries that had been excluded from the simpler model as they were outside the range of sizes of estuaries in the gradient domain.

Fig. 4 Multiple linear regression between mean salinity, estuary 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 illustrate the target domain. Refer to Fig. 3 for additional information.

3478 R . E . L E S T E R et al. On the basis of the improved plausibility, we would recommend the use of the second model, despite estuary basin volume being a nonsignificant parameter in the gradient domain. This additional nonclimate modifier 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 relationship from Step 2 is valid, but likely to yield less accurate estimates and only for estuaries of a comparable 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 predictions. 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 quantify likely changes in flow, salinity and fish species richness 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 warming, ~2050) are predicted for the target domain (Post et al., 2012). Based on these predictions, and the relationships 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, respectively (assuming that surface salinity changes are proportional to changes across the water column, given their close correlation). These estimates provide the basis for an assessment of the likely impacts on ecological function and structure, as well as an understanding

of the level of management intervention required (e.g. reductions in upstream water diversions) to protect biodiversity 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 example integrates data on fish species across multiple studies 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 thousands of kilometres away. The method enables quantitative predictions to be developed in the absence of long-term data sets or a detailed mechanistic understanding of likely responses to climatic changes. By using well-studied analogous systems in a spacefor-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 prediction of ecological response to climate change with consequent benefits for conservation and resource management (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, 20, 3471–3481

P R E D I C T I N G R E S P O N S E S O F D A T A - P O O R E C O S Y S T E M S 3479 (Jackson & Williams, 2004), but in the absence of another basis for quantitative climate-related predictions, using this approach appears justifiable as a starting point. Our method is also likely to be of substantial value even when reasonable data sets are available for the target 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 (approximately 2030 and 2050, respectively) are lower than historical 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 climaterelated change enables quantitatively informed extrapolation 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 applications of space-for-time substitutions (e.g. Heegaard & Vandvik, 2004; Wernberg et al., 2012; see Table S1) suggest 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 contiguous latitudinal or altitudinal region, but may also include more isolated examples in similar climatic zones across multiple continents (e.g. for desert ecosystems). Although further testing across a suite of ecosystem 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 wellstudied systems and those of comparable typology with few data.

Predicting climate-related changes in southern Australian 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 relationships are highly intuitive, but can include complex © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3471–3481

feedback loops and multiple interacting drivers, so quantitative estimates of flow under conditions of lower rainfall, for example, are available for few estuaries. These relationships are intended to predict long-term (e.g. decadal) changes associated with a changing climate, rather than short-term (e.g. intra- or inter-annual) fluctuations. For example, at short time scales, fish species 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 practitioners 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. Modelling 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 alternative approach correlating altered rainfall with changes in flow both attempt to characterise the impact of climate change on estuarine hydrology by quantifying freshwater inflows to estuaries, thus enabling a comparison. The comparison demonstrated a similar magnitude of flow although our method tended to provide lower flow predictions overall. This comparison demonstrated that our approach is generally consistent with the findings of another simple and commonly used estimate for changes in water availability. Similar quantitative 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 representative 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

3480 R . E . L E S T E R et al. 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 consistent with future ecological responses (Kerr et al., 2007). This includes the assumption that the distribution of extreme events will not change, which is unlikely to be true under all conditions. Ultimately though, all predictions of future ecological response are uncertain, 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 ecological response in data-poor ecosystems from distant analogous systems. Spatial patterns in biophysical relationships 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 distant analogous ecosystems (i.e. the target domain) enables robust predictions to be made where insufficient 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 Authority, Tracy Calvert and Andrew Maughan (Western Australian Department of Water), and David Tunbridge (University of Western Australia) for providing data and knowledge. We acknowledge Deakin University for funding to enable conceptdevelopment workshops. We thank Peter Fairweather, Gerry Quinn, Angela Arthington, Graeme Hays, Robert Naiman and Peter Davies and three anonymous reviewers for helpful comments on draft versions of this manuscript and/or insightful conversations regarding the concept. Finally, thanks to Ralph Mac Nally for assistance with testing relationships and Courtney Cummings and Damian Woodberry for assistance during the development 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 mean annual rainfall and log(x + 1)-transformed total annual flow at the gauge closest to the estuary head; (b) total annual estuarine flow standardised by estuary basin volume and mean annual surface salinity; (c) mean salinity (to 4 m depth) and estuarine fish richness as a proportion of the total regional richness and (d) mean salinity (to 4 m depth), estuary basin volume and estuarine fish richness as 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 existing methods to predict likely future climate-related change) with predicted flows based on relationships developed in Lester et al. The solid 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 in parentheses. Physical characteristics of estuaries used for case study. Note that Oyster Harbour is maintained to be permanently open.

© 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3471–3481

Predicting the likely response of data-poor ecosystems to climate change using space-for-time substitution across domains.

Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models c...
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