Dugong Dugon) and Their Seagrass Habitat in Shark Bay Marine Park Before and After the Marine Heatwave of 2010/11

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Dugongs & seagrass NESP report 2 2019

FINAL REPORT

Modelling the spatial relationship between dugong (Dugong dugon) and their seagrass habitat in Shark Bay Marine Park before and after the marine heatwave of 2010/11

Peter Bayliss1, Holly Raudino2, Marlee Hutton1, Kathy Murray2, Kelly Waples2 and Simone Strydom2

1CSIRO Oceans & Atmosphere Business Unit, Perth WA and Brisbane Qld 2Department of Biodiversity, Conservation and Attractions, Marine Science Program, WA

Dugongs & seagrass NESP report 2 2019

Contents

SUMMARY ...... 6 1 INTRODUCTION ...... 9

1.1 BACKGROUND ...... 9 1.2 AIMS OF THE STUDY ...... 9 2 HISTORICAL DUGONG SURVEYS – REPORT 1 TO NESP ...... 9

2.1 REVIEW OF PREVIOUS CHANGE ANALYSES ...... 9 2.2 2018 POPULATION ESTIMATES – CURRENT STATUS ...... 10 3 METHODS ...... 11

3.1 DESIGN FOR DUGONG POPULATION CHANGE ANALYSIS PRE- AND POST-MARINE HEAT WAVE ...... 11 3.2 DUGONG DISTRIBUTION AND RELATIVE ABUNDANCE OVER THE TIME SERIES (1989-2018) ...... 12 3.3 TURTLE DISTRIBUTION AND RELATIVE ABUNDANCE IN SHARK BAY BETWEEN 2002 & 2018...... 12 3.4 SEAGRASS EXTENT (2002-2016) AND SPECIES DISTRIBUTION AND RELATIVE ABUNDANCE (1981-2002) ...... 12 3.4.1 Extent ...... 12 3.4.2 Species composition ...... 13 3.5 FINE-SCALE SPATIAL GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) MODELS OF DUGONG/TURTLE-SEAGRASS HABITAT RELATIONSHIPS ...... 14 3.6 BROAD-SCALE SPATIAL MODELLING OF DUGONG– SEAGRASS HABITAT RELATIONSHIP ...... 14 4 RESULTS ...... 15

4.1 POPULATION CHANGE ANALYSIS BETWEEN 2007 AND 2018 – PRE- & POST-MARINE HEAT WAVE ...... 15 4.1.1 Shark Bay ...... 15 4.1.2 Ningaloo-Exmouth Gulf ...... 18 4.1.3 How many dugongs are there in Shark Bay? ...... 18 4.2 DUGONG DISTRIBUTION AND RELATIVE ABUNDANCE OVER THE TIME SERIES (1989-2018) ...... 20 4.2.1 Shark Bay ...... 20 4.2.2 Ningaloo-Exmouth Gulf ...... 20 4.3 TURTLE DISTRIBUTION AND RELATIVE ABUNDANCE IN SHARK BAY BETWEEN 2002 & 2018...... 20 4.4 SEAGRASS ...... 26 4.4.1 Seagrass extent (2002-2016) ...... 26 4.4.2 Seagrass species composition (1981-2002) and bathymetry ...... 26 4.5 SPATIAL MODELLING OF DUGONG AND TURTLE – SEAGRASS HABITAT RELATIONSHIPS ...... 32 4.5.1 The extent of seagrass attributes in the 1km Albers grid 2002-2016 ...... 32 4.5.2 Local GWR spatial models for dugongs, turtles and seagrass (2002-2018) ...... 32 4.5.3 Ordinary global multiple regression models of dugongs and seagrass (2002 vs. 2018) ...... 40 4.6 BROAD-SCALE SPATIAL MODELLING OF DUGONG– SEAGRASS HABITAT RELATIONSHIP ...... 43 5 DISCUSSION ...... 45 6 REFERENCES ...... 50 7 ACKNOWLEDGEMENTS ...... 55 8 APPENDICES ...... 55

ATTACHMENT 1 REPORT 1 TO NESP ...... 55 ATTACHMENT 2 SUPPLEMENTARY MATERIAL (FIGURES) ...... 55

Dugongs & seagrass NESP report 2 2019

List of Figures Figure 1a-d. 2-ANOVA comparing mean dugong transect density (numbers.km-2) by survey block between 2007 and 2018 for (a) Shark Bay and (c) Ningaloo-Exmouth Gulf, respectively (see Tables 1a & 2a, respectively). Trends in density by Block and Year are illustrated in (b) and (d) for each survey area, respectively. Transects are considered random replicates, densities are transformed to Ln (X+0.1), and means are least squares (LS) means. Transect densities were corrected for perception and availability visibility biases using the Pollock et al. (2006) method. The ANOVA F-ratio and degrees of freedom are indicated for each contrast, LS = least squares...... 16 Figure 2a-f. Relative abundance “hotspots” of dugongs mapped by Kernel smoothing using all sighting data in Shark Bay for the survey years of (a) 1989 (July), (b) 1994 (June), (c) 1999 (July), (d) 2002 (February), (e) 2007 ( May-June) and (d) 2018 (June), respectively. All observer counts were used to derive Kernel densities at 520m resolution and re-scaled to estimates of observed numbers in Shark Bay after adjusting for sampling intensity. Jenks Natural Breaks (Jenks 1967) was used to define 10 abundance intervals with red colours having the highest relative abundances and blue colours the lowest, with a colour-abundance range in between (orange – yellow-grey blue). The lowest relative abundance interval is coloured white to highlight abundance hotspots...... 21 Figure 3a-e. Relative abundance “hotspots” of dugongs mapped by Kernel smoothing using all sighting data in the Ningaloo Reef-Exmouth Gulf region for the survey years of (a) 1989 (July), (b) 1994 (June), (c) 1999 (July), (d) 2002 (February), (e) 2007 ( May-June) and (d) 2018 (June), respectively. All observer counts were used to derive Kernel densities at 520m resolution and re-scaled to estimates of observed numbers in the region after adjusting for sampling intensity. Jenks Natural Breaks (Jenks 1967) was used to define 10 abundance intervals with red colours having the highest relative abundances and blue colours the lowest, with a colour-abundance range in between (orange – yellow-grey blue). The lowest relative abundance interval is coloured white to highlight abundance hotspots...... 23 Figure 4a&b. Relative abundance “hotspots” of turtles mapped by Kernel smoothing using all sighting data in Shark Bay for the (a) 2002 (February) and (b) 2018 (June) survey years. All observer counts were used to derive Kernel densities at 520m resolution and re-scaled to estimates of observed numbers in Shark Bay after adjusting for sampling intensity. Jenks Natural Breaks (Jenks 1967) was used to define 10 abundance intervals with red colours having the highest relative abundances and blue colours the lowest, with a colour-abundance range in between (orange – yellow-grey blue). The lowest relative abundance interval is coloured white to highlight abundance hotspots...... 25 Figure 5a&b. The extent of dense (> 40% cover) and sparse (< 40% cover) seagrass mapped in Shark Bay using Landsat satellite images at 30m resolution for (a) 2002 and (b) 2016 (Strydom et al. 2019 in prep. & DBCA database)...... 28 Figure 6a&b. (a) Bathymetry map of Shark Bay at ~5m contour intervals (source DBCA July 2018) and (b) location of seagrass systematic field sample sites (n=885 sites from 1981 to 2002; Strydom et al. 2019 in prep. & DBCA database)...... 29 Figure 7a-c. (a) Percentage (%) occurrence of different seagrass genera and/or species by the 2002 seagrass map class (1 = Other; 2 = Dense seagrass; 3 = Sparse seagrass). (b) Mean bathymetry (m) of seagrass species occurrence by 2002 seagrass map class and (c) Mean occurrence (0-1) of dominant seagrass species that differed significantly by map class in 2002. Vertical bars are standard errors. Species composition and bathymetry data are from systematic field sample sites (1981 to 2002; Strydom et al. 2019 in prep. & DBCA database)...... 31 Figure 8a&b. (a) 1km spatial analysis grid across Shark Bay excluding non-World Heritage (WH) areas (GDA94 Albers projection). (b) Close up showing the intersection between the 1km grid and the 2002 extent of dense (dark green) and sparse (light green) seagrass...... 33 Figure 9a-d. Comparison of (a) observed distribution and abundance of dugongs (mean Kernel density/1km cell) for Shark Bay in 2002 to (b) that predicted by Geographically Weighted Regression (GWR). The explanatory variable is the area (km2) of sparse seagrass per cell. (c & d) Similarly for the predicted distribution and abundance of turtles predicted by GWR in relation to the extent (km2) of total seagrass/cell. See Table 10a and 11a for a summary of the GWR and Ordinary Least Squares (OLS)

iii

Dugongs & seagrass NESP report 2 2019 regression statistics, respectively. The extent of seagrass was measured in the same year as the dugong survey in 2002...... 37 Figure 10a-d. Comparison of (a) observed distribution and abundance of dugongs (mean Kernel density/1km cell) for Shark Bay in 2018 to (b) that predicted by Geographically Weighted Regression (GWR). The explanatory variable is the area (km2) of sparse seagrass per cell. (c & d) Similarly for the predicted distribution and abundance of turtles predicted by GWR in relation to the extent (km2) of total seagrass/cell. See Table 10c and 11b for a summary of the GWR and Ordinary Least Squares (OLS) regression statistics, respectively. The extent of seagrass was measured in 2016...... 38 Figure 11a-e. Comparison of (a) observed distribution and abundance of dugongs (mean Kernel density/1km cell) for Shark Bay in 2007 to that predicted by Geographically Weighted Regression (GWR) with (b) bathymetry and the (c) the mean probability of seagrass occurrence/cell derived from (d) dominant species presence/absence field data sampled across Shark Bay between 1996 and 2002. No turtle data were available for 2007, hence (e) is the GWR regression prediction between dugongs and bathymetry (mean mid-point depth contour interval, m). See Table 10b for a summary of the GWR statistics. No GWR dugong or turtle model using 2010 seagrass data resolved...... 39 Figure 12a-d. Multiple partial regression analysis between dugong relative density (numbers/1km grid cell) in Shark Bay in 2002 and the percentage cover of total seagrass / 1km grid cell (area dense + sparse seagrass) in 2002 and bathymetry (m). Dugong density is transformed to log10 (Y +1) and the percentage cover of seagrass variable transformed to arcsine (√proportions). Bathymetry data were untransformed. Nonlinear relationships were modelled by including quadratic polynomials of both explanatory variables, such that: ...... 41 Figure 13a-d. Multiple partial regression analysis between dugong relative density (numbers/1km grid cell) in Shark Bay in 2018 and the percentage cover of total seagrass / 1km grid cell (area dense + sparse seagrass) in 2016 and bathymetry (m). Dugong density is transformed to log10 (Y +1) and the percentage cover of seagrass variable transformed to arcsine (√proportions). Bathymetry data were untransformed. Nonlinear relationships were modelled by including quadratic polynomials of both explanatory variables, such that: ...... 42 Figure 14a-c. (a) Linear regression relationship between dugong population estimates for Shark Bay in 2002, 2007, 2012 and 2018 and the extent (km2) of sparse seagrass in 2002, 2010 and 2014 respectively. Population estimates were adjusted for visibility biases using the Pollock et al. (2006) method, and numbers were transformed of natural logs. (b) Observed (non-statistical) trend in the % of dugong calves summed across survey blocks 3-5 across for each survey year with the corresponding area (km2) of total seagrass. (c) Trend in the percentage of dugong calves counted during surveys between 1989 and 2018 showing a collapse in breeding recruitment in 2012, 1.5 years after the seagrass dieback in 2010/11. Only blocks 3-5 were surveyed in 2012, hence the trend in percentage calves between 2007 and 2018 is illustrated separately (dashed lines) for these blocks combined. The 0.3% value for 2012 (1 of 356) only relate to survey blocks 3-5 (c.f. (77 of 496 in the same blocks in 2018, or 15.5%), after Bayliss et al. (2018)...... 44

List of Tables

Table 1. Summary of available historical and contemporary data sets for population (dugongs & turtles) and spatial seagrass habitat analyses...... 11 Table 2a&b. Summary of a 2-ANOVA of differences in dugong density (Ln D + 0.1) in Shark Bay between survey block (n=8; 0 to 7) and year (n=2; 2007 vs. 2018 11 years elapse) for (a) a standard balanced ANOVA model treating transects as replicates, and (b) a GLM treating transects as a random effects factor (see Hodgson et al. 2013). ns=not significant at P<0.05...... 15 Table 3a&b. Summary of a 2-ANOVA of differences in dugong density (Ln D + 0.1) in Ningaloo-Exmouth Gulf region between survey block (n=2) and year (n=2; 2007 vs. 2018 11 years elapse) for (a) a standard balanced ANOVA model treating transects as replicates, and (b) a GLM treating transects as a random effects factor (see Hodgson et al. 2013). ns=not significant at P<0.05...... 18 Dugongs & seagrass NESP report 2 2019

Table 4. Summary of the GLM mixture model comparing regression relationships through the origin between dugong population estimates by blocks in 2018 derived by the Hagihara et al. (2014, 2018; HAGI) method with those derived by the Pollock et al. (2006; POLL) and Marsh and Sinclair (1989; MS) methods and the relative index of abundance (sum of all observer counts; INDEX). Blocks were Shark Bay (n=8) and Ninglaoo-Exmouth Gulf (n=2)...... 19 Table 5. Linear regression equations through the origin between survey block (n=10) population estimates in 2018 derived by the Hagihara et al. (2014, 2018) method with those derived by the Pollock et al. (2006; POLL) and Marsh and Sinclair (1989; MS) methods, and a relative index of abundance (INDEX). The slope of the regression equations provide correction factors (+ SE) to adjust alternative estimates of dugong abundance to the most recent and advanced method to adjust for visibility bias (both perception & availability biases)...... 20 Table 6. Summary of a 1-ANOVA of differences in mean bathymetry (m) of seagrass species present or absent (n=2 levels) on a sample site (n=878) in Shark Bay. Species composition and bathymetry data are from systematic field surveys (1981 to 2002, see Fig. 6b; Strydom et al. 2019 in prep. & DBCA database). df = 1/885, ns= not significant at P<0.05...... 26 Table 7. Summary of the mean bathymetry (m) where seagrass species were found to occur in each of the 2002 map classes (1=Other; 2=Dense seagrass; 3=Sparse seagrass)...... 30 Table 8. Summary of a 1-ANOVA of differences in mean occurrence (0-1) of seagrass species across seagrass map classes in 2002 (n=3 levels; 1=Other; 2=Dense seagrass; 3=Sparse seagrass) in Shark Bay. Species composition and bathymetry data are from systematic field surveys (1981 to 2002, see Fig. 6b; Strydom et al. 2019 in prep.). df = 2/885 (n=878 sample sites). The Newman–Keuls test is used for post- hoc contrasts between map classes. A 1-ANOVA is undertaken also between mean bathymetry (m) where they occur in each map class...... 30 Table 9a&b. Summary of Y-response and X-explanatory variable names used in the Geographical Weighted Regression (GWR) models to examine spatial relationships between dugong and turtle distribution and abundance and seagrass habitats in Shark Bay in 2002, 2007 and 2018, across a 1km grid (n=14,339 cells). Included are important GWR model parameter acronyms used to evaluation the models predictive reliability and performance...... 32 Table 10a-c. Summary of the Geographical Weighted Regression (GWR) models used to explore the spatial relationships between the distribution and relative abundance of dugongs and turtles in Shark Bay in (a) 2002, (b) 2007 and (c) 2018 to seagrass habitats in 2002, 2010 and 2016. See Table 9b for model parameter codes (a = regression intercept in model equations)...... 35 Table 11a&b. Summary of multiple regression equations between relative observed dugong density and quadratic polynomials for bathymetry and the percentage cover of total seagrass per 1 km grid cell for (a) Shark Bay in 2002 and in (b) 2018 (seagrass data are from 2016). Dugong density is transformed to log10 (Y +1) and the percentage cover of seagrass variable transformed to arcsine (√proportions). Bathymetry data were untransformed. Nonlinear relationships were modelled by including quadratic polynomials of both 2 2 explanatory variables, such that (See Fig. 11a-d): Log10 D = a + bathy + bathy + AS PCsgTot + AS PCsgTot ...... 40

Summary

1. Shark Bay is a global strong-hold for dugongs because of its extensive stands of seagrass. In the late summer of 2010/11 a marine heatwave occurred in WA coastal waters that had a significant impact on key marine habitats, including the large-scale loss of seagrass in Shark Bay Marine Park that has shown limited signs of recovery. An aerial survey of dugong populations in the Shark Bay-Ningaloo-Exmouth Gulf region was therefore undertaken in June 2018 to assess how dugong populations may have responded to the extensive loss of seagrass in 2011. The specific objectives, methodology, population-level analyses and results of that survey are documented in the first report of this project (Appendix 1; Bayliss et al. 2018). 2. The key results from the first report are: the number of dugongs in Shark Bay in 2018 was estimated at 18,555 + 3,396 (SE 18.3%) using the most updated visibility bias correction factors developed by Hagihara et al. (2014, 2018). The estimate for the Exmouth Gulf-Ningaloo region was 4,831 + 1,965 (SE 40.7%), producing a total of 23,386 + 3,124 (SE 16.8%) for both regions combined; preliminary analysis of population trends suggested that no major decline in either region before or after the seagrass dieback event could be detected, however a more comprehensive change analysis complimented with fine-scale spatial modelling of the relationship between dugongs and their seagrass habitat were recommended. Both recommendations comprise major objectives of the following report. 3. Two different 2-ANOVA models were used to assess change in dugong density in each survey region between 2007 and 2018. Both models in each region indicated that, overall, dugong abundance had not significantly decreased between 2007 and 2018, a period encompassing extensive seagrass dieback in Shark Bay. Taken together, the results cannot be used to confidently draw conclusions about potential large-scale movements between Shark Bay and the Ningaloo-Exmouth Gulf regions, in either direction, as a result of the seagrass dieback event in Shark Bay in 2010/11. The results of the change analysis suggest that, for Shark Bay at least, dugong populations have been relatively stable between 1989 and 2018 and supports a similar finding by Hodgson et al. (2008) for the period 1989 to 2007 although prior to the seagrass dieback event. 4. Kernel density extrapolation and smoothing methods were used on observed dugong sightings from the complete time series in Shark Bay (1989, 1994, 1999, 2002, 2007 & 2018) to identify and map dugong abundance hotspots and for fine-scale spatial modelling in relation to changes in the extent of seagrass. The extent of dense (> 40% cover) and sparse (< 40% cover) seagrass classes were mapped at 30m resolution in Shark Bay using the Landsat satellite collection and Sentinel 2 satellite images for the years 2002, 2010, 2014 and 2016. When validated with field data the mapping had on average an overall accuracy of 74%. 5. Dugong distribution and relative abundance and seagrass extent data in Shark Bay were paired for the years 2002-2002, 2007-2010 and 2016 and 2018 and integrated into a 1-km spatial grid for habitat modelling using Geographically Weighted Regression (GWR) models. A similar process was adopted for turtles for the 2002 and 2018 survey years. Dugong and turtle relative observed densities (nos/km2) compromised the dependent variable in the spatially explicit GRW models, and the following seagrass abundance variables were used as explanatory variables: total area of seagrass (km2); the areas (km2) of dense and sparse seagrass; the percentage (%) covers of both seagrass classes and the total. 6. All GWR models in all years apart from 2007 returned high overall adjusted R2 values predicting local dugong and turtle distribution and abundance patterns based on a single seagrass explanatory variable. All predicted model outputs compared favorably with observed data. For dugongs R2 ranged between 88% and 97% in 2002, and 73% to 97% in 2018. For turtles the range was 73% to 90% in 2002, and 73% to 85% in 2018. Model robustness was evaluated using 6

standard regression diagnostic procedures, which indicated that GWR models outperformed “global-scale” regression models that did not use optimal distance criteria for prediction. The optimal bandwidth or distance in the GWR models varied between 5 and 10 km. Of all seagrass mapping variables used to model dugong distribution and abundance, the extent or percentage cover of sparse seagrass habitat were generally the most predictive, particularly for 2018 (adjusted R2=97%). 7. The distribution and relative abundance of several species and/or genera of seagrass were mapped in Shark Bay also using historical field data collected systematically between 1981 and 2002. The frequency of occurrence of seagrass species was estimated for each 1-km grid cell, converted to a probability of occurrence value, and used as an alternative indicator of seagrass abundance in the 2002 and 2007 GWR models. The species-based relative abundance index of seagrass compared favourably with Landsat-derived seagrass maps in 2002 in terms of predicting local dugong (& turtle) distribution and abundance patterns, and provide a predictive dugong GWR model for the 2007-2010 pair. Species composition was examined for patterns in relation to bathymetry and the satellite-derived dense and sparse seagrass habitat maps. 8. A significant positive correlation was obtained between the combined dugong population estimates for survey blocks 3, 4 and 5 and the extent (km2) of sparse seagrass in those blocks over four time periods spanning the extensive seagrass dieback event in 2010/11. No significant correlations were obtained using the extent of dense seagrass. The extent of dense seagrass in the survey blocks prior to the dieback event was about twice that of sparse seagrass. In contrast, after the dieback event and during the recovery phase, they had similar extents. Between 2010 and 2014 the extent of total seagrass in the survey blocks decreased by 27%. However, the extent of dense seagrass decreased by 42% and, in contrast, the extent of sparse seagrass increased by 4%. Between 2014 and 2016 the extent of total seagrass increased by 6%, with dense and sparse seagrass increasing by 8% and 3%, respectively. This broad-scale spatial relationship between dugong and seagrass abundance (combined block areas = 5,891 km2) in combination with the fine-sale (1-km2) resolution of the GWR models suggest that dugongs in Shark Bay prefer sparse seagrass habitat, and a number of plausible hypotheses are proposed for future research. 9. One of the key results of the first report is reiterated in this report: that the trend in the percentage of dugong calves counted during surveys between 1989 and 2018 showed that breeding recruitment in 2012 most likely collapsed, 1.5 years after the seagrass dieback event in 2010/11. If significantly reduced juvenile recruitment (and/or mortality) was the major population dynamic response to the seagrass dieback event, then the response in terms of population abundance would not be detected for decades given the life history of dugongs. Furthermore, if adult mortality was initially buffered by an increase in preferred sparse seagrass habitat at the expense of dense seagrass habitat after the event, then this would likely reinforce the above prediction. 10. In summary, the lack of any significant and substantial change in dugong numbers in Shark Bay between 2007 and 2018, with no corresponding change in numbers in the Ningaloo-Exmouth Gulf region, is no reason for complacency. Within the context of an iconic conservation species within an iconic World Heritage marine park, our null result may simply reflect the existence of complex decadal-scale time lags between dugongs and seagrass after major perturbation events such as marine heatwaves, extensive freshwater flood events from coastal catchments, tropical cyclones, anthropogenic impacts or a combination of cumulative effects. Hence, the major recommendation of our final stage 2 report is to continue monitoring dugong populations in Shark Bay but at previously implemented 5-year intervals (the next one due in 2023) to avoid drift in the time series and, importantly, with contemporaneous monitoring of the condition of their seagrass habitats. The latter recommendation needs to include monitoring changes in species composition of seagrass (& other benthic communities) in addition to aerial extent, and 7

would involve collection of field data to both calibrate and validate seagrass maps derived from satellite imagery and concomitant dugong-seagrass fine-scale spatial habitat models. Such long- term paired data would provide a valuable knowledge base, not only for the conservation and management of dugongs and sea turtles, but for other components of the seagrass ecosystem.

1 Introduction

1.1 Background Dugong are one of the only extant sirenian species and herbivorous marine mammals in Australia, locally recognized as ‘near threatened’ (Woinarski et al. 2014) and globally as vulnerable to extinction (Marsh and Sobtzick 2015). The species is culturally valued through customary harvest (Marsh et al. 2015) and revered spiritually as totems across northern Australia and ecologically have high value as they serve the ecosystem through their function as grazers and dispersers of seagrass (McMahon et al. 2017). The main threats across their range include habitat loss and sedimentation from extreme weather events intensified by climate change and human activities, bycatch and subsistence hunting (Marsh and Sobtzick 2015; Marsh et al. 2015). Whilst dugong are widely distributed across the tropics a significant proportion are found in north Western Australian coastal waters from Shark Bay in the Gascoyne through to the Kimberley. Shark Bay has extensive stands of seagrass and has long been recognised as a global strong-hold for the species. The population size of dugongs in the Shark Bay and nearby Ningaloo-Exmouth Gulf regions has been monitored by aerial survey at approximately five year intervals since the late 1980’s (Gales et al. 2004; Holley et al. 2006; Anderson 1994; Hodgson et al. 2013; Preen et al. 1997; Bayliss et al. 2018). In the late summer of 2010/11 a marine heatwave occurred in WA coastal waters that had a significant impact on key marine habitats (Feng et al. 2013; Caputi et al. 2014; Fraser et al. 2014), including the large-scale loss of seagrass in Shark Bay Marine Park that has shown limited signs of recovery (Arias-Ortiz et al. 2018; DBCA 2019). An aerial survey of dugong populations in the Shark Bay-Ningaloo-Exmouth Gulf region was therefore undertaken in June 2018 to assess how dugong populations may have responded to the extensive loss of seagrass habitat and the results of that survey are documented in the first report of this project (Bayliss et al. 2018). The key results of the first report are: the number of dugongs in Shark Bay in 2018 was estimated at 18,555 + 3,396 (SE 18.3%) using the most updated visibility bias correction factors developed by Hagihara et al. (2014, 2018). The estimate for the Exmouth Gulf-Ningaloo region was 4,831 + 1,965 (SE 40.7%) dugongs, producing a total of 23,386 + 3,124 (SE 16.8%) for both regions; preliminary analysis of population trends suggested that no major population decline in either region could be detected since the seagrass dieback event, however more comprehensive change analysis complimented with fine-scale spatial modelling of the relationship between dugongs and their seagrass habitats were recommended, and comprise major objectives of this second report.

1.2 Aims of the study The aims of the second phase of this project are to: (i) undertake more comprehensive spatial and temporal analyses of the updated dataset in order to assess the status of current dugong populations within the context of all historical surveys; and (ii) develop fine-scale dugong-seagrass habitat models in Shark Bay to assess past and present trends in the distribution and relative abundance of dugongs in relation to seagrass condition following the extensive loss of seagrass habitat due to the marine heatwave in 2010/11.

2 Historical dugong surveys – Report 1 to NESP

2.1 Review of previous change analyses The most closely matched “whole of Shark Bay” dugong surveys before and after the 2010/11 seagrass dieback event were those undertaken in 2007 and 2018, respectively. Additionally, both 9

survey years included the Ningaloo-Exmouth Gulf regions and, hence, the 2007 and 2018 data sets comprise the before and after change analysis design reported here. In 2012, however, Hodgson et al. (2013) surveyed blocks 3-5 in Shark Bay as part of a study evaluating the efficacy of drones to monitor dugong populations and, fortuitously, was about 1.5 years after the seagrass dieback event. Preliminary analysis of historical dugong population trends indicates that Blocks 3-5 are generally high dugong abundance blocks (Hodgson et al. 2008; Bayliss et al. 2018) allowing an informal comparison using the combined block estimates reported by Hodgson et al. (2013). Their figures indicate no significant difference in dugong population estimates between 2007 and 2012 for those blocks (5,187 + 800 cf. 5,882 + 1,389, respectively). The most comprehensive change analysis of dugong abundance in Shark Bay and Ningaloo-Exmouth Gulf using the historical aerial survey time series data was that undertaken by Hodgson et al. (2008) for the period 1989 to 2007. They applied a robust ANOVA design to (Year, Block & Transects as factors) and corrected observed transect densities for visibility biases using the Pollock et al. (2006) method. They highlight that the Shark Bay dugong population has been qualitatively linked to populations in the Ningaloo-Exmouth Gulf region following an apparent decline in numbers following a tropical cyclone (TC Vance) that destroyed seagrass beds in the Ningaloo-Exmouth Gulf region in 1999 that coincided with increases in dugong numbers in Shark Bay. Gales et al. (2004) suggested that one reason for the cross-over trend is that dugongs had moved in response to the seagrass loss. However, the apparent changes in the dugong populations in the Shark Bay and Exmouth/Ningaloo regions following the 1999 cyclone in Exmouth were not reflected statistically in their standardised comparisons of relative dugong densities using a robust ANOVA design. Hodgson et al. (2008) attributed their non-significant result to the large variation in the distribution of dugongs within blocks swamping the variation between blocks and across survey years. They concluded that dugong aerial survey data are not robust enough to detect subtle changes in regional populations due to relatively small numbers of animals moving between regions. Their main conclusion was that, in Shark Bay at least, dugong populations had remained relatively stable since 1989.

2.2 2018 population estimates – current status An aerial survey of dugong populations in the Shark Bay-Ningaloo-Exmouth Gulf region was undertaken in June 2018 to assess how dugong populations may have responded to the extensive loss of seagrass due to the marine heatwave event in 2010/11, and preliminary results of that survey are reported by Bayliss et al. (2018). They estimated 18,555 + 3,396 (SE 18.3%) dugongs in Shark Bay using the most updated visibility bias correction factors developed by Hagihara et al. (2014, 2018), and 4,831 + 1,965 (SE 40.7%) in the Exmouth Gulf-Ningaloo region, a combined total of 23,386 + 3,124 (SE 16.8%). Preliminary analysis of population trends over the whole historical time series indicated that no major decline in either region before and after the seagrass dieback event could be detected. Nevertheless, Bayliss et al. (2018) recommended that a more a comprehensive year x block x transect design change analysis be undertaken between 2007 and 2018, and that this broad- scale analysis be complimented with fine-scale spatial modelling of the relationship between dugongs and their seagrass habitat. Both recommendations are major objectives of this follow-up report.

3 Methods

3.1 Design for dugong population change analysis pre- and post-marine heat wave Standardised aerial surveys of dugong populations in the Shark Bay and Ningaloo Reef-Exmouth Gulf regions of north-west Western Australian have been undertaken at approximately 5-y intervals between 1989 and 2018 (Table 1), providing a unique time series to monitor and assess population- level changes in relative abundance and broad-scale distribution. The dugong surveys encompassed the period of extensive seagrass loss in Shark Bay due to the marine heat wave of 2010-11, and subsequent seagrass mapping work (Table 1) provides contemporaneous data to examine detailed spatial relationships between the two at both fine and broad-scale levels of resolution. Historical dugong sighting data were collated from ancient databases and/or reconstructed where possible from GIS files and verified by matching reports or journal publications. The 2002 seagrass map provides a pre-heatwave baseline approximately 8 years prior to the event, and the 2010 map immediately prior to the event. The 2014 and 2016 seagrass maps provide detailed spatial-temporal assessments of recovery approximately 3 and 5 years after the event respectively (Strydom et al. 2019 in prep.). The 2002 seagrass and dugong data are contemporary while, in contrast, the 2007 dugong and 2010 seagrass data are separated by a 3-year interval and the 2016 seagrass and 2018 dugong data by 2 years. Given that only blocks 3-5 were surveyed in 2012, the population-level change analysis for the whole of Shark Bay pre- and post-heatwave event was necessarily constrained to the contrast between the 2007 and 2018 dugong surveys that are 11 years apart. Additionally, simultaneous surveys were also undertaken in both years in the relatively nearby Ningaloo-Exmouth Gulf region allowing some assessment of potential large-scale movements between the two. Most dugong surveys have been winter/winter-spring surveys, the only exception being the summer survey of 2002.

Table 1. Summary of available historical and contemporary data sets for population (dugongs & turtles) and spatial seagrass habitat analyses.

Interval Dugong surveys Dugongs Interval Seagrass map Survey region (y) Year-month Turtles (y) Year-months Shark Bay 0 1989 – July Both 5 1994 – June Both 5 1999 – July Both 3 2002 - February Both 0 2002 – Sept3 5 2007 – May/June Dugongs1 8 2010 – July/Sept3 5 2012 – Aug-Sept B3-52 Both 4 2014 – July/Aug/Sept3 6 2018 – June Both 4 2016 – July/Oct3

Ningaloo-Exmouth 1989 – July Both Gulf 1994 – June Both 1999 – July Both 2007 – May/June Dugongs1 2018 – June4 Both

1 Turtles counted and reported but georeferenced sighting data could not be retrieved. 2 See Hodgson et al. (2013). Only blocks 3, 4 and 5 were surveyed and approx. 1.5 years after the marine heatwave. 3 See Strydom et al. (2019 in prep.). Restricted to the World Heritage (WH) area of Shark Bay. 4 See Bayliss et al. (2018).

Comprehensive and robust change analysis of dugong distribution and abundance in Shark Bay between survey years has previously been undertaken by Hogdson et al. (2008) before the heatwave event (1989-2007), and only in the high abundance survey blocks (3-5) by Hodgson et al. (2013) after the heatwave event. The analysis undertaken for the present study therefore only focusses on the change between 2007 and 2018 being the new contribution. Given the importance of the change analysis, two ANOVA models with different survey design assumptions were therefore used and results compared. Model 1 is similar to that used by Hodgson et al. (2008): transect densities within survey block were corrected for visibility biases using the Pollock et al. (2006) method and logged transformed [Ln (y + 0.01)]; a linear mixed-effects ANOVA model was used that treated blocks and years as fixed effects and transects within blocks as a random effect. The mixed effects model allows maximum likelihood estimation of the random components of variance providing appropriate tests for differences between years, blocks and the block-year interaction (Hodgson et al. 2008). Model 2 is similar but assumes that transects are not a factor but randomly placed replicates within a standard balanced 2-ANOVA design, albeit potentially entailing some level of sample bias.

3.2 Dugong distribution and relative abundance over the time series (1989-2018) The sightings of dugongs by all tandem observers (port & starboard front, port & starboard rear positions) on and off transects were mapped to provide a relative index of distribution and abundance in the Shark Bay and Ningaloo-Exmouth Gulf survey regions between 1989 and 2018.

3.3 Turtle distribution and relative abundance in Shark Bay between 2002 & 2018. Turtle counts were similarly combined to provide a relative index of distribution and abundance in the Shark Bay and Ningaloo-Exmouth Gulf survey regions between 2002 and 2018 only, given the lack of raw sighting data for 2007.

3.4 Seagrass extent (2002-2016) and species distribution and relative abundance (1981-2002) 3.4.1 Extent Details of the methodology to map the extent of seagrass using satellite remote sensing images are found in the companion paper by Strydom et al. (2019 in prep.). The mapping extent is restricted to the World Heritage area (WHA) of the inside of Shark Bay and excludes a small area to the north of survey block 7 and coastal offshore areas. Seagrass extent of Shark Bay was estimated by classifying multispectral medium resolution satellite imagery from the Landsat collection and Sentinel-2 with pixel resolutions of 30m and 10m respectively (United States Geological Survey, https://www.usgs.gov/land-resources/nli/landsat; European Space Agency https://earth.esa.int/web/sentinel/missions/sentinel-2). Landsat satellites were used for the years 2002, 2010 and 2014 while Sentinel 2 imagery was used for 2016. Each year image dates were acquired between June and October to ensure the clearest images free from turbidity, glint and cloud were selected to cover the area as results some years required multiple images to achieve full clear image coverage. Images in each year were classified into three different habitat classes (dense perennial seagrass at > 40% cover; sparse perennial seagrass at < 40% cover; ‘other’ habitat) based on pixel colour and ground-truthing data from research and long-term monitoring programs. ‘Other’ habitat comprised sand, ephemeral seagrass, coral, macroalgae, pavement, shell and microbial mat. The extent of ephemeral seagrass species, however, could not be mapped with confidence due to their sparse nature and frequently changing distribution. Hence, the seagrass maps used in the

present study realistically only represents perennial seagrass distributions such as Amphibolis and Posidonia. The extent of seagrass variable used in predictive dugong habitat modelling therefore only comprised the dense seagrass (> 40% cover) and sparse seagrass (< 40% cover) classes. These classes were further modified to provide three additional classes after adjustment for mid-point percentage cover ranges (dense class 40-100% range, mid-point 70%; sparse class 1-40% range, mid-point 20%). Hence, the total percentage area of seagrass cover of any spatial analysis unit (1-km cells, see below) could be derived possibly providing a more reliable indicator of total seagrass abundance across both map classes than simply combining their individual areas. 3.4.2 Species composition Dugongs may prefer some seagrass species over others depending on life-history stage, palatability, accessibility and the possible inhibitory effects of grazing dense mono-specific stands (e.g. preferring Halophila sp., Halodule sp. & Cymodocea sp. over dense stands of Zoster sp.; Wake 1975 & Heinson et al. 1977). Hence, spatially-explicit species composition data may be useful adjuncts to seagrass extent data when predicting dugong (or turtle) distribution and abundance patterns, particularly at finer local scales. Historical systematic field survey data of seagrass in Shark Bay provides species composition data for the 1981 to 2002 period (specifically the years 1981, 1995, 1996, 1997 & 2002). Data were extracted from the WA DBCA Marine Science Program Habitat database, and seagrass genera and/or species recorded were: Posidonia sp. (Pos); Amphibolis sp. (Amp); Halophila ovalis (Halop_ov); Halophila spinulosa (Haloph_spi); Halophila sp. species unknown (Haloph_sp); Halodule uninervis (Halodule u); Syringodium sp. (Syringod); Zostera sp. (Zostera); and Cymodocea sp. (Cymodocea). The dominant seagrass species at each site is recorded in the database even though other seagrass species are present. A seagrass species richness metric is provided also for each site that summarises the total number of genera/species recorded in field notes (a maximum of 4 species across sites). Although ephemeral seagrass species were not mapped, they most likely occurred with and within perennial Amphibolis and Posidonia meadows as the habitat dataset illustrates (K. Murray pers. comm.). The point sample data collected over 21 years cover most the extent of Shark Bay (see Fig. 6b) and was therefore used to map genera/species distribution via an index of relative abundance based on the frequency of occurrence of dominant presence-only data. Relative abundance “hotspots” of each seagrass category were mapped at 520m grid resolution by Kernel density extrapolation and smoothing in a GIS (ESRI 2011) and, similarly, for all seagrass genera/species combined via the species richness index. Whilst the field surveys span two decades, 91% of the data (806/885) was collected over a 7-year period between 1996 and 2002. Accurate bathymetry data were collected also at each sample point. A 1-ANOVA was used to compare mean bathymetry (m) where seagrass species were present as the dominant species or not (i.e. absent or sub-dominant). The seagrass species data layer was intersected in a GIS with the 2002 seagrass extent data layer and outputs used to undertake a 2-ANOVA to compare the composition of dominant perennial seagrass species within and between all three seagrass map classes. Note that given the likely low accuracy of GPS data collected in the 1980s, and the fact that some sites may have been located on the boundary between map classes, results should be treated as preliminary in nature until the location of each sample site can be verified with 2002 aerial photography.

3.5 Fine-scale spatial Geographically Weighted Regression (GWR) models of dugong/turtle- seagrass habitat relationships Examination of fine-scale spatial relationships between dugong and seagrass abundance involved the following 3-step process in a GIS (& similarly for turtles).

i. Relative abundance “hotspots” of dugongs were mapped by Kernel smoothing (ESRI 2011) using all sighting data in Shark Bay for all survey years. Extrapolated Kernel densities were derived at 520m resolution and re-scaled to estimates of observed numbers in Shark Bay after adjusting for sampling intensity. Jenks Natural Breaks (Jenks 1967) were used to define 10 abundance intervals with red colours having the highest relative abundances and blue colours the lowest, with a colour-abundance range in between (orange – yellow-grey blue). See Bayliss et al. (2018) for details. ii. A 1-km spatial grid (GDA94 Australian Albers projection (EPSG:3577)) was developed for Shark Bay (n=14,339 cells) to overlay dugong, turtle, bathymetry and seagrass data layers. Grid cell data were obtained by intersecting all spatial layers separately and joining each into a combined layer for modelling relationships between variables. Mean values of higher resolution Kernel cell densities (~520m) were derived for each 1-km cell and comprises a relative observed density index (nos.km-2). Similarly a mean bathymetry (m) estimate was obtained for each 1-km cell by intersecting the bathymetry layer with the Albers spatial grid. Each cell contained the areas (km2 < 1.0) of each map class, and estimates of percentage cover of both perennial seagrass map classes as separate but related variables (e.g. a 50% area cover of dense seagrass is multiplied by 0.7 to adjust for its mid-point cover range). An additional seagrass abundance variable was created independent of the seagrass extent maps via the mean probability of occurrence of seagrass species (per 1-km cell) derived from their Kernel densities. iii. GWR models (Brunsdon et al. 1991; Frotheringham & Brunsdon 1999; Páez et al. 2011; Fotheringham & Oshua 2016; ESR 2011 - Mitchell 1999, 2005 & 2012; http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/geographically- weighted-regression.htm) were then developed using the 1-km amalgamated data grid to explore “local” spatial relationships between the relative abundance of dugongs and turtles for the 2002-2002, 2007-2010 and 2016-2018 dugong-seagrass data pairs. Relationships between dugong (& turtle) abundance and bathymetry were examined separately to avoid problems with multicollinearity, given that it is highly correlated with seagrass. iv. Diagnostic tools associated with Ordinary Least Squares (OLS) “global” regression models in ArcGIS (v10.6.1; ESRI 2011) were used to examine potential model design reliability such as possible variable omission and multicollinearity when more than one predictive variable is used. Multiple partial regression models were developed outside of a GIS environment also to further investigate potential issues associated with collinearity and nonlinearity of explanatory variables, well-known constraints in GRW models (see Wheeler & Tiefsdorf 2005). Note that “global” refers to a single overall statistical model for Shark Bay dugongs and should not be confused with global dugong populations.

3.6 Broad-scale spatial modelling of dugong– seagrass habitat relationship Bayliss and Freeland (1989) found a strong positive ranked relationship between dugong population estimates by survey block in the NT Gulf of Carpentaria and the extent of seagrass (km2) mapped by boat (I. Poiner pers. comm.) Bayliss et al. (2015) used linear regression analysis and found a similar

but stronger positive relationship between dugong and turtle population estimates by survey block and the extent of seagrass (km2) mapped by Landsat satellite images in the North Kimberley (dugongs: R2 = 94%, n=7, P<0.001; turtles R2=84%, n=7, P=0.017). These past results suggest that the relationship between dugong abundance and seagrass extent may manifest differently at global scales than local scales ascertained by GWR regression models and is therefore examined using the current times series in blocks 3, 4 and 5 given that it will include a matching dugong-seagrass data point immediately after the extensive seagrass loss. A linear regression analysis was undertaken on the combined dugong population estimate for the three blocks (Y) with the extent of total, dense and sparse seagrass classes (km2) in those blocks (X), using the paired dugong-seagrass years 2002- 2002, 2007-2010, 2012-2014 and 2018-2016 (n=4). Bayliss et al. (2018) found that there appeared to be no significant decrease in dugong abundance in Shark Bay in the three survey blocks immediately after the 2010/11 seagrass dieback in 2012 (via Hodgson et al. 2013) and across all survey blocks in 2018 (via Bayliss et al. 2018). However, they found that the percentage of calves significantly decreased to a low level as a possible response to the seagrass dieback event. Hence, although low on dfs (n=4), the percentage of dugong calves in those years were plotted against the extent of seagrass to ascertain whether or not there is a statistically significant trend.

4 Results

4.1 Population change analysis between 2007 and 2018 – pre- & post-marine heat wave 4.1.1 Shark Bay ANOVA model 1 (Table 2a) shows a significant Year effect (Fig. 1a) but also a significant Block x Year interaction (Fig. 1b) that is mostly explained by a 3.5 increase in dugong density in Block 1 between 2 2007 and 2018 (0.09 cf. 0.31 nos/km ; F1,23 = 13.7, P<0.01). In contrast, ANOVA model 2 (Table 2b) shows significant Block differences but no difference between Years or a significant Year by Block interaction. Both ANOVA models indicate that, overall, dugong abundance in Shark Bay had not significantly and substantially decreased between 2007 and 2018, a period encompassing the extensive seagrass dieback event.

Table 2a&b. Summary of a 2-ANOVA of differences in dugong density (Ln D + 0.1) in Shark Bay between survey block (n=8; 0 to 7) and year (n=2; 2007 vs. 2018 11 years elapse) for (a) a standard balanced ANOVA model treating transects as replicates, and (b) a GLM treating transects as a random effects factor (see Hodgson et al. 2013). ns = not significant at P<0.05. (a) Factor Effect df SS MS F P Block Fixed 1 111.29 15.90 17.85 < 0.001 Year Fixed 7 3.06 3.06 3.44 = 0.065/ns Block x Year Fixed 7 14.39 2.06 2.31 = 0.028 Error Random 170 151.41 0.90 Total 185 281.13

(b) Factor Effect df SS MS F P Block Fixed 7 125.0 17.86 38.3 < 0.001 Transect Random 20 45.2 2.26 Year Fixed 1 0.01 0.003 0.01 NS

Block x Transect Random 63 48.3 0.77 Block x Year Fixed 7 16.8 2.40 0.19 NS Transect x Year Random 20 29.2 1.46 Block x Trans x Year Random 58 27.0 0.47

(a) Shark Bay - Year (b) Shark Bay – Block x Year

Figure 1a-d. 2-ANOVA comparing mean dugong transect density (numbers.km-2) by survey block between 2007 and 2018 for (a) Shark Bay and (c) Ningaloo-Exmouth Gulf, respectively (see Tables 1a & 2a, respectively). Trends in density by Block and Year are illustrated in (b) and (d) for each survey area, respectively. Transects are considered random replicates, densities are transformed to Ln (X+0.01), and means are least squares (LS) means. Transect densities were corrected for perception and availability visibility biases using the Pollock et al. (2006) method. The ANOVA F-ratio and degrees of freedom are indicated for each contrast. Figure 1a in

Appendix 1 (Report 1: Bayliss et al. 2018) shows the locations of survey blocks and transects in Shark Bay, and that for Ningaloo-Exmouth Gulf region in Figure 1b).

4.1.2 Ningaloo-Exmouth Gulf ANOVA model 1 (Table 3a) shows significant Block and Year effects (Fig. 1c&d, respectively) and no significant Block by Year interaction. Densities in Exmouth Gulf are substantially higher than densities along Ningaloo Reef in both years. In contrast, ANOVA model 2 (Table 3b) shows a significant Block difference but no difference between years. Overall both sets of results suggest that dugong abundance may have possibly increased or not changed at all between 2007 and 2018, a period encompassing the extensive seagrass dieback event in Shark Bay. Taken together the survey results cannot be used to confidently draw conclusions about potential large-scale movements between Shark Bay and the Ningaloo-Exmouth Gulf regions as a result of changing food availability due to the seagrass dieback event in Shark Bay in 2010/11, in either direction.

Table 3a&b. Summary of a 2-ANOVA of differences in dugong density (Ln D + 0.1) in Ningaloo-Exmouth Gulf region between survey block (n=2) and year (n=2; 2007 vs. 2018 11 years elapse) for (a) a standard balanced ANOVA model treating transects as replicates, and (b) a GLM treating transects as a random effects factor (see Hodgson et al. 2013). ns=not significant at P<0.05.

(a)

Factor Effect df SS MS F P

Block Fixed 1 20.99 20.99 43.90 < 0.001

Year Fixed 1 2.63 2.63 5.51 = 0.021

Block x Year Fixed 1 1.19 1.19 2.49 ns

Error Random 120 57.36 0.48

Total 123 81.23

(b)

Factor Effect df SS MS F P

Block Fixed 1 14.6 14.63 31.80 < 0.001

Transect Random 42 19.1 0.45

Year Fixed 1 2.4 2.44 5.30 ns

Block x Transect Random 18 16.6 0.92

Block x Year Fixed 1 0.9 0.87 0.53 ns

Transect x Year Random 42 13.2 0.31

Block x Trans x Year Random 17 7.8 0.46

4.1.3 How many dugongs are there in Shark Bay? To help characterise the conservation status of dugongs in World Heritage Shark Bay in 2018, 7 years after the seagrass dieback event, Bayliss et al. (2018) derived estimates of population size using the following three methods to correct for both perception and availability biases: (1) the most updated and more robust Hagihara et al. (2014, 2018) method based on bathymetry (See Appendix 1 Bayliss

et al. 2018 section 2.3.1 & Bayliss et al. 2015 Appendix 4 page 31 for details); (2) the Pollock et al. (2006) method based on turbidity and sea state; and (3) the earlier Marsh and Sinclair (1989) method based on the proportions seen at the surface and under the water. The Hagihara method estimated 18,555 + 3,396 (SE 18.3%) dugongs for Shark Bay, which is similar to the 18,773 + 4,094 (SE 21.8%) estimated by the Marsh and Sinclair method. In contrast, the Pollock method estimated ~ 39% less dugongs at 11,778 + 2,151 (SE 18.3%). This contrast generally supports recent re-analysis of historical dugong population estimates for Torres Strait using the Hagihara method instead of the Pollock method, although the degree of underestimation is not as great (~15%). A more robust comparison of these estimates was undertaken on a Block basis (n=10, 8 Shark Bay blocks + 2 Ningaloo-Exmouth Gulf blocks) and included the simple relative abundance index used to derive the Kernel density maps (i.e. all observer transect counts combined). A GLM mixture model was used to compare the regression relationship between block population estimates derived by the Hagihara method (HAGI) to those derived by the Pollock (POLL), Marsh and Sinclair (MS) and Index (INDEX) methods.

Factor df SS MS F P POLL 1 316506 316505.9 81.68 < 0.001 MS 1 458 458.1 0.12 0.748 INDEX 1 30447 30446.7 7.86 = 0.049 POLL*MS 1 210458 210457.7 54.32 = 0.002 POLL*INDEX 1 130888 130888.0 33.78 = 0.004 MS*INDEX 1 190553 190553.0 49.18 = 0.002 Error 4 15499 3874.7 Total 10 126118880

Results (Table 4) show no difference between the HAGI and MS population estimates, significant differences between the HAGI and POLL estimates, an obvious difference between the simple INDEX and all other estimates, and obvious higher order interactions explained by differences in slopes and intercepts of each regression model. A comparison of the three linear regression equations (HAGL vs. POLL; HAGI vs. MS; HAGI vs. INDEX) is summarized in Table 5. All regressions are through the origin and includes two blocks with zero population estimates. The closeness of the HAGI and earlier MS estimates suggest that the proportion of dugongs observed at the surface and under the water is reasonable indicator of bathymetry used in the HAGI model, in the Shark Bay area during winter at least and which may not apply to other more turbid survey conditions elsewhere in northern Australian coastal waters.

Table 5. Linear regression equations through the origin between survey block (n=10) population estimates in 2018 derived by the Hagihara et al. (2014, 2018; HAGI) method with those derived by the Pollock et al. (2006; POLL) and Marsh and Sinclair (1989; MS) methods, and a relative index of abundance (INDEX). The slope of the regression equations provide correction factors (+ SE) to adjust alternative estimates of dugong abundance to the most recent and advanced method to adjust for visibility bias (both perception & availability biases).

X b* SE(b*) b SE(b) t(9) P adj % SE % variable regress R2 regress Diff POLL 0.998 0.023 1.62 0.038 43.18 < 0.001 99.5 259.5 -38 MS 0.996 0.030 0.97 0.030 32.85 < 0.001 99.1 340.6 +3 INDEX 0.993 0.038 28.47 1.095 26.00 < 0.001 98.5 429.3 -96

4.2 Dugong distribution and relative abundance over the time series (1989-2018)

4.2.1 Shark Bay The distribution and relative abundance of dugong sightings during standardised aerial survey in Shark Bay in (a) 1989 (July), (b) 1994 (June), (c) 1999 (July), (d) 2002 (February – summer survey), (e) 2007 (May-June) and (f) 2018 (June) are illustrated in Figure A1a-f (Supplementary Material), respectively. The relative abundance “hotspots” of dugongs mapped by Kernel smoothing in Shark Bay for the survey years of 1989 (July), 1994 (June), 1999 (July), 2002 (February), 2007 (May-June) and 2018 (June) are illustrated in Figure 2a-f, respectively. The distribution and abundance pattern for 2002 represents the only summer survey and contrasts to the all other surveys which are “winter-spring”.

4.2.2 Ningaloo-Exmouth Gulf The distribution and relative abundance of dugong sightings during standardised aerial survey in the Ningaloo Reef and Exmouth Gulf regions in 1989 (July), 1994 (June), 1999 (July), 2002 (February – summer survey), 2007 (May-June) and 2018 (June) are illustrated in Figure A3a-f (Supplementary Material), respectively. The relative abundance “hotspots” of dugongs mapped by Kernel smoothing for the survey years of 1989 (July), 1994 (June), 1999 (July), 2007 (May-June) and 2018 (June) are illustrated in Figure 3a-e, respectively (i.e. missing the 2002 survey).

4.3 Turtle distribution and relative abundance in Shark Bay between 2002 & 2018. The distribution and relative abundance of turtle sightings (all species combined) during standardised aerial survey in Shark Bay in 2002 (February) and 2018 (June) are illustrated in Figure A3a & b (Supplementary Material), respectively. The relative abundance “hotspots” of turtles mapped by Kernel smoothing in Shark Bay for the 2002 (February) and 2018 (June) survey years are illustrated in Figure 4a&b, respectively.

(a) (b) (c)

Figure 2a-f. Relative abundance “hotspots” of dugongs mapped by Kernel smoothing using all sighting data in Shark Bay for the survey years of (a) 1989 (July), (b) 1994 (June), (c) 1999 (July), (d) 2002 (February), (e) 2007 ( May-June) and (f) 2018 (June), respectively. All observer counts were used to derive Kernel densities at 520m resolution and re-scaled to estimates of observed numbers in Shark Bay after adjusting for sampling intensity. Jenks Natural Breaks (Jenks 1967) was used to define 10 abundance intervals with red colours having the highest relative abundances and blue colours the lowest, with a colour-abundance range in between (orange – yellow-grey blue). The lowest relative abundance interval is coloured white to highlight abundance hotspots.

(d) (e) (f)

Figure 2a-f. Continue.

(a) (b) (c)

Figure 3a-e. Relative abundance “hotspots” of dugongs mapped by Kernel smoothing using all sighting data in the Ningaloo Reef-Exmouth Gulf region for the survey years of (a) 1989 (July), (b) 1994 (June), (c) 1999 (July), (), (d) 2007 ( May-June) and (e) 2018 (June), respectively. Note the Ningaloo-Exmouth Gulf region was not surveyed in 2002. All observer counts were used to derive Kernel densities at 520m resolution and re-scaled to estimates of observed numbers in the region after adjusting for sampling intensity. Jenks Natural Breaks (Jenks 1967) was used to define 10 abundance intervals with red colours having the highest relative abundances and blue colours the lowest, with a colour-abundance range in between (orange – yellow-grey blue). The lowest relative abundance interval is coloured white to highlight abundance hotspots.

(d) (e)

Figure 3a-e continue.

(a) (b)

Figure 4a&b. Relative abundance “hotspots” of turtles mapped by Kernel smoothing using all sighting data in Shark Bay for the (a) 2002 (February) and (b) 2018 (June) survey years. All observer counts were used to derive Kernel densities at 520m resolution and re-scaled to estimates of observed numbers in Shark Bay after adjusting for sampling intensity. Jenks Natural Breaks (Jenks 1967) was used to define 10 abundance intervals with red colours having the highest relative abundances and blue colours the lowest, with a colour-abundance range in between (orange – yellow-grey blue). The lowest relative abundance interval is coloured white to highlight abundance hotspots.

4.4 Seagrass

4.4.1 Seagrass extent (2002-2016) The extent of dense (> 40% cover) and sparse (< 40% cover) seagrass mapped in Shark Bay using medium resolution satellite images for 2002, 2010, 2014 and 2016 are illustrated in Figure 5a-d, respectively, with an overall average accuracy of 74% when validated against field data (Strydom et al. 2019 in prep.). The estimated percentage loss in seagrass extent between 2010 and 2014 due to the 2010/11 marine heatwave varied between regions within Shark Bay, with the greatest loss found for the Western Gulf Region (~-36%) and is reported in detail in the companion paper by Strydom et al. (2019 in prep). Similarly, the recovery rate of seagrass extent after the heatwave event also varied by region within Shark Bay with the highest rate reported for the Western Gulf Region also (+13%) (Strydom et al. 2019 in prep.). Dugongs and turtles may respond to both fine-scale and larger-scale dynamic spatial variations in seagrass extent by shifting their own distribution and abundances patterns to accommodate changing conditions of food availability.

4.4.2 Seagrass species composition (1981-2002) and bathymetry The relative abundance “hotspots” (probability of occurrence) of seagrass genera and/or species mapped at 520m grid resolution by Kernel density extrapolation and smoothing is illustrated in Figure A4a-g of the Supplementary Material for: Posidonia sp.; Amphibolis sp.; Halophila ovalis, H. spinulosa & H. sp. combined; Halodule uninervis; Syringodium sp., Zostera sp. and Cymodocea sp. Respectively.

The distribution of different seagrass species in Shark Bay may be influenced by bathymetry amongst other key environmental factors, which in turn may influence the spatial use of seagrass habitats by dugongs for food. The spatial variation in bathymetry (at 5m contour intervals) across Shark Bay is illustrated in Figure 6a, and the systematic distribution of field seagrass sample sites between 1981 and 2002 is illustrated in Figure 6b showing good coverage. Table 6 summarises the 1-ANOVA tests for differences in mean bathymetry (m) between dominant presence-only and sub- dominant/absence sites by seagrass species, demonstrating strong water depth preferences.

Table 6. Summary of a 1-ANOVA of differences in mean bathymetry (m) of seagrass genera/species dominant present-only or sub-dominant/absent (n=2 levels) at a sample site (n=878) in Shark Bay. Species composition and bathymetry data are from systematic field surveys (1981 to 2002, see Fig. 6b; Strydom et al. 2019 in prep. & DBCA database). df = 1/885, ns= not significant at P<0.05.

Relative preference Factor F P (shallower/deeper than dominant presence-only bathymetry) Posidonia sp. 16.57 < 0.001 Shallower Amphibolis sp. 12.21 < 0.001 Shallower Halophila ovalis 8.23 < 0.001 Marginally deeper H. Spinulosa 3,41 = 0.065/ns Deeper H. sp. 4.11 =0.043 Shallower Halodule uninervis 4.30 = 0.038 Shallower Syringodium sp. 0.00 0.985/ns No difference Zostera sp. 11.66 < 0.001 Deeper Cymodocea sp. 6.02 = 0.143 Shallower

Species richness 5.81 < 0.001 Shallower

(a) (b)

Figure 5a&b. The extent of dense (> 40% cover) and sparse (< 40% cover) seagrass mapped in Shark Bay using Landsat satellite images at 30m resolution for (a) 2002 and (b) 2016 (Strydom et al. 2019 in prep. & DBCA database).

(a) (b)

Figure 6a&b. (a) Bathymetry map of Shark Bay at ~5m contour intervals (source DBCA July 2018) and (b) location of seagrass systematic field sample sites (n=885 sites from 1981 to 2002; Strydom et al. 2019 in prep. & DBCA marine habitat database).

Different seagrass extent map classes may exhibit differences in bathymetry and, hence, seagrass species composition that in turn may influence dugong (or turtle) use of the different classes. The percentage (%) occurrence of different seagrass genera and/or species by the 2002 seagrass map classes (1 = Other; 2 = Dense seagrass; 3 = Sparse seagrass) is illustrated in Figure 7a. The use of percentages adjusts for differences in absolute area between classes. Results show a strong preference for different classes by some species. Figure 7b illustrates the mean bathymetry (m) of seagrass species occurrence by 2002 seagrass map class (Table 7), and Figure 7c the mean occurrence (0-1) of dominant seagrass species that differed significantly by map class in 2002 (Table 8; see Strydom et al. 2019 in prep.).

Table 7. Summary of the mean bathymetry (m) where seagrass species were found to occur in each of the 2002 map classes (1=Other; 2=Dense seagrass; 3=Sparse seagrass).

Mean Bathy N Class SE -95% +95% N% (m) sample sites Other 10.73 0.20 10.34 11.11 594 68 SG Dense 7.81 0.34 7.14 8.49 192 22 SG Sparse 7.58 0.50 6.60 8.55 92 10 Total 878 100

Table 8. Summary of a 1-ANOVA of differences in mean occurrence (0-1) of seagrass species across seagrass map classes in 2002 (n=3 levels; 1=Other; 2=Dense seagrass; 3=Sparse seagrass) in Shark Bay. Species composition and bathymetry data are from systematic field surveys (1981 to 2002, see Fig. 6b; Strydom et al. 2019 in prep.). df = 2/885 (n=878 sample sites). The Newman–Keuls test is used for post-hoc contrasts between map classes. A 1-ANOVA is undertaken also between mean bathymetry (m) where they occur in each map class.

Factor F P 1: Other 2: Dense SG 3: Sparse SG Posidonia sp. 13.89 < 0.001 Different to 2 & 3 Different to 1 Different to 1 Amphibolis sp. 86.90 < 0.001 Different to 2 Different to 1 & 3 Different to 2 Halophila ovalis 1.70 = 0.183/ns H. Spinulosa 1.06 = 0.346/ns H. sp. 0.51 = 0.602/ns Halodule uninervis 1.54 = 0.215/ns Syringodium sp. 0.24 = 0.785/ns Zostera sp. 0.45 = 0.639/ns Cymodocea sp. 2.18 = 0.114/ns Species richness 38.51 < 0.001 Different to 2 & 3 Different to 1 & 3 Different to 1 & 2 Map classes 38.0 < 0.001 Different to 2 & 3 Different to 1 Different to 1

(a) % dominant seagrass genera/species by 2002 map class

(b) Mean bathy (m) vs. 2002 map class (c) Dominant seagrass genera/species vs. 2002 map class

Figure 7a-c. (a) Percentage (%) occurrence of different seagrass genera and/or species by the 2002 seagrass map class (1 = Other; 2 = Dense seagrass; 3 = Sparse seagrass). (b) Mean bathymetry (m) of seagrass species occurrence by 2002 seagrass map class and (c) Mean occurrence (0-1) of dominant seagrass species that differed significantly by map class in 2002. Vertical bars are standard errors. Species composition and bathymetry data are from systematic field sample sites (1981 to 2002; Strydom et al. 2019 in prep. & DBCA database).

4.5 Spatial modelling of dugong and turtle – seagrass habitat relationships Figure 8a illustrates the 1-km spatial analysis grid across Shark Bay (excluding non-World Heritage areas), and Figure 8b illustrates a close-up of the grid highlighting the intersection with the 2002 extent of dense (dark green) and sparse (light green) seagrass map layer.

4.5.1 The extent of seagrass attributes in the 1km Albers grid 2002-2016 The distribution and abundance of seagrass in Shark Bay in 2002 as indexed by the area (km2) per 1km grid cell for total seagrass area/cell, area of dense (>40% cover) and sparse (0-40%) seagrass/cell, is illustrated in Figure A5a-c in the Supplementary Material, respectively. Similar maps illustrate seagrass distribution and abundance for 2016 in Figures A6a-c in the Supplementary Material, respectively. Seagrass maps for 2010 and 2014 (pre- & post-seagrass dieback) are detailed in Strydom et al. 2019 in prep.). 4.5.2 Local GWR spatial models for dugongs, turtles and seagrass (2002-2018) The regression Y and X variable codes used in the GWR models are summarized in Table 9, and includes a brief list of acronyms used in GWR analysis.

Table 9a&b. Summary of Y-response and X-explanatory variable names used in the Geographical Weighted Regression (GWR) models to examine spatial relationships between dugong and turtle distribution and abundance and seagrass habitats in Shark Bay in 2002, 2007 and 2018, across a 1km grid (n=14,339 cells). Included are important GWR model parameter acronyms used to evaluation the models predictive reliability and performance.

(a) Variable type Code Variable description Intercept a Linear regression intercept/constant. Y-response D Relative dugong density /1km grid cell. Mean of Kernel densities. X-explanatory T Relative turtle density /1km grid cell. Mean of Kernel densities X-explanatory or Y AsgTot Total area (km2) of seagrass /1km grid cell. X-explanatory or Y Asgd Area (km2) of dense seagrass / 1km grid cell. X-explanatory or Y Asgs Area (km2) of sparse seagrass / 1km cell) X-explanatory or Y PCsgd Adjusted % cover of dense seagrass / 1km grid cell (% area x 0.70). X-explanatory or Y PCsgd Adjusted % cover of sparse seagrass / 1km grid cell (% area x 0.20). X-explanatory or Y PCsgTot Adjusted % cover of total seagrass / 1km grid cell (PCsgd + PCsgs) X-explanatory Bathy Mean bathymetry (m) / 1km grid cell (mid-point range of depth interval). Mean probability of seagrass species occurrences / 1km grid cell based on Kernel X-explanatory or Y ProbSG Probability of seagrass occurrence derived from field data (1996-2002; 7-y interval) and used as a surrogate relative density index. (b) GWR model term Model parameter description OLS Ordinary Least Squares global regression model. Corrected Akaike Information Criteria used to measure model performance and compare AICc different regression models including OLS global models. Sigma Estimated model standard deviation, used to estimate the AICc. Optimal distance (m) or number of neighbours (1 km grid = 1 neighbour) used to perform Bandwidth local regressions. Condition Statistic that evaluates local multicollinearity. Local GWR with CNs > 30 are unreliable. number Measure of goodness of fit of all data to model. Adjusted for number variables in the adj R2 model via df. Values range between 0.0 and 1.0 and indicate how well the local regression model fits Local R2 local observed y-values.

(a) (b)

Figure 8a&b. (a) 1km spatial analysis grid across Shark Bay excluding non-World Heritage (WH) areas (GDA94 Australian Albers projection). (b) Close up showing the intersection between the 1km grid and the 2002 extent of dense (dark green) and sparse (light green) seagrass.

A summary of the results of the GWR models used to examine the spatial relationships between the distribution and relative abundance of dugongs and turtles in Shark Bay in 2002, 2007 and 2018 and seagrass habitats mapped in 2002, 2010 and 2016, is presented in Table 10a-d). All GWR models in all years, apart from 2007, returned high overall adjusted R2 values predicting local dugong and turtle distribution and abundance patterns based on a single seagrass explanatory variable, and all compared favorably to Kernel density maps derived from observed data (2002: Fig. 9a&b for dugongs & 9c&d for turtles; 2018: Fig. 10a&b for dugongs & 10c&d for turtle). For dugongs the R2 range was 88% to 97% in 2002 (Table 10a) and 73% to 97% in 2018 (Table 10c). For turtles the range was 73% to 90% in 2002 (Table 10a) and 73% to 85% in 2018 (Table 10c) (there are no turtle data for 2007). The fact that such high explained variances were achieve with a single explanatory seagrass variable indicates that the usual problem of unstable GWR model predictions due to multicollinearity was likely avoided. The reason for the failure of 2007 dugong GWR models to resolve due to poor model design is unknown, but may be related to the 3-year time interval between dugong and seagrass variables. In contrast, however, the dugong-seagrass GWR model was highly predictive using the probability of seagrass occurrence metric derived from field species composition data, and which in turn was highly spatially correlated to bathymetry (Table 10d, R2 = 85% & 90%, respectively). The observed and predicted maps compare favorably (2007: Fig. 11a&b for dugongs; Fig. 11b&c for species abundance index vs. bathymetry). The probability of seagrass occurrence metric is used as a long- term mean index of seagrass abundance between 1981 and 2002 (n=21y). It was not used in post- 2002 models given that no field data since then has been added to the data base. The bandwidth or optimal distance used to perform local regressions was in the range 5-10 km for both turtles and dugongs using seagrass map data, and between 10 and 15 km for models using bathymetry as a predictive variable. The range of performance and diagnostic metrics available suggests robust local models: only a single explanatory variable was necessary in terms of prediction given that most R2 values obtained were very high; models that incorporated say seagrass and bathymetry in combination performed less as R2 substantially decreased; sigma (regression model standard deviation is low) and AICc acceptable compared to global models with poor R2 (ignore the – ve signs); and maximum condition number of all 14,339 local regressions is low and much < 30 (apart from some models that use bathymetry). In summary the overall performance of all GWR models is good, especially compared to OLS global regression models that had substantially less R2 (< 0.1% to ~5%), although with statistically significant regression coefficients likely due to substantial sample size. Examination of the spatial patterns of local regression residuals plotted against predicted data indicates that all models may have a degree of over prediction compared to randomly distributed residuals (Fig. 9Aa-c & 9Ag-I in Supplementary Material). However, this is considered a minor trade-off and likely due to the need to transform both dependent and independent data to normalise variances (see Fig. 9Ac, f &i in the Supplementary Material). More importantly, there were no strong patterns in the GWR residuals plotted against predictions indicating nonlinearity that may otherwise manifest in global models. Nevertheless, the 2007 GWR model does show complex non-random patterning of local residuals and directional bias, suggesting that another explanatory variable is missing. All these diagnostic issues of model design robustness are examined in the next section.

Table 10a-c. Summary of the Geographical Weighted Regression (GWR) models used to explore the spatial relationships between the distribution and relative abundance of dugongs and turtles in Shark Bay in (a) 2002, (b) 2007 and (c) 2018 to seagrass habitats in 2002, 2010 and 2016. See Table 9b for model parameter codes (a = regression intercept in model equation).

Year Adjusted Bandwidth Max local Section GWR Model Sigma AICc Max CN layers* % R2 (m) adjusted R2 (a) 2002 1. D = a + AsgTot 95.4 0.083 6,777 -36,146 6.3 48.0 vs. 2. D = a+ Asgd 96.5 0.071 6,078 -35,033 5 48

2002 3. D= a+ Asgs 95.3 0.083 6,777 -28,879 3.1 36.2

4. D = a+ PCsgTot 95.5 0.081 6,777 -31,172 4.9 53.1

5. D = a+ PCsgd 96.5 0.071 6,078 -35,041 4.6 47.9

6. D = a+ PCsgs 95.3 0.093 6,777 -30,594 3.1 34.8

7. D = a + RDspo 93.5 0.098 8,628 -6,582 22.00 98.2 8. D = a+ Bathy 87.7 0.134 11,155 -16,889 30.0 60.8

1. T=a + AsgTot 87.0 0.384 6,777 8,780 6.3 69.3

2. T = a+ Asgd 89.8 0.340 6,078 9,895 4.6 47.9

3. T= a+ Asgs 88.2 0.366 6,777 14,266 3.1 57.4

4. T = a+ PCsgTot 87.4 0.378 6,777 12,926 4.9 67.8

5. T = a+ PCsgd 79.4 0.412 10,576 15,337 3.1 71.6

6. T = a+ PCsgs 88.6 0.366 6,777 11,962 3.1 57.6

7. T = a + RDspo 79.2 0.485 8,628 20,020 22.00 97.5 8. T = a+ Bathy 73.3 0.549 11,155 23,567 30.0 80.9

1. AsgTot = a + Bathy 45.7 0.275 11,155 3,725 30.0 66.7

2. PCsgTot = a + Bathy 43.5 15.560 11,155 122,921 30.0 64.3 3. RDspo = a + AsgTot 44.4 0.245 8,628 3,894 22.00 44.8 All years 4. RDspo = a + Bathy 90.0 0.059 11,155 -40,392 30.0 80.5 (b) 2007 1. D = a + AsgTot Failed

vs. 2. D = a+ Asgd Failed

2010 3. D= a+ Asgs Failed

4. D = a+ PCsgTot Failed

5. D = a+ PCsgd Failed

6. D = a+ PCsgs Failed

7. D = a + RDspo 85.0 0.192 8,628 -6,582 22.00 81.7 8. D = a+ Bathy 76.3 0.241 11,155 -85 30.0 78.9

1. T=a + AsgTot No data

2. T = a+ Asgd No data

3. T= a+ Asgs No data

4. T = a+ PCsgTot No data

5. T = a+ PCsgd No data

6. T = a+ PCsgs No data

7. T = a + RDspo No data 8. T = a+ Bathy No data

1. AsgTot = a + Bathy 45.9 0.288 11,155 5,033 30.00 78.90

2016 3. D= a+ Asgs 79.8 0.481 8,666 19,790 2.5 55.2

4. D = a+ PCsgTot 80.2 0.476 8,666 20,053 3.5 56.5

5. D = a+ PCsgd 72.7 0.560 10,576 24,105 3.1 46.3

6. D = a+ PCsgs 97.0 0.129 8,666 -17,870 2.8 30.5

7. D = a+ Bathy 72.9 0.558 11,147 24,007 30.0 74.2

1. T=a + AsgTot 85.4 0.347 8,666 11,117 3.7 57.5

2. T = a+ Asgd 79.4 0.412 10,576 15,162 3.1 70.0

3. T= a+ Asgs 84.4 0.359 8,666 11,687 2.5 54.2

4. T = a+ PCsgTot 85.3 0.349 8,666 11,134 3.5 71.4

5. T = a+ PCsgd 79.4 0.412 10,576 15,337 3.1 71.6

6. T = a+ PCsgs 84.4 0.358 8,666 11,330 2.8 44.4

7. T = a+ Bathy 79.5 0.411 11,147 15,251 30.0 75.8

1. AsgTot = a + Bathy 79.5 0.411 11,147 3,327 30.0 66.7

2. PCsgTot = a + Bathy 77.8 0.429 1,268 120,014 30.0 68.8 3. RDspo = a + AsgTot Not done

Table 10. Continue. * = dugong survey year vs. seagrass map year; Failed = GWR model failed to resolve.

(a) (b)

Figure 9a-d. Comparison of (a) observed distribution and abundance of dugongs (mean Kernel density/1km cell) for Shark Bay in 2002 to (b) that predicted by Geographically Weighted Regression (GWR). The explanatory variable is the area (km2) of sparse seagrass per cell. (c & d) Similarly for the predicted distribution and abundance of turtles predicted by GWR in relation to the extent (km2) of total seagrass/cell. See Table 10a and 11a for a summary of the GWR and Ordinary Least Squares (OLS) regression statistics, respectively. The extent of seagrass was measured in the same year as the dugong survey in 2002.

(a) (b)

Figure 10a-d. Comparison of (a) observed distribution and abundance of dugongs (mean Kernel density/1km cell) for Shark Bay in 2018 to (b) that predicted by Geographically Weighted Regression (GWR). The explanatory variable is the area (km2) of sparse seagrass per cell. (c & d) Similarly for the predicted distribution and abundance of turtles predicted by GWR in relation to the extent (km2) of total seagrass/cell. See Table 10c and 11b for a summary of the GWR and Ordinary Least Squares (OLS) regression statistics, respectively. The extent of seagrass was measured in 2016.

(a) (b) (c)

(d) (e)

Figure 11a-e. Comparison of (a) observed distribution and abundance of dugongs (mean Kernel density/1km cell) for Shark Bay in 2007 to that predicted by Geographically Weighted Regression (GWR) with (b) bathymetry and the (c) the mean probability of seagrass occurrence/cell derived from (d) dominant species presence/absence field data sampled across Shark Bay between 1996 and 2002. No turtle data were available for 2007, hence (e) is the GWR regression prediction between dugongs and bathymetry (mean mid-point depth contour interval, m). See Table 10b for a summary of the GWR statistics. No GWR dugong or turtle model using 2010 seagrass data resolved. 39

4.5.3 Ordinary global multiple regression models of dugongs and seagrass (2002 vs. 2018) The OLS diagnostics tools in ArcGIS are computationally limiting in that it’s difficult to derive partial residuals of a thousands of local multiple regression model, and to also transform data or include nonlinear terms (e.g. quadratic polynomials) to increase model performance. This step was undertaken outside the GIS framework in a standard statistical package (Statistica v10; StatSoft 2012), with a focus on partial residual analysis of more than one explanatory regression variable and potential nonlinear effects. The constraint, however, is that the results only relate to a global model across the whole study area and not to 14,339 individual local regression models. Nevertheless, they may better inform interpretation of local GWR models as recommended by many authors (e.g. Brunsdon et al. 1996; Wheeler & Tiefelsdorf 2005). The OLS multiple regression model examined for 2002 and 2018 data was: 2 2 Log10 D = a + bathy + bathy + AS PCsgTot + AS PCsgTot

2 where D is the mean log10 transformed Kernel dugong density per 1-km grid cell, bathy and bathy are the mean bathymetry (m) and its quadratic polynomial, and ASPCsgTot and ASPCsgTot2 are the arcsine transformations of the adjusted percentage covers of total seagrass (both sparse & dense) per grid cell. Normal probability plots indicated that both dugong density and seagrass extent needed transformation to normalize data whereas bathymetry did not. Table 11a summarises the model results for 2002, and that for 2018 in Table 11b. All regression variables and intercepts are highly significant, with adjusted R2 values of 6.3% and 9.6% for 2002 and 2018 models respectively. Although it was possible to conveniently transform data and include nonlinear terms, the global model underperforms substantially compared to GWR models based on R2 alone.

Table 11a&b. Summary of multiple regression equations between relative observed dugong density and quadratic polynomials for bathymetry and the percentage cover of total seagrass per 1 km grid cell for (a) Shark Bay in 2002 and in (b) 2018 (seagrass data are from 2016). Dugong density is transformed to log10 (Y +1) and the percentage cover of seagrass variable transformed to arcsine (√proportions). Bathymetry data were untransformed. Nonlinear relationships were modelled by including quadratic polynomials of both explanatory 2 2 variables, such that (See Fig. 11a-d): Log10 D = a + bathy + bathy + AS PCsgTot + AS PCsgTot 2 (a) 2002 Y response = Log10 (dugongs); adjusted R = 6.3%; F(4,14006) = 236.9; P<0.001; regression SE = 0.091

Variable b* SE(b*) b SE(b) t(14,006) P Intercept 0.12 0.002 53.66 < 0.001 Bathy -0.29 0.02 -0.01 0.000 -13.03 < 0.001 Bathy2 0.08 0.02 0.0001 0.00002 3.68 < 0.001 AS(PCsgTot) 0.17 0.03 0.05 0.009 5.71 < 0.001 AS(PCsgTot)2 -0.09 0.03 -0.03 0.009 -2.99 = 0.003

2 (b) 2016-18 Y response = Log10 (dugongs); adjusted R = 9.6%; F(4,13712) = 365.6; P<0.001; regression SE = 0.162

Variable b* SE(b*) b SE(b) t(14,006) P Intercept 0.09 0.004 23.6794 < 0.001 Bathy 0.76 0.02 0.02 0.001 34.1876 < 0.001 Bathy2 -0.70 0.02 -0.001 0.00003 -31.6282 < 0.001 AS(PCsgTot) 0.08 0.03 0.04 0.015 2.8579 0.004 AS(PCsgTot)2 -0.22 0.03 -0.13 0.016 -8.1447 < 0.001 b* = standardised regression coefficient; b= regression coefficient. SE=standard error; t= students t-statistic; P=probability value.

(a) Bathy (b) Bathy2

Figure 12a-d. Multiple partial regression analysis between dugong relative density (numbers/1km grid cell) in Shark Bay in 2002 and the percentage cover of total seagrass / 1km grid cell (area dense + sparse seagrass) in

2002 and bathymetry (m). Dugong density is transformed to log10 (Y +1) and the percentage cover of seagrass variable transformed to arcsine (√proportions). Bathymetry data were untransformed. Nonlinear relationships were modelled by including quadratic polynomials of both explanatory variables, such that: D = a + Bathy + Bathy2 + PCsgTot + PCsgTot2. Table 11a summarises the multiple regression model.

Paired Y-X data are instantaneous with no optimal distance band employed, given the regression model is global (n=14,339 cells). The multiple regression results are summarized in Table 11a.

(b) Bathy (b) Bathy2

Figure 13a-d. Multiple partial regression analysis between dugong relative density (numbers/1km grid cell) in Shark Bay in 2018 and the percentage cover of total seagrass / 1km grid cell (area dense + sparse seagrass) in

2016 and bathymetry (m). Dugong density is transformed to log10 (Y +1) and the percentage cover of seagrass variable transformed to arcsine (√proportions). Bathymetry data were untransformed. Nonlinear relationships were modelled by including quadratic polynomials of both explanatory variables, such that: D = a + Bathy + Bathy2 + PCsgTot + PCsgTot2. Table 8a summarises the multiple regression model.

Paired Y-X data are instantaneous with no optimal distance band employed, given the regression model is global (n=14,339 cells). The multiple regression results are summarized in Table 11b.

Shark Bay Ningaloo-Exmouth Gulf dugong survey June 2018

Figure 12a-d plots the partial residuals of each variable in the 2002 multiple regression model (e.g. the variation in Y-dugong density on bathy whilst holding the effects of all other X-variables constant), and illustrates that both bathymetry and seagrass extent have optimal nonlinear values that are not captured adequately in a linear model. Dugong relative abundance in 2002 had a convex relationship with bathymetry (i.e. decreased then increased) and, in contrast, it had a concave relationship with seagrass extent (i.e. increased the decreased). Similarly, Figure 13a-d plots the partials residuals for the 2018 data. Dugong abundance had a similar convex relationship with seagrass extent and, in contrast and interestingly, had an opposite concave relationship with bathymetry.

4.6 Broad-scale spatial modelling of dugong– seagrass habitat relationship A significant linear and positive relationship was obtained between combined dugong population estimates for survey blocks 3, 4 and 5 and the extent of sparse seagrass in those blocks over four time periods spanning the extensive seagrass dieback in 2010/11 (Fig. 14a). No significant correlations were obtained using the extent of dense seagrass or total seagrass given that dense seagrass comprised most of the total. Preliminary analysis indicates that prior to the dieback the extent of dense seagrass in the survey blocks was about twice that of sparse seagrass and, in contrast, that after the dieback during the recovery period they had similar extents (2010: dense SG 1,257 km2 cf. sparse SG 609 km2; 2014: dense SG 729 km2 cf. sparse SG 634km2; 2016: dense SG 789 km2 cf. sparse SG 654 km2). Between 2010 and 2014 the extent of total seagrass decreased by 27% in the survey blocks but was differentially distributed between dense and sparse seagrass habitats; the extent of dense seagrass decreased by 42% and, in contrast, the extent of sparse seagrass increased by 4%. Between 2014 and 2016 the extent of total seagrass increased by 6%, with dense and sparse seagrass increasing by 8% and 3% respectively. These result in combination with results from the fine spatial resolution of the GWR models, especially for the 2016-2018 match-up (overall R2 of GWR model = 97%), suggest strongly that dugongs in Shark Bay prefer sparse seagrass habitat. Figure 14b illustrates the nonlinear trend (albeit non-statistical) in the percentage of dugong calves in survey blocks 3-5 for each survey year against the corresponding area (km2) of total seagrass and, if true, highlights the strong relationship between dugong reproductive success, juvenile recruitment and mortality, and food availability. The trend in the percentage of dugong calves counted during surveys between 1989 and 2018 showing a collapse in breeding recruitment in 2012, 1.5 years after the seagrass dieback in 2010/11, is illustrated in Figure 14c. Only blocks 3-5 were surveyed in 2012, hence the trend in percentage calves between 2007 and 2018 is illustrated separately (dashed lines) for these blocks combined. The 0.3% value for 2012 (1 of 356 observations) only relate to survey blocks 3-5 (c.f. 77 of 496 in the same blocks in 2018, or 15.5%). If reduced juvenile recruitment was the major population level response to the seagrass dieback event, especially if adult mortality was initially buffered by an increase in sparse seagrass feeding habitat at the expense of dense seagrass habitat, then the population level response would not be detected for decades given the species’ life history.

(a) (b)

(c)

Figure 14a-c. (a) Linear regression relationship between dugong population estimates for Shark Bay in 2002, 2007, 2012 and 2018 and the extent (km2) of sparse seagrass in 2002, 2010 and 2014 respectively. Population estimates were adjusted for visibility biases using the Pollock et al. (2006) method, and numbers were transformed of natural logs. (b) Observed (non-statistical) trend in the % of dugong calves summed across survey blocks 3-5 across for each survey year with the corresponding area (km2) of total seagrass. (c) Trend in the percentage of dugong calves counted during surveys between 1989 and 2018 showing a collapse in breeding recruitment in 2012, 1.5 years after the seagrass dieback in 2010/11. Only blocks 3-5 were surveyed in 2012, hence the trend in percentage calves between 2007 and 2018 is illustrated separately (dashed lines) for these blocks combined. The 0.3% value for 2012 (1 of 356) only relate to survey blocks 3-5 (c.f. (77 of 496 in the same blocks in 2018, or 15.5%), after Bayliss et al. (2018).

5 Discussion

The first aim of the present study was to undertake more comprehensive spatial and temporal analyses of the updated dugong aerial survey dataset in order to assess the status of current dugong populations in Shark Bay within the context of all historical surveys since 1989 and, in particular, since the extensive seagrass dieback event in 2010/11 due to a marine heatwave. The most closely matched “whole of Shark Bay” dugong surveys before and after the 2010/11 seagrass dieback event were those undertaken in 2007 and 2018, respectively. Additionally, both survey years included the Ningaloo-Exmouth Gulf regions and, hence, the 2007 and 2018 data sets comprise the before and after design for the change analysis reported here given the possibility of assessing potential large- scale movements between the nearby regions due to extensive loss of seagrass habitat. The more statistically comprehensive change analysis undertaken for this report, however, suggest that dugong abundance may have possibly increased marginally or not at all between 2007 and 2018 in both regions, a period encompassing the extensive seagrass dieback event in Shark Bay. Furthermore, given the inherently large sample variation in dugong counts between sample transects with blocks, our analyses cannot be used to confidently draw conclusions about potential large-scale movements in either direction between Shark Bay and the Ningaloo-Exmouth Gulf regions as a result of changing food availability. Our results generally support the conclusions from a previous comprehensive change analysis undertaken by Hodgson et al. (2008) for Shark Bay dugongs over the period 1989 to 2007. They concluded that dugong aerial survey data are not robust enough to detect subtle changes in regional populations due to relatively small numbers of animals moving between regions. The only caveat, however, is that non-significant ANOVA results, or any statistical contrast, are ambiguous; there may or may not be a difference. Hence, the design and analyses cannot say either way whether or not changes in abundance were subtle or minor. Nevertheless, we agree with their general conclusion that, in Shark Bay at least, dugong populations had remained relatively stable between 1989 and 2007, a conclusion that may also now apply to our updated change analysis 11 years later, albeit encompassing an extensive seagrass dieback event. Notably, a study by Nowicki et al. (2019) examined relative abundance of dugongs over a relatively localised area in the Eastern Gulf of Shark Bay, before (1997-2010) and following the seagrass loss (2012-2014), and found a 67.5% decrease in dugongs sighted. They suggested that this was a result of emigration rather than in situ mortality. Nowicki et al. (2019) findings suggests that there was some displacement of dugongs searching for seagrass soon after the seagrass loss that would not be detected by 5-yearly periodic monitoring of abundance by broad-scale aerial surveys. These results highlight that well-designed boat surveys can provide important knowledge of dugong population structure (e.g. sex, size/reproductive class) and seasonal use of seagrass habitats at local scales that broad-scale aerial surveys cannot provide. Hodgson et al. (2008) recommended that complementary studies on dugong population ecology be implemented to address the limitations of broad-scale aerial survey methodology to answer other important questions at smaller scales, such as seasonal movements and habitat use. They specifically identified satellite tracking and population genetics to investigate the interconnectedness between populations. Systematic aerial survey data has been used previously to model dugong habitat and population dynamics but at large geographic to continental scales, such as the study by Fuentes et al. (2016) to investigate the spatial and temporal variation in the effects of climatic variables on dugong calf production along the East Coast of Queensland to Torres Strait and, similarly, the study by Grech

and Marsh (2007) prioritising areas for dugong conservation in a marine protected area using a spatially explicit population model. Given the availability of medium resolution (30m pixels) seagrass maps derived from satellite sensors like Landsat and Sentinel 2, the second aim of the study was to develop fine-scale dugong- seagrass habitat models in Shark Bay to assess past and present trends in the distribution and relative abundance of dugongs in relation to seagrass condition prior to and following a seagrass dieback event. Predictive high resolution spatial models of the relationship between dugongs and seagrass may provide a valuable assessment tool to help evaluate potential impacts and recovery rates of dugong populations following major disturbances to seagrass habitats due to freshwater runoff from coastal catchments after extreme rainfall events (Preen & Marsh 1995; Walker et al. 2012 for Shark Bay; Meager & Limpus 2014), tropical cyclones (Gales et al. 2004) and anthropogenic impacts such as dredging (Vanderklift et al. 2017a,b). Kernel density extrapolation and smoothing methods were used on observed dugong sightings from the complete time series in Shark Bay to identify and map dugong abundance “hotspots” and for fine-scale spatial modelling in relation to changes in the extent of seagrass. The transformation of point source dugong sighting data to area-based polygon data was a necessary first step in the habitat modelling process because it facilitated the integration of two disparate data types. The accuracy associated with the extrapolation and smoothing process will obviously depend on the values of the point data and their distribution, particularly in relation to outliers. However, there is no simple GIS method to display uncertainty levels in tandem with density contours. Hence, an additional and simple form of spatial cluster analysis was used to assess general uncertainty in map outputs, one that uses a statistical significance test to identify “hot” and “cold” dugong abundance areas. These abundance ‘clusters’ were calculated in ArcMap (ESRI 2011; version 10.6.1) using the Getis–Ord Gi* test statistic (percentage z-score; after Getis and Ord 1992), and resulting polygons greater than 95% significance level were extrapolated over the 1-km grid and smoothed. The Getis- Ord indices, although statistically accounting for uncertainty, are qualitatively different to an observed dugong density metric, but were nevertheless useful to compare with the Kernel map outputs. All Kernel density maps produced similar “hotspot” maps to the Getis-Ord “hotspot” maps, suggesting that their uncertainty levels were not unduly influenced by data distribution or outliers. The availability of paired dugong density and seagrass abundance data therefore allowed use of more sophisticated statistical spatial modelling methods in GIS such as Geographically Weighted Regression (GWR) models. All GWR models in all years of paired data, apart from 2007, had high overall explained variance (adjusted R2) values and accurately predicted local dugong and turtle distribution and abundance patterns based on a single seagrass explanatory variable. Predicted model outputs compared favorably to spatially observed data. For dugong models the R2 range was 88% to 97% in 2002 and 73% to 97% in 2018. For turtles the range was 73% to 90% in 2002 and 73% to 85% in 2018. Standard regression diagnostic procedures were used to evaluate model robustness and indicated that the GWR modelling approach outperformed global-scale OLS regression models that do not use an optimal distance criteria for predictions at local scale. Nevertheless, although model performance as measured by R2 was low (6.3% cf. 9.6% for 2002 & 2018, respectively), the results are interesting in themselves. Dugong relative abundance in 2002 demonstrated a convex relationship with bathymetry and a concave relationship with seagrass extent. In 2018 dugong relative abundance

demonstrated a similar convex relationship with seagrass extent but, in contrast, an opposite concave relationship with bathymetry. It’s unlikely that these statistical relationships are artefactual and, if true, may reflect spatial changes in foraging conditions after the heatwave event, although this is speculation. Regardless, bathymetry and seagrass extent are likely highly inter-correlated in a complex manner and, whilst these effects may be important at a global level, the GWR model appears to account for these trends across many local levels. The optimal bandwidth or distance used in the GWR models varied between 5 and 10 km. Of all seagrass mapping variables used to model dugong distribution and abundance, the extent or percentage cover of sparse seagrass habitat were generally the most predictive variables, particularly for the 2016-2018 data pair (GWR model adjusted R2=97%). At a broader scale a significant positive correlation was obtained between the combined dugong population estimates for survey blocks 3, 4 and 5 and the extent (km2) of sparse seagrass in those blocks over four time periods spanning the extensive seagrass dieback event in 2010/11 (2002, 2010, 2014 & 2016). No significant correlations were obtained using the extent of dense seagrass. Prior to the dieback event the extent of dense seagrass in the survey blocks was about twice that of sparse seagrass and, in contrast, after the dieback event and during the recovery phase they had similar extents. For example, between 2010 and 2014 the extent of total seagrass in the survey blocks decreased by 27%. In contrast, however, the extent of dense seagrass decreased by 42% and the extent of sparse seagrass increased by 4%. These broad-scale results in combination with results from the fine spatial resolution of the GWR models, especially for the 2016-2018 match-up (overall R2 of GWR model = 97%), suggest that dugongs in Shark Bay prefer sparse seagrass habitat. Nevertheless, GWR results also show strong correlation with dense seagrass habitat and dense and sparse seagrass habitat combined for some modelling periods. Interestingly, the apparent preference for sparse seagrass habitat manifests at multiple scales (e.g. the combined block areas = 5,891 km2 in contrast to 1 km2 unit of the GWR models), and supports results of other studies of environmental or seasonal drivers of change in explaining patterns of dugong distribution and abundance (e.g. Anderson 1986) and, which if true, may have important implications with regards to the necessity of informing dugong conservation and management at several spatial and temporal scales (see Cleguer 2015 and Cleguer et al. 2017 for dugongs in New Caledonia). The reason for the preference for sparse seagrass habitat is unknown, however the following selection of plausible hypotheses for future research come to mind: (i) dugongs prefer seagrass species specific to sparse seagrass habitat; (ii) sparse habitat contains similar species to dense habitats but with life forms and/or growth structures (see McMahon et al. 2017) more suitable for grazing; (iii) it may simply be easier to forage ephemeral and opportunistic seagrass species in more open pastures; (iv) dugong grazing behavior may directly contribute to the development and maintenance of sparse seagrass habitats; and (v) any combination of the above hypotheses. With respect to (i), however, of all genera/species in the Shark Bay field data base, Amphibolis sp. was the only category that differed significantly (here greater than) in dominant occurrence between sparse and dense seagrass map classes and, interestingly, is a persistent meadow-forming species. Hypothesis (ii) may be relevant here, given that perennial meadow-forming seagrass species may be higher in tannins and/or toxins, and dugongs may prefer younger, more palatable plants (i.e. high in nitrogen & low in fibre; see Tol et al. 2016) of the same species. As reported by Heinson et al. (1977), Wake (1975) observed that dugongs fed on sparsely distributed Zostera capricorni in preference to dense old stands of the same species in adjacent areas. With respect to hypotheses (i) to (iii), sparse

seagrass habitat also includes Posidonia australis and, while dugong may not eat these species directly, they may nevertheless be attracted to ephemeral species such as Halophila ovalis that grow amongst the sparse P. australis and Amphibolis antarctica meadows. Hence, dugongs may prefer ‘sparse’ seagrass habitat as it would be easier to graze on the ephemerals when canopy forming species are less dense. With respect to (iv), herbivory is well-known to have major influences on the composition, biomass, growth rate and phenology of pastures (Caughley & Lawton 1981), and this applies to seagrass grazing systems (Vanderklift et al. 2017a). This hypothesis is important not just with respect to dugong habitat use, but also because sustained grazing may affect the recovery rate of seagrass after the dieback event (see Strydom et al. 2019 in prep. for details), albeit in both directions given that grazing may have both positive and negative effects on plant growth rates. Back of the envelop calculations suggest that, if ~18,000 adult dugongs each consume ~40 kg wet weight of seagrass/day (Lanyon 1991) in Shark Bay, then this equates to ~720,000 kg or 720 tonnes of seagrass offtake/day, about 262,800 tonnes/yr. That’s a lot of annual seagrass production or growth rate just to support dugongs, let alone turtles and other herbivore offtake on top of this (e.g. adult green turtles can consume up to 2 kg wet weight of seagrass/day; Gauss 2002). A major issue not addressed in this report is the potential cumulative impact of seagrass loss due to substantial freshwater input into Shark Bay at the same time as the state-wide 2010/2011 marine heatwave, a “perfect storm” of extreme events. The extensive loss of seagrass due to substantial freshwater inputs from extreme rainfall-flood events in coastal catchments is a well-known phenomenon along the east coast of northern Australia, particularly its potential to have a devastating impact on dugong populations ranging from mortality due to starvation to significant reduction of breeding. Preen and Marsh (1995) estimated that >1,000 km2 of seagrass habitat was lost from Hervey Bay in north Queensland following two flood events and a tropical cyclone in early 1992. They estimated that dugong populations slowly recovered reaching only about 34% of the pre- flood numbers just under 2 years after the events. Significantly, they reported that the proportion of calves in surveys declined from 22% to 2.2%, the same order of magnitude collapse as our estimates derived for Shark Bay after the 2010/11 seagrass dieback event to date solely attributed to the marine heatwave. In contrast, but in support of the potential population-level impact of freshwater inputs into coastal ecosystems, Meager and Limpus (2014) found that peak natural mortality of inshore dolphins and dugongs followed sustained periods of elevated freshwater discharge (9 months) and low air temperature (3 months), and concluded that at a regional scale their results point to a strong relationship between annual mortality and an index of El Nino-Southern Oscillation (see Bayliss & Ligtermoet 2017 for a tropical freshwater aquatic example). A total of 255 mm of rainfall was recorded at Carnarvon Airport (BoM station station 006011) in December 2010, with ~200mm of this total falling in a single day causing extensive flooding in coastal catchments and the delivery of large amounts of sediment into the bay ((Walker et al. 2012). The monthly total is the maximum on record over 74 years (1945 to 2018), and was about 14 times the mean monthly total. The maximum water level recorded at Carnarvon (1965-2016) was 1.44 m in February 2011. Seawater temperatures in Shark Bay in February 2011 were concomitantly among the highest recorded in the region with averages above 27 oC. In 2011 CSIRO, UWA, Curtin University and the Western Australian Marine Science Institution (WAMSI) initiated a study to examine the influence of both extreme events on the marine ecosystems of Shark Bay (Walker et al. 2012). The impact of the marine heatwave on seagrass ecosystems in Shark Bay has subsequently been reported by many studies (Thomson et al. 2014; Caputi et al. 2014; Arias-Ortiz et al. 2018). Importantly,

Strydom et al. (2019 in prep.) recently argued that the main cause of the extensive seagrass dieback across all of Shark Bay in early 2011 was more likely due to the marine heatwave because of the restricted locations and small spatial extents of potential freshwater-sediment discharge impact sites reported by Fraser et al. (2014). However, whilst appropriate data may not be presently available in order to clearly differentiate the cumulative and potentially confounding effects of substantial freshwater input and sediment discharge from the effects of the marine heatwave, this is not a deal breaker for our study. From the perspective of dugongs being an ecological assessment endpoint for climate related risks, we report on their population-level response and recovery along with their spatially-explicit relationship with seagrass using well-quantified estimates of extensive seagrass loss regardless of attribution given that both extreme events had co-occurred. Nevertheless, both extreme events can clearly have severe independent impacts on seagrass and ultimately dugongs. However, within the context of overall cumulative ecological risk it may be fortuitous that they had co-occurred rather than in rapid succession from one year to the next given that mortality from either simultaneous event can only occur once. Hence, the overall additive impact may have been far greater assuming that there were no antagonistic or synergistic effects between the two events. Regardless, there is no room for complacency with respect to the recurrence intervals of multiple future extreme weather events and the increasing probability for deadly sequencing in the face of climate change. Fourier time series analysis of the 74-year rainfall and 50-year water level records for Carnarvon indicates 12.5-year average recurrence intervals for both, and is not surprising given that the two variables were correlated with a two month time lag. Hence, there appears some regularity or predictability in the occurrence of extreme rainfall-flood events. A time series analysis of extreme seawater temperatures is therefore also warranted in order to examine the likelihoods of coherence, or end-to-end sequencing, between the two extreme events. The last recorded marine heatwave in the Shark Bay region occurred in 1998/99 (Ming et al. 2013), about 12 years before the 2010/11 marine heatwave. Additionally, Shark Bay was subjected to ‘very hot’ climate years following the 2010/11 heat wave event (Gilmore et al. 2019) that may have prolonged potential impacts such as slowing down the recovery rate of seagrass (Strydom et al. 2019 in prep.). Such contextual analyses may lead to a better understanding of potential natural and anthropogenic climate risks to seagrass ecosystems, particularly for coastal areas that are also subjected to sustained development pressures. Smith (2011) proposed a robust framework to assess ecological risks from extreme climate events that deals with all of the above issues and is highly relevant to this study (& see Burgman et al. 2012 for modelling extreme risks in ecology).

6 Conclusions and Recommendation

One of the key results of the first report by Bayliss et al. (2018) is reiterated in our concluding comments: that the trend in the percentage of dugong calves counted during surveys between 1989 and 2018 showed that breeding recruitment in 2012 collapsed, 1.5 years after the seagrass dieback event in 2010/11, and is similar to results obtained by Preen and Marsh (1995) for dugongs in Hervey Bay after extensive seagrass dieback due to a sequence of two major floods in coastal catchments followed by a tropical cyclone. If significantly reduced juvenile recruitment (and/or mortality) was the major population dynamic response to the seagrass dieback event, then the response in population abundance would not be detected for decades given the life history of dugongs. Furthermore, if adult mortality was initially buffered by an increase in preferred sparse seagrass feeding habitat at the expense of dense seagrass habitat after the dieback event, then this

would reinforce the above prediction. Wild et al. (2019) found reproductive output of dolphins was negatively impacted in Shark Bay following the seagrass loss and it is possible that dugongs were similarly affected. As suggested by Wild et al. (2019), reduced food may result in abortions, calf mortality (when the mother cannot support a dependent calf through nursing), delayed sexual maturity or suppressed ovulation, and these factors may also apply to dugongs to account for the lower proportion of calves observed directly following the extensive loss of seagrass habitat in 2010- 11. Given the above we therefore conclude that the lack of any significant and substantial change in dugong numbers in Shark Bay between 2007 and 2018, with no corresponding change in numbers in the Ningaloo-Exmouth Gulf region, is no reason for complacency. Within the context of a globally vulnerable iconic conservation species (Marsh & Sobtzick 2015) within an iconic World Heritage marine park, our null result may simply reflect the existence of complex decadal-scale time lags between dugongs and seagrass after major perturbation events such as marine heatwaves, extensive flood events from coastal catchments, tropical cyclones, anthropogenic impacts or a combination of cumulative effects. Hence, the major recommendation of our final stage 2 report is to continue monitoring dugong populations in Shark Bay but at previously implemented 5-year intervals to avoid drift in the time series (the next one is due in 2023) and, importantly, with contemporaneous fine- scale monitoring of the condition of their seagrass habitats. The latter recommendation needs to include monitoring changes in species composition of seagrass (& other benthic communities) in addition to aerial extent, and would involve collection of field data to both calibrate and validate seagrass maps derived from satellite imagery and concomitant dugong-seagrass spatial habitat models that depend on these maps. Such long-term paired data would provide a valuable knowledge base, not only for the conservation and management of dugongs and sea turtles, but for other highly valued components of seagrass ecosystems (e.g. see Burkholder et al. 2012, 2013) that are globally declining (~7 % loss p.a. or 110 km2 p.a.; Waycott et al. 2009). The knowledge gained in Shark Bay on how dugong populations respond to extensive and sudden loss of seagrass habitats is directly relevant to dugong populations outside of reserves, particularly those subjected to development pressures such as dredging and mining. Every major perturbation event that causes extensive loss of seagrass habitat, whether by anthropogenic or natural causes, should provide critical ecological analogues for the management of species dependent of seagrass habitats. This study also highlights gaps in our knowledge of dugong reproductive biology and life history parameters (e.g. gestation, period of dependency and calving intervals) that influence population recovery and resilience to pressures including these extreme weather events.

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8 Acknowledgements

Special thanks to our pilot Gail Nolan, our survey consultants Glenn Dunshea and Kym Reeve, and to Mike Rule, Cindy Bessey and Bart Huntley for contributions to the seagrass maps. We thank DBCA staff at Shark Bay and Exmouth for their hospitality and support, particularly Steve Mills, Steve Nicholson, Matt Smith, Dani Rob, Kristen Wren, Heather Barnes and Tim Hunt. We thank the local custodians, the Mulgana aboriginal people, for caretaking sea country and countrymen Jimmy, Dennis and Bobby Hoult and Benny Bellottie for long-term and contemporary insights into dugong abundance, distribution and habitat use. We also thank Ningaloo Aviation, Birds Eye View Ningaloo and Coral Coast helicopters for logistical support.

9 Appendices

Attachment 1 Report 1 to NESP Attachment 2 Supplementary Material (Figures)