Influence of Inundation Characteristics on the Distribution of Dryland Floodplain Vegetation Communities

Ecological Indicators 124 (2021) 107429

Contents lists available at ScienceDirect

Ecological Indicators

journal homepage: www.elsevier.com/locate/ecolind

Influence of inundation characteristics on the distribution of dryland floodplain vegetation communities

Sara Shaeri Karimi a,*, Neil Saintilan a, Li Wen b, Jonathan Cox c, Roozbeh Valavi d a Department of Earth and Environmental Sciences, Macquarie University, Sydney, Australia b Science, Economics and Insights Division, NSW Department of Planning, Industry and Environment, Sydney, Australia c Caribbean Institute for Meteorology and Hydrology, Husbands, St. James, Barbados d School of Biosciences, University of Melbourne, Parkville, Victoria, Australia

ARTICLE INFO ABSTRACT

Keywords: Semi-arid floodplain vegetation is an essential component of floodplain and terrestrial ecosystems due to the Flow-ecology model wide range of services provided for waterbirds, woodland birds, amphibians and mammals, together with their Electivity analysis contribution to natural carbon sequestration. Since overbank flooding has been considered the driving factor of Preferential flood regime vegetation distribution on floodplains, sustainable water resource management requires a better understanding Presence-absence data of the influenceof inundation on vegetation distribution. We examined the relationship between the distribution Upper Darling River Vegetation distribution of four flood-dependant vegetation communities and associated long term inundation metrics on the upper Darling River floodplainusing electivity analysis and a generalised additive model (GAM) by generating a total of 10,478 individual inundation maps with a high spatial resolution over a period of 29 years. Our results show that the four dominant vegetation communities are situated differently in relation to inundation attributes. The inundation metrics better explained the variation in the distribution of River Red Gum than those of Black box, Coolabah and Lignum communities. We found that the floodplainforests and woodlands in the upper Darling can survive longer periods of drought and shorter inundation duration than previously reported. The results provide practical information for ecosystem management by offering a means of predicting changes in vegetation dis tribution in relation to alteration in flowregime resulting from water planning arrangements or climate change.

1. Introduction providing a roosting and breeding habitat for waterbirds (Bino et al., 2020), woodland birds (McGinness et al., 2010), amphibians (Mac Nally Overbank floodingis a dominant driver of vegetation distribution on et al., 2017), and mammals (Iles et al., 2010), as well as influencing floodplains, including arid and semi-arid regions of high hydrological patterns of natural carbon sequestration (Whitaker et al., 2015). An variability. In these climatic settings, floodplain vegetation is particu understanding of the influence of inundation on floodplain vegetation larly important to regional biodiversity (Selwood et al., 2017). On the distribution is, therefore, key to interpreting and predicting the impact large intermittently inundated floodplains of semi-arid interior of water resource development on a range of ecosystem services asso Australia, vegetation communities are spatially distributed in relation to ciated with floodplains. periodic inundation and drought, which may influence seedling germi Floodplain vegetation has been modifiedboth by physical alterations nation, recruitment of seedlings to the adult cohort, and interspecies to the floodplain and modifications to flow regimes, through the competition (Capon, 2005; Capon and Brock, 2006; Rogers and Ralph imposition of dams and other flow control structures in many of the et al., 2010). Previous studies have related the distribution of floodplain world’s river basins (Mateo et al., 2014). The abstraction of water for vegetation communities to patterns of inundation derived from satellite irrigated agriculture has altered the timing and volume of available imagery (Bino et al., 2015; Thomas et al., 2015), with flood duration, water for floodplain inundation, and concern for third party flooding periodicity and maximum inter-floodperiod being important predictors impacts has further restricted the range of overbank flooding options (Wen et al., 2009, 2018). These vegetation communities, often struc accessible to water managers (MDBA, 2012). Apprehension about the turally distinct, shape patterns in the distribution of biota on floodplains, effect of water sharing arrangements on the viability and integrity of

* Corresponding author. E-mail address: [email protected] (S. Shaeri Karimi). https://doi.org/10.1016/j.ecolind.2021.107429 Received 12 August 2020; Received in revised form 21 December 2020; Accepted 17 January 2021 Available online 1 February 2021 1470-160X/© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/). S. Shaeri Karimi et al. Ecological Indicators 124 (2021) 107429 floodplain ecological communities has been a critical element of water River floodplain. We also compare our results with the findings of pre planning in many jurisdictions, including the Murray Darling Basin viously published research. (MDB), Australia’s most agriculturally important river basin. Water plans, therefore, require informed projections of the effect of watering 2. Materials and methods arrangements on the distribution of floodplainvegetation communities, and by implication, the ecological communities and ecosystem services 2.1. Study area they support. The Darling River catchment forms the northern half of the MDB, and The study area covers 1200 km2 of floodplain in the upper Darling the expansion of irrigated agriculture has been particularly rapid in River floodplain, located in north-western New South Wales (NSW) recent decades (MDBA, 2016). The development of on-farm water which is a part of the MDB in south-eastern Australia (Fig. 1c). The storages and large-scale water harvesting has reduced flows in the general topography of the study area is flatwith elevations across most Darling by one third compared to long-term averages (MDBA, 2016), a of the floodplainarea being <100 m above sea level (Geocentric Datum decline likely to impact on floodplain vegetation. While the current of Australia). The area belongs to Koppen¨ Climate zone of Bsh, i.e. hot distribution patterns reflect the historical inundation regimes, flooding semi-arid climate (Stern et al., 2000). The area is considered to have requirements of recruitment differ from those needed for maintenance extreme climatic variability with low rainfall throughout the year of mature communities (Rogers and Ralph et al., 2010). Inundation (Thoms and Sheldon, 2000) and average annual rainfall for the period of events over recent years are unlikely, therefore, to provide an accurate 1998–2020 totalling just 317.9 mm. The daily minimum and maximum ◦ ◦ picture of hydrological conditions associated with floodplainvegetation temperature is 4.2 C in July, and 37.7 C in January, respectively recruitment. (BOM, 2020). The characterisation of floodplain vegetation distribution based on The study area is situated between two of twelve “umbrella envi inundation history does not allow forward projections unless linked in ronmental assets” (MDBA, 2016) established by the Murray Darling some way to a predictive model of inundation. However, the represen Basin Authority for the monitoring of hydrological and ecological tar tation of floodplain inundation in river hydrology models is a chal gets across the northern basin. The segment contains several wetlands of lenging task. Detailed hydrodynamic representations of floodplain significance, including 320 km2 of Toorale National Park and State inundation are unviable over areas >1000 km2 (Teng et al., 2017) due to Conservation Area; however, grazing accounts for the primary land use excessive run times, and modelling is complicated by the comparative across the study reach (Crown Lands and Water Division, 2017). There lack of relief, and complex roughness associated with vegetation char are diverse types of vegetation in the study area. Mitchel Grass grassland acteristics (which may alter during a single flow event), and input and is the main vegetation type, covering >37% of the study area (444 km2), losses that are sometimes difficult to constrain within a model. followed by Coolabah woodland at 167 km2. The other communities of To overcome these challenges, we previously developed a spatially interest for this study (Lignum shrubland, River Red Gum forest/ explicit predictive inundation model for the upper Darling River flood woodland and Black Box woodland) cover a combined total area of 93 plain using machine learning algorithms (Shaeri Karimi et al., 2019). km2. Here, we utilised this model to characterise the inundation attributes associated with the key vegetation units on the upper Darling River 2.2. Vegetation information floodplain an area that supports flood-dependent woody vegetation communities characteristic of the MDB. These include the River Red The State Vegetation Type Map (SVTM) is provided by the NSW Gum (Eucalyptus camaldulensis), ubiquitously distributed through the Department of Planning, Industry and Environment for almost 80% of MDB as a forest or woodland dominant, and typical of more frequently NSW in ESRI shapefileformat. The SVTM represents the most complete inundated river reaches. Coolabah (Eucalyptus coolabah), a woodland and consistent information available about the distribution of Plant species more commonly found in the northern Murray Darling Basin Community Types (PCT) across NSW (https://www.environment.nsw. where conditions become more hydrologically variable, is often asso gov.au/vegetation/state-vegetation-type-map.htm). The mapping pro ciated with ephemeral creek lines. The Black Box (Eucalyptus largiflorens) cedure followed two parallel workflows of classification and spatial forms a woodland tolerant of the drier, less frequently inundated con analysis using the best available imagery, site survey records, and ditions of the distal floodplain.Lignum (Duma florulenta) is a floodplain environmental information (see OEH, 2017, for more detail). shrub, often densely forming, and tolerant of a range of drought and The Western region PCT map was used in this paper, which covers flood conditions. Their water requirements have been described in re the entire study area. Four vegetation communities (Fig. 1a) were cho views by Rogers and Ralph (2010), Roberts and Marston (2011) and sen as response variables for this study, including River Red Gum, Black Casanova (2015), though the evidence base for the hydrological de Box, Coolabah, and Lignum. The selected vegetation communities pendency of these species is often deduced from observations of limited constitute 21% of the study area: River Red Gum (3%), Black Box (1%), spatial or temporal scale. Therefore, the upper Darling floodplain is an Coolabah (14%), and Lignum (3%). appropriate location to model the impacts of water resource develop The vegetation presence/absence raster layer was generated using ment on the distribution of the aforementioned dominant flood- ArcGIS 10.6 (ESRI, 2018) from the SVTM vector with a spatial resolution dependant vegetation communities, given the importance of the river of 30 m × 30 m to match the inundation data (Fig. 1b) as a monitoring site (MDBA, 2016), and the availability of a high- resolution hydrological model and floodplain vegetation map. To ach 2.3. Inundation regime characterisations ieve this, the distribution of vegetation communities needs to be char acterised in relation to inundation metrics predictable from the We applied a previously developed and tested inundation model developed floodplain hydrology model. More specifically, we seek to (Shaeri Karimi et al., 2019) to predict daily wetland inundation at a 30 m determine the extent to which the distribution of E. camaldulensis, E. resolution as a function of hydrologic, climatic and topographic pa coolabah, E. largiflorens, and D. florulenta can be associated to flood rameters across the Darling River floodplain. The primary model input duration, flood periodicity and inter-flood duration as represented by variable, river discharge at the Bourke gauge, allowed us to represent our hydrological model of the upper Darling River floodplain, and inundation characteristics on the Darling River floodplain beyond the subsequently compare these results with the findings of previously inundation “snapshots” provided by the Landsat archive, with the published findings. If so, we can predict how modifications to flow further potential of hindcasting and projecting hydrological regime, either through water planning arrangements or climate change characteristics. might affect the spatial distribution of critical habitat on the Darling The model was used to simulate the daily time series of the

2 S. Shaeri Karimi et al. Ecological Indicators 124 (2021) 107429

Fig. 1. a) Distribution map of targeted vegetation communities in the study area. b) Presence/Absence map of vegetation communities. Red, orange, green and blue coloured areas show the presence of vegetation communities (pixel value = 1) for Black Box, Coolabah, Lignum and River Red Gum respectively, whereas grey areas have pixel values of 0 representing an absence of each vegetation type. c) Location of the Darling River catchment, within the MDB in south-eastern Australia. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) inundation extent from 1988 to 2016. According to Brennan et al. 2.4. Data analysis (2002), the majority of wetlands (>75%) in the study area commenced to flowat discharges between 14,000–50,000 ML/d. Accordingly, river The total inundation, the average duration of inundation, and the discharge higher than 30,000 ML/d at Bourke station was set as the maximum inter-flood maps were categorised into different classes. The overbank flow threshold (Cooney, 1994; Sheldon, 2017). Thus, inun three classified inundation measures were overlayed on the four dation maps for days with discharge higher than 30,000 were produced different presence/absence vegetation maps to count all pixels with the and for the rest of the days with a discharge record lower than the same vegetation type from each category of the inundation character threshold, a base map was created and inserted to complete the daily istics (Fig. 3). time series of inundation extent over 29 years for inundation analysis. More than ten thousand (10,478) individual inundation maps were 2.4.1. Electivity analysis generated and analysed pixel by pixel, to extract statistical summaries of Statistical associations between vegetation communities and the three major components of inundation regimes that are most important three calculated inundation measures were evaluated using an electivity for plant species over longer time-scales: frequency, duration and inter- index that has been applied successfully in landscape ecology (Pastor flood (Roberts et al., 2000; Thomas et al., 2015). and Broschart, 1990; Host et al., 1996; Pan et al., 1999; Jensen and In this study, total inundation (TI) was definedas the total number of Bourgeron, 2001; De Jager et al., 2016; Miller-Adamany et al., 2019). days that each pixel was inundated over the period of record. The This index was used to study whether a particular vegetation community average duration of inundation (ADI) was calculated as the average elects for a given inundation feature, and if so, then it provides water length of individual inundation events. Maximum inter-flood(MIF) was managers with a prescription of broad-scale conditions that may be characterised by identifying the maximum period between two required for successful water allocation under a given set of environ consecutive floodevents; in other words, the maximum number of days mental conditions (Theiling et al., 2012). without flooding. The formula to calculate the electivity indices of each vegetation TI, ADI, and MIF (inundation characteristics) were calculated for community for each class of TI, ADI, and MIF was (Jacobs, 1974; Jen each pixel using the statistical software R (R Core Team, 2019). The kins, 1979): maps are shown in Fig. 2. ⌈ ) )/ ) )⌉ Eij = ln rij 1 pij pij 1 rij (1)

where Eij was the electivity for vegetation type i on inundation class j.

3 S. Shaeri Karimi et al. Ecological Indicators 124 (2021) 107429

Fig. 2. Spatial distribution of a) total inundation; b) average duration; and c) maximum inter-flood. rij was the proportion of vegetation type i at inundation class j, and pij et al., 2005; Maggini et al., 2006; Guisan et al., 2017), a balanced was the proportion of the floodplainoccupied by inundation class j. An random-stratified sampling design (Hirzel and Guisan, 2002) was electivity index <0 indicates avoidance, whereas values >0 imply a followed. preference by the vegetation type for a given inundation class. For each vegetation community, all the presence pixels were The statistical significance of each Eij was tested using chi-square selected. To sample a number of absence records equal to the number of goodness of fit with one degree of freedom according to the following presences, the study area was stratified based on inundation charac equation in Jenkins (1979): teristics, including TI, ADI and MIF, using K-means clustering through [ ) ] 2 2 the “unsuperClass” function in the “RStoolbox” R package (Leutner X = E / 1/xij + 1/ mj xij + 1/yi + 1/(nt yi) (2) ij et al., 2019). A ratio of total presence/absence was randomly selected from each stratum out of absence pixels. This form of sampling indicates where x was the area of vegetation type i on inundation class j, y was ij i that our samples were roughly representative of the entire study area. the total area of vegetation type i in the floodplain, m was the area of j Prior to model fitting, the collinearity of inundation variables was inundation class j and n was the area of the entire floodplain. t tested. Based on Dormann et al. (2013), the total inundation was highly collinear with the average duration of inundation (|r| > 0.7). As inter 2.4.2. Generalised additive models pretability of the results and the unique information provided by all To improve our understanding of inundation effects on vegetation explanatory variables were the primary objectives of the models; hence, distribution, the relationship between inundation characteristics and instead of excluding the collinear variables or using principal compo vegetation occurrence was examined using generalised additive models nents analysis (Dormann et al., 2013; McElreath, 2016), two models (GAMs; Hastie and Tibshirani, 1990). GAMs were chosen because of the were built for each vegetation community: one as a function of average ecological interpretability of their non-parametric response curves duration and maximum inter-flood, and a second one as a function of (Lehmann et al., 2002). Another advantage of using GAMs is that they total inundation and maximum inter-flood. can determine the non-linear relationship between a response variable Binomial GAMs were performed with the “mgcv” R package (Wood, and multiple explanatory variables without any restrictive assumptions, 2011) using a logit link function. Restricted Maximum Likelihood which means being data-driven rather than model-driven (Guisan et al. (REML) was selected for the automated estimator of smoothing pa 2002). rameters (Marra and Wood, 2011). The basis dimensions for TI, ADI, and To build the GAMs, the presence/absence vegetation maps were used MIF were specified as 10 to allow for potentially complex non-linear as the response variable. For each of the four vegetation communities, a 2 relationships. The Akaike Information Criterion (AIC), adjusted R and model was fittedby inundation explanatory variables using four sets of the percentage of deviance explained were used to assess the goodness of sample data. To ensure an equal prevalence (0.5) between presences and fit of the models. Additionally, modelling results were evaluated using absences to avoid negative prevalence effects (Manel et al., 2001; Liu

4 S. Shaeri Karimi et al. Ecological Indicators 124 (2021) 107429

Fig. 3. Percentage of the four vegetation communities on each class of a) average duration of inundation, b) total inundation, and c) maximum inter-flood. partial response curves. and inundation parameters based on electivity analysis. The average duration of 7 days or less was only preferred by Black Box community. 3. Results 14 to 35 days of flood duration were favoured for both Coolabah and Lignum, while River Red Gum showed a strong positive association to A general overview of inundation metrics preferred by different the duration of 21 days and higher. The most preferred total inundation vegetation communities is shown in Fig. 3. It can be observed that classes are 3 months and less for Black Box, 9 months and more for almost 80% of the Black Box community was present in the areas with an Coolabah, 15 to 21 months for Lignum and 15 months and higher for the inundation duration of one week or less. Half of the Coolabah preferred River Red Gum community. There were strong preferences of a period of an average flood duration of two to three weeks, whereas, >70% of <10 years without flooding for Coolabah and River Red Gum commu Lignum appeared in areas with the average duration between 7 and 14 nities, while Lignum and Black Box showed a higher range of tolerance. days. Finally, 60% of the River Red Gum community occurred in regions Two different GAMs were applied to estimate how inundation pa with inundation duration of 8 weeks and higher (Fig. 3a). rameters influence the distribution of vegetation communities. GAM1 With regards to the total inundation (Fig. 3b), the 3-month inunda was built involving average duration of inundation, and maximum inter- tion class appeared to be the dominant class for Black Box (67%). About flood as explanatory variables, while total inundation and maximum 45% of the Lignum community occupied terrain that was inundated for inter-floodwere used for GAM2. Comparative results from the two GAMs 3–9 months in the entire period studied. >80% of River Red Gum are summarized in Table 2 for each vegetation community. All three appeared to have a total inundation of 18 to 24 months, and nearly 70% inundation variables included in the GAMs were statistically highly of Coolabah occurred in a range of 12 to 24 months. significant. In Fig. 3c, it is clear that River Red Gum, Coolabah and Lignum could Goodness of fit of the models ranged from 4% to 52% based on the survive a maximum of 5 to 15 years of inter-floodwhile this appeared to percentage of deviance explained which shows the percentage of vege be >25 years for more than half of the Black Box community. tation’s presence and absence variability explained by the models Table 1 shows the association between each vegetation community (Table 2). River Red Gum was the vegetation community best explained

5 S. Shaeri Karimi et al. Ecological Indicators 124 (2021) 107429

Table 1 Olden, 2018), the majority are for in-stream biota, particularly fish. Electivity indices for the four vegetation types and different inundation Moreover, the transferability of the empirical flow-ecologyrelationships characteristics. across space and time varies between species (Chen and Olden, 2018). In Inundation Measures Vegetation Communities this study, we quantified the water requirements of four major woody

Average Duration (day) Black Box Coolabah Lignum River Red Gum vegetation communities in the semi-arid Australian floodplain by link ing the community occurrence and the inundation history derived from – + 0 7 1.83* 1.99* 0.08* 5.02* a well-calibrated and spatially explicit fine-scale inundation model 7–14 1.40* 0.23* 0.09* 2.51* 14–21 1.34* +0.90* +0.22* 0.23* (Shaeri Karimi et al., 2019). We found that the floodplain forests and 21–28 1.36* +1.18* +0.20* +1.02* woodlands in the upper Darling were generally able to survive longer 28–35 1.19* +1.03* +0.47* +1.49* drought than suggested by previous studies (Table 3). Specifically, the – + + 35 42 0.97* 1.22* 0.12* 2.57* woodlands can survive longer drought and shorter inundation duration > 42 0.61* +0.30* 0.04 +2.96* than previously suggested. Our findings highlight the importance of Total Inundation (month) local-scale research for effective environmental water management. < + 3 1.82* 2.73* 0.55* 6.25* River Red Gum is the most studied of the four communities. How 3–6 0.12* 1.17* +0.58* 4.74* 6–9 1.49* 0.48* 0.06* 3.18* ever, much of current understanding of the water requirements of 9–12 1.67* +0.11* 0.53* 2.12* floodplain vegetation in the MDB has been derived from studies in the 12–15 1.30* +0.64* 0.17* 0.88* southern part of the basin (Roberts and Marston, 2011; Colloff et al., + + + 15–18 1.51* 1.04* 0.17* 0.09* 2015) where conditions are less arid and hydrologically variable than 18–21 1.31* +1.09* +0.47* +1.31* the northern basin. Further, subspecies of the dominant tree species may 21–24 0.96* +1.22* 0.15* +2.57* > 24 0.61* +0.30* 0.04 +2.96* differ across the basin. Both E. camaldulensis and E. coolabah have sub species arida more commonly encountered in the northern basin, which Max Inter-flood (year) < 5 0.61* +0.30* 0.04 +2.96* may confer differences in drought tolerance to those subspecies 5–10 1.64* +1.71* 0.10* +1.53* observed in the southern basin. These considerations along with the 10–15 0.48* 1.05* +0.54* 3.65* detail made possible by the mapping and hydrological modelling of 15–20 +1.35* 2.17* +1.48* 5.45* species at a landscape scale may explain some of the differences between 20–25 +1.63* 2.12* +0.40* 3.68* our findings and previous reviews of water tolerances of dominant > 25 +1.66* 3.59* 1.50* 6.63* floodplain tree species of the MDB (Table 3). * < Indicates that electivity index is significantly different from 0 at p 0.001 The maximum inter-flood period associated with the distribution of River Red Gum in the Darling is consistent with estimates provided by by inundation measures suggesting the most substantial impact of Doody et al. (2015) for the Yanga National Park in the Southern MDB, inundation on the presence and absence of this community. who suggested that while the species could survive droughts of up to 15 The low values of deviance explained for Lignum, Coolabah and years, below-average rainfall would likely leave the species in very poor Black Box, indicate that inundation variables are not sufficient to condition, and many would die. Mapping of the extent and condition of describe the vegetation distribution. Other environmental variables River Red Gum in the Macquarie Marshes in the northern MDB before, should be considered to improve the distribution model and achieve a during and following the “millennial drought” of 1999–2009 has higher proportion of explained deviance for these communities, espe demonstrated the tolerance of the species to droughts of decadal dura cially if the models are going to be used for prediction purposes. tion (Bino et al., 2015, Sandi et al., 2019). Individual vegetation responses to the inundation parameters were Black Box is a drought-hardy tree, occurring on floodplains with also identified using partial response curves and compared with elec irregular and infrequent flooding,but also in areas that are almost never tivity indices (Figs. 4 and 5). Visual comparison of the electivity indices flooded (Casanova 2015). The survival thresholds currently acknowl with GAM response curves for River Red Gum showed high similarity edged by water managers are mainly based on studies in South Australia among the results (Fig. 4). According to both methods, probability of such as the Chowilla floodplain,where the survival of floodplaintrees is River Red Gum presence was high at ADI > 21 days, TI > 450 days, and dependent on the infrequent flooding to freshen the shallow saline MIF < 12 years. The GAM response curves for other vegetation com groundwater (Doody et al. 2009). In our study area, the aquifer within munities also support the electivity results (Fig. 5). the reach of root system imposes a low risk to the floodplain trees in terms of salinity (Overton et al., 2018). The accessible fresher ground 4. Discussion water might contribute to the relatively longer inter-flood period. In the MDB, Lignum can occur as extensive shrubland and as un Knowledge of water requirements of wetland vegetation is critical derstory in woodlands such as Black Box and River Cooba (Roberts and for sustainable water resource management, aiming to maintain the Marston, 2011). It can persist in a relatively dormant state for long pe ecological integrity of the river-floodplain system (Swirepik et al., riods, growing only when conditions are favourable (i.e. flooding or 2016). A quantitative flow-ecology relationship is required to support heavy rainfall), reflecting the high resistance to water deficits (Wen environmental water managers in making decisions about how much et al., 2018). Our results showed that there were considerable overlaps and when to prescribe water releases from large dams to attain envi in the preferred maximum inter-floodperiod between Lignum and Black ronmental targets (Rosenfeld, 2017). Although there is substantial Box, reflecting the coexistence of the two species often observed in the literature on flow-ecology relationships (Webb et al., 2013; Chen and upper Darling floodplain.

Table 2 Results from GAMs performed between the presence/absence of four vegetation communities and inundation characteristics.

Community GAM1: s(ADI) + s(MIF) GAM2: s(TI) + s(MIF)

AIC R2 (adj) Dev. explained (%) AIC R2 (adj) Dev. explained (%)

Black Box 58,117 0.197 16.7 57,419 0.205 17.7 Coolabah 421,257 0.216 16.6 421,843 0.214 16.4 Lignum 108,176 0.051 3.73 106,543 0.071 5.19 River Red Gum 44,229 0.569 49.6 42,166 0.593 52

6 S. Shaeri Karimi et al. Ecological Indicators 124 (2021) 107429

Fig. 4. Visualization of the associations between the distribution of River Red Gum community and inundation metrics. a) Electivity plots indicating the rela tionship between River Red Gum presence or absence and average duration of inundation, total inundation and maximum inter-flood. Partial response curves of GAMs for River Red Gum community based on b) average duration and max inter-flood(GAM 1), and c) total inundation and max inter-flood(GAM 2). Shaded areas around the response curve are 95% confidence intervals of the estimated curves. The vertical bars on the x-axis indicate observation values of each inundation variables. The y-axis label indicates the covariate name and the estimated degrees of freedom of the smooth curve. The y-axis represents the probability of occurrence of River Red Gum in logit scale. The zero value indicates the probability of 50%; for example, the probability of occurrence of River Red Gum is higher than 50% in areas with the average duration of inundation higher than 20 days. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Despite being an iconic species in the hot and dry northern MDB and the investigated woodland communities and could have great effects on Lake Eyre Basin, the water requirements of Coolabah are not well the survival and persistence of the tree species. The shallow (fresh and defined( Roberts and Marston, 2011). Thus, our findingsfill a significant accessible) and deep (saline and non-available) aquifers are generally knowledge gap. Wen et al. (2018) found that Coolabah and Black Box not hydraulically connected, and the shallow aquifers are typically communities had comparable resilience to water deficitssuggesting they perched and recharged directly via the Darling River channel in high can both survive prolonged drought as confirmed in this study. river flows, and the floodplain in overbank events (NSW Government, The thresholds reported in this study are for the sustenance of the 2020). Therefore, the groundwater available for plants is also directly vegetation communities only because vegetation condition cannot be associated with inundation regimes, and the presence of sediment inferred from the current vegetation map. Under prolonged drought or aquifers may act as a buffer to prolong vegetation survival during insufficientinundation (i.e. short duration), all vegetation communities extended drought events. Second, Lignum can exhibit dieback following will be stressed and suffer a reduction in the ecological values they extensive drought (Doody and Overton, 2012), and recover of condition provide. For example, Lignum shrubland is an important habitat for following drought is longer on the Darling than for the floodplain tree colonially nesting waterbirds and the freckled duck (Foster, 2015), a species (Wen et al., 2018). The phase of recovery during the mapping contribution not provided in the degraded state. The management exercise is challenging to interpret, and may, therefore, present a con intention of the Murray Darling Basin Authority is to maintain at least servative estimate of the drought tolerance of Lignum. 70–90% of forests in good condition, a state which cannot be maintained if watering strategies shift towards the outer limits of drought tolerance. 5. Conclusion There are some limitations associated with the inference of flood tolerance and preference based on our hydrological modelling. First, By combining machine learning techniques and environmental pa access to groundwater is important to the survival of trees on the rameters along with the vegetation community occurrence to identify floodplain (Arthur et al., 2011; McGinness et al., 2013), and ground the relationship between vegetation distribution and inundation char water could not be represented in our model. Intermittent flood pulses acteristics, the technical challenge of flow-ecology modelling in flood during extensive droughts can recharge lateral bank groundwater re plains which is critical for sustainable water resource management, was serves to a distance of up to 120 m on the Murray River (Doody et al., overcome. In this study, the electivity indices better served our purpose 2014) providing a subsidy to River Red Gum. The wide range of flood than GAMs by clearly showing the positive or negative association be tolerances observed for Coolabah and Black Box in our study may relate tween each class of inundation metrics and vegetation occurrence. to their capacity to utilise groundwater to several metres’ depth in distal However, GAM results confirmedthat, although the hydrological regime floodplainlocations (Colloff et al., 2015). In our study system, the Upper is reported to be a critical factor influencing vegetation distribution, Darling alluvial groundwater can be conceptualised as having a shallow other environmental factors should be considered while studying (i.e. sediments of the Narrabri formation) and a deep (i.e. Gunnedah and vegetation distribution especially for Black Box, Coolabah and Lignum. Cubbaroo formations) aquifer system (Geoscience Australia, 2019). The This study has important implications for sustainable water resource shallow aquifer reaches a depth of 25 m below ground level (NSW management in the MDB as it enhances our understanding of the Government, 2020), thus, the shallow groundwater is within reach of inundation requirements of four key floodplainvegetation communities

7 S. Shaeri Karimi et al. Ecological Indicators 124 (2021) 107429

Fig. 5. a) Electivity plots indicating the relationship between vegetation occurrence and different inundation classes. b) Partial response curves of GAM1 (Left), and GAM2 (Right), showing the relationships between the four selected vegetation communities and explanatory variables. The y-axes represent the responses of vegetation presence and absence to the respective variables. For each variable, the smoothed function (s) from the model denotes the degrees of freedom. e.g., s(ADI, 8.63) means the smooth function for average duration having 8.63 degrees of freedom.

8 S. Shaeri Karimi et al. Ecological Indicators 124 (2021) 107429

Table 3 Chen, W., Olden, J.D., 2018. Evaluating transferability of flow–ecology relationships – Estimates of flooding preferences and tolerances of dominant woodland com across space, time and taxonomy. Fresh. Biol. 63 (8), 817 830. https://doi.org/ 10.1111/fwb.13041. munities for this study, compared to previous estimates (Roberts and Marston, Colloff, M.J., Caley, P., Saintilan, N., Pollino, C.A., Crossman, N.D., 2015. Long-term 2011; Casanova, 2015). ecological trends of flow-dependent ecosystems in a major regulated river basin. Mar. Fresh. Res. 66 (11), 957–969. https://doi.org/10.1071/MF14067. Community Preferred duration of inundation Maximum inter-flood period Cooney, T., 1994. Barwon-Darling River Riparian Health Report: Wetland Inundation. This study Previous findings This study Previous findings Department of Water Resources. Crown Lands & Water Division, 2017. Barwon-Darling Water Resource Plan: Surface > – – River Red Gum 2 months 5 7 months 12 years 5 7 years water resource description. http://www.industry.nsw.gov.au/__data/assets/ Coolabah 1–5 weeks Not known 15 years Not known pdf_file/0010/152938/Barwon-Darling.pdf. Lignum 1–3 weeks 3–7 months* 20–25 years 10 years De Jager, N.R., Rohweder, J.J., Yin, Y., Hoy, E., 2016. The Upper Mississippi River Black Box < 1 week 3–6 months >25 years 12–16 years floodscape: spatial patterns of flood inundation and associated plant community – * distributions. Appl. Veg. Sci. 19 (1), 164 172. https://doi.org/10.1111/avsc.12189. Not continuous. Doody, T.M., Benger, S.N., Pritchard, J.L., Overton, I.C., 2014. Ecological response of Eucalyptus camaldulensis (river red gum) to extended drought and flooding along the River Murray, South Australia (1997–2011) and implications for environmental critically and provides a tool for predicting the impact of future water flow management. Mar. Fresh. Res. 65 (12), 1082–1093. https://doi.org/10.1071/ development projects or climate change on floodplain vegetation MF13247. communities. Doody, T.M., Holland, K.L., Benyon, R.G., Jolly, I.D., 2009. Effect of groundwater freshening on riparian vegetation water balance. Hydrol. Process. 23 (24), 3485–3499. https://doi.org/10.1002/hyp.7460. CRediT authorship contribution statement Doody, T.M., Overton, I.C., 2012. Gol Gol wetlands health assessment: 2008-2011. Report prepared for the NSW Officeof Environment and Heritage. CSIRO Water for a Healthy Country, Adelaide, SA. Sara Shaeri Karimi: Conceptualization, Methodology, Investiga ´ ´ & Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carre, G., García Marquez, J. tion, Formal analysis, Writing - original draft, Writing - review edit R., Gruber, B., Lafourcade, B., Leitao,˜ P.J., Münkemüller, T., McClean, C., ing. Neil Saintilan: Conceptualization, Methodology, Supervision, Osborne, P.E., Reineking, B., Schroder,¨ B., Skidmore, A.K., Zurell, D., Lautenbach, S., Writing - original draft, Writing - review & editing. Li Wen: Conceptu 2013. Collinearity: a review of methods to deal with it and a simulation study – & evaluating their performance. Ecography 36 (1), 27 46. https://doi.org/10.1111/ alization, Resources, Writing - original draft, Writing - review editing. j.1600-0587.2012.07348.x. Jonathan Cox: Writing - original draft, Writing - review & editing. ESRI, 2018. ArcGIS Desktop: Release 10.6 Redlands, CA: Environmental Systems Roozbeh Valavi: Methodology, Writing - review & editing. Research Institute. Foster, N., 2015. Ecological considerations relating to flow related processes within the Barwon-Darling River: a guide for the Barwon-Darling Water Sharing Plan Declaration of Competing Interest Interagency Panel. NSW Office of Water, Sydney. Geoscience Australia, 2019. Australian Stratigraphic Units Database. https://asud.ga. gov.au/ (accessed 28 October 2020). The authors declare that they have no known competing financial Guisan, A., Edwards Jr, T.C., Hastie, T., 2002. Generalized linear and generalized interests or personal relationships that could have appeared to influence additive models in studies of species distributions: setting the scene. Ecol. Model. the work reported in this paper. 157 (2–3), 89–100. https://doi.org/10.1016/S0304-3800(02)00204-1. Guisan, A., Thuiller, W., Zimmermann, N.E., 2017. Habitat suitability and distribution models: with applications in R. Cambridge University Press. https://doi.org/ Acknowledgements 10.1017/9781139028271. Hastie, T., Tibshirani, R.J., 1990. Generalized Additive Models. Chapman & Hall, Macquarie University, Australia, funded this research under the In London. Hirzel, A., Guisan, A., 2002. Which is the optimal sampling strategy for habitat suitability ternational Macquarie University Research Training Program Scholar modelling. Ecol. Model. 157 (2–3), 331–341. https://doi.org/10.1016/S0304-3800 ship (iMQRTP) to Sara Shaeri Karimi. Roozbeh Valavi is supported by an (02)00203-X. Australian Government Research Training Program Scholarship and a Host, G.E., Polzer, P.L., Mladenoff, D.J., White, M.A., Crow, T.R., 1996. A quantitative approach to developing regional ecosystem classifications. Ecol. Appl. 6 (2), Rowden White Scholarship. 608–618. https://doi.org/10.2307/2269395. Iles, J., Kelleway, J., Kobayashi, T., Mazumder, D., Knowles, L., Priddel, D., Saintilan, N., References 2010. Grazing kangaroos act as local recyclers of energy on semi-arid floodplains. Aust. J. Zool. 58 (3), 145–149. https://doi.org/10.1071/ZO10020. Jacobs, J., 1974. Quantitative measurement of food selection. Oecologia 14 (4), Arthur, A.D., McGinness, H.M., McIntyre, S., 2011. The effect of changing irrigation 413–417. https://doi.org/10.1007/BF00384581. strategies on biodiversity. Final report to the National Program for Sustainable Jenkins, S.H., 1979. Seasonal and year-to-year differences in food selection by beavers. Irrigation. CSIRO Ecosystem Sciences, Canberra. https://www.yumpu.com/en/ Oecologia 44 (1), 112–116. https://doi.org/10.1007/BF00346408. document/read/33254476/the-effect-of-changing-irrigation-strategies-on-biodiv Jensen, M.E., Bourgeron, P.S. (Eds.), 2001. A guidebook for integrated ecological ersitypdf. assessments. Springer Science & Business Media, New York. Bino, G., Sisson, S.A., Kingsford, R.T., Thomas, R.F., Bowen, S., 2015. Developing state Lehmann, A., Overton, J.M., Leathwick, J.R., 2002. GRASP: generalized regression and transition models of floodplain vegetation dynamics as a tool for conservation analysis and spatial prediction. Ecol. Model. 157 (2–3), 189–207. https://doi.org/ decision-making: a case study of the Macquarie Marshes Ramsar wetland. J. Appl. 10.1016/S0304-3800(02)00195-3. Ecol. 52 (3), 654–664. https://doi.org/10.1111/1365-2664.12410. Leutner, B., Horning, N., Schwalb-Willmann, J., 2019. RStoolbox: Tools for Remote Bino, G., Brandis, K., Kingsford, R.T., Porter, J., 2020. Waterbird synchrony across Sensing Data Analysis. R package version 0.2.6. Retrieved from: https://CRAN.R- Australia’s highly variable dryland rivers–Risks and opportunities for conservation. project.org/package=RStoolbox. Biol. Conserv. 243, 108497 https://doi.org/10.1016/j.biocon.2020.108497. Liu, C., Berry, P.M., Dawson, T.P., Pearson, R.G., 2005. Selecting thresholds of BOM, 2020. Climate data online. Australian Bureau of Meteorology. Retrieved from htt occurrence in the prediction of species distributions. Ecography 28 (3), 385–393. p://www.bom.gov.au/climate/averages/tables/cw_048245.shtml. https://doi.org/10.1111/j.0906-7590.2005.03957.x. Brennan, S., O’Brien, M., Thoms, M., Maher, S., 2002. The physical character and flow Mac Nally, R., Horrocks, G.F., Lada, H., 2017. Anuran responses to pressures from high- criteria for wetlands along the Barwon-Darling River. CRC for Freshwater Ecology amplitude drought–flood–drought sequences under climate change. Clim. Change. Technical Report to the Department of Land and Water Conservation. 141 (2), 243–257. https://doi.org/10.1007/s10584-016-1890-z. Capon, S.J., 2005. Flood variability and spatial variation in plant community Maggini, R., Lehmann, A., Zimmermann, N.E., Guisan, A., 2006. Improving generalized composition and structure on a large arid floodplain. J. Arid Environ. 60 (2), regression analysis for the spatial prediction of forest communities. J. Biogeogr. 33, 283–302. https://doi.org/10.1016/j.jaridenv.2004.04.004. 1729–1749. https://doi.org/10.1111/j.1365-2699.2006.01465.x. Capon, S.J., Brock, M.A., 2006. Flooding, soil seed bank dynamics and vegetation Manel, S., Williams, H.C., Ormerod, S.J., 2001. Evaluating presence–absence models in resilience of a hydrologically variable desert floodplain.Fresh. Biol. 51 (2), 206–223. ecology: the need to account for prevalence. J. Appl. Ecol. 38, 921–931. https://doi. https://doi.org/10.1111/j.1365-2427.2005.01484.x. org/10.1046/j.1365-2664.2001.00647.x. Casanova, M.T., 2015. Review of Water Requirements for Key Floodplain Vegetation for Marra, G., Wood, S.N., 2011. Practical variable selection for generalized additive models. the Northern Basin; Literature review and expert knowledge assessment. Report to Comput. Stat. Data Anal. 55 (7), 2372–2387. https://doi.org/10.1016/j. the Murray-Darling Basin Authority, Charophyte Services, Lake Bolac. https://www. csda.2011.02.004. mdba.gov.au/sites/default/files/pubs/Review%20of%20Water%20Requirements% Mateo, C.M., Hanasaki, N., Komori, D., Tanaka, K., Kiguchi, M., Champathong, A., 20for%20Floodplain%20Vegetation_final.pdf. Sukhapunnaphan, T., Yamazaki, D., Oki, T., 2014. Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir

9 S. Shaeri Karimi et al. Ecological Indicators 124 (2021) 107429

management, and hydrodynamic models. Water Resour. Res. 50 (9), 7245–7266. vulnerability. Adv. Water Resour. 128, 168–182. https://doi.org/10.1016/j. https://doi.org/10.1002/2013WR014845. advwatres.2019.04.016. McElreath, R., 2016. Statistical rethinking: a Bayesian course with examples in R and Selwood, K.E., Clarke, R.H., McGeoch, M.A., Mac Nally, R., 2017. Green tongues into the Stan. CRC press, Boca Raton, FL. arid zone: river floodplains extend the distribution of terrestrial bird species. McGinness, H.M., Arthur, A.D., Davies, M., McIntyre, S., 2013. Floodplain woodland Ecosystems 20 (4), 745–756. https://doi.org/10.1007/s10021-016-0059-y. structure and condition: the relative influence of flood history and surrounding Shaeri Karimi, S., Saintilan, N., Wen, L., Valavi, R., 2019. Application of machine irrigation land use intensity in contrasting regions of a dryland river. Ecohydrol. 6, learning to model wetland inundation patterns across a large semi-arid floodplain. 201–213. https://doi.org/10.1002/eco.1259. Water Resour. Res., 55(11), 8765-8778. https://doi.org/10.1029/2019WR024884. McGinness, H.M., Arthur, A.D., Reid, J.R., 2010. Woodland bird declines in the Murray- Sheldon, F., 2017. Characterising the ecological effects of changes in the ’low-flow Darling Basin: are there links with floodplainchange? Rangeland J. 32 (3), 315–327. hydrology’ of the Barwon-Darling River. Advice to the Commonwealth https://doi.org/10.1071/RJ10016. Environmental Water Holder Office, Australian Rivers Institute, Griffith University. MDBA, 2012. Basin Plan 2012. Murray Darling Basin Authority, Canberra. Available https://www.environment.gov.au/system/files/resources/bb774e1f-d7fa-4825-88 from: https://www.legislation.gov.au/Details/F2018C00451 (accessed 20 June 51-cb5e5f1b3f51/files/characterising-eco-effects-changes-low-flow-barwon-darling. 2020). pdf. MDBA, 2016. Environmental Outcomes of the Northern Basin Review. Published by the Stern, H., De Hoedt, G., Ernst, J., 2000. Objective classification of Australian climates. Murray Darling Basin Authority, Canberra. https://www.mdba.gov.au/sites/default Aust. Meteorol. Mag. 49 (2), 87–96. /files/pubs/NBR-environmental-outcomes-Oct-16-v2_1.pdf. Swirepik, J.L., Burns, I.C., Dyer, F.J., Neave, I.A., O’brien, M.G., Pryde, G.M., Miller-Adamany, A., Baumann, D., Thomsen, M., 2019. Facilitating natural succession in Thompson, R.M., 2016. Establishing environmental water requirements for the a heavily invaded ecosystem. For. Ecol. Manage. 444, 235–243. https://doi.org/ Murray-Darling Basin, Australia’s largest developed river system. River Res. Appl. 32 10.1016/j.foreco.2019.04.043. (6), 1153–1165. https://doi.org/10.1002/rra.2975. NSW Government, 2020. Water Sharing Plan for the Darling Alluvial Groundwater Teng, J., Jakeman, A.J., Vaze, J., Croke, B.F., Dutta, D., Kim, S., 2017. Flood inundation Sources 2020. https://www.industry.nsw.gov.au/water/plans-programs/water-r modelling: a review of methods, recent advances and uncertainty analysis. Environ. esource-plans/drafts/darling-alluvium/components (accessed 28 October 2020). Modell. Software 90, 201–216. https://doi.org/10.1016/j.envsoft.2017.01.006. OEH, 2017, The NSW State Vegetation Type Map: Methodology for a regional-scale map Theiling, C.H., Bettis, E.A., Heitmeyer, M.E., 2012. Hydro-geomorphic classificationand of NSW plant community types, NSW Office of Environment and Heritage, Sydney, potential vegetation mapping for Upper Mississippi River bottomland restoration. Australia. https://www.environment.nsw.gov.au/resources/vegetation/nsw-state INTECH Open Access Publisher. https://doi.org/10.5772/28808. -vegetation-type-map-methodology-170134.pdf. Thomas, R.F., Kingsford, R.T., Lu, Y., Cox, S.J., Sims, N.C., Hunter, S.J., 2015. Mapping Overton, I.C, Coff, B., Mollison, D., Barling. R., Fels, K. Boyd, A., 2018. Black Box inundation in the heterogeneous floodplain wetlands of the Macquarie Marshes, Management Framework: a Framework for Managing Floodplain and Wetland Black using Landsat Thematic Mapper. J. Hydrol. 524, 194–213. https://doi.org/10.1016/ Box Eucalypts in the Murray-Darling Basin. Prepared by Jacobs Group (Australia) j.jhydrol.2015.02.029. Pty Ltd for the Commonwealth Environmental Water Office, Department of the Thoms, M.C., Sheldon, F., 2000. Water resource development and hydrological change in Environment and Energy. https://www.environment.gov.au/water/cewo/publicatio a large dryland river: the Barwon-Darling River. Australia. J. Hydrol. 228 (1–2), ns/black-box-management-framework. 10–21. https://doi.org/10.1016/S0022-1694(99)00191-2. Pan, D., Domon, G., De Blois, S., Bouchard, A., 1999. Temporal (1958–1993) and spatial Webb, J.A., Miller, K.A., King, E.L., de Little, S.C., Stewardson, M.J., Zimmerman, J.K., patterns of land use changes in Haut-Saint-Laurent (Quebec, Canada) and their Poff, N.L., 2013. Squeezing the most out of existing literature: a systematic re- relation to landscape physical attributes. Landsc. Ecol. 14 (1), 35–52. https://doi. analysis of published evidence on ecological responses to altered flows. Fresh. Biol. org/10.1023/A:1008022028804. 58 (12), 2439–2451. https://doi.org/10.1111/fwb.12234. Pastor, J., Broschart, M., 1990. The spatial pattern of a northern conifer-hardwood Wen, L., Ling, J., Saintilan, N., Rogers, K., 2009. An investigation of the hydrological landscape. Landsc. Ecol. 4 (1), 55–68. https://doi.org/10.1007/BF02573951. requirements of River Red Gum (Eucalyptus camaldulensis) Forest, using R Core Team, 2019. R: A language and environment for statistical computing. R classification and regression tree modelling ecosystems, land and water process Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. interactions. Ecohydrol. 2 (2), 143–155. https://doi.org/10.1002/eco.46. Roberts, J., Marston, F., 2011. Water regime for wetland and floodplain plants: a source Wen, L., Powell, M., Saintilan, N., 2018. Landscape position strongly affects the book for the Murray–Darling Basin, National Water Commission, Canberra. resistance and resilience to water deficit anomaly of floodplain vegetation Roberts, J., Young, B., Marston, F., 2000. Estimating the water requirements for plants of community. Ecohydrol. 11 (8), e2027 https://doi.org/10.1002/eco.2027. floodplainwetlands: a guide. CSIRO Land and Water, Canberra. https://library.dbca. Whitaker, K., Rogers, K., Saintilan, N., Mazumder, D., Wen, L., Morrison, R.J., 2015. wa.gov.au/static/FullTextFiles/070608.pdf. Vegetation persistence and carbon storage: implications for environmental water Rogers, K., Ralph, T.J. (Eds.), 2010. Floodplain wetland biota in the Murray-Darling management for P hragmites australia. Water Resour. Res. 51 (7), 5284–5300. Basin: water and habitat requirements. CSIRO publishing. https://doi.org/10.1002/2014WR016253. Rosenfeld, J.S., 2017. Developing flow–ecology relationships: implications of nonlinear Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood biological responses for water management. Fresh. Biol. 62 (8), 1305–1324. https:// estimation of semiparametric generalized linear models. J. R. Stat. Soc. Series B Stat. doi.org/10.1111/fwb.12948. Methodol. 73 (1), 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x. Sandi, S.G., Saco, P.M., Saintilan, N., Wen, L., Riccardi, G., Kuczera, G., Willgoose, G., Rodríguez, J.F., 2019. Detecting inundation thresholds for dryland wetland