Satellite telemetry reveals complex mixed movement strategies in ibis and spoonbills of Australia: implications for water and wetland management

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McGinness, Luke R. Lloyd-Jones, Freya Robinson, Art Langston, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4596537/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Movement Ecology → Version 1 posted 9 You are reading this latest preprint version Abstract Waterbird population and species diversity maintenance are important outcomes of wetland conservation management, but knowledge gaps regarding waterbird movements affect our ability to understand and predict waterbird responses to management at appropriate scales. Movement tracking using satellite telemetry is now allowing us to fill these knowledge gaps for nomadic waterbirds at continental scales, including in remote areas for which data have been historically difficult to acquire. We used GPS satellite telemetry to track the movements of 122 individuals of three species of ibis and spoonbills ( Threskiornithidae ) in Australia from 2016 to 2023. We analysed movement distances, residency, home range (when resident) and foraging-site fidelity. From this we derived implications for water and wetland management for waterbird conservation. This is the first multi-year movement tracking data for ibis and spoonbills in Australia, with some individuals tracked continuously for more than five years including from natal site to first breeding attempt. Tracking revealed both inter- and intra-specific variability in movement strategies, including residency, nomadism, and migration, with individuals switching between these behaviours. During periods of residency, home ranges and distances travelled to forage were highly variable and differed significantly between species. Sixty-five percent of identified residency areas were not associated with wetlands formally listed nationally or internationally as important. Tracking the movements of waterbirds provides context for coordinated allocation of management resources, such as provision of environmental water at appropriate places and times for maximum conservation benefit. This study highlights the geographic scales over which these birds function and shows how variable waterbird movements are. This illustrates the need to consider the full life cycle of these birds when making management decisions and evaluating management impacts. Increased knowledge of the spatio-temporal interactions of waterbirds with their resource needs over complete life cycles will continue to be essential for informing management aimed at increasing waterbird numbers and maintaining long-term diversity. environmental water satellite telemetry foraging nomadic partial migration behavioural plasticity conservation management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Waterbirds are highly mobile, with many species conducting long-distance movements of hundreds or thousands of kilometres in days or weeks [ 1 – 3 ]. Understanding these movements is key to effective management of waterbird populations [ 4 ], particularly since rates of wetland habitat loss are increasing due to water harvesting and climate drying [ 4 – 7 ]. Maintaining waterbird diversity and habitats are often important goals for water and wetland managers worldwide [ 8 – 10 ]. However, there are knowledge gaps that affect our ability to manage habitats and to predict waterbird responses at local, national and global scales. One of the most critical gaps is knowledge of waterbird movement behaviour and variability, particularly for inland species [ 4 , 11 – 13 ]. Waterbird movements can be complex, occurring along a continuum of variation where the behaviour of any one species or individual may be classified variously as: (1) Residency, including ‘sedentary movements’, ‘central-place movements’, and ‘commuting’, where individuals remain in one area or home range, usually returning to favoured locations between relatively short-distance foraging trips, and sometimes displaying territoriality (including during breeding periods); (2) Nomadism, including ‘facultative migration’ and ‘facultative movements’ where individuals move different distances and directions irregularly and seem to generally be responding opportunistically to resource availability, including ‘fugitive movements’ in response to disruption or disturbance of resources, individuals or flocks; or (3) Migration, sometimes called ‘obligatory migration’ or ‘seasonal migration’, where individuals regularly move relatively long distances predictably between particular locations and in consistent directions, usually seasonally or annually [ 14 – 17 ]. Adding to this complexity, individuals can show plasticity in their movement strategies over time [ 18 ]. Advances in telemetry are revealing complexities in movement patterns within and among individuals and species, including ‘partial migration’, where residency, nomadism or migration strategies may each be employed within a species by different individuals or by an individual at different times [ 19 , 20 ]. It has been suggested that there is greater variation in movement strategies in the southern hemisphere compared to the northern hemisphere due to greater variability in climatic factors [ 21 , 22 ]. However, there are relatively few satellite-telemetry studies tracking southern hemisphere species, especially inland waterbirds, and those few have usually involved relatively small numbers of individuals [ 12 , 23 – 25 ]. Moreover, understanding of nomadism is relatively limited compared to that for migration and residency [ 17 , 20 , 26 ]. Movement ecology is often poorly understood even for common and conspicuous taxa that are the focus of significant management investment. Examples include ibis, spoonbills, egrets, and herons that nest in large aggregations, often in Ramsar sites, in response to specific environmental conditions such as flooding [ 11 , 27 , 28 ]. Despite extensive leg-banding and other marking programs, resighting or recovery data are usually limited for these taxa. For example, 3 months after banding, post-dispersal from natal or nest sites [ 29 ]. Taxa such as these are of particular interest for wetland and waterbird management and policy makers because they often nest in areas that are susceptible to adverse effects of environmental change; consequently, their habitats and populations are the subject of intensive conservation management [ 30 – 34 ]. For species dependent on surface water, management can include the allocation of environmental water or ‘environmental flows’, to provide the quantity, timing, and quality of freshwater flows and levels necessary to sustain aquatic ecosystems [ 35 ]. Wetlands and other areas that receive environmental water (actively or passively), or that may be inundated by natural flooding if key constraints are overcome, are colloquially termed the ‘managed floodplain’ [ 36 ]. Understanding waterbird movements at fine spatial and temporal scales can maximise the efficacy of environmental water application, by guiding where and when to provide water and for how long. Australia has breeding populations of three ibis species and two spoonbill species in the Threskiornithidae family: Straw-necked ibis ( Threskiornis spinicollis ); Australian white ibis ( Threskiornis molucca ); glossy ibis ( Plegadis falcinellus ); royal spoonbill ( Platalea regia ); and yellow-billed spoonbill ( Platalea flavipes ). These species nest in aggregations of up to hundreds of thousands of birds when conditions are good, while in poor conditions they may not nest at all. Inland, these species are experiencing reduced breeding event frequency, size, and success, including mass nest abandonment events due to prematurely falling water levels [ 37 – 39 ]. The birds are dependent on wetland inundation for breeding, but the degree of breeding-site fidelity among individuals or groups is not well understood, even within the relatively well-studied breeding sites of the Murray-Darling Basin in south-eastern Australia, which is thought to be the core breeding area for these species, with 46% of aggregate-nesting wetlands in Australia [ 40 ]. Some breeding sites are used every year by the same species; but it is not known if it is the same individuals revisiting each time. There are major knowledge gaps about movements outside of breeding events. While these birds are known to be capable of moving at continental scales (many 100s – 1000s km) within a few months [ 28 , 29 , 40 , 41 ], some authors have suggested there may be regional sub-populations and seasonal migrations with site fidelity [ 42 , 43 ]. Leg-banding data suggest individuals that breed in eastern Australia mostly remain in eastern Australia and rarely move west; most recorded displacements of leg-banded birds have been from the south-east to the north-east [ 29 , 40 , 41 , 44 ]. Seasonal fluctuations in local abundance along with observations of flocks in flight suggest that east coast areas are important winter and drought refuge locations, while inland wetlands of the Murray-Darling Basin are important breeding sites [ 29 , 40 , 42 ]. However banding recoveries do not suggest any relationships among bird age, season, and movement distances or directions [ 29 , 40 , 41 ]. There has been very limited direct movement tracking using telemetry for ibis and spoonbill species in Australia. No royal spoonbills, yellow-billed spoonbills, or glossy ibis have been tracked prior to this study. In February 2000, two straw-necked ibis were fitted with satellite transmitters in the Macquarie Marshes in New South Wales (NSW) and tracked for c. two months [ 28 ]. Of these two, one bird flew 1,438 km north of the capture site within a month, while the second bird remained within 200 km of the capture site [ 28 ]. Tracking of Australian white ibis movements has largely focused on breeding sites in urban and suburban coastal environments, rather than natural inland wetlands [ 45 – 47 ]. For conservation and management, important knowledge gaps remain at the continental scale about movement patterns, movement variability, movement rates and movement timing for these species. At local to regional scales, questions such as how far birds travel to find food are also relevant for managers of waterbird populations and habitats. Answering these questions would assist water and wetland managers to identify key habitats associated with movements and to understand better the places, scales and times at which resources are required. This should in turn improve capacity to target land and water management actions (such as strategic watering and drying of wetlands), evaluate progress, and predict future outcomes for waterbirds. Over seven years, we used GPS (global positioning system) satellite telemetry to track the movements of three species in the Threskiornithidae family – straw-necked ibis, Australian white ibis, and royal spoonbills. We aimed to address knowledge gaps regarding intra and interspecific variation in movement strategies of these species post-dispersal from breeding sites and consider consequent implications for water, wetland and waterbird management. Methods Transmitter deployment Straw-necked ibis (‘SNI’; N = 73; 45 adults and 28 juveniles) was chosen as the primary species for transmitter deployment because it is a focal species for Australian wetland and water managers that nests in large numbers in major inland wetlands managed with environmental water. Two other species that frequently nest and forage with straw-necked ibis were also tracked, in smaller numbers, to explore potential differences among species: the royal spoonbill (‘RSB’; N = 42; 5 adults and 37 juveniles) and the Australian white ibis (‘AWI’; N = 7; 3 adults and 4 juveniles). Transmitters were deployed at eight breeding sites within the Murray-Darling Basin in south-eastern Australia (Fig. 1 ) between 2016 and 2023. The Murray-Darling Basin is ≥ 10 6 km 2 and is a primary focus for intensive water management and water policy reform in Australia. It contains 16 internationally significant wetlands, including some of the most important aggregate-nesting ibis and spoonbill breeding sites on the continent. Transmitters were attached as a ‘backpack’ using Teflon ribbon or Spectra ribbon (Bally Ribbon Mills™) harnesses, fitted either as wing-loops with a join at the keel (SNI and AWI and some RSB), or as leg-loops (most RSB). Harness design was based on designs used in other species [ 48 – 51 ] modified and improved over time, with different types of weak links used in different years. Transmitters weighed 12–40 g, ranging from < 1–5% of bird bodyweight. Solar-powered GPS transmitters with a fix resolution of 15–26 m were used, with data sent through either the Argos satellite network (Geotrak units) or the 3G network (Ornitela and Druid units). The frequency of fixes ranged from every min to every 6 h, depending on the transmitter type and programmed schedule. This was handled in analyses, with interpolation or down-scaling applied when appropriate (see below). Analyses Periods of data for nesting adults, for adults and juveniles still within breeding sites post-capture but pre-dispersal, and for birds tracked for < 30 days were removed, to ensure results reflected longer-term movement statistics and patterns. The final dataset comprised 122 individuals, 41,619 days of tracking data and 1,240,206 GPS fixes (Table 1 ). Analyses were done in R version 4.3.1 [ 48 ]. Summary statistics were calculated for each species and age class for tracking duration, distance travelled between roosts and foraging sites (maximum distance travelled from roost per day), roost-shift distances (midnight fix to midnight fix displacements), and cumulative distance travelled per hour (km h -1 ), per 24 hr (km d -1 ) and per year (km yr -1 ). Statistics for distances travelled were calculated separately for calendar months to account for seasonal differences, by species and age class. Permutation tests were used to asses differences in distributions between groups of interest e.g., species and age using the ‘percentileTest’ and ‘pairwisePercentileTest’ functions with 1000 replicates in the R package ‘rcompanion’ [ 52 , 53 ]. Table 1 Number of birds included in analyses by species and age group. Species and age group Total Straw-necked Ibis 73 Adult 45 Juvenile 28 Royal Spoonbill 42 Adult 5 Juvenile 37 Australian White Ibis 7 Adult 3 Juvenile 4 Grand Total 122 Residency Periods of residency were identified using the semivariance function (SVF) approach of CH Fleming, JM Calabrese, T Mueller, KA Olson, P Leimgruber and WF Fagan [ 54 ]. The SVF measures the variability in the distances between pairs of locations as a function of the time lag between those pairs and is calculated over all possible time lags up to the maximum. This method was initially trialled on both the entire movement track of each individual and various temporal sub-sections of the movement track, but sections longer than a few months rarely passed the residency tests required for estimations. Ultimately, monthly segments were chosen as the longest frequently resident sections of the telemetry tracks to explore changes in behaviour through time and to allow evaluation of seasonal patterns. To classify months of resident behaviour, a within-month Ornstein-Uhlenbeck-including-foraging (OUF) model was fitted to the empirical variogram using functions in the package ‘Continuous Time Movement Models’ (CTMM) [ 55 ]. The OUF model is frequently the best fitting model for home-range behaviour modes [ 54 ]. First- and second-order derivative checks were performed on the fitted OUF function to determine whether the function reached an asymptote within each month. Reaching an asymptote defined the months for each individual in which the bird was resident. For all birds, the odds of residency vs non-residency for each calendar month were computed from the counts in each of the two classes and the confidence interval from delta method approximation [ 56 ] for the odds as a function of the sample proportion estimate i.e., the standard error was computed on the proportion estimate and then mapped to the interval on the odds scale via the delta method. Using the resident/non-resident status over all months for all birds, we computed the odds of being resident as the ratio of the number of resident months to non-resident months. For example, if there were 100 instances of telemetry tracks covering the month of June (which could be multiple instances per bird if they were tracked for multiple years) then an odds of 2 indicates that 66 tracks from June were classified as resident versus 33 as non-resident. This was done for all birds combined, for individual species, and for age groups within species. Differences in the proportion of residency events in season were compared with the two-sample permutation test to compare two proportions implemented using the ‘twoSamplePermutationTestProportion’ function in the EnvStats R package [ 57 ] . Home range when resident Home ranges were calculated during periods of residency for each bird using the autocorrelated kernel density estimation (AKDE) method, fitted using the package ‘CTMM’ [ 58 ]. For each month that each bird was resident, the within-month 95% home range area was computed. Within-month home ranges were likely to overlap when an individual was resident for many months, resulting in an overestimation of the total home range for a given residency period. Therefore, a test for overlap of home range was performed within sequential pairs of months that were appropriate for home-range estimation using the Bhattacharyya coefficient (BC) method in the package ‘CTMM’ [ 59 ]. The BC quantifies the overlap between home ranges and provides a statistical test for whether the overlap is substantial enough to state they are distinct, given model uncertainty [ 59 ]. Home ranges were considered to overlap if the lower confidence interval was greater than 0.01 as in K Winner, MJ Noonan, CH Fleming, KA Olson, T Mueller, D Sheldon and JM Calabrese [ 59 ]. For the sets of months in which the home range values overlapped, the AKDE method was rerun to estimate a home range for a joint time period, which we refer to as a ‘home range block’. The mean and median over each individual’s discontiguous 95% blocks were summarised. Differences in home range among species or by age class were investigated by testing differences in medians via permutation tests using the ‘percentileTest’ function in the ‘rcompanion’ R package. Site fidelity To explore fidelity to foraging areas (locations and periods of time for which individuals roosted and foraged in the same area), sites used during periods of residency were mapped as utilisation distribution (‘UD’) polygons for each individual based on the start and end date of each residency period. The package ‘Recurse’ [ 60 ] was used to calculate visitation to each site polygon, in terms of arrival date, departure date, duration of occupancy, duration between visits, and number of distinct visits overall and by month, for each individual. Residency areas were mapped and characterised according to whether they were associated with: (a) sites listed by the Ramsar Convention on Wetlands of International Importance [ 61 ]; (b) sites listed by the Directory of Important Wetlands in Australia [ 62 ]; (c) the ‘managed floodplain’, being wetlands and other areas that can receive environmental water (actively or passively) or that may be inundated by natural flooding or high flows if key constraints are overcome [ 36 ]; and, (d) historical breeding sites for these and other aggregate-nesting species within the Murray-Darling Basin, which is thought to be the core breeding area for these species and for which breeding site locations are relatively well known and mapped compared to other parts of Australia. Results Tracking duration The maximum number of days that any individual was tracked was 2,233 days. Five adult and two juvenile SNI and two juvenile AWI were tracked for > 3 years (Table 2 ). Table 2 Summary statistics for number of days tracked per bird. Sp AgeCat Min Quartile_1 Median Mean Std_Dev Quartile_3 Max N (birds) AWI Adult 156 499.5 843 687 472 952 1062 3 AWI Juvenile 301 522 920 1093 855 1492 2233 4 RSB Adult 98 256 424 401 229 552 675 5 RSB Juvenile 32 55 111 196 189 332 709 37 SNI Adult 32 109 235 421 464 519 1844 45 SNI Juvenile 41 120 297 460 528 523 2233 28 Distance Tracked movements spanned almost the whole of eastern mainland Australia (Fig. 2 ). The longest distance travelled in an hour was 135 km, recorded by a juvenile SNI. The longest cumulative distance travelled within a 24-hr period was 857 km, recorded for a juvenile RSB; maximums for SNI and AWI were 662 km and 271 km respectively. The median cumulative distance travelled per 24-hr differed between species, with SNI travelling the furthest at 5.9 km d − 1 , AWI travelling 3.9 km d − 1 , and RSB traveling 5.4 km d − 1 (permutation P -value < 0.001 adjusted for multiple testing from pairwise permutation tests between medians (null hypothesis of no difference in medians) of each species; see Supplementary Fig. 1 for distributions). Cumulative distances travelled per day were lower in the late autumn and early winter months and higher in spring and summer for SNI and AWI; for RSB, mean distances travelled per day were highest in autumn (Fig. 3 ). Permutation P -values between medians and 80th percentiles between seasons within species were < 1e − 6 (null hypothesis of no difference in median distance travelled between seasons) except for summer and spring comparisons within AWI and SNI. RSB showed evidence for differences in 80th percentiles, except for autumn and spring and summer differences in medians. The 80th percentiles summarise the components of the monthly distribution that are long-distance movements while the medians summarise the central tendency of the heavily right-skewed distributions (see Supplementary Fig. 2 for summaries of full distributions). For those individuals with complete datasets of 365 days in a year (adjusted for removal of nesting periods and missing data), the maximum cumulative distance travelled by an individual was 15,549 km yr − 1 (an adult female SNI). Species also differed in the distances travelled from roosts to foraging sites (maximum distance travelled from roost per day; permutation test P < 0.001). Distances were shortest for AWI (median 1.6 km, mean 3.7 ± 10.7 km) compared to SNI (median 2.1 km, mean 10.8 ± 35.1 km) and RSB (median 2.4 km, mean 8.1 ± 27.9 km; Supplementary Fig. 3). Juvenile SNI travelled shorter distances (median 1.6 km) to forage than did adults (median 2.5 km; P < 0.001; Table 3 ). Daily distances travelled from the roost differed among months of the year for SNI and RSB but less so for AWI (Fig. 4 ). SNI travelled further from their roosts in spring and summer months compared to late autumn and early winter months (permutation test for median differences P -values < 1e − 6 for all season comparisons; 80th percentile permutation test P -values = 0.74 for the summer-spring comparison and 0.04 for the autumn-winter comparison). In contrast, RSB travelled farther to forage in late autumn, and foraged closer to roosts in summer (Fig. 4 ; permutation P -values between summer median distance travelled and all other seasons < 1e − 6 ). There was evidence for greater roost-site fidelity for AWI than for the other species, with median roost location shift distances of 0.3 km (mean 2.7 ± 10.7 km), compared to 0.6 for SNI (mean 9.4 ± 35.0 km) and 0.6 for RSB, (mean 7.8 ± 32.5 km; P < 0.001). Median roost location shift distances were also shorter for juvenile RSB and SNI (0.55 km and 0.45 km) compared to adults of these species (0.66 km and 0.78 km respectively; permutation P - <1e − 6 ). Table 3 Summary statistics for maximum distance travelled (km) from roost per day Species Age class Min Quartile 1 Median Mean Std Dev Quartile 3 Max N birds AWI Adult 0 0 1 4 16 4 267 3 AWI Juvenile 0 1 2 4 8 4 171 4 RSB Adult 0 1 2 9 28 6 415 5 RSB Juvenile 0 1 2 8 28 6 546 37 SNI Adult 0 1 2 13 40 6 580 45 SNI Juvenile 0 1 2 8 27 4 484 28 Residency Across all species, 25% of individuals had no period of residency in any month according to the criteria detailed above. All individuals with no residency periods were either SNI or RSB (SNI = 19 adults, 4 juveniles, accounting for 31.5% of tracked SNIs; RSB = 6 adults, 1 juvenile, accounting for 16.6% of tracked RSB). In contrast, all tracked Australian white ibis had multiple or extended periods of residency. The proportion of months spent resident overall ranged from 24% (adult SNI) to 33% (adult RSB) and 58% (adult AWI; Table 4 ). Straw-necked ibis were most likely to be resident in July, the middle of winter (Fig. 5 ), with higher proportion of resident months in autumn (prop. = 0.3) and winter (prop. = 0.38) with peaks in odds in May and June. The odds of residency were lowest in spring. Two sample permutation tests for differences in proportions of months resident within seasons showed winter proportions were greater than summer and spring (Null hypothesis prop.1 = prop.2, prop.winter – prop.summer = 0.38–0.24, permutation P -value = 0.001 and prop.winter – prop.spring = 0.38–0.16, permutation P -value = 1e-6). Autumn also showed evidence of a great proportion than spring (prop.autumn – prop.spring = 0.29–0.16, permutation P -value = 7e-4). Within age groups, adult SNI showed clearly increased odds of residency during winter (proportion resident months in winter = 0.4, prop.winter – prop.spring = 0.38–0.15, permutation P -value = 4e-5). Juvenile odds of residency among months were less consistent, with multiple small peaks in late summer, late autumn, and mid-winter and reduced odds in spring. Permutation P -values only showed evidence for autumn and winter being more resident than spring (prop.winter – prop.spring = 0.36–0.18, permutation P -value = 0.005 and prop.autumn– prop.spring = 0.36–0.18, permutation P -value = 0.007) but no evidence for differences between other seasonal proportions for juveniles. Royal spoonbill juveniles showed reduced residency in autumn (prop. = 0.24) and winter (prop. = 0.31), and increased odds in spring (prop. = 0.35) and summer (prop. = 0.39) (Fig. 5 ). Permutation P -values only showed marginal evidence for summer proportions being greater than autumn (prop.summer– prop.autumn = 0.15, permutation P -value = 0.04). Data for adult RSBs were limited but appeared to show no evidence for difference in seasons based on permutation tests for proportions. Juveniles showed similar results to the all-RSB analysis (Fig. 5 ). Australian white ibis odds of residency were relatively consistent through the year, with small peaks in autumn (March-April) and spring (November) but no evidence for differences in proportions of months resident in each of the seasons (Fig. 5 ). Table 4 Percentage time (months) classed as resident by species and age group. Species Age class % months resident by species and age group AWI Adult 58 AWI Juvenile 49 RSB Adult 33 RSB Juvenile 30 SNI Adult 24 SNI Juvenile 30 Home range when resident Home range sizes when resident differed among species for adults. AWI adults had significantly smaller home ranges (median 10.0 km 2 ) than SNI and RSB (medians 49.7 and 55.5 km 2 respectively; Fig. 6 ; Table 5 ; see permutation test results in Supplementary Fig. 5). Adult SNI and RSB also had larger median home ranges than juveniles of the same species (Fig. 6 ), but this was only significant for permutation testing of a greater median for adults for SNI (permutation P -value = 0.01, see Supplementary Fig. 6). In contrast, the median home range of juvenile AWI (34.4 km 2 ) was nearly four times greater than that of adult AWI (10.0 km 2 ), however there were limited data for AWI, so these observations should be treated with caution (permutation P -value = 0.02, see Supplementary Fig. 6). Table 5 Home range (km 2 ) statistics by species and age class. Species Age class Min 2.5 percentile Mean Median Sd. 97.5 percentile Max N Blocks N AWI Adult 0.01 0.1 9.3 10 8.8 23.5 25.1 7 3 AWI Juvenile 2.1 3.0 76.9 34.4 113 320 347 11 4 RSB Adult 0.01 0.8 83.3 55.5 91 245 257 8 4 RSB Juvenile 0.01 0.0 43.2 11.4 55.8 169 198 42 31 SNI Adult 1.0 1.9 84.4 49.7 120 415 608 53 26 SNI Juvenile 0.01 0.9 46.2 15.5 68.6 265 302 40 24 Site fidelity A total of 147 residency utilisation distribution areas (UDs) were identified, representing areas and periods of time for which individuals roosted and foraged in the same place. Of these, 81 areas were used by multiple individuals (55%). The maximum number of individuals tracked using any one residency area (UD) over the whole period of record was 25 (median 2.0; mean 3.9 ± Sd. = 4.4), at Barmah-Millewa Forest on the Murray River at the NSW-VIC border. Of the 89 individuals with periods of residency, 33 (37%) had more than one residency area identified. The largest number of residency areas identified for an individual was 7 areas, for an adult female SNI. The greatest number of separate residency periods identified for an individual was 25, for an adult female SNI tracked for > 5 year. The median time spent visiting any residency area was one day (mean 11.0 ± Sd. = 32.0; range < 1–679 days), while the median time between visits was three days (mean 36.0 ± Sd. = 126.0; range 1–1263 days). Arrivals and departures from residency areas were most frequent in March (autumn) and least frequent in July (winter; Supplementary Fig. 7). The median number of revisits to a residency area across all birds was six (mean 12.22 ± -18.67; range 1–110). Areas with the highest total revisits across all birds were: Barmah-Millewa Forest; Hird Swamp and Johnson Swamp in the Kerang Lakes area of the Loddon catchment; the adjacent Gunbower-Koondrook-Perricoota Forest area including Kow Swamp; and the Macquarie Marshes (Supplementary Table 1; Supplementary Fig. 8; Supplementary Fig. 9; Supplementary Fig. 10). Sixty-five percent of identified residency areas were not associated with wetlands listed nationally or internationally as important. There were 27 wetlands listed in the Directory of Important Wetlands in Australia used as residency areas (51 unique UDs; Fig. 7 ; Supplementary Table 1). Seven of these are listed under the Ramsar convention: Barmah Forest; Millewa Forest and Koondrook-Pericoota Forests (NSW Central Murray State Forests); the Macquarie Marshes; Gunbower Island (Gunbower Forest), Hird Swamp and Johnson Swamp (Kerang Wetlands). These wetlands are also known breeding sites for aggregate-nesting species. Seventy-four percent of the residency areas identified were located within the Murray-Darling Basin. Overall, 42 of the 147 residency areas overlapped known breeding wetlands for aggregate-nesting species within the Murray-Darling Basin (29%; Fig. 8 ; Table 6 ). Almost half of the 147 residency areas overlapped the ‘managed floodplain’ (72; 49%; Fig. 8 ), including all the 42 breeding area overlaps. Table 6 Known breeding areas used during residency periods for roosting and foraging within the Murray-Darling Basin. Catchment Breeding wetland Number of residency areas (UDs) Murray Barmah-Millewa Forest 14 Murrumbidgee Gayini Floodplains 5 Loddon Kerang Lakes 4 Macquarie Macquarie Marshes 4 Goulburn Goulburn River Floodplains 3 Lachlan Lower Lachlan Floodplains 3 Broken Broken Creek Wetlands 2 Murray Gunbower-Koondrook-Perricoota Forest 2 Lachlan Cuba Dam 1 Lachlan Mid Lachlan Floodplains 1 Murrumbidgee Mid Murrumbidgee River Floodplains 1 Ovens Ovens River Floodplains 1 Goulburn Wallenjoe Wetlands 1 Discussion Long-term research is important for understanding survival strategies in long-lived species [ 63 , 64 ]. This study is the first to track multi-year movements of substantial numbers of ibis and spoonbills in Australia using satellite telemetry. From it we have quantified movement distances and timing, residency and non-residency, home range size when resident, and species and individual movement strategies. A range of scales of movement was apparent, from movements of only tens of km over months to movements of hundreds of km through eastern Australia within a few days or weeks. Movement strategies within and among species The tracking results reported here have revealed mixed movement strategies within and among the three species tracked (Fig. 9 ). Each species had individuals displaying plasticity in behaviours across the movement spectrum over time, using different movement strategies at both annual and sub-annual temporal scales, or demonstrating different ‘personalities’ [ 65 , 66 ]. Overall, each species tended toward differences in dominant movement strategy. The dominant strategy for straw-necked ibis as a species was clearly nomadism, while the dominant strategy for Australian white ibis was residency – with the caveat that only seven AWI were tracked. The dominant strategy for royal spoonbills was less clear, but with only 33% of months spent resident by adult spoonbills and relatively low residency odds for juveniles, is also likely to be nomadism. The odds of residency for juveniles were lower than for adults for both straw-necked ibis and royals spoonbills, implying that juveniles are potentially more likely to take advantage of opportunities presented by climate, weather and management actions than adults [ 67 ] – but also potentially more difficult to support with management in predictable ways [ 1 , 68 ]. None of the individuals tracked were classical ‘obligate migrants’ for the entire duration of tracking, but some individuals moved seasonally north-south between the same places in some years and showed some site fidelity. For example, in the first two years of tracking, the movements of one adult female SNI suggested that this individual was classically migratory, moving between the same breeding and overwintering sites at about the same time each year (Fig. 10 ). However, in the third year, this individual diverted from the usual route to the previous overwintering site and instead flew to an area experiencing extensive flooding hundreds of kilometres away. This individual then flew east via a different route to the usual overwintering site, arriving several months later than the previous two years. There was also flexibility in the timing of movements in terms of departures and arrivals, but with this flexibility set within seasonal patterns. For example, individuals that migrated north-south in autumn and spring varied in the precise timing of their movements and were flexible about the routes and time taken to reach their destinations. Benefits and vulnerability arising from flexible movement strategies While the variability in movement strategies documented in this study is known to be common among vertebrates, it remains less well studied than ‘typical’ migratory movements despite indications that such plasticity significantly influences the capacity of species and populations to cope with environmental change [ 20 , 69 ]. Satellite-telemetry studies are increasingly demonstrating that mixed movement strategies are more common in birds than previously thought [ 19 , 34 , 70 – 72 ]. It has been estimated that > 36% of Australian land bird species may be ‘partially migratory’ [ 21 ] and it is likely that this also applies to waterbird species. This is because Australian inland and especially freshwater environments are subject to extreme variability and unpredictability. Plasticity in movement strategies is thought to be a population characteristic that facilitates species persistence in the face of spatial and temporal variation in resources, environmental conditions and their predictability [ 20 , 69 , 70 , 72 , 73 ]. For such plasticity to persist in a species, there must be benefits to individuals using each strategy when integrated over time [ 70 , 72 , 73 ]. For example, an individual ibis remaining resident near a breeding area through winter may have first choice of prime nesting sites in spring or be able to take advantage of good breeding conditions early. In contrast, an individual moving long distances (either migrating or moving nomadically) may be able to avoid unfavourable weather or resource conditions, particularly in winter, thereby improving body condition and surviving for longer. A nomadic or strategy-flexible individual may take advantage of relatively unpredictable booms in water and food resources but can move away from unfavourable conditions and choose to return to favoured breeding sites when conditions are suitable. The latter strategy is likely to be the most advantageous when climatic and resource conditions are highly unpredictable and highly variable – as they are in Australia [ 74 – 76 ]. An ability to switch between strategies among years allows the species to deal with climatic and weather variability and associated resource fluctuations [ 69 ]; this is particularly the case for straw-necked ibis due to their broad diet and their ability to successfully forage in terrestrial environments for long periods [ 41 , 44 , 77 , 78 ]. This may explain the high proportion of individuals that are classed as ‘non-resident’ but not typically migratory in this study. In the long-term, such a nomadic strategy is likely to allow these species to adapt to climate change better than some other species that are either classically migratory or highly resident and are, therefore, vulnerable to change at particular sites [ 17 , 79 ]. However, this will differ among species and individual dependence on, or fidelity to, important breeding areas or sites, as suggested for RSBs in New Zealand [ 80 ]. While long distance movements and migration can provide benefits, they also have energy costs and can increase the risk of mortality relative to staying resident [ 81 , 82 ]. This is particularly the case if conditions in ‘resident’ or breeding sites remain good or improve over time, or when conditions elsewhere are becoming more unpredictable or less suitable. In the northern hemisphere, C Buchan, JJ Gilroy, I Catry and AMA Franco [ 83 ] found evidence in some partially migratory populations of passerine birds, mammals, and reptiles that resident individuals showed greater survival than migratory individuals, counter to author expectations. They and others have suggested that climate change and other anthropogenic changes, such as urbanisation, may be altering conditions to either favour residency or to promote strategic plasticity in birds, depending on the species and situation [ 19 , 84 , 85 ]. This seems likely for AWI, which, even with the low numbers this study tracked, appears to display residency. It has been suggested for this species that the relatively high and reliable resource availability in coastal urban environments, together with a loss of resources inland, may have resulted in a split of some sub-populations from a larger more mobile population [ 45 , 86 ]. The spread of breeding populations of the AWI into coastal cities, with extensive use of parks and recreation areas, rubbish or landfill sites and areas near airports, has caused conflict with humans [ 45 – 47 ]. Radio-telemetry studies suggest that AWI are largely sedentary in urban environments, with strong site fidelity and most movements occurring within c. 50 km of their breeding or nesting colonies [ 87 ]. This suggests reduced habitat and population connectivity between urban populations and inland populations of AWI, similar to that for the American white ibis ( Eudocimus albus ) in Florida USA [ 88 ], despite AWI being capable of rapid long-distance movements inland [ 27 , 29 ]. Some ibis and spoonbill species in the northern hemisphere show evidence of within-species variation in movement strategies geographically, for example the northern bald ibis and Eurasian spoonbill [ 34 , 81 , 82 ], with some suggestion that this is at least partly driven by regional differences in resource availability [ 89 ]. Further satellite tracking from a wider range of sites will assist with evaluating such assertions. In general, the implications of the plasticity of movement strategies documented here for management are that provision of suitable resources for such species must still be undertaken within appropriate seasons according to life cycle stage (e.g. nesting), but the location, timing and duration of such provisions can differ and still have benefits. Effectively, management strategies for these species and their habitats can and should also be ‘plastic’ and adaptable. Foraging site fidelity There is some foraging site fidelity and there are clearly important sites used by multiple individuals and revisited frequently. In addition, while many individual foraging sites used by the three species reported upon here are not recognised by existing conservation or management strategies (such as the Directory of Important Wetlands in Australia or the Ramsar Convention), or are outside of the Murray-Darling Basin and its ‘managed floodplain’, the most heavily used sites generally do fall within these priority management areas. While the true relative importance of non-listed and listed sites for overall population dynamics remains unknown, managers will need to take bird use of non-listed sites into account when evaluating species responses to management actions, such as environmental watering. For example, while environmental watering may be employed in one area, birds may choose to use other sites if conditions are perceived to be better there. A lack of perceived responses to management actions (listing sites, or priority management actions) may arise from such bird responses but not be a true indicator of failure per se. The availability of food in foraging habitats and their distance from suitable roosting or nesting sites will be critical in determining responses and are potentially manageable. For example, statistics describing distances travelled from roosting sites to foraging sites such as those derived from satellite tracking and described here enable development of threshold distances from roosts within which sites could be prioritised and environmental watering or other management to support foraging areas or food availability could be applied. The degree and timing of residency also allows managers to assess which species or age groups are more likely to be reliant on management of particular sites for long periods or at particular times. Similarly, understanding home-range sizes when resident allows assessment of the area of resources such as environmental watering needed to support species or age groups when resident. It is essential that evaluation of site-based waterbird responses considers these types of contextual information beyond the site, namely, at landscape and whole of basin scales. This is most likely to be the case in relatively wet periods such as La Niña, when natural rainfall and flooding produce vast areas of highly productive foraging and breeding habitats, often long distances from regularly managed wetland sites. Implications of large-scale movements This tracking has revealed high levels of connectivity in eastern Australia for these species, from the south to the north, contrasting with an apparent disconnect between eastern and western populations. The degree and timing of connectivity is likely to be influenced by wind and water conditions as well as by season, as shown for SNI in previous work [ 90 ]. This suggests that the lack of species presence records in intervening areas and the lack of recoveries of banded individuals that have moved between eastern and western Australia is most likely because of the extent of arid environments between the latter, rather than because of the scarcity of human observers in these areas, as previously suggested [ 40 , 41 , 44 ]. Within eastern Australia, it is clear that individuals can move long distances quickly and may visit multiple sites within a season over broad areas before selecting a place for temporary residency or nesting. Exploration of potential nesting sites can start in late winter (e.g. August) and can cover hundreds of km for a single individual. This suggests that managers wishing to provide suitable foraging, refuge or breeding habitat may need to coordinate among sites at these broader spatial scales. For example, at some sites starting watering early, particularly in southern sites where natural flood regimes and cues would have typically started much earlier than in the north, and at other sites starting watering later in the breeding season. These results emphasise the need for multi-scale thinking in planning environmental water allocations for and managing expectations regarding inland waterbird behavioural and population responses. This, together with multiple major breeding sites for these species being in the Murray-Darling Basin of eastern Australia, reinforces the importance of environmental water management for waterbirds at Basin-scale in influencing the maintenance of overall species populations in eastern Australia [ 91 ]. Conclusion Many waterbird species are highly mobile and move across jurisdictional boundaries and at continental scales, which makes strategic conservation management challenging. Information about individual-, population- and species-level movement strategies, flexibility and plasticity is essential for evidence-based management. For example, prioritisation of sites for environmental watering to support juvenile survival and adult recovery from breeding can be informed by data on site-fidelity, dispersal routes, and foraging sites. Long-term satellite tracking is a valuable tool in providing this entire lifecycle information and can be used to resolve knowledge gaps, enabling management decisions to be made with improved understanding of waterbird movements and site use. This is the most detailed study to date of Australian waterbird movements, with 122 individuals tracked between 2016 and 2023. Characterisation of long-term movements provided evidence for variability in movement strategies, including plasticity over an individual’s lifetime. The dominant strategy of our focal species, SNI, was nomadism; however, this varied among individuals, with some evidence for partial migration. Overall, increased knowledge of the interaction of waterbirds with their environment across their entire life cycles will be increasingly important for informing policy and management decisions and predictions aimed at increasing waterbird abundance and maintaining waterbird diversity. Declarations Author Contribution H.M.M. conceived the idea and led the project, data collection, data processing, planning and interpretation of analyses, and writing the manuscript. L.L.-J. led the advanced analyses and modelling and co-wrote the manuscript. H.M.M., F.R., L.G.O., S.R., J.M.M., M.P., M.D., J.H., and M.V.J conducted the primary fieldwork and data collection. L.L.-J., F.R., A.L., J.H., M.V.J. and H.M. processed and mapped the data. R.K., K.B., V.D. and R.M.N. provided research direction and design advice at the commencement of the project. Acknowledgement The authors express their gratitude for the assistance of colleagues, collaborators and volunteers with fieldwork, and the support of program leaders. Data Availability The data supporting the conclusions of this article are available on reasonable request to the corresponding author. Funding declaration The original research that formed the basis of this article was co-funded by the Commonwealth Environmental Water Holder’s Office (CEWH/CEWO) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) through the CEWH Monitoring, Evaluation and Research project (2019-2024) and the CEWO Environmental Watering Knowledge and Research project (2015-2018), administered through the Commonwealth Environmental Water Office within the Department of Climate Change, Energy, the Environment and Water and its precursors. The research also benefited from co-investment by the Lake Cowal Conservation Centre, and from in-kind support from the Royal Botanic Garden Sydney (John Martin), NSW Department of Planning and Environment and its precursors, and the Goulburn-Broken Catchment Management Authority (Keith Ward). 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Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Movement Ecology → Version 1 posted Editorial decision: Revision requested 01 Sep, 2024 Reviews received at journal 27 Aug, 2024 Reviews received at journal 05 Aug, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviewers invited by journal 14 Jul, 2024 Editor assigned by journal 20 Jun, 2024 Submission checks completed at journal 20 Jun, 2024 First submitted to journal 17 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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McGinness","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3QMWrDMBSA4WcM6fJMV0PBvoK8GAqmvopCQZOHhC4dOhgE7hUSegmPHToIBNGiAxgKbYqgWROydGorNWknO3jsoH8SD388SwA+3z+MCIDwcAjX4md0VgPQ0yTg7nApJuRAUIwkpcDjKD71vSOKb8zs9gVypQM5fyzKdGnM9u2pgPNFvy31KuMLfQO5rkAuNZu2zyyP6TuDuOsnpKMZjxoKeYczGTWSkotqAlRIgCHyutnx6JNai8SSL/tjOtw6kg5uQbul/iMiqDu0L2AJGSK6mj/giiLRzJHraavdXQTDTK8HXky1e7yjCVHS7KPmqkzvpdl9iCJJVP+W33DExOfz+Xyj+wY6WWpA0UJWVgAAAABJRU5ErkJggg==","orcid":"","institution":"CSIRO Environment","correspondingAuthor":true,"prefix":"","firstName":"Heather","middleName":"M.","lastName":"McGinness","suffix":""},{"id":323827947,"identity":"025d45a0-fc83-4d78-ab38-4e53a61459d3","order_by":1,"name":"Luke R. Lloyd-Jones","email":"","orcid":"","institution":"CSIRO Data61","correspondingAuthor":false,"prefix":"","firstName":"Luke","middleName":"R.","lastName":"Lloyd-Jones","suffix":""},{"id":323827948,"identity":"5b19567d-c395-4d5c-bdb2-73d867651677","order_by":2,"name":"Freya Robinson","email":"","orcid":"","institution":"CSIRO Health and Biosecurity","correspondingAuthor":false,"prefix":"","firstName":"Freya","middleName":"","lastName":"Robinson","suffix":""},{"id":323827949,"identity":"cb2153ae-dd50-4bfa-8eae-1e939a9156f3","order_by":3,"name":"Art Langston","email":"","orcid":"","institution":"CSIRO Environment","correspondingAuthor":false,"prefix":"","firstName":"Art","middleName":"","lastName":"Langston","suffix":""},{"id":323827950,"identity":"7408109e-8222-4a6f-ae77-d5e7fe8b25b9","order_by":4,"name":"Louis G. O’Neill","email":"","orcid":"","institution":"CSIRO Environment","correspondingAuthor":false,"prefix":"","firstName":"Louis","middleName":"G.","lastName":"O’Neill","suffix":""},{"id":323827951,"identity":"ed7f1e08-ea1d-485e-b5b4-f38eb8f7ac1e","order_by":5,"name":"Shoshana Rapley","email":"","orcid":"","institution":"CSIRO Environment","correspondingAuthor":false,"prefix":"","firstName":"Shoshana","middleName":"","lastName":"Rapley","suffix":""},{"id":323827952,"identity":"529710fc-4683-4531-a175-3edf5d3edc21","order_by":6,"name":"Micha V. Jackson","email":"","orcid":"","institution":"CSIRO Environment","correspondingAuthor":false,"prefix":"","firstName":"Micha","middleName":"V.","lastName":"Jackson","suffix":""},{"id":323827953,"identity":"622c0a8e-9517-4988-9418-eb0ff5fbc1aa","order_by":7,"name":"Jessica Hodgson","email":"","orcid":"","institution":"CSIRO Environment","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Hodgson","suffix":""},{"id":323827954,"identity":"e9804846-f180-44c9-9b2d-9620bc83695f","order_by":8,"name":"Melissa Piper","email":"","orcid":"","institution":"CSIRO Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"","lastName":"Piper","suffix":""},{"id":323827955,"identity":"dbb0a8f1-ae7f-4969-b547-9abaa73c76d4","order_by":9,"name":"Micah Davies","email":"","orcid":"","institution":"CSIRO Environment","correspondingAuthor":false,"prefix":"","firstName":"Micah","middleName":"","lastName":"Davies","suffix":""},{"id":323827956,"identity":"f6d4da70-e062-43ca-abd4-ebcd0b31c678","order_by":10,"name":"John M. Martin","email":"","orcid":"","institution":"Royal Botanic Gardens Sydney","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"M.","lastName":"Martin","suffix":""},{"id":323827957,"identity":"a7a45a7b-b195-4310-bc0d-47cf7f091a26","order_by":11,"name":"Richard Kingsford","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Kingsford","suffix":""},{"id":323827958,"identity":"9a6f794b-3a43-4fd4-a51d-d806a1dbe11e","order_by":12,"name":"Kate Brandis","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Kate","middleName":"","lastName":"Brandis","suffix":""},{"id":323827959,"identity":"bdce7288-f015-447e-b332-cf6d7127db74","order_by":13,"name":"Veronica Doerr","email":"","orcid":"","institution":"CSIRO Environment","correspondingAuthor":false,"prefix":"","firstName":"Veronica","middleName":"","lastName":"Doerr","suffix":""},{"id":323827960,"identity":"25ff1a2d-05a6-4450-a212-5d4f0623249b","order_by":14,"name":"Ralph Mac Nally","email":"","orcid":"","institution":"University of Canberra","correspondingAuthor":false,"prefix":"","firstName":"Ralph","middleName":"Mac","lastName":"Nally","suffix":""}],"badges":[],"createdAt":"2024-06-18 01:36:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4596537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4596537/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40462-024-00515-4","type":"published","date":"2024-11-26T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60601379,"identity":"28790956-7672-497e-8a38-e2ad02b598eb","added_by":"auto","created_at":"2024-07-18 16:05:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":277585,"visible":true,"origin":"","legend":"\u003cp\u003eSites at which straw-necked ibis, Australian white ibis and royal spoonbill were fitted with transmitters, south-eastern Australia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/b6999bdf5ae8cc4e3006a7d5.png"},{"id":60599386,"identity":"b2b90a1f-c5c6-49be-88ca-b7e44ec11ea5","added_by":"auto","created_at":"2024-07-18 15:57:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6189056,"visible":true,"origin":"","legend":"\u003cp\u003ePanel a) shows all satellite-tracked SNI, RSB and AWI movements in eastern Australia combined, 2016 – 2024 (\u003cem\u003eN\u003c/em\u003e=122) with different colours designate different species. Panel b) shows satellite-tracked SNI movements (\u003cem\u003eN\u003c/em\u003e=73) with different colours representing individual tracks. Panel c) shows satellite-tracked RSB movements (\u003cem\u003eN\u003c/em\u003e=42) and panel d) AWI movements across eastern Australia (\u003cem\u003eN\u003c/em\u003e=7).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/41244ffb3caa7f7b3f88e4b8.png"},{"id":60599390,"identity":"bc80d1d6-9002-475c-ab06-ab54fcef82c5","added_by":"auto","created_at":"2024-07-18 15:57:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67605,"visible":true,"origin":"","legend":"\u003cp\u003eMean cumulative distance travelled per 24 h, by month and species. Central solid line is the trend in the median values. Dotted lines show the trend in the lower 10\u003csup\u003eth\u003c/sup\u003e percentile and the upper 80\u003csup\u003eth\u003c/sup\u003e percentile, which were chosen so that the change in variation can be observed between months, but the median trends are evident (see Supplementary Figure 2 for full distributions of cumulative distances by month).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/3b3149bdbcdcec455b5211af.png"},{"id":60599388,"identity":"13f92914-e8a3-44bd-9354-75ff90dfac42","added_by":"auto","created_at":"2024-07-18 15:57:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74698,"visible":true,"origin":"","legend":"\u003cp\u003eMean distance travelled to forage from roost site, by species and month. Central solid line is the trend in the median values. Dotted lines show the trend in the lower 10\u003csup\u003eth\u003c/sup\u003e percentile and the upper 80\u003csup\u003eth\u003c/sup\u003e percentile, which were chosen so that the change in variance can be observed between months, but the median trend can be visually appreciated (see Supplementary Figure 4 for full distributions of cumulative distances by month).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/230a8840b108b25cfba65c36.png"},{"id":60599384,"identity":"c42b9f8f-284b-4f00-b5d4-be0eb8761c72","added_by":"auto","created_at":"2024-07-18 15:57:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":497340,"visible":true,"origin":"","legend":"\u003cp\u003eOdds of residency (black line) per calendar month. Dashed lines indicate the upper and lower 95% confidence interval (CI) for the odds.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/9ab6b2679f730ab5884bfa0a.png"},{"id":60599393,"identity":"f1ca2d13-e1c5-4b47-9ed2-eef40d585bec","added_by":"auto","created_at":"2024-07-18 15:57:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41664,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of 95% autocorrelated kernel density estimated (AKDE) home range block area distributions by species and age class. Numbers above boxes correspond to median values.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/d47cff7b32eeeb7697108a18.png"},{"id":60601384,"identity":"2d42c0fe-2547-4e5e-8a41-b71a5089097a","added_by":"auto","created_at":"2024-07-18 16:05:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":193164,"visible":true,"origin":"","legend":"\u003cp\u003eResidency areas for non-nesting ibis and spoonbills, and associated wetlands listed under the Directory of Important Wetlands in Australia, including seven sites listed under Ramsar.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/7f924edc5f6a497eaa02b68e.png"},{"id":60601383,"identity":"d1016a3a-0c45-4b4d-a103-a025344cfcf5","added_by":"auto","created_at":"2024-07-18 16:05:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":296339,"visible":true,"origin":"","legend":"\u003cp\u003eResidency areas for non-nesting ibis and spoonbills mapped with known nesting sites and ‘managed floodplain’ within the Murray-Darling Basin.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/dfefc90d4870fe867787bbf1.png"},{"id":60601380,"identity":"26f8fe36-b896-4ca7-989c-166413e968b6","added_by":"auto","created_at":"2024-07-18 16:05:18","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":85196,"visible":true,"origin":"","legend":"\u003cp\u003eSimple representation of a spectrum of movement strategies and their relationships to resource predictability and weather stochasticity.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/770a5642175012dae0d6697e.png"},{"id":60599391,"identity":"d544bc00-7d51-46a5-a8b1-953e6a007495","added_by":"auto","created_at":"2024-07-18 15:57:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":342337,"visible":true,"origin":"","legend":"\u003cp\u003eSatellite tracked movements of an adult female straw-necked ibis between December 2017 and October 2022.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/3f30f4d59fa7a33621375b68.png"},{"id":70382158,"identity":"0e6e37bc-a8b5-4e25-ab59-00b1fae79fae","added_by":"auto","created_at":"2024-12-02 16:24:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12503420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/a1ce2ea7-d909-4de2-a7e5-dbb5ef196bad.pdf"},{"id":60599394,"identity":"dd604b1e-0dcc-4d03-8fd8-854f9e52b9c4","added_by":"auto","created_at":"2024-07-18 15:57:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2955763,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4596537/v1/05dd66c468849a0fe3dd6a9f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Satellite telemetry reveals complex mixed movement strategies in ibis and spoonbills of Australia: implications for water and wetland management","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWaterbirds are highly mobile, with many species conducting long-distance movements of hundreds or thousands of kilometres in days or weeks [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Understanding these movements is key to effective management of waterbird populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], particularly since rates of wetland habitat loss are increasing due to water harvesting and climate drying [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Maintaining waterbird diversity and habitats are often important goals for water and wetland managers worldwide [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, there are knowledge gaps that affect our ability to manage habitats and to predict waterbird responses at local, national and global scales. One of the most critical gaps is knowledge of waterbird movement behaviour and variability, particularly for inland species [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWaterbird movements can be complex, occurring along a continuum of variation where the behaviour of any one species or individual may be classified variously as: (1) Residency, including \u0026lsquo;sedentary movements\u0026rsquo;, \u0026lsquo;central-place movements\u0026rsquo;, and \u0026lsquo;commuting\u0026rsquo;, where individuals remain in one area or home range, usually returning to favoured locations between relatively short-distance foraging trips, and sometimes displaying territoriality (including during breeding periods); (2) Nomadism, including \u0026lsquo;facultative migration\u0026rsquo; and \u0026lsquo;facultative movements\u0026rsquo; where individuals move different distances and directions irregularly and seem to generally be responding opportunistically to resource availability, including \u0026lsquo;fugitive movements\u0026rsquo; in response to disruption or disturbance of resources, individuals or flocks; or (3) Migration, sometimes called \u0026lsquo;obligatory migration\u0026rsquo; or \u0026lsquo;seasonal migration\u0026rsquo;, where individuals regularly move relatively long distances predictably between particular locations and in consistent directions, usually seasonally or annually [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Adding to this complexity, individuals can show plasticity in their movement strategies over time [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdvances in telemetry are revealing complexities in movement patterns within and among individuals and species, including \u0026lsquo;partial migration\u0026rsquo;, where residency, nomadism or migration strategies may each be employed within a species by different individuals or by an individual at different times [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It has been suggested that there is greater variation in movement strategies in the southern hemisphere compared to the northern hemisphere due to greater variability in climatic factors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, there are relatively few satellite-telemetry studies tracking southern hemisphere species, especially inland waterbirds, and those few have usually involved relatively small numbers of individuals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, understanding of nomadism is relatively limited compared to that for migration and residency [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMovement ecology is often poorly understood even for common and conspicuous taxa that are the focus of significant management investment. Examples include ibis, spoonbills, egrets, and herons that nest in large aggregations, often in Ramsar sites, in response to specific environmental conditions such as flooding [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Despite extensive leg-banding and other marking programs, resighting or recovery data are usually limited for these taxa. For example, \u0026lt; 0.8% of ibis and spoonbills banded in Australia have been resighted or had their bands found\u0026thinsp;\u0026gt;\u0026thinsp;3 months after banding, post-dispersal from natal or nest sites [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Taxa such as these are of particular interest for wetland and waterbird management and policy makers because they often nest in areas that are susceptible to adverse effects of environmental change; consequently, their habitats and populations are the subject of intensive conservation management [\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. For species dependent on surface water, management can include the allocation of environmental water or \u0026lsquo;environmental flows\u0026rsquo;, to provide the quantity, timing, and quality of freshwater flows and levels necessary to sustain aquatic ecosystems [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Wetlands and other areas that receive environmental water (actively or passively), or that may be inundated by natural flooding if key constraints are overcome, are colloquially termed the \u0026lsquo;managed floodplain\u0026rsquo; [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Understanding waterbird movements at fine spatial and temporal scales can maximise the efficacy of environmental water application, by guiding where and when to provide water and for how long.\u003c/p\u003e \u003cp\u003eAustralia has breeding populations of three ibis species and two spoonbill species in the Threskiornithidae family: Straw-necked ibis (\u003cem\u003eThreskiornis spinicollis\u003c/em\u003e); Australian white ibis (\u003cem\u003eThreskiornis molucca\u003c/em\u003e); glossy ibis (\u003cem\u003ePlegadis falcinellus\u003c/em\u003e); royal spoonbill (\u003cem\u003ePlatalea regia\u003c/em\u003e); and yellow-billed spoonbill (\u003cem\u003ePlatalea flavipes\u003c/em\u003e). These species nest in aggregations of up to hundreds of thousands of birds when conditions are good, while in poor conditions they may not nest at all. Inland, these species are experiencing reduced breeding event frequency, size, and success, including mass nest abandonment events due to prematurely falling water levels [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The birds are dependent on wetland inundation for breeding, but the degree of breeding-site fidelity among individuals or groups is not well understood, even within the relatively well-studied breeding sites of the Murray-Darling Basin in south-eastern Australia, which is thought to be the core breeding area for these species, with 46% of aggregate-nesting wetlands in Australia [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Some breeding sites are used every year by the same species; but it is not known if it is the same individuals revisiting each time.\u003c/p\u003e \u003cp\u003eThere are major knowledge gaps about movements outside of breeding events. While these birds are known to be capable of moving at continental scales (many 100s \u0026ndash; 1000s km) within a few months [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], some authors have suggested there may be regional sub-populations and seasonal migrations with site fidelity [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Leg-banding data suggest individuals that breed in eastern Australia mostly remain in eastern Australia and rarely move west; most recorded displacements of leg-banded birds have been from the south-east to the north-east [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Seasonal fluctuations in local abundance along with observations of flocks in flight suggest that east coast areas are important winter and drought refuge locations, while inland wetlands of the Murray-Darling Basin are important breeding sites [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However banding recoveries do not suggest any relationships among bird age, season, and movement distances or directions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere has been very limited direct movement tracking using telemetry for ibis and spoonbill species in Australia. No royal spoonbills, yellow-billed spoonbills, or glossy ibis have been tracked prior to this study. In February 2000, two straw-necked ibis were fitted with satellite transmitters in the Macquarie Marshes in New South Wales (NSW) and tracked for c. two months [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Of these two, one bird flew 1,438 km north of the capture site within a month, while the second bird remained within 200 km of the capture site [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Tracking of Australian white ibis movements has largely focused on breeding sites in urban and suburban coastal environments, rather than natural inland wetlands [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor conservation and management, important knowledge gaps remain at the continental scale about movement patterns, movement variability, movement rates and movement timing for these species. At local to regional scales, questions such as how far birds travel to find food are also relevant for managers of waterbird populations and habitats. Answering these questions would assist water and wetland managers to identify key habitats associated with movements and to understand better the places, scales and times at which resources are required. This should in turn improve capacity to target land and water management actions (such as strategic watering and drying of wetlands), evaluate progress, and predict future outcomes for waterbirds.\u003c/p\u003e \u003cp\u003eOver seven years, we used GPS (global positioning system) satellite telemetry to track the movements of three species in the Threskiornithidae family \u0026ndash; straw-necked ibis, Australian white ibis, and royal spoonbills. We aimed to address knowledge gaps regarding intra and interspecific variation in movement strategies of these species post-dispersal from breeding sites and consider consequent implications for water, wetland and waterbird management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTransmitter deployment\u003c/h2\u003e \u003cp\u003eStraw-necked ibis (\u0026lsquo;SNI\u0026rsquo;; N\u0026thinsp;=\u0026thinsp;73; 45 adults and 28 juveniles) was chosen as the primary species for transmitter deployment because it is a focal species for Australian wetland and water managers that nests in large numbers in major inland wetlands managed with environmental water. Two other species that frequently nest and forage with straw-necked ibis were also tracked, in smaller numbers, to explore potential differences among species: the royal spoonbill (\u0026lsquo;RSB\u0026rsquo;; N\u0026thinsp;=\u0026thinsp;42; 5 adults and 37 juveniles) and the Australian white ibis (\u0026lsquo;AWI\u0026rsquo;; N\u0026thinsp;=\u0026thinsp;7; 3 adults and 4 juveniles).\u003c/p\u003e \u003cp\u003eTransmitters were deployed at eight breeding sites within the Murray-Darling Basin in south-eastern Australia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) between 2016 and 2023. The Murray-Darling Basin is \u0026ge;\u0026thinsp;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e and is a primary focus for intensive water management and water policy reform in Australia. It contains 16 internationally significant wetlands, including some of the most important aggregate-nesting ibis and spoonbill breeding sites on the continent.\u003c/p\u003e \u003cp\u003eTransmitters were attached as a \u0026lsquo;backpack\u0026rsquo; using Teflon ribbon or Spectra ribbon (Bally Ribbon Mills\u0026trade;) harnesses, fitted either as wing-loops with a join at the keel (SNI and AWI and some RSB), or as leg-loops (most RSB). Harness design was based on designs used in other species [\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] modified and improved over time, with different types of weak links used in different years. Transmitters weighed 12\u0026ndash;40 g, ranging from \u0026lt;\u0026thinsp;1\u0026ndash;5% of bird bodyweight.\u003c/p\u003e \u003cp\u003eSolar-powered GPS transmitters with a fix resolution of 15\u0026ndash;26 m were used, with data sent through either the Argos satellite network (Geotrak units) or the 3G network (Ornitela and Druid units). The frequency of fixes ranged from every min to every 6 h, depending on the transmitter type and programmed schedule. This was handled in analyses, with interpolation or down-scaling applied when appropriate (see below).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnalyses\u003c/h2\u003e \u003cp\u003ePeriods of data for nesting adults, for adults and juveniles still within breeding sites post-capture but pre-dispersal, and for birds tracked for \u0026lt;\u0026thinsp;30 days were removed, to ensure results reflected longer-term movement statistics and patterns. The final dataset comprised 122 individuals, 41,619 days of tracking data and 1,240,206 GPS fixes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalyses were done in R version 4.3.1 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Summary statistics were calculated for each species and age class for tracking duration, distance travelled between roosts and foraging sites (maximum distance travelled from roost per day), roost-shift distances (midnight fix to midnight fix displacements), and cumulative distance travelled per hour (km h\u003csup\u003e-1\u003c/sup\u003e), per 24 hr (km d\u003csup\u003e-1\u003c/sup\u003e) and per year (km yr\u003csup\u003e-1\u003c/sup\u003e). Statistics for distances travelled were calculated separately for calendar months to account for seasonal differences, by species and age class. Permutation tests were used to asses differences in distributions between groups of interest e.g., species and age using the \u0026lsquo;percentileTest\u0026rsquo; and \u0026lsquo;pairwisePercentileTest\u0026rsquo; functions with 1000 replicates in the R package \u0026lsquo;rcompanion\u0026rsquo; [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of birds included in analyses by species and age group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies and age group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStraw-necked Ibis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuvenile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRoyal Spoonbill\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuvenile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAustralian White Ibis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuvenile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrand Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e122\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eResidency\u003c/h2\u003e \u003cp\u003ePeriods of residency were identified using the semivariance function (SVF) approach of CH Fleming, JM Calabrese, T Mueller, KA Olson, P Leimgruber and WF Fagan [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The SVF measures the variability in the distances between pairs of locations as a function of the time lag between those pairs and is calculated over all possible time lags up to the maximum. This method was initially trialled on both the entire movement track of each individual and various temporal sub-sections of the movement track, but sections longer than a few months rarely passed the residency tests required for estimations. Ultimately, monthly segments were chosen as the longest frequently resident sections of the telemetry tracks to explore changes in behaviour through time and to allow evaluation of seasonal patterns. To classify months of resident behaviour, a within-month Ornstein-Uhlenbeck-including-foraging (OUF) model was fitted to the empirical variogram using functions in the package \u0026lsquo;Continuous Time Movement Models\u0026rsquo; (CTMM) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The OUF model is frequently the best fitting model for home-range behaviour modes [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. First- and second-order derivative checks were performed on the fitted OUF function to determine whether the function reached an asymptote within each month. Reaching an asymptote defined the months for each individual in which the bird was resident.\u003c/p\u003e \u003cp\u003eFor all birds, the odds of residency vs non-residency for each calendar month were computed from the counts in each of the two classes and the confidence interval from delta method approximation [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] for the odds as a function of the sample proportion estimate i.e., the standard error was computed on the proportion estimate and then mapped to the interval on the odds scale via the delta method. Using the resident/non-resident status over all months for all birds, we computed the odds of being resident as the ratio of the number of resident months to non-resident months. For example, if there were 100 instances of telemetry tracks covering the month of June (which could be multiple instances per bird if they were tracked for multiple years) then an odds of 2 indicates that 66 tracks from June were classified as resident versus 33 as non-resident. This was done for all birds combined, for individual species, and for age groups within species. Differences in the proportion of residency events in season were compared with the two-sample permutation test to compare two proportions implemented using the \u0026lsquo;twoSamplePermutationTestProportion\u0026rsquo; function in the EnvStats R package [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHome range when resident\u003c/h2\u003e \u003cp\u003eHome ranges were calculated during periods of residency for each bird using the autocorrelated kernel density estimation (AKDE) method, fitted using the package \u0026lsquo;CTMM\u0026rsquo; [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. For each month that each bird was resident, the within-month 95% home range area was computed. Within-month home ranges were likely to overlap when an individual was resident for many months, resulting in an overestimation of the total home range for a given residency period. Therefore, a test for overlap of home range was performed within sequential pairs of months that were appropriate for home-range estimation using the Bhattacharyya coefficient (BC) method in the package \u0026lsquo;CTMM\u0026rsquo; [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The BC quantifies the overlap between home ranges and provides a statistical test for whether the overlap is substantial enough to state they are distinct, given model uncertainty [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Home ranges were considered to overlap if the lower confidence interval was greater than 0.01 as in K Winner, MJ Noonan, CH Fleming, KA Olson, T Mueller, D Sheldon and JM Calabrese [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. For the sets of months in which the home range values overlapped, the AKDE method was rerun to estimate a home range for a joint time period, which we refer to as a \u0026lsquo;home range block\u0026rsquo;. The mean and median over each individual\u0026rsquo;s discontiguous 95% blocks were summarised. Differences in home range among species or by age class were investigated by testing differences in medians via permutation tests using the \u0026lsquo;percentileTest\u0026rsquo; function in the \u0026lsquo;rcompanion\u0026rsquo; R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSite fidelity\u003c/h2\u003e \u003cp\u003eTo explore fidelity to foraging areas (locations and periods of time for which individuals roosted and foraged in the same area), sites used during periods of residency were mapped as utilisation distribution (\u0026lsquo;UD\u0026rsquo;) polygons for each individual based on the start and end date of each residency period. The package \u0026lsquo;Recurse\u0026rsquo; [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] was used to calculate visitation to each site polygon, in terms of arrival date, departure date, duration of occupancy, duration between visits, and number of distinct visits overall and by month, for each individual.\u003c/p\u003e \u003cp\u003eResidency areas were mapped and characterised according to whether they were associated with: (a) sites listed by the Ramsar Convention on Wetlands of International Importance [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]; (b) sites listed by the Directory of Important Wetlands in Australia [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]; (c) the \u0026lsquo;managed floodplain\u0026rsquo;, being wetlands and other areas that can receive environmental water (actively or passively) or that may be inundated by natural flooding or high flows if key constraints are overcome [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]; and, (d) historical breeding sites for these and other aggregate-nesting species within the Murray-Darling Basin, which is thought to be the core breeding area for these species and for which breeding site locations are relatively well known and mapped compared to other parts of Australia.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTracking duration\u003c/h2\u003e \u003cp\u003eThe maximum number of days that any individual was tracked was 2,233 days. Five adult and two juvenile SNI and two juvenile AWI were tracked for \u0026gt;\u0026thinsp;3 years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics for number of days tracked per bird.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgeCat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile_1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd_Dev\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQuartile_3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003e(birds)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e499.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDistance\u003c/h2\u003e \u003cp\u003eTracked movements spanned almost the whole of eastern mainland Australia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The longest distance travelled in an hour was 135 km, recorded by a juvenile SNI. The longest cumulative distance travelled within a 24-hr period was 857 km, recorded for a juvenile RSB; maximums for SNI and AWI were 662 km and 271 km respectively. The median cumulative distance travelled per 24-hr differed between species, with SNI travelling the furthest at 5.9 km d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, AWI travelling 3.9 km d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and RSB traveling 5.4 km d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 adjusted for multiple testing from pairwise permutation tests between medians (null hypothesis of no difference in medians) of each species; see Supplementary Fig.\u0026nbsp;1 for distributions). Cumulative distances travelled per day were lower in the late autumn and early winter months and higher in spring and summer for SNI and AWI; for RSB, mean distances travelled per day were highest in autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Permutation \u003cem\u003eP\u003c/em\u003e-values between medians and 80th percentiles between seasons within species were \u0026lt;\u0026thinsp;1e\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e (null hypothesis of no difference in median distance travelled between seasons) except for summer and spring comparisons within AWI and SNI. RSB showed evidence for differences in 80th percentiles, except for autumn and spring and summer differences in medians. The 80th percentiles summarise the components of the monthly distribution that are long-distance movements while the medians summarise the central tendency of the heavily right-skewed distributions (see Supplementary Fig.\u0026nbsp;2 for summaries of full distributions). For those individuals with complete datasets of 365 days in a year (adjusted for removal of nesting periods and missing data), the maximum cumulative distance travelled by an individual was 15,549 km yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (an adult female SNI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecies also differed in the distances travelled from roosts to foraging sites (maximum distance travelled from roost per day; permutation test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Distances were shortest for AWI (median 1.6 km, mean 3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7 km) compared to SNI (median 2.1 km, mean 10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;35.1 km) and RSB (median 2.4 km, mean 8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;27.9 km; Supplementary Fig.\u0026nbsp;3). Juvenile SNI travelled shorter distances (median 1.6 km) to forage than did adults (median 2.5 km; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDaily distances travelled from the roost differed among months of the year for SNI and RSB but less so for AWI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). SNI travelled further from their roosts in spring and summer months compared to late autumn and early winter months (permutation test for median differences \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;1e\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e for all season comparisons; 80th percentile permutation test \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;=\u0026thinsp;0.74 for the summer-spring comparison and 0.04 for the autumn-winter comparison). In contrast, RSB travelled farther to forage in late autumn, and foraged closer to roosts in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; permutation \u003cem\u003eP\u003c/em\u003e-values between summer median distance travelled and all other seasons\u0026thinsp;\u0026lt;\u0026thinsp;1e\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThere was evidence for greater roost-site fidelity for AWI than for the other species, with median roost location shift distances of 0.3 km (mean 2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7 km), compared to 0.6 for SNI (mean 9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;35.0 km) and 0.6 for RSB, (mean 7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;32.5 km; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Median roost location shift distances were also shorter for juvenile RSB and SNI (0.55 km and 0.45 km) compared to adults of these species (0.66 km and 0.78 km respectively; permutation \u003cem\u003eP\u003c/em\u003e- \u0026lt;1e\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics for maximum distance travelled (km) from roost per day\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd Dev\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN birds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResidency\u003c/h2\u003e \u003cp\u003eAcross all species, 25% of individuals had no period of residency in any month according to the criteria detailed above. All individuals with no residency periods were either SNI or RSB (SNI\u0026thinsp;=\u0026thinsp;19 adults, 4 juveniles, accounting for 31.5% of tracked SNIs; RSB\u0026thinsp;=\u0026thinsp;6 adults, 1 juvenile, accounting for 16.6% of tracked RSB). In contrast, all tracked Australian white ibis had multiple or extended periods of residency. The proportion of months spent resident overall ranged from 24% (adult SNI) to 33% (adult RSB) and 58% (adult AWI; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStraw-necked ibis were most likely to be resident in July, the middle of winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with higher proportion of resident months in autumn (prop. = 0.3) and winter (prop. = 0.38) with peaks in odds in May and June. The odds of residency were lowest in spring. Two sample permutation tests for differences in proportions of months resident within seasons showed winter proportions were greater than summer and spring (Null hypothesis prop.1\u0026thinsp;=\u0026thinsp;prop.2, prop.winter \u0026ndash; prop.summer\u0026thinsp;=\u0026thinsp;0.38\u0026ndash;0.24, permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.001 and prop.winter \u0026ndash; prop.spring\u0026thinsp;=\u0026thinsp;0.38\u0026ndash;0.16, permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1e-6). Autumn also showed evidence of a great proportion than spring (prop.autumn \u0026ndash; prop.spring\u0026thinsp;=\u0026thinsp;0.29\u0026ndash;0.16, permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;7e-4). Within age groups, adult SNI showed clearly increased odds of residency during winter (proportion resident months in winter\u0026thinsp;=\u0026thinsp;0.4, prop.winter \u0026ndash; prop.spring\u0026thinsp;=\u0026thinsp;0.38\u0026ndash;0.15, permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;4e-5). Juvenile odds of residency among months were less consistent, with multiple small peaks in late summer, late autumn, and mid-winter and reduced odds in spring. Permutation \u003cem\u003eP\u003c/em\u003e-values only showed evidence for autumn and winter being more resident than spring (prop.winter \u0026ndash; prop.spring\u0026thinsp;=\u0026thinsp;0.36\u0026ndash;0.18, permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.005 and prop.autumn\u0026ndash; prop.spring\u0026thinsp;=\u0026thinsp;0.36\u0026ndash;0.18, permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.007) but no evidence for differences between other seasonal proportions for juveniles.\u003c/p\u003e \u003cp\u003eRoyal spoonbill juveniles showed reduced residency in autumn (prop. = 0.24) and winter (prop. = 0.31), and increased odds in spring (prop. = 0.35) and summer (prop. = 0.39) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Permutation \u003cem\u003eP\u003c/em\u003e-values only showed marginal evidence for summer proportions being greater than autumn (prop.summer\u0026ndash; prop.autumn\u0026thinsp;=\u0026thinsp;0.15, permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.04). Data for adult RSBs were limited but appeared to show no evidence for difference in seasons based on permutation tests for proportions. Juveniles showed similar results to the all-RSB analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Australian white ibis odds of residency were relatively consistent through the year, with small peaks in autumn (March-April) and spring (November) but no evidence for differences in proportions of months resident in each of the seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage time (months) classed as resident by species and age group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% months resident by species and age group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHome range when resident\u003c/h2\u003e \u003cp\u003eHome range sizes when resident differed among species for adults. AWI adults had significantly smaller home ranges (median 10.0 km\u003csup\u003e2\u003c/sup\u003e ) than SNI and RSB (medians 49.7 and 55.5 km\u003csup\u003e2\u003c/sup\u003e respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; see permutation test results in Supplementary Fig.\u0026nbsp;5). Adult SNI and RSB also had larger median home ranges than juveniles of the same species (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), but this was only significant for permutation testing of a greater median for adults for SNI (permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.01, see Supplementary Fig.\u0026nbsp;6). In contrast, the median home range of juvenile AWI (34.4 km\u003csup\u003e2\u003c/sup\u003e) was nearly four times greater than that of adult AWI (10.0 km\u003csup\u003e2\u003c/sup\u003e), however there were limited data for AWI, so these observations should be treated with caution (permutation \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.02, see Supplementary Fig.\u0026nbsp;6).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHome range (km\u003csup\u003e2\u003c/sup\u003e) statistics by species and age class.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5 percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSd.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.5 percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN Blocks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAWI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAdult\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJuvenile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSite fidelity\u003c/h2\u003e \u003cp\u003eA total of 147 residency utilisation distribution areas (UDs) were identified, representing areas and periods of time for which individuals roosted and foraged in the same place. Of these, 81 areas were used by multiple individuals (55%). The maximum number of individuals tracked using any one residency area (UD) over the whole period of record was 25 (median 2.0; mean 3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;Sd. = 4.4), at Barmah-Millewa Forest on the Murray River at the NSW-VIC border. Of the 89 individuals with periods of residency, 33 (37%) had more than one residency area identified. The largest number of residency areas identified for an individual was 7 areas, for an adult female SNI. The greatest number of separate residency \u003cem\u003eperiods\u003c/em\u003e identified for an individual was 25, for an adult female SNI tracked for \u0026gt;\u0026thinsp;5\u0026nbsp;year. The median time spent visiting any residency area was one day (mean 11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;Sd. = 32.0; range\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026ndash;679 days), while the median time between visits was three days (mean 36.0\u0026thinsp;\u0026plusmn;\u0026thinsp;Sd. = 126.0; range 1\u0026ndash;1263 days). Arrivals and departures from residency areas were most frequent in March (autumn) and least frequent in July (winter; Supplementary Fig.\u0026nbsp;7). The median number of revisits to a residency area across all birds was six (mean 12.22 \u0026plusmn; -18.67; range 1\u0026ndash;110). Areas with the highest total revisits across all birds were: Barmah-Millewa Forest; Hird Swamp and Johnson Swamp in the Kerang Lakes area of the Loddon catchment; the adjacent Gunbower-Koondrook-Perricoota Forest area including Kow Swamp; and the Macquarie Marshes (Supplementary Table\u0026nbsp;1; Supplementary Fig.\u0026nbsp;8; Supplementary Fig.\u0026nbsp;9; Supplementary Fig.\u0026nbsp;10).\u003c/p\u003e \u003cp\u003eSixty-five percent of identified residency areas were not associated with wetlands listed nationally or internationally as important. There were 27 wetlands listed in the Directory of Important Wetlands in Australia used as residency areas (51 unique UDs; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e; Supplementary Table\u0026nbsp;1). Seven of these are listed under the Ramsar convention: Barmah Forest; Millewa Forest and Koondrook-Pericoota Forests (NSW Central Murray State Forests); the Macquarie Marshes; Gunbower Island (Gunbower Forest), Hird Swamp and Johnson Swamp (Kerang Wetlands). These wetlands are also known breeding sites for aggregate-nesting species.\u003c/p\u003e \u003cp\u003eSeventy-four percent of the residency areas identified were located within the Murray-Darling Basin. Overall, 42 of the 147 residency areas overlapped known breeding wetlands for aggregate-nesting species within the Murray-Darling Basin (29%; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Almost half of the 147 residency areas overlapped the \u0026lsquo;managed floodplain\u0026rsquo; (72; 49%; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), including all the 42 breeding area overlaps.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKnown breeding areas used during residency periods for roosting and foraging within the Murray-Darling Basin.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatchment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreeding wetland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of residency areas (UDs)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMurray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarmah-Millewa Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMurrumbidgee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGayini Floodplains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoddon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKerang Lakes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacquarie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacquarie Marshes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoulburn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoulburn River Floodplains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLachlan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Lachlan Floodplains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBroken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBroken Creek Wetlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMurray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGunbower-Koondrook-Perricoota Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLachlan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCuba Dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLachlan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMid Lachlan Floodplains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMurrumbidgee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMid Murrumbidgee River Floodplains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvens River Floodplains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoulburn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWallenjoe Wetlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLong-term research is important for understanding survival strategies in long-lived species [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This study is the first to track multi-year movements of substantial numbers of ibis and spoonbills in Australia using satellite telemetry. From it we have quantified movement distances and timing, residency and non-residency, home range size when resident, and species and individual movement strategies. A range of scales of movement was apparent, from movements of only tens of km over months to movements of hundreds of km through eastern Australia within a few days or weeks.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMovement strategies within and among species\u003c/h2\u003e \u003cp\u003eThe tracking results reported here have revealed mixed movement strategies within and among the three species tracked (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Each species had individuals displaying plasticity in behaviours across the movement spectrum over time, using different movement strategies at both annual and sub-annual temporal scales, or demonstrating different \u0026lsquo;personalities\u0026rsquo; [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Overall, each species tended toward differences in dominant movement strategy. The dominant strategy for straw-necked ibis as a species was clearly nomadism, while the dominant strategy for Australian white ibis was residency \u0026ndash; with the caveat that only seven AWI were tracked. The dominant strategy for royal spoonbills was less clear, but with only 33% of months spent resident by adult spoonbills and relatively low residency odds for juveniles, is also likely to be nomadism. The odds of residency for juveniles were lower than for adults for both straw-necked ibis and royals spoonbills, implying that juveniles are potentially more likely to take advantage of opportunities presented by climate, weather and management actions than adults [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] \u0026ndash; but also potentially more difficult to support with management in predictable ways [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNone of the individuals tracked were classical \u0026lsquo;obligate migrants\u0026rsquo; for the entire duration of tracking, but some individuals moved seasonally north-south between the same places in some years and showed some site fidelity. For example, in the first two years of tracking, the movements of one adult female SNI suggested that this individual was classically migratory, moving between the same breeding and overwintering sites at about the same time each year (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). However, in the third year, this individual diverted from the usual route to the previous overwintering site and instead flew to an area experiencing extensive flooding hundreds of kilometres away. This individual then flew east via a different route to the usual overwintering site, arriving several months later than the previous two years. There was also flexibility in the timing of movements in terms of departures and arrivals, but with this flexibility set within seasonal patterns. For example, individuals that migrated north-south in autumn and spring varied in the precise timing of their movements and were flexible about the routes and time taken to reach their destinations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBenefits and vulnerability arising from flexible movement strategies\u003c/h2\u003e \u003cp\u003eWhile the variability in movement strategies documented in this study is known to be common among vertebrates, it remains less well studied than \u0026lsquo;typical\u0026rsquo; migratory movements despite indications that such plasticity significantly influences the capacity of species and populations to cope with environmental change [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Satellite-telemetry studies are increasingly demonstrating that mixed movement strategies are more common in birds than previously thought [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. It has been estimated that \u0026gt;\u0026thinsp;36% of Australian land bird species may be \u0026lsquo;partially migratory\u0026rsquo; [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and it is likely that this also applies to waterbird species. This is because Australian inland and especially freshwater environments are subject to extreme variability and unpredictability. Plasticity in movement strategies is thought to be a population characteristic that facilitates species persistence in the face of spatial and temporal variation in resources, environmental conditions and their predictability [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. For such plasticity to persist in a species, there must be benefits to individuals using each strategy when integrated over time [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. For example, an individual ibis remaining resident near a breeding area through winter may have first choice of prime nesting sites in spring or be able to take advantage of good breeding conditions early. In contrast, an individual moving long distances (either migrating or moving nomadically) may be able to avoid unfavourable weather or resource conditions, particularly in winter, thereby improving body condition and surviving for longer.\u003c/p\u003e \u003cp\u003eA nomadic or strategy-flexible individual may take advantage of relatively unpredictable booms in water and food resources but can move away from unfavourable conditions and choose to return to favoured breeding sites when conditions are suitable. The latter strategy is likely to be the most advantageous when climatic and resource conditions are highly unpredictable and highly variable \u0026ndash; as they are in Australia [\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. An ability to switch between strategies among years allows the species to deal with climatic and weather variability and associated resource fluctuations [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]; this is particularly the case for straw-necked ibis due to their broad diet and their ability to successfully forage in terrestrial environments for long periods [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. This may explain the high proportion of individuals that are classed as \u0026lsquo;non-resident\u0026rsquo; but not typically migratory in this study. In the long-term, such a nomadic strategy is likely to allow these species to adapt to climate change better than some other species that are either classically migratory or highly resident and are, therefore, vulnerable to change at particular sites [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. However, this will differ among species and individual dependence on, or fidelity to, important breeding areas or sites, as suggested for RSBs in New Zealand [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile long distance movements and migration can provide benefits, they also have energy costs and can increase the risk of mortality relative to staying resident [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. This is particularly the case if conditions in \u0026lsquo;resident\u0026rsquo; or breeding sites remain good or improve over time, or when conditions elsewhere are becoming more unpredictable or less suitable. In the northern hemisphere, C Buchan, JJ Gilroy, I Catry and AMA Franco [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] found evidence in some partially migratory populations of passerine birds, mammals, and reptiles that resident individuals showed greater survival than migratory individuals, counter to author expectations. They and others have suggested that climate change and other anthropogenic changes, such as urbanisation, may be altering conditions to either favour residency or to promote strategic plasticity in birds, depending on the species and situation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis seems likely for AWI, which, even with the low numbers this study tracked, appears to display residency. It has been suggested for this species that the relatively high and reliable resource availability in coastal urban environments, together with a loss of resources inland, may have resulted in a split of some sub-populations from a larger more mobile population [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. The spread of breeding populations of the AWI into coastal cities, with extensive use of parks and recreation areas, rubbish or landfill sites and areas near airports, has caused conflict with humans [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Radio-telemetry studies suggest that AWI are largely sedentary in urban environments, with strong site fidelity and most movements occurring within c. 50 km of their breeding or nesting colonies [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. This suggests reduced habitat and population connectivity between urban populations and inland populations of AWI, similar to that for the American white ibis (\u003cem\u003eEudocimus albus\u003c/em\u003e) in Florida USA [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], despite AWI being capable of rapid long-distance movements inland [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Some ibis and spoonbill species in the northern hemisphere show evidence of within-species variation in movement strategies geographically, for example the northern bald ibis and Eurasian spoonbill [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], with some suggestion that this is at least partly driven by regional differences in resource availability [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Further satellite tracking from a wider range of sites will assist with evaluating such assertions.\u003c/p\u003e \u003cp\u003eIn general, the implications of the plasticity of movement strategies documented here for management are that provision of suitable resources for such species must still be undertaken within appropriate seasons according to life cycle stage (e.g. nesting), but the location, timing and duration of such provisions can differ and still have benefits. Effectively, management strategies for these species and their habitats can and should also be \u0026lsquo;plastic\u0026rsquo; and adaptable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eForaging site fidelity\u003c/h2\u003e \u003cp\u003eThere is some foraging site fidelity and there are clearly important sites used by multiple individuals and revisited frequently. In addition, while many individual foraging sites used by the three species reported upon here are not recognised by existing conservation or management strategies (such as the Directory of Important Wetlands in Australia or the Ramsar Convention), or are outside of the Murray-Darling Basin and its \u0026lsquo;managed floodplain\u0026rsquo;, the most heavily used sites generally do fall within these priority management areas. While the true relative importance of non-listed and listed sites for overall population dynamics remains unknown, managers will need to take bird use of non-listed sites into account when evaluating species responses to management actions, such as environmental watering. For example, while environmental watering may be employed in one area, birds may choose to use other sites if conditions are perceived to be better there. A lack of perceived responses to management actions (listing sites, or priority management actions) may arise from such bird responses but not be a true indicator of failure per se.\u003c/p\u003e \u003cp\u003eThe availability of food in foraging habitats and their distance from suitable roosting or nesting sites will be critical in determining responses and are potentially manageable. For example, statistics describing distances travelled from roosting sites to foraging sites such as those derived from satellite tracking and described here enable development of threshold distances from roosts within which sites could be prioritised and environmental watering or other management to support foraging areas or food availability could be applied. The degree and timing of residency also allows managers to assess which species or age groups are more likely to be reliant on management of particular sites for long periods or at particular times. Similarly, understanding home-range sizes when resident allows assessment of the area of resources such as environmental watering needed to support species or age groups when resident. It is essential that evaluation of site-based waterbird responses considers these types of contextual information beyond the site, namely, at landscape and whole of basin scales. This is most likely to be the case in relatively wet periods such as La Ni\u0026ntilde;a, when natural rainfall and flooding produce vast areas of highly productive foraging and breeding habitats, often long distances from regularly managed wetland sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImplications of large-scale movements\u003c/h2\u003e \u003cp\u003eThis tracking has revealed high levels of connectivity in eastern Australia for these species, from the south to the north, contrasting with an apparent disconnect between eastern and western populations. The degree and timing of connectivity is likely to be influenced by wind and water conditions as well as by season, as shown for SNI in previous work [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. This suggests that the lack of species presence records in intervening areas and the lack of recoveries of banded individuals that have moved between eastern and western Australia is most likely because of the extent of arid environments between the latter, rather than because of the scarcity of human observers in these areas, as previously suggested [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Within eastern Australia, it is clear that individuals can move long distances quickly and may visit multiple sites within a season over broad areas before selecting a place for temporary residency or nesting. Exploration of potential nesting sites can start in late winter (e.g. August) and can cover hundreds of km for a single individual.\u003c/p\u003e \u003cp\u003eThis suggests that managers wishing to provide suitable foraging, refuge or breeding habitat may need to coordinate among sites at these broader spatial scales. For example, at some sites starting watering early, particularly in southern sites where natural flood regimes and cues would have typically started much earlier than in the north, and at other sites starting watering later in the breeding season. These results emphasise the need for multi-scale thinking in planning environmental water allocations for and managing expectations regarding inland waterbird behavioural and population responses. This, together with multiple major breeding sites for these species being in the Murray-Darling Basin of eastern Australia, reinforces the importance of environmental water management for waterbirds at Basin-scale in influencing the maintenance of overall species populations in eastern Australia [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMany waterbird species are highly mobile and move across jurisdictional boundaries and at continental scales, which makes strategic conservation management challenging. Information about individual-, population- and species-level movement strategies, flexibility and plasticity is essential for evidence-based management. For example, prioritisation of sites for environmental watering to support juvenile survival and adult recovery from breeding can be informed by data on site-fidelity, dispersal routes, and foraging sites. Long-term satellite tracking is a valuable tool in providing this entire lifecycle information and can be used to resolve knowledge gaps, enabling management decisions to be made with improved understanding of waterbird movements and site use.\u003c/p\u003e \u003cp\u003eThis is the most detailed study to date of Australian waterbird movements, with 122 individuals tracked between 2016 and 2023. Characterisation of long-term movements provided evidence for variability in movement strategies, including plasticity over an individual\u0026rsquo;s lifetime. The dominant strategy of our focal species, SNI, was nomadism; however, this varied among individuals, with some evidence for partial migration. Overall, increased knowledge of the interaction of waterbirds with their environment across their entire life cycles will be increasingly important for informing policy and management decisions and predictions aimed at increasing waterbird abundance and maintaining waterbird diversity.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.M.M. conceived the idea and led the project, data collection, data processing, planning and interpretation of analyses, and writing the manuscript. L.L.-J. led the advanced analyses and modelling and co-wrote the manuscript. H.M.M., F.R., L.G.O., S.R., J.M.M., M.P., M.D., J.H., and M.V.J conducted the primary fieldwork and data collection. L.L.-J., F.R., A.L., J.H., M.V.J. and H.M. processed and mapped the data. R.K., K.B., V.D. and R.M.N. provided research direction and design advice at the commencement of the project.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors express their gratitude for the assistance of colleagues, collaborators and volunteers with fieldwork, and the support of program leaders.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the conclusions of this article are available on reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003eFunding declaration\u003c/p\u003e\n\u003cp\u003eThe original research that formed the basis of this article was co-funded by the Commonwealth Environmental Water Holder\u0026rsquo;s Office (CEWH/CEWO) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) through the CEWH\u0026nbsp;Monitoring, Evaluation and Research project (2019-2024) and the CEWO Environmental Watering Knowledge and Research project (2015-2018), administered through the Commonwealth Environmental Water Office within the Department of Climate Change, Energy, the Environment and Water and its precursors. The research also benefited from co-investment by the Lake Cowal Conservation Centre, and from in-kind support from the Royal Botanic Garden Sydney (John Martin), NSW Department of Planning and Environment and its precursors, and the Goulburn-Broken Catchment Management Authority (Keith Ward).\u003c/p\u003e\n\u003cp\u003eAnimal ethics statement\u003c/p\u003e\n\u003cp\u003eAll research protocols were approved by an authorized Animal Care and Ethics Committee, according to the Australian code of practice for the care and use of animals for scientific purposes. On-ground fieldwork activities were conducted under New South Wales and Victoria Scientific Licences 102180 and 10010534.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDodman T, Diagana C: \u003cstrong\u003eMovements of waterbirds within Africa and their conservation implications\u003c/strong\u003e. \u003cem\u003eOstrich \u003c/em\u003e2007, \u003cstrong\u003e78\u003c/strong\u003e(2):149-154.\u003c/li\u003e\n\u003cli\u003eDonnelly JP, Naugle DE, Collins DP, Dugger BD, Allred BW, Tack JD, Dreitz VJ: \u003cstrong\u003eSynchronizing conservation to seasonal wetland hydrology and waterbird migration in semi-arid landscapes\u003c/strong\u003e. \u003cem\u003eEcosphere \u003c/em\u003e2019, \u003cstrong\u003e10\u003c/strong\u003e(6).\u003c/li\u003e\n\u003cli\u003ePedler RD, Ribot RFH, Bennett ATD: \u003cstrong\u003eExtreme nomadism in desert waterbirds: flights of the banded stilt\u003c/strong\u003e. \u003cem\u003eBiology Letters \u003c/em\u003e2014, \u003cstrong\u003e10\u003c/strong\u003e(10).\u003c/li\u003e\n\u003cli\u003eHaig SM, Mehlman DW, Oring LW: \u003cstrong\u003eAvian Movements and Wetland Connectivity in Landscape Conservation\u003c/strong\u003e. \u003cem\u003eConservation Biology \u003c/em\u003e1998, \u003cstrong\u003e12\u003c/strong\u003e(4):749-758.\u003c/li\u003e\n\u003cli\u003eAmano T, Sz\u0026eacute;kely T, Sandel B, Nagy S, Mundkur T, Langendoen T, Blanco D, Soykan CU, Sutherland WJ: \u003cstrong\u003eSuccessful conservation of global waterbird populations depends on effective governance\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2018, \u003cstrong\u003e553\u003c/strong\u003e(7687):199-202.\u003c/li\u003e\n\u003cli\u003eKirby JS, Stattersfield AJ, Butchart SHM, Evans MI, Grimmett RFA, Jones VR, O\u0026apos;Sullivan J, Tucker GM, Newton I: \u003cstrong\u003eKey conservation issues for migratory land- and waterbird species on the world\u0026apos;s major flyways\u003c/strong\u003e. \u003cem\u003eBird Conservation International \u003c/em\u003e2008, \u003cstrong\u003e18\u003c/strong\u003e(S1):S49-S73.\u003c/li\u003e\n\u003cli\u003eDavidson NC: \u003cstrong\u003eHow much wetland has the world lost? 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Movement tracking using satellite telemetry is now allowing us to fill these knowledge gaps for nomadic waterbirds at continental scales, including in remote areas for which data have been historically difficult to acquire. We used GPS satellite telemetry to track the movements of 122 individuals of three species of ibis and spoonbills (\u003cem\u003eThreskiornithidae\u003c/em\u003e) in Australia from 2016 to 2023. We analysed movement distances, residency, home range (when resident) and foraging-site fidelity. From this we derived implications for water and wetland management for waterbird conservation. This is the first multi-year movement tracking data for ibis and spoonbills in Australia, with some individuals tracked continuously for more than five years including from natal site to first breeding attempt. Tracking revealed both inter- and intra-specific variability in movement strategies, including residency, nomadism, and migration, with individuals switching between these behaviours. During periods of residency, home ranges and distances travelled to forage were highly variable and differed significantly between species. Sixty-five percent of identified residency areas were not associated with wetlands formally listed nationally or internationally as important. Tracking the movements of waterbirds provides context for coordinated allocation of management resources, such as provision of environmental water at appropriate places and times for maximum conservation benefit. This study highlights the geographic scales over which these birds function and shows how variable waterbird movements are. This illustrates the need to consider the full life cycle of these birds when making management decisions and evaluating management impacts. Increased knowledge of the spatio-temporal interactions of waterbirds with their resource needs over complete life cycles will continue to be essential for informing management aimed at increasing waterbird numbers and maintaining long-term diversity.\u003c/p\u003e","manuscriptTitle":"Satellite telemetry reveals complex mixed movement strategies in ibis and spoonbills of Australia: implications for water and wetland management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 15:57:12","doi":"10.21203/rs.3.rs-4596537/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-01T22:18:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-28T03:37:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-05T07:16:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9981106181882007512876808623859679787","date":"2024-07-16T15:50:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115048734749288532021116034130694659806","date":"2024-07-16T15:12:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-14T13:06:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-20T11:47:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-20T11:47:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Movement Ecology","date":"2024-06-18T01:34:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"movement-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"move","sideBox":"Learn more about [Movement Ecology](http://movementecologyjournal.biomedcentral.com/)","snPcode":"40462","submissionUrl":"https://submission.nature.com/new-submission/40462/3","title":"Movement Ecology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"412583d3-8815-498b-a188-fc5be0417cd0","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T16:00:07+00:00","versionOfRecord":{"articleIdentity":"rs-4596537","link":"https://doi.org/10.1186/s40462-024-00515-4","journal":{"identity":"movement-ecology","isVorOnly":false,"title":"Movement Ecology"},"publishedOn":"2024-11-26 15:57:07","publishedOnDateReadable":"November 26th, 2024"},"versionCreatedAt":"2024-07-18 15:57:12","video":"","vorDoi":"10.1186/s40462-024-00515-4","vorDoiUrl":"https://doi.org/10.1186/s40462-024-00515-4","workflowStages":[]},"version":"v1","identity":"rs-4596537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4596537","identity":"rs-4596537","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}